From e92c23fa524a472c2213b24e0a50a714aa5867f1 Mon Sep 17 00:00:00 2001 From: root Date: Mon, 25 Sep 2023 08:03:23 +0000 Subject: [PATCH 01/24] Set devcontainer, Add readme for AMC-CBN --- .devcontainer/devcontainer.json | 20 ++++++++++++++++++++ README_amc-cbn.md | 18 ++++++++++++++++++ 2 files changed, 38 insertions(+) create mode 100644 .devcontainer/devcontainer.json create mode 100644 README_amc-cbn.md diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json new file mode 100644 index 0000000000..53708812b8 --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,20 @@ +{ + "build": { "dockerfile": "../docker/Dockerfile" }, + "runArgs": [ + "--gpus", + "all", + "--shm-size", + "8g" + ], + "mounts": [ + "type=bind,source=/home/${localEnv:USER}/.ssh,target=/root/.ssh,readonly" + ], + "customizations": { + "vscode": { + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance" + ] + } + } +} \ No newline at end of file diff --git a/README_amc-cbn.md b/README_amc-cbn.md new file mode 100644 index 0000000000..6426e9c8c8 --- /dev/null +++ b/README_amc-cbn.md @@ -0,0 +1,18 @@ +## README_AMC-CBN + +### Dataset +- ADE20K: Download ADE20K dataset from http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip + +### Settings +- logger: change `vis_backends` type to `WandbVisBackend` in `configs/_base_/default_runtime.py` +- wandb logging url: https://wandb.ai/amccbn/mmsegmentation-tool + +### Installation +- Download pretrained vit from https://github.com/open-mmlab/mmpretrain/tree/master/configs/vision_transformer and move it to `pretrain/` +- Used [vit checkpoint](vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py) for training vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py +- `pip install wandb` + +### Train +``` +tools/dist_train.sh configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py [num_gpus] --work-dir logs +``` From 99929e4ed753cb3a02e7d95e054563e4dbdd286d Mon Sep 17 00:00:00 2001 From: root Date: Tue, 26 Sep 2023 00:19:25 +0000 Subject: [PATCH 02/24] Modified shutdownAction in devcontainer.json --- .devcontainer/devcontainer.json | 3 ++- .gitignore | 2 ++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 53708812b8..171d43f66e 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -16,5 +16,6 @@ "ms-python.vscode-pylance" ] } - } + }, + "shutdownAction": "none" } \ No newline at end of file diff --git a/.gitignore b/.gitignore index 787d13ec67..c4525a5c4f 100644 --- a/.gitignore +++ b/.gitignore @@ -118,3 +118,5 @@ mmseg/.mim # Pytorch *.pth + +logs/ \ No newline at end of file From 4d49023ad4e41d42e84d3667844f908f51ae3253 Mon Sep 17 00:00:00 2001 From: root Date: Wed, 27 Sep 2023 02:42:30 +0000 Subject: [PATCH 03/24] minor fix --- README_amc-cbn.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README_amc-cbn.md b/README_amc-cbn.md index 6426e9c8c8..01e91ee7dc 100644 --- a/README_amc-cbn.md +++ b/README_amc-cbn.md @@ -6,6 +6,7 @@ ### Settings - logger: change `vis_backends` type to `WandbVisBackend` in `configs/_base_/default_runtime.py` - wandb logging url: https://wandb.ai/amccbn/mmsegmentation-tool +- Adjust the number of epochs depending on the batch size. ### Installation - Download pretrained vit from https://github.com/open-mmlab/mmpretrain/tree/master/configs/vision_transformer and move it to `pretrain/` @@ -14,5 +15,6 @@ ### Train ``` -tools/dist_train.sh configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py [num_gpus] --work-dir logs +tools/dist_train.sh configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py [num_gpus] --work-dir logs/vit-upernet +tools/dist_train.sh configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py [num_gpus] --work-dir logs/res-101-upernet ``` From 2587bebd4d673048dfc1ef2723c88e80688cfdf9 Mon Sep 17 00:00:00 2001 From: root Date: Fri, 6 Oct 2023 07:03:54 +0000 Subject: [PATCH 04/24] minor fix --- .gitignore | 3 ++- README_amc-cbn.md | 17 ++++++++++++----- docker/Dockerfile | 1 + mmseg/datasets/__init__.py | 3 ++- mmseg/datasets/angiography.py | 19 +++++++++++++++++++ 5 files changed, 36 insertions(+), 7 deletions(-) create mode 100644 mmseg/datasets/angiography.py diff --git a/.gitignore b/.gitignore index c4525a5c4f..ce225418b5 100644 --- a/.gitignore +++ b/.gitignore @@ -119,4 +119,5 @@ mmseg/.mim # Pytorch *.pth -logs/ \ No newline at end of file +logs/ +*.png \ No newline at end of file diff --git a/README_amc-cbn.md b/README_amc-cbn.md index 01e91ee7dc..3189cea64f 100644 --- a/README_amc-cbn.md +++ b/README_amc-cbn.md @@ -1,20 +1,27 @@ -## README_AMC-CBN +# README_AMC-CBN -### Dataset +## Dataset - ADE20K: Download ADE20K dataset from http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip +- Angiography: + - `mmseg/datasets/angiography.py` -### Settings +## Settings - logger: change `vis_backends` type to `WandbVisBackend` in `configs/_base_/default_runtime.py` - wandb logging url: https://wandb.ai/amccbn/mmsegmentation-tool - Adjust the number of epochs depending on the batch size. -### Installation +## Installation - Download pretrained vit from https://github.com/open-mmlab/mmpretrain/tree/master/configs/vision_transformer and move it to `pretrain/` - Used [vit checkpoint](vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py) for training vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py - `pip install wandb` -### Train +## Train ``` tools/dist_train.sh configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py [num_gpus] --work-dir logs/vit-upernet tools/dist_train.sh configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py [num_gpus] --work-dir logs/res-101-upernet ``` + +## Issues +- To create instance of `dataset` in the jupyter notebook, `init_default_scope('mmseg')` must be called. + - https://mmsegmentation.readthedocs.io/en/latest/advanced_guides/datasets.html#main-interfaces +- After adding a custom dataset class, you need to register the class to `datasets/__init__.py` file. \ No newline at end of file diff --git a/docker/Dockerfile b/docker/Dockerfile index 26420ddbbb..e0efc81172 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -33,3 +33,4 @@ WORKDIR /mmsegmentation ENV FORCE_CUDA="1" RUN pip install -r requirements.txt RUN pip install --no-cache-dir -e . +RUN pip install wandb diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index a2bdb63d01..3c45e485c5 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -1,6 +1,7 @@ # Copyright (c) OpenMMLab. All rights reserved. # yapf: disable from .ade import ADE20KDataset +from .angiography import AngiographyDataset from .basesegdataset import BaseCDDataset, BaseSegDataset from .bdd100k import BDD100KDataset from .chase_db1 import ChaseDB1Dataset @@ -60,5 +61,5 @@ 'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset', 'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile', 'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset', - 'NYUDataset' + 'NYUDataset', 'AngiographyDataset' ] diff --git a/mmseg/datasets/angiography.py b/mmseg/datasets/angiography.py new file mode 100644 index 0000000000..5982e8ba86 --- /dev/null +++ b/mmseg/datasets/angiography.py @@ -0,0 +1,19 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmseg.registry import DATASETS +from .basesegdataset import BaseSegDataset + + +@DATASETS.register_module() +class AngiographyDataset(BaseSegDataset): + """Angiography dataset. + """ + METAINFO = dict( + classes=('contrast',), + palette=[[0, 0, 0]]) + + def __init__(self, + img_suffix='.png', + seg_map_suffix='.png', + **kwargs) -> None: + super().__init__( + img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs) From 27e6cee8b627244a8cd7d4e327944ae3b17e4a09 Mon Sep 17 00:00:00 2001 From: root Date: Mon, 16 Oct 2023 00:52:50 +0000 Subject: [PATCH 05/24] Add CAG dataset --- README_amc-cbn.md | 6 +- configs/_base_/datasets/cag.py | 65 +++++++++++++++++++ .../upernet_r101_4xb4-160k_cag-512x512.py | 11 ++++ mmseg/datasets/__init__.py | 4 +- mmseg/datasets/{angiography.py => cag.py} | 2 +- 5 files changed, 83 insertions(+), 5 deletions(-) create mode 100644 configs/_base_/datasets/cag.py create mode 100644 configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py rename mmseg/datasets/{angiography.py => cag.py} (91%) diff --git a/README_amc-cbn.md b/README_amc-cbn.md index 3189cea64f..35696379aa 100644 --- a/README_amc-cbn.md +++ b/README_amc-cbn.md @@ -17,8 +17,10 @@ ## Train ``` -tools/dist_train.sh configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py [num_gpus] --work-dir logs/vit-upernet -tools/dist_train.sh configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py [num_gpus] --work-dir logs/res-101-upernet +tools/dist_train.sh configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py [num_gpus] --work-dir logs/vit-upernet-ade20k +tools/dist_train.sh configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py [num_gpus] --work-dir logs/res-101-upernet-ade20k + +tools/dist_train.sh configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py [num_gpus] --work-dir logs/res-101-upernet-cag ``` ## Issues diff --git a/configs/_base_/datasets/cag.py b/configs/_base_/datasets/cag.py new file mode 100644 index 0000000000..1748b9d42d --- /dev/null +++ b/configs/_base_/datasets/cag.py @@ -0,0 +1,65 @@ +# dataset settings +dataset_type = 'CoronaryAngiographyDataset' +data_root = 'data/cag' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(512, 512), + ratio_range=(0.9, 1.0), + keep_ratio=True), + dict( + type='Pad', + size=(512, 512), + ), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(512, 512), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations', reduce_zero_label=True), + dict(type='PackSegInputs') +] +img_ratios = [1.0] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=4, + num_workers=4, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + pipeline=test_pipeline)) +test_dataloader = val_dataloader + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py b/configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py new file mode 100644 index 0000000000..87f853bf72 --- /dev/null +++ b/configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/upernet_r50.py', '../_base_/datasets/cag.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=150), + auxiliary_head=dict(num_classes=150), + pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index 3c45e485c5..8c7f4a7705 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -1,7 +1,7 @@ # Copyright (c) OpenMMLab. All rights reserved. # yapf: disable from .ade import ADE20KDataset -from .angiography import AngiographyDataset +from .cag import CoronaryAngiographyDataset from .basesegdataset import BaseCDDataset, BaseSegDataset from .bdd100k import BDD100KDataset from .chase_db1 import ChaseDB1Dataset @@ -61,5 +61,5 @@ 'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset', 'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile', 'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset', - 'NYUDataset', 'AngiographyDataset' + 'NYUDataset', 'CoronaryAngiographyDataset' ] diff --git a/mmseg/datasets/angiography.py b/mmseg/datasets/cag.py similarity index 91% rename from mmseg/datasets/angiography.py rename to mmseg/datasets/cag.py index 5982e8ba86..56ffbf159c 100644 --- a/mmseg/datasets/angiography.py +++ b/mmseg/datasets/cag.py @@ -4,7 +4,7 @@ @DATASETS.register_module() -class AngiographyDataset(BaseSegDataset): +class CoronaryAngiographyDataset(BaseSegDataset): """Angiography dataset. """ METAINFO = dict( From b451a0188e2bc3121b60d1cc1aa9e97afc0caaf1 Mon Sep 17 00:00:00 2001 From: root Date: Fri, 20 Oct 2023 06:51:14 +0000 Subject: [PATCH 06/24] Add albumentation augmentation pipelines --- configs/_base_/datasets/cag.py | 25 ++-- mmseg/datasets/transforms/transforms.py | 157 ++++++++++++++++++++++++ 2 files changed, 174 insertions(+), 8 deletions(-) diff --git a/configs/_base_/datasets/cag.py b/configs/_base_/datasets/cag.py index 1748b9d42d..73eb166e07 100644 --- a/configs/_base_/datasets/cag.py +++ b/configs/_base_/datasets/cag.py @@ -1,19 +1,28 @@ # dataset settings dataset_type = 'CoronaryAngiographyDataset' data_root = 'data/cag' +# augmentation setting from YoungIn's jupyter notebook train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( - type='RandomResize', - scale=(512, 512), - ratio_range=(0.9, 1.0), - keep_ratio=True), + type='AlbuShiftScaleRotateTransform', + scale_limit=(-0.2,0), + rotate_limit=20, + shift_limit=0.1, + border_mode=0, value=[0.3,0.4,0.5], + p=1 + ), + dict( + type='AlbuRandomContrastTransform', + limit=0.4, + p=0.5 + ), dict( - type='Pad', - size=(512, 512), + type='AlbuGaussNoiseTransform', + var_limit=(0, 0.01), + p=0.5 ), - dict(type='PhotoMetricDistortion'), dict(type='PackSegInputs') ] test_pipeline = [ @@ -21,7 +30,7 @@ dict(type='Resize', scale=(512, 512), keep_ratio=True), # add loading annotation after ``Resize`` because ground truth # does not need to do resize data transform - dict(type='LoadAnnotations', reduce_zero_label=True), + dict(type='LoadAnnotations'), dict(type='PackSegInputs') ] img_ratios = [1.0] diff --git a/mmseg/datasets/transforms/transforms.py b/mmseg/datasets/transforms/transforms.py index 082ae5b440..11bea97cf0 100644 --- a/mmseg/datasets/transforms/transforms.py +++ b/mmseg/datasets/transforms/transforms.py @@ -15,6 +15,7 @@ from mmengine.utils import is_tuple_of from numpy import random from scipy.ndimage import gaussian_filter +import albumentations as album from mmseg.datasets.dataset_wrappers import MultiImageMixDataset from mmseg.registry import TRANSFORMS @@ -2512,3 +2513,159 @@ def transform(self, results: dict) -> dict: results['img'] = img return results + + +class BaseAlbuTransform(BaseTransform): + + def __repr__(self): + repr_str = self.__class__.__name__ + num_params = len(self.params) + for i, (k, v) in enumerate(self.params.items()): + if i == 0: + repr_str += f'({k}={v}, ' + elif 0 < i < num_params - 1: + repr_str += f'{k}={v}, ' + elif i == num_params - 1: + repr_str += f'{k}={v})' + return repr_str + + +@TRANSFORMS.register_module() +class AlbuShiftScaleRotateTransform(BaseAlbuTransform): + """ + Wrapper for album.ShiftScaleRotate + + Required Keys: + + - img + - gt_seg_map + + Modified Keys: + + - img + - gt_seg_map + + Args: + shift_limit ((float, float) or float): shift factor range for both height and width. If shift_limit + is a single float value, the range will be (-shift_limit, shift_limit). Absolute values for lower and + upper bounds should lie in range [0, 1]. Default: (-0.0625, 0.0625). + scale_limit ((float, float) or float): scaling factor range. If scale_limit is a single float value, the + range will be (-scale_limit, scale_limit). Note that the scale_limit will be biased by 1. + If scale_limit is a tuple, like (low, high), sampling will be done from the range (1 + low, 1 + high). + Default: (-0.1, 0.1). + rotate_limit ((int, int) or int): rotation range. If rotate_limit is a single int value, the + range will be (-rotate_limit, rotate_limit). Default: (-45, 45). + interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of: + cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. + Default: cv2.INTER_LINEAR. + border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of: + cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. + Default: cv2.BORDER_REFLECT_101 + value (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT. + mask_value (int, float, + list of int, + list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks. + shift_limit_x ((float, float) or float): shift factor range for width. If it is set then this value + instead of shift_limit will be used for shifting width. If shift_limit_x is a single float value, + the range will be (-shift_limit_x, shift_limit_x). Absolute values for lower and upper bounds should lie in + the range [0, 1]. Default: None. + shift_limit_y ((float, float) or float): shift factor range for height. If it is set then this value + instead of shift_limit will be used for shifting height. If shift_limit_y is a single float value, + the range will be (-shift_limit_y, shift_limit_y). Absolute values for lower and upper bounds should lie + in the range [0, 1]. Default: None. + rotate_method (str): rotation method used for the bounding boxes. Should be one of "largest_box" or "ellipse". + Default: "largest_box" + p (float): probability of applying the transform. Default: 0.5. + """ + def __init__(self, **kwargs): + self.params = kwargs + self.albu_transform = album.ShiftScaleRotate(**kwargs) + + def transform(self, results: dict) -> dict: + """Transform function to perform `album.ShiftScaleRotate`. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with images distorted. + """ + aug = self.albu_transform(image=results['img'], mask=results['gt_seg_map']) + results['img'] = aug['image'] + results['gt_seg_map'] = aug['mask'] + return results + + +@TRANSFORMS.register_module() +class AlbuRandomContrastTransform(BaseAlbuTransform): + """ + Wrapper for album.RandomContrast + + Required Keys: + + - img + + Modified Keys: + + - img + + Args: + limit ((float, float) or float): factor range for changing contrast. + If limit is a single float, the range will be (-limit, limit). Default: (-0.2, 0.2). + p (float): probability of applying the transform. Default: 0.5. + """ + def __init__(self, **kwargs): + self.params = kwargs + self.albu_transform = album.RandomContrast(**kwargs) + + def transform(self, results: dict) -> dict: + """Transform function to perform `album.ShiftScaleRotate`. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with images distorted. + """ + aug = self.albu_transform(image=results['img']) + results['img'] = aug['image'] + return results + + +@TRANSFORMS.register_module() +class AlbuGaussNoiseTransform(BaseAlbuTransform): + """ + Wrapper for album.GaussNoise + + Required Keys: + + - img + + Modified Keys: + + - img + + Args: + var_limit ((float, float) or float): variance range for noise. If var_limit is a single float, the range + will be (0, var_limit). Default: (10.0, 50.0). + mean (float): mean of the noise. Default: 0 + per_channel (bool): if set to True, noise will be sampled for each channel independently. + Otherwise, the noise will be sampled once for all channels. Default: True + p (float): probability of applying the transform. Default: 0.5. + """ + def __init__(self, **kwargs): + self.params = kwargs + self.albu_transform = album.GaussNoise(**kwargs) + + def transform(self, results: dict) -> dict: + """Transform function to perform `album.ShiftScaleRotate`. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with images distorted. + """ + aug = self.albu_transform(image=results['img']) + results['img'] = aug['image'] + return results From 5f5d4d8af1ee4c0d36b5c1df30a638f0ea13d086 Mon Sep 17 00:00:00 2001 From: root Date: Fri, 20 Oct 2023 06:54:55 +0000 Subject: [PATCH 07/24] Update README --- README_amc-cbn.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README_amc-cbn.md b/README_amc-cbn.md index 35696379aa..b0047ff7d4 100644 --- a/README_amc-cbn.md +++ b/README_amc-cbn.md @@ -23,7 +23,10 @@ tools/dist_train.sh configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py [nu tools/dist_train.sh configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py [num_gpus] --work-dir logs/res-101-upernet-cag ``` +## Transforms +- Additional augmentation functions using albumentation can be found in `mmseg/datasets/transforms/transforms.py` + ## Issues - To create instance of `dataset` in the jupyter notebook, `init_default_scope('mmseg')` must be called. - https://mmsegmentation.readthedocs.io/en/latest/advanced_guides/datasets.html#main-interfaces -- After adding a custom dataset class, you need to register the class to `datasets/__init__.py` file. \ No newline at end of file +- After adding a custom dataset class, you need to register the class to `datasets/__init__.py` file. From a10711ac12a3cd48fbbd67080d3d3203e9c2bacb Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Mon, 15 Apr 2024 08:15:58 +0000 Subject: [PATCH 08/24] 2024.04.15 --- .devcontainer/devcontainer.json | 3 -- configs/_base_/datasets/cag.py | 23 ++++++-- configs/_base_/schedules/schedule_160k.py | 2 +- ...se_upernet_8xb2-amp-160k_ade20k-512x512.py | 2 +- ...-base_upernet_8xb2-amp-160k_cag-512x512.py | 54 +++++++++++++++++++ mmseg/datasets/cag.py | 4 +- mmseg/utils/class_names.py | 9 ++++ 7 files changed, 87 insertions(+), 10 deletions(-) create mode 100644 configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 171d43f66e..c39e1225f7 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -6,9 +6,6 @@ "--shm-size", "8g" ], - "mounts": [ - "type=bind,source=/home/${localEnv:USER}/.ssh,target=/root/.ssh,readonly" - ], "customizations": { "vscode": { "extensions": [ diff --git a/configs/_base_/datasets/cag.py b/configs/_base_/datasets/cag.py index 73eb166e07..2870d2b4e9 100644 --- a/configs/_base_/datasets/cag.py +++ b/configs/_base_/datasets/cag.py @@ -1,6 +1,6 @@ # dataset settings dataset_type = 'CoronaryAngiographyDataset' -data_root = 'data/cag' +data_root = '/workspaces/mmsegmentation-1/cag' # augmentation setting from YoungIn's jupyter notebook train_pipeline = [ dict(type='LoadImageFromFile'), @@ -23,6 +23,8 @@ var_limit=(0, 0.01), p=0.5 ), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), dict(type='PackSegInputs') ] test_pipeline = [ @@ -42,6 +44,10 @@ [ dict(type='Resize', scale_factor=r, keep_ratio=True) for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] ]) ] @@ -68,7 +74,18 @@ img_path='images/validation', seg_map_path='annotations/validation'), pipeline=test_pipeline)) -test_dataloader = val_dataloader +test_dataloader = dict( + batch_size=1, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='images/test', + seg_map_path='annotations/test'), + pipeline=test_pipeline)) val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) -test_evaluator = val_evaluator +test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) diff --git a/configs/_base_/schedules/schedule_160k.py b/configs/_base_/schedules/schedule_160k.py index 60d7bec762..712888d87b 100644 --- a/configs/_base_/schedules/schedule_160k.py +++ b/configs/_base_/schedules/schedule_160k.py @@ -20,6 +20,6 @@ timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), param_scheduler=dict(type='ParamSchedulerHook'), - checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=16000), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=10000), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='SegVisualizationHook')) diff --git a/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py b/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py index b8eae174e9..b146635db2 100644 --- a/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py +++ b/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py @@ -51,4 +51,4 @@ # By default, models are trained on 8 GPUs with 2 images per GPU train_dataloader = dict(batch_size=2) val_dataloader = dict(batch_size=1) -test_dataloader = val_dataloader +test_dataloader = dict(batch_size=1) diff --git a/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py b/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py new file mode 100644 index 0000000000..0225c1e9f7 --- /dev/null +++ b/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_mae.py', '../_base_/datasets/cag.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + backbone=dict( + type='MAE', + img_size=(512, 512), + patch_size=16, + embed_dims=768, + num_layers=12, + num_heads=12, + mlp_ratio=4, + init_values=1.0, + drop_path_rate=0.1, + out_indices=[3, 5, 7, 11]), + neck=dict(embed_dim=768, rescales=[4, 2, 1, 0.5]), + decode_head=dict( + in_channels=[768, 768, 768, 768], num_classes=150, channels=768), + auxiliary_head=dict(in_channels=768, num_classes=150), + test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341))) + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict( + type='AdamW', lr=1e-4, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.65), + constructor='LayerDecayOptimizerConstructor') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=160000, + by_epoch=False, + ) +] + +# mixed precision +fp16 = dict(loss_scale='dynamic') + +# By default, models are trained on 8 GPUs with 2 images per GPU +train_dataloader = dict(batch_size=2) +val_dataloader = dict(batch_size=1) +test_dataloader = dict(batch_size=1) diff --git a/mmseg/datasets/cag.py b/mmseg/datasets/cag.py index 56ffbf159c..b5151ab080 100644 --- a/mmseg/datasets/cag.py +++ b/mmseg/datasets/cag.py @@ -8,8 +8,8 @@ class CoronaryAngiographyDataset(BaseSegDataset): """Angiography dataset. """ METAINFO = dict( - classes=('contrast',), - palette=[[0, 0, 0]]) + classes=('background','contrast'), + palette=[[0, 0, 0], [255, 0, 0]]) def __init__(self, img_suffix='.png', diff --git a/mmseg/utils/class_names.py b/mmseg/utils/class_names.py index 122e63fcc4..fa79572565 100644 --- a/mmseg/utils/class_names.py +++ b/mmseg/utils/class_names.py @@ -1,6 +1,14 @@ # Copyright (c) OpenMMLab. All rights reserved. from mmengine.utils import is_str +def cag_classes(): + return [ + 'background', 'contrast' + ] + +def cag_palette(): + return [[0, 0, 0], [255, 0, 0]] + def cityscapes_classes(): """Cityscapes class names for external use.""" @@ -440,6 +448,7 @@ def bdd100k_palette(): dataset_aliases = { + 'cag' : ['cag'], 'cityscapes': ['cityscapes'], 'ade': ['ade', 'ade20k'], 'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug'], From b979d4638e097524f2545d07f8950c894e2680ce Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Tue, 16 Apr 2024 01:22:44 +0000 Subject: [PATCH 09/24] 2024.04.16 --- configs/_base_/datasets/cag.py | 2 +- mmseg/datasets/__init__.py | 2 +- mmsegmentation | 1 + 3 files changed, 3 insertions(+), 2 deletions(-) create mode 160000 mmsegmentation diff --git a/configs/_base_/datasets/cag.py b/configs/_base_/datasets/cag.py index 2870d2b4e9..3f0f268a49 100644 --- a/configs/_base_/datasets/cag.py +++ b/configs/_base_/datasets/cag.py @@ -1,6 +1,6 @@ # dataset settings dataset_type = 'CoronaryAngiographyDataset' -data_root = '/workspaces/mmsegmentation-1/cag' +data_root = './cag' # augmentation setting from YoungIn's jupyter notebook train_pipeline = [ dict(type='LoadImageFromFile'), diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index 8c7f4a7705..eda74852fa 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -38,7 +38,7 @@ PhotoMetricDistortion, RandomCrop, RandomCutOut, RandomMosaic, RandomRotate, RandomRotFlip, Rerange, ResizeShortestEdge, ResizeToMultiple, RGB2Gray, - SegRescale) + SegRescale, AlbuShiftScaleRotateTransform, AlbuRandomContrastTransform, AlbuGaussNoiseTransform) from .voc import PascalVOCDataset # yapf: enable diff --git a/mmsegmentation b/mmsegmentation new file mode 160000 index 0000000000..b040e147ad --- /dev/null +++ b/mmsegmentation @@ -0,0 +1 @@ +Subproject commit b040e147adfa027bbc071b624bedf0ae84dfc922 From f377e256a229ba26c1ef4ff6764ab1afcac99933 Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Tue, 16 Apr 2024 05:55:21 +0000 Subject: [PATCH 10/24] 2024.04.16 --- a.ipynb | 126 ++++++++++++++++++++++ configs/_base_/datasets/cag.py | 9 +- configs/_base_/schedules/schedule_160k.py | 4 +- mmseg/datasets/__init__.py | 4 +- mmseg/datasets/cag.py | 7 +- mmseg/datasets/transforms/__init__.py | 4 +- mmseg/datasets/transforms/formatting.py | 5 +- mmseg/datasets/transforms/transforms.py | 2 +- mmsegmentation | 1 - 9 files changed, 147 insertions(+), 15 deletions(-) create mode 100644 a.ipynb delete mode 160000 mmsegmentation diff --git a/a.ipynb b/a.ipynb new file mode 100644 index 0000000000..39e36b55cb --- /dev/null +++ b/a.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAAsTAAALEwEAmpwYAAAC00lEQVR4nO3YsQ3DMAwAwcjIahnBU2aE7BZmASGd4S/uSqph8yCgNTMPoOe4ewFgT5wQJU6IEidEiROinv8eX8fpKxcu9vm+127uckKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocULUmpm7dwA2XE6IEidEiROixAlR4oQocULUD0EoC8mGDyx+AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Path to the image file\n", + "image_path = '/workspaces/mmsegmentation-1/MAEOUT_240416/00001.png'\n", + "\n", + "# Load the image using OpenCV\n", + "img = cv2.imread(image_path, 0)\n", + "\n", + "# Convert BGR to RGB (OpenCV uses BGR by default)\n", + "\n", + "# Display the image using Matplotlib\n", + "plt.imshow(img)\n", + "plt.axis('off') # Turn off axis\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]\n", + " ...\n", + " [0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]]\n", + "\n", + " [[0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]\n", + " ...\n", + " [0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]]\n", + "\n", + " [[0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]\n", + " ...\n", + " [0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]]\n", + "\n", + " ...\n", + "\n", + " [[0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]\n", + " ...\n", + " [0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]]\n", + "\n", + " [[0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]\n", + " ...\n", + " [0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]]\n", + "\n", + " [[0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]\n", + " ...\n", + " [0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]]]\n" + ] + } + ], + "source": [ + "print(img)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/configs/_base_/datasets/cag.py b/configs/_base_/datasets/cag.py index 3f0f268a49..7c9f4c7d1e 100644 --- a/configs/_base_/datasets/cag.py +++ b/configs/_base_/datasets/cag.py @@ -1,10 +1,10 @@ # dataset settings dataset_type = 'CoronaryAngiographyDataset' -data_root = './cag' +data_root = './cag/' # augmentation setting from YoungIn's jupyter notebook train_pipeline = [ dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), + dict(type='LoadAnnotations', reduce_zero_label=False), dict( type='AlbuShiftScaleRotateTransform', scale_limit=(-0.2,0), @@ -15,7 +15,8 @@ ), dict( type='AlbuRandomContrastTransform', - limit=0.4, + brightness_limit=(-0.2, 0.2), + contrast_limit=(-0.2, 0.2), p=0.5 ), dict( @@ -32,7 +33,7 @@ dict(type='Resize', scale=(512, 512), keep_ratio=True), # add loading annotation after ``Resize`` because ground truth # does not need to do resize data transform - dict(type='LoadAnnotations'), + dict(type='LoadAnnotations', reduce_zero_label=False), dict(type='PackSegInputs') ] img_ratios = [1.0] diff --git a/configs/_base_/schedules/schedule_160k.py b/configs/_base_/schedules/schedule_160k.py index 712888d87b..9bf7f58175 100644 --- a/configs/_base_/schedules/schedule_160k.py +++ b/configs/_base_/schedules/schedule_160k.py @@ -13,13 +13,13 @@ ] # training schedule for 160k train_cfg = dict( - type='IterBasedTrainLoop', max_iters=160000, val_interval=16000) + type='IterBasedTrainLoop', max_iters=160000, val_interval=1000) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), param_scheduler=dict(type='ParamSchedulerHook'), - checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=10000), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='SegVisualizationHook')) diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index eda74852fa..2cb0ec0694 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -59,7 +59,7 @@ 'BioMedicalRandomGamma', 'BioMedical3DPad', 'RandomRotFlip', 'SynapseDataset', 'REFUGEDataset', 'MapillaryDataset_v1', 'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset', - 'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile', + 'LoadMultipleRSImageFromFile', 'CoronaryAngiographyDataset', 'LoadSingleRSImageFromFile', 'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset', - 'NYUDataset', 'CoronaryAngiographyDataset' + 'NYUDataset' ] diff --git a/mmseg/datasets/cag.py b/mmseg/datasets/cag.py index b5151ab080..90123e76e0 100644 --- a/mmseg/datasets/cag.py +++ b/mmseg/datasets/cag.py @@ -14,6 +14,11 @@ class CoronaryAngiographyDataset(BaseSegDataset): def __init__(self, img_suffix='.png', seg_map_suffix='.png', + reduce_zero_label=False, + **kwargs) -> None: super().__init__( - img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs) + img_suffix=img_suffix, + seg_map_suffix=seg_map_suffix, + reduce_zero_label=reduce_zero_label, + **kwargs) diff --git a/mmseg/datasets/transforms/__init__.py b/mmseg/datasets/transforms/__init__.py index 125f070818..9fb64c78ac 100644 --- a/mmseg/datasets/transforms/__init__.py +++ b/mmseg/datasets/transforms/__init__.py @@ -13,7 +13,7 @@ RandomDepthMix, RandomFlip, RandomMosaic, RandomRotate, RandomRotFlip, Rerange, Resize, ResizeShortestEdge, ResizeToMultiple, RGB2Gray, - SegRescale) + SegRescale, AlbuGaussNoiseTransform, AlbuRandomContrastTransform, AlbuShiftScaleRotateTransform) # yapf: enable __all__ = [ @@ -26,5 +26,5 @@ 'BioMedical3DRandomFlip', 'BioMedicalRandomGamma', 'BioMedical3DPad', 'RandomRotFlip', 'Albu', 'LoadSingleRSImageFromFile', 'ConcatCDInput', 'LoadMultipleRSImageFromFile', 'LoadDepthAnnotation', 'RandomDepthMix', - 'RandomFlip', 'Resize' + 'RandomFlip', 'Resize', 'AlbuGaussNoiseTransform', 'AlbuRandomContrastTransform', 'AlbuShiftScaleRotateTransform' ] diff --git a/mmseg/datasets/transforms/formatting.py b/mmseg/datasets/transforms/formatting.py index bd250551e9..a66bf966bc 100644 --- a/mmseg/datasets/transforms/formatting.py +++ b/mmseg/datasets/transforms/formatting.py @@ -64,14 +64,15 @@ def transform(self, results: dict) -> dict: if 'img' in results: img = results['img'] if len(img.shape) < 3: - img = np.expand_dims(img, -1) + img = np.stack([img] * 3, axis=-1) if not img.flags.c_contiguous: + img = (img - np.min(img))/np.max(img) img = to_tensor(np.ascontiguousarray(img.transpose(2, 0, 1))) else: img = img.transpose(2, 0, 1) + img = (img - np.min(img))/np.max(img) img = to_tensor(img).contiguous() packed_results['inputs'] = img - data_sample = SegDataSample() if 'gt_seg_map' in results: if len(results['gt_seg_map'].shape) == 2: diff --git a/mmseg/datasets/transforms/transforms.py b/mmseg/datasets/transforms/transforms.py index 11bea97cf0..687e883cea 100644 --- a/mmseg/datasets/transforms/transforms.py +++ b/mmseg/datasets/transforms/transforms.py @@ -2616,7 +2616,7 @@ class AlbuRandomContrastTransform(BaseAlbuTransform): """ def __init__(self, **kwargs): self.params = kwargs - self.albu_transform = album.RandomContrast(**kwargs) + self.albu_transform = album.RandomBrightnessContrast(**kwargs) def transform(self, results: dict) -> dict: """Transform function to perform `album.ShiftScaleRotate`. diff --git a/mmsegmentation b/mmsegmentation deleted file mode 160000 index b040e147ad..0000000000 --- a/mmsegmentation +++ /dev/null @@ -1 +0,0 @@ -Subproject commit b040e147adfa027bbc071b624bedf0ae84dfc922 From 9a37bfc4677b71cc3700335da03631a3b04c2e9d Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Tue, 16 Apr 2024 07:11:35 +0000 Subject: [PATCH 11/24] 2024.04.16 --- MAEOUT_240416/vis_data/config.py | 349 ++++++++++++++++++++++ a.ipynb | 56 +--- configs/_base_/datasets/cag.py | 43 +-- configs/_base_/schedules/schedule_160k.py | 2 +- mmseg/datasets/cag.py | 4 +- mmseg/utils/class_names.py | 4 +- 6 files changed, 381 insertions(+), 77 deletions(-) create mode 100644 MAEOUT_240416/vis_data/config.py diff --git a/MAEOUT_240416/vis_data/config.py b/MAEOUT_240416/vis_data/config.py new file mode 100644 index 0000000000..cfaf78522b --- /dev/null +++ b/MAEOUT_240416/vis_data/config.py @@ -0,0 +1,349 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = './cag/' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=1000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '/workspaces/mmsegmentation-1/work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512/iter_1000.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='./cag/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='./cag/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='./cag/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='./MAEOUT_240416', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512' diff --git a/a.ipynb b/a.ipynb index 39e36b55cb..c0e6542391 100644 --- a/a.ipynb +++ b/a.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -31,7 +31,7 @@ "# Convert BGR to RGB (OpenCV uses BGR by default)\n", "\n", "# Display the image using Matplotlib\n", - "plt.imshow(img)\n", + "plt.imshow(img*255)\n", "plt.axis('off') # Turn off axis\n", "plt.show()\n" ] @@ -45,60 +45,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]\n", - " ...\n", - " [0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]]\n", - "\n", - " [[0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]\n", - " ...\n", - " [0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]]\n", - "\n", - " [[0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]\n", - " ...\n", - " [0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]]\n", - "\n", - " ...\n", - "\n", - " [[0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]\n", - " ...\n", - " [0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]]\n", - "\n", - " [[0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]\n", - " ...\n", - " [0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]]\n", - "\n", - " [[0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]\n", - " ...\n", - " [0 0 0]\n", - " [0 0 0]\n", - " [0 0 0]]]\n" + "(512, 512)\n" ] } ], "source": [ - "print(img)" + "print(img.shape)" ] } ], diff --git a/configs/_base_/datasets/cag.py b/configs/_base_/datasets/cag.py index 7c9f4c7d1e..d5d9649fd1 100644 --- a/configs/_base_/datasets/cag.py +++ b/configs/_base_/datasets/cag.py @@ -1,29 +1,29 @@ # dataset settings dataset_type = 'CoronaryAngiographyDataset' -data_root = './cag/' +data_root = 'data/cag' # augmentation setting from YoungIn's jupyter notebook train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=False), - dict( - type='AlbuShiftScaleRotateTransform', - scale_limit=(-0.2,0), - rotate_limit=20, - shift_limit=0.1, - border_mode=0, value=[0.3,0.4,0.5], - p=1 - ), - dict( - type='AlbuRandomContrastTransform', - brightness_limit=(-0.2, 0.2), - contrast_limit=(-0.2, 0.2), - p=0.5 - ), - dict( - type='AlbuGaussNoiseTransform', - var_limit=(0, 0.01), - p=0.5 - ), + # dict( + # type='AlbuShiftScaleRotateTransform', + # scale_limit=(-0.2,0), + # rotate_limit=20, + # shift_limit=0.1, + # border_mode=0, value=[0.3,0.4,0.5], + # p=1 + # ), + # dict( + # type='AlbuRandomContrastTransform', + # brightness_limit=(-0.2, 0.2), + # contrast_limit=(-0.2, 0.2), + # p=0.5 + # ), + # dict( + # type='AlbuGaussNoiseTransform', + # var_limit=(0, 0.01), + # p=0.5 + # ), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict(type='PackSegInputs') @@ -60,6 +60,7 @@ dataset=dict( type=dataset_type, data_root=data_root, + reduce_zero_label=False, data_prefix=dict( img_path='images/training', seg_map_path='annotations/training'), pipeline=train_pipeline)) @@ -71,6 +72,7 @@ dataset=dict( type=dataset_type, data_root=data_root, + reduce_zero_label=False, data_prefix=dict( img_path='images/validation', seg_map_path='annotations/validation'), @@ -83,6 +85,7 @@ dataset=dict( type=dataset_type, data_root=data_root, + reduce_zero_label=False, data_prefix=dict( img_path='images/test', seg_map_path='annotations/test'), diff --git a/configs/_base_/schedules/schedule_160k.py b/configs/_base_/schedules/schedule_160k.py index 9bf7f58175..20a804cbec 100644 --- a/configs/_base_/schedules/schedule_160k.py +++ b/configs/_base_/schedules/schedule_160k.py @@ -22,4 +22,4 @@ param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000), sampler_seed=dict(type='DistSamplerSeedHook'), - visualization=dict(type='SegVisualizationHook')) + visualization=dict(type='SegVisualizationHook', draw=True)) diff --git a/mmseg/datasets/cag.py b/mmseg/datasets/cag.py index 90123e76e0..da607faf94 100644 --- a/mmseg/datasets/cag.py +++ b/mmseg/datasets/cag.py @@ -8,8 +8,8 @@ class CoronaryAngiographyDataset(BaseSegDataset): """Angiography dataset. """ METAINFO = dict( - classes=('background','contrast'), - palette=[[0, 0, 0], [255, 0, 0]]) + classes=('contrast', 'background'), + palette=[[255, 0, 0], [0, 0, 0]]) def __init__(self, img_suffix='.png', diff --git a/mmseg/utils/class_names.py b/mmseg/utils/class_names.py index fa79572565..30177ac0e0 100644 --- a/mmseg/utils/class_names.py +++ b/mmseg/utils/class_names.py @@ -3,11 +3,11 @@ def cag_classes(): return [ - 'background', 'contrast' + 'contrast', 'background' ] def cag_palette(): - return [[0, 0, 0], [255, 0, 0]] + return [[255, 0, 0], [0, 0, 0]] def cityscapes_classes(): From 229a6c8d554a9ac145e8941769bf13bd45c9123e Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Tue, 16 Apr 2024 07:56:12 +0000 Subject: [PATCH 12/24] 2024.04.16 --- configs/_base_/datasets/cag.py | 40 +++++++++++------------ configs/_base_/schedules/schedule_160k.py | 4 +-- mmseg/datasets/cag.py | 4 +-- mmseg/datasets/transforms/formatting.py | 4 +-- mmseg/utils/class_names.py | 4 +-- 5 files changed, 28 insertions(+), 28 deletions(-) diff --git a/configs/_base_/datasets/cag.py b/configs/_base_/datasets/cag.py index d5d9649fd1..367e346b9f 100644 --- a/configs/_base_/datasets/cag.py +++ b/configs/_base_/datasets/cag.py @@ -5,27 +5,27 @@ train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=False), - # dict( - # type='AlbuShiftScaleRotateTransform', - # scale_limit=(-0.2,0), - # rotate_limit=20, - # shift_limit=0.1, - # border_mode=0, value=[0.3,0.4,0.5], - # p=1 - # ), - # dict( - # type='AlbuRandomContrastTransform', - # brightness_limit=(-0.2, 0.2), - # contrast_limit=(-0.2, 0.2), - # p=0.5 - # ), - # dict( - # type='AlbuGaussNoiseTransform', - # var_limit=(0, 0.01), - # p=0.5 - # ), + dict( + type='AlbuShiftScaleRotateTransform', + scale_limit=(-0.2,0), + rotate_limit=20, + shift_limit=0.1, + border_mode=0, value=[0.3,0.4,0.5], + p=1 + ), + dict( + type='AlbuRandomContrastTransform', + brightness_limit=(-0.2, 0.2), + contrast_limit=(-0.2, 0.2), + p=0.5 + ), + dict( + type='AlbuGaussNoiseTransform', + var_limit=(0, 0.01), + p=0.5 + ), dict(type='RandomFlip', prob=0.5), - dict(type='PhotoMetricDistortion'), + # dict(type='PhotoMetricDistortion'), dict(type='PackSegInputs') ] test_pipeline = [ diff --git a/configs/_base_/schedules/schedule_160k.py b/configs/_base_/schedules/schedule_160k.py index 20a804cbec..83887f5cb5 100644 --- a/configs/_base_/schedules/schedule_160k.py +++ b/configs/_base_/schedules/schedule_160k.py @@ -13,13 +13,13 @@ ] # training schedule for 160k train_cfg = dict( - type='IterBasedTrainLoop', max_iters=160000, val_interval=1000) + type='IterBasedTrainLoop', max_iters=160000, val_interval=500) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), param_scheduler=dict(type='ParamSchedulerHook'), - checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=500), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='SegVisualizationHook', draw=True)) diff --git a/mmseg/datasets/cag.py b/mmseg/datasets/cag.py index da607faf94..7eb7ba43bb 100644 --- a/mmseg/datasets/cag.py +++ b/mmseg/datasets/cag.py @@ -8,8 +8,8 @@ class CoronaryAngiographyDataset(BaseSegDataset): """Angiography dataset. """ METAINFO = dict( - classes=('contrast', 'background'), - palette=[[255, 0, 0], [0, 0, 0]]) + classes=('background', 'contrast'), + palette=[[0, 0, 0], [255, 0, 0]]) def __init__(self, img_suffix='.png', diff --git a/mmseg/datasets/transforms/formatting.py b/mmseg/datasets/transforms/formatting.py index a66bf966bc..ef9a783f6b 100644 --- a/mmseg/datasets/transforms/formatting.py +++ b/mmseg/datasets/transforms/formatting.py @@ -66,11 +66,11 @@ def transform(self, results: dict) -> dict: if len(img.shape) < 3: img = np.stack([img] * 3, axis=-1) if not img.flags.c_contiguous: - img = (img - np.min(img))/np.max(img) + # img = (img - np.min(img))/np.max(img) img = to_tensor(np.ascontiguousarray(img.transpose(2, 0, 1))) else: img = img.transpose(2, 0, 1) - img = (img - np.min(img))/np.max(img) + # img = (img - np.min(img))/np.max(img) img = to_tensor(img).contiguous() packed_results['inputs'] = img data_sample = SegDataSample() diff --git a/mmseg/utils/class_names.py b/mmseg/utils/class_names.py index 30177ac0e0..e93555e12d 100644 --- a/mmseg/utils/class_names.py +++ b/mmseg/utils/class_names.py @@ -3,11 +3,11 @@ def cag_classes(): return [ - 'contrast', 'background' + 'background', 'contrast' ] def cag_palette(): - return [[255, 0, 0], [0, 0, 0]] + return [[0, 0, 0], [255, 0, 0]] def cityscapes_classes(): From c0702489f2ed7d26383705073a42fc01afed4d01 Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Tue, 16 Apr 2024 08:29:36 +0000 Subject: [PATCH 13/24] 2024.04.16 --- configs/_base_/schedules/schedule_160k.py | 6 +- mmseg/datasets/transforms/transforms.py | 68 +- nohup.out | 1981 +++++++++++++++++++++ 3 files changed, 2018 insertions(+), 37 deletions(-) create mode 100644 nohup.out diff --git a/configs/_base_/schedules/schedule_160k.py b/configs/_base_/schedules/schedule_160k.py index 83887f5cb5..1221375d8c 100644 --- a/configs/_base_/schedules/schedule_160k.py +++ b/configs/_base_/schedules/schedule_160k.py @@ -13,13 +13,13 @@ ] # training schedule for 160k train_cfg = dict( - type='IterBasedTrainLoop', max_iters=160000, val_interval=500) + type='IterBasedTrainLoop', max_iters=160000, val_interval=10000) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), param_scheduler=dict(type='ParamSchedulerHook'), - checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=500), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=10000), sampler_seed=dict(type='DistSamplerSeedHook'), - visualization=dict(type='SegVisualizationHook', draw=True)) + visualization=dict(type='SegVisualizationHook')) diff --git a/mmseg/datasets/transforms/transforms.py b/mmseg/datasets/transforms/transforms.py index 687e883cea..916186c3af 100644 --- a/mmseg/datasets/transforms/transforms.py +++ b/mmseg/datasets/transforms/transforms.py @@ -671,40 +671,40 @@ def contrast(self, img: np.ndarray) -> np.ndarray: alpha=random.uniform(self.contrast_lower, self.contrast_upper)) return img - def saturation(self, img: np.ndarray) -> np.ndarray: - """Saturation distortion. - - Args: - img (np.ndarray): The input image. - Returns: - np.ndarray: Image after saturation change. - """ - - if random.randint(2): - img = mmcv.bgr2hsv(img) - img[:, :, 1] = self.convert( - img[:, :, 1], - alpha=random.uniform(self.saturation_lower, - self.saturation_upper)) - img = mmcv.hsv2bgr(img) - return img - - def hue(self, img: np.ndarray) -> np.ndarray: - """Hue distortion. - - Args: - img (np.ndarray): The input image. - Returns: - np.ndarray: Image after hue change. - """ - - if random.randint(2): - img = mmcv.bgr2hsv(img) - img[:, :, - 0] = (img[:, :, 0].astype(int) + - random.randint(-self.hue_delta, self.hue_delta)) % 180 - img = mmcv.hsv2bgr(img) - return img + # def saturation(self, img: np.ndarray) -> np.ndarray: + # """Saturation distortion. + + # Args: + # img (np.ndarray): The input image. + # Returns: + # np.ndarray: Image after saturation change. + # """ + + # if random.randint(2): + # img = mmcv.bgr2hsv(img) + # img[:, :, 1] = self.convert( + # img[:, :, 1], + # alpha=random.uniform(self.saturation_lower, + # self.saturation_upper)) + # img = mmcv.hsv2bgr(img) + # return img + + # def hue(self, img: np.ndarray) -> np.ndarray: + # """Hue distortion. + + # Args: + # img (np.ndarray): The input image. + # Returns: + # np.ndarray: Image after hue change. + # """ + + # if random.randint(2): + # img = mmcv.bgr2hsv(img) + # img[:, :, + # 0] = (img[:, :, 0].astype(int) + + # random.randint(-self.hue_delta, self.hue_delta)) % 180 + # img = mmcv.hsv2bgr(img) + # return img def transform(self, results: dict) -> dict: """Transform function to perform photometric distortion on images. diff --git a/nohup.out b/nohup.out new file mode 100644 index 0000000000..918bb3b173 --- /dev/null +++ b/nohup.out @@ -0,0 +1,1981 @@ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/16 08:10:01 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1275060518 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1275060518 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/16 08:10:02 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/16 08:10:04 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' + +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.layers.3.ffn.layers.1.bias as id 4 + +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14set param backbone.layers.4.gamma_1 as id 5 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.cls_token as id 0 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.pos_embed as id 0set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.0.gamma_1 as id 1set param backbone.layers.5.attn.qkv.weight as id 6 + +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6set param backbone.layers.0.ln1.bias as id 1 + +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6set param backbone.layers.0.attn.relative_position_bias_table as id 1 + +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.6.gamma_1 as id 7set param backbone.layers.0.attn.qkv.bias as id 1 + +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.0.ffn.layers.0.0.weight as id 1 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + +set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param backbone.layers.6.ffn.layers.1.bias as id 7set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.1.attn.proj.weight as id 2 + +set param backbone.layers.7.gamma_2 as id 8set param backbone.layers.1.attn.proj.bias as id 2 + +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.7.ln1.bias as id 8 + +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.7.attn.proj.weight as id 8 + +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.7.ffn.layers.1.bias as id 8 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.2.attn.relative_position_bias_table as id 3set param backbone.layers.8.gamma_2 as id 9 + +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.8.attn.proj.bias as id 9 + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.2.ffn.layers.1.weight as id 3set param backbone.layers.8.ffn.layers.1.weight as id 9 + +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.2.ffn.layers.1.bias as id 3 + +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.3.gamma_2 as id 4set param backbone.layers.9.ln1.weight as id 10 + +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.3.attn.qkv.weight as id 4set param backbone.layers.9.attn.proj.weight as id 10 + +set param backbone.layers.9.attn.proj.bias as id 10set param backbone.layers.3.attn.qkv.bias as id 4 + +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.4.gamma_1 as id 5 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11set param backbone.layers.4.ln1.weight as id 5 + +set param backbone.layers.4.ln1.bias as id 5set param backbone.layers.10.ln2.weight as id 11 + +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11set param backbone.layers.4.attn.qkv.weight as id 5 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.11.ln1.weight as id 12set param backbone.layers.4.ln2.bias as id 5 + +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.4.ffn.layers.1.weight as id 5set param backbone.layers.11.attn.proj.bias as id 12 + +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.11.ln2.weight as id 12 + +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param backbone.layers.11.ffn.layers.1.weight as id 12set param backbone.layers.5.ln1.weight as id 6 + +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6set param neck.upsample_4x.0.weight as id 13 + +set param backbone.layers.5.attn.qkv.bias as id 6set param neck.upsample_4x.0.bias as id 13 + +set param backbone.layers.5.attn.proj.weight as id 6set param neck.upsample_4x.1.weight as id 13 + +set param neck.upsample_4x.1.bias as id 13set param backbone.layers.5.attn.proj.bias as id 6 + +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.5.ln2.weight as id 6 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.5.ln2.bias as id 6 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param decode_head.conv_seg.weight as id 13 + +set param decode_head.conv_seg.bias as id 13set param backbone.layers.5.ffn.layers.1.bias as id 6 + +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7set param decode_head.psp_modules.0.1.conv.weight as id 13 + +set param backbone.layers.6.ln1.weight as id 7set param decode_head.psp_modules.0.1.bn.weight as id 13 + +set param decode_head.psp_modules.0.1.bn.bias as id 13set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param backbone.layers.6.attn.qkv.weight as id 7set param decode_head.lateral_convs.0.bn.bias as id 13 + +set param backbone.layers.6.attn.qkv.bias as id 7 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param backbone.layers.6.attn.proj.weight as id 7set param decode_head.lateral_convs.1.bn.bias as id 13 + +set param backbone.layers.6.attn.proj.bias as id 7 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13set param backbone.layers.6.ln2.weight as id 7 + +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param backbone.layers.6.ln2.bias as id 7 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param backbone.layers.7.gamma_1 as id 8set param decode_head.fpn_convs.2.bn.bias as id 13 + +set param backbone.layers.7.gamma_2 as id 8set param decode_head.fpn_bottleneck.conv.weight as id 13 + +set param decode_head.fpn_bottleneck.bn.weight as id 13set param backbone.layers.7.ln1.weight as id 8 + +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8set param auxiliary_head.conv_seg.weight as id 13 + +set param auxiliary_head.conv_seg.bias as id 13set param backbone.layers.7.attn.qkv.bias as id 8 + +set param auxiliary_head.convs.0.conv.weight as id 13set param backbone.layers.7.attn.proj.weight as id 8 + +set param auxiliary_head.convs.0.bn.weight as id 13 +set param backbone.layers.7.attn.proj.bias as id 8 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", 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This disables kernel caching. Specified directory is /root/.cache/torch/kernels. This warning will appear only once per process. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/cuda/jit_utils.cpp:860.) + tensor.erfinv_() +/opt/conda/lib/python3.8/site-packages/mmengine/model/weight_init.py:649: UserWarning: Specified kernel cache directory could not be created! This disables kernel caching. Specified directory is /root/.cache/torch/kernels. This warning will appear only once per process. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/cuda/jit_utils.cpp:860.) + tensor.erfinv_() +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +/opt/conda/lib/python3.8/site-packages/mmengine/model/weight_init.py:649: UserWarning: Specified kernel cache directory could not be created! This disables kernel caching. Specified directory is /root/.cache/torch/kernels. This warning will appear only once per process. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/cuda/jit_utils.cpp:860.) + tensor.erfinv_() +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +/opt/conda/lib/python3.8/site-packages/mmengine/model/weight_init.py:649: UserWarning: Specified kernel cache directory could not be created! This disables kernel caching. Specified directory is /root/.cache/torch/kernels. This warning will appear only once per process. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/cuda/jit_utils.cpp:860.) + tensor.erfinv_() +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +04/16 08:10:06 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/16 08:10:06 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/16 08:10:06 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512. +04/16 08:11:03 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:27:28 time: 1.0323 data_time: 0.0047 memory: 8935 loss: 7.1374 decode.loss_ce: 5.1061 decode.acc_seg: 2.7090 aux.loss_ce: 2.0313 aux.acc_seg: 0.0000 +04/16 08:11:54 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 2 days, 0:06:13 time: 1.0280 data_time: 0.0044 memory: 8462 loss: 6.8298 decode.loss_ce: 4.8244 decode.acc_seg: 26.8021 aux.loss_ce: 2.0054 aux.acc_seg: 0.1680 +04/16 08:12:46 - mmengine - INFO - Iter(train) [ 150/160000] base_lr: 9.9401e-06 lr: 3.6750e-08 eta: 1 day, 23:16:43 time: 1.0264 data_time: 0.0042 memory: 8462 loss: 6.3951 decode.loss_ce: 4.4258 decode.acc_seg: 57.5527 aux.loss_ce: 1.9693 aux.acc_seg: 27.9995 +04/16 08:13:37 - mmengine - INFO - Iter(train) [ 200/160000] base_lr: 1.3276e-05 lr: 4.9083e-08 eta: 1 day, 22:48:42 time: 1.0202 data_time: 0.0040 memory: 8462 loss: 5.8755 decode.loss_ce: 3.9571 decode.acc_seg: 70.3098 aux.loss_ce: 1.9185 aux.acc_seg: 25.9087 +04/16 08:14:28 - mmengine - INFO - Iter(train) [ 250/160000] base_lr: 1.6611e-05 lr: 6.1415e-08 eta: 1 day, 22:27:43 time: 1.0164 data_time: 0.0046 memory: 8462 loss: 5.1345 decode.loss_ce: 3.2971 decode.acc_seg: 80.0583 aux.loss_ce: 1.8375 aux.acc_seg: 45.8790 +04/16 08:15:19 - mmengine - INFO - Iter(train) [ 300/160000] base_lr: 1.9947e-05 lr: 7.3747e-08 eta: 1 day, 22:12:04 time: 1.0117 data_time: 0.0040 memory: 8462 loss: 4.6058 decode.loss_ce: 2.8301 decode.acc_seg: 84.3102 aux.loss_ce: 1.7756 aux.acc_seg: 37.2120 +04/16 08:16:09 - mmengine - INFO - Iter(train) [ 350/160000] base_lr: 2.3282e-05 lr: 8.6079e-08 eta: 1 day, 21:58:41 time: 1.0075 data_time: 0.0046 memory: 8462 loss: 3.9343 decode.loss_ce: 2.2677 decode.acc_seg: 90.3549 aux.loss_ce: 1.6666 aux.acc_seg: 51.3355 +04/16 08:16:59 - mmengine - INFO - Iter(train) [ 400/160000] base_lr: 2.6618e-05 lr: 9.8412e-08 eta: 1 day, 21:47:59 time: 1.0072 data_time: 0.0041 memory: 8462 loss: 3.2720 decode.loss_ce: 1.7116 decode.acc_seg: 92.8013 aux.loss_ce: 1.5604 aux.acc_seg: 62.0686 +04/16 08:17:50 - mmengine - INFO - Iter(train) [ 450/160000] base_lr: 2.9953e-05 lr: 1.1074e-07 eta: 1 day, 21:39:08 time: 1.0060 data_time: 0.0041 memory: 8462 loss: 2.5921 decode.loss_ce: 1.1644 decode.acc_seg: 97.5016 aux.loss_ce: 1.4277 aux.acc_seg: 68.5354 +04/16 08:18:40 - mmengine - INFO - Iter(train) [ 500/160000] base_lr: 3.3289e-05 lr: 1.2308e-07 eta: 1 day, 21:31:50 time: 1.0055 data_time: 0.0042 memory: 8462 loss: 2.0673 decode.loss_ce: 0.7804 decode.acc_seg: 97.7400 aux.loss_ce: 1.2869 aux.acc_seg: 67.5140 +04/16 08:19:30 - mmengine - INFO - Iter(train) [ 550/160000] base_lr: 3.6624e-05 lr: 1.3541e-07 eta: 1 day, 21:25:21 time: 1.0046 data_time: 0.0044 memory: 8462 loss: 1.6140 decode.loss_ce: 0.5170 decode.acc_seg: 96.9492 aux.loss_ce: 1.0970 aux.acc_seg: 83.8314 +04/16 08:20:20 - mmengine - INFO - Iter(train) [ 600/160000] base_lr: 3.9960e-05 lr: 1.4774e-07 eta: 1 day, 21:19:55 time: 1.0043 data_time: 0.0044 memory: 8462 loss: 1.3474 decode.loss_ce: 0.3544 decode.acc_seg: 97.2013 aux.loss_ce: 0.9931 aux.acc_seg: 82.7944 +04/16 08:21:11 - mmengine - INFO - Iter(train) [ 650/160000] base_lr: 4.3296e-05 lr: 1.6007e-07 eta: 1 day, 21:14:59 time: 1.0030 data_time: 0.0046 memory: 8462 loss: 1.0697 decode.loss_ce: 0.2786 decode.acc_seg: 98.2925 aux.loss_ce: 0.7911 aux.acc_seg: 94.7325 +04/16 08:22:01 - mmengine - INFO - Iter(train) [ 700/160000] base_lr: 4.6631e-05 lr: 1.7240e-07 eta: 1 day, 21:10:12 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.8254 decode.loss_ce: 0.2025 decode.acc_seg: 98.8443 aux.loss_ce: 0.6229 aux.acc_seg: 97.8575 +04/16 08:22:51 - mmengine - INFO - Iter(train) [ 750/160000] base_lr: 4.9967e-05 lr: 1.8474e-07 eta: 1 day, 21:05:44 time: 0.9987 data_time: 0.0043 memory: 8462 loss: 0.6389 decode.loss_ce: 0.1660 decode.acc_seg: 96.1958 aux.loss_ce: 0.4728 aux.acc_seg: 94.6260 +04/16 08:23:41 - mmengine - INFO - Iter(train) [ 800/160000] base_lr: 5.3302e-05 lr: 1.9707e-07 eta: 1 day, 21:01:16 time: 0.9981 data_time: 0.0045 memory: 8462 loss: 0.5138 decode.loss_ce: 0.1463 decode.acc_seg: 97.7625 aux.loss_ce: 0.3675 aux.acc_seg: 96.5370 +04/16 08:24:30 - mmengine - INFO - Iter(train) [ 850/160000] base_lr: 5.6638e-05 lr: 2.0940e-07 eta: 1 day, 20:57:08 time: 0.9965 data_time: 0.0042 memory: 8462 loss: 0.4012 decode.loss_ce: 0.1269 decode.acc_seg: 98.1451 aux.loss_ce: 0.2743 aux.acc_seg: 96.9545 +04/16 08:25:20 - mmengine - INFO - Iter(train) [ 900/160000] base_lr: 5.9973e-05 lr: 2.2173e-07 eta: 1 day, 20:53:32 time: 0.9979 data_time: 0.0043 memory: 8462 loss: 0.3109 decode.loss_ce: 0.0988 decode.acc_seg: 98.7818 aux.loss_ce: 0.2121 aux.acc_seg: 97.1060 +04/16 08:26:10 - mmengine - INFO - Iter(train) [ 950/160000] base_lr: 6.3309e-05 lr: 2.3407e-07 eta: 1 day, 20:50:07 time: 0.9976 data_time: 0.0040 memory: 8462 loss: 0.2525 decode.loss_ce: 0.0933 decode.acc_seg: 97.1182 aux.loss_ce: 0.1593 aux.acc_seg: 94.3285 +04/16 08:27:00 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 08:27:00 - mmengine - INFO - Iter(train) [ 1000/160000] base_lr: 6.6644e-05 lr: 2.4640e-07 eta: 1 day, 20:46:57 time: 0.9972 data_time: 0.0042 memory: 8462 loss: 0.2075 decode.loss_ce: 0.0836 decode.acc_seg: 98.2325 aux.loss_ce: 0.1239 aux.acc_seg: 97.9246 +04/16 08:27:50 - mmengine - INFO - Iter(train) [ 1050/160000] base_lr: 6.9980e-05 lr: 2.5873e-07 eta: 1 day, 20:44:03 time: 0.9977 data_time: 0.0040 memory: 8462 loss: 0.1839 decode.loss_ce: 0.0781 decode.acc_seg: 98.1035 aux.loss_ce: 0.1059 aux.acc_seg: 97.7102 +04/16 08:28:40 - mmengine - INFO - Iter(train) [ 1100/160000] base_lr: 7.3316e-05 lr: 2.7106e-07 eta: 1 day, 20:41:20 time: 0.9965 data_time: 0.0041 memory: 8462 loss: 0.1804 decode.loss_ce: 0.0764 decode.acc_seg: 96.8763 aux.loss_ce: 0.1039 aux.acc_seg: 96.4491 +04/16 08:29:30 - mmengine - INFO - Iter(train) [ 1150/160000] base_lr: 7.6651e-05 lr: 2.8339e-07 eta: 1 day, 20:38:45 time: 0.9978 data_time: 0.0045 memory: 8462 loss: 0.1393 decode.loss_ce: 0.0634 decode.acc_seg: 97.7964 aux.loss_ce: 0.0759 aux.acc_seg: 96.7056 From 5c668f324f3c9d64be51cbda205f02d0b1c337e3 Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Wed, 17 Apr 2024 08:33:45 +0000 Subject: [PATCH 14/24] 2024.04.17 --- MAEOUT_240416/vis_data/config.py | 349 - a.ipynb | 326 +- configs/_base_/datasets/cag.py | 4 +- .../upernet_r101_4xb4-160k_cag-512x512.py | 4 +- nohup.out | 7448 +++++++++++++++++ 5 files changed, 7765 insertions(+), 366 deletions(-) delete mode 100644 MAEOUT_240416/vis_data/config.py diff --git a/MAEOUT_240416/vis_data/config.py b/MAEOUT_240416/vis_data/config.py deleted file mode 100644 index cfaf78522b..0000000000 --- a/MAEOUT_240416/vis_data/config.py +++ /dev/null @@ -1,349 +0,0 @@ -crop_size = ( - 512, - 512, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 512, - 512, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = './cag/' -dataset_type = 'CoronaryAngiographyDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=1000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(draw=True, type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -fp16 = dict(loss_scale='dynamic') -img_ratios = [ - 1.0, -] -launcher = 'pytorch' -load_from = '/workspaces/mmsegmentation-1/work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512/iter_1000.pth' -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=768, - in_index=2, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=150, - num_convs=1, - type='FCNHead'), - backbone=dict( - act_cfg=dict(type='GELU'), - attn_drop_rate=0.0, - drop_path_rate=0.1, - embed_dims=768, - img_size=( - 512, - 512, - ), - in_channels=3, - init_values=1.0, - mlp_ratio=4, - norm_cfg=dict(eps=1e-06, type='LN'), - norm_eval=False, - num_heads=12, - num_layers=12, - out_indices=[ - 3, - 5, - 7, - 11, - ], - patch_size=16, - type='MAE'), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 512, - 512, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=768, - dropout_ratio=0.1, - in_channels=[ - 768, - 768, - 768, - 768, - ], - in_index=[ - 0, - 1, - 2, - 3, - ], - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=150, - pool_scales=( - 1, - 2, - 3, - 6, - ), - type='UPerHead'), - neck=dict( - embed_dim=768, rescales=[ - 4, - 2, - 1, - 0.5, - ], type='Feature2Pyramid'), - pretrained='/workspaces/mmsegmentation-1/converted_model.pth', - test_cfg=dict(crop_size=( - 512, - 512, - ), mode='slide', stride=( - 341, - 341, - )), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - constructor='LayerDecayOptimizerConstructor', - optimizer=dict( - betas=( - 0.9, - 0.999, - ), lr=0.0001, type='AdamW', weight_decay=0.05), - paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, by_epoch=False, end=1500, start_factor=1e-06, - type='LinearLR'), - dict( - begin=1500, - by_epoch=False, - end=160000, - eta_min=0.0, - power=1.0, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=1, - dataset=dict( - data_prefix=dict( - img_path='images/test', seg_map_path='annotations/test'), - data_root='./cag/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 512, - 512, - ), type='Resize'), - dict(reduce_zero_label=False, type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - reduce_zero_label=False, - type='CoronaryAngiographyDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 512, - 512, - ), type='Resize'), - dict(reduce_zero_label=False, type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict( - max_iters=160000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=2, - dataset=dict( - data_prefix=dict( - img_path='images/training', seg_map_path='annotations/training'), - data_root='./cag/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(reduce_zero_label=False, type='LoadAnnotations'), - dict( - border_mode=0, - p=1, - rotate_limit=20, - scale_limit=( - -0.2, - 0, - ), - shift_limit=0.1, - type='AlbuShiftScaleRotateTransform', - value=[ - 0.3, - 0.4, - 0.5, - ]), - dict( - brightness_limit=( - -0.2, - 0.2, - ), - contrast_limit=( - -0.2, - 0.2, - ), - p=0.5, - type='AlbuRandomContrastTransform'), - dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( - 0, - 0.01, - )), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - reduce_zero_label=False, - type='CoronaryAngiographyDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(reduce_zero_label=False, type='LoadAnnotations'), - dict( - border_mode=0, - p=1, - rotate_limit=20, - scale_limit=( - -0.2, - 0, - ), - shift_limit=0.1, - type='AlbuShiftScaleRotateTransform', - value=[ - 0.3, - 0.4, - 0.5, - ]), - dict( - brightness_limit=( - -0.2, - 0.2, - ), - contrast_limit=( - -0.2, - 0.2, - ), - p=0.5, - type='AlbuRandomContrastTransform'), - dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( - 0, - 0.01, - )), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=1, - dataset=dict( - data_prefix=dict( - img_path='images/validation', - seg_map_path='annotations/validation'), - data_root='./cag/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 512, - 512, - ), type='Resize'), - dict(reduce_zero_label=False, type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - reduce_zero_label=False, - type='CoronaryAngiographyDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - save_dir='./MAEOUT_240416', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512' diff --git a/a.ipynb b/a.ipynb index c0e6542391..817ad0b913 100644 --- a/a.ipynb +++ b/a.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -21,37 +21,337 @@ "source": [ "import cv2\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "\n", - "# Path to the image file\n", - "image_path = '/workspaces/mmsegmentation-1/MAEOUT_240416/00001.png'\n", + "# Define the paths to the images\n", + "image_path_output = '/workspaces/mmsegmentation-1/MAEOUT_240416/00000.png'\n", + "image_path_img = '/workspaces/mmsegmentation-1/data/cag/images/test/00000.png'\n", + "image_path_label = '/workspaces/mmsegmentation-1/data/cag/annotations/test/00000.png'\n", "\n", - "# Load the image using OpenCV\n", - "img = cv2.imread(image_path, 0)\n", "\n", - "# Convert BGR to RGB (OpenCV uses BGR by default)\n", + "# Load the images using OpenCV\n", + "label_img = cv2.imread(image_path_label, 0)\n", + "img = cv2.imread(image_path_img)\n", + "output_img = cv2.imread(image_path_output, 0)\n", "\n", - "# Display the image using Matplotlib\n", - "plt.imshow(img*255)\n", - "plt.axis('off') # Turn off axis\n", + "# Create masks for pixels where label and output are both 1, label is 1 but output is 0, and label is 0 but output is 1\n", + "true_mask = (label_img == 1) & (output_img == 1)\n", + "false_mask = (label_img == 1) & (output_img == 0)\n", + "the_false_mask = (label_img == 0) & (output_img == 1)\n", + "\n", + "# Create an array to store the visualization\n", + "visualization = np.zeros((label_img.shape[0], label_img.shape[1], 3), dtype=np.uint8)\n", + "\n", + "# Define colors\n", + "green = [0, 0, 255] # Red for overlapping parts\n", + "yellow = [0, 255, 255] # Yellow for parts where prediction is not possible\n", + "blue = [0, 255, 0] # Green for parts where prediction is better\n", + "\n", + "# Assign colors to the visualization array based on the masks\n", + "visualization[true_mask] = green\n", + "visualization[false_mask] = yellow\n", + "visualization[the_false_mask] = blue\n", + "\n", + "# Display the result\n", + "plt.imshow(cv2.cvtColor(visualization, cv2.COLOR_BGR2RGB))\n", + "plt.axis('off')\n", "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00285.png\n", + "IoU: 94.63\n", + "F1 Score: 97.24\n", + "Precision: 96.41\n", + "Recall: 98.09\n" + ] + }, + { + "data": { + "image/png": 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VMWIvCp6PBSAaICTwvHcCkBfSS+pODUMfYA2KDTVuWIhsiBpmNR09DT8hj8gvXWer5tFAKsM8FJ3SEWNzzs06OBclt0VJaa80E02ENTGCIZvtxAH7+cjGcBfo7ihkRhh5I0cTdo5+OFczmvzMnTpm1t104PTKPbzOAdkcYMLL+bjJMcNmk0o4TBy+50+0dhXBpTcc8XkZJxPH0Iyj+fnm5mZRH2TA5HxABno0EZQhIhHPxknU5bMoMEqNcKtsGgvLPYnOGIxrfCw2fal4MvIbFJox8jt7V+djGIOJBfdZmtU0/HLB2kaFA1xYWCjKKuvr6yVGkPujfCbTUBy8NGNDjuvr6zvmcXh4vo8DoGlkbm6uMCie49yNaI+jxuiRgYk6s7zOf52vc00mkyJSc29SioWFhVKKhd5hiOYWmBPlK+u2+4hxBM4fHYlxwOYeWDecl6sQtgV+7rTnnI2Tv4ExEAAsKBdwgs+a8vbnUAYLzjDGrVeLi4sFbKZH02RKldUluTbkNKFABwz1PfdXIliEx8+B6UQBlBx42+l0ii4n12NZQLcfEsWttOStZrzJf5iXmWgulAFISnSjm8U5NnMgHyK/RgGr+TfGDDtsx4MiorhmxZGZn2sGuZr3IwvGlqTIWQ3J0ScM1Y7b8zNL6ohl4srzdrrB2Ew8Op2zo8DRMBfWi+YWHD1Om3H4e4bpu10HIoRGo1HRE2lh0V5HzgecRHnxwFtbW4UC28Nw72RWL3O+Q7dLo9EoIDAKT+2v3W4X96LpG4N0UzvdMfasvV6v1Btr2p/CcbUUgyLhuFhEe3MiNIw0HtYLBbSuwmjGRhQ3XY/ygwqIrJPJpGCLYXYZG7VT7umUAUaZfMvMKcQFn8EhOwVAUQ3hzeybAbfhMl8cAo4AfcA4XVtkTibkgOfojy9gJdGV1kgzrcgUPeI5rJkrADZ0nJ+bDswhEMkd/Z1PI2vy92/LOBkAXtnRB+HhQSFpmACFbBoFmCSTWFtbKzwx92LB+/1+4aExUkPG0WiU06dPF0YJueK81osFrGIxyW8QNItGKQKngwclanN/lLrf7xe7HrrdbhF5aEqHqTPsgUbnWW7zAn4yRubsmigRjnu51YxoiNwgIPguRX0UxXAdlMPzuFeV3QWiI09KSCapzIYy1iqpyNwgufwd7uXP2bAZZ5Uld3GfFGVlZaVALuiJKwI4VxCQn4GDQxbWSeQymczKQ6QhzNkNKHYijqw7XQc2ThJ7QywXl01s0E7n3AlBOKwTWVkwPBaD5ncI2+wbykWk3dzcTKfTKbwZvZhEXBhR7sM4HKmJ8iabEL7H7nzajsL5EIbfbDZL+zYd2U2YsYiGT2YNTaYgc9bBsBA4R46czDalE1VRTEdCw1eQBuNA5tybdU9mnUXMC1KQOboOy71Ze8NvZO89r8gFJ814zd6z9uSS9fr2BmzmZLSGEeFQmBtGaVhrtOJcFNlUmXjDX2TDeJ2yGeI7eHxbxomyInTyJHsclILBuc5YhUJAFhrYvUvEnUIoKOyb+0lpnDds4/uDwaBEWphKt8K6S8jbp1xrsyFyH+AVUZ88Dm/KYnuTcjLrP+71egV0rdfrxf5T7lXNp4nUzjFHo1ERSZIUzB9piNvQmAMGAeSjhFRdJ+YP/K9GJ+eerBe/t+wMTxmHSRJ0CWMz2cX3XeM2ceR1db0TpeeZztdJT/i+IbPhMvuPWedq9xuw2feDCEvKZzoxdjfVGAHudh24CYFIUCVfjOtdQ6omvQiV70N8QCmTM3k7lYmO0WiUtbW1LC0tJUkBh7na7XZp54J3ANi4klneggHjPfHieHXDKysvCs0YMARKI+TIo9F23y1EElHZcmDhUPKFhYWzyAS8bTJDD8Bh10p3Iric83onBfMzMjHcdG5np4pxA5GTmUPB8ZkQRO4oImQNDtbsp9lQRyHkZdRlsszQ0TVuZFpFOjg0b7rg/l4XoqQhNY7Fa4OcnVciRzslR1d/brfrwMbpZJ6zbrrdbjEIope9OQrvRJjLE19eXi6iDlHD0cZUt7tV3PpkmIQRekd6NV9hYVkEFsBMocswKC/RwTAIeJzMOprIUclHifAoMb2z5OReRI/R43MqgHMhp3VNzzASg8FxoBDMz0V1561EDhsP5Eg1v+fZRC6UEjk4xTGqsH7gRDAWl4W4zLS6dzcpQ0bGaMdJEElSYu3tBMh7XdIDjQBdQUh26rXa9kZ8l+HI5T1+M8E4fdtE9Tqn9j2zeWtra5lMJrnggguKxaMojSexsqEQ1eiFN3Qe5AhGDosyYDij0fYRHt4Zn2y3oAFrnfdgVJubm6WjSGBN2+12YTgIlMVkLGYqUSJqj2bvUHY/k7EYrjkqWfFRFGTteZhhxLjYQMzvcVB2UkRVZGEkYFIC4wGG2QEwv2rvrY0Q+RmyYZhVMoR5UN5x5EQHLCN/r5peWGfQU77LuUvJjOQzAWUYXKvVig0QPB89hP13HoyzJB91Pm6HCvTFQAks6NFO1znBWhaz0+mk2+1mdXU1y8vLBWtpuIE3sldFgM4VWGAM26QB92MxUBwWFUIICEV0cOQwtAE246HNwNojA98x2CoUhbTgs669cnQo7LKJIRbQZRC+R2dLtTxgBhtFIA/EU5ucAHYDq3FckGXck/UiYo5G26ccghwMtenjZasdcmOOThmIkvwbpWc+/hxoZn19vZT7uWTh/NuGggGiO97jaTRiWZrHMNR1To8zdI3dzG29Xi+293Ff57nos8lLIz8iL9/9G8k5je85HpNjFlkQCw7o6cFjdFaqao7oTiGimkkdjM7sG4qEIMiHXLu04HgeBFJ1fi4jsHBAR1oHKY9AljBmM83MZTqdFs3peGOPFwOvUvCO+CABkx/MKZmd4et8HuXCaJETa1Zlwp3b+vNWIPdHu8yB3O2ATfB4zBhtvV4v5GqihLVGod1pg/442qNjbhJwfugojEPCQWLYhpysqdeoCusdbFirKsPPWrCGyB5ZQFh+28ZZZSYxUITi3Q4MnN8xGYTn3A8PTiRz/pSkYHLtTV2n4xnu6mHBUA6zdlUWFsaNBXe+aa+MIrtkRPmH+zjqYTDO+RivaX7n4y5XIUczrs7bklnD+tLSUkGcEUVJE+x0zLYSJUx+ucXSZI8bTcw6G8IzN581hPwwPhM8zp/5jKGvc1dyPJTbeZt3wJDv8dwkBflU7ddtNpvF2cnoSjJLvUBSXksMy5HaaMQMOzLm/qwrKQZI6rxhrQUK1ONhHOybbO8wcB42nU4LdpIJVxNqoBwCRzlYVBQeogDlNEGSzPIE/o2nw5DtMBiDc0cW1WSSFZPnogwmUMyaJrPclEVCSSmn2LPzGRbOJIdzOCOQZrNZlGFOnz6d06dPZzAY5OKLL87x48dLpA/fY46Mi3wR2XBvDJs5I3cMmLXDuXku1pOq7uDUHZl4Jk6W5++0dcz3w9kRDMjd0JUqWYdeoGtmhVkXpysOEtVc3I0J2AGy4rmG4awx951MJllZWSnSwirpec7GiaewAtrz4mmJLBge9Ug3oXuwhrvVMgX3xWB2SvhRAkdNNzHTxpbkLKjFWHEQjiyUPazEREaeZyX1hl5HceYKtKoeNl0t+KOU1WZw50OGROvr60Wf6ObmZk6dOpXFxcUcOXKkxJ5CVqE0hmUoOHNnbDgnKw4Ka+dBjs/4bZCw0YzFa21ZI0dzFU57zNba2TLOVqtVbFqwYzGBY2dphFJlVQ3/eR7lEcvBumr4bUdrtEiA4TxnTv9PUqrt73TteeS0BeMIh8FBdlh4GBNQiNPerGwI0JEBTO9nw0RSikhSMLYrKytF/oeh4BxwAM4nqiQCC2janDHwGeckLBrzQ5ldxMcIYZiBeIY7VdjK74hQrVartI1oOp3VlIHCGCywkHGZnGKcnNfruh/P90t4iI7ONX2ioEtLyQxNuP2Pe7rMYHhomfJM59VVuTjy4Ahd7sLBuHMJo6jm0yZfkBXPw1m7Xm9Sx/fBMdkecDbVOaDvW1tbxTtqmHvVAZ6zcVp5fCPnY+QEXgjIEucCeF1HKjcsOG9xBFpYWChO8TOxgYBRVgTGImFUHgdGyO9JyjF4OyGTN2b3EC6LDDTnOchmdXW1+A5KgSwwEmTsaE5UIJdCsTBSUgZkaIMyeWX2krFxUoHl7c3qwP5Dhw6l2+2W1pHfe+eRGzxYX/cKo6TOD2n2Nix2yci5nfN81t4RizV1nojBsPbWOcZg520Sh4t14uLFUDY4IjXfQ0+4l5EDz4U5Jz3hfrtd++acKLQfjGciSTe7hRAQtBve+a5rYfwfKOzknDG0Wq0cPny4gAicweLd7xgUG1lhCM0Sm+E0/OMZEDJ8BiTA2FgEvmPFM3RmFw4lCEfNKjHifAWHhwG7zxdD9liRKeNBCQ4dOpQkRcmHuWBoGC4wy07Fkce5Os8yU7pTTsjPMRA7YSs8BoiBud2PNSAyYnCMGaeEHKnDVrkFH+FZq9VKfcc2LCKu2wiRnZlzOwVsw2QoCIKA47otTp+A4pMJd7v2zTmT2Tk3KAYGlcxOL+ffeGIWA+VzPREvlKQQIBNwzuNuFbe0XXjhhRkMtt+qhfBQQkoIGLjrWURclJXF8OWcGIHyDBsiBuQT6clTKFAvLy8XW9p2kgdsKzDKPa5mHfkOCgx7CSylE8aten6VIDIhYlJvXV9fL8FujBdUwljw8kZOKKdzZRTYCIYxoPwoOOsNAsC5JSk5cHSG+Tu1skFYD+wo7QCtq4zTuuNGiyqKw8gMg/08N3fg7FjzbrdblKBYa6OP8zJOTw4P5ijE//HE3hng/M85BgND2ERgFLEKWZggwm42m8UbtE6fPl1smuY1CyiFD/Ll++4EgUbH2SBoxraxsVHKIU1EmDRIZiSZz0DijWHVUoNJKeqmjJUx4v2JdhizFZ0oZgJlOt1uTqABwbVXFNpsNBATxbLjNULi72o5wjU/5EPK4Lqf19PQlDGawKsaQa/XK/EGjoDIokocIRccBs6OMUP0Od+zwfjYSqMKy9m6bz4DHbOz47Mm+zzX8zJOh35HJRMm5G2j0ag47s/K4AGiCCw8/6+SRYa2znf4/2SyfVzFsWPHcurUqWLx7A0ZM89xNFpcXMxoNCratOhzxciAyC+88ELRWsY8bVzIxGUCdvIzb2SEogDzyH02NjZKu/h9xGOyHflNcmHAIAIa3nFOdAPZCKgJI3vkgZIBkZG5cyGTFzRdGN76c3yXsTk62vGhlM77kItZVusGDsNQlMYNclKvPd8HcqIj1UPkrFM4FU51REYOKhiVnYiJIeuhZY2scCzMfS9C6Jwb3w3vGARKjpey4VrZnR8Bn3yhBF4Yvs8iGXotLS2lXq8Xr2ZzlAOKecO2GVwn9vaUfL/b7ZZeBExkJ1qYYTbRQ45p0gkvigH5ALJOp1McAN3r9XLo0KFC2byLBPlgmOT0oAMizEUXXZROp5PV1dXSEZA2HuZDQRzFYe6uaZMfMZ7RaFQ4LNaLi4jkaGWHS1Rzfs28jJBYX+exztUN1+3ErCuGwIyH/Bo9MTJhnZOUoDI672huh2Jb4POsj/NNkAf6RgPP34hxsrBAWA8A7+teRP64TpjMakX1erlLBk9qj5+klPt4DC61AJFM+hg6OJ8jirDgePOdhEyfLPd3LynNAOS3GB9MqhXLzRUoNBGR3HF9fb04GsQtbciFPHJ9fb1om5xMZhuor7rqquKFwhiDYRaGjJwwFBTbjC9r42iAwvq1CKwJjtcbDnbKs70+1ingqXXLqRTriJ64HETux5oyJ77D5+xUDYf5edWRGM3xhwDE9+x4HeVrtVpxEBs675SF/Bv0tNt1oAO+uPAysLPeWc4xHTQxs+hcMFUoMgtF9HFeaaYQ5TARwJ5Hw0HKDIwRQ+C+LAz3NLOYzJhk4C6La7LKiwjET2Y7K3gjl4vlPNcK7VxtMpmk0+kUvbdEXGSCI0DRXnzxxcIgOcf2kksuKWTCzx31a7Va6Sxc1qPKqhp6WyZ2YrXa7O1wyawnmeK6P498zUoSkXCYGLLXDmeAsaMvyAZdnE6npW4z1w4NMZFzv98v2hrRMdc2QT/M2zwFRoRxOQhwvhJjTFLSR+6JDo3H49KZSedlnNWLyGYCwAbklxSh2NUOfEcEf9eenkk49/KC49VZZASAUhHh+B6eHeFZobhs/DgXvLUJCQ4qcwTEWM0KOlKCHojczrH4/mi0vSuEseGpvXWM85JY+Ol0WtSAyQM3NzeLtj7WgLkYXViJidZOO5CPO6J8L5yiowxG69SkmmtCrLiWCYfAv12/NltK7m1yB4jt99wMh8Ni7CbCTFqCvIDi1dIUa2cihwhcTYXMsqOznHLB6xxJPcidnfr9jRgnAwWmAWMNlRhItReRRUPICI5oaWbPNLgX18/hOwjdbJ6dAYuJkqGYPlvI3gsWjzGY/MERAL18TIqjHONgvjgPOxMWGqh05MiRUj6Gp3YOSw5JxDh06FCOHj1a2jx8+vTpbGxsFE6FvLZYcHlviusgECsc9+QtaEQsEx3O3VF6nxho5GR9MWmCMyZ9gY3HQWBsIBqfJogxGQU4IvIMP4vfM17Wr9qwX4321kXWLJmlBOiYy2ysM87UjLidzrdlnDYQFgVvgZAcKVgchA18cTRjwO7kYec4Buxn2rAwuGp+64S8SjaYOWOB3HqGI8GgDEUxUHvPfr9feEIUiL+5H7KCvTUJwUU6wCZwQ/V2u52lpaW0Wq0888wzWVlZyXi83ZLX7XZz4YUXptPpZDweZ21tLadOnSpOImS+nAaITByZMQyTLiiWFce5mom9ZNZSCMvK90AFLh/4D0ZVbTjBaNzm5s/zfG+3wnkhb8aEUfFd1gUDQT9YU6dj5JVVctP1Z4zcvcRJSmNhfDgAGyZs/27Xvsbp6OfTBdz94wi40wLwbwRTvbdDOxMmelB3I08EGkHQVIkkvybPJ6nXauWmfENpckDDDPJLl3QMfRAuLW3z8/M5duxYYaREKOeaeGLnb85vJpNZzRdZbhMLozzzzDOFM+h2uzl8+HAOHTpUgurkmcyr0+kUsM/7QVHsKtoBboIIaP5IZmUWIiVKzFpj7Gx05oLtJZp5veiLZe7+Y9LQRBCyR26uEuBYLE/G5dTEMsIRVcnEKruMUbmZxgZMYGG+Rj8Yp3NkZHrexmkv6TyICTab2y9fnU6nRQcRhkurnTtTDDm9SCTkVRiLp3Y+x2I4/wB+8n0fRUkuBjTjSBHuxWesOCyiFRBvjkEScWhq7vV6OXz4cC6++OKzcleeUyWqTEygJI5Sk8kk9c0X8uqb/iAPPfSyIodnrGtrayVW1CWAra2toubKz5NZLoW8q96dzzjfR5HddpnM3tblNkl34QDR3UONU2RNMDB+ZxTD3zgFDI714f+j0eykQNISnmPiCiP2uqInZlT5PLmo0YadFDIh6gK9narxDOftro+fN6w1vWzWzaSQQzxbyGCvzMwyUXshmheIjNwHehxBGoZiVPZq3knCgsP08Vo75zJ4UuY0Go0KWDkezxoJfGg2srATcdF6OBzm8ccfT7PZLM5VYhGt9HYEGGuz2Uy32y0d9DU3N5dOv5+3v+99uWrwlTz6fafzwNol2dqa5NSpU4X8gM1JivQAuI78THIgT+aK3EziEMExAhTNKAjHW/2+eYOFhYV0u91SDsh8vXMFpa7VakVOiRPh50QcohRRyPLFsFh/IilzxoiRCfczg2yiCOdD3m+drObPjJc0xWPgj88gwim69HPOxmkFQ6lQcE7MQ/A8DINxqDecZaJVL2d4iiGgxDS6k8+6QO8knShLVEOJuQ+HYfX7/UKRTYBwP6KKmVkiEl5veXk5R48eLQTe7/fzjW98I+PxOJdeemnBQFYXnfH6nsD2zc3N1Bv1XPXwQ7nzd9+f4888k1qS9556OKcuWMv7rr08W1sLWVtbKwxuMpkUpRwUD8cI2cL6mNTiu87hnYez9rDIKCMMsplWFJBnst7MHeOgDIQRV/8Qqezc+b8jEffwXNzhZOIQ/WUtfE/3g7ubCcKMORjV4MwJSBirmWbnu8iy1WoVTSfomY/KOSfjtDGZPcUDA7Pc0IxhQOoYqnjAQAW3zmGYCA/vQx5JNPRJ8KbGa7Vacey+m8Rp8SIi4NkdScyyOaoRdfjbZ502Go1cdtllmZ+fz+rqajY3N/Piiy8WhxFffvnlpUOxPB4rmeHjdDLJ5tzXc9UnfjUXPrNerEHruWl+5sVnsn7rWj7UvD3DQStra2tFo0SSAmpjQJ1OJ51Op/RmMtbATf+QN8jBuaujIVEjmXl8KzJlIkNR52Uof9UJ8hlvUkC/XAP3Wb2sjVnw6jox5ipz7FzbDt25sOGmkYBzVvc2+3fOodET9N25sQnVna59mxAYGApWq9VKlD7egxyThSVqVilroi5e2N6tyvwar1dZMrdw4ZXNDhuOs2gWjgkglMAvpaWjxvkjsNGJ/vHjx7O4uJjnn38+vV4vq6urWVhYyOrqak6fPl3AMlP5VnTGREnnygceyJ2//qtZfmE91WtumPyz31vLI//w6ZxcvyX1cfnwqVOnTqXb7abb7Rab1CF3zCQnsy1xoB8bOIgGp2nYZ7IEZV5YWCjyfFBStUkD+QPfMRIMqdpU7yYMnoE+kfO73unIxroDyTFi4Kb5C/Me3N+G73wZPTESsvGxnuZYHHCqdV8TrOdsnBYgA3Kize/wNAgYBcR4EKK9iWtGDNIJvmGIYRJGYSLBtLajLhDYdVBYzEIAynPtxaoC97PtZGq17caCK664IltbWzl9+nQBezY2NrK2tpbDhw8X3/VmXxS/3W6nluTKr3wld/zyL2fhzFufd7rmhsl/++FH8i9/upVR/9a0T7UzGoyK4zpwKuTBzqtodFhdXS3xB4Zzlp1rj4bnQPjJZLu76YILLiilLz7i1GUXRzV4AuRhB2Kn7rGRt9lhsk6O2Oirnw+zn8y6pJyTE7WrjRomg5zGdbvdQk+N4DwfnLxJRsuwikaq176Rk5vwf7x/NZHm50BR56vVSIGhgdP5vw0XoeGFgCn2iJw4T/HcL+rxHDY3N4t8bCeF43mQVkQVlAdkgNJQomG+jJHe2tOnTxfPX11dLWCPYT3yaTQaaTYa6XziE3nNv/23md/DMJOkluSab0zzL3/twfzML38jh06/I3c9fFfqmTVlI0vWxM6ON2NDulRRDUaOogL/HEnW1tYKKEvEdG0Pw7bh2PESVQztXO5Arl4Hl0b4m5QhKR97yn0hkdA9/s3JFzgQ6ziHiFXTLxNldB+5M8g5MOvKyRPJLJJ7DsvLy8XWxZ2uPY2zGiWBT8BGkllONHeIRrDABGo/KKlrefztBWBBk1ldEeGQz7KgZlkhL4CgLJhLB9zPRWM8v2EZf9PaxoI4l8Z7uzsEZwQMgr0mYqDYk8kkg8Yg7c99PK/5f/9C5s+QPAe5rn0s+Y//eCPf92cfz3eMviPHpseSbBslh3olKeWbVZk75zUry3zMLqIPyMw5ZjJ7BSAohB04Ti9QWJNL3JsI49QFOSI3lz7QlWp+R25aTZ+4Lztx7Cid3hiVVR2HkSPzxiGCHtFZl2AYl8eK7px3ndNRxB05ZlQNjVgE5yn8m4JzMqv7ufjvSFuNoCYWiIB4Sv7mPZTcn3FibERefu7GcLydozKRxowvCssi8h131NTr9SwtLZW+S0TivKLCYTS28uSjv5i3/r/+IvNruxMDu10vezz55781ztYtjYw3Zx091V0SzM9tcdWGDre72cGgnMm2cUK2EZ1YZ0oI1gvWje86V7UD80mJRlrMw89nzUFoGKrrq0Q0LtYLmbg2iY4CQ23o6GS9PutbNtuLbpqQdDpknoNngbIccHa79o2cVU/rVjQU0EX2aj7g+iSD9wtj8GwI157OUMB1OSIoi9br9QqhwSLybAiZJCWh2JmgUN4Iy1j4P3OrEjkmCoBqjqrT6bToeeV0hFarlfF0lKce/qX8yP/+L3Lk1LkbZpJM5lt5+6tvyl9Pu+mlDJ3sLMbjcVGXNFPoKISyw4bTx0ouBhRGxuT4yMXRCtm4MYTnsGb8nZR7dI2YyNEhrLg3v8cZ26FzX5fxHGkJBDgSQ2kTjSZyeLadsnNJ0h4bM8/yyYTI3DD9vI0zmSkhykz0YCGAuhSS2+12ETGAixBF7Npn0VqtVlGvc1Qz3EAo1U4UL6DrmMl2P6npcUcBYM3CwkLhCd0WhkApPsMUVvOeqnJz/yorSgQZDAbFDvtRa5Rn7/8f8xP//K/SWTk/wxy1WvnSj/54vjF8V4bjRur1cqsjssHBurSUzBwva8TLfkzQAekhvZC5GXCiGwbrXBWIuVOkIHJ5rXHijN2RH7YVjsKR0k0L9Xq9aDzBuRKR+S7b0Wy0OFQ3O1j/Xes2QYlsXXlg/OiJHbUJVncs7XTta5wMCOU0pOUhDutEVQwJw+aENJMBkDcMFK9dZRLp1+R75H0I3mSNmVSioVk9vueXEZkwMctsY6ahgAXhj2G/oRJ/+23dGxsbmYzHOf6Ve/K9//Y/p7Oy9/sZd7vGc3P58k/8RB5/z3syHI4zGg0K7w+aQTkcFSAoDMVwgOvr6yWixKc10DHFfG0IGBclGD7DvZEjkQU583NHVDs4nAmdWzh48wFGac5jqxDbJQ3DThsIUdbIijHaKIH1yNpOx8QXcufZ6DYOjrwaY93pOpBx7nRD13wQkutg9oQ+vNhKbOM1BKnCSe/i9+nzCADjcJLu7p5klreibBtnWFGeDWThc4aFdINQ8DdEhwwzYeDxmWza2jydq7/yeO78hV/Jwtr5G+ZX/9E/yjff9a5Mz6wByubnJilyKx/ZwXxAQNW1dqcURlntnGEtHS0NLUEa1TdxcYQJRoQBsIY8z86y2mXFRbBIZumR81izvEYClgNO3OUZEzQ4Hc8R3XXOad0xUeXyjnNu8l4TYjtdBzoJwXmAqWUzeVz2yEwWT2mKm8lVqXPa6vBOeOxer1d03oDzGSOejuciKH7GYuKpeb9GdZ7JDMa4n5KcivHivT0PQxqzwRjDwsIkt5/43dz6y19KY2333e97Xf2F5As//d5sfM/3ZDoYZDIclhSXsQPbIMIcvTGQagsb85xOp0VPbLXW67ouMrNRGDV4Q7Xb6/geimxG1rm658Pv4RVwvOYLbCze1eRGAV92stU8k1M20DPfG33GidhQ/QwMH0djR+3I7GBTvfY0TsMGjAQPwORQDgse5QcWuAULxeH8HJQExg7jdQTmTFZyI8bgzcEI2YRMkqK0Ajx2lMFpEAFMPFnxGBcGTn+uYTAL4sJ/s9k803e6mXdc8ad59T/7Ymqn9pL47tewmfyHn78h7de/I8fPIAXLtUrKGW4Z3lOCSmZM8/LyctbX14vjQFkXvu96JEZh6G7EQnphwzfZw2Xyx5wAqMykFuNFtqyRx+QSEPDUeamfCaojwvJdpwCkS8jTZSO+S5pG+mMnxfe4p+vrrj+fN6w1ocJNUVYX8c2A2WC9OAyCfMh1JXvKZJYL4CHp7kAY1dYsxprMIpvJDkcUkxUorskkkx7AY/IYIoHzmSo8NxHVarWyuDDN26/487zyl+9N7eT+RrjTNU3y13d28q3uu3PJ05v51ty3ChRBQdxRgr+dlyF/jHYwGBSnF1Z7VXEurVartLcTT49Rs44obZXEYyzIw9CSs3WJTNYRF/eTlCIZn2ONTeAgB6IUOTP65fIOekGqg+74Z065HPG5lwOUCUI+i9Pk51QpGF81WFSvA8FaP4xFZKAsGMpiI7XSJykZiRk6BNFqtUqTwWM6kebnViSzvCwSk4c8sPHYcaB8jpIoM9819DJEsXNxTXhmuLVc/+iX86r//u7UTpwfKztN8rk3d/LBn/7RHD55ZSaTSbHpularFa99oBQyHs82UzMWfpekMBDOn0Wm8/PzWV5eLtbZTg8nhrIDWXFKOFobEE7NZxk5GiflvJH7mHQyYQXEtGNmvUiB7IhdGnK0ZC6G9uYYiOawsYwZuExENUoxeuNZMN84HO5nI/626pyFgshQqjmnPUqSglll0mZyTUtzH3Iz7gOMZSJEBryz252g94G8CADDxeh4RlJu7SK/ALZ4dw3ek+iDU/HimyRwjlGr1dJuTvPqp38z1/y7T6e2fv6G+dSNN+b+H/mRXJsrMjp8tmIyDjOmGC7n/3h8zH8wGGRjYyNLS0tF6Yl1ddsc88dxAiOTlGrSQEsiLGuFM0e5jSq8j5d1N4TGWCBhXALxv82+OjpjKDhh66v10GNCpt5nye95phGhn+HecgyTtAmZOLJubW1ldXV11/U/0IuMDBH9ACARng3lxUM5f0DQFjyRGAgyGo2Kpga8l72ZDdwX92TxjeNNeNBXypjcyOAX1xiyGnLZu+MxnasUR1D0ern9j/4wL7/nU6n1zs8wN+eTJ19xUz71Qz+Z4bibxrj8RuckBeFjWOUx+9Ry50zOS93tQ/SDOcWJVaMQY+C7OGO/tImIQAS1ElueJn3QKwzMZQ4TL5PJpDiDymUW9JA1hWewPiYz48NJGfnwvWr50A0eljM6XCWNcNqMF4ToNsXhcJiTJ3fPdQ5ECJnwcCgH81dbxbzf0tHLbXJm0ah/AUW5HFGdMyFABIRT8MtODbtMbyezlyftBNFrtVp6vV5pCxl5rut0LLChWK1Wy4vDb+bdv/dnufFzd2f38vLe16SWPHbbjfmrn/1HqW8tpFGf7axwjzALDYxFAfyWMzy3IwdlFNZuY2OjpHAobTKDdS5XURtGN3AEyAooze/tIKv3pjnDCMTbxUhJDKH5TDVtIje2rjryo3tjOTo3WmBsyNK6h4HuZAc4fOZv2IvOkMrhBCDnqiyyr31hLdGBBUEwZtJsOAieSIYiW1hAJud4KB7eiklgDM59arXZ9iQM0pCMRcBj2ysbuiB4PsP3TUzgRW3EfNc1r1qtliOPPpp3/OdfzJWPvnDehtlbTH7tf3Zjmlf8TBamyxnWhoVSeC4gCefrePsqQ+geZhQCxYYcggVnTsBk5IEx8PwqmnBpyjpBFETRnfclOevALJ5tYzBZ6KhoVtSwFwfE/JOUjIl1bjZnZ2Dh5JEd8/I7U/mZDdwo0kaJfru+ib6xHktLSzly5MiuurCncVrg1STYTBPwFgXnD4uT5KxJIGDnnigbnr3KkpHP4s1NPJm5ZUHxeHYsjqAslgvc4/G46NmkfISi7qQc3Kf79a/nHb/0Szn0wgt7iXTPq7+Q/Mp/9fJsXf9T6Q6XkvFsMzLb2cifkBVjZGyO8Cg1zKjljIJxfm91l7/lb+dqmRo5WFmRKwYFAsHoTJYYhjsKYRTWK2Tt8RjVGdrzM9aU6E5qg/y4p9cTA0aOVb3iORCOOAPLl+fiBJwuoVPtdjtHjx7dVR/2jZzOc4CCOxFBCIp+VYTsSJvMdsWzkHSScG+UwY0I1X5Rd34gwOTsTd/AbhNSkAh2DN4gi6B9vIlrZ4uLi8U9arVa6o16Dn3j8bztl34py9+GYY5arfznn7stG9d8f+ZX59Kb9gqY6kZ0ZMfB0c7HzIxSR6Z8BCpBhktLS0Uq4e4mHCfzt0NDyarNBB6DuQSzx26zJG1gHViz0aj8hi/npBgdkdelOEc0xsT9Ga/fRGAngePm3uYa6JYy023mudpCaLKpisD4bjKD3xCSu137EkIoLfVGPIqNkqgEMWDvitFBHAGbuBCEf+8CN9CFvAgl5OcYpokiiAv+YPwmHpyPIlDubfhuggnlM6X/Qu2bueLT/99vK2KOWq185r3vTe/6N2dplGwMN0pNBTgqjvNgnBsbGwUcc57tWq/bJZk/+y19jCMysHxgtKfT2cZmlxzcKYRxGOqZsyB94OBsl69QchMs3NekkA0W3fSzHfkZA8/nb4y1SggxFne0OS814kLnnEs7pTL05w8IEkfiMe92Hcg4q1CFqIUwLCgiEv9GWEQfrioZQ4Rzhz8ejZ/zvMXFxWLHPkIkOrAQhlsYsqGMcyizj4zJtVTux1gLT7q1lXf8xh/lVZ89sZcY97zGr2jl7u/54Tz8sjelNp2mkcw2Yp+B8d6e5AZy8keXDVw0tyIzduZBuQoDpCaJMprQIfpWUYeNw40DjI95EJXc74xBWM+qeablz70Zi/kK1rwaGau8wmQye/8LqGw0GpW6ztAhv03NUdo2wf+T2cn3zBHZswMKffRccJS7XQfqEEKJmTTewkws8KXX6xVKgld0kp6UX0dOPsJ33DIFo4UwvHD8wbOiyAgaw4RVxaHgPd0N4lKNzyXyz73bptlsZmEyye2/+Zu58dPnz8qOXtHKF/63/yCP1d+dyUY/Y3lScstk5hCAhe7t9XtnrHBJuQEd+YFMnIIAs5AXn2HN2+12oagYAgSeT0vwurjkggKCfvi8CR0ch5sDuA8y4LusEzlfMnOe1dwRJMS9vKuEz9uh+2fIOknpOybB0IdqgOIybK/C3qT8Jr7qdSDjRIGBtVbonVi16XR6FpRkoK47MbiqQTAZ10g5uWA8HhdlAUgSK6RhVxXy4q35HdETwZILAfmYu53UdDpNc2srd/zO7+Tln/70eRvmqSsuzYP/q+/LI5PXZjKa7dBHjs5rffK429EajUbW19dz+vTpdLvds/IXHJYZR+embiAAkZAfIlM75uq4DGONUOy8IaQ4agQDNAnjdSafQ4dwGoaXJl54FqUvxsI8p9Np8UoKHHeVkeUix+dyQwbyMAR304kDAvkoaQa6j3PFTkajUdYXzj5lkevA59aS99CiZyYM63dvLYO2t0VoTBqheqcESTKLD1zlPm6343mGazx7NJr1bQ6Hs/d1uEfWSlHNF9xUDqRrtVrpdGq56yv/Odd/6lOpTc+9wWCa5MlrD+fun/359JqXJ5NJarUZVERZTbmz9Yv5s/jAvM3NzdKuGWRAS6VlQmS2DHFWThHM/NZqs83W1ZwPXcAoDefMkuPkkCNGh0Fb2R0AknJXjfM7s+UYbBVuNxqNdDqdwjjQX+Axeuq0jXlwP5eK/PlkZnRVRIAumT8x+plOp3l4/uH8mx//N/kX+Rc76sqBjdMETTLzyvaU5IF8B69jpjeZdY2gPCgZ3gbBOll2jsLn+T7FbP7NgvF5v1jG/Z8mHcgN1tbWCsiHwRY5a3q58+Tv5Pr/8KnUzq/xJ6uHmvnd//7v5vL2yzLa6BWLb3iHvHAuRBsz3Yw9KZ/16ohGHomR8ZlWq1UQPJzWbuVE8VFgkzeOWqyJHTH5MVDW88M5+meMn2jtY2XM6tuRmvjD6aJTyczxYDg4BhsekRS5OHUyIWUWnzHZJrwei4uLJcIOp8bvaRaZTqc5MTqR37vj93JqafdtSgd+s7XJEyZkpXD0Mr52TcoHe9kzU6KYTqdZXV0t4BmG1Gq1ijc/G2YRefm5oSFvWjbDW+SLZ6KA+0GBM2zE5m+EurDQyp29387L/y+fTu3FvaS2+9VfSH77v7wpFy68NcPBsKQIJjmm02lxtm61LOBcGpjbaDSKZngUEmNmDZyHESVxUK4XVyMGSm0Cz6frmURya6T7c3ku6+rUBYNst9tFqSiZpSfe/sUYHOn4vA2FQEL0NhtrgtOlKadw/J4IbntwPmkyCPRhtpkAhOFSmjlVO5VffM0v5r4r78teedE5vTwXOn9jY6MgGZKUjAJvyGXI4RKKmbokhcfs9/vF5mCTD37dnxvrnQsYz6MoLApjQdGrNSp2aZhoolg+nU5zyfxDufa3P5faC+ffxP7I61+Zyc3/JJOtWkajGdPqOhqRCGW0kzPpYjljkNPp9il/3W63qOv5aJgqueK6XTLriLIDgAuALyDfN1wjuqGI8AF27ig8joRo6V1IrA91Z5CA2WDWq9HYPuIGJwB3we9dLsFgkSO6YFTi6O5csUoC4US92dpyIl1y7oq+1ev1rDXW8gu3/kK+cNkX9jTM5ByMk4m02+2srq6WhO2JYQhMAsGhBOSaZuMggszIsTgsitu+qLnyM9r4yNNMBKFkLNBgMCiMn5pbtZ8XwyjYu/rpvO6BD2TuE7vvWt/vuvv17dz70/8gncFyppnltMnMURHF8LjANuejGCbw1A0hjHt9fb3EZALtLC9DWaIl42i320VEdm+r4TKKafhL95ahr3XHimzmGGNwfs+4uar12ipKq8J9fo9+Mjd3TrkxAQfDeN0OSa6KwaJbrkRQWUDHbQuFU52eyu/f9D/k7svu2dcwk3MwTitAp9MptgOZvat2juB1gG94KH5nOt37El3DQmjkjbSiISwvCEJ1AR5F8eIgWMM0ou10Oi1qYdvet5m75j+UY//umYOK6qyrt7iYR+78B+k2rsl0Up47DowFdBQ1wcIcq0e87ERcYODc1w7H5zGhtPV6vTgLKZk5NZhhILJl7Z9xMR/XGQ2LcSrokQ3d8zRqwFHhwDF+fsYaur2UObr8hn7gBHDQGDXz5h6shZlV9MtGR5S3YVcZ7tFolLnmel794L/PP/+/35Mv/cfk0ev215tzgrXJtrF0Op1sbGwUUIK8A8NqNGYbY6HEURSUxRNhsTEol2i8YCiAPRkKgXPwaQfVKA2EIt/k+BNoeB/FgkItTZ7Pjf/2i6nt/ZaEXa/Nqxfz52/++1m78bVZrtUzrU9LhoBs5ubmsrGxUcC1RqNR/M4GSO6M7KqN7+Px9mnysLcoJBuFMTAbwNzcXFFGcp3Za2u463U1ZLYTcOROyl01RgxeD+d8vi/kHyiLe5utrTa1kN/Zsdmo3KRgvTWcZ57k6cjcwcWBxMSU5zA/38otz/9yLvk/fy6XrCW/88PJD/3n5LGXJRc/f3Fy0c66c07GyUChp1EeJlz1pGbIeOMVwjDUcr5BTY/PEX0bjUaR6/o7boxAUG63MrPoqMw9TMhAIhXwejDIqz7xiSw8uPv7LHaVVZLNNy3l0//FP8za1mvSUTR3729y9sYB/g2krW4+Jzoid0cqFMuK6BwdhfPp+yZacAA+IZ98EuiGU/DzQDU4EvIvnx7BOE3m2AkYtiIfclCil/NuEzZuPOd7NjZHMozbbLbLHT46pUo4MTfQmO/rsRMIGo1aXta6O5f9m7tTO/O2jdd8MfndH0z+1//uqrzn7v8i+Wc769A5GSdCaTabhXHaGL3IJi6saCgDC2HmllKMIxyK664iPL+VJ5nlGSYEIDAgsxyp+bfrti5rXPTkk3n5X96b2jkeljed3z7B4JPv/bFsbFyYubkyfLWSkyP3+/3CCBkv8yQakSfxMh3OdHVUQ+EbjVnvq+WE8WDcIBWXOLhssKwTcBQZG9W0Wq3CQJvNZqkfF0bcqMS1ZKCoI6BhN84dx8JcHLnMY3Bt69kwCwun0mo1Mhw2Mh4vlb5jltppEAZahbI4guph43Yc2/dNrm19Lq/9f/xaml+YtenVkrzmgeRXnhzlY0vn+WZrL6gXLElJ0CgCUNf9tUzaymIyxOQEnqian2K4Jg5YMJJwe1A7A7xhu90uMcTVhgeUgJLOcHUl7/zgn6dzJtof9JomWflnl+ee2380m71L00i5DdLGzzzMWFq2hkaG6EQRvlPaJSPvjSGh2DbsZMYFkIaAWmBbqQNS7iByWN78G8Xks+gPcvXaEp2TlKKaHS2frdVmpzGg9O4GY54JLzauZ7m7nuXGC7n8hS+k87UX0vx8L8ePP5xaptkaLOb5F69PfqyWp8Y35bmLrk9v87KMxmcTliakHB1JNdzpZjQAorh48GBe9//5tbS+cDaROLxzLs9d+N2pf+M893NWyyJV7I4RMjD+7U4gkxUshhfN56vwRixHMr7D9320JvmIuzQMW/m8yQ2/NMdzJIep1WpZmJ9P+4k/zaVPfmMv8Zx1TZM8e/PN+esbfj6rW3NpNuslxTUER45EB46jbLfbRS+ryQwMCHiP8WJE3A+DrMJnk3gYFMQQ8seoIMNcZvHZt44SREbWu9WanYRBrgY6wHF7TYHayYxQ4r5EW9pGm81mwcons6b9er2e9uIkF2w9mtue/liOffjxtE+/mHw1ZzWLNDPINbkn+WxydefubN3QyYvfcXkevOSdeTwvz+RMecZzZdzoP8/HEZm8Gg6HmY7HOXz6dG7/93+Q1qM7GOb8fP76jT+ap599e5rN8zzgy5HG0ZOfAb28ePaoJgGYLL/HAE1lA+9s6NwL1s4MoGEEDKW7Wlwu6PV6peMOgcvUMTHeRqOR9a2v5sd/9RNp7r6bZ8fr4VsO5Uv/85/NVmsxOQN/bJxJipfatlqtUs8rCo7Dco5s+Ebr29raWpEfWWkM0zAOHEO3283a2lqp2wjkwfMc4SxfCCOz5nayjJs3dGNo6BDRE0Pk9AVgO/rj3t7pdPa+UDsRem63nVkv17c/l1u/8Oks/c4LaX1rUF2WXa/aRrJw70YuvfehXHTokdx07Orc+5a3ZO0Vr0j/6NFS95plw1xYH+fG84NBrr7nntz++7+fxZWVs545nJvLZ374h/Poda/P3HA2p52uA222ruJ6M1iGHygiD2Rh+B6L6LIARuWEnETdVHS16wg4WGUugcnu3cWYKdeYLsdIku1o2u5v5Dt+8Y9z5OS50bOnD9Xyje//h6kfujTp9QvW03CN55LDkKMRQabTadbX14sIiuxdfkImOBg2VfMcNknjfHx/nAXfd63QjCSwjXFi9EQuWhvtBNCVZNaOxzrZgbJ+1f2k/G14ztoypmazqVfUj3PFhU/lTfe8P8d/76nUn5puQ5fzvBor41y28mgufvzxbC4v5+E3vSkPvv712bjggiQzhGWHz88Hg0HGk1FObz2d7/ulP8jVX34g9cnZEXGa5HN/5+/ksbe8Jc3W7OT63a59XwGYlOGsc55qrudwz3c8ETNgpt15hiEXioei4XGBQbTh2bCqzQwoP4pFU4NzWbfuLbV7efNXfjmXffXhc95t8qGfuitbt96R+ubsJULkVjgvN3wb9lkpbWAYg2HVeDwucncaKpgHa0HkYvH5TjVPxdhxBEmKxnoimHcX2aFh5GZtiYQ2WhueiSSXy2D/aUFEn5LZK+pN9hw7Os1bN96Xq3/hK2l8bnjOhN1eV2MySef06dz2J3+SGz75yXz9TW/Is9/9ypxoXp7pXDPTabeY32QySWNzM+ONL+Wir9yf2z5xX655tLez7tSSJ2+5MY+99a2pKU1z6lK9zrnOyQ0xUrwku/KTGQOIN3dkxSN6oTF2iBzT80kKBTJbixe11yYC4DgwOsPrZAaTgVTbUHiQV47en8v+41fP2TC32u10jn93RluNpDarLZowA9IZTRgJuPnAUKcKp/wdZIqiOMqCOHAAGAgOEQYcORAh2cHBHDAWWu1wijhJxsW6OZ/GeNEDF/JNEvlz9FAjB8pAEJGdhV7e/PXfyBX/+oHUzr3CdeCrlqSzspLb/vTPM/zUX2V4+0J6VxzOs8duSu3GWvL700zG0xx/9NF0nn8iS2t750D9d3Zz/499T6Y5XHAEVU6nep2zcZokcBGbXlv+OI+xYvE7R2MiCYbnRaT+lcz211E3RdHY61l9T6c7iLzA0OAuvRxZeiHXv//zqe2+vW7Ha5rkM++8OCcuvygTNReY1bZMiH50uHBEJe2IOBQ7Iu7JvLxtjw3MRFpqlNQOgdHOQf2Ga8aFc3C/qp9vws2ESZXFhEwiStP8wHriKHAAPieKCO8XTfH80WiUufpa3vT1/5Qr/vWD/381zOrVWhmk9ZFB2lnNscY3k3qS3dHoWVf/nd187Kd+NicGN2VubtakYIS503VOpZRqkRdYhDcFQhEJzArys2oUc3kgSQFVofWtrERUIBcRFOWbn5/P6upqAV0pobhsgXJVGxUuO/mVLPzFOVpmksF8PV976zszPy6/l5MxO6qh4LCMLhclKY3RqMFlBBQZo4OgIbdGvjCn5O7IkciUzNIM561GJD5dzgX/al6K8ToXZq5GCzhKIyUMO5kd5oyzZm3H43EWh+t5x6/+p1z55QdTO5cW51oyOVzP4OJ28sYkf51MH59mftxLvX8eSer4zJ8DPrv3zqV88qf/cZ4b3JzxeNbsACI5b1hrw0SB8KjOAVgMvF71nB7gXVLeB2dYU4W9LgtQ2EZJk1lea8Ki1+uVjpB0Zw1zQFEoR7RarWQyzjV//LnUdj8Zf9frsRu6WTjyyrTmZocZW/EsAyC8CQ7Gh1K79ZALw6MGScSZTrf3Y7LFDSXHMKs9tMgpSbHli4YEs8OMwevochjOpdpLTbR0tDUTjVO0M+Eib+W5lLzq9XrmW6t58+O/livvffDA+eV0Plm740ieff3L8uxrXpPn+7dlupSM7xxnsNrLFSsP5MpHvpSr7vlaGo//zeatyTaiWn3Txfn0T/xgvtW/Kc3mrFkDEpSAstt1oFIKF5GTFjOTQ/RyooDuBCKKuQRiFs95pBlO8jVgDQpNy5XLNnyW3IXoyTamTqdTcgr8ezgc5nCeyZEHnz6vBRhc8Ya05i/IaDgqlXuS2cFl5BgYKnO1o6tu1yJygR6Q82Qye+szBlh90ZMJCzZTJylQB4bv/5ODdrvdJDOHwRjs4TEw159xtGYxq87XDG8BVSsnB4IGuFqt9bxp41dy+f/wlQMZ0LSenH7t0XzpXXfl65e+PpPG0TR723Os9WvJfDI60skTR9+Rx696cy79wcdyx4u/nQv+0zdT++w0+TaNdJpka2Eun/+u784z735neqPF1Gqz/l074CoHUb32NE68tKMVf2MQSbnNiz8sqEkaK60Jg0ajURTIkxQwOZk1E/DZWq1Wev8nvyPqcn/+jcHaKzv/Go/HueTDD6X15LlvB+svL+fxd747w8Es0ruMxDyAl8ybcVXnQZShyYIxshb0/7quZtKJuTvlwNicP7rRA0IPp4VTZF3n5+ezvr6eWq1W5KNmkHEyjM3NDSapXEZDf1gbnlWF65PJMNdf9de5/F9+5UA55uD4fL78rjfk8697S3qjpdS36mk0RhmNpkVnlOvvw8kkz63elA+3/+tc+S8+k1c8+bF0f+VUWvcMkt0Pxdv1WllOPvuGS/LF29+UQxe9Oe2FdjItdzyxVtVcf6dr38hJtHKHvo3BEYtaZDVf2GlHSjJjHClCe2+cD/QlclCQRgFN8PAM76pot9sFFc8Rk6b0x+NxJv1+rnrggXNmaKfN5PG/88asXnBBpuOz3yvp0o9ZUpoQbJTMxygCBSYS9/v9UgmFZxgm40QNZdkBw3iqkNF9x+T6rB/lF0dNR0B0xHtt/bvqZ91UPhqNiqMzjZyYR6PRyDXX3J/bPv47qX1sn7VIsvGew/nw7T+RZ6+4LtNpMxn1CzQAnLTjRPe2G1EW8+WVt+XrC6/P8f/FU7n0K1/LlR+4J4efej71FyY76sZ0OcnVSa93NL1JN3/wA3N57pKb029dn876ofS3trKlEhCypMONAHHeR2PaG7MIrkOZlZ1Op8UCm7ZH0dzmlMzeM4In45iKJGdFQO/qn0xmLywy22U47QiBUQLBcCrUGxefey6XPv743qtfXZgkJ667Mg/9nXdnOpkdbEYkAZKa4GEhYFqrjRq08BmdJCkROtPptPSacuRJDkNU9X5WHBFzx0GimFXSD4NnXabTaUHSOaoSCZA9TggW2rqBwfEc0henOKQ+8AWNxlauP/xHaf7i1r5Qs/d3DuXjP/5P82LvusyfcVreNUXEJjfHYbi9cduZdfPc6k05efUr8/l/8q4cPfVo1p+9L2/4yxOZkC50apn+yCSnu0fz3LE7Mhpdm83Nw2m3GrlyuL3RfXNhs+RYSf9cd3aKtdt1IELIC2ihuxnBJAA/N83uGpzJHO4PqeTanPOvJKVICcMINDW1z+dc/EYRPI5ms5lr778/zT2gxU7Xwy+v5/3/9btz4dZSarVZK5wjOvknMNqODRnhwUEnjLX6Oe5NB5QbQFjg6vasamM55I8/73W0AxgOh1lbWys5MYzXr7dw0wfrwt+kJa5TksPaSbpFziWWiy66O0ceeyTZZ4/7xuHD+cgb/3FObt6QM8tegsuO7CAA4LfLbuh7gRo6h7Myd0u2Dr88779kpeiKmpubSwbJ5OQkh0eHz+y8GWfuzJyWl5eLFlKTXkacrKNlttN1oAO+uIHLFq4f8kC8ZtVYkvIueecglBb4XdWbeOGpZ2L4CMBUPQvvpm4iBvMpIlp9lI3huUHaaZI//gdXZXn+ttRHs3NacUQQKBgE0IqoYPLD7DM/Z/zu/DEctyxhqKv1SndO4cQcvXAYrKtLWfyf3NIOjaYA15tZb48vSYk0xPA8D0N77s0YGo16rr3iI6n/n8Z75n6TWi1f/v7vz6nLX5XaZHZ6AZERtIZ8iZbUU1kP9AOnx1oxpna7XZBmLg/iADudTql8Rs5svsScB/rMWu527WmcwJVkBqFcDqhS/nhd5yh8HvbUpwAAmRzZTFbgAJLtZmef2ToYDNLr9QohYsRuc3N9zx1IjH9xc5TXfvrcX6UwOnJD5rfm05grv8nZebWdjV9ia5IMBfURIOSmGB+IAvkTjckn3XOMQ+L7GDHrYecAjPbxpEYXjGt9fbv2u7S0VNyHsbo5wmSWN8vbmXNZj6ycOO1u9+kceeCx5GN7r8PKpZfmm3fdVcyXch5jci0ZmMlYNjY20ul0SmtDOuIc3btsTFhOp9P0+/2iv/uCCy4oVSCMMplztaTFGu52HWjLmHMCoiLRizqZaXNDUTwnZIg7h9wehmLgcRECEJYIhaL6hD6U0I0LRCCEQz5qEuLFzlPZavTT3VsHzrpuefqWvHDzQikiWylxRFUiolbbPr19eXm5ME6ikmVq5diJMMEA+fzi4mIRYQ2PbcSUVEzq8Qw7U6cCRFh2rqBw6IXb/4CDvJ6QtSd6ETlYE6c7rCHr3WyeTOsveskeBPq0lnzrva9Mb2EhIxEsdkiNRqMwLOSGrDc3N7O+vl5CLy7bmeDiCJter1d6CRPOaHV1Nb1eL8vLy+l0OmfZiXP8qiP14XLV60B1TkNQBkytkcW2QTq3cB8ukcKLz3dZXP7NfZ3TuuukVqul3W4XeYMpeUNtnynk8slwOMzxh5/Mwsa5l1BaajLHkeA0EDbG5J0zQDgWDObUkWUwGKTT6aTdbpfeUYkCcwICKIDvFPlQZk0DbJNDEXAazvcx8iq8Q4aUUDBSDBknYiNDZ1gbdrAYQjpC4zAYL2t/xfjTyfv27t6ZvryeJ+96Q+Yyg+jVNMu5LmOirRHn2Ov10u12i/GDJvg/8wMdgChg3Ymoa2trWVlZyfLycpaWlkrIwEgxKW8GOW9Y6y9WjdMPQSAuH/BdPBeQAjiF4JxrAVeZiE9IqzZUV082oOHBh1i5v3NhYaEQPB7yqm8mnXPt0TySTC+Y5Weu8VYbwRmzoaMjlplUbxz2yXLAWKcG7m21YnM0JTVjk3c+bNskiTcpEFlZSzeRGKFwsWY8A0Z9PB4XPb/u0iJqel18v4LbODlO7fm9l+HFm67JSvOKgslFHuieCRg7H4KCYW+/3y/1G/seli9/4wz4GfIZDAbFQeRHjx4t7pnMNvdzD2T7bRFCJiHwcgw4mUUOfm+vkJTP6jEz6NzGXptyBNuHTEbt9B0Lz0QDJ9gRUUxY2IOf83VVsnXNIOO1WRO75QRRwsI7whjCoQSz/YkpRfVq/rK4uFjc07lMUj7WxMd8mjTjflUI6W1sEFCUYux8UULnXxwOjaLVarN6HuuNM0IWzskp1xjqdrvTLP3uU8k+S/PIte/KaLRQcoiWsZ/DM4xO0C9q7Bik0zU7QSMJNpUn5WNEibgcUN7tdouoTW3ZPA7f3+3at0PISTuLlJQ3PDNAw1y+g4fmQmAYF94ThakygFYQ93S63paU3/dZhVmMzUl+kkym526cq2tHcuLE0YyzWSwiymUCit+hKEn55a02GsbpaG+Py/eMTuyckBERF9YYo0lmTeWGebCuJik8Rq+jld/IyISLm9+JvNVuIcbCvZgLz2qM17J08qk912B6YTJ85aw32G2BrrsbQjNGyxK5s2eVDeA2cEgi+ArqpxCZJvhAe8iletYvOTgoropEqteexslmXm/ZQgDFDc7Q6+SgzmMQRjLD8q630ZOL5+O+CwsLpcO77BTOWqjp7FQ7chuauvv9fpFnuPtka2srqSX3vuzevHNPNTj76n/XcjaHS6nXZw3jVjYIMMN4j9W5BjmxFwkvTscVsvSJEaAVDIXPJOW3KqO4IAdkiyPA8A2xzPRSU0VuGJ45ADdU8H1gsmu7Ngzu7ZMOMIjxc+NMP77PGjSO5ETruoyH40L+7pCywaJXjAu9Ys1IAdBFt0siY3MXyN5kGZ+DBa42/BNleQ4bNBqNRhFRd7r2ZWstQMokhSDH5XddOqlngYEyLGz1MxiOF2l+fj5LS0vFcZEYIIqG0gPHOAW+3++XtjvBjkIYcf/BYJBGq5G19treWrDD9fjGnanXZ4yoa2LIxxAfJXZ0MASuyg+PjPESGalXojgoG/d3NMRoLVeTR4zD0dvNDaw95JMdI/LzMSRmX03OeP1ZZxy9Sw12KMuPPZbGHgxmkkxePZdJfSnNZvk0BTsQ5MTPqo7MRKLZXKB5vT7rTKNKQM5PVHVPNPrJ80xyoitOK0CN592+x4PNmrpXsbjJGSWj8OodKygikzdMTVJEDmASAlxcXMyxY8cKAwMuMDkWwTVAEnK/DbvRaBSR1J51fnF2MNZBr9WlRu67+XjmWrMT4TA819Cq+yNReHK5akeRXw3HZ0xcISvWIZmdLs7zfF87BeezZpiJCHyetSicl/IiG5TzM+TrQ5px2igqP7eTr5bA+Pf8/HyOPPVUGqO9+/Wmt273NjcyO6OIe5ugQu9wRujeTnJFb9msztjQKVojcZom8DhCFOiLrDw2Iq8bYxYWFs6/lMJEMCpT7QzAntIe30pgAgOF2qmOh0DwhAsLC1leXi6RUZAYho9mbUejUVFYhqWliZ5nFq1i43M7CGI8105jfHWxyCgixWhIqOrB2UA/54zM3STEaDQqjN4RySjE0c5G7LydV9u557XKttp5Vkk5nAXR04wnsmd9kxmTz/oYumKgKKJ3YXg7YaPRSK1ZyzNLz+SWfdbhqaduz+jmMndhRGcCzuSW8170qErI4HwoH7ln2/prFASJCevuTjpIJ/YXI2/GVQ10vg5U50QANjSzjigTi49RO0q6XcsG6V7cJCWI4vKI4ZajDormE+F4S5aJLJS+6BAaT/Pqr706yaP7qMLs+pPvrWXtUC1NsZSOEsBanALPtlwMiZiDd8Xj0JAnhJtTBS8832O9nAJYluw4wfAYG9CZdXYkdfHcRuRoXDUO7s3a4qAwBnSFZ4GUms1mxvVxvnb8oXznXoswn4xvPZykntFo1qyCoSJTw+pqXpukcGDoHodus36gIqIsaUGVSCPV8r/9XM8d/TTDv7S0tMtE9zHOfr9fnBiQlM8Pmk6nRa+rGTIuG7QZP3o/6/V6CX5auTmGxCQIyufT4Pg+UMI5T1JuV+P3pZrcaPdd6Dtdl754bV4ctkpIgnFZkXFCPM80O5/BQPmu5eTIaw/N951LmplMZtuy8OZ2UGbUbYj+P3KqkkOORCb5UG7WxLVSnHg1+ntPLSnA/Px8NrOZ/c63nMzVs3ndkYzXZic/uufV609ZifXick4KmYnu0ofs5hAMzqjBzLpPNvAaWrcxaPTSAWy3a/f2hMx2f7jtyBQ4yo8HTGYlFggJDI3/O5y7YQDI5vwWJWebD4pTq9UKCMnn8NL8fDKZFGQQQp+bmyu6ipKkUd9z+mddS6eXMt6cGTdjpdPECCIpN5SbXHN5odGYNbljUHwHp4JTgtlDAZ03EcWrhIQJI9+TMa2vrxfr4fVhfZEviMWEFs7H7Ypmr722jNUnVRgxJWci8T7bEEaj+Tz/3C2l/BZZGjLaoSBPoiqyw5G22+10u90sLy+n0Wik2+0WxCJ6y1xZK4+bZ1TLgjgrv9vFsN/GvNO1b50TxcPQvFeQyxjfzJgX0Cwa965CDkMPn0Q+nW6/TsBN3qbzUTQzc8msOE/nCpCM/Gnj2nrGS/U01g5W7+y1ehlNR6mNZ1vAKHuYkay+1p4oSPQkWqFYZlphEF2+AtoSdZE/uX018vIzM4bJDM2gbEA72tcwQENrvsO/3feMHN0q5yiMkvLsJCWyxVGpXq+nNarldZ/bew1GCwsZ1Wbn32IQRHSTfER+9AKZ4XSQDVDf+oUMnW86aqJnLuEkZX13Pk+ExXHw2fM2ThJ4vD45jDG1c1EE4sGjhHjzKltmA3VJwRuYMUJYMec4TBLh0+4GKUQ0Go9n7W10sTwyvTIbtU6Wc7CSysPXPZxes5eF/kLR2WM54AjIb13HgmBw5wkMNJ0kOA4iGkafpBg3jgvlIWJi8Cgfxl4+j6dVKBRIhbofRuumium0/J5L52jMmXwLA6lGB6cv1S1cyazJYjQaZW4yzase2BvWfusVr8hGt5vaGZ6BXmTn2JQoXF6xruCIWBNHd/cE4wBMNnlXVNW4qvN3sDJyoV2w1Wqdf53TNTiTMK5roRhuFqjCqqpHs5AwUOeFyax5HiNFGVqtVtG/6OjKPVgEF8ZRFOAyReXGYH5fGOXrhoenOXHVOOPJrOuIBcErE5F8Lg9Kg0xZdBTadD/3wuj4HR53PB4Xnh4CgvzIBIS5Aa+NcylSC2RUjeQuSViWlBGMYlg7O25+x1pyPi+OzOUW7r/fYZWTzIJGMivv8H+CiFl75IJz8NiAp93u9knunU6nRNyhcy4fuSuKeaDjrmiY3WYsOGVSRSPQ6rWncZKDOKlnwTEcby62EWEIDA7DxqMAR5ikd16wUCw4i88ETa44apnRNYTgdzCT1LIWFxdTO4e889VfGOfTb++n1lgslZZYTDy1m8UZN3MmJ3K/p3NtLmTo+iG5NnPk594U4FyLSN5ut0tNBpAgRIFqKcwMuss6zjsZg8doJ8AcvD6MgfuYDEuSyXhcHIi10zVNMtDpgHyPeeHwTHJh/DzPOTeEkbfTQVghf5yKSSGnIcwhmZ26QCBwamc+gpop49vtOtCh0s4peQCRwovhfzMRfo5QnD+5ZMJzzCCaSOCPGVgWG4FQk4NFxAgZg+tqo9Eok8Y008tqyem9pDC7FjZ7ueKZJ/LMVceK55BL2FnYUZkYYCEM9/m/Sx6M2583yWJoicGjeM6LgLk4JQyMNIXP83OcCEpmOFar1UooBq/v1AFIT97stMQ5J46SnLOI2HsY5plB5Juvf33JGMzOWv/QNZxVlTm2jFwKcxnJjQOkKu12u4DDjvjJzNh98oR5CZN+7kja7dq3CQHB4VXtFfkMHsZdOHhXs3bG7/4+iokh2gOTh9lBsEcRJXUjApEaBeDfNpJCAVvzeeKam3PzVz6xt1KcueYHybFnx3n2mnrB6Lnx38+0bPhjMsbzdJrQaDSKPI9IQKN11aCRBYVzfg/0JaI53ah6b5NuTl9wOnYQlLeQM8Zl1OJuJ8gWIxqiaHWtJ5NJMtpIrbY3OdcQoeKo5c4pns28XRvf2tpKv99Po9Eova4QxONUxIROtT7qQAKCMfSlz9tBxqWe3dI9Xwc6CcGRgD8I102+4/G4BJfsKcDdSUr1TUI+z2Ow9DJaQRAkUWttba3Y1/niiy+WtjvR/1mQDXNzZwm1Xm9mNGrvqQw7KkijfOwFTQ84AJdPkJ9l5FILSgExwPgsd5CK3z7m3Ma1RkgNK7/zPBrfuT8yZeyMAefHGiXbio9CM247yWo5iHUgrzLpZAfGml347CPpbJzcVe6jK+ayedEsDfA8iOKGyeYzer1e+v3+WUbn7XY4U7PaREm6vCingAzNmLNehu3J7KXE3jtrx73btW/k9IU3ZaF5IDDRmN5exPmjowYeqvo94B1vLtvJyxvPe/8kbCaKgRKb+HARelo/t/dlcF/eo9ntdguH5HnYe7NgGABydRO/kQQ/4+coPbmgd0JY8U3MsNsHQzYJhUHamXlXCusFamGcfi8LaUWVGXVJyXk0DtHEmfPtWq2W+tNbqa3tvh6nO1dkfeGK1Kcp7lMlVcxv4DTgRpCTS0PoE+ijmgd2Op0sLCzk1KlThTGvrq6eRQJxb693MoPxlpEdYru9e3DY1zidbBtPMwi2laF8jn6GEs4tfW/v0bQn5OeLi4tFTgcsZuMxR3mghERu58KONBg3Xn04HObFt12eyV/WUx8cfG8nyoxBsrkWQfP/KnPpXlR7fsMoe3sWFUVycz2f4d9EDvJ8iKMkpajI8RomNexAiQr8DOVm/WAayd2TGeJBHjY4own+bwKK0kK9Xs8Vz39qT7m/8J5rzpwfO8vfSWlYU+RLlw8vbmLNQDl2hnao6B+6xPqwtqPRKEtLS0UaV03BmJfX3I7N9gFJttu1b+e34QnekAG7k8eUO9+rEg4s5k7CdENxq9Uqmg6ckzlSuBtlfn4+7XY7KysrxUL4nSBWfvf2jsfjPNe/MpNpI/X9tt6fudYX11Nv1DPXKp8eYGeF0jsy+phJxoeB8TcwnnmORqNix/7y8nKpkRxH5R5iGh18T9eEvXfUdesq+ZTMdqgQtdlxhBFiuL5QWB/JgfMwFB8Oh8W7a4CAvZzM1hd273MeH2nmuRtfk8lkmul0ZhSG3DgxIiUHbzGGJKVtYHZOEGfIwac2MheMiTVFz73Bg4sUqmqYdnzf1q4UJ+9mshicyQ5+Zmas2uXCd82IoTzV5LucG9ZLLVCOOEze+Qw72lEYFg5ldf7GWA56PXTzQzl2+L1pz7eLuh0QEUbPCkq0M4NnT21WE5lQiwSVOM+hzggzCKJAtmYAaXKwTIGh5D8YCZDN65PMjoZ0DuV8F4MFXbnOiOG7K8Ylp2IvbGOUOz/w/lz34Kld5d5vHMpz8zfM4Pt8MhwP88zhZ/Jc+7lsbm1mcbyYa795bRqDRtbW1gqE4A0CVWfDHwiibrdbkH3osCMoOsh6VoMMKZSJNUdnAoMrB7td+x7wxWJVDY6Q7mKwE3K8Cf9HcSwk56UYmpNqDJbGaEgTs3B45G63m62trayvr5cIDu7NoqCwjH88Hu9b+C7JpHGGqW3Op9PppFarFfnd4cOHk8wcAnJgYXBwRhIom1ncavfRZDIpFMc1ZBafSObaKsbmGjRwj6Mek9mbymhoYK3sjK0PrHm1tonjYO+s0QlNGTwbQ200GplORnnZV/80L/vgZ1PbYyHGtUmeaz2XL97wcFaaK3nw4gfzQveFrM+tZ3P+zGs8xvVc9tRlueszd+XGh24snsXRoXZcyMeIjmhrRMS6sBXMfcQm4tA5ZOYyiysJbowAWu927ZtzImQXpI3Njd0NYV2g9r2AShivlYfPuXyCQQOZIEPcEZJsJ96cB8v93brmrhcEU6/X07qsldqtSe7ZU07Fdcnz9Sxcu5BMZpDHjeXVHf92Zv57bW2tZBTJrJiOR3UNkBw3SelUN04j5z5EWhjbqmyJFDgUOAFYVW9tMnJhLnaa3g6GI8cZm6wxpGMs4/E4k/E4F9/7xdz8H/4wzbW9X4lxqn0q/4fv/j/m1KHBriTepDHJk1c9mecufi5z75/Lbc/cNtu7q0YEnwhIFCUlcfulHcl4PM7S0lJBTppY4jOgEZN5yM06glPbK2omByilOD/0QoPrqzkof1hE097OITFWex1HFO5h+rnaCeI8F2/mfXeG1S5hUK8bDoepXVTP5JKDdwm99wPJR++qZXM0LMbG/M0aJykUguf78kFgVmjn1+12uzhQCogLhIfi964fM6KMh0hRZcRNoCEzjwW4jLKZNMIBupOG3zkPNQHX6/XS6XSK8dWSXPTFL+bN/+E/ZO4AZwc/em2ysbiV6QGWajA3yOnu6UInkxSRz6U9kGC32y1SHHYXbW5uFu+L4egYtk/SMolDcr5OiuP7u4xGyrOTTlSvA51bi/F4+xhKxMTxBEQtjAUvg8KgAEl5exkwCqPGUPkbyICg7BTcxIBBolhmBlFIf//UqaU8/fTLck2+uv+qJ8lklj8A7wxhWBzGTu5oGZqqN5GA0sN6Li4u5qKLLsrq6mpxlo+bKbyZGcRhtpe1YizIifoc6MT3qKYckBk833kpjoc1JlJyegUnRCBvtqctjMe5/oMfzM0f/nDmNzYOJPaPvzXZWtj/c61BK6/73Oty01dvKvSpysyy8QGZ0DBQLPGZNAIyqRpk3PsM9KU7zevqtMA5u3finHfjexUCOj+00fFZJkvXjE/Ww7vawzPoZBbdSL6JLMANBE0Ut1e2V3Q901DchAz33YYz9YxGu9PZ1auooVXqW8zFpI9ZWZ5pCMTeU0cn7sVc5ubmSifmmRQzC+nancsWjBkFI5KYda3VZm/+prxF7c+EB44GGAhygBnn2d5MTfqSJONRPxeuPZTX/PlHc+mHv5ba9GDZfn9h2zh3X5SkMW7k2m9em7fd87Zc/fjVmYwmGYy2dYk1QP6MGXnhlEzcNBqNHDlypNTk4cvpAcw5CMRBh/s7L3fp6bzZ2iSlRa12XyQpGYyNz4dE8Y4JDxpBEPKdhyI48q7hcJiVlZViUmZC3aXh+p/HyM9R8mq+NHl5M9P7c6D9KdOcOc+34nScczN3N5sn5UOWiaS+DEutNMwB+QAnqwV1Q3fnOCge8waFeE2cR/EsWGP3kjJXPmenwvOpNXM+63BtLRd+9Su59fm/yhUffyz1kzu/kHa367GXJQ+86uyFmBvOZXFrMa/72uty2yO35dhzx9JKK+PmONPGbAsiB3R5HycR0SjDzTWG7t6wwb+BrMiDd68kKbYsgvIcUbGXJAXTv9t1oPY97wN0zdBG6d+ZqbOy8TkbBtHVjKoFlqTYx+n6Hfd2ZwflBX4P1MAgULKkHGkeuvyO3JAvHkhRRo1Raou1zE1mjcwoPgwmjqwgnc4oBbkuP0tSkDBJ+YBmzwND97/NVKNojqZ818wv3zHMx6l4/6JhtxlcvkPEJBLxMz9/Mpmk1Rzl4q9+NTf84Qdz2SOPpLlHq9pe1323JaeP1HPB6pEcO3ksVz95dZqNZq5/+Pocnx7P8uZyxqNxJtNJavVaUT+0zq2trZUciqMa46VhxWQjskDnWVfgrPN7atLJ7PQK3q7u7iHzJeddSjGJgLE4L3R9Jym/Pp1/EylRJAbEwlZZTEcfN3S7rIBCocyGvDvBaJdUHOHJ4U4vt7PeTZbW91eUhRe/lflnHs34klcU/b/2tjYavKsb8A1dMV4vMuUPNzEw/8XFxZJBuUaKfGAiMWDnXQsLC6WIyDqQnsCIE9m5Bz9vt9uFApsbsMFur+ko808/l1vv/lBe+bm70zi1N/Gx1zWtJc9fd3v+6Z+9Ltc/d31ak1Zqw20nwQkdRnbIHiItScFGc8QIcmR+wFy3oSK36qZ/w3SCFsScf+YuKhh8ZGbupIqefB2olJKUSwGGZ2ZQfZyDmT0GbsrdZQ7Xgch5UGQEa3bYPweq4r2IXAjRR2NCajh6TiaT3HvDKLdcW8ttX9o/B1pemea2P/l0Pv8zLy/G4nsm2+UNd43we9hYSAAW3dusHOH4G7bWRs9CO1WA5l9bWyvk7nTE6MPjNSIhQmBsrt3REmcG0nlavV5Pd+HF3Py1j+bG3/hC2s+unRN8rV7Tw7U8/PbXpn3oh3PL050ZQqhvj3dpaakwUGTjujGODsNCV9BPHD05tg0Px8Mpis7vrefoHQaNXhnN4IxBFuvr68WBAXvVOvfNOVEsBp3M6HLqQXgHlMlwoBpNk/JZtSiDoS1CWFhYyPr6euHRbJyUQlwvdS4Ek2kCxXkeghwOh7n46Uty4QsXJnluX4WpJTn2ta+l/uKLybFjpQjOIrqUUs03Go1G0VDhyObdGlXlQR6GVV4bRzDmbZaWNUBZ7SSpKXuDOI6Mw9CsfHY2jG17XLVcXftCXvtHf5XjH3wytb1LePte08PJw/+b1+bjR38og0ErC/VZrzJQ1c0FDgg4GsZIlHIpED2r17c3CUAqGq34FEP0le4zH72CPibls61wutYB1irZNuLzfnlutenAxINpd3fBmJVCUSEI/DqB6iJvbGwUeQJN2yiae0a52u12sQUI5ZpOt4/hWFhYKA6tAsY5n4LCBhrWG7V8+Dta+Ye/cjDFOfzss7n8rz+Xx979ntIZOlUj8xlAyYwAwIiAYSAGR3wWlc84OjuNsAz5Y5hrZeGzwH6cXtX54uyYB/CddeX7RSvcXD83nPhE3viBP83cF/d+lcJBrq03LubRn3197qn9QIbrtdRq05Ie2iG5pGMnQpkrmb1awmy3m9SJriAxWFTq5tzPJRi/TY2/cV4lsvGMg1xZWckLL7yQ0WiU5eXlXHzxxVlcXDz/1zGw0CyMt95w4c3xZkQvcj7CvhvOq4oDLOW75GpE406nU/I2rsPhJV2T3dzcLAwcr0WuxPhdt00tWazdmkntqRxkB1l9Os3c0x/J+votaTYPlyIS47ShmZX2bhYiHApiUofverMB93fLpFsS3biNIpq1dY5pmIZD4d50zfAdHAnEktnL+dZ63vLEb+Wa/+eXUztYyXLXazzXyPPfdXXu+/7vyNOjV59xNtvKyxpX2WsQATuVqgw98iGHd0nESAvysOqAijWXQ6yWtcx2+72sVQKo0WhkY2Mj6+vrRcD4ttha8heSYzwKxocACPMmblBUJ71MAiiMcaNUGJEpeWpIRO5arVbkYV4Iey3DZf5vA2bRiHZPvOKGrC7/ZQ6v7O7JfN159/PZeNvd+fLhN5eKyy6dUEs1e4uzQ+mJaD4XyeUNOyIzuCw2huroZuVAkXyUikkooi3jwyFwTxwqLYP0yTabzVx15WO582t/kcv/9SPflmFOG0n/5kO5/13fnQdvuCvDwVxarfKLlbm8tu6JdW2XvwvW+Mw8QAqOqH6dhY/IBKoaDaFr3jZpNMfvXG7CYeLgDh06lKWlpTSbzcJAd7v2zTnxtiiNa107Dc4KxP+BpI4O3ptoBpN748FZBCYOVAaqVB0A0BFFdO6Gd8S7uv1sOjmWL91+LG/9yNMHUKek3pvmtj/6ZB7+e7emfuyiTCaT9Pv90pk91VYx+jaRFQSLyxDuIsIAeXsanpbxm2jyPQxlk3KJBmSSzF5DyDpzfyAi43Rk6fV623n6hV/Od538jbT/1XpynoY5TdI7dChff/sb88h735aNyfHUp0lLeZpzRYIC643+IVvvgDFiweFYj52eueSCAfuYF/QWZ2AHYXnyMwwO44ToO3z4cKFvBKa1td2PZd3TOHu97XeyUwogUplmNoRl8CgPPyffBII6BzQlz30NXQw9DC2AkpxKQOeKC/QsoMsdZtSIWttQbz7fvO7GjD/2dBoH3EV29N4TedPr/jSfPf5TqdXKZ+PS0oWR4LDcEIBBmDwj+iM3yiuGTva2boKw58dA2crmMhZOg3GST7GWyJ97eCfF/Pwkr7j6k3nDU3+V9n+7cV6GWRjlG96Qh972tqweOZLaVi212sxYbDiet8kf5IYR+TA3SidOqaqED8jE6MFVBuezGJmN2JERFIIN8DsTVdbDZrOZQ4cOlVjz6nWgY0rwQt5hYDyOciQzbM4gTYowUZhAf4/7mHm0xwS2EXmqL4E1vDORZWbTL6/B68GaNhqNrF98e7YWPpv2Gae031VLcsOH7s9XLn8yp45ecZbhV7uiOp1OyZPb86M4JlrsmFyvbTQaRb7slKBKyyOfxcXZUZ4806QT9/VGbT5bjg6bef3m7+fmf/Pp1D87TQ6WARTXNMnkwmYevPM9+eqb35r1o0dTbzbTkH6YwXZTg7kFYHr1xAvrELVLM8rJ7DBrUgv0jHTExFuVgUcf0XGcLePCqfL+F6I+40L2wGRO8tvt2hfWeiA2JudyhlFWNrOMTNCRwcrhA7h8fwsViE0+gbNwzlCFs/zetdbxeFwcf1IiAI5dnmde9rJc+8ADB67PzT22mWP3fSwn3/GjyXTWcI9S42W9Idy/Q744PEdCPmfPTnSuytKlJGRjA0QRnRfzTLw/cnMOivw79Ufyjo//di774COpvXhu5y4lyfh4I89+9yvz0Ju+O8/MvTzjSVLTPHEQrK/zZEcj5AVCYs2T2QYMd+SgO/yetcGYuD9yT2a8B04Sh29Dw2Fh0Pwcx+v52KnYEYP+drv2PVTawmNAhgQmhUzQuPCNMEwuMFEisvc9Or8gD2OhSLiB0Y6CFjyOg3vZyIHkdO+wOKPRKA+94Y5c+7UHkgN2mtWSXPfF+3LfO96axdrlJUfjMooXEHnxTEfIZKaEyAYYT3QDFRjegQzwyjzH/zYh5ghi3oAygQm2zmOP5G2f//c58ucncsDTXGbzTrL6+uP5zDt+NKdfeVuS+UxHowwGmyXj2Yk0s6w8V5wa62623uw2UQrn5N5q6xoy4Q8Bwu2WrKH13uSZYTbkmW0HPYYYMi+y27XvljF7dhYeD0W0RMloC6uSDnzHZRRPhntAjiAYvus8w1uQvED1er04mZsFtEANuxEoiT8edDqdZuWupYweaKb1mYP3gV75eD8X3f/RvHjjj5SgajIzNBwKRlk1XPe8Mhaz0yiYoRdKZBjvEo7/7UjtaMzz6Ud1nlSv19M7/Ui+99/9Yo48d+LA8ijm1U2+/iO35TOv+JFsLR5KVrcymfSLOTSbzQLWQUyxRiZtkONOZRQQWjLb2F51WqRD6KWPpxmPZ3taQX44ChwEpSrWlnHZMVgnmYPX3B1MBJBqH3P12nPrqhcfhsk3ZCD+vwVr9tbw1zVQhLyxsVE6Gc5w051DOAR3uvB/zsfZ3Nwsmhoc6bkXitHv9wtyCpbvxOpV+eYbbjyY9iHEafKD/+m+XPGlLxXlJs/bBJnh/tzcXPESHj5TzQutfFbUpLzDgWf2er3iRTxuMHDZpNrbzFqxFgUi2dzMq/7sD3Po+efPSR5JsrWwkAf+q7fkY7f/WDZanULWlK/YtWJnZNhaJYWsC5BUdiRAzSqjzeWe5NFoVLDOyLhaAjSh6LzfP6vuDsL4cQx8h+9DTjF2s+U7XfvWOf2wpNwBxP9dr6zmMbSHoVRudQOuwi6iiG5YYJIsTDJLxN0QYSaOsdPx4U4OFg0ojZIwznp9Lg/c8O5c+trHs3j3wanIhf5WXv9XH86Jm67MaNQuUEZVUXzIcjLbI8nCI0uf1uAX8ODJXXphHbxG/nySUk7v6E7ewyYDjLY23cxrP/D7edVHv3ygxgxfK8vJB/7hW7Ny/DvTGs+lVpsxqoapKG61Y6wKNc2qMgeiJOw4c/WWLn8fo3cbqH/HM3iuyybmW3ykjNFZcrYD4H08Nk5kzveqTsTXvmytT31j8vbEzmnc8jWZbNf9KAUkZQYXOLG2tlbyMjZs10qdbyAcM7tWPBQO48AbQhYQgV03ZX6NRiPPjK7Ln7/hh/OOr/9qDp8+eJJ18TNP5dX3fzRfvOuHSujAc0BheC5GzNydXzHm5OzX/RkWuy2x0djuQuFnJhyqkNZIhnujrFc+ck9e9YVPpb6H8ux0TWrJ+/7L12Tj8reluTlJfTwu5VkmzJwy4bCdLpl5RQaG7LsZnw0KVhuj9eshcPI4DtbC94a7QI9AdkBjl7WIwugSDtHvJGUejPm8t4yhAPztbUVM3mwp3oVJ7JQ/YbT+XZUdpCULYe+UOyDEyWRSCLzdbhceFiWnowVDRhFcKnBetv3McZ686eY8+PrX5q4P/vWBI0dtlLzmzz6TZ255VR7pX1Nqg0Nu9fr24dM2ENAG0Bo5ufeVN1tD0jB+w2inIRinO2wglJwHmVWcTCap12q57CtfyVt//fdTXz/H0/CTPPGa29O+5CfTai4k3dnr1/0clySMjJLZjg4uIy3uwedcd0fGGMF4PC66yJAvOgwx49yP3yXlDQtuoDHSszM0n8GzvQHBeSlR3g0wu137llKYvCl2hGMYwOSYoD2+lcg0N8J1kowh9/v9UqcMXsY7UVgwlBFnYY9rZpiLCGpHgtJDimSQfPy73p7LH/p6rnzsxYOIaVugL45y5R9+KU9831UFtAY6u7yBZzXSsCJb7mtrayUU4lKIj+DYqTbJczDMZFYjRvFR1sWFWi780pfz7l//7bTXD7C5Vdc0yeO3357P/czPpNHtppHZUSg4QNY6mRkdiCuZnVyILpmYsmNFNnY86CBIr0o2+rnOYWFWcVpJzjqT1umCkQ3jNNRmHBizyUf+7fud9zElrrtV34dBQoynYLGTFEpQpZlpiQL3IxC852g0KhhXogZG6G1QPnjJ3SvkYhhftY8VWESUcr7GXk8ENhqN0p2/JH/+M9+TH/+/vi+djYPD21s/+9k8cdNNeermmwul5AhLGFTgEkZEfsnCN5vN4tgLOxwIFYy63++n0+kU6MGQzGUAfucSlaF/vVbLa07/SW79nQ+nuX5uJxZMkzxxxx25+2d/NllaSkNyTGalG5w7TmOn3M9QEt3hHhipoSQXczMaS3LWodboiHULWVrv0G+aZpIU60dkdKDiGSalmJ/ZWv5v5LnbtW8pxd7YN9qJXaySOAjc568aajBos5hEZMgOjIvFQomJSI1Go6jvVRk3GEsEgoBZpOp+xWopo9FspnH0rnzyu7+a9/zu3QeGt/XJJK/56Edz8oYbMlHrnmEUSoeMms3ZpmoMGrLDtVBkiMJTXnEXELv63WXFM3CopA6dTifD4TDH77svN/36J87ZMFeWkwff+bo8/b0/l3q3m+m43IZpFpUIVWX7udxLjY6YvTUMTmavQiA/NaGEzN1FBbmGc4TVxjCRM7oFWnO91BGwSowyR++UwUYcUbGXwWCw5+l7e5ZS3O2PEhv3IyAMw2wrA3IzggmHer1etFwxed550u12i420fs8hQneuQenEJyW43uUyAXMZDrf3eK6srBReEc8HRCSajmq1PPzq9+Sb1x7fW0t11ZJc9eijue0zn0nnzFulLY+qcvkPiML7V/HCKLpTDNfm+CyowO1tyUz5cXxE4uP33pv3vO99aa+fW6Ps6nLyq/+7u/LAD/1MsrRUguc+dQIYye/taE1uwSW4nIaxMGe3yIFCeP8Jr/mbTMrnxWLkOHPrXDVy25BALt1ut+RYm81mOp1OsTvHiNKVBZwTf9BBlxb3uvY0TiuN65TkKPbMQGCIIB9tiSJVldMQgsvkEIKAfaXliTNgeLbPh3He5ujN/RxdNjc30+v1CiGhxHy/oLwXj+dPf+qt6e/eBnnWVUty092fzGJ9pZgrC8TrDdfW1tLr9bK2tpaTJ08WBxon5b2Z1MWAdK75zc3NZXl5OYuLi1lYWCjqptUojYGQp83Pz2c6P8n66T/Me37919PZY3fETtc0yUNvuTPda/5RlrJcQk1EFhuC38+SlI9CtbP15gnGaoPEEFlvQ0OMzPVFfo8xu5EBXTCqMlPMHPgMv2MOJrG4p++DPHi+9X6/MkpyAEIIaFksijC9O4UQELkjwvVioDBVrG3G1g3svh9N47yACPgHrCXqJdsej0hsGAIEch7CHBiriSIWe25uLrnkjdm46CNZfOLg5FD35Au54alP5YErvy+TybRgXL0dCXkAX3u9XmlT8Gg0Kl5bx7xQmFarlYWFheL1dPzMrCUOzgrcaDQybo3ywsnfyT/5159Ie+PcWNmtueSpW+/IN9/7T9OdtjOezMowRlmwm6yTDQ7nwtqbKfVGZMNiuABHWNc8vX6OjPyO8ZBbYixEV+5nJ05wMYvuEgjBxtUA8t9kdgqDDRXmHkSw27VvnRMrdx2Sn1E8Z+JMhv5PBMgCQFZwb5QIY+EcF2hyFgOvTJJer9dz6NChUvMCC0suaWPnHgjVkbFajuHgMOfUw+EwC5P5TH9kOfm/vXjg/tLaVnLjn30qj/wXb8lk4aKSHO2QrGi8ZMgti7zvM9l2VjC/ZiXNePPiJ1AFz0zOMLVz4zwy+O38N//qEzl+8twMc9hM/vm/uiBXNN+bSzqdNM7kbkTxRqNRKpWhrIyBFMV5JwQQc/L3OMCLIz5xtuicITvysaHZsJCTv+uojT7iANBnO36vTa/XK3QHPYGMNAz3BgtITwei3a4DbxkjgmEko9GoSHiJLiwI0bbapcLFz7kfnp3JjMfjov3OECGZNWqD9yGUXBJIZhS6qWwSdTwo4wYF0JXD5TxuPGnkq7e/I0ev+U9pPHrwox67XzyVo/c9kZU3XF4in5iba6woAqfoJdsvPAIttNvtAqn4M56H0wZgGDCWnz3U+lze8r6P5cLnz72O+ZH3LKd15EfSezZ5+umnCyLKjgCZUQ5z9PHa46xxUD6ZIknp9R9Oj/g8z0EG6KlPqofRN0OezEqBpC7kqoabRG6Qm9M77mE2nLKV2y2tW0aNrlDsdh24fc95HEJzoRvBu2PHMMfek4m5RlV9QzWLZ9hiJXaNC3iHAHEgtVqtOARsOBxmbW2tlB+z+2I02j50mOiJ0NbW1oqXCY3H4zx86o5c8h0P5rpf+Nyer6vzVRtPc/PXPpJPvu7VGQ7Lu3qAX+TSo9EoGxsbhYI2Go3iQG28rQkgDABizaQHJSbnqBsbG/lWHsi7fvk38vYPnfu2r43l5Xz9jh/PxSvXZX28XpyFg2E6ojmdIdK49MbYcLZEfHTBbD5rVoWWOPNqIwD5OSUP9NCb0NEnDAk4bOIKG2BM1VfH2ziTWfOCnQgE5NraWkGourWzt8fe4X1LKRghZAsPNEMFnsYA7UWhrIfDYdEQ4DY/Hy0IVnebHx6nWhO1Abr9yrCIZw6Hw8IQfWasCRYUycbjss62Uxrn01d9Z45d8PUcOfnCgRS6luT45x/P3Dufz+DiS4sIwrs9WTCMDCUzI8nY+Lxf/GqirdVqFSeMo9Qo9Gg0ynj9dK7/yAfytg+fx3myjeSL3/u29LtXp6XoAjpxOQqdIJrAfLPejMmsJaiC9TehiMPxiXl2SOiFoye6a/TDH+RRrT1WO38wfG+c5/nVsk61mmFSCWhLmsiYSGd2u/bdlWIPb7bKiS2Ds8dksBY63wO6GTYAPzidzIZT7URCmGaKPUbXnvBgnU4nR48ezfLycjqdTrrdbol5o0F+Y2Oj+B4Og6bwra2tnJ47lHu+87sy3GOTbPVqne7nzm+9L9PpoHB0djYoqMkpWFyiE4rhGnGn08ny8nLptHEU0F69Xq9nsLWZ9hffl+/6nQfPuZE9SV54+/F88223FMRctSBPnksuRxmMvBE9YO2qDKcv2GcYepTa/IQNA+cO+05Jyt08JqzMInNm7Xi8vY91aWkpnU6nKOHhMJkP60AaxxrinByZnbcSzbEpN2Dsdu2bc5pJAwLiNRGQI+FO3zdZhMeZTqelSME9WASMDtiDQE3WJOUDwYA+PAfDxpEQSX0WDEZNKSKZUeFJ+YxdYO/X77gj199/f66+//69xFdctSRHP/zNLN7yfDZySREd6HSyF+50OklmZSwcSbfbzWAwyKlTpzIej4uOI941w9GQJjbw3Ju9Xi784F/krl+/+8DnI/kaXdnKl9/7XRk2Xpbl5VYRGUhxkI93vrAWtAr6LWZuxsf4Wq1WsTZJuY8W44J9N8FjqGvIaA4DA6gebcKYzT9YbsmMd+EePhQAZ+OgATLw2UJGe47w6Olu176lFDOaGIFhJdDFyowhufharQ1hcEAbL5hhs5k/4IxLOCgC0QgDsqc2AeFTEbj8IlqemaQ454i6KnCyVqvloe/5zlz+1FfTfPFgR5u3v7aWVzzyydz/yp8ojAl5sdAm3FBq13SRI3ALRtuMp51fvV7PVr+fo3/2Z3ndb/5mWnv0ce52TeeT+9/1PXmy+7ZkNHtdu8kXZGKE1G63s7W1VYq0KCTK6k4mHCPygCgjJ7czTVI6XI4501DiGqUDAiUNSlfT6bQo2zjq42Cc+ph93Yn45KJ/HKebzAzc+ujDqne79jROyBUegGA9YYwTJUpSCApyCI+G4Jwo8xkzYBYuRgU0sNe0MPnjNivgH8K00DwHe0uO2idn4VVuPoipXq9n87W358mn3pmrf/2DByOHxsl1f/zZPHXb92dr7pJCdvzN4iEjDNhGWa9vvx8EyDqdTtPpdEqlB+Bmv9/PeDjMxR/6UF7/m7+ZuXM0zGmSwXwz9/7d78zjb397hoOZk0PGPpeJv8nbWFO3I/p0C1IgH4mCPKrEjMkko6piLfRKA5fGXA9HL71JH4O1cfI71sDElklO0hznoC6n8EzGip24o6jT6Zx/4zvQy68lt/LY00NgUKs0a2qihv9jvHyGi2M07eHG43FxSsJoNCpqXj4RwbCXv+25iExEKcbHRmMbuAW9ublZkDcUlLe95kIe/cEfyvH770/3vmf21vQz1/wDqzn8yQfz7DsuLuaHQ3E9baemD5hbE1gbZ94KzXo4rxpsbuaSv/zLvOG3fuucI+aokZy88pJ84J++J2nfkUZvLhxb6cPAkCtoxjVI/k/jBKgLA8PB+nRG9Ik1YW7J7L0i5Ibk3z5yFSMBWVgvcLrWV5wIMNQNN3YSrLuDU7U8yNxMMqGvNmD0D6O17levPQkhJ7csPmHegzcDRxKMl2TiZsq43GZmDF5l3mjf81YeFhUWkGcms7qsPZ1LQU7Yk9lu98FgkF6vV7z0lQjsBnqcRL/fz/roSB76+Z/O5NoD7bxLbTrN8XvuyVh7BIE2LLibLnAwS0tLxev3FhcX0+12izGCBiCRBoNBtoZbear/0dzxgd8+L8P8vZ+8NO//Fz+fuQvenMEZosqQbmNjo1TOga3tdDpF9Ad9dDqdYteMiSP0C6WG/EEeRFcMkf9jpDwb0s6OwiUy1hl5u/yHfAkAVTTlMhB6BXrAEDFu0Ax5uPeR2gk7AJhL2ena0zgNN4g6TtKZON4EeAasoEzCn2TmkYA93oHA71igra2tojbJxDgjh7Hg2VBmn73q6J7MyB1yTxbECt5sNoujJTgikcZ6nA9QaTwe56ljr86D/90PZtLeU5TFdfWX785FJz5RLL7pe2RtZQB6Uex3j2r1VPLpdLtrZfjUE/n+f/P76a6e28Gyk1otD7zzHTn5jv9lWoNjpdp0NbdyBOcz3r4Hu8kYFxcXizd0QXahtD5flnWCLTX/gB7wTP6dpHiOUx4M23CSkkmS4hnI3r3k6CGG7T/mV9xs43yZPNypGXrJv/mz27WnRoHR7S2qjcn8jIdUMTgG4vwBY3G+57CPx+OzJP98ByIEIeAFzRxWC8QsEt9ByREQAkTw9vJbW1ulDhLQwfbcp/n68ruz8nNX7iXK4mqsj3LDX/xZNjdOll7NUPy+MTv1rd1ulyAYCknUQNl9DMag18sFX/jtvPzB0+dUy5zU63nwXe/KF77/76U7OlYqQQAxzaYzFmSC/Fwi8EFjfGZzczNra2tFdw7GQlePjR6kwkZzs7DMlzQIQ7aBOCfk5yAxkIhRGsbmQGPngAwMbU1cORc28kOvHYCImnt1CO1b52QATIp8gsG71MAEMSh7LISFYRH98PrOwUzysFBASSKc31YMtjeTV43yCB7B8TnXNnEUrt0Cbx2tWWjmOko3j93y3Zl2DmYIx77yjRyafjW9Xq+0iK4TA9HckuimiyoRhhJd+Mgj+Z4PPHywgZy5xvXkj3/s6nzxH/z9bE7LJ+PbkSJHw0Lka4a+3++XOp2Ykzu6QE+euxUZPcEJW4lxGCAgn2BHQOHCeGx0OG4jLZ6NvImyNjj+Xy0b4pC8JtV72yiRFzLY7drTOM1UErGIGjYeFtHexcbg/NSYnuiEB0UI3jyLd0EAVZraz0Ww1UUGDrJYhjWwocBLFrJ6ZAj7P4m8GCaG/OTynVm9/LK9xDlToMenyT2bRYsg+xBxgsAtM6OUFHyqIY0KzG915URe/dBH09gDKu10ffPaxTzz7p9OmrO9syi8UYVfX4dxuN/Uu0Ro4Hd6gTNEpt4Usb6+XiIaUX6nFxgzeukynwkxlyick9rRuCRoNtVBxg7KcNZOn2CC4cKH+NjOpNynyzPsuHa6zunN1iZYnMfZWEy2sGB4DjNkJphYZGAM97WwUU6egQHbU8J+8XkvnKFjNdLTXkVOwDyTFDtcyHVRvGrOO8piTi8u5tB+Aj1zXX/R5/PY4BXp9/sFMcEC4zwwUByFGWznq61Wa3sv6IkXc+UDT54TnN2aT77xnh/I8tw1GW+V8912u31WGrCwsJB+v194fHfbWLZOV4jEVf3ge0RI5u936XjrIPdPyq9M4D7uvuE7LsFU2VRDUMNYR0UulzzsOAzzcSwOWNwTMsqdYTiy3a49jZOoQ9KMN6r2sCIA9z4arriQixLyex+9aVjZbDaLNj4n7TQbVCOJjTFJ6dwXhO88yd7SuZRbw2wMi4uLRaRqnzndwPlPs9XKl99xc66675G9RLq9WEnmfm8zk58qn0ELJEQBcFogC6MFSCryzYWFhRx/5rnMnzo3dva+N16SJ9/17nQmi1ntrxbKZaeapKTIQETk6j2UXn90iLnxM9c5ce4wuGxUaDZnJzmYHWXd3WDAPZNZKsZ4TPoRydFV1s7NNIa+6Ip1noqBoTCXkZ2ZWXTWhJjnvtu17wFfeB/2EA6Hw1KkQjDkQ248wOAYjL0l/0cwGDp4HYXztiG+Z8YNls6G7Tc8mbH1s5x3+EBlzu1ptVpFjovgXV8E6pthvGTr0j0NoST4SS3Ly92SU3MNlnouSocjWl9fP6ujajqdZn5uLhetrqY1OXh/3ulDyT3vfmsuHHYyrZ/9HhrWEJk6OlIyMM9Qr293BuEYSRvMUCazlkuvC1HF5/eYWES/lpeXS8SfyRZgtUt73J/I5QjpQICu+FlmqtFP5/y0TrpUYzSG0+dCZ90xtRdbu++WMYSAx0hmRmkyh/97MbiH74Ox4CkRFI3BhqGOgo569sabm5vFYhBJUHAgBNGA7xOxgWbOVcfjcZHzme7ndx5PkoI13Q0O7XZduP5ojk5W0jh6rFjclZWVosHC9wPCsQ7ueiIqjDc386qvfvXAz5/Ukj/73htzevGV6WxslOqTKNThw4czGAxK0Bt5uh5t44QB5/+sC+sOuzoeb+/ZdduceQYbj2EpSK1KWNFAgI7ZMDFwjKeakpkpRo/txF2LdC7qvNHy4HNuNvDlbW7n3b5nitzh157QLB1CMlPrqAh8wVjYiuPtZBifF5yckMhNd5D7bNkBwffIR9wOyDiJjPa+EA5AKgzdXS/smqkuGrsuvnH8eG5tt7N4gPd7Nr4xzPi5zUwvm+U+5HjMGUXjWH/kbigFzDvd6qcxPdh7RadJ/vy7lvKl174ty2u1rDfWi0PVFhcXCxhJrrS8vJxTp06VjMGOi2M/gOVGKawHugTB503J1iuTYUZfGLf7eGlaMLtuPWWt7cwwXMNgDIlIVuVX0Gvy452cAM8ypGW+yIKf+xl7Xfu+AtDHMlRpe+cRCAeh4A3xLkzOdLRZXON7omiz2SwdHTgez9r47JlNBiQzapuFxbj5Q844Go1KL7RFaBgmDQhEBJQQpeN73H/16HIGN7az+MX9jWRam+bZ5WdzZO6aUlnCvZ/8oSkCeVcd33Q6zfFnn87S6YOdb/StS+q57y1/L0url6XRnHVBee2IluRqGEfVANzX6uM4qqWDqkIzdrcu0hqJYYJe+LdTJDOwVaLSHUTsbzXPwOeM1ixP0ifnlThQxm8jI8h4fWzgHh9zTnKWo61e+7a1mC0lelBc5vf2Hva4RDovKEYDO+o6JvVElxX4jnOFer1eHGOCV+JkNqACZBKGZBhqRXPBO5lB9fF4XEREjwXygzeZQWAkSRa7+cblt+0n0u15TCZpP/ZAUafl2czReQmK5WK+XxQ7mUzSa44zOkAX4aRez0d//N1ZOvTqUo/qiRMn8txzzxVMbLXntdvtpt1uF5+nkaCaq+HciJjssXTtmZ5lFLoKIavOHGN3uyLGWnWshsZ2+obd1X3IJgTtQNAVr5FzYTPsjrImOI0wneqBOIzqqteey8mgfSi0qWhfzicxGISBZ0JICNdRE+V0pOZ5NEJjbCwEBupd8eSS7rRBICyIYTXGACxhoV3nRJAsBkLHaIFpjUYjG0sXZVKr7fsCoMYkWX720Tx983oWRguFDIGEXlTn8o5I/D0cDnPRU6McWtnzkUmS3g2HU7v2+9JtLWR9br2UttAPi8wgZ0gDyOtcpHcOR+QkmhOVcJrswnD0Qrbcy9HXfatuiUM3bZi7VQiQEymK5cjvTEBBxpmIQj+d5zJPEEEyg/ogrGoVgntZ5nsZ556R09S3yyFABdPVGKRzQyKtmVYPCM+IMWEwHC6N8FFOouPq6mrhfTc2Noq8FSOx4DEy560YWZV0oKvD7BzfxUHh7e0tvajP3HprJnvUrnzd9elTuXCrXjCxNG0zX7OyKBXOzZHK9b79rqdvfnUahw+n3W4XpyhgeKwprCkXz2F8QDXXhlnHZHb+rH/HZmunOtzXJB2GD0m4U47odAY4zHNcH3dVgcjn/l87X/Ss0+kUpzDwmWrTjR0SDgh7Af3QWWbkU42w1YMDqte+xsnA3KFhpWSAKIz3xSXl40vwaERGlIJIAVSG/HBtDyUy84si0PtKpGaBIKYYI4Zf/T5solvFarVaUZdKZi+s4R5EE+5BNJ0cOZKVyw7WKXTJk+u57Av3lXIQw1c7reoiotAo5l6UvK/R+FCm03J9sdFolGROgwPlDefZOJJkdhyld9X4XjYoouPi4mKR8wNNuYfzbncFMVeMCNjN3O2AWS9kZwILuVn3CrmcSRWS8sugTY7ZMTJXIysClDvIWDeiM7I0fN/t2rfx3UJ2xw0eKJl1hXjALvAnZU/iMoBpdyfSCBemcnFxsThbhkOUWXjuZeP2jgizabR98WyMzkrBolD/bDQaxf7ERqNRfMb5LkqzOT+fteMHe3VDbTrNlV+4J41MS944mbW2uVYGsjAkRKlM9+92TZNMarMGdFjvQ4cOZWlpKUtLS6W1oc6LrHBeGIdJKhQbfajX64XT9EuiqkZjB29jZb7onR0YMie3NW9AWY3PAlFJk8xDVHXQ1QA7SBOXoD9sg3E5GvMszn7CZkjnzNGYXKte+5ZSbGBmyvA8LAzYHaEk5QO5mCzwEOGz0Agcr2Osj7dKUtoLiMEzaRd2DVmcjKNcSflMGfItC72a0+B0Wq1Wut1u0dIHJIRgWb3yykw///kDtdFdNvhKjh55PM+evKqot5mAQ35ElSJCT2abC1C4/a7hXD2ff+slaZ75rIkX5oxy9/v94l0hlKxsYKyLnSs5mEsMRGLg+ljPJh2AZ/DZulZm1s3ogXU0T4B+oocgHe5pR52kxE9UZczFnM0Uc5nkIrhYB3keOooO26nt5VT3jJyGKr4ZC+ow7+jK71xMxzi5MAIuohaeheeTgzQajeIlR8vLy+l2u8XY3EDAItnjk8sgOOclKEi1VmaCoNrAv76+XkA1Fta7bF685Za9xFpegOeGqT/eKxTBuTryQpEpJXHahCPWXrlL8aw0c3R0XZLZu1hAHDDkjhTD4bD0/haTNDhuGFT+WAHZmnXBBRcURo8e0YzAOHjlBHDXDsc1ZZ8vi9NFB3YicRinyybV2qeJIR8MUM017bT5tzdtOCc11HUkxwEzr2+LEHJUojhvpjJJ4RXxMoaPzpcwai+yIQaejue5F9KCInchigB75ubmiiNFKLNUWVYWj3sZGlV7eJ0D4YCm02lRqAfiUgIi0nzz+m6+ec0B33p0Mrl8cm8phXCbl/Ms8kTn/ox7r9yF6547kucvmnVyMc9qQ0Gj0SgOO0ZGyMvMPbJ0qoM8khnicSRrNpsl3XCrHc7G7ZrIhVMJnV5Ut4s5ujoicW86kxzZrOfoEt9x/u9IaPTAGLnMdeDgXPryfeEMdrsOXOd0Pct/4+3Yqc9F1K16BhTL9DILz+8RLErnaMrvSf7NnPqcF6IebB4R1YtXzZE3NjZKOYoL4MyJMaBQromePHkyJ06cSH91MR/4wWP7iTbJdhN8+49OlOh63hvKIlJURy4osImSvXIXrhsfSi5caxXR35CNKOj8G0Winru1tVXULDEu1t/kCMeqeC1xPCaRkjIXMR6PSyco4Fi5eIOaIbGJHDveQr618mZqcxSOzkYLfNbwHCSI8Rqh2R6QHZfJUJwJ0daOZadrX0IoKZ96kMygKxGLBWYxTfAACxGGyQjf0zmik38miOAddXEOZnURJONCgCgT8JjvOeeoJvsmJFyvdZmBPI37DAaDrK6uZTy9IsODHS2UQ6efSn108izFcD5jkoJ54UySZHjBfLbm935Ody1pDWbQGQeAgvJsH6DsiOHXJLAOJqt8BhCvx6CEhdzdVMB6QPhhlFZ8GxaRm7U27LWTIYc2RHV90p93PRRk50ialM/GZQ2c6kAiOQXiuegT3yGQVNncna491cfemAFX2TXnkoY5nph3ivM5/rYRA2kXFxeLPIiFNWHh/A4hmzyBTaVLiXF0u91SZGSslAxGo1HxXhTKNjzfuzToZnIjxeHDh2fM33iSp697eU4e+3wueXb/+mPn/hczefxERoeP7aggRKZ2u10ituyhv3ZzNzdfMZ9rH9n73KBabdYQggxBKOSfyMUOdTweFx1COENkhBxgf1kXnw3kFjrWEz0wtKsSODgqEIo3TCAnO2QcCoZhPTHSwDF5rlWUxhowjioZxmcM432ha8zP5Cps9nmXUnaCb+6CMLSoUs1AxmazmW63WzrZ25GOSZnx47t4UhbCrDFCBcdXczUL1oLkMlRhPuScjsp8DvjljbxeAJ7Vbrdz6NChXDO+NV+9/dq9xFtcjfEo1/XuLx3WjFIxRiIl5BWeHvlcePrSfPPaI3s/pz7MZUfvLtbT5SDfd319PbVaLUtLSwVSoIXN0QA5sH6UdKbTaQGDmQfrDRHEmrojB3SDkbEmdgZGKpTZIBJNBGHUPMdGTHriXJdqgYki/uZCP43y0HtHbp5fRSSOtpbhbteexul8h9CNotoLJil5LPC0t+iYALK3wiid15kdtMGYNCIvs5fkd1XBcSD0cDgsMYrOeclxXKYAtsPysTAspql4M3Dz8/O54NDxTK//rkz2oMq5auNk7t5+CWIbLWCIVaNAPpPJJKPaNJ97/d6LXducpvGHGyXHmMyIOs4dBn1A3jAv3sidzEgyHBZyMWFiNINc7WRJbYi+c3NzRf5IPsYckTEyQR7olrfSOWoyJgg+EBKBgj92IFWEB3ph5xT35j7YBfZA6chticzJUNvk2U7XvputETJ1KASE8rNQ/j9/o7CUVDCmKuywcdqgiZDkKlwIgiP/Ecrc3PYr6SFM8NIIFAWBTPJCAueAi4yTn5lSJ0pSZGfMJpKS5OTVV2fj8OEsvbD/G8nWxw9mNOmnVmudlesgd+S1ublZehHUcDjMNNMcXbkgybf2fM5kfXbYtp0T7CzOzQiGDdREx36/n8XFxYLAwZmxRiAR7ueuKzO47DiygzOBVIWSbqlzWYz1cEO7e7SNSMjrqxAY3eTZ/I0Ouz5rBtfIwH88F6Mw57Z8d7drX7aWhaoSJK6zEWVQHEcZD4AJ8nv3dSbl0839OVPwhjIcVkzPp5sg8K5zc7MX1cDmuhPECzIcDovokcyYRJdhnMgb4vEZ7j8YDLKxtJQXrzzYkZm33bOW9mb5NAjkyQ4OM45EWPqLR5ujXHXyxoz3WdEr77sv49XVIh3o9Xp54YUXCiLIBuTUAF1g3XFmJlhMHLoVkbU1U0vEdKpULZ9x4SgwINepWRNDYxwMQcMEkWGreRBHzyrLjI4yFxxlMovshq5mrr1WjuLc+7wJIb5sTwqU4JXbDAKlNXvL4FBmPAhK7MiMsKvMsOEvgkhmb6eilc8wukpT+/um05Py6XKwj4ZP9oyOji7ZeJFNOEwmk/TeuJTpvdm3W6gxHmeh38/mkSMlAsoR0ztC6vXZubAo+yfe2cnr/rieI6f32IbU72ewuZneGQdCDu1SmPdVMmdkbATka3FxsYCN/oy7dqw/rJOZTNAM8I8WOAg6og06YDhdjbI4H3MTpBwYsuuozJ01xbBdk3QKR36NUwdWo0fJzAkZWZksq9VqJea/eu3bIYTRWRmZhBN1J7cuifiMHyIRvzOTy2XBVbtzMBqo/uXl5Rw7duysrg28FQdCkz9ZwMBbE0Aeh3e8Y8COts5LGFv1d7VaLd/o3nkAfJLMr6/nyH33Fffy3KusNvkhuQ7yvvKp6zI3au/5nPWF1Tx27OtFrggjjtypG1pBOR7V3r/f72dtba20M8Upioki/kYmwForv8tVEFCGr1Wm1VESQggewlHP5TL+VCFokqKxBR3EYTiAYOguzTk42XGjB1VSlTGzrshlp2tf4wRaIUAzpEQjJ94ocDUvBXZWjdj5KlS2WTwMw4vj3KbdbmdpaakEd1AMH2LNwvBdQxd7PsMQFNU5BovO8xGyd68wlsFgkMHN9UyuOwAplOSF+SeyubVZup8jJYuJQZlFnU6n2WjN5dEbbtjzOcefH+euzz5X7IVl3fg3rDeQkfey+P2afGZtbe2s184ns/QF6IpcQE8YqPO+Xq9XYnuRLyjIxXxzFY72VRYUvXRqhbEhU+5t0onIjhPhXugCfdieK4GM+2CEJhNtQ07tdrsO9DoGb5My6eGkHPYLL1GNuHyOC2HhRSx4HzJlqGv4W2VcfdKbWTW6XhCinYSjHkJkvIzLNUcWnTn6NAeUxPlzkjy7cnlOL124l5iL640ffzKTytYp2EDnZn7FgfOf5vx8Vo8dy15dts1x8uqvfTH1WoreVz/HeR/zQdl4hQG64TEZNjpH5XPV1Mg6w9nA9PeiH6ybj0lBttwP43KJhN97DXmey1SeJ+tV7UDidxgf962WkhykQIzVqoRbE83l7Hbta5wosPMPLyDCNlvq3QXuCEE4RCcvmNnbaj0Kb83vgEB4cMgi7gmpAzNmUscKhTKQv0IaeRx8vkpoOGfa2Ngoju1gfHjF5vx8xn9vbv+kM0l9eCJpnCjVCtfX14tzjFByZOw8m4V/5JZXZdLaB0c/PMxgMCztmYQ4I6+F6bYzNCHE8xy9ibjOz91ggIyrKQSRy6Ual0KQa1KujdpguDelJXJBjMl64OczJwwGPUIGVfiLvqNj6D08gV8P4ipEFQEgHxzATte+R2OS0FcZStd1UBqUkgmyoEQ0LrNzXjCigneJ0P5lSMz96FBqNpvFvkHyDmN/58ZuGmCOdjDMg+hlZTO5YOXiTF/yasPdpJEnX/GGXHjsG8mJvaSdXPr0IJc8u5bVl7UKWTiNIO9D9qAJHEqSLN7aTu3OJJ/e/TmbcyuZdvupTdpFKahWqxWbBlxT9MuAqgZKiYxzh0x4EJ0cPVlzO2PWYCdIytwxFOaMQrud0c+1zrk0BtqqduW4U8jGbohtTgOd8JycVwL/XXc1Aen57pVzHqj70/APGImS8G/+drMCgrZQiDp81nDWL8CZTGav5oO8SGYn87l8wyIwHjwbsJZFtsdiXCZxgKruNFpcXCzmBBFglg8l5Z4ou/etnqjdlsHr/yhzf7Sxr6yfan8+C4NXnOUIoeip5zEnF8gbjUa2JvW82L80x/LUrs+4+onVXPdUP89cWm6TIwons7ZLEz52qEQX8k0MsMriVhn/KiNfzQ9Ze5wozt+w1ERMUn69hnXAY3K6gVztHFhvnAf3Z61xYLSOcgFXk5RShKrOILtqNcMcTPXal0c0+8VCMjgGAZww6cL+PiIeCjWZTIrwjhDdAshgHZn4mfMsG5m9JUYLRKFZmxomYwWyuHbpP95XiKdnDIbWyMhwxTn2dDrN6d5leeZVB2Ntf+x/PJnlWi3dbrfI36rdQY5g7sTZnv9C+r3u3ms6TqbDWTOIS09mnfv9fqbT2V5V55lOMZiz17RqDNYRl0UwBubKfe0UktmrQXYiVgx5PScTgHbQ5ivcqIIBcS+XZ/g/RkWuzHlDpHLoHikHulflLBhbNYqX1mmvRXR+CLFiJg1vauUwceTaJQrrRagWZ/FY1XYsvBhwmc+yGF54jiohX4I8gW1j0dbX17OxsZFGo1HaosVYDdEwehSDMgcLZoMxm0yEGY2meeplr8qksbd11pIcfeqpNMenikPOqu2INCTQu2qvbrSy31VLuefTpYnhcFiUSWhRpJUTRWMniR1nr9crkShmKk0MJbNdG9wPZEDehoG6O4vx8u9qtMRAKBFhKKylobOhqWE3TKzljhOiLc8lQRCH96VWG1XQTeZtB/Btl1K8gBhsklKLnCOSF8EsGIbkA4xt3D4M2GydBYugvHCuGxHx+DwKDuFB/cxMHuNEyK5htVrbR5IYYqIInU6ndBqdWV2TXbVaLSfnrs/my5f2NZr6eJjjT9xfgj3If21trRgvMBb5M3fa+va7WFv+jfxoADDr6LUkP2ONUMper1eslaNrMqtR+9k4zGRmTKQRvNbe0dmG5ujHWPz2LuuKgwnO2qxpNbL5gIAqAYrD8DxYd5fZeC7fd33VsHxubi7d7u4oZ1/jdBLsPYVEHOefZlEZBLDLHgild/65trZWMlqTElZ2w1kIGKIthoiCmGVLtqMp8NZjq9ZAkxRQG+XiXiguz4Cw4F7efO7yzmbreE7cfP2eZY4kaYyTlz/9zXTac8W+U+QMBDcJkqQEt+qtesb1fc4TaiZbk0ER5UA6wPlq0wiKhcwdHbxzxLyA2UpDb2SKfLmPuQunIRArREXW3zDVKMdlLXTJ32M8/E3AMLy17vvfRm3WT9dwvRbojp0bc0OWe137ulhHLSZfTagRNH+juNVdAIUC1WclDYq643H57WBAR7OuwA/fC1aThSJ34rP8mwWAlPKBXoa9br9ypwiGwff4PGO0XJJZHsz4k+TrN7w9kz1yDK6LPnVvlntfK6KgYRiKXm1rq9e3T1Bobp1MfeXxPe+/etXhrF19uBgrpah6vV46wbAafcyqIzPDVgyZCOr0BnmaMExmig9E9BGnfpa7iNxUwvOJmtzXuubLLYiQiNXnuC3QBBnO0LwH3zWDb2Nk/ehqc9/At0UIIdhq+aHqXYiEwElPgjyT+xmWsiAIqQoTvGj8H4NJZkddmMxAOZIUAqgSAyyqOz5gbu1I3MwAjESZGbf3JzJHFNHymUwm+daVN+bZl798L5EnSVpPbeaKv/pAXnzxhfR6vaJ7hvvh2Dx/CKSH85lc/K2939HZGx3KxvBQ0UiCIbmOa8W1c8bpIh9zA44KtVqt5Lwg6JCtCTmTicgT4wGG+mxaxuio6JwbR+l0g4hdJZBMuPF9ZLFT9cEGy2csm2p+a2he1ekq3K9eBzpDiL/x3PZOfgC5nRk17xTBKPk8gzdxxOX2J0czLwiTNsXvSMbnWRg7GASEYQKNTTa4e8nElhnqZJYr81kWjudhTJvjcb787ndn1DrAifB/9fU8s/pEVlZWive+MF5a75yrDYfDPLLxSL7V+3Bq+7yic9BoZHiG5ndB3+iF9WEubjzBqWHEniuycYfMTkSJox2Oh8s1R5CRWw3REwcOdIK1dKkGY/Pa8xwuGzX35rOTyaSUU5sE4/PVNkt2S5msM0m2k85XrwOVUhg8jcFVIYDdyTer0QovUTUAs1WOuAzcE8Ar8yyzcDbgKqPHvatsIXPzhl/nFSbC2JblyM7Cr6+vl9rOkI89qlno9Ve8Iv2jR/cTey578nQm0/sKgipJ8TqKXq9XnIrA27a/9ey38tELP5y7PvlCGvsY55de+coMzzhOGjPcEmjCD4eHPFB2/g+09ukGGBa5J2mBo0+Ss/bVGqaaC0DpMXL/jhSFiOhcljnYKfB9VwgM0Q1TWTNaPu1snfsbMZpltmMyWQlCQAa7XXsapxXUND243UaSlN+tQmsYhoLRMhhDASKfKWcbLkLFe8MkuiBOTmhlTlKKrO79tZHiJfF4eFoWCUfjQ68QMM9mfFVnU2UYR91unv3eV2e6T8WjNUwu/fpncuqyk7nwwgtz7NixEkF0+PDh0t7FcX2cJy78Qt7zob3vO2w2s37BBZnXC3mdL7EVEHmAlpgT88IJun5N+QKZcE//291jyewkQQzKyMMbv3Ee/B+Dqjp4P9MGR+T0CRdERcNcjBq9M6StQmbG4Pk5mHAvR1VHTX9vp2vf93Oi2N7JXYV+JoZg8EzVU/IwOwcEIn9BWCxWNVm2ErEIVWMwAUCUdZ6AQbubqLrgZhe5J1GAHteqjEwa8KcKvYrm7fE4j7zhzlzzoY+k+c3dc8Nakh/+rdP52mN/mUf//nWpLS0VikSEKHKbVi2fvOnj+elfPpmj+7yi8/7XHMm3broujZQJLKIjrKvn4TSGwjrKh4LxOxTSKQbfJ781WWY9sqNDP1zuAeImKUpjrD3PJSAgexuFI7KNB4fvfBDd4NnOXx2QGLeDgOdZ1RfGwv5ZQ+vqtW8Tgi3chkE0MStqb+czPt0xRLIPVAQW8R0ME6E4H2LBq3VXxmZam5omgqzCIWC4BY6S2mC5rxfIubYhujudMCCTOORqL7Relqe/5/a9RJ8kObSa3PmX9+XO970vvenJkpwYW2+xlw/d/KG82P+D/OSvjdPco4oyaNVyz3e9MdPGfCFf1haIXH3tgh2emVfnf27EYJ78v6qs/J5/c6+kfEQKskPXcAoYLrqB/vEsoyDuzf0Nq9ETPmOdcGR3Q4mj8E5MK3rEz4GxjMeOCGezF1u7b+O7PQMLhEcjGlQN2DkCC+rP0OGSzN4ehoI7Ka9GCCZsA8KDGsJUYZU7MtyQYEV0g7cX3wvtxaahGnkgG6MKy49jPehceuRN35tLvvilzH1u77dg16bTXPupT2aw/kA+8BPX5Lkrbsvo0DQvtNfzxVfcm+H8av7pvz2Rn/mP0xw9teet8rVbL0j/ojvT3GpmONkec7vdLoyA8oXlQX8ociCyECVYK6KYzzuqljm4j/PMyWRSHPrtBvqq0mJ4IB8zn271456uoRtxVbmKKjnFGhvOE1lt3HZaVYLUP0PviJaed/Vz1WvfohueqNohgfBNO2OIKCJe1Zgf5g2D895KjAGFMHOIgRS5mxJySArgCIvAdiWXR8gTaFqATHCB2QpiZSACuOCNQ2FsyMKQ2+Pic6da1+eh9/5AXnXvb6a2tXdrQm2avOLeU7nugVP5xlVfzOZC8v6/l1z51eRVDyQ/9hvZM2ImyaCVrF775rQbF2Q0NyrJ0NCr0Zi9Uc2IBQQwPz9fbNPjyA/nbM7HDCGTWZN4ozF7jQVwuLoJAkNwXmeY2Wg0Cmjo8VeJRhu19dEMMmvFM5ALOs/8XW7jO+5GM/fin5vFtSF/WzknN/EAjb2TmZHy0IKgGM9OFzOdbA9lJtjelYiEsphBJBcht0CgNhwUAAUxe4s3rDY4k1M6Z+J+KACKg0L6wCijAxYSo/bxkuRzk8kkj13xHbnoLQ/k+Ie/dKA3ks0Nk+sf2f73zQ8c4AtnrmmSJ15xVb7wHW/IwmB2TCMXMkUerGW/3y8pKLKsRo5km+jhBU+gDiKFyUAimuVWr8/qnjvVCm1wyM6pinP+JCVDZ62dezotsaF6nXHSBCRHfe//NXvLc0yWutmf+VSrA7tdB4qcLm0QTcjnzKQyWe+9c1ThbzOmCBYDcm6BUkBSOBqxiDRmm3QCrqKEzj+A0wgR7+86WtWpYFCUEJhvofzylDgMnk0eY4iNgo+azTzw8z+Y13efzOIfvJh9e/vO8+ovL+feH/zH6U6OZFyblQGQuZXT+SYbBew4MSRHXtIEH+lh2GhD7nQ6RXmq0WgUB7QRldErRx10kPU1aWfdZI3cjmhnifyrumdS0fV057KGtKwz+aRZey7/m7TN+SbP2CvnPFD7HmytTz+oRhX+mBTxRPFE/N943QvowrJJJxM0RFMm5jyRsfF9b2+q7jSoGhrCRnERHt1BGJmPt/A8XOslZzOThyf1Aj238bJ8/qf/m/R+4NiBTks4l2ua5Mu3zOevfvIHsnHRRUlS2qmPp3f5wM5kbm7urB5jcwrIezotNx14LbifDxGjvklpyAgK+Gm05ijIOtqp4DCcXlX5g2oOivw5E9nBhHUzAWl9T8rHmdhhMQcirEtH1Vya5+x27fvyXBTKA8fwzHDxc+cKDMAejssG6M8hKOelbtWy0SEUv9+DnJhcg9P3JpPtLg/uT9QcDoel0wvIsf2mZiIIrx10noYh40i8uBi/iSoTE4z12frL8tc/+d/l8v77c/GnPpOltb1W5WDXoJV85k1z+chP/HAuHd6ViEWmHc6OCaY92e6rNcxkfVknO0FgGmvqup+dKuvtCMx9qrDSCMxR2+mQ5Z+kcCSgHBuK18iOgPsxNmRQrbEzFxNFdkSOwjgOvsPz+Dl6jeNyelG99mVrGaC9JJDPpRKzZRas2ctqhMTwMcCdQr/zCOcYfMZsHPfGeF1shtHDSOz1gOmgA56NgfmQMObnkoxrdIZY5LLAGhfz+X2SDAbDvNC4Mk//o5/LZa+5I7f+1p/k8BNPpL4Hk7fbNU3Sayfv+7nL8qXv/M7c/NjthcF4PcbjcYEIDAPb7XbhNMzQNxqNYvM1OrCwsFAYuRXc3Tl8jjVArtUOGsub+7G9zzyES1XIkvk4ujrVYrxu8URXjALNX5ijqNfrBZMMf8DvPQc+7/5ijBbY7/LUXkxtcoDIidAYNJOvsrBOyvFeeEOzZZAuMLFuCWNBKa+YGfabkXmu9+Ex3nq9XuwcMfVfNQ4cgiEnDsi7V6rQD8Pi3hinDd/EEDDdyuTcBYXc2tpKvdbMiVvemE+9/I5cfM89edmf/knmTjyR5dWDGem4nnzwuzv5+Pe/Jld0/27e8PyF2WptZXO8WXym0dg+7pK2w8XFxeL0O7ORnqu/a4hYrW8Slf3aDnTF5xwls5JLr9crIKCdPCUe1sD8g8eEo0bfuIehN3rJPZy+cO9qTul1ajQaBQmUzJpzPHdzJf4837csTQadN1trj2WogNGg+AjG0BUDNEOGEJzTVFvIvM3IjQZmhCGI/Ex7LKAyitHpdIo2LHst8oZk1s/LmOilJXLSxOxobUKJn1t2jo40TlRP90OOfHdrayvDubls3HVXHr/j1Rm98OW84Q++kPqJh3Lxo89lrtJU9NxFyVNXtDLuXp6733llBle/My/rHct8bz79Ub+4P03q9IkuLy9nY2OjVBA3HHOKMp1O0+v1SnI1kuDeKB/OmaYT5otMNjc3i3d3Vp1UNfrxHNaHjf2TyaSoGSPvwWBQGJ7XA931PXg3DPpUNVx0x+NIZigymRE9dgzcjzmhZ+gwNlRFhjtdBzrgy5EgmZ04gNAZHBHKAzBBw4CZlNlCG+9O7y2sKr4ZM0Nn8h73/5oKN5VvMspsHswwHtgnxruLxZHA+VGSIm+GIOIPSrUT5U7OzPNrtWbqF96ev/65OzJY6OfiJ06kuXWGUR6NMhqP88yRUZ6+vJmr1q/KUr2RyWCS+tzMYLwGbNlCwagT4jg8N5NmyAUdcP5URRIYqdESEBp5+wwpFJTnOI+z4+V5jNPElaE0+kE0c05sw0cPrVuGp9zHcNcQmjUEfSEvthGaf/C4GDvjOO+c03DFrVIM3mEeZWXgTMRR0cbgvkt7FRNPTAAlQ4FNRDkqMw5f3BvG2bkrJRd3DjFmanRuB/QBxlVChfwIQ6YZw7CJ6IvBmhxBLhyzacjYarUyP+hk9YrlktFPp9OMR6Nc+Pwko9Y4zWatdNQla2PnZ1LF8Mpb/WAYOZvVJAqKyj3NZA4GgyKPNUdhmaCQLo84D3ad2U4fOVTRT7WswjjRHdad9UL+jNGb8x3Fq2iiyqqaI6lGQvS7mvMaMRninpdxJrPtXiyKcykilEkQ/w5iiMOiLHSzo4Z/GKMJJzexu9maz3jLDsL00f4YQRVWQBJgXJubm8UGYubNmBgXi8TzzepyIQPKJkQLH5dJzsXiVbuqTIAgB7pzaMDHGbGVzO/6YG6Gac67UBwYaJSTezuCIAsU3OSeIZvTG+biMhTOgYPV3GwC6jIX4K1o6ABrjZHxO1IMZG9DMAGYlIOJjcPnD+NUYIDNjzjaoU/+4+1l1kmTkE4hdrv2NE635iUzj2QSpupxEDSKVW3+thFixLzOnO/yeXvlap3I/2eBiV78H2KBxTN1XT1b1/nSwsJCKd/ghUgor7eUJTPYXyWfWAS/x4O52xtDr3v+RC+2cOEIeE8KMiEHc86OA6CE5PoidU5em2hSwpATphmY72NRTIDhfOxsHJX4twmken12FhPwG1RjFGE9tDFVoyrcQZWnAFoiaxBX1al4XM4jzS1UOYpkZuTIhd9ZH6oMP1GVn583rHUibc+LcTAQBm9YAWRFWbkfEBHCwB4FheC+TAr62kpZq9WK/YMcpej8Fu/u7/AzwxGEbDiVpBgb43B+ZmiN8ZLbIhvDG57jkopzNcM1nlndfZPMziVCiRyxcAZWGPeRGvrThcNGZfJRM6bIlPl4k4PHipExNx/SxpoRIVFe1oPPjEajgqDBYdiJ41yc8qA/Tk2QHRcO0evBPV0ORL+5jyMv96lyI9VmGeRip8MJ/TZQ20KSHZHXgYzTkwUaICAbHZ8l0gA3LCQfT5KkpAgYlhNmIJHLGdWcF6OyErHoJjZgcnEoJhKs8I7GTv6TWdsZXs81XsNkRxArgg2GPIgLB8PPdiJInLdXn1v1/nym1+uVojwQ2Y5vY2PjLLIDmOx0xg0GIARHL+QIq9toNIojVFgjFNH5KlGGNV5dXS21Bzo3Bx57LUk3kKvnxnfQv0ajUUD/wWBQHD2C7PwCLcuEMaPn1fKaZYIjQg+dHtlGnALsdu0bOT14R1KMjUGwu8M5nuuUNAFU4R9wxwwp0QZvPhqNSufM4N1MjOCVnReQj7meachCfmWviXNx0m44ZBiNB3UUBnYSpVgUlATGlO+iZOPxOOvr64W8qoaGU0ChzJRW54bxTqfTUvEcZRmPxzl16lQReSw3/ob0cqTwM1kjIh7jYE4YkDt0jCJwWERWIi/fM6nI3Picx8hlZ1FlnY1MTp8+XcoPjfysl86tTTZWnaVTOtaZ1kTsB5nhSN0bcN4558rKSk6dOlU0J6M8jihMDC9pA2QStVota2trRfscim1lcO7D4q2vr5fayBx9km1P1+v1zmLZnDs4AuFc3A5oUqKaU/A9lJoFshG4YcEKmsxIH3edAMWrebxhLIvncpA3KE+n0yLCsB5EI8vUa+B58RnPxYZrCEa+iyGxNsBJ502Mjb2y1TY32iVxpsjN78cxRGZNq3VD5sBYGSfwu9oPzJqzxkDaalQkMHjdnSY5DXLagCwhsNA/o8CqswfyVlGmrz2NczAY5Nlnny2M00ynlQrFgRlEYbzAHILlovdO3TkmgOzhDMncoWIWFQGYHXM0R/GdK7pVazweF+QNSuY8wgqUpCCHXKJBTs5RvGe0ShKgHMA1IglKaG+OsrlRg7GaiLJsUDQ7KxSeNdjY2CilDjY6v6MEcog5GmabwGO9yD9RaG8ZI51xqQmlxlCcntDJxO+AkKw7pJX10+SYCTPyVes5umEEhdMxxEc2rLejeZLS9sAkZ+XEzAc9c332nIzzxIkTeeKJJ0rRrhq9gDzeXoUSopQ2EgsE5hAho7BO2JMZ5EJ4a2trpRPX+IzzPrwyi16r1YpXBHLBgE6n04LUQdCGotyfnyWzw58wdrac8TfzocY3GAyydOYcoGrUdD+r3y3qNkg7AEM60ApjMjyzEWOUGxsbRecOUTyZGRdyxqB3cgA0XDCXyWTGLjvKcEKgo5vJlVqtVvTrImMcHt8hDUC/7PBdHsGxLS4uFo4chwxywEE4PUIXd0oJHO1NRtkGcOh2tl4b5OTgg9w2NzeLLYw7XTUbzEvXS9dL19+e6wAvpXvpeul66fqf4nrJOF+6Xrr+ll4vGedL10vX39LrJeN86Xrp+lt6vWScL10vXX9Lr5eM86Xrpetv6fX/A8cXo8r1KXPOAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00316.png\n", + "IoU: 94.75\n", + "F1 Score: 97.31\n", + "Precision: 97.20\n", + "Recall: 97.41\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00452.png\n", + "IoU: 94.78\n", + "F1 Score: 97.32\n", + "Precision: 96.21\n", + "Recall: 98.46\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average IoU: 82.83\n", + "Average F1 Score: 90.30\n", + "Average Precision: 90.21\n", + "Average Recall: 90.89\n" + ] + } + ], + "source": [ + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "\n", + "iou_list, f1_score_list, precision_list, recall_list = [],[],[],[]\n", + "\n", + "file_name_list = os.listdir('/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240417/')\n", + "file_name_list = sorted(file_name_list)\n", + "\n", + "def evaluate_segmentation(label, output):\n", + " # Flatten label and output arrays\n", + " label_flat = label.flatten()\n", + " output_flat = output.flatten()\n", + "\n", + " # True positives, false positives, false negatives\n", + " TP = np.sum(np.logical_and(label_flat == 1, output_flat == 1))\n", + " FP = np.sum(np.logical_and(label_flat == 0, output_flat == 1))\n", + " FN = np.sum(np.logical_and(label_flat == 1, output_flat == 0))\n", + "\n", + " # Intersection over Union (IoU)\n", + " intersection = TP\n", + " union = TP + FP + FN\n", + " iou = intersection / union if union > 0 else 0\n", + "\n", + " # Precision\n", + " precision = TP / (TP + FP) if (TP + FP) > 0 else 0\n", + "\n", + " # Recall\n", + " recall = TP / (TP + FN) if (TP + FN) > 0 else 0\n", + "\n", + " # F1 score\n", + " f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n", + "\n", + " return iou, f1_score, precision, recall\n", + "\n", + "\n", + "for file_name in file_name_list:\n", + " image_path_output = f'/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240417/{file_name}'\n", + " image_path_img = f'/workspaces/mmsegmentation-1/data/cag/images/test/{file_name}'\n", + " image_path_label = f'/workspaces/mmsegmentation-1/data/cag/annotations/test/{file_name}'\n", + "\n", + " label_img = cv2.imread(image_path_label, 0)\n", + " img = cv2.imread(image_path_img)\n", + " output_img = cv2.imread(image_path_output, 0)\n", + "\n", + " iou, f1_score, precision, recall = evaluate_segmentation(label_img, output_img)\n", + " \n", + " iou_list.append(iou)\n", + " f1_score_list.append(f1_score)\n", + " precision_list.append(precision)\n", + " recall_list.append(recall)\n", + " \n", + " if f1_score > 0.972 and f1_score < 0.975:\n", + " # Create a mask for pixels where label and output are both 1\n", + " overlap_mask = (label_img == 1) & (output_img == 1)\n", + "\n", + " # Create a mask for pixels where prediction is not possible (label is 1 but output is 0)\n", + " not_possible_mask = (label_img == 1) & (output_img == 0)\n", + "\n", + " # Create a mask for pixels where prediction is better (output is 1 but label is 0)\n", + " better_prediction_mask = (label_img == 0) & (output_img == 1)\n", + "\n", + " # Create a copy of the original image\n", + " result_img = img.copy()\n", + "\n", + " # Define colors\n", + " green = (0, 255, 0) # Green for overlapping parts\n", + " red = (0, 0, 255) # Red for parts where prediction is not possible\n", + " yellow = (0, 255, 255) # Yellow for parts where prediction is better\n", + "\n", + " # Draw masks on the result image\n", + " result_img[overlap_mask] = red\n", + " result_img[not_possible_mask] = yellow\n", + " result_img[better_prediction_mask] = green\n", + "\n", + " # Display the result\n", + " print(file_name)\n", + " print(\"IoU: %.2f\" % (iou*100))\n", + " print(\"F1 Score: %.2f\" % (f1_score*100))\n", + " print(\"Precision: %.2f\" % (precision*100))\n", + " print(\"Recall: %.2f\" % (recall*100))\n", + " \n", + " plt.imshow(cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB))\n", + " plt.axis('off')\n", + " plt.show()\n", + " \n", + "iou_average = sum(iou_list) / len(iou_list)\n", + "f1_score_average = sum(f1_score_list) / len(f1_score_list)\n", + "precision_average = sum(precision_list) / len(precision_list)\n", + "recall_average = sum(recall_list) / len(recall_list)\n", + "\n", + "print(\"Average IoU: %.2f\" % (iou_average * 100))\n", + "print(\"Average F1 Score: %.2f\" % (f1_score_average * 100))\n", + "print(\"Average Precision: %.2f\" % (precision_average * 100))\n", + "print(\"Average Recall: %.2f\" % (recall_average * 100))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(512, 512)\n" + "1421\n" ] } ], "source": [ - "print(img.shape)" + "print(len(file_name_list))" ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of sheets for portion 1: 2400.0\n", + "Number of sheets for portion 2: 800.0\n", + "Number of sheets for portion 3: 800.0\n" + ] + } + ], + "source": [ + "total_sheets = 4000\n", + "\n", + "# Calculate the number of sheets for each portion\n", + "portion_1 = total_sheets * 6 / (6 + 2 + 2)\n", + "portion_2 = total_sheets * 2 / (6 + 2 + 2)\n", + "portion_3 = total_sheets * 2 / (6 + 2 + 2)\n", + "\n", + "print(\"Number of sheets for portion 1:\", portion_1)\n", + "print(\"Number of sheets for portion 2:\", portion_2)\n", + "print(\"Number of sheets for portion 3:\", portion_3)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4262\n", + "2400\n" + ] + } + ], + "source": [ + "import os\n", + "#annotations\n", + "# images\n", + "# Directory containing the images\n", + "directory = '/workspaces/mmsegmentation-1/data/cag/annotations/training'\n", + "print(len(os.listdir('/workspaces/mmsegmentation-1/data/cag/annotations/training')))\n", + "# Iterate over the files in the directory\n", + "for filename in os.listdir(directory):\n", + " # Check if the filename is in the range to be deleted\n", + " if filename.endswith('.png'):\n", + " file_number = int(filename.split('.')[0])\n", + " if file_number >= 2400:\n", + " # Construct the full file path\n", + " file_path = os.path.join(directory, filename)\n", + " # Delete the file\n", + " os.remove(file_path)\n", + " \n", + "print(len(os.listdir('/workspaces/mmsegmentation-1/data/cag/annotations/training')))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/configs/_base_/datasets/cag.py b/configs/_base_/datasets/cag.py index 367e346b9f..b8935bdcbe 100644 --- a/configs/_base_/datasets/cag.py +++ b/configs/_base_/datasets/cag.py @@ -91,5 +91,5 @@ seg_map_path='annotations/test'), pipeline=test_pipeline)) -val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) -test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mFscore']) +test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mFscore']) diff --git a/configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py b/configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py index 87f853bf72..1e80ddc436 100644 --- a/configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py +++ b/configs/upernet/upernet_r101_4xb4-160k_cag-512x512.py @@ -6,6 +6,6 @@ data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, - decode_head=dict(num_classes=150), - auxiliary_head=dict(num_classes=150), + decode_head=dict(num_classes=2), + auxiliary_head=dict(num_classes=2), pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/nohup.out b/nohup.out index 918bb3b173..353438b038 100644 --- a/nohup.out +++ b/nohup.out @@ -1979,3 +1979,7451 @@ Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/conver 04/16 08:27:50 - mmengine - INFO - Iter(train) [ 1050/160000] base_lr: 6.9980e-05 lr: 2.5873e-07 eta: 1 day, 20:44:03 time: 0.9977 data_time: 0.0040 memory: 8462 loss: 0.1839 decode.loss_ce: 0.0781 decode.acc_seg: 98.1035 aux.loss_ce: 0.1059 aux.acc_seg: 97.7102 04/16 08:28:40 - mmengine - INFO - Iter(train) [ 1100/160000] base_lr: 7.3316e-05 lr: 2.7106e-07 eta: 1 day, 20:41:20 time: 0.9965 data_time: 0.0041 memory: 8462 loss: 0.1804 decode.loss_ce: 0.0764 decode.acc_seg: 96.8763 aux.loss_ce: 0.1039 aux.acc_seg: 96.4491 04/16 08:29:30 - mmengine - INFO - Iter(train) [ 1150/160000] base_lr: 7.6651e-05 lr: 2.8339e-07 eta: 1 day, 20:38:45 time: 0.9978 data_time: 0.0045 memory: 8462 loss: 0.1393 decode.loss_ce: 0.0634 decode.acc_seg: 97.7964 aux.loss_ce: 0.0759 aux.acc_seg: 96.7056 +04/16 08:30:20 - mmengine - INFO - Iter(train) [ 1200/160000] base_lr: 7.9987e-05 lr: 2.9573e-07 eta: 1 day, 20:36:19 time: 0.9976 data_time: 0.0041 memory: 8462 loss: 0.1442 decode.loss_ce: 0.0723 decode.acc_seg: 97.6814 aux.loss_ce: 0.0719 aux.acc_seg: 96.8489 +04/16 08:31:09 - mmengine - INFO - Iter(train) [ 1250/160000] base_lr: 8.3322e-05 lr: 3.0806e-07 eta: 1 day, 20:34:06 time: 0.9977 data_time: 0.0044 memory: 8462 loss: 0.1477 decode.loss_ce: 0.0750 decode.acc_seg: 97.8846 aux.loss_ce: 0.0727 aux.acc_seg: 97.8550 +04/16 08:31:59 - mmengine - INFO - Iter(train) [ 1300/160000] base_lr: 8.6658e-05 lr: 3.2039e-07 eta: 1 day, 20:31:57 time: 0.9982 data_time: 0.0041 memory: 8462 loss: 0.1178 decode.loss_ce: 0.0598 decode.acc_seg: 98.3858 aux.loss_ce: 0.0580 aux.acc_seg: 97.7406 +04/16 08:32:49 - mmengine - INFO - Iter(train) [ 1350/160000] base_lr: 8.9993e-05 lr: 3.3272e-07 eta: 1 day, 20:29:53 time: 0.9979 data_time: 0.0041 memory: 8462 loss: 0.1109 decode.loss_ce: 0.0613 decode.acc_seg: 97.9813 aux.loss_ce: 0.0496 aux.acc_seg: 97.3036 +04/16 08:33:39 - mmengine - INFO - Iter(train) [ 1400/160000] base_lr: 9.3329e-05 lr: 3.4506e-07 eta: 1 day, 20:27:50 time: 0.9982 data_time: 0.0044 memory: 8462 loss: 0.1039 decode.loss_ce: 0.0561 decode.acc_seg: 97.9427 aux.loss_ce: 0.0478 aux.acc_seg: 97.4939 +04/16 08:34:29 - mmengine - INFO - Iter(train) [ 1450/160000] base_lr: 9.6664e-05 lr: 3.5739e-07 eta: 1 day, 20:25:53 time: 0.9973 data_time: 0.0041 memory: 8462 loss: 0.1053 decode.loss_ce: 0.0591 decode.acc_seg: 98.8289 aux.loss_ce: 0.0462 aux.acc_seg: 98.2611 +04/16 08:35:19 - mmengine - INFO - Iter(train) [ 1500/160000] base_lr: 1.0000e-04 lr: 3.6972e-07 eta: 1 day, 20:24:03 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.0957 decode.loss_ce: 0.0518 decode.acc_seg: 98.0558 aux.loss_ce: 0.0439 aux.acc_seg: 97.3547 +04/16 08:36:09 - mmengine - INFO - Iter(train) [ 1550/160000] base_lr: 9.9969e-05 lr: 3.6961e-07 eta: 1 day, 20:22:18 time: 0.9972 data_time: 0.0040 memory: 8462 loss: 0.1076 decode.loss_ce: 0.0628 decode.acc_seg: 96.7735 aux.loss_ce: 0.0448 aux.acc_seg: 96.2715 +04/16 08:36:59 - mmengine - INFO - Iter(train) [ 1600/160000] base_lr: 9.9938e-05 lr: 3.6949e-07 eta: 1 day, 20:20:37 time: 0.9957 data_time: 0.0043 memory: 8462 loss: 0.0848 decode.loss_ce: 0.0441 decode.acc_seg: 98.0389 aux.loss_ce: 0.0407 aux.acc_seg: 97.3362 +04/16 08:37:49 - mmengine - INFO - Iter(train) [ 1650/160000] base_lr: 9.9906e-05 lr: 3.6937e-07 eta: 1 day, 20:19:02 time: 0.9975 data_time: 0.0042 memory: 8462 loss: 0.0880 decode.loss_ce: 0.0508 decode.acc_seg: 98.3910 aux.loss_ce: 0.0371 aux.acc_seg: 97.4863 +04/16 08:38:38 - mmengine - INFO - Iter(train) [ 1700/160000] base_lr: 9.9874e-05 lr: 3.6926e-07 eta: 1 day, 20:17:28 time: 0.9974 data_time: 0.0040 memory: 8462 loss: 0.0877 decode.loss_ce: 0.0514 decode.acc_seg: 98.4911 aux.loss_ce: 0.0362 aux.acc_seg: 97.8970 +04/16 08:39:28 - mmengine - INFO - Iter(train) [ 1750/160000] base_lr: 9.9843e-05 lr: 3.6914e-07 eta: 1 day, 20:15:54 time: 0.9977 data_time: 0.0044 memory: 8462 loss: 0.0777 decode.loss_ce: 0.0452 decode.acc_seg: 97.4014 aux.loss_ce: 0.0325 aux.acc_seg: 97.5168 +04/16 08:40:18 - mmengine - INFO - Iter(train) [ 1800/160000] base_lr: 9.9811e-05 lr: 3.6902e-07 eta: 1 day, 20:14:21 time: 0.9983 data_time: 0.0044 memory: 8462 loss: 0.0848 decode.loss_ce: 0.0489 decode.acc_seg: 98.3080 aux.loss_ce: 0.0359 aux.acc_seg: 97.6761 +04/16 08:41:08 - mmengine - INFO - Iter(train) [ 1850/160000] base_lr: 9.9780e-05 lr: 3.6891e-07 eta: 1 day, 20:12:52 time: 0.9967 data_time: 0.0042 memory: 8462 loss: 0.0878 decode.loss_ce: 0.0555 decode.acc_seg: 98.5981 aux.loss_ce: 0.0323 aux.acc_seg: 98.0816 +04/16 08:41:58 - mmengine - INFO - Iter(train) [ 1900/160000] base_lr: 9.9748e-05 lr: 3.6879e-07 eta: 1 day, 20:11:23 time: 0.9979 data_time: 0.0047 memory: 8462 loss: 0.0696 decode.loss_ce: 0.0407 decode.acc_seg: 98.9454 aux.loss_ce: 0.0289 aux.acc_seg: 98.5573 +04/16 08:42:48 - mmengine - INFO - Iter(train) [ 1950/160000] base_lr: 9.9717e-05 lr: 3.6867e-07 eta: 1 day, 20:09:59 time: 0.9975 data_time: 0.0041 memory: 8462 loss: 0.0650 decode.loss_ce: 0.0386 decode.acc_seg: 98.4501 aux.loss_ce: 0.0264 aux.acc_seg: 97.8333 +04/16 08:43:38 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 08:43:38 - mmengine - INFO - Iter(train) [ 2000/160000] base_lr: 9.9685e-05 lr: 3.6856e-07 eta: 1 day, 20:08:37 time: 0.9983 data_time: 0.0048 memory: 8462 loss: 0.0783 decode.loss_ce: 0.0462 decode.acc_seg: 98.0640 aux.loss_ce: 0.0322 aux.acc_seg: 97.0270 +04/16 08:44:28 - mmengine - INFO - Iter(train) [ 2050/160000] base_lr: 9.9654e-05 lr: 3.6844e-07 eta: 1 day, 20:07:15 time: 0.9977 data_time: 0.0046 memory: 8462 loss: 0.0654 decode.loss_ce: 0.0391 decode.acc_seg: 98.8264 aux.loss_ce: 0.0263 aux.acc_seg: 98.0137 +04/16 08:45:17 - mmengine - INFO - Iter(train) [ 2100/160000] base_lr: 9.9622e-05 lr: 3.6832e-07 eta: 1 day, 20:05:56 time: 0.9981 data_time: 0.0042 memory: 8462 loss: 0.0681 decode.loss_ce: 0.0394 decode.acc_seg: 99.0908 aux.loss_ce: 0.0287 aux.acc_seg: 98.6416 +04/16 08:45:48 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 08:46:07 - mmengine - INFO - Iter(train) [ 2150/160000] base_lr: 9.9591e-05 lr: 3.6821e-07 eta: 1 day, 20:04:41 time: 0.9983 data_time: 0.0042 memory: 8462 loss: 0.0750 decode.loss_ce: 0.0456 decode.acc_seg: 98.0452 aux.loss_ce: 0.0294 aux.acc_seg: 96.4743 +04/16 08:46:57 - mmengine - INFO - Iter(train) [ 2200/160000] base_lr: 9.9559e-05 lr: 3.6809e-07 eta: 1 day, 20:03:24 time: 0.9969 data_time: 0.0043 memory: 8462 loss: 0.0626 decode.loss_ce: 0.0372 decode.acc_seg: 98.7965 aux.loss_ce: 0.0254 aux.acc_seg: 98.4007 +04/16 08:47:47 - mmengine - INFO - Iter(train) [ 2250/160000] base_lr: 9.9527e-05 lr: 3.6797e-07 eta: 1 day, 20:02:07 time: 0.9962 data_time: 0.0041 memory: 8462 loss: 0.0595 decode.loss_ce: 0.0356 decode.acc_seg: 99.3166 aux.loss_ce: 0.0238 aux.acc_seg: 98.3961 +04/16 08:48:37 - mmengine - INFO - Iter(train) [ 2300/160000] base_lr: 9.9496e-05 lr: 3.6786e-07 eta: 1 day, 20:00:51 time: 0.9996 data_time: 0.0047 memory: 8462 loss: 0.0570 decode.loss_ce: 0.0344 decode.acc_seg: 99.5049 aux.loss_ce: 0.0226 aux.acc_seg: 98.8173 +04/16 08:49:27 - mmengine - INFO - Iter(train) [ 2350/160000] base_lr: 9.9464e-05 lr: 3.6774e-07 eta: 1 day, 19:59:38 time: 0.9978 data_time: 0.0044 memory: 8462 loss: 0.0608 decode.loss_ce: 0.0368 decode.acc_seg: 97.5445 aux.loss_ce: 0.0240 aux.acc_seg: 97.2980 +04/16 08:50:17 - mmengine - INFO - Iter(train) [ 2400/160000] base_lr: 9.9433e-05 lr: 3.6762e-07 eta: 1 day, 19:58:24 time: 0.9973 data_time: 0.0044 memory: 8462 loss: 0.0567 decode.loss_ce: 0.0334 decode.acc_seg: 98.2815 aux.loss_ce: 0.0232 aux.acc_seg: 97.8489 +04/16 08:51:07 - mmengine - INFO - Iter(train) [ 2450/160000] base_lr: 9.9401e-05 lr: 3.6751e-07 eta: 1 day, 19:57:11 time: 0.9974 data_time: 0.0041 memory: 8462 loss: 0.0542 decode.loss_ce: 0.0325 decode.acc_seg: 98.7648 aux.loss_ce: 0.0217 aux.acc_seg: 98.1602 +04/16 08:51:57 - mmengine - INFO - Iter(train) [ 2500/160000] base_lr: 9.9370e-05 lr: 3.6739e-07 eta: 1 day, 19:56:01 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0509 decode.loss_ce: 0.0299 decode.acc_seg: 99.0875 aux.loss_ce: 0.0210 aux.acc_seg: 98.3164 +04/16 08:52:46 - mmengine - INFO - Iter(train) [ 2550/160000] base_lr: 9.9338e-05 lr: 3.6727e-07 eta: 1 day, 19:54:50 time: 0.9980 data_time: 0.0044 memory: 8462 loss: 0.0517 decode.loss_ce: 0.0306 decode.acc_seg: 98.6189 aux.loss_ce: 0.0211 aux.acc_seg: 98.1190 +04/16 08:53:36 - mmengine - INFO - Iter(train) [ 2600/160000] base_lr: 9.9307e-05 lr: 3.6716e-07 eta: 1 day, 19:53:41 time: 0.9969 data_time: 0.0042 memory: 8462 loss: 0.0544 decode.loss_ce: 0.0320 decode.acc_seg: 98.2189 aux.loss_ce: 0.0223 aux.acc_seg: 97.2631 +04/16 08:54:26 - mmengine - INFO - Iter(train) [ 2650/160000] base_lr: 9.9275e-05 lr: 3.6704e-07 eta: 1 day, 19:52:30 time: 0.9963 data_time: 0.0040 memory: 8462 loss: 0.0589 decode.loss_ce: 0.0355 decode.acc_seg: 98.5888 aux.loss_ce: 0.0233 aux.acc_seg: 98.1344 +04/16 08:55:16 - mmengine - INFO - Iter(train) [ 2700/160000] base_lr: 9.9244e-05 lr: 3.6692e-07 eta: 1 day, 19:51:22 time: 0.9980 data_time: 0.0045 memory: 8462 loss: 0.0546 decode.loss_ce: 0.0329 decode.acc_seg: 98.9714 aux.loss_ce: 0.0218 aux.acc_seg: 98.4739 +04/16 08:56:06 - mmengine - INFO - Iter(train) [ 2750/160000] base_lr: 9.9212e-05 lr: 3.6681e-07 eta: 1 day, 19:50:14 time: 0.9976 data_time: 0.0042 memory: 8462 loss: 0.0515 decode.loss_ce: 0.0310 decode.acc_seg: 99.3862 aux.loss_ce: 0.0204 aux.acc_seg: 98.8497 +04/16 08:56:56 - mmengine - INFO - Iter(train) [ 2800/160000] base_lr: 9.9180e-05 lr: 3.6669e-07 eta: 1 day, 19:49:06 time: 0.9974 data_time: 0.0041 memory: 8462 loss: 0.0478 decode.loss_ce: 0.0284 decode.acc_seg: 99.3832 aux.loss_ce: 0.0194 aux.acc_seg: 98.8251 +04/16 08:57:46 - mmengine - INFO - Iter(train) [ 2850/160000] base_lr: 9.9149e-05 lr: 3.6657e-07 eta: 1 day, 19:47:59 time: 0.9973 data_time: 0.0040 memory: 8462 loss: 0.0514 decode.loss_ce: 0.0303 decode.acc_seg: 99.1659 aux.loss_ce: 0.0211 aux.acc_seg: 98.4051 +04/16 08:58:36 - mmengine - INFO - Iter(train) [ 2900/160000] base_lr: 9.9117e-05 lr: 3.6646e-07 eta: 1 day, 19:46:52 time: 0.9975 data_time: 0.0040 memory: 8462 loss: 0.0500 decode.loss_ce: 0.0297 decode.acc_seg: 99.0492 aux.loss_ce: 0.0203 aux.acc_seg: 98.0768 +04/16 08:59:25 - mmengine - INFO - Iter(train) [ 2950/160000] base_lr: 9.9086e-05 lr: 3.6634e-07 eta: 1 day, 19:45:48 time: 0.9980 data_time: 0.0047 memory: 8462 loss: 0.0498 decode.loss_ce: 0.0296 decode.acc_seg: 98.2616 aux.loss_ce: 0.0202 aux.acc_seg: 97.5533 +04/16 09:00:15 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 09:00:15 - mmengine - INFO - Iter(train) [ 3000/160000] base_lr: 9.9054e-05 lr: 3.6622e-07 eta: 1 day, 19:44:44 time: 0.9967 data_time: 0.0041 memory: 8462 loss: 0.0492 decode.loss_ce: 0.0290 decode.acc_seg: 99.5146 aux.loss_ce: 0.0203 aux.acc_seg: 98.7753 +04/16 09:01:05 - mmengine - INFO - Iter(train) [ 3050/160000] base_lr: 9.9023e-05 lr: 3.6611e-07 eta: 1 day, 19:43:40 time: 0.9972 data_time: 0.0041 memory: 8462 loss: 0.0420 decode.loss_ce: 0.0242 decode.acc_seg: 99.1709 aux.loss_ce: 0.0178 aux.acc_seg: 98.9540 +04/16 09:01:55 - mmengine - INFO - Iter(train) [ 3100/160000] base_lr: 9.8991e-05 lr: 3.6599e-07 eta: 1 day, 19:42:37 time: 0.9975 data_time: 0.0040 memory: 8462 loss: 0.0550 decode.loss_ce: 0.0337 decode.acc_seg: 97.5500 aux.loss_ce: 0.0213 aux.acc_seg: 97.6732 +04/16 09:02:45 - mmengine - INFO - Iter(train) [ 3150/160000] base_lr: 9.8960e-05 lr: 3.6587e-07 eta: 1 day, 19:41:34 time: 0.9973 data_time: 0.0041 memory: 8462 loss: 0.0424 decode.loss_ce: 0.0242 decode.acc_seg: 99.4364 aux.loss_ce: 0.0183 aux.acc_seg: 98.9170 +04/16 09:03:35 - mmengine - INFO - Iter(train) [ 3200/160000] base_lr: 9.8928e-05 lr: 3.6576e-07 eta: 1 day, 19:40:29 time: 0.9974 data_time: 0.0043 memory: 8462 loss: 0.0388 decode.loss_ce: 0.0224 decode.acc_seg: 98.3208 aux.loss_ce: 0.0164 aux.acc_seg: 97.7089 +04/16 09:04:25 - mmengine - INFO - Iter(train) [ 3250/160000] base_lr: 9.8897e-05 lr: 3.6564e-07 eta: 1 day, 19:39:27 time: 0.9980 data_time: 0.0043 memory: 8462 loss: 0.0416 decode.loss_ce: 0.0248 decode.acc_seg: 99.2579 aux.loss_ce: 0.0168 aux.acc_seg: 98.5394 +04/16 09:05:15 - mmengine - INFO - Iter(train) [ 3300/160000] base_lr: 9.8865e-05 lr: 3.6552e-07 eta: 1 day, 19:38:26 time: 0.9982 data_time: 0.0045 memory: 8462 loss: 0.0442 decode.loss_ce: 0.0261 decode.acc_seg: 99.2485 aux.loss_ce: 0.0182 aux.acc_seg: 98.9531 +04/16 09:06:05 - mmengine - INFO - Iter(train) [ 3350/160000] base_lr: 9.8833e-05 lr: 3.6541e-07 eta: 1 day, 19:37:25 time: 0.9977 data_time: 0.0042 memory: 8462 loss: 0.0524 decode.loss_ce: 0.0322 decode.acc_seg: 97.8529 aux.loss_ce: 0.0202 aux.acc_seg: 97.3797 +04/16 09:06:54 - mmengine - INFO - Iter(train) [ 3400/160000] base_lr: 9.8802e-05 lr: 3.6529e-07 eta: 1 day, 19:36:25 time: 0.9973 data_time: 0.0043 memory: 8462 loss: 0.0447 decode.loss_ce: 0.0265 decode.acc_seg: 98.5550 aux.loss_ce: 0.0182 aux.acc_seg: 98.0677 +04/16 09:07:44 - mmengine - INFO - Iter(train) [ 3450/160000] base_lr: 9.8770e-05 lr: 3.6517e-07 eta: 1 day, 19:35:26 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0530 decode.loss_ce: 0.0320 decode.acc_seg: 99.4623 aux.loss_ce: 0.0210 aux.acc_seg: 98.9674 +04/16 09:08:34 - mmengine - INFO - Iter(train) [ 3500/160000] base_lr: 9.8739e-05 lr: 3.6506e-07 eta: 1 day, 19:34:25 time: 0.9973 data_time: 0.0041 memory: 8462 loss: 0.0466 decode.loss_ce: 0.0287 decode.acc_seg: 99.1919 aux.loss_ce: 0.0179 aux.acc_seg: 98.7110 +04/16 09:09:24 - mmengine - INFO - Iter(train) [ 3550/160000] base_lr: 9.8707e-05 lr: 3.6494e-07 eta: 1 day, 19:33:25 time: 0.9972 data_time: 0.0039 memory: 8462 loss: 0.0358 decode.loss_ce: 0.0201 decode.acc_seg: 99.3044 aux.loss_ce: 0.0157 aux.acc_seg: 98.6568 +04/16 09:10:14 - mmengine - INFO - Iter(train) [ 3600/160000] base_lr: 9.8676e-05 lr: 3.6482e-07 eta: 1 day, 19:32:24 time: 0.9962 data_time: 0.0041 memory: 8462 loss: 0.0403 decode.loss_ce: 0.0239 decode.acc_seg: 99.0747 aux.loss_ce: 0.0163 aux.acc_seg: 98.2910 +04/16 09:11:04 - mmengine - INFO - Iter(train) [ 3650/160000] base_lr: 9.8644e-05 lr: 3.6471e-07 eta: 1 day, 19:31:23 time: 0.9971 data_time: 0.0042 memory: 8462 loss: 0.0385 decode.loss_ce: 0.0218 decode.acc_seg: 99.1697 aux.loss_ce: 0.0166 aux.acc_seg: 98.4314 +04/16 09:11:54 - mmengine - INFO - Iter(train) [ 3700/160000] base_lr: 9.8613e-05 lr: 3.6459e-07 eta: 1 day, 19:30:24 time: 0.9977 data_time: 0.0045 memory: 8462 loss: 0.0433 decode.loss_ce: 0.0254 decode.acc_seg: 98.5950 aux.loss_ce: 0.0179 aux.acc_seg: 97.6034 +04/16 09:12:44 - mmengine - INFO - Iter(train) [ 3750/160000] base_lr: 9.8581e-05 lr: 3.6447e-07 eta: 1 day, 19:29:26 time: 0.9987 data_time: 0.0046 memory: 8462 loss: 0.0491 decode.loss_ce: 0.0297 decode.acc_seg: 99.2804 aux.loss_ce: 0.0193 aux.acc_seg: 98.4362 +04/16 09:13:34 - mmengine - INFO - Iter(train) [ 3800/160000] base_lr: 9.8550e-05 lr: 3.6436e-07 eta: 1 day, 19:28:27 time: 0.9966 data_time: 0.0042 memory: 8462 loss: 0.0460 decode.loss_ce: 0.0283 decode.acc_seg: 99.6212 aux.loss_ce: 0.0178 aux.acc_seg: 99.1259 +04/16 09:14:23 - mmengine - INFO - Iter(train) [ 3850/160000] base_lr: 9.8518e-05 lr: 3.6424e-07 eta: 1 day, 19:27:29 time: 0.9976 data_time: 0.0043 memory: 8462 loss: 0.0404 decode.loss_ce: 0.0241 decode.acc_seg: 98.6940 aux.loss_ce: 0.0163 aux.acc_seg: 97.9586 +04/16 09:15:13 - mmengine - INFO - Iter(train) [ 3900/160000] base_lr: 9.8486e-05 lr: 3.6412e-07 eta: 1 day, 19:26:31 time: 0.9969 data_time: 0.0045 memory: 8462 loss: 0.0406 decode.loss_ce: 0.0241 decode.acc_seg: 98.7534 aux.loss_ce: 0.0165 aux.acc_seg: 98.0196 +04/16 09:16:03 - mmengine - INFO - Iter(train) [ 3950/160000] base_lr: 9.8455e-05 lr: 3.6401e-07 eta: 1 day, 19:25:32 time: 0.9976 data_time: 0.0045 memory: 8462 loss: 0.0419 decode.loss_ce: 0.0252 decode.acc_seg: 99.4352 aux.loss_ce: 0.0167 aux.acc_seg: 98.8262 +04/16 09:16:53 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 09:16:53 - mmengine - INFO - Iter(train) [ 4000/160000] base_lr: 9.8423e-05 lr: 3.6389e-07 eta: 1 day, 19:24:34 time: 0.9976 data_time: 0.0042 memory: 8462 loss: 0.0354 decode.loss_ce: 0.0202 decode.acc_seg: 98.6773 aux.loss_ce: 0.0152 aux.acc_seg: 97.7581 +04/16 09:17:43 - mmengine - INFO - Iter(train) [ 4050/160000] base_lr: 9.8392e-05 lr: 3.6377e-07 eta: 1 day, 19:23:36 time: 0.9966 data_time: 0.0042 memory: 8462 loss: 0.0412 decode.loss_ce: 0.0250 decode.acc_seg: 97.9300 aux.loss_ce: 0.0162 aux.acc_seg: 97.4089 +04/16 09:18:33 - mmengine - INFO - Iter(train) [ 4100/160000] base_lr: 9.8360e-05 lr: 3.6366e-07 eta: 1 day, 19:22:38 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0445 decode.loss_ce: 0.0277 decode.acc_seg: 98.4241 aux.loss_ce: 0.0168 aux.acc_seg: 97.7720 +04/16 09:19:23 - mmengine - INFO - Iter(train) [ 4150/160000] base_lr: 9.8329e-05 lr: 3.6354e-07 eta: 1 day, 19:21:39 time: 0.9965 data_time: 0.0042 memory: 8462 loss: 0.0387 decode.loss_ce: 0.0222 decode.acc_seg: 99.3420 aux.loss_ce: 0.0164 aux.acc_seg: 98.7667 +04/16 09:20:13 - mmengine - INFO - Iter(train) [ 4200/160000] base_lr: 9.8297e-05 lr: 3.6342e-07 eta: 1 day, 19:20:42 time: 0.9976 data_time: 0.0041 memory: 8462 loss: 0.0386 decode.loss_ce: 0.0228 decode.acc_seg: 98.6504 aux.loss_ce: 0.0158 aux.acc_seg: 98.1169 +04/16 09:21:03 - mmengine - INFO - Iter(train) [ 4250/160000] base_lr: 9.8266e-05 lr: 3.6331e-07 eta: 1 day, 19:19:45 time: 0.9970 data_time: 0.0043 memory: 8462 loss: 0.0370 decode.loss_ce: 0.0221 decode.acc_seg: 98.6811 aux.loss_ce: 0.0149 aux.acc_seg: 98.2132 +04/16 09:21:52 - mmengine - INFO - Iter(train) [ 4300/160000] base_lr: 9.8234e-05 lr: 3.6319e-07 eta: 1 day, 19:18:48 time: 0.9976 data_time: 0.0044 memory: 8462 loss: 0.0386 decode.loss_ce: 0.0231 decode.acc_seg: 99.5756 aux.loss_ce: 0.0155 aux.acc_seg: 99.0969 +04/16 09:22:42 - mmengine - INFO - Iter(train) [ 4350/160000] base_lr: 9.8203e-05 lr: 3.6307e-07 eta: 1 day, 19:17:51 time: 0.9989 data_time: 0.0047 memory: 8462 loss: 0.0338 decode.loss_ce: 0.0193 decode.acc_seg: 99.4419 aux.loss_ce: 0.0145 aux.acc_seg: 98.6378 +04/16 09:23:32 - mmengine - INFO - Iter(train) [ 4400/160000] base_lr: 9.8171e-05 lr: 3.6296e-07 eta: 1 day, 19:16:55 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0375 decode.loss_ce: 0.0225 decode.acc_seg: 98.9044 aux.loss_ce: 0.0150 aux.acc_seg: 98.0289 +04/16 09:24:22 - mmengine - INFO - Iter(train) [ 4450/160000] base_lr: 9.8139e-05 lr: 3.6284e-07 eta: 1 day, 19:15:58 time: 0.9972 data_time: 0.0047 memory: 8462 loss: 0.0413 decode.loss_ce: 0.0249 decode.acc_seg: 99.3196 aux.loss_ce: 0.0163 aux.acc_seg: 98.7162 +04/16 09:25:12 - mmengine - INFO - Iter(train) [ 4500/160000] base_lr: 9.8108e-05 lr: 3.6273e-07 eta: 1 day, 19:15:00 time: 0.9966 data_time: 0.0045 memory: 8462 loss: 0.0434 decode.loss_ce: 0.0268 decode.acc_seg: 98.5575 aux.loss_ce: 0.0166 aux.acc_seg: 97.8743 +04/16 09:26:02 - mmengine - INFO - Iter(train) [ 4550/160000] base_lr: 9.8076e-05 lr: 3.6261e-07 eta: 1 day, 19:14:02 time: 0.9963 data_time: 0.0043 memory: 8462 loss: 0.0366 decode.loss_ce: 0.0225 decode.acc_seg: 99.2420 aux.loss_ce: 0.0141 aux.acc_seg: 99.0316 +04/16 09:26:52 - mmengine - INFO - Iter(train) [ 4600/160000] base_lr: 9.8045e-05 lr: 3.6249e-07 eta: 1 day, 19:13:05 time: 0.9984 data_time: 0.0043 memory: 8462 loss: 0.0461 decode.loss_ce: 0.0290 decode.acc_seg: 99.2903 aux.loss_ce: 0.0171 aux.acc_seg: 98.5401 +04/16 09:27:41 - mmengine - INFO - Iter(train) [ 4650/160000] base_lr: 9.8013e-05 lr: 3.6238e-07 eta: 1 day, 19:12:08 time: 0.9968 data_time: 0.0048 memory: 8462 loss: 0.0399 decode.loss_ce: 0.0238 decode.acc_seg: 98.9355 aux.loss_ce: 0.0161 aux.acc_seg: 97.9330 +04/16 09:28:31 - mmengine - INFO - Iter(train) [ 4700/160000] base_lr: 9.7982e-05 lr: 3.6226e-07 eta: 1 day, 19:11:10 time: 0.9964 data_time: 0.0043 memory: 8462 loss: 0.0359 decode.loss_ce: 0.0218 decode.acc_seg: 98.7331 aux.loss_ce: 0.0141 aux.acc_seg: 98.1449 +04/16 09:29:21 - mmengine - INFO - Iter(train) [ 4750/160000] base_lr: 9.7950e-05 lr: 3.6214e-07 eta: 1 day, 19:10:13 time: 0.9964 data_time: 0.0043 memory: 8462 loss: 0.0330 decode.loss_ce: 0.0199 decode.acc_seg: 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8462 loss: 0.0437 decode.loss_ce: 0.0267 decode.acc_seg: 98.8968 aux.loss_ce: 0.0170 aux.acc_seg: 98.6990 +04/16 09:33:30 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 09:33:30 - mmengine - INFO - Iter(train) [ 5000/160000] base_lr: 9.7792e-05 lr: 3.6156e-07 eta: 1 day, 19:05:33 time: 0.9968 data_time: 0.0041 memory: 8462 loss: 0.0353 decode.loss_ce: 0.0210 decode.acc_seg: 99.4287 aux.loss_ce: 0.0143 aux.acc_seg: 99.0656 +04/16 09:34:20 - mmengine - INFO - Iter(train) [ 5050/160000] base_lr: 9.7761e-05 lr: 3.6144e-07 eta: 1 day, 19:04:37 time: 0.9966 data_time: 0.0041 memory: 8462 loss: 0.0453 decode.loss_ce: 0.0280 decode.acc_seg: 99.3763 aux.loss_ce: 0.0173 aux.acc_seg: 98.9359 +04/16 09:35:10 - mmengine - INFO - Iter(train) [ 5100/160000] base_lr: 9.7729e-05 lr: 3.6133e-07 eta: 1 day, 19:03:41 time: 0.9977 data_time: 0.0045 memory: 8462 loss: 0.0360 decode.loss_ce: 0.0216 decode.acc_seg: 99.3940 aux.loss_ce: 0.0144 aux.acc_seg: 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decode.acc_seg: 99.2064 aux.loss_ce: 0.0145 aux.acc_seg: 98.6559 +04/16 09:39:19 - mmengine - INFO - Iter(train) [ 5350/160000] base_lr: 9.7572e-05 lr: 3.6074e-07 eta: 1 day, 18:59:03 time: 0.9974 data_time: 0.0045 memory: 8462 loss: 0.0340 decode.loss_ce: 0.0205 decode.acc_seg: 99.4291 aux.loss_ce: 0.0135 aux.acc_seg: 98.8754 +04/16 09:40:09 - mmengine - INFO - Iter(train) [ 5400/160000] base_lr: 9.7540e-05 lr: 3.6063e-07 eta: 1 day, 18:58:08 time: 0.9968 data_time: 0.0043 memory: 8462 loss: 0.0366 decode.loss_ce: 0.0218 decode.acc_seg: 99.3673 aux.loss_ce: 0.0149 aux.acc_seg: 98.6958 +04/16 09:40:59 - mmengine - INFO - Iter(train) [ 5450/160000] base_lr: 9.7509e-05 lr: 3.6051e-07 eta: 1 day, 18:57:12 time: 0.9963 data_time: 0.0045 memory: 8462 loss: 0.0440 decode.loss_ce: 0.0265 decode.acc_seg: 98.9367 aux.loss_ce: 0.0175 aux.acc_seg: 98.4566 +04/16 09:41:49 - mmengine - INFO - Iter(train) [ 5500/160000] base_lr: 9.7477e-05 lr: 3.6039e-07 eta: 1 day, 18:56:16 time: 0.9957 data_time: 0.0042 memory: 8462 loss: 0.0370 decode.loss_ce: 0.0222 decode.acc_seg: 99.1501 aux.loss_ce: 0.0148 aux.acc_seg: 98.9159 +04/16 09:42:39 - mmengine - INFO - Iter(train) [ 5550/160000] base_lr: 9.7445e-05 lr: 3.6028e-07 eta: 1 day, 18:55:22 time: 0.9966 data_time: 0.0041 memory: 8462 loss: 0.0297 decode.loss_ce: 0.0172 decode.acc_seg: 99.3139 aux.loss_ce: 0.0125 aux.acc_seg: 98.8188 +04/16 09:43:29 - mmengine - INFO - Iter(train) [ 5600/160000] base_lr: 9.7414e-05 lr: 3.6016e-07 eta: 1 day, 18:54:27 time: 0.9961 data_time: 0.0042 memory: 8462 loss: 0.0362 decode.loss_ce: 0.0216 decode.acc_seg: 99.2807 aux.loss_ce: 0.0145 aux.acc_seg: 98.4791 +04/16 09:44:18 - mmengine - INFO - Iter(train) [ 5650/160000] base_lr: 9.7382e-05 lr: 3.6004e-07 eta: 1 day, 18:53:32 time: 0.9976 data_time: 0.0045 memory: 8462 loss: 0.0384 decode.loss_ce: 0.0235 decode.acc_seg: 99.5243 aux.loss_ce: 0.0149 aux.acc_seg: 98.6925 +04/16 09:45:08 - mmengine - INFO - Iter(train) [ 5700/160000] base_lr: 9.7351e-05 lr: 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INFO - Iter(train) [ 5900/160000] base_lr: 9.7225e-05 lr: 3.5946e-07 eta: 1 day, 18:48:59 time: 0.9982 data_time: 0.0055 memory: 8462 loss: 0.0308 decode.loss_ce: 0.0182 decode.acc_seg: 99.1032 aux.loss_ce: 0.0125 aux.acc_seg: 98.3934 +04/16 09:49:17 - mmengine - INFO - Iter(train) [ 5950/160000] base_lr: 9.7193e-05 lr: 3.5934e-07 eta: 1 day, 18:48:04 time: 0.9968 data_time: 0.0044 memory: 8462 loss: 0.0358 decode.loss_ce: 0.0219 decode.acc_seg: 99.2605 aux.loss_ce: 0.0139 aux.acc_seg: 98.6856 +04/16 09:50:07 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 09:50:07 - mmengine - INFO - Iter(train) [ 6000/160000] base_lr: 9.7161e-05 lr: 3.5923e-07 eta: 1 day, 18:47:10 time: 0.9970 data_time: 0.0041 memory: 8462 loss: 0.0337 decode.loss_ce: 0.0205 decode.acc_seg: 99.1886 aux.loss_ce: 0.0132 aux.acc_seg: 98.7307 +04/16 09:50:57 - mmengine - INFO - Iter(train) [ 6050/160000] base_lr: 9.7130e-05 lr: 3.5911e-07 eta: 1 day, 18:46:15 time: 0.9956 data_time: 0.0043 memory: 8462 loss: 0.0309 decode.loss_ce: 0.0182 decode.acc_seg: 99.3471 aux.loss_ce: 0.0127 aux.acc_seg: 98.9792 +04/16 09:51:47 - mmengine - INFO - Iter(train) [ 6100/160000] base_lr: 9.7098e-05 lr: 3.5899e-07 eta: 1 day, 18:45:20 time: 0.9949 data_time: 0.0041 memory: 8462 loss: 0.0321 decode.loss_ce: 0.0186 decode.acc_seg: 99.1684 aux.loss_ce: 0.0135 aux.acc_seg: 98.6595 +04/16 09:52:37 - mmengine - INFO - Iter(train) [ 6150/160000] base_lr: 9.7067e-05 lr: 3.5888e-07 eta: 1 day, 18:44:25 time: 0.9968 data_time: 0.0046 memory: 8462 loss: 0.0433 decode.loss_ce: 0.0277 decode.acc_seg: 99.3948 aux.loss_ce: 0.0156 aux.acc_seg: 98.8504 +04/16 09:53:27 - mmengine - INFO - Iter(train) [ 6200/160000] base_lr: 9.7035e-05 lr: 3.5876e-07 eta: 1 day, 18:43:30 time: 0.9963 data_time: 0.0042 memory: 8462 loss: 0.0343 decode.loss_ce: 0.0209 decode.acc_seg: 99.0700 aux.loss_ce: 0.0133 aux.acc_seg: 98.5773 +04/16 09:54:16 - mmengine - INFO - Iter(train) [ 6250/160000] base_lr: 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- mmengine - INFO - Iter(train) [ 6450/160000] base_lr: 9.6878e-05 lr: 3.5818e-07 eta: 1 day, 18:38:57 time: 0.9969 data_time: 0.0045 memory: 8462 loss: 0.0304 decode.loss_ce: 0.0181 decode.acc_seg: 99.2731 aux.loss_ce: 0.0124 aux.acc_seg: 98.5628 +04/16 09:58:25 - mmengine - INFO - Iter(train) [ 6500/160000] base_lr: 9.6846e-05 lr: 3.5806e-07 eta: 1 day, 18:38:03 time: 0.9959 data_time: 0.0044 memory: 8462 loss: 0.0323 decode.loss_ce: 0.0195 decode.acc_seg: 98.5191 aux.loss_ce: 0.0128 aux.acc_seg: 97.7932 +04/16 09:59:15 - mmengine - INFO - Iter(train) [ 6550/160000] base_lr: 9.6814e-05 lr: 3.5794e-07 eta: 1 day, 18:37:09 time: 0.9971 data_time: 0.0048 memory: 8462 loss: 0.0379 decode.loss_ce: 0.0234 decode.acc_seg: 99.3090 aux.loss_ce: 0.0145 aux.acc_seg: 99.0202 +04/16 10:00:05 - mmengine - INFO - Iter(train) [ 6600/160000] base_lr: 9.6783e-05 lr: 3.5783e-07 eta: 1 day, 18:36:14 time: 0.9966 data_time: 0.0043 memory: 8462 loss: 0.0382 decode.loss_ce: 0.0229 decode.acc_seg: 99.1682 aux.loss_ce: 0.0153 aux.acc_seg: 98.7230 +04/16 10:00:55 - mmengine - INFO - Iter(train) [ 6650/160000] base_lr: 9.6751e-05 lr: 3.5771e-07 eta: 1 day, 18:35:20 time: 0.9966 data_time: 0.0043 memory: 8462 loss: 0.0407 decode.loss_ce: 0.0246 decode.acc_seg: 98.9925 aux.loss_ce: 0.0161 aux.acc_seg: 98.5542 +04/16 10:01:45 - mmengine - INFO - Iter(train) [ 6700/160000] base_lr: 9.6720e-05 lr: 3.5759e-07 eta: 1 day, 18:34:27 time: 0.9964 data_time: 0.0045 memory: 8462 loss: 0.0347 decode.loss_ce: 0.0211 decode.acc_seg: 98.9086 aux.loss_ce: 0.0136 aux.acc_seg: 98.3412 +04/16 10:02:35 - mmengine - INFO - Iter(train) [ 6750/160000] base_lr: 9.6688e-05 lr: 3.5748e-07 eta: 1 day, 18:33:33 time: 0.9980 data_time: 0.0042 memory: 8462 loss: 0.0358 decode.loss_ce: 0.0218 decode.acc_seg: 99.2535 aux.loss_ce: 0.0140 aux.acc_seg: 98.5071 +04/16 10:03:24 - mmengine - INFO - Iter(train) [ 6800/160000] base_lr: 9.6657e-05 lr: 3.5736e-07 eta: 1 day, 18:32:40 time: 0.9962 data_time: 0.0044 memory: 8462 loss: 0.0338 decode.loss_ce: 0.0205 decode.acc_seg: 99.4959 aux.loss_ce: 0.0133 aux.acc_seg: 98.9510 +04/16 10:04:14 - mmengine - INFO - Iter(train) [ 6850/160000] base_lr: 9.6625e-05 lr: 3.5724e-07 eta: 1 day, 18:31:46 time: 0.9964 data_time: 0.0042 memory: 8462 loss: 0.0336 decode.loss_ce: 0.0201 decode.acc_seg: 99.3446 aux.loss_ce: 0.0135 aux.acc_seg: 98.9294 +04/16 10:05:04 - mmengine - INFO - Iter(train) [ 6900/160000] base_lr: 9.6594e-05 lr: 3.5713e-07 eta: 1 day, 18:30:52 time: 0.9963 data_time: 0.0046 memory: 8462 loss: 0.0331 decode.loss_ce: 0.0198 decode.acc_seg: 99.5260 aux.loss_ce: 0.0133 aux.acc_seg: 98.6692 +04/16 10:05:54 - mmengine - INFO - Iter(train) [ 6950/160000] base_lr: 9.6562e-05 lr: 3.5701e-07 eta: 1 day, 18:29:59 time: 0.9967 data_time: 0.0046 memory: 8462 loss: 0.0321 decode.loss_ce: 0.0187 decode.acc_seg: 99.4892 aux.loss_ce: 0.0134 aux.acc_seg: 99.0576 +04/16 10:06:44 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 10:06:44 - mmengine - INFO - Iter(train) [ 7000/160000] base_lr: 9.6531e-05 lr: 3.5689e-07 eta: 1 day, 18:29:05 time: 0.9960 data_time: 0.0046 memory: 8462 loss: 0.0254 decode.loss_ce: 0.0143 decode.acc_seg: 99.4932 aux.loss_ce: 0.0110 aux.acc_seg: 98.8232 +04/16 10:07:34 - mmengine - INFO - Iter(train) [ 7050/160000] base_lr: 9.6499e-05 lr: 3.5678e-07 eta: 1 day, 18:28:12 time: 0.9958 data_time: 0.0041 memory: 8462 loss: 0.0332 decode.loss_ce: 0.0205 decode.acc_seg: 99.0192 aux.loss_ce: 0.0127 aux.acc_seg: 98.2471 +04/16 10:08:23 - mmengine - INFO - Iter(train) [ 7100/160000] base_lr: 9.6467e-05 lr: 3.5666e-07 eta: 1 day, 18:27:19 time: 0.9957 data_time: 0.0041 memory: 8462 loss: 0.0317 decode.loss_ce: 0.0194 decode.acc_seg: 99.5409 aux.loss_ce: 0.0123 aux.acc_seg: 98.7761 +04/16 10:09:13 - mmengine - INFO - Iter(train) [ 7150/160000] base_lr: 9.6436e-05 lr: 3.5654e-07 eta: 1 day, 18:26:26 time: 0.9973 data_time: 0.0046 memory: 8462 loss: 0.0292 decode.loss_ce: 0.0174 decode.acc_seg: 99.0015 aux.loss_ce: 0.0118 aux.acc_seg: 98.7383 +04/16 10:10:03 - mmengine - INFO - Iter(train) [ 7200/160000] base_lr: 9.6404e-05 lr: 3.5643e-07 eta: 1 day, 18:25:33 time: 0.9981 data_time: 0.0041 memory: 8462 loss: 0.0318 decode.loss_ce: 0.0184 decode.acc_seg: 99.5110 aux.loss_ce: 0.0134 aux.acc_seg: 98.5100 +04/16 10:10:53 - mmengine - INFO - Iter(train) [ 7250/160000] base_lr: 9.6373e-05 lr: 3.5631e-07 eta: 1 day, 18:24:40 time: 0.9971 data_time: 0.0042 memory: 8462 loss: 0.0427 decode.loss_ce: 0.0266 decode.acc_seg: 98.6675 aux.loss_ce: 0.0161 aux.acc_seg: 98.4175 +04/16 10:11:43 - mmengine - INFO - Iter(train) [ 7300/160000] base_lr: 9.6341e-05 lr: 3.5619e-07 eta: 1 day, 18:23:47 time: 0.9957 data_time: 0.0042 memory: 8462 loss: 0.0292 decode.loss_ce: 0.0170 decode.acc_seg: 99.3309 aux.loss_ce: 0.0122 aux.acc_seg: 98.8367 +04/16 10:12:33 - mmengine - INFO - Iter(train) [ 7350/160000] base_lr: 9.6310e-05 lr: 3.5608e-07 eta: 1 day, 18:22:54 time: 0.9966 data_time: 0.0043 memory: 8462 loss: 0.0334 decode.loss_ce: 0.0192 decode.acc_seg: 99.2157 aux.loss_ce: 0.0141 aux.acc_seg: 98.7862 +04/16 10:13:22 - mmengine - INFO - Iter(train) [ 7400/160000] base_lr: 9.6278e-05 lr: 3.5596e-07 eta: 1 day, 18:22:01 time: 0.9970 data_time: 0.0046 memory: 8462 loss: 0.0284 decode.loss_ce: 0.0166 decode.acc_seg: 99.2878 aux.loss_ce: 0.0118 aux.acc_seg: 98.8157 +04/16 10:14:12 - mmengine - INFO - Iter(train) [ 7450/160000] base_lr: 9.6247e-05 lr: 3.5584e-07 eta: 1 day, 18:21:08 time: 0.9957 data_time: 0.0043 memory: 8462 loss: 0.0394 decode.loss_ce: 0.0249 decode.acc_seg: 99.0482 aux.loss_ce: 0.0145 aux.acc_seg: 98.3685 +04/16 10:15:02 - mmengine - INFO - Iter(train) [ 7500/160000] base_lr: 9.6215e-05 lr: 3.5573e-07 eta: 1 day, 18:20:15 time: 0.9963 data_time: 0.0044 memory: 8462 loss: 0.0347 decode.loss_ce: 0.0213 decode.acc_seg: 99.2590 aux.loss_ce: 0.0134 aux.acc_seg: 99.0177 +04/16 10:15:52 - mmengine - INFO - Iter(train) [ 7550/160000] base_lr: 9.6184e-05 lr: 3.5561e-07 eta: 1 day, 18:19:21 time: 0.9945 data_time: 0.0044 memory: 8462 loss: 0.0362 decode.loss_ce: 0.0219 decode.acc_seg: 99.4312 aux.loss_ce: 0.0143 aux.acc_seg: 98.5786 +04/16 10:16:42 - mmengine - INFO - Iter(train) [ 7600/160000] base_lr: 9.6152e-05 lr: 3.5549e-07 eta: 1 day, 18:18:27 time: 0.9960 data_time: 0.0045 memory: 8462 loss: 0.0369 decode.loss_ce: 0.0225 decode.acc_seg: 99.1697 aux.loss_ce: 0.0144 aux.acc_seg: 98.7450 +04/16 10:17:31 - mmengine - INFO - Iter(train) [ 7650/160000] base_lr: 9.6120e-05 lr: 3.5538e-07 eta: 1 day, 18:17:35 time: 0.9966 data_time: 0.0046 memory: 8462 loss: 0.0293 decode.loss_ce: 0.0172 decode.acc_seg: 99.2083 aux.loss_ce: 0.0121 aux.acc_seg: 98.6141 +04/16 10:18:21 - mmengine - INFO - Iter(train) [ 7700/160000] base_lr: 9.6089e-05 lr: 3.5526e-07 eta: 1 day, 18:16:42 time: 0.9966 data_time: 0.0043 memory: 8462 loss: 0.0255 decode.loss_ce: 0.0147 decode.acc_seg: 98.9990 aux.loss_ce: 0.0108 aux.acc_seg: 98.5468 +04/16 10:19:11 - mmengine - INFO - Iter(train) [ 7750/160000] base_lr: 9.6057e-05 lr: 3.5514e-07 eta: 1 day, 18:15:49 time: 0.9962 data_time: 0.0042 memory: 8462 loss: 0.0340 decode.loss_ce: 0.0202 decode.acc_seg: 99.3233 aux.loss_ce: 0.0138 aux.acc_seg: 98.8796 +04/16 10:20:01 - mmengine - INFO - Iter(train) [ 7800/160000] base_lr: 9.6026e-05 lr: 3.5503e-07 eta: 1 day, 18:14:56 time: 0.9964 data_time: 0.0042 memory: 8462 loss: 0.0342 decode.loss_ce: 0.0212 decode.acc_seg: 99.3309 aux.loss_ce: 0.0129 aux.acc_seg: 98.4568 +04/16 10:20:51 - mmengine - INFO - Iter(train) [ 7850/160000] base_lr: 9.5994e-05 lr: 3.5491e-07 eta: 1 day, 18:14:03 time: 0.9965 data_time: 0.0046 memory: 8462 loss: 0.0345 decode.loss_ce: 0.0211 decode.acc_seg: 99.7005 aux.loss_ce: 0.0134 aux.acc_seg: 99.2964 +04/16 10:21:41 - mmengine - INFO - Iter(train) [ 7900/160000] base_lr: 9.5963e-05 lr: 3.5479e-07 eta: 1 day, 18:13:10 time: 0.9969 data_time: 0.0046 memory: 8462 loss: 0.0323 decode.loss_ce: 0.0190 decode.acc_seg: 99.2842 aux.loss_ce: 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- mmengine - INFO - Iter(train) [ 8300/160000] base_lr: 9.5710e-05 lr: 3.5386e-07 eta: 1 day, 18:06:08 time: 0.9962 data_time: 0.0043 memory: 8462 loss: 0.0302 decode.loss_ce: 0.0178 decode.acc_seg: 99.3151 aux.loss_ce: 0.0124 aux.acc_seg: 98.9853 +04/16 10:29:09 - mmengine - INFO - Iter(train) [ 8350/160000] base_lr: 9.5679e-05 lr: 3.5374e-07 eta: 1 day, 18:05:15 time: 0.9968 data_time: 0.0052 memory: 8462 loss: 0.0287 decode.loss_ce: 0.0167 decode.acc_seg: 99.5903 aux.loss_ce: 0.0120 aux.acc_seg: 99.0244 +04/16 10:29:59 - mmengine - INFO - Iter(train) [ 8400/160000] base_lr: 9.5647e-05 lr: 3.5363e-07 eta: 1 day, 18:04:23 time: 0.9959 data_time: 0.0043 memory: 8462 loss: 0.0308 decode.loss_ce: 0.0186 decode.acc_seg: 99.3853 aux.loss_ce: 0.0122 aux.acc_seg: 98.7701 +04/16 10:30:48 - mmengine - INFO - Iter(train) [ 8450/160000] base_lr: 9.5616e-05 lr: 3.5351e-07 eta: 1 day, 18:03:30 time: 0.9964 data_time: 0.0045 memory: 8462 loss: 0.0365 decode.loss_ce: 0.0223 decode.acc_seg: 99.5937 aux.loss_ce: 0.0143 aux.acc_seg: 99.1161 +04/16 10:31:38 - mmengine - INFO - Iter(train) [ 8500/160000] base_lr: 9.5584e-05 lr: 3.5339e-07 eta: 1 day, 18:02:38 time: 0.9969 data_time: 0.0050 memory: 8462 loss: 0.0301 decode.loss_ce: 0.0183 decode.acc_seg: 99.2689 aux.loss_ce: 0.0118 aux.acc_seg: 98.8541 +04/16 10:32:28 - mmengine - INFO - Iter(train) [ 8550/160000] base_lr: 9.5553e-05 lr: 3.5328e-07 eta: 1 day, 18:01:45 time: 0.9951 data_time: 0.0043 memory: 8462 loss: 0.0368 decode.loss_ce: 0.0231 decode.acc_seg: 98.9004 aux.loss_ce: 0.0137 aux.acc_seg: 98.5189 +04/16 10:33:18 - mmengine - INFO - Iter(train) [ 8600/160000] base_lr: 9.5521e-05 lr: 3.5316e-07 eta: 1 day, 18:00:53 time: 0.9961 data_time: 0.0044 memory: 8462 loss: 0.0374 decode.loss_ce: 0.0226 decode.acc_seg: 99.1444 aux.loss_ce: 0.0149 aux.acc_seg: 98.4131 +04/16 10:34:08 - mmengine - INFO - Iter(train) [ 8650/160000] base_lr: 9.5490e-05 lr: 3.5304e-07 eta: 1 day, 18:00:00 time: 0.9953 data_time: 0.0046 memory: 8462 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day, 17:56:30 time: 0.9962 data_time: 0.0047 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0129 decode.acc_seg: 99.1808 aux.loss_ce: 0.0099 aux.acc_seg: 98.4518 +04/16 10:38:17 - mmengine - INFO - Iter(train) [ 8900/160000] base_lr: 9.5332e-05 lr: 3.5246e-07 eta: 1 day, 17:55:38 time: 0.9968 data_time: 0.0045 memory: 8462 loss: 0.0284 decode.loss_ce: 0.0170 decode.acc_seg: 99.2653 aux.loss_ce: 0.0114 aux.acc_seg: 98.7051 +04/16 10:39:06 - mmengine - INFO - Iter(train) [ 8950/160000] base_lr: 9.5300e-05 lr: 3.5234e-07 eta: 1 day, 17:54:45 time: 0.9955 data_time: 0.0039 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0188 decode.acc_seg: 99.2916 aux.loss_ce: 0.0124 aux.acc_seg: 98.5813 +04/16 10:39:56 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 10:39:56 - mmengine - INFO - Iter(train) [ 9000/160000] base_lr: 9.5269e-05 lr: 3.5223e-07 eta: 1 day, 17:53:53 time: 0.9953 data_time: 0.0041 memory: 8462 loss: 0.0279 decode.loss_ce: 0.0161 decode.acc_seg: 99.4371 aux.loss_ce: 0.0118 aux.acc_seg: 98.8926 +04/16 10:40:46 - mmengine - INFO - Iter(train) [ 9050/160000] base_lr: 9.5237e-05 lr: 3.5211e-07 eta: 1 day, 17:53:01 time: 0.9974 data_time: 0.0047 memory: 8462 loss: 0.0281 decode.loss_ce: 0.0167 decode.acc_seg: 99.0015 aux.loss_ce: 0.0114 aux.acc_seg: 98.5495 +04/16 10:41:36 - mmengine - INFO - Iter(train) [ 9100/160000] base_lr: 9.5206e-05 lr: 3.5199e-07 eta: 1 day, 17:52:08 time: 0.9961 data_time: 0.0042 memory: 8462 loss: 0.0305 decode.loss_ce: 0.0184 decode.acc_seg: 99.4125 aux.loss_ce: 0.0121 aux.acc_seg: 98.7947 +04/16 10:42:26 - mmengine - INFO - Iter(train) [ 9150/160000] base_lr: 9.5174e-05 lr: 3.5188e-07 eta: 1 day, 17:51:16 time: 0.9959 data_time: 0.0041 memory: 8462 loss: 0.0324 decode.loss_ce: 0.0195 decode.acc_seg: 99.2474 aux.loss_ce: 0.0129 aux.acc_seg: 98.8632 +04/16 10:43:15 - mmengine - INFO - Iter(train) [ 9200/160000] base_lr: 9.5143e-05 lr: 3.5176e-07 eta: 1 day, 17:50:24 time: 0.9963 data_time: 0.0044 memory: 8462 loss: 0.0319 decode.loss_ce: 0.0186 decode.acc_seg: 99.4095 aux.loss_ce: 0.0133 aux.acc_seg: 98.9197 +04/16 10:44:05 - mmengine - INFO - Iter(train) [ 9250/160000] base_lr: 9.5111e-05 lr: 3.5164e-07 eta: 1 day, 17:49:31 time: 0.9948 data_time: 0.0041 memory: 8462 loss: 0.0284 decode.loss_ce: 0.0168 decode.acc_seg: 99.4268 aux.loss_ce: 0.0116 aux.acc_seg: 98.7555 +04/16 10:44:55 - mmengine - INFO - Iter(train) [ 9300/160000] base_lr: 9.5079e-05 lr: 3.5153e-07 eta: 1 day, 17:48:39 time: 0.9956 data_time: 0.0046 memory: 8462 loss: 0.0292 decode.loss_ce: 0.0171 decode.acc_seg: 99.3982 aux.loss_ce: 0.0121 aux.acc_seg: 99.1098 +04/16 10:45:45 - mmengine - INFO - Iter(train) [ 9350/160000] base_lr: 9.5048e-05 lr: 3.5141e-07 eta: 1 day, 17:47:47 time: 0.9962 data_time: 0.0048 memory: 8462 loss: 0.0273 decode.loss_ce: 0.0162 decode.acc_seg: 99.3837 aux.loss_ce: 0.0112 aux.acc_seg: 98.9866 +04/16 10:46:35 - mmengine - INFO - Iter(train) [ 9400/160000] base_lr: 9.5016e-05 lr: 3.5130e-07 eta: 1 day, 17:46:55 time: 0.9950 data_time: 0.0044 memory: 8462 loss: 0.0246 decode.loss_ce: 0.0146 decode.acc_seg: 99.3452 aux.loss_ce: 0.0099 aux.acc_seg: 98.9985 +04/16 10:47:24 - mmengine - INFO - Iter(train) [ 9450/160000] base_lr: 9.4985e-05 lr: 3.5118e-07 eta: 1 day, 17:46:02 time: 0.9954 data_time: 0.0045 memory: 8462 loss: 0.0284 decode.loss_ce: 0.0171 decode.acc_seg: 99.1913 aux.loss_ce: 0.0113 aux.acc_seg: 98.4587 +04/16 10:48:14 - mmengine - INFO - Iter(train) [ 9500/160000] base_lr: 9.4953e-05 lr: 3.5106e-07 eta: 1 day, 17:45:10 time: 0.9956 data_time: 0.0043 memory: 8462 loss: 0.0302 decode.loss_ce: 0.0182 decode.acc_seg: 99.3170 aux.loss_ce: 0.0121 aux.acc_seg: 99.3048 +04/16 10:49:04 - mmengine - INFO - Iter(train) [ 9550/160000] base_lr: 9.4922e-05 lr: 3.5095e-07 eta: 1 day, 17:44:18 time: 0.9964 data_time: 0.0043 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0154 decode.acc_seg: 99.4465 aux.loss_ce: 0.0113 aux.acc_seg: 99.1415 +04/16 10:49:54 - mmengine - INFO - Iter(train) [ 9600/160000] base_lr: 9.4890e-05 lr: 3.5083e-07 eta: 1 day, 17:43:26 time: 0.9974 data_time: 0.0041 memory: 8462 loss: 0.0400 decode.loss_ce: 0.0244 decode.acc_seg: 99.1270 aux.loss_ce: 0.0156 aux.acc_seg: 98.4724 +04/16 10:50:44 - mmengine - INFO - Iter(train) [ 9650/160000] base_lr: 9.4859e-05 lr: 3.5071e-07 eta: 1 day, 17:42:35 time: 0.9968 data_time: 0.0047 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0171 decode.acc_seg: 98.8754 aux.loss_ce: 0.0115 aux.acc_seg: 98.0198 +04/16 10:51:34 - mmengine - INFO - Iter(train) [ 9700/160000] base_lr: 9.4827e-05 lr: 3.5060e-07 eta: 1 day, 17:41:43 time: 0.9967 data_time: 0.0042 memory: 8462 loss: 0.0336 decode.loss_ce: 0.0210 decode.acc_seg: 99.1304 aux.loss_ce: 0.0125 aux.acc_seg: 98.6174 +04/16 10:52:23 - mmengine - INFO - Iter(train) [ 9750/160000] base_lr: 9.4796e-05 lr: 3.5048e-07 eta: 1 day, 17:40:52 time: 0.9964 data_time: 0.0042 memory: 8462 loss: 0.0250 decode.loss_ce: 0.0143 decode.acc_seg: 99.3227 aux.loss_ce: 0.0107 aux.acc_seg: 98.9199 +04/16 10:53:13 - mmengine - INFO - Iter(train) [ 9800/160000] base_lr: 9.4764e-05 lr: 3.5036e-07 eta: 1 day, 17:40:00 time: 0.9967 data_time: 0.0042 memory: 8462 loss: 0.0249 decode.loss_ce: 0.0142 decode.acc_seg: 99.2334 aux.loss_ce: 0.0107 aux.acc_seg: 98.5453 +04/16 10:54:03 - mmengine - INFO - Iter(train) [ 9850/160000] base_lr: 9.4732e-05 lr: 3.5025e-07 eta: 1 day, 17:39:08 time: 0.9969 data_time: 0.0045 memory: 8462 loss: 0.0288 decode.loss_ce: 0.0172 decode.acc_seg: 99.1528 aux.loss_ce: 0.0115 aux.acc_seg: 98.8901 +04/16 10:54:53 - mmengine - INFO - Iter(train) [ 9900/160000] base_lr: 9.4701e-05 lr: 3.5013e-07 eta: 1 day, 17:38:16 time: 0.9961 data_time: 0.0046 memory: 8462 loss: 0.0258 decode.loss_ce: 0.0146 decode.acc_seg: 99.2846 aux.loss_ce: 0.0112 aux.acc_seg: 98.7749 +04/16 10:55:43 - mmengine - INFO - Iter(train) [ 9950/160000] base_lr: 9.4669e-05 lr: 3.5001e-07 eta: 1 day, 17:37:24 time: 0.9963 data_time: 0.0043 memory: 8462 loss: 0.0276 decode.loss_ce: 0.0161 decode.acc_seg: 99.2397 aux.loss_ce: 0.0116 aux.acc_seg: 98.6727 +04/16 10:56:32 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 10:56:32 - mmengine - INFO - Iter(train) [ 10000/160000] base_lr: 9.4638e-05 lr: 3.4990e-07 eta: 1 day, 17:36:32 time: 0.9959 data_time: 0.0044 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0167 decode.acc_seg: 98.9981 aux.loss_ce: 0.0115 aux.acc_seg: 98.5960 +04/16 10:56:32 - mmengine - INFO - Saving checkpoint at 10000 iterations +04/16 10:56:44 - mmengine - INFO - Iter(val) [ 50/355] eta: 0:00:43 time: 0.1151 data_time: 0.0014 memory: 7125 +04/16 10:56:50 - mmengine - INFO - Iter(val) [100/355] eta: 0:00:33 time: 0.1154 data_time: 0.0015 memory: 4004 +04/16 10:56:55 - mmengine - INFO - Iter(val) [150/355] eta: 0:00:25 time: 0.1157 data_time: 0.0014 memory: 4004 +04/16 10:57:01 - mmengine - INFO - Iter(val) [200/355] eta: 0:00:19 time: 0.1153 data_time: 0.0013 memory: 4004 +04/16 10:57:07 - mmengine - INFO - Iter(val) [250/355] eta: 0:00:12 time: 0.1155 data_time: 0.0015 memory: 4004 +04/16 10:57:13 - mmengine - INFO - Iter(val) [300/355] eta: 0:00:06 time: 0.1156 data_time: 0.0015 memory: 4004 +04/16 10:57:18 - mmengine - INFO - Iter(val) [350/355] eta: 0:00:00 time: 0.1153 data_time: 0.0014 memory: 4004 +04/16 10:57:19 - mmengine - INFO - per class results: +04/16 10:57:19 - mmengine - INFO - ++------------+-------+-------+ +| Class | IoU | Acc | ++------------+-------+-------+ +| background | 99.1 | 99.54 | +| contrast | 80.58 | 89.43 | ++------------+-------+-------+ +04/16 10:57:19 - mmengine - INFO - Iter(val) [355/355] aAcc: 99.1300 mIoU: 89.8400 mAcc: 94.4800 data_time: 0.0021 time: 0.1196 +04/16 10:58:09 - mmengine - INFO - Iter(train) [ 10050/160000] base_lr: 9.4606e-05 lr: 3.4978e-07 eta: 1 day, 17:35:45 time: 0.9948 data_time: 0.0042 memory: 8462 loss: 0.0320 decode.loss_ce: 0.0194 decode.acc_seg: 98.7612 aux.loss_ce: 0.0126 aux.acc_seg: 98.2853 +04/16 10:58:59 - mmengine - INFO - Iter(train) [ 10100/160000] base_lr: 9.4575e-05 lr: 3.4966e-07 eta: 1 day, 17:34:52 time: 0.9946 data_time: 0.0044 memory: 8462 loss: 0.0302 decode.loss_ce: 0.0176 decode.acc_seg: 99.2897 aux.loss_ce: 0.0126 aux.acc_seg: 98.5819 +04/16 10:59:49 - mmengine - INFO - Iter(train) [ 10150/160000] base_lr: 9.4543e-05 lr: 3.4955e-07 eta: 1 day, 17:33:59 time: 0.9944 data_time: 0.0045 memory: 8462 loss: 0.0380 decode.loss_ce: 0.0233 decode.acc_seg: 99.3277 aux.loss_ce: 0.0147 aux.acc_seg: 98.6393 +04/16 11:00:38 - mmengine - INFO - Iter(train) [ 10200/160000] base_lr: 9.4512e-05 lr: 3.4943e-07 eta: 1 day, 17:33:07 time: 0.9957 data_time: 0.0046 memory: 8462 loss: 0.0314 decode.loss_ce: 0.0186 decode.acc_seg: 99.6262 aux.loss_ce: 0.0128 aux.acc_seg: 99.1808 +04/16 11:01:28 - mmengine - INFO - Iter(train) [ 10250/160000] base_lr: 9.4480e-05 lr: 3.4931e-07 eta: 1 day, 17:32:14 time: 0.9943 data_time: 0.0044 memory: 8462 loss: 0.0305 decode.loss_ce: 0.0182 decode.acc_seg: 99.1917 aux.loss_ce: 0.0123 aux.acc_seg: 98.9431 +04/16 11:02:18 - mmengine - INFO - Iter(train) [ 10300/160000] base_lr: 9.4449e-05 lr: 3.4920e-07 eta: 1 day, 17:31:21 time: 0.9958 data_time: 0.0042 memory: 8462 loss: 0.0281 decode.loss_ce: 0.0170 decode.acc_seg: 98.8918 aux.loss_ce: 0.0111 aux.acc_seg: 98.5752 +04/16 11:03:08 - mmengine - INFO - Iter(train) [ 10350/160000] base_lr: 9.4417e-05 lr: 3.4908e-07 eta: 1 day, 17:30:28 time: 0.9944 data_time: 0.0044 memory: 8462 loss: 0.0278 decode.loss_ce: 0.0161 decode.acc_seg: 99.0158 aux.loss_ce: 0.0117 aux.acc_seg: 98.1525 +04/16 11:03:57 - mmengine - INFO - Iter(train) [ 10400/160000] base_lr: 9.4385e-05 lr: 3.4896e-07 eta: 1 day, 17:29:36 time: 0.9941 data_time: 0.0044 memory: 8462 loss: 0.0324 decode.loss_ce: 0.0188 decode.acc_seg: 98.5664 aux.loss_ce: 0.0135 aux.acc_seg: 97.8355 +04/16 11:04:47 - mmengine - INFO - Iter(train) [ 10450/160000] base_lr: 9.4354e-05 lr: 3.4885e-07 eta: 1 day, 17:28:43 time: 0.9949 data_time: 0.0043 memory: 8462 loss: 0.0288 decode.loss_ce: 0.0179 decode.acc_seg: 99.4333 aux.loss_ce: 0.0109 aux.acc_seg: 98.8943 +04/16 11:05:37 - mmengine - INFO - Iter(train) [ 10500/160000] base_lr: 9.4322e-05 lr: 3.4873e-07 eta: 1 day, 17:27:51 time: 0.9961 data_time: 0.0041 memory: 8462 loss: 0.0295 decode.loss_ce: 0.0182 decode.acc_seg: 99.4612 aux.loss_ce: 0.0113 aux.acc_seg: 99.0013 +04/16 11:06:27 - mmengine - INFO - Iter(train) [ 10550/160000] base_lr: 9.4291e-05 lr: 3.4861e-07 eta: 1 day, 17:26:58 time: 0.9948 data_time: 0.0042 memory: 8462 loss: 0.0325 decode.loss_ce: 0.0198 decode.acc_seg: 99.4640 aux.loss_ce: 0.0127 aux.acc_seg: 98.8817 +04/16 11:07:16 - mmengine - INFO - Iter(train) [ 10600/160000] base_lr: 9.4259e-05 lr: 3.4850e-07 eta: 1 day, 17:26:05 time: 0.9941 data_time: 0.0042 memory: 8462 loss: 0.0332 decode.loss_ce: 0.0205 decode.acc_seg: 99.3015 aux.loss_ce: 0.0127 aux.acc_seg: 98.8506 +04/16 11:08:06 - mmengine - INFO - Iter(train) [ 10650/160000] base_lr: 9.4228e-05 lr: 3.4838e-07 eta: 1 day, 17:25:13 time: 0.9958 data_time: 0.0044 memory: 8462 loss: 0.0316 decode.loss_ce: 0.0187 decode.acc_seg: 99.4198 aux.loss_ce: 0.0129 aux.acc_seg: 98.6265 +04/16 11:08:56 - mmengine - INFO - Iter(train) [ 10700/160000] base_lr: 9.4196e-05 lr: 3.4826e-07 eta: 1 day, 17:24:20 time: 0.9951 data_time: 0.0047 memory: 8462 loss: 0.0340 decode.loss_ce: 0.0209 decode.acc_seg: 98.6649 aux.loss_ce: 0.0131 aux.acc_seg: 98.0890 +04/16 11:09:45 - mmengine - INFO - Iter(train) [ 10750/160000] base_lr: 9.4165e-05 lr: 3.4815e-07 eta: 1 day, 17:23:28 time: 0.9944 data_time: 0.0043 memory: 8462 loss: 0.0256 decode.loss_ce: 0.0151 decode.acc_seg: 99.4286 aux.loss_ce: 0.0105 aux.acc_seg: 99.2020 +04/16 11:10:35 - mmengine - INFO - Iter(train) [ 10800/160000] base_lr: 9.4133e-05 lr: 3.4803e-07 eta: 1 day, 17:22:35 time: 0.9947 data_time: 0.0044 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0156 decode.acc_seg: 99.4251 aux.loss_ce: 0.0111 aux.acc_seg: 98.9100 +04/16 11:11:25 - mmengine - INFO - Iter(train) [ 10850/160000] base_lr: 9.4102e-05 lr: 3.4791e-07 eta: 1 day, 17:21:43 time: 0.9950 data_time: 0.0045 memory: 8462 loss: 0.0330 decode.loss_ce: 0.0200 decode.acc_seg: 99.2067 aux.loss_ce: 0.0130 aux.acc_seg: 98.6345 +04/16 11:12:15 - mmengine - INFO - Iter(train) [ 10900/160000] base_lr: 9.4070e-05 lr: 3.4780e-07 eta: 1 day, 17:20:50 time: 0.9961 data_time: 0.0047 memory: 8462 loss: 0.0331 decode.loss_ce: 0.0201 decode.acc_seg: 99.0942 aux.loss_ce: 0.0130 aux.acc_seg: 98.8865 +04/16 11:13:04 - mmengine - INFO - Iter(train) [ 10950/160000] base_lr: 9.4038e-05 lr: 3.4768e-07 eta: 1 day, 17:19:58 time: 0.9935 data_time: 0.0043 memory: 8462 loss: 0.0336 decode.loss_ce: 0.0195 decode.acc_seg: 98.7394 aux.loss_ce: 0.0141 aux.acc_seg: 97.6879 +04/16 11:13:54 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 11:13:54 - mmengine - INFO - Iter(train) [ 11000/160000] base_lr: 9.4007e-05 lr: 3.4756e-07 eta: 1 day, 17:19:05 time: 0.9938 data_time: 0.0042 memory: 8462 loss: 0.0322 decode.loss_ce: 0.0194 decode.acc_seg: 99.1959 aux.loss_ce: 0.0128 aux.acc_seg: 98.8623 +04/16 11:14:44 - mmengine - INFO - Iter(train) [ 11050/160000] base_lr: 9.3975e-05 lr: 3.4745e-07 eta: 1 day, 17:18:13 time: 0.9953 data_time: 0.0046 memory: 8462 loss: 0.0272 decode.loss_ce: 0.0161 decode.acc_seg: 99.1056 aux.loss_ce: 0.0112 aux.acc_seg: 98.4341 +04/16 11:15:34 - mmengine - INFO - Iter(train) [ 11100/160000] base_lr: 9.3944e-05 lr: 3.4733e-07 eta: 1 day, 17:17:21 time: 0.9950 data_time: 0.0048 memory: 8462 loss: 0.0280 decode.loss_ce: 0.0165 decode.acc_seg: 99.5041 aux.loss_ce: 0.0114 aux.acc_seg: 99.0475 +04/16 11:16:23 - mmengine - INFO - Iter(train) [ 11150/160000] base_lr: 9.3912e-05 lr: 3.4721e-07 eta: 1 day, 17:16:28 time: 0.9954 data_time: 0.0043 memory: 8462 loss: 0.0342 decode.loss_ce: 0.0206 decode.acc_seg: 99.3380 aux.loss_ce: 0.0136 aux.acc_seg: 98.7478 +04/16 11:17:13 - mmengine - INFO - Iter(train) [ 11200/160000] base_lr: 9.3881e-05 lr: 3.4710e-07 eta: 1 day, 17:15:36 time: 0.9939 data_time: 0.0045 memory: 8462 loss: 0.0323 decode.loss_ce: 0.0196 decode.acc_seg: 98.4217 aux.loss_ce: 0.0127 aux.acc_seg: 98.0209 +04/16 11:18:03 - mmengine - INFO - Iter(train) [ 11250/160000] base_lr: 9.3849e-05 lr: 3.4698e-07 eta: 1 day, 17:14:43 time: 0.9943 data_time: 0.0043 memory: 8462 loss: 0.0288 decode.loss_ce: 0.0165 decode.acc_seg: 99.4074 aux.loss_ce: 0.0123 aux.acc_seg: 98.8914 +04/16 11:18:52 - mmengine - INFO - Iter(train) [ 11300/160000] base_lr: 9.3818e-05 lr: 3.4686e-07 eta: 1 day, 17:13:51 time: 0.9954 data_time: 0.0046 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0164 decode.acc_seg: 99.5909 aux.loss_ce: 0.0111 aux.acc_seg: 99.1802 +04/16 11:19:42 - mmengine - INFO - Iter(train) [ 11350/160000] base_lr: 9.3786e-05 lr: 3.4675e-07 eta: 1 day, 17:12:58 time: 0.9951 data_time: 0.0047 memory: 8462 loss: 0.0365 decode.loss_ce: 0.0222 decode.acc_seg: 98.8888 aux.loss_ce: 0.0142 aux.acc_seg: 98.4755 +04/16 11:20:32 - mmengine - INFO - Iter(train) [ 11400/160000] base_lr: 9.3755e-05 lr: 3.4663e-07 eta: 1 day, 17:12:06 time: 0.9944 data_time: 0.0041 memory: 8462 loss: 0.0253 decode.loss_ce: 0.0143 decode.acc_seg: 99.4547 aux.loss_ce: 0.0110 aux.acc_seg: 99.2214 +04/16 11:21:22 - mmengine - INFO - Iter(train) [ 11450/160000] base_lr: 9.3723e-05 lr: 3.4651e-07 eta: 1 day, 17:11:14 time: 0.9953 data_time: 0.0045 memory: 8462 loss: 0.0300 decode.loss_ce: 0.0172 decode.acc_seg: 99.3704 aux.loss_ce: 0.0127 aux.acc_seg: 98.9031 +04/16 11:22:11 - mmengine - INFO - Iter(train) [ 11500/160000] base_lr: 9.3691e-05 lr: 3.4640e-07 eta: 1 day, 17:10:22 time: 0.9945 data_time: 0.0046 memory: 8462 loss: 0.0290 decode.loss_ce: 0.0171 decode.acc_seg: 99.1337 aux.loss_ce: 0.0119 aux.acc_seg: 98.4346 +04/16 11:23:01 - mmengine - INFO - Iter(train) [ 11550/160000] base_lr: 9.3660e-05 lr: 3.4628e-07 eta: 1 day, 17:09:29 time: 0.9945 data_time: 0.0041 memory: 8462 loss: 0.0346 decode.loss_ce: 0.0213 decode.acc_seg: 98.7728 aux.loss_ce: 0.0133 aux.acc_seg: 98.3486 +04/16 11:23:51 - mmengine - INFO - Iter(train) [ 11600/160000] base_lr: 9.3628e-05 lr: 3.4616e-07 eta: 1 day, 17:08:37 time: 0.9948 data_time: 0.0043 memory: 8462 loss: 0.0306 decode.loss_ce: 0.0183 decode.acc_seg: 99.3786 aux.loss_ce: 0.0122 aux.acc_seg: 98.8174 +04/16 11:24:41 - mmengine - INFO - Iter(train) [ 11650/160000] base_lr: 9.3597e-05 lr: 3.4605e-07 eta: 1 day, 17:07:45 time: 0.9939 data_time: 0.0041 memory: 8462 loss: 0.0272 decode.loss_ce: 0.0166 decode.acc_seg: 98.9901 aux.loss_ce: 0.0106 aux.acc_seg: 98.5510 +04/16 11:25:30 - mmengine - INFO - Iter(train) [ 11700/160000] base_lr: 9.3565e-05 lr: 3.4593e-07 eta: 1 day, 17:06:53 time: 0.9942 data_time: 0.0043 memory: 8462 loss: 0.0282 decode.loss_ce: 0.0169 decode.acc_seg: 98.5504 aux.loss_ce: 0.0113 aux.acc_seg: 98.2452 +04/16 11:26:20 - mmengine - INFO - Iter(train) [ 11750/160000] base_lr: 9.3534e-05 lr: 3.4581e-07 eta: 1 day, 17:06:01 time: 0.9935 data_time: 0.0044 memory: 8462 loss: 0.0272 decode.loss_ce: 0.0155 decode.acc_seg: 99.5529 aux.loss_ce: 0.0117 aux.acc_seg: 98.8819 +04/16 11:27:10 - mmengine - INFO - Iter(train) [ 11800/160000] base_lr: 9.3502e-05 lr: 3.4570e-07 eta: 1 day, 17:05:08 time: 0.9942 data_time: 0.0051 memory: 8462 loss: 0.0256 decode.loss_ce: 0.0150 decode.acc_seg: 99.4202 aux.loss_ce: 0.0105 aux.acc_seg: 98.8888 +04/16 11:27:59 - mmengine - INFO - Iter(train) [ 11850/160000] base_lr: 9.3471e-05 lr: 3.4558e-07 eta: 1 day, 17:04:16 time: 0.9937 data_time: 0.0043 memory: 8462 loss: 0.0357 decode.loss_ce: 0.0216 decode.acc_seg: 99.3042 aux.loss_ce: 0.0141 aux.acc_seg: 98.7051 +04/16 11:28:49 - mmengine - INFO - Iter(train) [ 11900/160000] base_lr: 9.3439e-05 lr: 3.4546e-07 eta: 1 day, 17:03:24 time: 0.9942 data_time: 0.0044 memory: 8462 loss: 0.0263 decode.loss_ce: 0.0155 decode.acc_seg: 99.2916 aux.loss_ce: 0.0108 aux.acc_seg: 98.7413 +04/16 11:29:39 - mmengine - INFO - Iter(train) [ 11950/160000] base_lr: 9.3408e-05 lr: 3.4535e-07 eta: 1 day, 17:02:32 time: 0.9944 data_time: 0.0045 memory: 8462 loss: 0.0297 decode.loss_ce: 0.0176 decode.acc_seg: 99.4255 aux.loss_ce: 0.0121 aux.acc_seg: 98.8607 +04/16 11:30:29 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 11:30:29 - mmengine - INFO - Iter(train) [ 12000/160000] base_lr: 9.3376e-05 lr: 3.4523e-07 eta: 1 day, 17:01:39 time: 0.9935 data_time: 0.0042 memory: 8462 loss: 0.0265 decode.loss_ce: 0.0156 decode.acc_seg: 99.4846 aux.loss_ce: 0.0108 aux.acc_seg: 98.9838 +04/16 11:31:18 - mmengine - INFO - Iter(train) [ 12050/160000] base_lr: 9.3344e-05 lr: 3.4511e-07 eta: 1 day, 17:00:47 time: 0.9943 data_time: 0.0046 memory: 8462 loss: 0.0276 decode.loss_ce: 0.0161 decode.acc_seg: 99.1394 aux.loss_ce: 0.0115 aux.acc_seg: 98.3816 +04/16 11:32:08 - mmengine - INFO - Iter(train) [ 12100/160000] base_lr: 9.3313e-05 lr: 3.4500e-07 eta: 1 day, 16:59:55 time: 0.9947 data_time: 0.0047 memory: 8462 loss: 0.0287 decode.loss_ce: 0.0165 decode.acc_seg: 98.9498 aux.loss_ce: 0.0122 aux.acc_seg: 98.0978 +04/16 11:32:58 - mmengine - INFO - Iter(train) [ 12150/160000] base_lr: 9.3281e-05 lr: 3.4488e-07 eta: 1 day, 16:59:03 time: 0.9948 data_time: 0.0044 memory: 8462 loss: 0.0307 decode.loss_ce: 0.0181 decode.acc_seg: 99.2891 aux.loss_ce: 0.0126 aux.acc_seg: 98.4135 +04/16 11:33:47 - mmengine - INFO - Iter(train) [ 12200/160000] base_lr: 9.3250e-05 lr: 3.4476e-07 eta: 1 day, 16:58:11 time: 0.9930 data_time: 0.0043 memory: 8462 loss: 0.0251 decode.loss_ce: 0.0144 decode.acc_seg: 99.4017 aux.loss_ce: 0.0107 aux.acc_seg: 98.8657 +04/16 11:34:37 - mmengine - INFO - Iter(train) [ 12250/160000] base_lr: 9.3218e-05 lr: 3.4465e-07 eta: 1 day, 16:57:19 time: 0.9949 data_time: 0.0044 memory: 8462 loss: 0.0304 decode.loss_ce: 0.0180 decode.acc_seg: 99.2184 aux.loss_ce: 0.0124 aux.acc_seg: 98.4539 +04/16 11:35:27 - mmengine - INFO - Iter(train) [ 12300/160000] base_lr: 9.3187e-05 lr: 3.4453e-07 eta: 1 day, 16:56:27 time: 0.9947 data_time: 0.0047 memory: 8462 loss: 0.0266 decode.loss_ce: 0.0162 decode.acc_seg: 99.5399 aux.loss_ce: 0.0104 aux.acc_seg: 99.2146 +04/16 11:36:17 - mmengine - INFO - Iter(train) [ 12350/160000] base_lr: 9.3155e-05 lr: 3.4441e-07 eta: 1 day, 16:55:35 time: 0.9950 data_time: 0.0052 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0142 decode.acc_seg: 99.5495 aux.loss_ce: 0.0101 aux.acc_seg: 99.3206 +04/16 11:37:06 - mmengine - INFO - Iter(train) [ 12400/160000] base_lr: 9.3124e-05 lr: 3.4430e-07 eta: 1 day, 16:54:43 time: 0.9944 data_time: 0.0044 memory: 8462 loss: 0.0243 decode.loss_ce: 0.0144 decode.acc_seg: 99.3097 aux.loss_ce: 0.0099 aux.acc_seg: 98.9460 +04/16 11:37:56 - mmengine - INFO - Iter(train) [ 12450/160000] base_lr: 9.3092e-05 lr: 3.4418e-07 eta: 1 day, 16:53:51 time: 0.9944 data_time: 0.0046 memory: 8462 loss: 0.0290 decode.loss_ce: 0.0171 decode.acc_seg: 99.2306 aux.loss_ce: 0.0119 aux.acc_seg: 98.2155 +04/16 11:38:46 - mmengine - INFO - Iter(train) [ 12500/160000] base_lr: 9.3061e-05 lr: 3.4406e-07 eta: 1 day, 16:52:59 time: 0.9961 data_time: 0.0047 memory: 8462 loss: 0.0296 decode.loss_ce: 0.0176 decode.acc_seg: 99.5613 aux.loss_ce: 0.0120 aux.acc_seg: 99.1644 +04/16 11:39:35 - mmengine - INFO - Iter(train) [ 12550/160000] base_lr: 9.3029e-05 lr: 3.4395e-07 eta: 1 day, 16:52:07 time: 0.9937 data_time: 0.0050 memory: 8462 loss: 0.0256 decode.loss_ce: 0.0157 decode.acc_seg: 98.9496 aux.loss_ce: 0.0099 aux.acc_seg: 98.8653 +04/16 11:40:25 - mmengine - INFO - Iter(train) [ 12600/160000] base_lr: 9.2997e-05 lr: 3.4383e-07 eta: 1 day, 16:51:15 time: 0.9946 data_time: 0.0047 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0163 decode.acc_seg: 99.4997 aux.loss_ce: 0.0112 aux.acc_seg: 99.2428 +04/16 11:41:15 - mmengine - INFO - Iter(train) [ 12650/160000] base_lr: 9.2966e-05 lr: 3.4371e-07 eta: 1 day, 16:50:23 time: 0.9950 data_time: 0.0044 memory: 8462 loss: 0.0287 decode.loss_ce: 0.0168 decode.acc_seg: 99.2878 aux.loss_ce: 0.0118 aux.acc_seg: 98.7865 +04/16 11:42:05 - mmengine - INFO - Iter(train) [ 12700/160000] base_lr: 9.2934e-05 lr: 3.4360e-07 eta: 1 day, 16:49:31 time: 0.9945 data_time: 0.0044 memory: 8462 loss: 0.0293 decode.loss_ce: 0.0178 decode.acc_seg: 99.4368 aux.loss_ce: 0.0115 aux.acc_seg: 99.0410 +04/16 11:42:54 - mmengine - INFO - Iter(train) [ 12750/160000] base_lr: 9.2903e-05 lr: 3.4348e-07 eta: 1 day, 16:48:39 time: 0.9947 data_time: 0.0046 memory: 8462 loss: 0.0268 decode.loss_ce: 0.0154 decode.acc_seg: 99.3006 aux.loss_ce: 0.0115 aux.acc_seg: 98.5382 +04/16 11:43:44 - mmengine - INFO - Iter(train) [ 12800/160000] base_lr: 9.2871e-05 lr: 3.4336e-07 eta: 1 day, 16:47:47 time: 0.9946 data_time: 0.0047 memory: 8462 loss: 0.0293 decode.loss_ce: 0.0174 decode.acc_seg: 99.5275 aux.loss_ce: 0.0119 aux.acc_seg: 98.9567 +04/16 11:44:34 - mmengine - INFO - Iter(train) [ 12850/160000] base_lr: 9.2840e-05 lr: 3.4325e-07 eta: 1 day, 16:46:56 time: 0.9946 data_time: 0.0047 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0146 decode.acc_seg: 99.4595 aux.loss_ce: 0.0106 aux.acc_seg: 99.2319 +04/16 11:45:24 - mmengine - INFO - Iter(train) [ 12900/160000] base_lr: 9.2808e-05 lr: 3.4313e-07 eta: 1 day, 16:46:04 time: 0.9946 data_time: 0.0043 memory: 8462 loss: 0.0278 decode.loss_ce: 0.0164 decode.acc_seg: 99.1468 aux.loss_ce: 0.0113 aux.acc_seg: 98.7642 +04/16 11:46:13 - mmengine - INFO - Iter(train) [ 12950/160000] base_lr: 9.2777e-05 lr: 3.4301e-07 eta: 1 day, 16:45:12 time: 0.9943 data_time: 0.0042 memory: 8462 loss: 0.0248 decode.loss_ce: 0.0147 decode.acc_seg: 99.4877 aux.loss_ce: 0.0101 aux.acc_seg: 99.1333 +04/16 11:47:03 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 11:47:03 - mmengine - INFO - Iter(train) [ 13000/160000] base_lr: 9.2745e-05 lr: 3.4290e-07 eta: 1 day, 16:44:21 time: 0.9950 data_time: 0.0043 memory: 8462 loss: 0.0273 decode.loss_ce: 0.0157 decode.acc_seg: 99.0637 aux.loss_ce: 0.0116 aux.acc_seg: 98.3202 +04/16 11:47:53 - mmengine - INFO - Iter(train) [ 13050/160000] base_lr: 9.2714e-05 lr: 3.4278e-07 eta: 1 day, 16:43:29 time: 0.9945 data_time: 0.0042 memory: 8462 loss: 0.0249 decode.loss_ce: 0.0142 decode.acc_seg: 99.5186 aux.loss_ce: 0.0108 aux.acc_seg: 98.9353 +04/16 11:48:42 - mmengine - INFO - Iter(train) [ 13100/160000] base_lr: 9.2682e-05 lr: 3.4266e-07 eta: 1 day, 16:42:38 time: 0.9956 data_time: 0.0051 memory: 8462 loss: 0.0273 decode.loss_ce: 0.0160 decode.acc_seg: 98.8503 aux.loss_ce: 0.0113 aux.acc_seg: 98.3902 +04/16 11:49:32 - mmengine - INFO - Iter(train) [ 13150/160000] base_lr: 9.2650e-05 lr: 3.4255e-07 eta: 1 day, 16:41:46 time: 0.9939 data_time: 0.0043 memory: 8462 loss: 0.0238 decode.loss_ce: 0.0137 decode.acc_seg: 99.2641 aux.loss_ce: 0.0101 aux.acc_seg: 98.5931 +04/16 11:50:22 - mmengine - INFO - Iter(train) [ 13200/160000] base_lr: 9.2619e-05 lr: 3.4243e-07 eta: 1 day, 16:40:54 time: 0.9940 data_time: 0.0044 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0168 decode.acc_seg: 99.5317 aux.loss_ce: 0.0106 aux.acc_seg: 99.2304 +04/16 11:51:12 - mmengine - INFO - Iter(train) [ 13250/160000] base_lr: 9.2587e-05 lr: 3.4231e-07 eta: 1 day, 16:40:03 time: 0.9957 data_time: 0.0048 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0135 decode.acc_seg: 99.4019 aux.loss_ce: 0.0102 aux.acc_seg: 99.0068 +04/16 11:52:01 - mmengine - INFO - Iter(train) [ 13300/160000] base_lr: 9.2556e-05 lr: 3.4220e-07 eta: 1 day, 16:39:11 time: 0.9942 data_time: 0.0041 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0134 decode.acc_seg: 99.4535 aux.loss_ce: 0.0103 aux.acc_seg: 99.1028 +04/16 11:52:51 - mmengine - INFO - Iter(train) [ 13350/160000] base_lr: 9.2524e-05 lr: 3.4208e-07 eta: 1 day, 16:38:20 time: 0.9946 data_time: 0.0043 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0164 decode.acc_seg: 99.1343 aux.loss_ce: 0.0119 aux.acc_seg: 98.5346 +04/16 11:53:41 - mmengine - INFO - Iter(train) [ 13400/160000] base_lr: 9.2493e-05 lr: 3.4196e-07 eta: 1 day, 16:37:28 time: 0.9935 data_time: 0.0045 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0137 decode.acc_seg: 99.4165 aux.loss_ce: 0.0103 aux.acc_seg: 98.7953 +04/16 11:54:31 - mmengine - INFO - Iter(train) [ 13450/160000] base_lr: 9.2461e-05 lr: 3.4185e-07 eta: 1 day, 16:36:36 time: 0.9943 data_time: 0.0047 memory: 8462 loss: 0.0217 decode.loss_ce: 0.0126 decode.acc_seg: 99.5184 aux.loss_ce: 0.0091 aux.acc_seg: 99.1095 +04/16 11:55:20 - mmengine - INFO - Iter(train) [ 13500/160000] base_lr: 9.2430e-05 lr: 3.4173e-07 eta: 1 day, 16:35:45 time: 0.9945 data_time: 0.0047 memory: 8462 loss: 0.0259 decode.loss_ce: 0.0152 decode.acc_seg: 99.6181 aux.loss_ce: 0.0107 aux.acc_seg: 99.2376 +04/16 11:56:10 - mmengine - INFO - Iter(train) [ 13550/160000] base_lr: 9.2398e-05 lr: 3.4161e-07 eta: 1 day, 16:34:53 time: 0.9947 data_time: 0.0050 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0159 decode.acc_seg: 99.5602 aux.loss_ce: 0.0116 aux.acc_seg: 99.0578 +04/16 11:57:00 - mmengine - INFO - Iter(train) [ 13600/160000] base_lr: 9.2367e-05 lr: 3.4150e-07 eta: 1 day, 16:34:02 time: 0.9954 data_time: 0.0044 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0135 decode.acc_seg: 99.0862 aux.loss_ce: 0.0094 aux.acc_seg: 98.6214 +04/16 11:57:49 - mmengine - INFO - Iter(train) [ 13650/160000] base_lr: 9.2335e-05 lr: 3.4138e-07 eta: 1 day, 16:33:10 time: 0.9946 data_time: 0.0043 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0169 decode.acc_seg: 98.9897 aux.loss_ce: 0.0117 aux.acc_seg: 98.2307 +04/16 11:58:39 - mmengine - INFO - Iter(train) [ 13700/160000] base_lr: 9.2303e-05 lr: 3.4126e-07 eta: 1 day, 16:32:18 time: 0.9947 data_time: 0.0049 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0135 decode.acc_seg: 99.2607 aux.loss_ce: 0.0099 aux.acc_seg: 98.7852 +04/16 11:59:29 - mmengine - INFO - Iter(train) [ 13750/160000] base_lr: 9.2272e-05 lr: 3.4115e-07 eta: 1 day, 16:31:27 time: 0.9950 data_time: 0.0045 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0119 decode.acc_seg: 99.5897 aux.loss_ce: 0.0095 aux.acc_seg: 99.0618 +04/16 12:00:19 - mmengine - INFO - Iter(train) [ 13800/160000] base_lr: 9.2240e-05 lr: 3.4103e-07 eta: 1 day, 16:30:35 time: 0.9938 data_time: 0.0044 memory: 8462 loss: 0.0342 decode.loss_ce: 0.0200 decode.acc_seg: 99.3093 aux.loss_ce: 0.0143 aux.acc_seg: 98.5952 +04/16 12:01:08 - mmengine - INFO - Iter(train) [ 13850/160000] base_lr: 9.2209e-05 lr: 3.4091e-07 eta: 1 day, 16:29:43 time: 0.9951 data_time: 0.0046 memory: 8462 loss: 0.0277 decode.loss_ce: 0.0163 decode.acc_seg: 99.1318 aux.loss_ce: 0.0114 aux.acc_seg: 98.6946 +04/16 12:01:58 - mmengine - INFO - Iter(train) [ 13900/160000] base_lr: 9.2177e-05 lr: 3.4080e-07 eta: 1 day, 16:28:52 time: 0.9937 data_time: 0.0044 memory: 8462 loss: 0.0281 decode.loss_ce: 0.0166 decode.acc_seg: 99.5358 aux.loss_ce: 0.0115 aux.acc_seg: 99.1428 +04/16 12:02:48 - mmengine - INFO - Iter(train) [ 13950/160000] base_lr: 9.2146e-05 lr: 3.4068e-07 eta: 1 day, 16:28:00 time: 0.9948 data_time: 0.0042 memory: 8462 loss: 0.0272 decode.loss_ce: 0.0156 decode.acc_seg: 99.3177 aux.loss_ce: 0.0116 aux.acc_seg: 98.7206 +04/16 12:03:37 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 12:03:37 - mmengine - INFO - Iter(train) [ 14000/160000] base_lr: 9.2114e-05 lr: 3.4056e-07 eta: 1 day, 16:27:09 time: 0.9954 data_time: 0.0040 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0141 decode.acc_seg: 99.4854 aux.loss_ce: 0.0099 aux.acc_seg: 99.0868 +04/16 12:04:27 - mmengine - INFO - Iter(train) [ 14050/160000] base_lr: 9.2083e-05 lr: 3.4045e-07 eta: 1 day, 16:26:18 time: 0.9941 data_time: 0.0044 memory: 8462 loss: 0.0268 decode.loss_ce: 0.0153 decode.acc_seg: 99.2085 aux.loss_ce: 0.0115 aux.acc_seg: 98.5888 +04/16 12:05:17 - mmengine - INFO - Iter(train) [ 14100/160000] base_lr: 9.2051e-05 lr: 3.4033e-07 eta: 1 day, 16:25:27 time: 0.9947 data_time: 0.0044 memory: 8462 loss: 0.0273 decode.loss_ce: 0.0160 decode.acc_seg: 98.7940 aux.loss_ce: 0.0113 aux.acc_seg: 97.9073 +04/16 12:06:07 - mmengine - INFO - Iter(train) [ 14150/160000] base_lr: 9.2020e-05 lr: 3.4022e-07 eta: 1 day, 16:24:36 time: 0.9947 data_time: 0.0045 memory: 8462 loss: 0.0269 decode.loss_ce: 0.0162 decode.acc_seg: 99.2874 aux.loss_ce: 0.0108 aux.acc_seg: 98.6807 +04/16 12:06:56 - mmengine - INFO - Iter(train) [ 14200/160000] base_lr: 9.1988e-05 lr: 3.4010e-07 eta: 1 day, 16:23:44 time: 0.9949 data_time: 0.0043 memory: 8462 loss: 0.0257 decode.loss_ce: 0.0147 decode.acc_seg: 99.4144 aux.loss_ce: 0.0109 aux.acc_seg: 98.7972 +04/16 12:07:46 - mmengine - INFO - Iter(train) [ 14250/160000] base_lr: 9.1956e-05 lr: 3.3998e-07 eta: 1 day, 16:22:53 time: 0.9943 data_time: 0.0041 memory: 8462 loss: 0.0231 decode.loss_ce: 0.0131 decode.acc_seg: 99.4913 aux.loss_ce: 0.0100 aux.acc_seg: 99.1066 +04/16 12:08:36 - mmengine - INFO - Iter(train) [ 14300/160000] base_lr: 9.1925e-05 lr: 3.3987e-07 eta: 1 day, 16:22:02 time: 0.9946 data_time: 0.0044 memory: 8462 loss: 0.0310 decode.loss_ce: 0.0182 decode.acc_seg: 98.8054 aux.loss_ce: 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decode.loss_ce: 0.0149 decode.acc_seg: 99.7185 aux.loss_ce: 0.0110 aux.acc_seg: 99.2022 +04/16 12:12:45 - mmengine - INFO - Iter(train) [ 14550/160000] base_lr: 9.1767e-05 lr: 3.3928e-07 eta: 1 day, 16:17:45 time: 0.9941 data_time: 0.0046 memory: 8462 loss: 0.0257 decode.loss_ce: 0.0151 decode.acc_seg: 99.2897 aux.loss_ce: 0.0106 aux.acc_seg: 98.7783 +04/16 12:13:34 - mmengine - INFO - Iter(train) [ 14600/160000] base_lr: 9.1736e-05 lr: 3.3917e-07 eta: 1 day, 16:16:54 time: 0.9946 data_time: 0.0045 memory: 8462 loss: 0.0285 decode.loss_ce: 0.0163 decode.acc_seg: 99.3795 aux.loss_ce: 0.0122 aux.acc_seg: 98.6528 +04/16 12:14:24 - mmengine - INFO - Iter(train) [ 14650/160000] base_lr: 9.1704e-05 lr: 3.3905e-07 eta: 1 day, 16:16:03 time: 0.9949 data_time: 0.0046 memory: 8462 loss: 0.0238 decode.loss_ce: 0.0137 decode.acc_seg: 99.6058 aux.loss_ce: 0.0101 aux.acc_seg: 99.0711 +04/16 12:15:14 - mmengine - INFO - Iter(train) [ 14700/160000] base_lr: 9.1673e-05 lr: 3.3893e-07 eta: 1 day, 16:15:12 time: 0.9943 data_time: 0.0043 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0133 decode.acc_seg: 99.6855 aux.loss_ce: 0.0103 aux.acc_seg: 99.2374 +04/16 12:16:04 - mmengine - INFO - Iter(train) [ 14750/160000] base_lr: 9.1641e-05 lr: 3.3882e-07 eta: 1 day, 16:14:21 time: 0.9943 data_time: 0.0048 memory: 8462 loss: 0.0272 decode.loss_ce: 0.0160 decode.acc_seg: 99.1642 aux.loss_ce: 0.0111 aux.acc_seg: 98.5998 +04/16 12:16:53 - mmengine - INFO - Iter(train) [ 14800/160000] base_lr: 9.1609e-05 lr: 3.3870e-07 eta: 1 day, 16:13:29 time: 0.9947 data_time: 0.0049 memory: 8462 loss: 0.0255 decode.loss_ce: 0.0145 decode.acc_seg: 99.4167 aux.loss_ce: 0.0110 aux.acc_seg: 98.8081 +04/16 12:17:43 - mmengine - INFO - Iter(train) [ 14850/160000] base_lr: 9.1578e-05 lr: 3.3858e-07 eta: 1 day, 16:12:38 time: 0.9937 data_time: 0.0048 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0116 decode.acc_seg: 99.6367 aux.loss_ce: 0.0088 aux.acc_seg: 99.2485 +04/16 12:18:33 - mmengine - INFO - Iter(train) [ 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memory: 8462 loss: 0.0275 decode.loss_ce: 0.0164 decode.acc_seg: 98.5355 aux.loss_ce: 0.0111 aux.acc_seg: 98.1300 +04/16 12:21:51 - mmengine - INFO - Iter(train) [ 15100/160000] base_lr: 9.1420e-05 lr: 3.3800e-07 eta: 1 day, 16:08:20 time: 0.9952 data_time: 0.0045 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0151 decode.acc_seg: 99.1245 aux.loss_ce: 0.0117 aux.acc_seg: 98.4695 +04/16 12:22:41 - mmengine - INFO - Iter(train) [ 15150/160000] base_lr: 9.1389e-05 lr: 3.3788e-07 eta: 1 day, 16:07:29 time: 0.9931 data_time: 0.0042 memory: 8462 loss: 0.0282 decode.loss_ce: 0.0168 decode.acc_seg: 99.2170 aux.loss_ce: 0.0115 aux.acc_seg: 98.6338 +04/16 12:23:31 - mmengine - INFO - Iter(train) [ 15200/160000] base_lr: 9.1357e-05 lr: 3.3777e-07 eta: 1 day, 16:06:37 time: 0.9934 data_time: 0.0043 memory: 8462 loss: 0.0281 decode.loss_ce: 0.0162 decode.acc_seg: 99.2514 aux.loss_ce: 0.0118 aux.acc_seg: 98.8026 +04/16 12:24:21 - mmengine - INFO - Iter(train) [ 15250/160000] base_lr: 9.1325e-05 lr: 3.3765e-07 eta: 1 day, 16:05:46 time: 0.9944 data_time: 0.0042 memory: 8462 loss: 0.0270 decode.loss_ce: 0.0156 decode.acc_seg: 99.1510 aux.loss_ce: 0.0114 aux.acc_seg: 98.5109 +04/16 12:25:10 - mmengine - INFO - Iter(train) [ 15300/160000] base_lr: 9.1294e-05 lr: 3.3753e-07 eta: 1 day, 16:04:55 time: 0.9937 data_time: 0.0043 memory: 8462 loss: 0.0269 decode.loss_ce: 0.0160 decode.acc_seg: 99.3240 aux.loss_ce: 0.0110 aux.acc_seg: 98.5308 +04/16 12:26:00 - mmengine - INFO - Iter(train) [ 15350/160000] base_lr: 9.1262e-05 lr: 3.3742e-07 eta: 1 day, 16:04:04 time: 0.9943 data_time: 0.0043 memory: 8462 loss: 0.0271 decode.loss_ce: 0.0154 decode.acc_seg: 99.4852 aux.loss_ce: 0.0117 aux.acc_seg: 98.7577 +04/16 12:26:50 - mmengine - INFO - Iter(train) [ 15400/160000] base_lr: 9.1231e-05 lr: 3.3730e-07 eta: 1 day, 16:03:13 time: 0.9947 data_time: 0.0046 memory: 8462 loss: 0.0249 decode.loss_ce: 0.0147 decode.acc_seg: 99.4936 aux.loss_ce: 0.0102 aux.acc_seg: 99.1734 +04/16 12:27:39 - mmengine - INFO - Iter(train) [ 15450/160000] base_lr: 9.1199e-05 lr: 3.3718e-07 eta: 1 day, 16:02:22 time: 0.9952 data_time: 0.0047 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0139 decode.acc_seg: 99.0118 aux.loss_ce: 0.0094 aux.acc_seg: 98.8165 +04/16 12:28:29 - mmengine - INFO - Iter(train) [ 15500/160000] base_lr: 9.1168e-05 lr: 3.3707e-07 eta: 1 day, 16:01:30 time: 0.9940 data_time: 0.0045 memory: 8462 loss: 0.0258 decode.loss_ce: 0.0150 decode.acc_seg: 99.4658 aux.loss_ce: 0.0108 aux.acc_seg: 98.7791 +04/16 12:29:19 - mmengine - INFO - Iter(train) [ 15550/160000] base_lr: 9.1136e-05 lr: 3.3695e-07 eta: 1 day, 16:00:39 time: 0.9945 data_time: 0.0044 memory: 8462 loss: 0.0274 decode.loss_ce: 0.0157 decode.acc_seg: 98.7837 aux.loss_ce: 0.0116 aux.acc_seg: 97.6768 +04/16 12:30:09 - mmengine - INFO - Iter(train) [ 15600/160000] base_lr: 9.1105e-05 lr: 3.3683e-07 eta: 1 day, 15:59:48 time: 0.9941 data_time: 0.0043 memory: 8462 loss: 0.0265 decode.loss_ce: 0.0149 decode.acc_seg: 99.6019 aux.loss_ce: 0.0116 aux.acc_seg: 98.9788 +04/16 12:30:58 - mmengine - INFO - Iter(train) [ 15650/160000] base_lr: 9.1073e-05 lr: 3.3672e-07 eta: 1 day, 15:58:57 time: 0.9956 data_time: 0.0040 memory: 8462 loss: 0.0234 decode.loss_ce: 0.0128 decode.acc_seg: 99.5907 aux.loss_ce: 0.0106 aux.acc_seg: 99.0166 +04/16 12:31:48 - mmengine - INFO - Iter(train) [ 15700/160000] base_lr: 9.1042e-05 lr: 3.3660e-07 eta: 1 day, 15:58:06 time: 0.9950 data_time: 0.0043 memory: 8462 loss: 0.0314 decode.loss_ce: 0.0188 decode.acc_seg: 99.2195 aux.loss_ce: 0.0126 aux.acc_seg: 98.7101 +04/16 12:32:38 - mmengine - INFO - Iter(train) [ 15750/160000] base_lr: 9.1010e-05 lr: 3.3648e-07 eta: 1 day, 15:57:15 time: 0.9938 data_time: 0.0042 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0149 decode.acc_seg: 99.3534 aux.loss_ce: 0.0102 aux.acc_seg: 98.4583 +04/16 12:33:27 - mmengine - INFO - Iter(train) [ 15800/160000] base_lr: 9.0978e-05 lr: 3.3637e-07 eta: 1 day, 15:56:24 time: 0.9938 data_time: 0.0044 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0121 decode.acc_seg: 99.6956 aux.loss_ce: 0.0097 aux.acc_seg: 99.3204 +04/16 12:34:17 - mmengine - INFO - Iter(train) [ 15850/160000] base_lr: 9.0947e-05 lr: 3.3625e-07 eta: 1 day, 15:55:33 time: 0.9939 data_time: 0.0044 memory: 8462 loss: 0.0235 decode.loss_ce: 0.0128 decode.acc_seg: 99.3752 aux.loss_ce: 0.0107 aux.acc_seg: 98.9956 +04/16 12:35:07 - mmengine - INFO - Iter(train) [ 15900/160000] base_lr: 9.0915e-05 lr: 3.3613e-07 eta: 1 day, 15:54:41 time: 0.9955 data_time: 0.0047 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0144 decode.acc_seg: 99.2025 aux.loss_ce: 0.0103 aux.acc_seg: 98.9445 +04/16 12:35:57 - mmengine - INFO - Iter(train) [ 15950/160000] base_lr: 9.0884e-05 lr: 3.3602e-07 eta: 1 day, 15:53:51 time: 0.9944 data_time: 0.0045 memory: 8462 loss: 0.0289 decode.loss_ce: 0.0168 decode.acc_seg: 99.4741 aux.loss_ce: 0.0121 aux.acc_seg: 99.0055 +04/16 12:36:46 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 12:36:46 - mmengine - INFO - Iter(train) [ 16000/160000] base_lr: 9.0852e-05 lr: 3.3590e-07 eta: 1 day, 15:52:59 time: 0.9950 data_time: 0.0045 memory: 8462 loss: 0.0251 decode.loss_ce: 0.0143 decode.acc_seg: 99.3509 aux.loss_ce: 0.0108 aux.acc_seg: 98.8085 +04/16 12:37:36 - mmengine - INFO - Iter(train) [ 16050/160000] base_lr: 9.0821e-05 lr: 3.3578e-07 eta: 1 day, 15:52:09 time: 0.9947 data_time: 0.0047 memory: 8462 loss: 0.0339 decode.loss_ce: 0.0205 decode.acc_seg: 98.8548 aux.loss_ce: 0.0134 aux.acc_seg: 98.2023 +04/16 12:38:26 - mmengine - INFO - Iter(train) [ 16100/160000] base_lr: 9.0789e-05 lr: 3.3567e-07 eta: 1 day, 15:51:18 time: 0.9947 data_time: 0.0044 memory: 8462 loss: 0.0333 decode.loss_ce: 0.0198 decode.acc_seg: 98.9243 aux.loss_ce: 0.0135 aux.acc_seg: 98.3068 +04/16 12:39:16 - mmengine - INFO - Iter(train) [ 16150/160000] base_lr: 9.0758e-05 lr: 3.3555e-07 eta: 1 day, 15:50:27 time: 0.9947 data_time: 0.0045 memory: 8462 loss: 0.0289 decode.loss_ce: 0.0170 decode.acc_seg: 99.2973 aux.loss_ce: 0.0119 aux.acc_seg: 98.9229 +04/16 12:40:05 - mmengine - INFO - Iter(train) [ 16200/160000] base_lr: 9.0726e-05 lr: 3.3543e-07 eta: 1 day, 15:49:35 time: 0.9933 data_time: 0.0043 memory: 8462 loss: 0.0289 decode.loss_ce: 0.0165 decode.acc_seg: 99.2540 aux.loss_ce: 0.0124 aux.acc_seg: 98.7711 +04/16 12:40:55 - mmengine - INFO - Iter(train) [ 16250/160000] base_lr: 9.0695e-05 lr: 3.3532e-07 eta: 1 day, 15:48:44 time: 0.9938 data_time: 0.0045 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0135 decode.acc_seg: 99.6256 aux.loss_ce: 0.0093 aux.acc_seg: 99.3422 +04/16 12:41:45 - mmengine - INFO - Iter(train) [ 16300/160000] base_lr: 9.0663e-05 lr: 3.3520e-07 eta: 1 day, 15:47:53 time: 0.9942 data_time: 0.0042 memory: 8462 loss: 0.0241 decode.loss_ce: 0.0139 decode.acc_seg: 99.5008 aux.loss_ce: 0.0102 aux.acc_seg: 99.0074 +04/16 12:42:34 - mmengine - INFO - Iter(train) [ 16350/160000] base_lr: 9.0631e-05 lr: 3.3508e-07 eta: 1 day, 15:47:02 time: 0.9941 data_time: 0.0045 memory: 8462 loss: 0.0244 decode.loss_ce: 0.0141 decode.acc_seg: 99.3250 aux.loss_ce: 0.0102 aux.acc_seg: 98.9412 +04/16 12:43:24 - mmengine - INFO - Iter(train) [ 16400/160000] base_lr: 9.0600e-05 lr: 3.3497e-07 eta: 1 day, 15:46:11 time: 0.9948 data_time: 0.0041 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0136 decode.acc_seg: 98.9716 aux.loss_ce: 0.0104 aux.acc_seg: 98.4613 +04/16 12:44:14 - mmengine - INFO - Iter(train) [ 16450/160000] base_lr: 9.0568e-05 lr: 3.3485e-07 eta: 1 day, 15:45:20 time: 0.9952 data_time: 0.0043 memory: 8462 loss: 0.0307 decode.loss_ce: 0.0182 decode.acc_seg: 99.5695 aux.loss_ce: 0.0125 aux.acc_seg: 98.6841 +04/16 12:45:04 - mmengine - INFO - Iter(train) [ 16500/160000] base_lr: 9.0537e-05 lr: 3.3473e-07 eta: 1 day, 15:44:30 time: 0.9952 data_time: 0.0043 memory: 8462 loss: 0.0269 decode.loss_ce: 0.0154 decode.acc_seg: 99.6611 aux.loss_ce: 0.0115 aux.acc_seg: 99.2939 +04/16 12:45:53 - mmengine - INFO - Iter(train) [ 16550/160000] base_lr: 9.0505e-05 lr: 3.3462e-07 eta: 1 day, 15:43:39 time: 0.9945 data_time: 0.0047 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0127 decode.acc_seg: 99.2729 aux.loss_ce: 0.0103 aux.acc_seg: 98.8014 +04/16 12:46:43 - mmengine - INFO - Iter(train) [ 16600/160000] base_lr: 9.0474e-05 lr: 3.3450e-07 eta: 1 day, 15:42:48 time: 0.9943 data_time: 0.0049 memory: 8462 loss: 0.0271 decode.loss_ce: 0.0158 decode.acc_seg: 99.4303 aux.loss_ce: 0.0113 aux.acc_seg: 98.9616 +04/16 12:47:33 - mmengine - INFO - Iter(train) [ 16650/160000] base_lr: 9.0442e-05 lr: 3.3438e-07 eta: 1 day, 15:41:57 time: 0.9940 data_time: 0.0044 memory: 8462 loss: 0.0253 decode.loss_ce: 0.0146 decode.acc_seg: 99.6136 aux.loss_ce: 0.0107 aux.acc_seg: 99.3130 +04/16 12:48:23 - mmengine - INFO - Iter(train) [ 16700/160000] base_lr: 9.0411e-05 lr: 3.3427e-07 eta: 1 day, 15:41:06 time: 0.9938 data_time: 0.0043 memory: 8462 loss: 0.0289 decode.loss_ce: 0.0167 decode.acc_seg: 99.1507 aux.loss_ce: 0.0121 aux.acc_seg: 98.4793 +04/16 12:49:12 - mmengine - INFO - Iter(train) [ 16750/160000] base_lr: 9.0379e-05 lr: 3.3415e-07 eta: 1 day, 15:40:15 time: 0.9950 data_time: 0.0047 memory: 8462 loss: 0.0268 decode.loss_ce: 0.0155 decode.acc_seg: 99.3820 aux.loss_ce: 0.0113 aux.acc_seg: 98.8203 +04/16 12:50:02 - mmengine - INFO - Iter(train) [ 16800/160000] base_lr: 9.0348e-05 lr: 3.3403e-07 eta: 1 day, 15:39:25 time: 0.9943 data_time: 0.0041 memory: 8462 loss: 0.0265 decode.loss_ce: 0.0158 decode.acc_seg: 99.6620 aux.loss_ce: 0.0107 aux.acc_seg: 99.1005 +04/16 12:50:52 - mmengine - INFO - Iter(train) [ 16850/160000] base_lr: 9.0316e-05 lr: 3.3392e-07 eta: 1 day, 15:38:34 time: 0.9947 data_time: 0.0042 memory: 8462 loss: 0.0254 decode.loss_ce: 0.0147 decode.acc_seg: 99.4753 aux.loss_ce: 0.0107 aux.acc_seg: 98.9782 +04/16 12:51:41 - mmengine - INFO - Iter(train) [ 16900/160000] base_lr: 9.0284e-05 lr: 3.3380e-07 eta: 1 day, 15:37:43 time: 0.9941 data_time: 0.0042 memory: 8462 loss: 0.0260 decode.loss_ce: 0.0151 decode.acc_seg: 99.6439 aux.loss_ce: 0.0109 aux.acc_seg: 99.1451 +04/16 12:52:31 - mmengine - INFO - Iter(train) [ 16950/160000] base_lr: 9.0253e-05 lr: 3.3368e-07 eta: 1 day, 15:36:52 time: 0.9949 data_time: 0.0046 memory: 8462 loss: 0.0249 decode.loss_ce: 0.0146 decode.acc_seg: 99.0032 aux.loss_ce: 0.0103 aux.acc_seg: 98.3549 +04/16 12:53:21 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 12:53:21 - mmengine - INFO - Iter(train) [ 17000/160000] base_lr: 9.0221e-05 lr: 3.3357e-07 eta: 1 day, 15:36:01 time: 0.9946 data_time: 0.0045 memory: 8462 loss: 0.0250 decode.loss_ce: 0.0144 decode.acc_seg: 99.5878 aux.loss_ce: 0.0106 aux.acc_seg: 99.1272 +04/16 12:54:11 - mmengine - INFO - Iter(train) [ 17050/160000] base_lr: 9.0190e-05 lr: 3.3345e-07 eta: 1 day, 15:35:10 time: 0.9936 data_time: 0.0044 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0135 decode.acc_seg: 99.3713 aux.loss_ce: 0.0102 aux.acc_seg: 98.7221 +04/16 12:55:00 - mmengine - INFO - 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0.0101 aux.acc_seg: 99.2603 +04/16 12:58:19 - mmengine - INFO - Iter(train) [ 17300/160000] base_lr: 9.0032e-05 lr: 3.3287e-07 eta: 1 day, 15:30:56 time: 0.9933 data_time: 0.0043 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0141 decode.acc_seg: 99.4154 aux.loss_ce: 0.0098 aux.acc_seg: 98.8899 +04/16 12:59:09 - mmengine - INFO - Iter(train) [ 17350/160000] base_lr: 9.0001e-05 lr: 3.3275e-07 eta: 1 day, 15:30:05 time: 0.9952 data_time: 0.0042 memory: 8462 loss: 0.0260 decode.loss_ce: 0.0150 decode.acc_seg: 99.5564 aux.loss_ce: 0.0110 aux.acc_seg: 99.2086 +04/16 12:59:59 - mmengine - INFO - Iter(train) [ 17400/160000] base_lr: 8.9969e-05 lr: 3.3263e-07 eta: 1 day, 15:29:14 time: 0.9945 data_time: 0.0044 memory: 8462 loss: 0.0249 decode.loss_ce: 0.0138 decode.acc_seg: 99.5892 aux.loss_ce: 0.0111 aux.acc_seg: 98.9199 +04/16 13:00:48 - mmengine - INFO - Iter(train) [ 17450/160000] base_lr: 8.9937e-05 lr: 3.3252e-07 eta: 1 day, 15:28:23 time: 0.9939 data_time: 0.0042 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0130 decode.acc_seg: 99.6269 aux.loss_ce: 0.0093 aux.acc_seg: 99.3746 +04/16 13:01:38 - mmengine - INFO - Iter(train) [ 17500/160000] base_lr: 8.9906e-05 lr: 3.3240e-07 eta: 1 day, 15:27:33 time: 0.9939 data_time: 0.0042 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0136 decode.acc_seg: 99.3824 aux.loss_ce: 0.0103 aux.acc_seg: 98.6378 +04/16 13:02:28 - mmengine - INFO - Iter(train) [ 17550/160000] base_lr: 8.9874e-05 lr: 3.3228e-07 eta: 1 day, 15:26:42 time: 0.9943 data_time: 0.0044 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0136 decode.acc_seg: 99.5794 aux.loss_ce: 0.0103 aux.acc_seg: 99.0973 +04/16 13:03:18 - mmengine - INFO - Iter(train) [ 17600/160000] base_lr: 8.9843e-05 lr: 3.3217e-07 eta: 1 day, 15:25:51 time: 0.9961 data_time: 0.0044 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0140 decode.acc_seg: 99.5371 aux.loss_ce: 0.0107 aux.acc_seg: 99.2817 +04/16 13:04:07 - mmengine - INFO - Iter(train) [ 17650/160000] base_lr: 8.9811e-05 lr: 3.3205e-07 eta: 1 day, 15:25:00 time: 0.9952 data_time: 0.0046 memory: 8462 loss: 0.0285 decode.loss_ce: 0.0169 decode.acc_seg: 99.0692 aux.loss_ce: 0.0116 aux.acc_seg: 98.6195 +04/16 13:04:57 - mmengine - INFO - Iter(train) [ 17700/160000] base_lr: 8.9780e-05 lr: 3.3193e-07 eta: 1 day, 15:24:10 time: 0.9953 data_time: 0.0046 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0142 decode.acc_seg: 99.5008 aux.loss_ce: 0.0097 aux.acc_seg: 98.8279 +04/16 13:05:47 - mmengine - INFO - Iter(train) [ 17750/160000] base_lr: 8.9748e-05 lr: 3.3182e-07 eta: 1 day, 15:23:19 time: 0.9949 data_time: 0.0046 memory: 8462 loss: 0.0279 decode.loss_ce: 0.0164 decode.acc_seg: 99.3298 aux.loss_ce: 0.0115 aux.acc_seg: 98.9090 +04/16 13:06:37 - mmengine - INFO - Iter(train) [ 17800/160000] base_lr: 8.9717e-05 lr: 3.3170e-07 eta: 1 day, 15:22:28 time: 0.9940 data_time: 0.0041 memory: 8462 loss: 0.0266 decode.loss_ce: 0.0154 decode.acc_seg: 99.4667 aux.loss_ce: 0.0112 aux.acc_seg: 98.8150 +04/16 13:07:26 - mmengine - INFO - Iter(train) [ 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memory: 8462 loss: 0.0242 decode.loss_ce: 0.0132 decode.acc_seg: 99.1476 aux.loss_ce: 0.0109 aux.acc_seg: 98.5649 +04/16 13:10:45 - mmengine - INFO - Iter(train) [ 18050/160000] base_lr: 8.9559e-05 lr: 3.3112e-07 eta: 1 day, 15:18:15 time: 0.9946 data_time: 0.0043 memory: 8462 loss: 0.0243 decode.loss_ce: 0.0138 decode.acc_seg: 99.5646 aux.loss_ce: 0.0105 aux.acc_seg: 98.5634 +04/16 13:11:35 - mmengine - INFO - Iter(train) [ 18100/160000] base_lr: 8.9527e-05 lr: 3.3100e-07 eta: 1 day, 15:17:25 time: 0.9949 data_time: 0.0046 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0125 decode.acc_seg: 99.3540 aux.loss_ce: 0.0105 aux.acc_seg: 98.6666 +04/16 13:12:25 - mmengine - INFO - Iter(train) [ 18150/160000] base_lr: 8.9496e-05 lr: 3.3088e-07 eta: 1 day, 15:16:34 time: 0.9943 data_time: 0.0045 memory: 8462 loss: 0.0263 decode.loss_ce: 0.0153 decode.acc_seg: 99.7435 aux.loss_ce: 0.0110 aux.acc_seg: 99.3992 +04/16 13:13:15 - mmengine - INFO - Iter(train) [ 18200/160000] base_lr: 8.9464e-05 lr: 3.3077e-07 eta: 1 day, 15:15:43 time: 0.9945 data_time: 0.0043 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0144 decode.acc_seg: 99.2609 aux.loss_ce: 0.0108 aux.acc_seg: 98.9428 +04/16 13:14:04 - mmengine - INFO - Iter(train) [ 18250/160000] base_lr: 8.9433e-05 lr: 3.3065e-07 eta: 1 day, 15:14:52 time: 0.9944 data_time: 0.0042 memory: 8462 loss: 0.0253 decode.loss_ce: 0.0142 decode.acc_seg: 99.3624 aux.loss_ce: 0.0111 aux.acc_seg: 99.0913 +04/16 13:14:54 - mmengine - INFO - Iter(train) [ 18300/160000] base_lr: 8.9401e-05 lr: 3.3053e-07 eta: 1 day, 15:14:02 time: 0.9963 data_time: 0.0044 memory: 8462 loss: 0.0258 decode.loss_ce: 0.0145 decode.acc_seg: 99.6546 aux.loss_ce: 0.0113 aux.acc_seg: 99.3649 +04/16 13:15:44 - mmengine - INFO - Iter(train) [ 18350/160000] base_lr: 8.9370e-05 lr: 3.3042e-07 eta: 1 day, 15:13:11 time: 0.9934 data_time: 0.0042 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0115 decode.acc_seg: 99.4238 aux.loss_ce: 0.0090 aux.acc_seg: 98.7648 +04/16 13:16:34 - mmengine - INFO - Iter(train) [ 18400/160000] base_lr: 8.9338e-05 lr: 3.3030e-07 eta: 1 day, 15:12:21 time: 0.9954 data_time: 0.0042 memory: 8462 loss: 0.0319 decode.loss_ce: 0.0187 decode.acc_seg: 99.6195 aux.loss_ce: 0.0133 aux.acc_seg: 99.2496 +04/16 13:17:23 - mmengine - INFO - Iter(train) [ 18450/160000] base_lr: 8.9307e-05 lr: 3.3018e-07 eta: 1 day, 15:11:30 time: 0.9944 data_time: 0.0042 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0118 decode.acc_seg: 99.5138 aux.loss_ce: 0.0094 aux.acc_seg: 99.0393 +04/16 13:18:13 - mmengine - INFO - Iter(train) [ 18500/160000] base_lr: 8.9275e-05 lr: 3.3007e-07 eta: 1 day, 15:10:39 time: 0.9942 data_time: 0.0043 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0125 decode.acc_seg: 99.7066 aux.loss_ce: 0.0101 aux.acc_seg: 99.0858 +04/16 13:19:03 - mmengine - INFO - Iter(train) [ 18550/160000] base_lr: 8.9243e-05 lr: 3.2995e-07 eta: 1 day, 15:09:48 time: 0.9949 data_time: 0.0045 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0113 decode.acc_seg: 99.6571 aux.loss_ce: 0.0087 aux.acc_seg: 99.1039 +04/16 13:19:52 - mmengine - INFO - Iter(train) [ 18600/160000] base_lr: 8.9212e-05 lr: 3.2983e-07 eta: 1 day, 15:08:58 time: 0.9944 data_time: 0.0043 memory: 8462 loss: 0.0249 decode.loss_ce: 0.0140 decode.acc_seg: 99.3109 aux.loss_ce: 0.0109 aux.acc_seg: 98.7213 +04/16 13:20:42 - mmengine - INFO - Iter(train) [ 18650/160000] base_lr: 8.9180e-05 lr: 3.2972e-07 eta: 1 day, 15:08:07 time: 0.9938 data_time: 0.0042 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0132 decode.acc_seg: 99.6332 aux.loss_ce: 0.0104 aux.acc_seg: 98.9868 +04/16 13:21:32 - mmengine - INFO - Iter(train) [ 18700/160000] base_lr: 8.9149e-05 lr: 3.2960e-07 eta: 1 day, 15:07:16 time: 0.9950 data_time: 0.0046 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0125 decode.acc_seg: 99.3567 aux.loss_ce: 0.0098 aux.acc_seg: 98.7898 +04/16 13:22:22 - mmengine - INFO - Iter(train) [ 18750/160000] base_lr: 8.9117e-05 lr: 3.2948e-07 eta: 1 day, 15:06:25 time: 0.9939 data_time: 0.0043 memory: 8462 loss: 0.0244 decode.loss_ce: 0.0139 decode.acc_seg: 99.5955 aux.loss_ce: 0.0105 aux.acc_seg: 99.0759 +04/16 13:23:11 - mmengine - INFO - Iter(train) [ 18800/160000] base_lr: 8.9086e-05 lr: 3.2937e-07 eta: 1 day, 15:05:35 time: 0.9947 data_time: 0.0044 memory: 8462 loss: 0.0246 decode.loss_ce: 0.0140 decode.acc_seg: 99.1087 aux.loss_ce: 0.0106 aux.acc_seg: 98.5701 +04/16 13:24:01 - mmengine - INFO - Iter(train) [ 18850/160000] base_lr: 8.9054e-05 lr: 3.2925e-07 eta: 1 day, 15:04:44 time: 0.9952 data_time: 0.0048 memory: 8462 loss: 0.0241 decode.loss_ce: 0.0138 decode.acc_seg: 99.3853 aux.loss_ce: 0.0104 aux.acc_seg: 98.9166 +04/16 13:24:51 - mmengine - INFO - Iter(train) [ 18900/160000] base_lr: 8.9023e-05 lr: 3.2914e-07 eta: 1 day, 15:03:54 time: 0.9946 data_time: 0.0043 memory: 8462 loss: 0.0270 decode.loss_ce: 0.0159 decode.acc_seg: 99.3099 aux.loss_ce: 0.0111 aux.acc_seg: 98.9237 +04/16 13:25:41 - mmengine - INFO - Iter(train) [ 18950/160000] base_lr: 8.8991e-05 lr: 3.2902e-07 eta: 1 day, 15:03:03 time: 0.9949 data_time: 0.0046 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0152 decode.acc_seg: 99.2718 aux.loss_ce: 0.0109 aux.acc_seg: 98.5825 +04/16 13:26:30 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 13:26:30 - mmengine - INFO - Iter(train) [ 19000/160000] base_lr: 8.8960e-05 lr: 3.2890e-07 eta: 1 day, 15:02:13 time: 0.9959 data_time: 0.0047 memory: 8462 loss: 0.0269 decode.loss_ce: 0.0155 decode.acc_seg: 99.3855 aux.loss_ce: 0.0114 aux.acc_seg: 98.9611 +04/16 13:27:20 - mmengine - INFO - Iter(train) [ 19050/160000] base_lr: 8.8928e-05 lr: 3.2879e-07 eta: 1 day, 15:01:23 time: 0.9954 data_time: 0.0045 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0158 decode.acc_seg: 99.3736 aux.loss_ce: 0.0108 aux.acc_seg: 98.9206 +04/16 13:28:10 - mmengine - INFO - Iter(train) [ 19100/160000] base_lr: 8.8896e-05 lr: 3.2867e-07 eta: 1 day, 15:00:32 time: 0.9939 data_time: 0.0042 memory: 8462 loss: 0.0258 decode.loss_ce: 0.0151 decode.acc_seg: 99.3345 aux.loss_ce: 0.0108 aux.acc_seg: 98.7389 +04/16 13:29:00 - mmengine - INFO - Iter(train) [ 19150/160000] base_lr: 8.8865e-05 lr: 3.2855e-07 eta: 1 day, 14:59:42 time: 0.9954 data_time: 0.0042 memory: 8462 loss: 0.0285 decode.loss_ce: 0.0166 decode.acc_seg: 99.3147 aux.loss_ce: 0.0119 aux.acc_seg: 98.7106 +04/16 13:29:49 - mmengine - INFO - Iter(train) [ 19200/160000] base_lr: 8.8833e-05 lr: 3.2844e-07 eta: 1 day, 14:58:51 time: 0.9954 data_time: 0.0046 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0136 decode.acc_seg: 99.3536 aux.loss_ce: 0.0100 aux.acc_seg: 98.9351 +04/16 13:30:39 - mmengine - INFO - Iter(train) [ 19250/160000] base_lr: 8.8802e-05 lr: 3.2832e-07 eta: 1 day, 14:58:01 time: 0.9949 data_time: 0.0047 memory: 8462 loss: 0.0250 decode.loss_ce: 0.0145 decode.acc_seg: 99.7335 aux.loss_ce: 0.0105 aux.acc_seg: 99.4125 +04/16 13:31:29 - mmengine - INFO - Iter(train) [ 19300/160000] base_lr: 8.8770e-05 lr: 3.2820e-07 eta: 1 day, 14:57:11 time: 0.9966 data_time: 0.0045 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0157 decode.acc_seg: 99.1705 aux.loss_ce: 0.0117 aux.acc_seg: 98.4364 +04/16 13:32:19 - mmengine - INFO - Iter(train) [ 19350/160000] base_lr: 8.8739e-05 lr: 3.2809e-07 eta: 1 day, 14:56:20 time: 0.9969 data_time: 0.0044 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0130 decode.acc_seg: 99.3145 aux.loss_ce: 0.0094 aux.acc_seg: 98.8594 +04/16 13:33:08 - mmengine - INFO - Iter(train) [ 19400/160000] base_lr: 8.8707e-05 lr: 3.2797e-07 eta: 1 day, 14:55:30 time: 0.9945 data_time: 0.0047 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0131 decode.acc_seg: 99.2586 aux.loss_ce: 0.0097 aux.acc_seg: 98.9151 +04/16 13:33:58 - mmengine - INFO - Iter(train) [ 19450/160000] base_lr: 8.8676e-05 lr: 3.2785e-07 eta: 1 day, 14:54:40 time: 0.9957 data_time: 0.0045 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0132 decode.acc_seg: 99.3841 aux.loss_ce: 0.0109 aux.acc_seg: 98.5268 +04/16 13:34:48 - mmengine - INFO - Iter(train) [ 19500/160000] base_lr: 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13:38:07 - mmengine - INFO - Iter(train) [ 19700/160000] base_lr: 8.8518e-05 lr: 3.2727e-07 eta: 1 day, 14:50:28 time: 0.9953 data_time: 0.0051 memory: 8462 loss: 0.0281 decode.loss_ce: 0.0164 decode.acc_seg: 98.9395 aux.loss_ce: 0.0118 aux.acc_seg: 98.3028 +04/16 13:38:57 - mmengine - INFO - Iter(train) [ 19750/160000] base_lr: 8.8486e-05 lr: 3.2715e-07 eta: 1 day, 14:49:38 time: 0.9940 data_time: 0.0044 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0119 decode.acc_seg: 99.4602 aux.loss_ce: 0.0096 aux.acc_seg: 99.1415 +04/16 13:39:47 - mmengine - INFO - Iter(train) [ 19800/160000] base_lr: 8.8455e-05 lr: 3.2704e-07 eta: 1 day, 14:48:47 time: 0.9945 data_time: 0.0045 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0132 decode.acc_seg: 99.2502 aux.loss_ce: 0.0095 aux.acc_seg: 98.8523 +04/16 13:40:36 - mmengine - INFO - Iter(train) [ 19850/160000] base_lr: 8.8423e-05 lr: 3.2692e-07 eta: 1 day, 14:47:58 time: 0.9951 data_time: 0.0045 memory: 8462 loss: 0.0255 decode.loss_ce: 0.0144 decode.acc_seg: 99.4022 aux.loss_ce: 0.0112 aux.acc_seg: 98.5693 +04/16 13:41:26 - mmengine - INFO - Iter(train) [ 19900/160000] base_lr: 8.8392e-05 lr: 3.2680e-07 eta: 1 day, 14:47:07 time: 0.9962 data_time: 0.0044 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0129 decode.acc_seg: 99.5338 aux.loss_ce: 0.0098 aux.acc_seg: 98.9962 +04/16 13:42:16 - mmengine - INFO - Iter(train) [ 19950/160000] base_lr: 8.8360e-05 lr: 3.2669e-07 eta: 1 day, 14:46:17 time: 0.9960 data_time: 0.0045 memory: 8462 loss: 0.0232 decode.loss_ce: 0.0131 decode.acc_seg: 99.4450 aux.loss_ce: 0.0101 aux.acc_seg: 99.0223 +04/16 13:43:06 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 13:43:06 - mmengine - INFO - Iter(train) [ 20000/160000] base_lr: 8.8329e-05 lr: 3.2657e-07 eta: 1 day, 14:45:27 time: 0.9964 data_time: 0.0046 memory: 8462 loss: 0.0244 decode.loss_ce: 0.0140 decode.acc_seg: 99.5642 aux.loss_ce: 0.0104 aux.acc_seg: 99.0322 +04/16 13:43:06 - mmengine - INFO - Saving checkpoint at 20000 iterations +04/16 13:43:16 - mmengine - INFO - Iter(val) [ 50/355] eta: 0:00:35 time: 0.1152 data_time: 0.0014 memory: 4004 +04/16 13:43:21 - mmengine - INFO - Iter(val) [100/355] eta: 0:00:29 time: 0.1157 data_time: 0.0017 memory: 4004 +04/16 13:43:27 - mmengine - INFO - Iter(val) [150/355] eta: 0:00:23 time: 0.1154 data_time: 0.0015 memory: 4004 +04/16 13:43:33 - mmengine - INFO - Iter(val) [200/355] eta: 0:00:17 time: 0.1154 data_time: 0.0014 memory: 4004 +04/16 13:43:39 - mmengine - INFO - Iter(val) [250/355] eta: 0:00:12 time: 0.1157 data_time: 0.0016 memory: 4004 +04/16 13:43:44 - mmengine - INFO - Iter(val) [300/355] eta: 0:00:06 time: 0.1155 data_time: 0.0015 memory: 4004 +04/16 13:43:50 - mmengine - INFO - Iter(val) [350/355] eta: 0:00:00 time: 0.1157 data_time: 0.0016 memory: 4004 +04/16 13:43:51 - mmengine - INFO - per class results: +04/16 13:43:51 - mmengine - INFO - ++------------+-------+-------+ +| Class | IoU | Acc | ++------------+-------+-------+ +| background | 99.18 | 99.59 | +| contrast | 82.13 | 90.09 | ++------------+-------+-------+ +04/16 13:43:51 - mmengine - INFO - Iter(val) [355/355] aAcc: 99.2100 mIoU: 90.6500 mAcc: 94.8400 data_time: 0.0017 time: 0.1157 +04/16 13:44:41 - mmengine - INFO - Iter(train) [ 20050/160000] base_lr: 8.8297e-05 lr: 3.2645e-07 eta: 1 day, 14:44:39 time: 0.9948 data_time: 0.0044 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0136 decode.acc_seg: 99.4276 aux.loss_ce: 0.0102 aux.acc_seg: 99.0868 +04/16 13:45:31 - mmengine - INFO - Iter(train) [ 20100/160000] base_lr: 8.8266e-05 lr: 3.2634e-07 eta: 1 day, 14:43:48 time: 0.9965 data_time: 0.0043 memory: 8462 loss: 0.0308 decode.loss_ce: 0.0174 decode.acc_seg: 99.2613 aux.loss_ce: 0.0134 aux.acc_seg: 98.5760 +04/16 13:46:20 - mmengine - INFO - Iter(train) [ 20150/160000] base_lr: 8.8234e-05 lr: 3.2622e-07 eta: 1 day, 14:42:58 time: 0.9954 data_time: 0.0045 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0141 decode.acc_seg: 99.6801 aux.loss_ce: 0.0105 aux.acc_seg: 99.2981 +04/16 13:47:10 - mmengine - INFO - Iter(train) [ 20200/160000] base_lr: 8.8202e-05 lr: 3.2610e-07 eta: 1 day, 14:42:08 time: 0.9946 data_time: 0.0043 memory: 8462 loss: 0.0231 decode.loss_ce: 0.0127 decode.acc_seg: 99.7377 aux.loss_ce: 0.0105 aux.acc_seg: 99.4703 +04/16 13:48:00 - mmengine - INFO - Iter(train) [ 20250/160000] base_lr: 8.8171e-05 lr: 3.2599e-07 eta: 1 day, 14:41:18 time: 0.9958 data_time: 0.0044 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0116 decode.acc_seg: 99.5728 aux.loss_ce: 0.0095 aux.acc_seg: 99.2064 +04/16 13:48:50 - mmengine - INFO - Iter(train) [ 20300/160000] base_lr: 8.8139e-05 lr: 3.2587e-07 eta: 1 day, 14:40:28 time: 0.9955 data_time: 0.0047 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0122 decode.acc_seg: 99.5043 aux.loss_ce: 0.0100 aux.acc_seg: 98.6240 +04/16 13:49:40 - mmengine - INFO - Iter(train) [ 20350/160000] base_lr: 8.8108e-05 lr: 3.2575e-07 eta: 1 day, 14:39:38 time: 0.9963 data_time: 0.0045 memory: 8462 loss: 0.0226 decode.loss_ce: 0.0126 decode.acc_seg: 99.4381 aux.loss_ce: 0.0100 aux.acc_seg: 98.7467 +04/16 13:50:29 - mmengine - INFO - Iter(train) [ 20400/160000] base_lr: 8.8076e-05 lr: 3.2564e-07 eta: 1 day, 14:38:48 time: 0.9958 data_time: 0.0042 memory: 8462 loss: 0.0234 decode.loss_ce: 0.0130 decode.acc_seg: 99.5968 aux.loss_ce: 0.0104 aux.acc_seg: 99.2714 +04/16 13:51:19 - mmengine - INFO - Iter(train) [ 20450/160000] base_lr: 8.8045e-05 lr: 3.2552e-07 eta: 1 day, 14:37:58 time: 0.9963 data_time: 0.0043 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0124 decode.acc_seg: 99.3410 aux.loss_ce: 0.0096 aux.acc_seg: 98.7488 +04/16 13:52:09 - mmengine - INFO - Iter(train) [ 20500/160000] base_lr: 8.8013e-05 lr: 3.2540e-07 eta: 1 day, 14:37:08 time: 0.9954 data_time: 0.0043 memory: 8462 loss: 0.0272 decode.loss_ce: 0.0159 decode.acc_seg: 98.9864 aux.loss_ce: 0.0114 aux.acc_seg: 98.5086 +04/16 13:52:59 - mmengine - INFO - Iter(train) [ 20550/160000] base_lr: 8.7982e-05 lr: 3.2529e-07 eta: 1 day, 14:36:18 time: 0.9956 data_time: 0.0042 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0151 decode.acc_seg: 99.5337 aux.loss_ce: 0.0110 aux.acc_seg: 98.8176 +04/16 13:53:49 - mmengine - INFO - Iter(train) [ 20600/160000] base_lr: 8.7950e-05 lr: 3.2517e-07 eta: 1 day, 14:35:28 time: 0.9963 data_time: 0.0046 memory: 8462 loss: 0.0235 decode.loss_ce: 0.0128 decode.acc_seg: 99.7038 aux.loss_ce: 0.0108 aux.acc_seg: 99.2350 +04/16 13:54:38 - mmengine - INFO - Iter(train) [ 20650/160000] base_lr: 8.7919e-05 lr: 3.2505e-07 eta: 1 day, 14:34:38 time: 0.9973 data_time: 0.0042 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0114 decode.acc_seg: 99.1310 aux.loss_ce: 0.0084 aux.acc_seg: 98.4112 +04/16 13:55:28 - mmengine - INFO - Iter(train) [ 20700/160000] base_lr: 8.7887e-05 lr: 3.2494e-07 eta: 1 day, 14:33:48 time: 0.9956 data_time: 0.0044 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0154 decode.acc_seg: 99.6151 aux.loss_ce: 0.0108 aux.acc_seg: 99.3122 +04/16 13:56:18 - mmengine - INFO - Iter(train) [ 20750/160000] base_lr: 8.7855e-05 lr: 3.2482e-07 eta: 1 day, 14:32:58 time: 0.9951 data_time: 0.0046 memory: 8462 loss: 0.0284 decode.loss_ce: 0.0161 decode.acc_seg: 99.7086 aux.loss_ce: 0.0122 aux.acc_seg: 99.2863 +04/16 13:57:08 - mmengine - INFO - Iter(train) [ 20800/160000] base_lr: 8.7824e-05 lr: 3.2470e-07 eta: 1 day, 14:32:08 time: 0.9958 data_time: 0.0041 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0133 decode.acc_seg: 99.6466 aux.loss_ce: 0.0097 aux.acc_seg: 98.8922 +04/16 13:57:58 - mmengine - INFO - Iter(train) [ 20850/160000] base_lr: 8.7792e-05 lr: 3.2459e-07 eta: 1 day, 14:31:17 time: 0.9961 data_time: 0.0045 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0125 decode.acc_seg: 99.4942 aux.loss_ce: 0.0094 aux.acc_seg: 98.9948 +04/16 13:58:47 - mmengine - INFO - Iter(train) [ 20900/160000] base_lr: 8.7761e-05 lr: 3.2447e-07 eta: 1 day, 14:30:28 time: 0.9965 data_time: 0.0044 memory: 8462 loss: 0.0258 decode.loss_ce: 0.0144 decode.acc_seg: 99.0498 aux.loss_ce: 0.0115 aux.acc_seg: 98.2670 +04/16 13:59:37 - mmengine - INFO - Iter(train) [ 20950/160000] base_lr: 8.7729e-05 lr: 3.2435e-07 eta: 1 day, 14:29:38 time: 0.9974 data_time: 0.0047 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0140 decode.acc_seg: 99.5800 aux.loss_ce: 0.0102 aux.acc_seg: 99.3896 +04/16 14:00:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 14:00:27 - mmengine - INFO - Iter(train) [ 21000/160000] base_lr: 8.7698e-05 lr: 3.2424e-07 eta: 1 day, 14:28:48 time: 0.9980 data_time: 0.0053 memory: 8462 loss: 0.0248 decode.loss_ce: 0.0142 decode.acc_seg: 98.9771 aux.loss_ce: 0.0105 aux.acc_seg: 98.5836 +04/16 14:01:17 - mmengine - INFO - Iter(train) [ 21050/160000] base_lr: 8.7666e-05 lr: 3.2412e-07 eta: 1 day, 14:27:58 time: 0.9961 data_time: 0.0044 memory: 8462 loss: 0.0297 decode.loss_ce: 0.0173 decode.acc_seg: 99.3839 aux.loss_ce: 0.0124 aux.acc_seg: 98.7478 +04/16 14:02:07 - mmengine - INFO - Iter(train) [ 21100/160000] base_lr: 8.7635e-05 lr: 3.2400e-07 eta: 1 day, 14:27:08 time: 0.9969 data_time: 0.0050 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0132 decode.acc_seg: 99.4356 aux.loss_ce: 0.0101 aux.acc_seg: 99.1446 +04/16 14:02:57 - mmengine - INFO - Iter(train) [ 21150/160000] base_lr: 8.7603e-05 lr: 3.2389e-07 eta: 1 day, 14:26:19 time: 0.9967 data_time: 0.0043 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0133 decode.acc_seg: 99.4202 aux.loss_ce: 0.0094 aux.acc_seg: 98.9567 +04/16 14:03:47 - mmengine - INFO - Iter(train) [ 21200/160000] base_lr: 8.7572e-05 lr: 3.2377e-07 eta: 1 day, 14:25:29 time: 0.9969 data_time: 0.0047 memory: 8462 loss: 0.0217 decode.loss_ce: 0.0123 decode.acc_seg: 99.7370 aux.loss_ce: 0.0095 aux.acc_seg: 99.4749 +04/16 14:04:36 - mmengine - INFO - Iter(train) [ 21250/160000] base_lr: 8.7540e-05 lr: 3.2365e-07 eta: 1 day, 14:24:39 time: 0.9969 data_time: 0.0046 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0130 decode.acc_seg: 99.6424 aux.loss_ce: 0.0106 aux.acc_seg: 99.0780 +04/16 14:05:26 - mmengine - INFO - Iter(train) [ 21300/160000] base_lr: 8.7508e-05 lr: 3.2354e-07 eta: 1 day, 14:23:49 time: 0.9962 data_time: 0.0044 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0129 decode.acc_seg: 99.6460 aux.loss_ce: 0.0099 aux.acc_seg: 99.1171 +04/16 14:06:16 - mmengine - INFO - Iter(train) [ 21350/160000] base_lr: 8.7477e-05 lr: 3.2342e-07 eta: 1 day, 14:22:59 time: 0.9978 data_time: 0.0043 memory: 8462 loss: 0.0257 decode.loss_ce: 0.0149 decode.acc_seg: 99.1144 aux.loss_ce: 0.0108 aux.acc_seg: 98.3063 +04/16 14:07:06 - mmengine - INFO - Iter(train) [ 21400/160000] base_lr: 8.7445e-05 lr: 3.2330e-07 eta: 1 day, 14:22:10 time: 0.9970 data_time: 0.0046 memory: 8462 loss: 0.0260 decode.loss_ce: 0.0144 decode.acc_seg: 99.5527 aux.loss_ce: 0.0116 aux.acc_seg: 98.5941 +04/16 14:07:56 - mmengine - INFO - Iter(train) [ 21450/160000] base_lr: 8.7414e-05 lr: 3.2319e-07 eta: 1 day, 14:21:20 time: 0.9966 data_time: 0.0044 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0123 decode.acc_seg: 99.2031 aux.loss_ce: 0.0091 aux.acc_seg: 98.9466 +04/16 14:08:45 - mmengine - INFO - Iter(train) [ 21500/160000] base_lr: 8.7382e-05 lr: 3.2307e-07 eta: 1 day, 14:20:30 time: 0.9976 data_time: 0.0047 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0150 decode.acc_seg: 99.6807 aux.loss_ce: 0.0111 aux.acc_seg: 99.3599 +04/16 14:09:35 - mmengine - INFO - Iter(train) [ 21550/160000] base_lr: 8.7351e-05 lr: 3.2295e-07 eta: 1 day, 14:19:40 time: 0.9962 data_time: 0.0050 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0134 decode.acc_seg: 99.3223 aux.loss_ce: 0.0105 aux.acc_seg: 98.9008 +04/16 14:10:25 - mmengine - INFO - Iter(train) [ 21600/160000] base_lr: 8.7319e-05 lr: 3.2284e-07 eta: 1 day, 14:18:50 time: 0.9974 data_time: 0.0045 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0122 decode.acc_seg: 99.5829 aux.loss_ce: 0.0098 aux.acc_seg: 99.2950 +04/16 14:11:15 - mmengine - INFO - Iter(train) [ 21650/160000] base_lr: 8.7288e-05 lr: 3.2272e-07 eta: 1 day, 14:18:00 time: 0.9974 data_time: 0.0046 memory: 8462 loss: 0.0248 decode.loss_ce: 0.0135 decode.acc_seg: 99.1840 aux.loss_ce: 0.0113 aux.acc_seg: 98.6668 +04/16 14:12:05 - mmengine - INFO - Iter(train) [ 21700/160000] base_lr: 8.7256e-05 lr: 3.2260e-07 eta: 1 day, 14:17:11 time: 0.9964 data_time: 0.0043 memory: 8462 loss: 0.0257 decode.loss_ce: 0.0143 decode.acc_seg: 99.3885 aux.loss_ce: 0.0114 aux.acc_seg: 98.5630 +04/16 14:12:55 - mmengine - INFO - Iter(train) [ 21750/160000] base_lr: 8.7225e-05 lr: 3.2249e-07 eta: 1 day, 14:16:21 time: 0.9972 data_time: 0.0046 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0154 decode.acc_seg: 99.4652 aux.loss_ce: 0.0107 aux.acc_seg: 99.2119 +04/16 14:13:44 - mmengine - INFO - Iter(train) [ 21800/160000] base_lr: 8.7193e-05 lr: 3.2237e-07 eta: 1 day, 14:15:31 time: 0.9969 data_time: 0.0043 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0128 decode.acc_seg: 99.5832 aux.loss_ce: 0.0098 aux.acc_seg: 98.6763 +04/16 14:14:34 - mmengine - INFO - Iter(train) [ 21850/160000] base_lr: 8.7161e-05 lr: 3.2225e-07 eta: 1 day, 14:14:41 time: 0.9957 data_time: 0.0047 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0125 decode.acc_seg: 99.3341 aux.loss_ce: 0.0100 aux.acc_seg: 98.8565 +04/16 14:15:24 - mmengine - INFO - Iter(train) [ 21900/160000] base_lr: 8.7130e-05 lr: 3.2214e-07 eta: 1 day, 14:13:51 time: 0.9981 data_time: 0.0044 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0117 decode.acc_seg: 99.6178 aux.loss_ce: 0.0089 aux.acc_seg: 99.0368 +04/16 14:16:14 - mmengine - INFO - Iter(train) [ 21950/160000] base_lr: 8.7098e-05 lr: 3.2202e-07 eta: 1 day, 14:13:02 time: 0.9969 data_time: 0.0043 memory: 8462 loss: 0.0278 decode.loss_ce: 0.0158 decode.acc_seg: 98.3555 aux.loss_ce: 0.0120 aux.acc_seg: 97.7139 +04/16 14:17:04 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 14:17:04 - mmengine - INFO - Iter(train) [ 22000/160000] base_lr: 8.7067e-05 lr: 3.2190e-07 eta: 1 day, 14:12:13 time: 0.9964 data_time: 0.0045 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0116 decode.acc_seg: 99.6542 aux.loss_ce: 0.0088 aux.acc_seg: 99.0362 +04/16 14:17:54 - mmengine - INFO - Iter(train) [ 22050/160000] base_lr: 8.7035e-05 lr: 3.2179e-07 eta: 1 day, 14:11:23 time: 0.9976 data_time: 0.0048 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0124 decode.acc_seg: 99.2950 aux.loss_ce: 0.0098 aux.acc_seg: 98.9662 +04/16 14:18:44 - mmengine - INFO - Iter(train) [ 22100/160000] base_lr: 8.7004e-05 lr: 3.2167e-07 eta: 1 day, 14:10:33 time: 0.9961 data_time: 0.0043 memory: 8462 loss: 0.0235 decode.loss_ce: 0.0133 decode.acc_seg: 99.2270 aux.loss_ce: 0.0103 aux.acc_seg: 98.7913 +04/16 14:19:34 - mmengine - INFO - Iter(train) [ 22150/160000] base_lr: 8.6972e-05 lr: 3.2155e-07 eta: 1 day, 14:09:43 time: 0.9974 data_time: 0.0046 memory: 8462 loss: 0.0232 decode.loss_ce: 0.0128 decode.acc_seg: 99.4585 aux.loss_ce: 0.0104 aux.acc_seg: 98.9992 +04/16 14:20:23 - mmengine - INFO - Iter(train) [ 22200/160000] base_lr: 8.6941e-05 lr: 3.2144e-07 eta: 1 day, 14:08:53 time: 0.9968 data_time: 0.0043 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0113 decode.acc_seg: 99.6887 aux.loss_ce: 0.0098 aux.acc_seg: 99.2519 +04/16 14:21:13 - mmengine - INFO - Iter(train) [ 22250/160000] base_lr: 8.6909e-05 lr: 3.2132e-07 eta: 1 day, 14:08:04 time: 0.9971 data_time: 0.0043 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0120 decode.acc_seg: 99.6883 aux.loss_ce: 0.0100 aux.acc_seg: 99.4377 +04/16 14:22:03 - mmengine - INFO - Iter(train) [ 22300/160000] base_lr: 8.6878e-05 lr: 3.2120e-07 eta: 1 day, 14:07:14 time: 0.9976 data_time: 0.0047 memory: 8462 loss: 0.0231 decode.loss_ce: 0.0137 decode.acc_seg: 99.3752 aux.loss_ce: 0.0094 aux.acc_seg: 98.8932 +04/16 14:22:53 - mmengine - INFO - Iter(train) [ 22350/160000] base_lr: 8.6846e-05 lr: 3.2109e-07 eta: 1 day, 14:06:24 time: 0.9970 data_time: 0.0046 memory: 8462 loss: 0.0255 decode.loss_ce: 0.0146 decode.acc_seg: 99.4810 aux.loss_ce: 0.0109 aux.acc_seg: 98.9985 +04/16 14:23:43 - mmengine - INFO - Iter(train) [ 22400/160000] base_lr: 8.6814e-05 lr: 3.2097e-07 eta: 1 day, 14:05:34 time: 0.9965 data_time: 0.0044 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0125 decode.acc_seg: 99.4696 aux.loss_ce: 0.0103 aux.acc_seg: 99.0412 +04/16 14:24:33 - mmengine - INFO - Iter(train) [ 22450/160000] base_lr: 8.6783e-05 lr: 3.2085e-07 eta: 1 day, 14:04:44 time: 0.9965 data_time: 0.0042 memory: 8462 loss: 0.0241 decode.loss_ce: 0.0134 decode.acc_seg: 99.5745 aux.loss_ce: 0.0107 aux.acc_seg: 99.1385 +04/16 14:25:22 - mmengine - INFO - Iter(train) [ 22500/160000] base_lr: 8.6751e-05 lr: 3.2074e-07 eta: 1 day, 14:03:55 time: 0.9967 data_time: 0.0041 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0126 decode.acc_seg: 99.4711 aux.loss_ce: 0.0099 aux.acc_seg: 99.0034 +04/16 14:26:12 - mmengine - INFO - Iter(train) [ 22550/160000] base_lr: 8.6720e-05 lr: 3.2062e-07 eta: 1 day, 14:03:05 time: 0.9976 data_time: 0.0051 memory: 8462 loss: 0.0253 decode.loss_ce: 0.0149 decode.acc_seg: 99.3826 aux.loss_ce: 0.0104 aux.acc_seg: 98.5819 +04/16 14:27:02 - mmengine - INFO - Iter(train) [ 22600/160000] base_lr: 8.6688e-05 lr: 3.2050e-07 eta: 1 day, 14:02:15 time: 0.9971 data_time: 0.0044 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0123 decode.acc_seg: 99.5838 aux.loss_ce: 0.0100 aux.acc_seg: 99.1564 +04/16 14:27:52 - mmengine - INFO - Iter(train) [ 22650/160000] base_lr: 8.6657e-05 lr: 3.2039e-07 eta: 1 day, 14:01:25 time: 0.9963 data_time: 0.0046 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0124 decode.acc_seg: 99.5415 aux.loss_ce: 0.0100 aux.acc_seg: 98.6750 +04/16 14:28:42 - mmengine - INFO - Iter(train) [ 22700/160000] base_lr: 8.6625e-05 lr: 3.2027e-07 eta: 1 day, 14:00:36 time: 0.9968 data_time: 0.0045 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0123 decode.acc_seg: 99.6025 aux.loss_ce: 0.0098 aux.acc_seg: 99.0717 +04/16 14:29:32 - mmengine - INFO - Iter(train) [ 22750/160000] base_lr: 8.6594e-05 lr: 3.2015e-07 eta: 1 day, 13:59:46 time: 0.9963 data_time: 0.0043 memory: 8462 loss: 0.0257 decode.loss_ce: 0.0146 decode.acc_seg: 99.0210 aux.loss_ce: 0.0111 aux.acc_seg: 98.5020 +04/16 14:30:21 - mmengine - INFO - Iter(train) [ 22800/160000] base_lr: 8.6562e-05 lr: 3.2004e-07 eta: 1 day, 13:58:56 time: 0.9976 data_time: 0.0049 memory: 8462 loss: 0.0238 decode.loss_ce: 0.0131 decode.acc_seg: 99.3656 aux.loss_ce: 0.0107 aux.acc_seg: 98.9552 +04/16 14:31:11 - mmengine - INFO - Iter(train) [ 22850/160000] base_lr: 8.6531e-05 lr: 3.1992e-07 eta: 1 day, 13:58:06 time: 0.9968 data_time: 0.0046 memory: 8462 loss: 0.0266 decode.loss_ce: 0.0149 decode.acc_seg: 99.5199 aux.loss_ce: 0.0117 aux.acc_seg: 98.6288 +04/16 14:32:01 - mmengine - INFO - Iter(train) [ 22900/160000] base_lr: 8.6499e-05 lr: 3.1980e-07 eta: 1 day, 13:57:16 time: 0.9954 data_time: 0.0044 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.6670 aux.loss_ce: 0.0086 aux.acc_seg: 99.4059 +04/16 14:32:51 - mmengine - INFO - Iter(train) [ 22950/160000] base_lr: 8.6467e-05 lr: 3.1969e-07 eta: 1 day, 13:56:27 time: 0.9961 data_time: 0.0044 memory: 8462 loss: 0.0249 decode.loss_ce: 0.0138 decode.acc_seg: 99.1858 aux.loss_ce: 0.0111 aux.acc_seg: 98.5119 +04/16 14:33:41 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 14:33:41 - mmengine - INFO - Iter(train) [ 23000/160000] base_lr: 8.6436e-05 lr: 3.1957e-07 eta: 1 day, 13:55:37 time: 0.9963 data_time: 0.0046 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0136 decode.acc_seg: 99.4303 aux.loss_ce: 0.0109 aux.acc_seg: 98.9567 +04/16 14:34:31 - mmengine - INFO - Iter(train) [ 23050/160000] base_lr: 8.6404e-05 lr: 3.1945e-07 eta: 1 day, 13:54:47 time: 0.9973 data_time: 0.0046 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0117 decode.acc_seg: 99.4234 aux.loss_ce: 0.0097 aux.acc_seg: 98.9040 +04/16 14:35:20 - mmengine - INFO - Iter(train) [ 23100/160000] base_lr: 8.6373e-05 lr: 3.1934e-07 eta: 1 day, 13:53:57 time: 0.9962 data_time: 0.0043 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0116 decode.acc_seg: 99.3435 aux.loss_ce: 0.0090 aux.acc_seg: 99.0650 +04/16 14:36:10 - mmengine - INFO - Iter(train) [ 23150/160000] base_lr: 8.6341e-05 lr: 3.1922e-07 eta: 1 day, 13:53:07 time: 0.9965 data_time: 0.0042 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0115 decode.acc_seg: 99.4799 aux.loss_ce: 0.0100 aux.acc_seg: 98.9277 +04/16 14:37:00 - mmengine - INFO - Iter(train) [ 23200/160000] base_lr: 8.6310e-05 lr: 3.1910e-07 eta: 1 day, 13:52:18 time: 0.9983 data_time: 0.0044 memory: 8462 loss: 0.0297 decode.loss_ce: 0.0155 decode.acc_seg: 99.3931 aux.loss_ce: 0.0142 aux.acc_seg: 98.6179 +04/16 14:37:50 - mmengine - INFO - Iter(train) [ 23250/160000] base_lr: 8.6278e-05 lr: 3.1899e-07 eta: 1 day, 13:51:29 time: 0.9978 data_time: 0.0045 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0127 decode.acc_seg: 99.5281 aux.loss_ce: 0.0096 aux.acc_seg: 98.7810 +04/16 14:38:40 - mmengine - INFO - Iter(train) [ 23300/160000] base_lr: 8.6247e-05 lr: 3.1887e-07 eta: 1 day, 13:50:39 time: 0.9974 data_time: 0.0043 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0129 decode.acc_seg: 99.6302 aux.loss_ce: 0.0108 aux.acc_seg: 98.9265 +04/16 14:39:30 - mmengine - INFO - Iter(train) [ 23350/160000] base_lr: 8.6215e-05 lr: 3.1875e-07 eta: 1 day, 13:49:49 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0103 decode.acc_seg: 99.5640 aux.loss_ce: 0.0087 aux.acc_seg: 98.7467 +04/16 14:40:20 - mmengine - INFO - Iter(train) [ 23400/160000] base_lr: 8.6184e-05 lr: 3.1864e-07 eta: 1 day, 13:49:00 time: 0.9982 data_time: 0.0043 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0118 decode.acc_seg: 99.4350 aux.loss_ce: 0.0097 aux.acc_seg: 98.9246 +04/16 14:41:10 - mmengine - INFO - Iter(train) [ 23450/160000] base_lr: 8.6152e-05 lr: 3.1852e-07 eta: 1 day, 13:48:10 time: 0.9973 data_time: 0.0047 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0135 decode.acc_seg: 99.6048 aux.loss_ce: 0.0104 aux.acc_seg: 99.3065 +04/16 14:41:59 - mmengine - INFO - Iter(train) [ 23500/160000] base_lr: 8.6120e-05 lr: 3.1840e-07 eta: 1 day, 13:47:20 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0104 decode.acc_seg: 99.5548 aux.loss_ce: 0.0091 aux.acc_seg: 99.1333 +04/16 14:42:49 - mmengine - INFO - Iter(train) [ 23550/160000] base_lr: 8.6089e-05 lr: 3.1829e-07 eta: 1 day, 13:46:31 time: 0.9973 data_time: 0.0044 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0113 decode.acc_seg: 99.6290 aux.loss_ce: 0.0093 aux.acc_seg: 99.0456 +04/16 14:43:39 - mmengine - INFO - Iter(train) [ 23600/160000] base_lr: 8.6057e-05 lr: 3.1817e-07 eta: 1 day, 13:45:41 time: 0.9973 data_time: 0.0043 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0132 decode.acc_seg: 99.5073 aux.loss_ce: 0.0105 aux.acc_seg: 99.1844 +04/16 14:44:29 - mmengine - INFO - Iter(train) [ 23650/160000] base_lr: 8.6026e-05 lr: 3.1806e-07 eta: 1 day, 13:44:52 time: 0.9975 data_time: 0.0046 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0154 decode.acc_seg: 99.3158 aux.loss_ce: 0.0113 aux.acc_seg: 98.8556 +04/16 14:45:19 - mmengine - INFO - Iter(train) [ 23700/160000] base_lr: 8.5994e-05 lr: 3.1794e-07 eta: 1 day, 13:44:02 time: 0.9964 data_time: 0.0046 memory: 8462 loss: 0.0226 decode.loss_ce: 0.0121 decode.acc_seg: 99.5440 aux.loss_ce: 0.0104 aux.acc_seg: 98.9670 +04/16 14:46:09 - mmengine - INFO - Iter(train) [ 23750/160000] base_lr: 8.5963e-05 lr: 3.1782e-07 eta: 1 day, 13:43:12 time: 0.9973 data_time: 0.0045 memory: 8462 loss: 0.0221 decode.loss_ce: 0.0123 decode.acc_seg: 99.6754 aux.loss_ce: 0.0098 aux.acc_seg: 99.3891 +04/16 14:46:59 - mmengine - INFO - Iter(train) [ 23800/160000] base_lr: 8.5931e-05 lr: 3.1771e-07 eta: 1 day, 13:42:23 time: 0.9976 data_time: 0.0042 memory: 8462 loss: 0.0253 decode.loss_ce: 0.0143 decode.acc_seg: 99.6031 aux.loss_ce: 0.0109 aux.acc_seg: 98.8400 +04/16 14:47:49 - mmengine - INFO - Iter(train) [ 23850/160000] base_lr: 8.5900e-05 lr: 3.1759e-07 eta: 1 day, 13:41:33 time: 0.9988 data_time: 0.0047 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0122 decode.acc_seg: 99.3511 aux.loss_ce: 0.0103 aux.acc_seg: 98.2054 +04/16 14:48:38 - mmengine - INFO - Iter(train) [ 23900/160000] base_lr: 8.5868e-05 lr: 3.1747e-07 eta: 1 day, 13:40:44 time: 0.9977 data_time: 0.0045 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0124 decode.acc_seg: 99.4944 aux.loss_ce: 0.0101 aux.acc_seg: 98.6906 +04/16 14:49:28 - mmengine - INFO - Iter(train) [ 23950/160000] base_lr: 8.5837e-05 lr: 3.1736e-07 eta: 1 day, 13:39:54 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0126 decode.acc_seg: 99.4253 aux.loss_ce: 0.0103 aux.acc_seg: 98.7873 +04/16 14:50:18 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 14:50:18 - mmengine - INFO - Iter(train) [ 24000/160000] base_lr: 8.5805e-05 lr: 3.1724e-07 eta: 1 day, 13:39:05 time: 0.9982 data_time: 0.0044 memory: 8462 loss: 0.0232 decode.loss_ce: 0.0121 decode.acc_seg: 99.3252 aux.loss_ce: 0.0111 aux.acc_seg: 98.3219 +04/16 14:51:08 - mmengine - INFO - Iter(train) [ 24050/160000] base_lr: 8.5773e-05 lr: 3.1712e-07 eta: 1 day, 13:38:16 time: 0.9982 data_time: 0.0045 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0095 decode.acc_seg: 99.6187 aux.loss_ce: 0.0083 aux.acc_seg: 99.1653 +04/16 14:51:58 - mmengine - INFO - Iter(train) [ 24100/160000] base_lr: 8.5742e-05 lr: 3.1701e-07 eta: 1 day, 13:37:26 time: 0.9977 data_time: 0.0045 memory: 8462 loss: 0.0226 decode.loss_ce: 0.0124 decode.acc_seg: 99.3769 aux.loss_ce: 0.0102 aux.acc_seg: 98.9765 +04/16 14:52:48 - mmengine - INFO - Iter(train) [ 24150/160000] base_lr: 8.5710e-05 lr: 3.1689e-07 eta: 1 day, 13:36:37 time: 0.9972 data_time: 0.0051 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0121 decode.acc_seg: 99.5529 aux.loss_ce: 0.0101 aux.acc_seg: 98.9731 +04/16 14:53:38 - mmengine - INFO - Iter(train) [ 24200/160000] base_lr: 8.5679e-05 lr: 3.1677e-07 eta: 1 day, 13:35:47 time: 0.9972 data_time: 0.0050 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0127 decode.acc_seg: 98.8163 aux.loss_ce: 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decode.loss_ce: 0.0128 decode.acc_seg: 99.5234 aux.loss_ce: 0.0103 aux.acc_seg: 98.9836 +04/16 14:57:47 - mmengine - INFO - Iter(train) [ 24450/160000] base_lr: 8.5521e-05 lr: 3.1619e-07 eta: 1 day, 13:31:40 time: 0.9968 data_time: 0.0043 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0121 decode.acc_seg: 99.2128 aux.loss_ce: 0.0104 aux.acc_seg: 98.7537 +04/16 14:58:37 - mmengine - INFO - Iter(train) [ 24500/160000] base_lr: 8.5489e-05 lr: 3.1607e-07 eta: 1 day, 13:30:50 time: 0.9977 data_time: 0.0044 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0116 decode.acc_seg: 99.5535 aux.loss_ce: 0.0095 aux.acc_seg: 99.1903 +04/16 14:59:27 - mmengine - INFO - Iter(train) [ 24550/160000] base_lr: 8.5458e-05 lr: 3.1596e-07 eta: 1 day, 13:30:01 time: 0.9969 data_time: 0.0046 memory: 8462 loss: 0.0248 decode.loss_ce: 0.0129 decode.acc_seg: 99.3225 aux.loss_ce: 0.0119 aux.acc_seg: 98.6050 +04/16 15:00:17 - mmengine - INFO - Iter(train) [ 24600/160000] base_lr: 8.5426e-05 lr: 3.1584e-07 eta: 1 day, 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aux.acc_seg: 98.7545 +04/16 15:06:57 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 15:06:57 - mmengine - INFO - Iter(train) [ 25000/160000] base_lr: 8.5174e-05 lr: 3.1491e-07 eta: 1 day, 13:22:37 time: 0.9990 data_time: 0.0051 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0123 decode.acc_seg: 99.5230 aux.loss_ce: 0.0100 aux.acc_seg: 98.9000 +04/16 15:07:46 - mmengine - INFO - Iter(train) [ 25050/160000] base_lr: 8.5142e-05 lr: 3.1479e-07 eta: 1 day, 13:21:47 time: 0.9994 data_time: 0.0046 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0138 decode.acc_seg: 98.9008 aux.loss_ce: 0.0124 aux.acc_seg: 98.2924 +04/16 15:08:36 - mmengine - INFO - Iter(train) [ 25100/160000] base_lr: 8.5111e-05 lr: 3.1467e-07 eta: 1 day, 13:20:58 time: 0.9984 data_time: 0.0048 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0111 decode.acc_seg: 99.5007 aux.loss_ce: 0.0089 aux.acc_seg: 99.1041 +04/16 15:09:26 - mmengine - INFO - Iter(train) [ 25150/160000] base_lr: 8.5079e-05 lr: 3.1456e-07 eta: 1 day, 13:20:08 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0103 decode.acc_seg: 99.6618 aux.loss_ce: 0.0096 aux.acc_seg: 99.2456 +04/16 15:10:16 - mmengine - INFO - Iter(train) [ 25200/160000] base_lr: 8.5048e-05 lr: 3.1444e-07 eta: 1 day, 13:19:19 time: 0.9974 data_time: 0.0043 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0127 decode.acc_seg: 99.2535 aux.loss_ce: 0.0100 aux.acc_seg: 98.7143 +04/16 15:11:06 - mmengine - INFO - Iter(train) [ 25250/160000] base_lr: 8.5016e-05 lr: 3.1432e-07 eta: 1 day, 13:18:29 time: 0.9972 data_time: 0.0046 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0105 decode.acc_seg: 99.4162 aux.loss_ce: 0.0098 aux.acc_seg: 99.0307 +04/16 15:11:56 - mmengine - INFO - Iter(train) [ 25300/160000] base_lr: 8.4985e-05 lr: 3.1421e-07 eta: 1 day, 13:17:40 time: 0.9987 data_time: 0.0045 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0116 decode.acc_seg: 99.5352 aux.loss_ce: 0.0098 aux.acc_seg: 99.0580 +04/16 15:12:46 - mmengine - INFO - Iter(train) [ 25350/160000] base_lr: 8.4953e-05 lr: 3.1409e-07 eta: 1 day, 13:16:51 time: 0.9987 data_time: 0.0050 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0108 decode.acc_seg: 99.6500 aux.loss_ce: 0.0092 aux.acc_seg: 99.0580 +04/16 15:13:36 - mmengine - INFO - Iter(train) [ 25400/160000] base_lr: 8.4922e-05 lr: 3.1397e-07 eta: 1 day, 13:16:01 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0226 decode.loss_ce: 0.0130 decode.acc_seg: 99.6540 aux.loss_ce: 0.0096 aux.acc_seg: 99.1856 +04/16 15:14:26 - mmengine - INFO - Iter(train) [ 25450/160000] base_lr: 8.4890e-05 lr: 3.1386e-07 eta: 1 day, 13:15:12 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0122 decode.acc_seg: 99.4015 aux.loss_ce: 0.0097 aux.acc_seg: 98.8869 +04/16 15:15:16 - mmengine - INFO - Iter(train) [ 25500/160000] base_lr: 8.4859e-05 lr: 3.1374e-07 eta: 1 day, 13:14:22 time: 0.9982 data_time: 0.0047 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0115 decode.acc_seg: 99.6840 aux.loss_ce: 0.0105 aux.acc_seg: 99.2466 +04/16 15:16:06 - mmengine - INFO - Iter(train) [ 25550/160000] base_lr: 8.4827e-05 lr: 3.1362e-07 eta: 1 day, 13:13:33 time: 0.9979 data_time: 0.0046 memory: 8462 loss: 0.0258 decode.loss_ce: 0.0139 decode.acc_seg: 99.6510 aux.loss_ce: 0.0119 aux.acc_seg: 99.0093 +04/16 15:16:56 - mmengine - INFO - Iter(train) [ 25600/160000] base_lr: 8.4795e-05 lr: 3.1351e-07 eta: 1 day, 13:12:44 time: 0.9991 data_time: 0.0044 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0105 decode.acc_seg: 99.4869 aux.loss_ce: 0.0095 aux.acc_seg: 98.7789 +04/16 15:17:45 - mmengine - INFO - Iter(train) [ 25650/160000] base_lr: 8.4764e-05 lr: 3.1339e-07 eta: 1 day, 13:11:54 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0117 decode.acc_seg: 99.4572 aux.loss_ce: 0.0097 aux.acc_seg: 98.7028 +04/16 15:18:35 - mmengine - INFO - Iter(train) [ 25700/160000] base_lr: 8.4732e-05 lr: 3.1327e-07 eta: 1 day, 13:11:05 time: 0.9985 data_time: 0.0046 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0129 decode.acc_seg: 99.4556 aux.loss_ce: 0.0101 aux.acc_seg: 98.9740 +04/16 15:19:25 - mmengine - INFO - Iter(train) [ 25750/160000] base_lr: 8.4701e-05 lr: 3.1316e-07 eta: 1 day, 13:10:15 time: 0.9978 data_time: 0.0047 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0120 decode.acc_seg: 99.6185 aux.loss_ce: 0.0102 aux.acc_seg: 99.2373 +04/16 15:20:15 - mmengine - INFO - Iter(train) [ 25800/160000] base_lr: 8.4669e-05 lr: 3.1304e-07 eta: 1 day, 13:09:26 time: 0.9973 data_time: 0.0045 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0126 decode.acc_seg: 99.5419 aux.loss_ce: 0.0103 aux.acc_seg: 99.0185 +04/16 15:21:05 - mmengine - INFO - Iter(train) [ 25850/160000] base_lr: 8.4638e-05 lr: 3.1292e-07 eta: 1 day, 13:08:36 time: 0.9979 data_time: 0.0046 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0107 decode.acc_seg: 99.6773 aux.loss_ce: 0.0090 aux.acc_seg: 99.1724 +04/16 15:21:55 - mmengine - INFO - Iter(train) [ 25900/160000] base_lr: 8.4606e-05 lr: 3.1281e-07 eta: 1 day, 13:07:47 time: 0.9980 data_time: 0.0044 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0115 decode.acc_seg: 99.6471 aux.loss_ce: 0.0095 aux.acc_seg: 99.1873 +04/16 15:22:45 - mmengine - INFO - Iter(train) [ 25950/160000] base_lr: 8.4575e-05 lr: 3.1269e-07 eta: 1 day, 13:06:58 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0125 decode.acc_seg: 99.7402 aux.loss_ce: 0.0105 aux.acc_seg: 99.2634 +04/16 15:23:35 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 15:23:35 - mmengine - INFO - Iter(train) [ 26000/160000] base_lr: 8.4543e-05 lr: 3.1257e-07 eta: 1 day, 13:06:08 time: 0.9976 data_time: 0.0045 memory: 8462 loss: 0.0235 decode.loss_ce: 0.0133 decode.acc_seg: 99.7139 aux.loss_ce: 0.0103 aux.acc_seg: 99.3311 +04/16 15:24:25 - mmengine - INFO - Iter(train) [ 26050/160000] base_lr: 8.4512e-05 lr: 3.1246e-07 eta: 1 day, 13:05:18 time: 0.9977 data_time: 0.0045 memory: 8462 loss: 0.0238 decode.loss_ce: 0.0136 decode.acc_seg: 99.5901 aux.loss_ce: 0.0102 aux.acc_seg: 99.0496 +04/16 15:25:15 - mmengine - INFO - Iter(train) [ 26100/160000] base_lr: 8.4480e-05 lr: 3.1234e-07 eta: 1 day, 13:04:29 time: 0.9992 data_time: 0.0043 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0117 decode.acc_seg: 99.5930 aux.loss_ce: 0.0101 aux.acc_seg: 99.1802 +04/16 15:26:05 - mmengine - INFO - Iter(train) [ 26150/160000] base_lr: 8.4448e-05 lr: 3.1222e-07 eta: 1 day, 13:03:40 time: 0.9985 data_time: 0.0049 memory: 8462 loss: 0.0203 decode.loss_ce: 0.0109 decode.acc_seg: 99.6428 aux.loss_ce: 0.0094 aux.acc_seg: 99.0723 +04/16 15:26:55 - mmengine - INFO - Iter(train) [ 26200/160000] base_lr: 8.4417e-05 lr: 3.1211e-07 eta: 1 day, 13:02:51 time: 0.9999 data_time: 0.0044 memory: 8462 loss: 0.0221 decode.loss_ce: 0.0119 decode.acc_seg: 99.4940 aux.loss_ce: 0.0102 aux.acc_seg: 99.0963 +04/16 15:27:45 - mmengine - INFO - Iter(train) [ 26250/160000] base_lr: 8.4385e-05 lr: 3.1199e-07 eta: 1 day, 13:02:01 time: 0.9977 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0111 decode.acc_seg: 99.3864 aux.loss_ce: 0.0090 aux.acc_seg: 98.7890 +04/16 15:28:35 - mmengine - INFO - Iter(train) [ 26300/160000] base_lr: 8.4354e-05 lr: 3.1187e-07 eta: 1 day, 13:01:12 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0111 decode.acc_seg: 99.6614 aux.loss_ce: 0.0099 aux.acc_seg: 99.0555 +04/16 15:29:24 - mmengine - INFO - Iter(train) [ 26350/160000] base_lr: 8.4322e-05 lr: 3.1176e-07 eta: 1 day, 13:00:22 time: 0.9977 data_time: 0.0047 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0115 decode.acc_seg: 99.6431 aux.loss_ce: 0.0098 aux.acc_seg: 99.2271 +04/16 15:30:14 - mmengine - INFO - Iter(train) [ 26400/160000] base_lr: 8.4291e-05 lr: 3.1164e-07 eta: 1 day, 12:59:33 time: 0.9981 data_time: 0.0045 memory: 8462 loss: 0.0212 decode.loss_ce: 0.0118 decode.acc_seg: 99.6040 aux.loss_ce: 0.0094 aux.acc_seg: 98.8987 +04/16 15:31:04 - mmengine - INFO - Iter(train) [ 26450/160000] base_lr: 8.4259e-05 lr: 3.1152e-07 eta: 1 day, 12:58:44 time: 0.9987 data_time: 0.0042 memory: 8462 loss: 0.0203 decode.loss_ce: 0.0112 decode.acc_seg: 99.5342 aux.loss_ce: 0.0091 aux.acc_seg: 99.0847 +04/16 15:31:54 - mmengine - INFO - Iter(train) [ 26500/160000] base_lr: 8.4228e-05 lr: 3.1141e-07 eta: 1 day, 12:57:54 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0121 decode.acc_seg: 99.5789 aux.loss_ce: 0.0093 aux.acc_seg: 99.3631 +04/16 15:32:44 - mmengine - INFO - Iter(train) [ 26550/160000] base_lr: 8.4196e-05 lr: 3.1129e-07 eta: 1 day, 12:57:05 time: 0.9983 data_time: 0.0044 memory: 8462 loss: 0.0231 decode.loss_ce: 0.0126 decode.acc_seg: 99.3521 aux.loss_ce: 0.0105 aux.acc_seg: 99.0625 +04/16 15:33:34 - mmengine - INFO - Iter(train) [ 26600/160000] base_lr: 8.4165e-05 lr: 3.1117e-07 eta: 1 day, 12:56:15 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.3067 aux.loss_ce: 0.0087 aux.acc_seg: 98.6843 +04/16 15:34:24 - mmengine - INFO - Iter(train) [ 26650/160000] base_lr: 8.4133e-05 lr: 3.1106e-07 eta: 1 day, 12:55:26 time: 0.9978 data_time: 0.0043 memory: 8462 loss: 0.0221 decode.loss_ce: 0.0119 decode.acc_seg: 99.5811 aux.loss_ce: 0.0102 aux.acc_seg: 99.1932 +04/16 15:35:14 - mmengine - INFO - Iter(train) [ 26700/160000] base_lr: 8.4101e-05 lr: 3.1094e-07 eta: 1 day, 12:54:37 time: 0.9995 data_time: 0.0048 memory: 8462 loss: 0.0243 decode.loss_ce: 0.0137 decode.acc_seg: 99.4766 aux.loss_ce: 0.0107 aux.acc_seg: 98.7249 +04/16 15:36:04 - mmengine - INFO - Iter(train) [ 26750/160000] base_lr: 8.4070e-05 lr: 3.1082e-07 eta: 1 day, 12:53:47 time: 0.9989 data_time: 0.0048 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.5125 aux.loss_ce: 0.0087 aux.acc_seg: 98.9407 +04/16 15:36:54 - mmengine - INFO - Iter(train) [ 26800/160000] base_lr: 8.4038e-05 lr: 3.1071e-07 eta: 1 day, 12:52:58 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0132 decode.acc_seg: 99.5655 aux.loss_ce: 0.0104 aux.acc_seg: 98.9796 +04/16 15:37:44 - mmengine - INFO - Iter(train) [ 26850/160000] base_lr: 8.4007e-05 lr: 3.1059e-07 eta: 1 day, 12:52:09 time: 0.9972 data_time: 0.0042 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0119 decode.acc_seg: 99.6017 aux.loss_ce: 0.0097 aux.acc_seg: 99.2483 +04/16 15:38:34 - mmengine - INFO - Iter(train) [ 26900/160000] base_lr: 8.3975e-05 lr: 3.1047e-07 eta: 1 day, 12:51:19 time: 0.9973 data_time: 0.0045 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0106 decode.acc_seg: 99.6767 aux.loss_ce: 0.0090 aux.acc_seg: 99.3874 +04/16 15:39:24 - mmengine - INFO - Iter(train) [ 26950/160000] base_lr: 8.3944e-05 lr: 3.1036e-07 eta: 1 day, 12:50:30 time: 0.9977 data_time: 0.0043 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0115 decode.acc_seg: 99.6105 aux.loss_ce: 0.0098 aux.acc_seg: 98.9498 +04/16 15:40:14 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 15:40:14 - mmengine - INFO - Iter(train) [ 27000/160000] base_lr: 8.3912e-05 lr: 3.1024e-07 eta: 1 day, 12:49:40 time: 0.9982 data_time: 0.0043 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0132 decode.acc_seg: 99.3624 aux.loss_ce: 0.0107 aux.acc_seg: 98.8785 +04/16 15:41:04 - mmengine - INFO - Iter(train) [ 27050/160000] base_lr: 8.3881e-05 lr: 3.1012e-07 eta: 1 day, 12:48:51 time: 0.9987 data_time: 0.0046 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0112 decode.acc_seg: 99.6258 aux.loss_ce: 0.0102 aux.acc_seg: 99.1985 +04/16 15:41:53 - mmengine - INFO - Iter(train) [ 27100/160000] base_lr: 8.3849e-05 lr: 3.1001e-07 eta: 1 day, 12:48:02 time: 0.9979 data_time: 0.0046 memory: 8462 loss: 0.0257 decode.loss_ce: 0.0140 decode.acc_seg: 99.5079 aux.loss_ce: 0.0117 aux.acc_seg: 99.1341 +04/16 15:42:43 - mmengine - INFO - Iter(train) [ 27150/160000] base_lr: 8.3818e-05 lr: 3.0989e-07 eta: 1 day, 12:47:12 time: 0.9989 data_time: 0.0048 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0108 decode.acc_seg: 99.4413 aux.loss_ce: 0.0086 aux.acc_seg: 98.9935 +04/16 15:43:33 - mmengine - INFO - Iter(train) [ 27200/160000] base_lr: 8.3786e-05 lr: 3.0977e-07 eta: 1 day, 12:46:23 time: 0.9989 data_time: 0.0043 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0119 decode.acc_seg: 99.2996 aux.loss_ce: 0.0103 aux.acc_seg: 98.4583 +04/16 15:44:23 - mmengine - INFO - Iter(train) [ 27250/160000] base_lr: 8.3754e-05 lr: 3.0966e-07 eta: 1 day, 12:45:34 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0110 decode.acc_seg: 99.5111 aux.loss_ce: 0.0099 aux.acc_seg: 98.9071 +04/16 15:45:13 - mmengine - INFO - Iter(train) [ 27300/160000] base_lr: 8.3723e-05 lr: 3.0954e-07 eta: 1 day, 12:44:44 time: 0.9976 data_time: 0.0043 memory: 8462 loss: 0.0232 decode.loss_ce: 0.0123 decode.acc_seg: 99.5283 aux.loss_ce: 0.0110 aux.acc_seg: 98.9485 +04/16 15:46:03 - mmengine - INFO - Iter(train) [ 27350/160000] base_lr: 8.3691e-05 lr: 3.0942e-07 eta: 1 day, 12:43:55 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0113 decode.acc_seg: 99.7271 aux.loss_ce: 0.0103 aux.acc_seg: 99.2552 +04/16 15:46:53 - mmengine - INFO - Iter(train) [ 27400/160000] base_lr: 8.3660e-05 lr: 3.0931e-07 eta: 1 day, 12:43:05 time: 0.9980 data_time: 0.0042 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.6401 aux.loss_ce: 0.0087 aux.acc_seg: 99.2111 +04/16 15:47:43 - mmengine - INFO - Iter(train) [ 27450/160000] base_lr: 8.3628e-05 lr: 3.0919e-07 eta: 1 day, 12:42:16 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0101 decode.acc_seg: 99.7450 aux.loss_ce: 0.0085 aux.acc_seg: 99.3895 +04/16 15:48:33 - mmengine - INFO - Iter(train) [ 27500/160000] base_lr: 8.3597e-05 lr: 3.0907e-07 eta: 1 day, 12:41:26 time: 0.9986 data_time: 0.0041 memory: 8462 loss: 0.0212 decode.loss_ce: 0.0110 decode.acc_seg: 99.5293 aux.loss_ce: 0.0102 aux.acc_seg: 98.3387 +04/16 15:49:23 - mmengine - INFO - Iter(train) [ 27550/160000] base_lr: 8.3565e-05 lr: 3.0896e-07 eta: 1 day, 12:40:37 time: 0.9984 data_time: 0.0047 memory: 8462 loss: 0.0226 decode.loss_ce: 0.0123 decode.acc_seg: 99.7822 aux.loss_ce: 0.0103 aux.acc_seg: 99.4503 +04/16 15:50:13 - mmengine - INFO - Iter(train) [ 27600/160000] base_lr: 8.3534e-05 lr: 3.0884e-07 eta: 1 day, 12:39:48 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0140 decode.acc_seg: 99.2548 aux.loss_ce: 0.0112 aux.acc_seg: 98.5176 +04/16 15:51:03 - mmengine - INFO - Iter(train) [ 27650/160000] base_lr: 8.3502e-05 lr: 3.0872e-07 eta: 1 day, 12:38:58 time: 0.9982 data_time: 0.0045 memory: 8462 loss: 0.0257 decode.loss_ce: 0.0153 decode.acc_seg: 99.4677 aux.loss_ce: 0.0105 aux.acc_seg: 98.7925 +04/16 15:51:53 - mmengine - INFO - Iter(train) [ 27700/160000] base_lr: 8.3471e-05 lr: 3.0861e-07 eta: 1 day, 12:38:09 time: 0.9994 data_time: 0.0048 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0112 decode.acc_seg: 99.8020 aux.loss_ce: 0.0095 aux.acc_seg: 99.4364 +04/16 15:52:43 - mmengine - INFO - Iter(train) [ 27750/160000] base_lr: 8.3439e-05 lr: 3.0849e-07 eta: 1 day, 12:37:20 time: 0.9992 data_time: 0.0043 memory: 8462 loss: 0.0203 decode.loss_ce: 0.0105 decode.acc_seg: 99.5148 aux.loss_ce: 0.0098 aux.acc_seg: 99.0330 +04/16 15:53:33 - mmengine - INFO - Iter(train) [ 27800/160000] base_lr: 8.3407e-05 lr: 3.0837e-07 eta: 1 day, 12:36:30 time: 0.9991 data_time: 0.0044 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0112 decode.acc_seg: 99.4135 aux.loss_ce: 0.0102 aux.acc_seg: 98.6155 +04/16 15:54:23 - mmengine - INFO - Iter(train) [ 27850/160000] base_lr: 8.3376e-05 lr: 3.0826e-07 eta: 1 day, 12:35:41 time: 0.9990 data_time: 0.0047 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0109 decode.acc_seg: 99.5617 aux.loss_ce: 0.0100 aux.acc_seg: 99.0740 +04/16 15:55:13 - mmengine - INFO - Iter(train) [ 27900/160000] base_lr: 8.3344e-05 lr: 3.0814e-07 eta: 1 day, 12:34:52 time: 0.9997 data_time: 0.0049 memory: 8462 loss: 0.0212 decode.loss_ce: 0.0115 decode.acc_seg: 99.6744 aux.loss_ce: 0.0096 aux.acc_seg: 99.0763 +04/16 15:56:03 - mmengine - INFO - Iter(train) [ 27950/160000] base_lr: 8.3313e-05 lr: 3.0802e-07 eta: 1 day, 12:34:02 time: 0.9996 data_time: 0.0047 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0097 decode.acc_seg: 99.7650 aux.loss_ce: 0.0077 aux.acc_seg: 99.2826 +04/16 15:56:52 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 15:56:52 - mmengine - INFO - Iter(train) [ 28000/160000] base_lr: 8.3281e-05 lr: 3.0791e-07 eta: 1 day, 12:33:13 time: 0.9996 data_time: 0.0046 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0131 decode.acc_seg: 99.3725 aux.loss_ce: 0.0106 aux.acc_seg: 98.1825 +04/16 15:57:42 - mmengine - INFO - Iter(train) [ 28050/160000] base_lr: 8.3250e-05 lr: 3.0779e-07 eta: 1 day, 12:32:24 time: 0.9979 data_time: 0.0045 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0116 decode.acc_seg: 99.7627 aux.loss_ce: 0.0093 aux.acc_seg: 99.5249 +04/16 15:58:32 - mmengine - INFO - Iter(train) [ 28100/160000] base_lr: 8.3218e-05 lr: 3.0767e-07 eta: 1 day, 12:31:34 time: 0.9990 data_time: 0.0048 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0107 decode.acc_seg: 99.4638 aux.loss_ce: 0.0102 aux.acc_seg: 98.6311 +04/16 15:59:22 - mmengine - INFO - Iter(train) [ 28150/160000] base_lr: 8.3187e-05 lr: 3.0756e-07 eta: 1 day, 12:30:45 time: 0.9989 data_time: 0.0044 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0109 decode.acc_seg: 99.5501 aux.loss_ce: 0.0093 aux.acc_seg: 98.6595 +04/16 16:00:12 - mmengine - INFO - Iter(train) [ 28200/160000] base_lr: 8.3155e-05 lr: 3.0744e-07 eta: 1 day, 12:29:55 time: 0.9978 data_time: 0.0047 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0104 decode.acc_seg: 99.5430 aux.loss_ce: 0.0094 aux.acc_seg: 98.7978 +04/16 16:01:02 - mmengine - INFO - Iter(train) [ 28250/160000] base_lr: 8.3124e-05 lr: 3.0732e-07 eta: 1 day, 12:29:06 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0103 decode.acc_seg: 99.5480 aux.loss_ce: 0.0095 aux.acc_seg: 99.3269 +04/16 16:01:52 - mmengine - INFO - Iter(train) [ 28300/160000] base_lr: 8.3092e-05 lr: 3.0721e-07 eta: 1 day, 12:28:17 time: 0.9994 data_time: 0.0045 memory: 8462 loss: 0.0217 decode.loss_ce: 0.0116 decode.acc_seg: 99.4350 aux.loss_ce: 0.0102 aux.acc_seg: 98.5794 +04/16 16:02:42 - mmengine - INFO - Iter(train) [ 28350/160000] base_lr: 8.3060e-05 lr: 3.0709e-07 eta: 1 day, 12:27:27 time: 0.9992 data_time: 0.0046 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.5749 aux.loss_ce: 0.0087 aux.acc_seg: 99.1053 +04/16 16:03:32 - mmengine - INFO - Iter(train) [ 28400/160000] base_lr: 8.3029e-05 lr: 3.0697e-07 eta: 1 day, 12:26:38 time: 0.9986 data_time: 0.0044 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0109 decode.acc_seg: 99.4873 aux.loss_ce: 0.0096 aux.acc_seg: 98.4062 +04/16 16:04:22 - mmengine - INFO - Iter(train) [ 28450/160000] base_lr: 8.2997e-05 lr: 3.0686e-07 eta: 1 day, 12:25:48 time: 0.9976 data_time: 0.0044 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0116 decode.acc_seg: 99.2525 aux.loss_ce: 0.0109 aux.acc_seg: 98.8028 +04/16 16:05:12 - mmengine - INFO - Iter(train) [ 28500/160000] base_lr: 8.2966e-05 lr: 3.0674e-07 eta: 1 day, 12:24:59 time: 0.9990 data_time: 0.0044 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0124 decode.acc_seg: 99.4305 aux.loss_ce: 0.0099 aux.acc_seg: 98.6626 +04/16 16:06:02 - mmengine - INFO - Iter(train) [ 28550/160000] base_lr: 8.2934e-05 lr: 3.0663e-07 eta: 1 day, 12:24:09 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0115 decode.acc_seg: 99.7007 aux.loss_ce: 0.0094 aux.acc_seg: 99.1093 +04/16 16:06:52 - mmengine - INFO - Iter(train) [ 28600/160000] base_lr: 8.2903e-05 lr: 3.0651e-07 eta: 1 day, 12:23:20 time: 0.9982 data_time: 0.0045 memory: 8462 loss: 0.0208 decode.loss_ce: 0.0113 decode.acc_seg: 99.5255 aux.loss_ce: 0.0096 aux.acc_seg: 98.9964 +04/16 16:07:42 - mmengine - INFO - Iter(train) [ 28650/160000] base_lr: 8.2871e-05 lr: 3.0639e-07 eta: 1 day, 12:22:31 time: 0.9989 data_time: 0.0045 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0108 decode.acc_seg: 99.5737 aux.loss_ce: 0.0095 aux.acc_seg: 98.8533 +04/16 16:08:32 - mmengine - INFO - Iter(train) [ 28700/160000] base_lr: 8.2840e-05 lr: 3.0628e-07 eta: 1 day, 12:21:41 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0116 decode.acc_seg: 99.5186 aux.loss_ce: 0.0098 aux.acc_seg: 98.9225 +04/16 16:09:22 - mmengine - INFO - Iter(train) [ 28750/160000] base_lr: 8.2808e-05 lr: 3.0616e-07 eta: 1 day, 12:20:52 time: 0.9987 data_time: 0.0047 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0135 decode.acc_seg: 99.5628 aux.loss_ce: 0.0112 aux.acc_seg: 98.9569 +04/16 16:10:12 - mmengine - INFO - Iter(train) [ 28800/160000] base_lr: 8.2777e-05 lr: 3.0604e-07 eta: 1 day, 12:20:03 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0103 decode.acc_seg: 99.4392 aux.loss_ce: 0.0094 aux.acc_seg: 98.5386 +04/16 16:11:02 - mmengine - INFO - Iter(train) [ 28850/160000] base_lr: 8.2745e-05 lr: 3.0593e-07 eta: 1 day, 12:19:13 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0101 decode.acc_seg: 99.7364 aux.loss_ce: 0.0089 aux.acc_seg: 99.1697 +04/16 16:11:52 - mmengine - INFO - Iter(train) [ 28900/160000] base_lr: 8.2713e-05 lr: 3.0581e-07 eta: 1 day, 12:18:24 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0105 decode.acc_seg: 99.4009 aux.loss_ce: 0.0099 aux.acc_seg: 98.6269 +04/16 16:12:41 - mmengine - INFO - Iter(train) [ 28950/160000] base_lr: 8.2682e-05 lr: 3.0569e-07 eta: 1 day, 12:17:34 time: 0.9990 data_time: 0.0047 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0118 decode.acc_seg: 99.3357 aux.loss_ce: 0.0100 aux.acc_seg: 98.6673 +04/16 16:13:31 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 16:13:31 - mmengine - INFO - Iter(train) [ 29000/160000] base_lr: 8.2650e-05 lr: 3.0558e-07 eta: 1 day, 12:16:45 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0123 decode.acc_seg: 99.5920 aux.loss_ce: 0.0107 aux.acc_seg: 99.0730 +04/16 16:14:21 - mmengine - INFO - Iter(train) [ 29050/160000] base_lr: 8.2619e-05 lr: 3.0546e-07 eta: 1 day, 12:15:56 time: 0.9994 data_time: 0.0041 memory: 8462 loss: 0.0212 decode.loss_ce: 0.0112 decode.acc_seg: 99.6086 aux.loss_ce: 0.0100 aux.acc_seg: 99.0261 +04/16 16:15:11 - mmengine - INFO - Iter(train) [ 29100/160000] base_lr: 8.2587e-05 lr: 3.0534e-07 eta: 1 day, 12:15:06 time: 0.9987 data_time: 0.0044 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0115 decode.acc_seg: 99.7440 aux.loss_ce: 0.0101 aux.acc_seg: 99.4856 +04/16 16:16:01 - mmengine - INFO - Iter(train) [ 29150/160000] base_lr: 8.2556e-05 lr: 3.0523e-07 eta: 1 day, 12:14:17 time: 0.9984 data_time: 0.0043 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0107 decode.acc_seg: 99.3132 aux.loss_ce: 0.0096 aux.acc_seg: 98.8047 +04/16 16:16:51 - mmengine - INFO - Iter(train) [ 29200/160000] base_lr: 8.2524e-05 lr: 3.0511e-07 eta: 1 day, 12:13:27 time: 0.9989 data_time: 0.0043 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0133 decode.acc_seg: 99.7334 aux.loss_ce: 0.0103 aux.acc_seg: 99.4469 +04/16 16:17:41 - mmengine - INFO - Iter(train) [ 29250/160000] base_lr: 8.2493e-05 lr: 3.0499e-07 eta: 1 day, 12:12:38 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0115 decode.acc_seg: 99.2943 aux.loss_ce: 0.0094 aux.acc_seg: 98.6359 +04/16 16:18:31 - mmengine - INFO - Iter(train) [ 29300/160000] base_lr: 8.2461e-05 lr: 3.0488e-07 eta: 1 day, 12:11:49 time: 0.9982 data_time: 0.0047 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6403 aux.loss_ce: 0.0084 aux.acc_seg: 98.9340 +04/16 16:19:21 - mmengine - INFO - Iter(train) [ 29350/160000] base_lr: 8.2430e-05 lr: 3.0476e-07 eta: 1 day, 12:10:59 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0126 decode.acc_seg: 99.5003 aux.loss_ce: 0.0107 aux.acc_seg: 99.0429 +04/16 16:20:11 - mmengine - INFO - Iter(train) [ 29400/160000] base_lr: 8.2398e-05 lr: 3.0464e-07 eta: 1 day, 12:10:10 time: 0.9985 data_time: 0.0047 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0106 decode.acc_seg: 99.5972 aux.loss_ce: 0.0094 aux.acc_seg: 99.3414 +04/16 16:21:01 - mmengine - INFO - Iter(train) [ 29450/160000] base_lr: 8.2366e-05 lr: 3.0453e-07 eta: 1 day, 12:09:20 time: 0.9988 data_time: 0.0051 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0117 decode.acc_seg: 99.4379 aux.loss_ce: 0.0098 aux.acc_seg: 98.8281 +04/16 16:21:51 - mmengine - INFO - Iter(train) [ 29500/160000] base_lr: 8.2335e-05 lr: 3.0441e-07 eta: 1 day, 12:08:31 time: 0.9987 data_time: 0.0046 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0100 decode.acc_seg: 99.5880 aux.loss_ce: 0.0078 aux.acc_seg: 99.0520 +04/16 16:22:41 - mmengine - INFO - Iter(train) [ 29550/160000] base_lr: 8.2303e-05 lr: 3.0429e-07 eta: 1 day, 12:07:41 time: 0.9985 data_time: 0.0042 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0115 decode.acc_seg: 99.6632 aux.loss_ce: 0.0101 aux.acc_seg: 99.0210 +04/16 16:23:31 - mmengine - INFO - Iter(train) [ 29600/160000] base_lr: 8.2272e-05 lr: 3.0418e-07 eta: 1 day, 12:06:52 time: 0.9980 data_time: 0.0043 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0099 decode.acc_seg: 99.5785 aux.loss_ce: 0.0092 aux.acc_seg: 99.1531 +04/16 16:24:21 - mmengine - INFO - Iter(train) [ 29650/160000] base_lr: 8.2240e-05 lr: 3.0406e-07 eta: 1 day, 12:06:02 time: 0.9999 data_time: 0.0044 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0102 decode.acc_seg: 99.5041 aux.loss_ce: 0.0090 aux.acc_seg: 98.8997 +04/16 16:25:11 - mmengine - INFO - Iter(train) [ 29700/160000] base_lr: 8.2209e-05 lr: 3.0394e-07 eta: 1 day, 12:05:13 time: 0.9982 data_time: 0.0046 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.7015 aux.loss_ce: 0.0085 aux.acc_seg: 99.2313 +04/16 16:26:01 - mmengine - INFO - Iter(train) [ 29750/160000] base_lr: 8.2177e-05 lr: 3.0383e-07 eta: 1 day, 12:04:23 time: 0.9992 data_time: 0.0046 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.5207 aux.loss_ce: 0.0086 aux.acc_seg: 98.9408 +04/16 16:26:51 - mmengine - INFO - Iter(train) [ 29800/160000] base_lr: 8.2146e-05 lr: 3.0371e-07 eta: 1 day, 12:03:34 time: 0.9992 data_time: 0.0043 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0101 decode.acc_seg: 99.5872 aux.loss_ce: 0.0093 aux.acc_seg: 98.7730 +04/16 16:27:41 - mmengine - INFO - Iter(train) [ 29850/160000] base_lr: 8.2114e-05 lr: 3.0359e-07 eta: 1 day, 12:02:45 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0112 decode.acc_seg: 99.4427 aux.loss_ce: 0.0101 aux.acc_seg: 98.6961 +04/16 16:28:30 - mmengine - INFO - Iter(train) [ 29900/160000] base_lr: 8.2083e-05 lr: 3.0348e-07 eta: 1 day, 12:01:55 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0098 decode.acc_seg: 99.5665 aux.loss_ce: 0.0081 aux.acc_seg: 98.8626 +04/16 16:29:20 - mmengine - INFO - Iter(train) [ 29950/160000] base_lr: 8.2051e-05 lr: 3.0336e-07 eta: 1 day, 12:01:06 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0107 decode.acc_seg: 99.7433 aux.loss_ce: 0.0089 aux.acc_seg: 99.2117 +04/16 16:30:10 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 16:30:10 - mmengine - INFO - Iter(train) [ 30000/160000] base_lr: 8.2019e-05 lr: 3.0324e-07 eta: 1 day, 12:00:17 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0109 decode.acc_seg: 99.3954 aux.loss_ce: 0.0101 aux.acc_seg: 98.5298 +04/16 16:30:10 - mmengine - INFO - Saving checkpoint at 30000 iterations +04/16 16:30:20 - mmengine - INFO - Iter(val) [ 50/355] eta: 0:00:35 time: 0.1152 data_time: 0.0013 memory: 4004 +04/16 16:30:26 - mmengine - INFO - Iter(val) [100/355] eta: 0:00:29 time: 0.1154 data_time: 0.0016 memory: 4004 +04/16 16:30:32 - mmengine - INFO - Iter(val) [150/355] eta: 0:00:23 time: 0.1154 data_time: 0.0013 memory: 4004 +04/16 16:30:38 - mmengine - INFO - Iter(val) [200/355] eta: 0:00:17 time: 0.1155 data_time: 0.0015 memory: 4004 +04/16 16:30:43 - mmengine - INFO - Iter(val) [250/355] eta: 0:00:12 time: 0.1157 data_time: 0.0015 memory: 4004 +04/16 16:30:49 - mmengine - INFO - Iter(val) [300/355] eta: 0:00:06 time: 0.1157 data_time: 0.0014 memory: 4004 +04/16 16:30:55 - mmengine - INFO - Iter(val) [350/355] eta: 0:00:00 time: 0.1158 data_time: 0.0014 memory: 4004 +04/16 16:30:56 - mmengine - INFO - per class results: +04/16 16:30:56 - mmengine - INFO - ++------------+-------+-------+ +| Class | IoU | Acc | ++------------+-------+-------+ +| background | 99.2 | 99.61 | +| contrast | 82.37 | 89.96 | ++------------+-------+-------+ +04/16 16:30:56 - mmengine - INFO - Iter(val) [355/355] aAcc: 99.2200 mIoU: 90.7800 mAcc: 94.7900 data_time: 0.0016 time: 0.1158 +04/16 16:31:46 - mmengine - INFO - Iter(train) [ 30050/160000] base_lr: 8.1988e-05 lr: 3.0313e-07 eta: 1 day, 11:59:29 time: 0.9975 data_time: 0.0045 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0104 decode.acc_seg: 99.6412 aux.loss_ce: 0.0094 aux.acc_seg: 98.9674 +04/16 16:32:36 - mmengine - INFO - Iter(train) [ 30100/160000] base_lr: 8.1956e-05 lr: 3.0301e-07 eta: 1 day, 11:58:39 time: 0.9978 data_time: 0.0043 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0113 decode.acc_seg: 99.5163 aux.loss_ce: 0.0093 aux.acc_seg: 99.0168 +04/16 16:33:26 - mmengine - INFO - Iter(train) [ 30150/160000] base_lr: 8.1925e-05 lr: 3.0289e-07 eta: 1 day, 11:57:50 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0100 decode.acc_seg: 99.6508 aux.loss_ce: 0.0088 aux.acc_seg: 98.9639 +04/16 16:34:16 - mmengine - INFO - Iter(train) [ 30200/160000] base_lr: 8.1893e-05 lr: 3.0278e-07 eta: 1 day, 11:57:01 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6592 aux.loss_ce: 0.0080 aux.acc_seg: 99.2422 +04/16 16:35:06 - mmengine - INFO - Iter(train) [ 30250/160000] base_lr: 8.1862e-05 lr: 3.0266e-07 eta: 1 day, 11:56:11 time: 0.9984 data_time: 0.0042 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.6014 aux.loss_ce: 0.0085 aux.acc_seg: 99.0728 +04/16 16:35:56 - mmengine - INFO - Iter(train) [ 30300/160000] base_lr: 8.1830e-05 lr: 3.0254e-07 eta: 1 day, 11:55:21 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0087 decode.acc_seg: 99.6727 aux.loss_ce: 0.0084 aux.acc_seg: 99.1022 +04/16 16:36:46 - mmengine - INFO - Iter(train) [ 30350/160000] base_lr: 8.1799e-05 lr: 3.0243e-07 eta: 1 day, 11:54:32 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0105 decode.acc_seg: 99.5253 aux.loss_ce: 0.0093 aux.acc_seg: 99.0820 +04/16 16:37:36 - mmengine - INFO - Iter(train) [ 30400/160000] base_lr: 8.1767e-05 lr: 3.0231e-07 eta: 1 day, 11:53:43 time: 0.9994 data_time: 0.0046 memory: 8462 loss: 0.0249 decode.loss_ce: 0.0138 decode.acc_seg: 99.5796 aux.loss_ce: 0.0111 aux.acc_seg: 98.9058 +04/16 16:38:25 - mmengine - INFO - Iter(train) [ 30450/160000] base_lr: 8.1736e-05 lr: 3.0219e-07 eta: 1 day, 11:52:53 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0100 decode.acc_seg: 99.4627 aux.loss_ce: 0.0093 aux.acc_seg: 98.7928 +04/16 16:39:15 - mmengine - INFO - Iter(train) [ 30500/160000] base_lr: 8.1704e-05 lr: 3.0208e-07 eta: 1 day, 11:52:04 time: 0.9983 data_time: 0.0045 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0118 decode.acc_seg: 99.2430 aux.loss_ce: 0.0088 aux.acc_seg: 98.7139 +04/16 16:40:05 - mmengine - INFO - Iter(train) [ 30550/160000] base_lr: 8.1672e-05 lr: 3.0196e-07 eta: 1 day, 11:51:14 time: 0.9990 data_time: 0.0044 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0096 decode.acc_seg: 99.6105 aux.loss_ce: 0.0086 aux.acc_seg: 99.0822 +04/16 16:40:55 - mmengine - INFO - Iter(train) [ 30600/160000] base_lr: 8.1641e-05 lr: 3.0184e-07 eta: 1 day, 11:50:25 time: 0.9981 data_time: 0.0043 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0106 decode.acc_seg: 99.5234 aux.loss_ce: 0.0098 aux.acc_seg: 98.7244 +04/16 16:41:45 - mmengine - INFO - Iter(train) [ 30650/160000] base_lr: 8.1609e-05 lr: 3.0173e-07 eta: 1 day, 11:49:35 time: 0.9984 data_time: 0.0043 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0092 decode.acc_seg: 99.6655 aux.loss_ce: 0.0091 aux.acc_seg: 99.1932 +04/16 16:42:35 - mmengine - INFO - Iter(train) [ 30700/160000] base_lr: 8.1578e-05 lr: 3.0161e-07 eta: 1 day, 11:48:46 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0114 decode.acc_seg: 99.4728 aux.loss_ce: 0.0095 aux.acc_seg: 98.6988 +04/16 16:43:25 - mmengine - INFO - Iter(train) [ 30750/160000] base_lr: 8.1546e-05 lr: 3.0149e-07 eta: 1 day, 11:47:56 time: 0.9980 data_time: 0.0044 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0109 decode.acc_seg: 99.5340 aux.loss_ce: 0.0089 aux.acc_seg: 98.9395 +04/16 16:44:15 - mmengine - INFO - Iter(train) [ 30800/160000] base_lr: 8.1515e-05 lr: 3.0138e-07 eta: 1 day, 11:47:06 time: 0.9982 data_time: 0.0048 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0122 decode.acc_seg: 99.4019 aux.loss_ce: 0.0100 aux.acc_seg: 98.8768 +04/16 16:45:05 - mmengine - INFO - Iter(train) [ 30850/160000] base_lr: 8.1483e-05 lr: 3.0126e-07 eta: 1 day, 11:46:17 time: 0.9981 data_time: 0.0043 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0121 decode.acc_seg: 99.5239 aux.loss_ce: 0.0103 aux.acc_seg: 98.8468 +04/16 16:45:55 - mmengine - INFO - Iter(train) [ 30900/160000] base_lr: 8.1452e-05 lr: 3.0114e-07 eta: 1 day, 11:45:27 time: 0.9981 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0097 decode.acc_seg: 99.6664 aux.loss_ce: 0.0081 aux.acc_seg: 99.2163 +04/16 16:46:45 - mmengine - INFO - Iter(train) [ 30950/160000] base_lr: 8.1420e-05 lr: 3.0103e-07 eta: 1 day, 11:44:38 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0226 decode.loss_ce: 0.0129 decode.acc_seg: 99.3189 aux.loss_ce: 0.0097 aux.acc_seg: 98.7026 +04/16 16:47:35 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 16:47:35 - mmengine - INFO - Iter(train) [ 31000/160000] base_lr: 8.1389e-05 lr: 3.0091e-07 eta: 1 day, 11:43:48 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0234 decode.loss_ce: 0.0122 decode.acc_seg: 99.5394 aux.loss_ce: 0.0112 aux.acc_seg: 98.7713 +04/16 16:48:25 - mmengine - INFO - Iter(train) [ 31050/160000] base_lr: 8.1357e-05 lr: 3.0079e-07 eta: 1 day, 11:42:59 time: 0.9982 data_time: 0.0048 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0121 decode.acc_seg: 99.6630 aux.loss_ce: 0.0101 aux.acc_seg: 99.1295 +04/16 16:49:15 - mmengine - INFO - Iter(train) [ 31100/160000] base_lr: 8.1325e-05 lr: 3.0068e-07 eta: 1 day, 11:42:09 time: 0.9985 data_time: 0.0043 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0118 decode.acc_seg: 99.3177 aux.loss_ce: 0.0112 aux.acc_seg: 98.3019 +04/16 16:50:05 - mmengine - INFO - Iter(train) [ 31150/160000] base_lr: 8.1294e-05 lr: 3.0056e-07 eta: 1 day, 11:41:20 time: 0.9979 data_time: 0.0043 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0091 decode.acc_seg: 99.6361 aux.loss_ce: 0.0078 aux.acc_seg: 99.0808 +04/16 16:50:55 - mmengine - INFO - Iter(train) [ 31200/160000] base_lr: 8.1262e-05 lr: 3.0044e-07 eta: 1 day, 11:40:30 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0113 decode.acc_seg: 99.6029 aux.loss_ce: 0.0094 aux.acc_seg: 99.0639 +04/16 16:51:44 - mmengine - INFO - Iter(train) [ 31250/160000] base_lr: 8.1231e-05 lr: 3.0033e-07 eta: 1 day, 11:39:41 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.6359 aux.loss_ce: 0.0086 aux.acc_seg: 99.1867 +04/16 16:52:34 - mmengine - INFO - Iter(train) [ 31300/160000] base_lr: 8.1199e-05 lr: 3.0021e-07 eta: 1 day, 11:38:51 time: 0.9991 data_time: 0.0047 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0116 decode.acc_seg: 99.6639 aux.loss_ce: 0.0101 aux.acc_seg: 99.0749 +04/16 16:53:24 - mmengine - INFO - Iter(train) [ 31350/160000] base_lr: 8.1168e-05 lr: 3.0009e-07 eta: 1 day, 11:38:02 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0105 decode.acc_seg: 99.4705 aux.loss_ce: 0.0095 aux.acc_seg: 98.8827 +04/16 16:54:14 - mmengine - INFO - Iter(train) [ 31400/160000] base_lr: 8.1136e-05 lr: 2.9998e-07 eta: 1 day, 11:37:12 time: 0.9984 data_time: 0.0046 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0098 decode.acc_seg: 99.3767 aux.loss_ce: 0.0094 aux.acc_seg: 98.6240 +04/16 16:55:04 - mmengine - INFO - Iter(train) [ 31450/160000] base_lr: 8.1105e-05 lr: 2.9986e-07 eta: 1 day, 11:36:23 time: 0.9981 data_time: 0.0041 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0107 decode.acc_seg: 99.5136 aux.loss_ce: 0.0099 aux.acc_seg: 98.8297 +04/16 16:55:54 - mmengine - INFO - Iter(train) [ 31500/160000] base_lr: 8.1073e-05 lr: 2.9974e-07 eta: 1 day, 11:35:33 time: 0.9987 data_time: 0.0046 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0111 decode.acc_seg: 99.4501 aux.loss_ce: 0.0095 aux.acc_seg: 98.7457 +04/16 16:56:44 - mmengine - INFO - Iter(train) [ 31550/160000] base_lr: 8.1042e-05 lr: 2.9963e-07 eta: 1 day, 11:34:44 time: 0.9987 data_time: 0.0046 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0129 decode.acc_seg: 99.0498 aux.loss_ce: 0.0108 aux.acc_seg: 98.4348 +04/16 16:57:34 - mmengine - INFO - Iter(train) [ 31600/160000] base_lr: 8.1010e-05 lr: 2.9951e-07 eta: 1 day, 11:33:54 time: 0.9999 data_time: 0.0044 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0095 decode.acc_seg: 99.6456 aux.loss_ce: 0.0090 aux.acc_seg: 99.1667 +04/16 16:58:24 - mmengine - INFO - Iter(train) [ 31650/160000] base_lr: 8.0978e-05 lr: 2.9939e-07 eta: 1 day, 11:33:05 time: 0.9995 data_time: 0.0042 memory: 8462 loss: 0.0258 decode.loss_ce: 0.0147 decode.acc_seg: 99.6164 aux.loss_ce: 0.0111 aux.acc_seg: 98.7766 +04/16 16:59:14 - mmengine - INFO - Iter(train) [ 31700/160000] base_lr: 8.0947e-05 lr: 2.9928e-07 eta: 1 day, 11:32:15 time: 0.9980 data_time: 0.0043 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0090 decode.acc_seg: 99.6387 aux.loss_ce: 0.0088 aux.acc_seg: 99.3845 +04/16 17:00:04 - mmengine - INFO - Iter(train) [ 31750/160000] base_lr: 8.0915e-05 lr: 2.9916e-07 eta: 1 day, 11:31:25 time: 0.9979 data_time: 0.0043 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0101 decode.acc_seg: 99.6805 aux.loss_ce: 0.0091 aux.acc_seg: 99.3076 +04/16 17:00:54 - mmengine - INFO - Iter(train) [ 31800/160000] base_lr: 8.0884e-05 lr: 2.9904e-07 eta: 1 day, 11:30:36 time: 0.9976 data_time: 0.0051 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0118 decode.acc_seg: 99.4101 aux.loss_ce: 0.0103 aux.acc_seg: 98.7715 +04/16 17:01:44 - mmengine - INFO - Iter(train) [ 31850/160000] base_lr: 8.0852e-05 lr: 2.9893e-07 eta: 1 day, 11:29:46 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0116 decode.acc_seg: 99.4753 aux.loss_ce: 0.0099 aux.acc_seg: 98.6017 +04/16 17:02:34 - mmengine - INFO - Iter(train) [ 31900/160000] base_lr: 8.0821e-05 lr: 2.9881e-07 eta: 1 day, 11:28:57 time: 1.0000 data_time: 0.0044 memory: 8462 loss: 0.0203 decode.loss_ce: 0.0111 decode.acc_seg: 99.3425 aux.loss_ce: 0.0093 aux.acc_seg: 98.9721 +04/16 17:03:24 - mmengine - INFO - Iter(train) [ 31950/160000] base_lr: 8.0789e-05 lr: 2.9869e-07 eta: 1 day, 11:28:07 time: 0.9999 data_time: 0.0050 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.6935 aux.loss_ce: 0.0085 aux.acc_seg: 99.3494 +04/16 17:04:14 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 17:04:14 - mmengine - INFO - Iter(train) [ 32000/160000] base_lr: 8.0758e-05 lr: 2.9858e-07 eta: 1 day, 11:27:18 time: 0.9979 data_time: 0.0043 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0099 decode.acc_seg: 99.3664 aux.loss_ce: 0.0090 aux.acc_seg: 98.5855 +04/16 17:05:04 - mmengine - INFO - Iter(train) [ 32050/160000] base_lr: 8.0726e-05 lr: 2.9846e-07 eta: 1 day, 11:26:28 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0208 decode.loss_ce: 0.0114 decode.acc_seg: 99.4728 aux.loss_ce: 0.0095 aux.acc_seg: 98.7848 +04/16 17:05:53 - mmengine - INFO - Iter(train) [ 32100/160000] base_lr: 8.0695e-05 lr: 2.9834e-07 eta: 1 day, 11:25:39 time: 0.9981 data_time: 0.0042 memory: 8462 loss: 0.0221 decode.loss_ce: 0.0118 decode.acc_seg: 99.6059 aux.loss_ce: 0.0103 aux.acc_seg: 99.2264 +04/16 17:06:43 - mmengine - INFO - Iter(train) [ 32150/160000] base_lr: 8.0663e-05 lr: 2.9823e-07 eta: 1 day, 11:24:49 time: 0.9984 data_time: 0.0042 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0116 decode.acc_seg: 99.4173 aux.loss_ce: 0.0109 aux.acc_seg: 98.8316 +04/16 17:07:33 - mmengine - INFO - Iter(train) [ 32200/160000] base_lr: 8.0631e-05 lr: 2.9811e-07 eta: 1 day, 11:24:00 time: 0.9995 data_time: 0.0042 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0104 decode.acc_seg: 99.5943 aux.loss_ce: 0.0095 aux.acc_seg: 99.0021 +04/16 17:08:23 - mmengine - INFO - Iter(train) [ 32250/160000] base_lr: 8.0600e-05 lr: 2.9799e-07 eta: 1 day, 11:23:10 time: 0.9984 data_time: 0.0047 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0108 decode.acc_seg: 99.5726 aux.loss_ce: 0.0102 aux.acc_seg: 99.0511 +04/16 17:09:13 - mmengine - INFO - Iter(train) [ 32300/160000] base_lr: 8.0568e-05 lr: 2.9788e-07 eta: 1 day, 11:22:20 time: 0.9987 data_time: 0.0043 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.5050 aux.loss_ce: 0.0085 aux.acc_seg: 99.0969 +04/16 17:10:03 - mmengine - INFO - Iter(train) [ 32350/160000] base_lr: 8.0537e-05 lr: 2.9776e-07 eta: 1 day, 11:21:31 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0103 decode.acc_seg: 99.6323 aux.loss_ce: 0.0098 aux.acc_seg: 98.8977 +04/16 17:10:53 - mmengine - INFO - Iter(train) [ 32400/160000] base_lr: 8.0505e-05 lr: 2.9764e-07 eta: 1 day, 11:20:41 time: 0.9981 data_time: 0.0043 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0111 decode.acc_seg: 99.4240 aux.loss_ce: 0.0104 aux.acc_seg: 98.7015 +04/16 17:11:43 - mmengine - INFO - Iter(train) [ 32450/160000] base_lr: 8.0474e-05 lr: 2.9753e-07 eta: 1 day, 11:19:52 time: 0.9994 data_time: 0.0047 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0111 decode.acc_seg: 99.4419 aux.loss_ce: 0.0097 aux.acc_seg: 98.7825 +04/16 17:12:33 - mmengine - INFO - Iter(train) [ 32500/160000] base_lr: 8.0442e-05 lr: 2.9741e-07 eta: 1 day, 11:19:02 time: 0.9996 data_time: 0.0049 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0100 decode.acc_seg: 99.7042 aux.loss_ce: 0.0094 aux.acc_seg: 99.1493 +04/16 17:13:23 - mmengine - INFO - Iter(train) [ 32550/160000] base_lr: 8.0411e-05 lr: 2.9729e-07 eta: 1 day, 11:18:13 time: 0.9991 data_time: 0.0046 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0103 decode.acc_seg: 99.6092 aux.loss_ce: 0.0090 aux.acc_seg: 98.9584 +04/16 17:14:13 - mmengine - INFO - Iter(train) [ 32600/160000] base_lr: 8.0379e-05 lr: 2.9718e-07 eta: 1 day, 11:17:23 time: 0.9984 data_time: 0.0047 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0118 decode.acc_seg: 99.5871 aux.loss_ce: 0.0093 aux.acc_seg: 99.2870 +04/16 17:15:03 - mmengine - INFO - Iter(train) [ 32650/160000] base_lr: 8.0348e-05 lr: 2.9706e-07 eta: 1 day, 11:16:34 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0113 decode.acc_seg: 99.6393 aux.loss_ce: 0.0105 aux.acc_seg: 98.9820 +04/16 17:15:53 - mmengine - INFO - Iter(train) [ 32700/160000] base_lr: 8.0316e-05 lr: 2.9694e-07 eta: 1 day, 11:15:44 time: 0.9999 data_time: 0.0046 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0102 decode.acc_seg: 99.5453 aux.loss_ce: 0.0093 aux.acc_seg: 99.1310 +04/16 17:16:43 - mmengine - INFO - Iter(train) [ 32750/160000] base_lr: 8.0284e-05 lr: 2.9683e-07 eta: 1 day, 11:14:55 time: 0.9980 data_time: 0.0050 memory: 8462 loss: 0.0212 decode.loss_ce: 0.0108 decode.acc_seg: 99.5314 aux.loss_ce: 0.0104 aux.acc_seg: 98.5731 +04/16 17:17:33 - mmengine - INFO - Iter(train) [ 32800/160000] base_lr: 8.0253e-05 lr: 2.9671e-07 eta: 1 day, 11:14:05 time: 0.9977 data_time: 0.0046 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0092 decode.acc_seg: 99.7011 aux.loss_ce: 0.0095 aux.acc_seg: 99.1590 +04/16 17:18:23 - mmengine - INFO - Iter(train) [ 32850/160000] base_lr: 8.0221e-05 lr: 2.9659e-07 eta: 1 day, 11:13:16 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0116 decode.acc_seg: 99.6649 aux.loss_ce: 0.0095 aux.acc_seg: 99.2958 +04/16 17:19:13 - mmengine - INFO - Iter(train) [ 32900/160000] base_lr: 8.0190e-05 lr: 2.9648e-07 eta: 1 day, 11:12:26 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0101 decode.acc_seg: 99.5682 aux.loss_ce: 0.0090 aux.acc_seg: 98.9614 +04/16 17:20:03 - mmengine - INFO - Iter(train) [ 32950/160000] base_lr: 8.0158e-05 lr: 2.9636e-07 eta: 1 day, 11:11:37 time: 0.9982 data_time: 0.0045 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0110 decode.acc_seg: 99.2937 aux.loss_ce: 0.0106 aux.acc_seg: 98.3685 +04/16 17:20:53 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 17:20:53 - mmengine - INFO - Iter(train) [ 33000/160000] base_lr: 8.0127e-05 lr: 2.9624e-07 eta: 1 day, 11:10:47 time: 0.9979 data_time: 0.0043 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0107 decode.acc_seg: 99.2453 aux.loss_ce: 0.0090 aux.acc_seg: 98.4140 +04/16 17:21:42 - mmengine - INFO - Iter(train) [ 33050/160000] base_lr: 8.0095e-05 lr: 2.9613e-07 eta: 1 day, 11:09:57 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0124 decode.acc_seg: 99.5264 aux.loss_ce: 0.0104 aux.acc_seg: 99.0314 +04/16 17:22:32 - mmengine - INFO - Iter(train) [ 33100/160000] base_lr: 8.0064e-05 lr: 2.9601e-07 eta: 1 day, 11:09:08 time: 0.9981 data_time: 0.0047 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0097 decode.acc_seg: 99.4806 aux.loss_ce: 0.0090 aux.acc_seg: 98.8743 +04/16 17:23:22 - mmengine - INFO - Iter(train) [ 33150/160000] base_lr: 8.0032e-05 lr: 2.9589e-07 eta: 1 day, 11:08:18 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0091 decode.acc_seg: 99.6393 aux.loss_ce: 0.0085 aux.acc_seg: 99.2161 +04/16 17:24:12 - mmengine - INFO - Iter(train) [ 33200/160000] base_lr: 8.0001e-05 lr: 2.9578e-07 eta: 1 day, 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aux.acc_seg: 98.8697 +04/16 17:30:52 - mmengine - INFO - Iter(train) [ 33600/160000] base_lr: 7.9748e-05 lr: 2.9485e-07 eta: 1 day, 11:00:53 time: 0.9981 data_time: 0.0047 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0101 decode.acc_seg: 99.7540 aux.loss_ce: 0.0089 aux.acc_seg: 99.4677 +04/16 17:31:42 - mmengine - INFO - Iter(train) [ 33650/160000] base_lr: 7.9717e-05 lr: 2.9473e-07 eta: 1 day, 11:00:03 time: 0.9982 data_time: 0.0046 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0099 decode.acc_seg: 99.6511 aux.loss_ce: 0.0087 aux.acc_seg: 99.1922 +04/16 17:32:32 - mmengine - INFO - Iter(train) [ 33700/160000] base_lr: 7.9685e-05 lr: 2.9461e-07 eta: 1 day, 10:59:14 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0087 decode.acc_seg: 99.5108 aux.loss_ce: 0.0084 aux.acc_seg: 98.9992 +04/16 17:33:22 - mmengine - INFO - Iter(train) [ 33750/160000] base_lr: 7.9653e-05 lr: 2.9450e-07 eta: 1 day, 10:58:24 time: 0.9994 data_time: 0.0050 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0115 decode.acc_seg: 99.4774 aux.loss_ce: 0.0101 aux.acc_seg: 99.0191 +04/16 17:34:12 - mmengine - INFO - Iter(train) [ 33800/160000] base_lr: 7.9622e-05 lr: 2.9438e-07 eta: 1 day, 10:57:35 time: 0.9994 data_time: 0.0048 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0114 decode.acc_seg: 99.5604 aux.loss_ce: 0.0100 aux.acc_seg: 98.9998 +04/16 17:35:02 - mmengine - INFO - Iter(train) [ 33850/160000] base_lr: 7.9590e-05 lr: 2.9426e-07 eta: 1 day, 10:56:45 time: 0.9989 data_time: 0.0046 memory: 8462 loss: 0.0231 decode.loss_ce: 0.0126 decode.acc_seg: 99.5131 aux.loss_ce: 0.0105 aux.acc_seg: 99.0900 +04/16 17:35:52 - mmengine - INFO - Iter(train) [ 33900/160000] base_lr: 7.9559e-05 lr: 2.9415e-07 eta: 1 day, 10:55:56 time: 0.9991 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.6851 aux.loss_ce: 0.0083 aux.acc_seg: 99.1272 +04/16 17:36:42 - mmengine - INFO - Iter(train) [ 33950/160000] base_lr: 7.9527e-05 lr: 2.9403e-07 eta: 1 day, 10:55:06 time: 0.9993 data_time: 0.0043 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0114 decode.acc_seg: 99.4322 aux.loss_ce: 0.0101 aux.acc_seg: 99.0152 +04/16 17:37:32 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 17:37:32 - mmengine - INFO - Iter(train) [ 34000/160000] base_lr: 7.9496e-05 lr: 2.9391e-07 eta: 1 day, 10:54:17 time: 0.9988 data_time: 0.0049 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0102 decode.acc_seg: 99.2540 aux.loss_ce: 0.0092 aux.acc_seg: 98.8462 +04/16 17:38:22 - mmengine - INFO - Iter(train) [ 34050/160000] base_lr: 7.9464e-05 lr: 2.9380e-07 eta: 1 day, 10:53:27 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0091 decode.acc_seg: 99.6376 aux.loss_ce: 0.0088 aux.acc_seg: 99.0265 +04/16 17:39:12 - mmengine - INFO - Iter(train) [ 34100/160000] base_lr: 7.9433e-05 lr: 2.9368e-07 eta: 1 day, 10:52:38 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.5995 aux.loss_ce: 0.0084 aux.acc_seg: 98.9571 +04/16 17:40:01 - mmengine - INFO - Iter(train) [ 34150/160000] base_lr: 7.9401e-05 lr: 2.9356e-07 eta: 1 day, 10:51:48 time: 0.9989 data_time: 0.0045 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0101 decode.acc_seg: 99.6189 aux.loss_ce: 0.0092 aux.acc_seg: 99.4095 +04/16 17:40:51 - mmengine - INFO - Iter(train) [ 34200/160000] base_lr: 7.9370e-05 lr: 2.9345e-07 eta: 1 day, 10:50:58 time: 0.9987 data_time: 0.0044 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0095 decode.acc_seg: 99.7444 aux.loss_ce: 0.0088 aux.acc_seg: 99.5531 +04/16 17:41:41 - mmengine - INFO - Iter(train) [ 34250/160000] base_lr: 7.9338e-05 lr: 2.9333e-07 eta: 1 day, 10:50:09 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0203 decode.loss_ce: 0.0104 decode.acc_seg: 99.7259 aux.loss_ce: 0.0099 aux.acc_seg: 99.3155 +04/16 17:42:31 - mmengine - INFO - Iter(train) [ 34300/160000] base_lr: 7.9306e-05 lr: 2.9321e-07 eta: 1 day, 10:49:19 time: 0.9994 data_time: 0.0048 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0096 decode.acc_seg: 99.6750 aux.loss_ce: 0.0089 aux.acc_seg: 99.1764 +04/16 17:43:21 - mmengine - INFO - Iter(train) [ 34350/160000] base_lr: 7.9275e-05 lr: 2.9310e-07 eta: 1 day, 10:48:30 time: 0.9992 data_time: 0.0043 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0094 decode.acc_seg: 99.5104 aux.loss_ce: 0.0087 aux.acc_seg: 98.8253 +04/16 17:44:11 - mmengine - INFO - Iter(train) [ 34400/160000] base_lr: 7.9243e-05 lr: 2.9298e-07 eta: 1 day, 10:47:40 time: 0.9996 data_time: 0.0046 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0098 decode.acc_seg: 99.6490 aux.loss_ce: 0.0096 aux.acc_seg: 99.0376 +04/16 17:45:01 - mmengine - INFO - Iter(train) [ 34450/160000] base_lr: 7.9212e-05 lr: 2.9286e-07 eta: 1 day, 10:46:51 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0246 decode.loss_ce: 0.0135 decode.acc_seg: 99.4993 aux.loss_ce: 0.0111 aux.acc_seg: 99.0532 +04/16 17:45:51 - mmengine - INFO - Iter(train) [ 34500/160000] base_lr: 7.9180e-05 lr: 2.9275e-07 eta: 1 day, 10:46:01 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0106 decode.acc_seg: 99.4734 aux.loss_ce: 0.0100 aux.acc_seg: 98.9143 +04/16 17:46:41 - mmengine - INFO - Iter(train) [ 34550/160000] base_lr: 7.9149e-05 lr: 2.9263e-07 eta: 1 day, 10:45:12 time: 0.9996 data_time: 0.0044 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0104 decode.acc_seg: 99.6119 aux.loss_ce: 0.0095 aux.acc_seg: 99.0353 +04/16 17:47:31 - mmengine - INFO - Iter(train) [ 34600/160000] base_lr: 7.9117e-05 lr: 2.9251e-07 eta: 1 day, 10:44:22 time: 0.9991 data_time: 0.0041 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0103 decode.acc_seg: 99.6735 aux.loss_ce: 0.0091 aux.acc_seg: 99.1041 +04/16 17:48:21 - mmengine - INFO - Iter(train) [ 34650/160000] base_lr: 7.9086e-05 lr: 2.9240e-07 eta: 1 day, 10:43:32 time: 0.9985 data_time: 0.0043 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0092 decode.acc_seg: 99.4553 aux.loss_ce: 0.0090 aux.acc_seg: 98.6092 +04/16 17:49:11 - mmengine - INFO - Iter(train) [ 34700/160000] base_lr: 7.9054e-05 lr: 2.9228e-07 eta: 1 day, 10:42:43 time: 0.9992 data_time: 0.0045 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0088 decode.acc_seg: 99.6115 aux.loss_ce: 0.0087 aux.acc_seg: 99.1495 +04/16 17:50:01 - mmengine - INFO - Iter(train) [ 34750/160000] base_lr: 7.9023e-05 lr: 2.9216e-07 eta: 1 day, 10:41:53 time: 0.9990 data_time: 0.0046 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0131 decode.acc_seg: 99.5756 aux.loss_ce: 0.0098 aux.acc_seg: 99.0211 +04/16 17:50:51 - mmengine - INFO - Iter(train) [ 34800/160000] base_lr: 7.8991e-05 lr: 2.9205e-07 eta: 1 day, 10:41:04 time: 0.9985 data_time: 0.0049 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0113 decode.acc_seg: 99.5152 aux.loss_ce: 0.0105 aux.acc_seg: 98.7673 +04/16 17:51:41 - mmengine - INFO - Iter(train) [ 34850/160000] base_lr: 7.8959e-05 lr: 2.9193e-07 eta: 1 day, 10:40:14 time: 0.9986 data_time: 0.0047 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.3679 aux.loss_ce: 0.0084 aux.acc_seg: 98.8329 +04/16 17:52:31 - mmengine - INFO - Iter(train) [ 34900/160000] base_lr: 7.8928e-05 lr: 2.9181e-07 eta: 1 day, 10:39:25 time: 0.9992 data_time: 0.0048 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0099 decode.acc_seg: 99.6708 aux.loss_ce: 0.0092 aux.acc_seg: 99.1367 +04/16 17:53:21 - mmengine - INFO - Iter(train) [ 34950/160000] base_lr: 7.8896e-05 lr: 2.9170e-07 eta: 1 day, 10:38:35 time: 0.9986 data_time: 0.0044 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0114 decode.acc_seg: 99.3448 aux.loss_ce: 0.0105 aux.acc_seg: 98.7076 +04/16 17:54:11 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 17:54:11 - mmengine - INFO - Iter(train) [ 35000/160000] base_lr: 7.8865e-05 lr: 2.9158e-07 eta: 1 day, 10:37:46 time: 0.9994 data_time: 0.0045 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0101 decode.acc_seg: 99.6401 aux.loss_ce: 0.0095 aux.acc_seg: 99.0271 +04/16 17:55:01 - mmengine - INFO - Iter(train) [ 35050/160000] base_lr: 7.8833e-05 lr: 2.9146e-07 eta: 1 day, 10:36:56 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.6319 aux.loss_ce: 0.0085 aux.acc_seg: 99.1037 +04/16 17:55:51 - mmengine - INFO - Iter(train) [ 35100/160000] base_lr: 7.8802e-05 lr: 2.9135e-07 eta: 1 day, 10:36:07 time: 0.9993 data_time: 0.0044 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0118 decode.acc_seg: 99.6220 aux.loss_ce: 0.0105 aux.acc_seg: 99.0681 +04/16 17:56:41 - mmengine - INFO - Iter(train) [ 35150/160000] base_lr: 7.8770e-05 lr: 2.9123e-07 eta: 1 day, 10:35:17 time: 1.0000 data_time: 0.0044 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0114 decode.acc_seg: 99.3393 aux.loss_ce: 0.0105 aux.acc_seg: 98.2107 +04/16 17:57:31 - mmengine - INFO - Iter(train) [ 35200/160000] base_lr: 7.8739e-05 lr: 2.9111e-07 eta: 1 day, 10:34:28 time: 0.9992 data_time: 0.0045 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0105 decode.acc_seg: 99.5390 aux.loss_ce: 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decode.loss_ce: 0.0094 decode.acc_seg: 99.7528 aux.loss_ce: 0.0094 aux.acc_seg: 99.4188 +04/16 18:01:40 - mmengine - INFO - Iter(train) [ 35450/160000] base_lr: 7.8581e-05 lr: 2.9053e-07 eta: 1 day, 10:30:20 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0105 decode.acc_seg: 99.6632 aux.loss_ce: 0.0096 aux.acc_seg: 99.0841 +04/16 18:02:30 - mmengine - INFO - Iter(train) [ 35500/160000] base_lr: 7.8549e-05 lr: 2.9041e-07 eta: 1 day, 10:29:30 time: 0.9994 data_time: 0.0048 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0096 decode.acc_seg: 99.5234 aux.loss_ce: 0.0088 aux.acc_seg: 98.9374 +04/16 18:03:20 - mmengine - INFO - Iter(train) [ 35550/160000] base_lr: 7.8518e-05 lr: 2.9030e-07 eta: 1 day, 10:28:41 time: 0.9996 data_time: 0.0049 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0106 decode.acc_seg: 99.2283 aux.loss_ce: 0.0084 aux.acc_seg: 98.6685 +04/16 18:04:10 - mmengine - INFO - Iter(train) [ 35600/160000] base_lr: 7.8486e-05 lr: 2.9018e-07 eta: 1 day, 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aux.acc_seg: 98.7633 +04/16 18:10:50 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 18:10:50 - mmengine - INFO - Iter(train) [ 36000/160000] base_lr: 7.8234e-05 lr: 2.8925e-07 eta: 1 day, 10:21:15 time: 0.9981 data_time: 0.0047 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0094 decode.acc_seg: 99.6029 aux.loss_ce: 0.0090 aux.acc_seg: 98.8834 +04/16 18:11:40 - mmengine - INFO - Iter(train) [ 36050/160000] base_lr: 7.8202e-05 lr: 2.8913e-07 eta: 1 day, 10:20:26 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0097 decode.acc_seg: 99.7293 aux.loss_ce: 0.0087 aux.acc_seg: 99.2447 +04/16 18:12:30 - mmengine - INFO - Iter(train) [ 36100/160000] base_lr: 7.8171e-05 lr: 2.8901e-07 eta: 1 day, 10:19:36 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0109 decode.acc_seg: 99.2603 aux.loss_ce: 0.0105 aux.acc_seg: 98.5594 +04/16 18:13:20 - mmengine - INFO - Iter(train) [ 36150/160000] base_lr: 7.8139e-05 lr: 2.8890e-07 eta: 1 day, 10:18:47 time: 0.9995 data_time: 0.0047 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0099 decode.acc_seg: 99.5678 aux.loss_ce: 0.0093 aux.acc_seg: 98.7810 +04/16 18:14:10 - mmengine - INFO - Iter(train) [ 36200/160000] base_lr: 7.8108e-05 lr: 2.8878e-07 eta: 1 day, 10:17:57 time: 0.9992 data_time: 0.0047 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0100 decode.acc_seg: 99.6346 aux.loss_ce: 0.0093 aux.acc_seg: 99.3034 +04/16 18:15:00 - mmengine - INFO - Iter(train) [ 36250/160000] base_lr: 7.8076e-05 lr: 2.8866e-07 eta: 1 day, 10:17:07 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0101 decode.acc_seg: 99.5901 aux.loss_ce: 0.0094 aux.acc_seg: 98.9717 +04/16 18:15:50 - mmengine - INFO - Iter(train) [ 36300/160000] base_lr: 7.8045e-05 lr: 2.8855e-07 eta: 1 day, 10:16:18 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0092 decode.acc_seg: 99.5510 aux.loss_ce: 0.0093 aux.acc_seg: 98.6420 +04/16 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decode.acc_seg: 99.6563 aux.loss_ce: 0.0087 aux.acc_seg: 99.3046 +04/16 18:20:00 - mmengine - INFO - Iter(train) [ 36550/160000] base_lr: 7.7887e-05 lr: 2.8796e-07 eta: 1 day, 10:12:10 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0103 decode.acc_seg: 99.3256 aux.loss_ce: 0.0092 aux.acc_seg: 98.7541 +04/16 18:20:50 - mmengine - INFO - Iter(train) [ 36600/160000] base_lr: 7.7855e-05 lr: 2.8785e-07 eta: 1 day, 10:11:21 time: 0.9989 data_time: 0.0047 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0094 decode.acc_seg: 99.6321 aux.loss_ce: 0.0097 aux.acc_seg: 98.8707 +04/16 18:21:40 - mmengine - INFO - Iter(train) [ 36650/160000] base_lr: 7.7824e-05 lr: 2.8773e-07 eta: 1 day, 10:10:31 time: 0.9993 data_time: 0.0045 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0090 decode.acc_seg: 99.4877 aux.loss_ce: 0.0087 aux.acc_seg: 98.6252 +04/16 18:22:30 - mmengine - INFO - Iter(train) [ 36700/160000] base_lr: 7.7792e-05 lr: 2.8761e-07 eta: 1 day, 10:09:41 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.7061 aux.loss_ce: 0.0087 aux.acc_seg: 99.2981 +04/16 18:23:20 - mmengine - INFO - Iter(train) [ 36750/160000] base_lr: 7.7761e-05 lr: 2.8750e-07 eta: 1 day, 10:08:52 time: 0.9982 data_time: 0.0046 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0104 decode.acc_seg: 99.5789 aux.loss_ce: 0.0096 aux.acc_seg: 99.2010 +04/16 18:24:10 - mmengine - INFO - Iter(train) [ 36800/160000] base_lr: 7.7729e-05 lr: 2.8738e-07 eta: 1 day, 10:08:02 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0096 decode.acc_seg: 99.6603 aux.loss_ce: 0.0098 aux.acc_seg: 99.0469 +04/16 18:25:00 - mmengine - INFO - Iter(train) [ 36850/160000] base_lr: 7.7698e-05 lr: 2.8726e-07 eta: 1 day, 10:07:13 time: 0.9994 data_time: 0.0050 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0107 decode.acc_seg: 99.7566 aux.loss_ce: 0.0094 aux.acc_seg: 99.3483 +04/16 18:25:50 - mmengine - INFO - Iter(train) [ 36900/160000] base_lr: 7.7666e-05 lr: 2.8715e-07 eta: 1 day, 10:06:23 time: 1.0014 data_time: 0.0045 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0094 decode.acc_seg: 99.6845 aux.loss_ce: 0.0087 aux.acc_seg: 99.1907 +04/16 18:26:40 - mmengine - INFO - Iter(train) [ 36950/160000] base_lr: 7.7635e-05 lr: 2.8703e-07 eta: 1 day, 10:05:34 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0217 decode.loss_ce: 0.0114 decode.acc_seg: 99.5628 aux.loss_ce: 0.0103 aux.acc_seg: 98.9550 +04/16 18:27:30 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 18:27:30 - mmengine - INFO - Iter(train) [ 37000/160000] base_lr: 7.7603e-05 lr: 2.8691e-07 eta: 1 day, 10:04:44 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0097 decode.acc_seg: 99.5579 aux.loss_ce: 0.0095 aux.acc_seg: 98.8516 +04/16 18:28:20 - mmengine - INFO - Iter(train) [ 37050/160000] base_lr: 7.7571e-05 lr: 2.8680e-07 eta: 1 day, 10:03:55 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.7751 aux.loss_ce: 0.0082 aux.acc_seg: 99.3525 +04/16 18:29:10 - mmengine - INFO - Iter(train) [ 37100/160000] base_lr: 7.7540e-05 lr: 2.8668e-07 eta: 1 day, 10:03:05 time: 1.0001 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0088 decode.acc_seg: 99.5516 aux.loss_ce: 0.0078 aux.acc_seg: 98.9372 +04/16 18:29:59 - mmengine - INFO - Iter(train) [ 37150/160000] base_lr: 7.7508e-05 lr: 2.8656e-07 eta: 1 day, 10:02:16 time: 0.9991 data_time: 0.0046 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.6342 aux.loss_ce: 0.0087 aux.acc_seg: 99.2096 +04/16 18:30:49 - mmengine - INFO - Iter(train) [ 37200/160000] base_lr: 7.7477e-05 lr: 2.8645e-07 eta: 1 day, 10:01:26 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0093 decode.acc_seg: 99.1800 aux.loss_ce: 0.0082 aux.acc_seg: 98.7444 +04/16 18:31:39 - mmengine - INFO - Iter(train) [ 37250/160000] base_lr: 7.7445e-05 lr: 2.8633e-07 eta: 1 day, 10:00:37 time: 0.9992 data_time: 0.0048 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0090 decode.acc_seg: 99.4818 aux.loss_ce: 0.0086 aux.acc_seg: 99.1272 +04/16 18:32:29 - mmengine - INFO - Iter(train) [ 37300/160000] base_lr: 7.7414e-05 lr: 2.8621e-07 eta: 1 day, 9:59:47 time: 0.9982 data_time: 0.0048 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0099 decode.acc_seg: 99.5996 aux.loss_ce: 0.0090 aux.acc_seg: 98.9307 +04/16 18:33:19 - mmengine - INFO - Iter(train) [ 37350/160000] base_lr: 7.7382e-05 lr: 2.8610e-07 eta: 1 day, 9:58:57 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0102 decode.acc_seg: 99.6471 aux.loss_ce: 0.0096 aux.acc_seg: 99.0953 +04/16 18:34:09 - mmengine - INFO - Iter(train) [ 37400/160000] base_lr: 7.7351e-05 lr: 2.8598e-07 eta: 1 day, 9:58:08 time: 0.9992 data_time: 0.0046 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0089 decode.acc_seg: 99.7519 aux.loss_ce: 0.0086 aux.acc_seg: 99.5729 +04/16 18:34:59 - mmengine - INFO - Iter(train) [ 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decode.acc_seg: 99.5834 aux.loss_ce: 0.0092 aux.acc_seg: 99.1297 +04/16 18:41:39 - mmengine - INFO - Iter(train) [ 37850/160000] base_lr: 7.7067e-05 lr: 2.8493e-07 eta: 1 day, 9:50:42 time: 0.9996 data_time: 0.0044 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6132 aux.loss_ce: 0.0082 aux.acc_seg: 99.1386 +04/16 18:42:29 - mmengine - INFO - Iter(train) [ 37900/160000] base_lr: 7.7035e-05 lr: 2.8481e-07 eta: 1 day, 9:49:52 time: 0.9993 data_time: 0.0049 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0105 decode.acc_seg: 99.5394 aux.loss_ce: 0.0094 aux.acc_seg: 99.1041 +04/16 18:43:19 - mmengine - INFO - Iter(train) [ 37950/160000] base_lr: 7.7004e-05 lr: 2.8470e-07 eta: 1 day, 9:49:03 time: 1.0004 data_time: 0.0044 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0117 decode.acc_seg: 99.0931 aux.loss_ce: 0.0097 aux.acc_seg: 98.3505 +04/16 18:44:09 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 18:44:09 - mmengine - INFO - 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aux.acc_seg: 99.4211 +04/16 18:47:29 - mmengine - INFO - Iter(train) [ 38200/160000] base_lr: 7.6846e-05 lr: 2.8412e-07 eta: 1 day, 9:44:55 time: 0.9987 data_time: 0.0043 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0096 decode.acc_seg: 99.6744 aux.loss_ce: 0.0089 aux.acc_seg: 99.0860 +04/16 18:48:19 - mmengine - INFO - Iter(train) [ 38250/160000] base_lr: 7.6814e-05 lr: 2.8400e-07 eta: 1 day, 9:44:06 time: 1.0001 data_time: 0.0050 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.6277 aux.loss_ce: 0.0083 aux.acc_seg: 99.1913 +04/16 18:49:09 - mmengine - INFO - Iter(train) [ 38300/160000] base_lr: 7.6783e-05 lr: 2.8388e-07 eta: 1 day, 9:43:16 time: 1.0000 data_time: 0.0043 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0099 decode.acc_seg: 99.6052 aux.loss_ce: 0.0090 aux.acc_seg: 99.2760 +04/16 18:49:59 - mmengine - INFO - Iter(train) [ 38350/160000] base_lr: 7.6751e-05 lr: 2.8377e-07 eta: 1 day, 9:42:26 time: 0.9981 data_time: 0.0045 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0111 decode.acc_seg: 99.5485 aux.loss_ce: 0.0103 aux.acc_seg: 98.7944 +04/16 18:50:49 - mmengine - INFO - Iter(train) [ 38400/160000] base_lr: 7.6720e-05 lr: 2.8365e-07 eta: 1 day, 9:41:37 time: 0.9983 data_time: 0.0045 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0105 decode.acc_seg: 99.6445 aux.loss_ce: 0.0091 aux.acc_seg: 99.1837 +04/16 18:51:39 - mmengine - INFO - Iter(train) [ 38450/160000] base_lr: 7.6688e-05 lr: 2.8353e-07 eta: 1 day, 9:40:47 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0093 decode.acc_seg: 99.5972 aux.loss_ce: 0.0093 aux.acc_seg: 98.8462 +04/16 18:52:29 - mmengine - INFO - Iter(train) [ 38500/160000] base_lr: 7.6657e-05 lr: 2.8342e-07 eta: 1 day, 9:39:58 time: 0.9984 data_time: 0.0044 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0088 decode.acc_seg: 99.6521 aux.loss_ce: 0.0084 aux.acc_seg: 99.0269 +04/16 18:53:19 - mmengine - INFO - Iter(train) [ 38550/160000] base_lr: 7.6625e-05 lr: 2.8330e-07 eta: 1 day, 9:39:08 time: 0.9995 data_time: 0.0050 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0081 decode.acc_seg: 99.6111 aux.loss_ce: 0.0084 aux.acc_seg: 99.1764 +04/16 18:54:09 - mmengine - INFO - Iter(train) [ 38600/160000] base_lr: 7.6594e-05 lr: 2.8318e-07 eta: 1 day, 9:38:18 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0109 decode.acc_seg: 99.6038 aux.loss_ce: 0.0100 aux.acc_seg: 98.9075 +04/16 18:54:59 - mmengine - INFO - Iter(train) [ 38650/160000] base_lr: 7.6562e-05 lr: 2.8307e-07 eta: 1 day, 9:37:29 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.7551 aux.loss_ce: 0.0082 aux.acc_seg: 99.3549 +04/16 18:55:49 - mmengine - INFO - Iter(train) [ 38700/160000] base_lr: 7.6530e-05 lr: 2.8295e-07 eta: 1 day, 9:36:39 time: 0.9990 data_time: 0.0046 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0108 decode.acc_seg: 99.6368 aux.loss_ce: 0.0094 aux.acc_seg: 99.2714 +04/16 18:56:39 - mmengine - INFO - Iter(train) [ 38750/160000] base_lr: 7.6499e-05 lr: 2.8283e-07 eta: 1 day, 9:35:50 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.7028 aux.loss_ce: 0.0086 aux.acc_seg: 99.0713 +04/16 18:57:29 - mmengine - INFO - Iter(train) [ 38800/160000] base_lr: 7.6467e-05 lr: 2.8272e-07 eta: 1 day, 9:35:00 time: 0.9993 data_time: 0.0047 memory: 8462 loss: 0.0212 decode.loss_ce: 0.0122 decode.acc_seg: 99.4604 aux.loss_ce: 0.0091 aux.acc_seg: 98.9594 +04/16 18:58:19 - mmengine - INFO - Iter(train) [ 38850/160000] base_lr: 7.6436e-05 lr: 2.8260e-07 eta: 1 day, 9:34:11 time: 1.0001 data_time: 0.0050 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0095 decode.acc_seg: 99.6334 aux.loss_ce: 0.0094 aux.acc_seg: 98.6723 +04/16 18:59:09 - mmengine - INFO - Iter(train) [ 38900/160000] base_lr: 7.6404e-05 lr: 2.8248e-07 eta: 1 day, 9:33:21 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0098 decode.acc_seg: 99.7036 aux.loss_ce: 0.0090 aux.acc_seg: 99.3160 +04/16 18:59:59 - mmengine - INFO - Iter(train) [ 38950/160000] base_lr: 7.6373e-05 lr: 2.8237e-07 eta: 1 day, 9:32:32 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0102 decode.acc_seg: 99.7040 aux.loss_ce: 0.0084 aux.acc_seg: 99.3305 +04/16 19:00:49 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 19:00:49 - mmengine - INFO - Iter(train) [ 39000/160000] base_lr: 7.6341e-05 lr: 2.8225e-07 eta: 1 day, 9:31:42 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0103 decode.acc_seg: 99.6683 aux.loss_ce: 0.0098 aux.acc_seg: 99.2292 +04/16 19:01:39 - mmengine - INFO - Iter(train) [ 39050/160000] base_lr: 7.6310e-05 lr: 2.8213e-07 eta: 1 day, 9:30:53 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0098 decode.acc_seg: 99.3977 aux.loss_ce: 0.0091 aux.acc_seg: 98.8224 +04/16 19:02:29 - mmengine - INFO - Iter(train) [ 39100/160000] base_lr: 7.6278e-05 lr: 2.8202e-07 eta: 1 day, 9:30:03 time: 1.0014 data_time: 0.0046 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0099 decode.acc_seg: 99.6540 aux.loss_ce: 0.0094 aux.acc_seg: 99.2640 +04/16 19:03:19 - mmengine - INFO - Iter(train) [ 39150/160000] base_lr: 7.6247e-05 lr: 2.8190e-07 eta: 1 day, 9:29:14 time: 0.9989 data_time: 0.0046 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0091 decode.acc_seg: 99.6185 aux.loss_ce: 0.0087 aux.acc_seg: 99.2218 +04/16 19:04:09 - mmengine - INFO - Iter(train) [ 39200/160000] base_lr: 7.6215e-05 lr: 2.8178e-07 eta: 1 day, 9:28:24 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0108 decode.acc_seg: 99.2207 aux.loss_ce: 0.0105 aux.acc_seg: 98.3152 +04/16 19:04:59 - mmengine - INFO - Iter(train) [ 39250/160000] base_lr: 7.6183e-05 lr: 2.8167e-07 eta: 1 day, 9:27:34 time: 0.9994 data_time: 0.0043 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0093 decode.acc_seg: 99.7082 aux.loss_ce: 0.0088 aux.acc_seg: 99.0751 +04/16 19:05:49 - mmengine - INFO - Iter(train) [ 39300/160000] base_lr: 7.6152e-05 lr: 2.8155e-07 eta: 1 day, 9:26:45 time: 0.9989 data_time: 0.0045 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.4886 aux.loss_ce: 0.0083 aux.acc_seg: 99.0915 +04/16 19:06:39 - mmengine - INFO - Iter(train) [ 39350/160000] base_lr: 7.6120e-05 lr: 2.8143e-07 eta: 1 day, 9:25:55 time: 1.0006 data_time: 0.0042 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0091 decode.acc_seg: 99.7728 aux.loss_ce: 0.0085 aux.acc_seg: 99.3750 +04/16 19:07:29 - mmengine - INFO - Iter(train) [ 39400/160000] base_lr: 7.6089e-05 lr: 2.8132e-07 eta: 1 day, 9:25:06 time: 1.0001 data_time: 0.0042 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0090 decode.acc_seg: 99.6607 aux.loss_ce: 0.0090 aux.acc_seg: 99.2296 +04/16 19:08:19 - mmengine - INFO - Iter(train) [ 39450/160000] base_lr: 7.6057e-05 lr: 2.8120e-07 eta: 1 day, 9:24:16 time: 1.0009 data_time: 0.0044 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0110 decode.acc_seg: 99.5613 aux.loss_ce: 0.0092 aux.acc_seg: 99.1129 +04/16 19:09:09 - mmengine - INFO - Iter(train) [ 39500/160000] base_lr: 7.6026e-05 lr: 2.8108e-07 eta: 1 day, 9:23:27 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0086 decode.acc_seg: 99.5474 aux.loss_ce: 0.0085 aux.acc_seg: 98.8457 +04/16 19:09:59 - mmengine - INFO - Iter(train) [ 39550/160000] base_lr: 7.5994e-05 lr: 2.8097e-07 eta: 1 day, 9:22:37 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0106 decode.acc_seg: 99.4545 aux.loss_ce: 0.0101 aux.acc_seg: 98.9027 +04/16 19:10:49 - mmengine - INFO - Iter(train) [ 39600/160000] base_lr: 7.5963e-05 lr: 2.8085e-07 eta: 1 day, 9:21:48 time: 1.0012 data_time: 0.0046 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0102 decode.acc_seg: 99.7526 aux.loss_ce: 0.0096 aux.acc_seg: 99.2126 +04/16 19:11:39 - mmengine - INFO - Iter(train) [ 39650/160000] base_lr: 7.5931e-05 lr: 2.8073e-07 eta: 1 day, 9:20:58 time: 1.0005 data_time: 0.0045 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0095 decode.acc_seg: 99.6218 aux.loss_ce: 0.0093 aux.acc_seg: 98.9985 +04/16 19:12:29 - mmengine - INFO - Iter(train) [ 39700/160000] base_lr: 7.5900e-05 lr: 2.8062e-07 eta: 1 day, 9:20:09 time: 1.0000 data_time: 0.0043 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0102 decode.acc_seg: 99.7480 aux.loss_ce: 0.0093 aux.acc_seg: 99.3753 +04/16 19:13:19 - mmengine - INFO - Iter(train) [ 39750/160000] base_lr: 7.5868e-05 lr: 2.8050e-07 eta: 1 day, 9:19:19 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.5581 aux.loss_ce: 0.0084 aux.acc_seg: 98.9313 +04/16 19:14:09 - mmengine - INFO - Iter(train) [ 39800/160000] base_lr: 7.5836e-05 lr: 2.8038e-07 eta: 1 day, 9:18:30 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0231 decode.loss_ce: 0.0119 decode.acc_seg: 99.5714 aux.loss_ce: 0.0112 aux.acc_seg: 98.8041 +04/16 19:14:59 - mmengine - INFO - Iter(train) [ 39850/160000] base_lr: 7.5805e-05 lr: 2.8027e-07 eta: 1 day, 9:17:40 time: 0.9999 data_time: 0.0050 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.5243 aux.loss_ce: 0.0079 aux.acc_seg: 99.1131 +04/16 19:15:49 - mmengine - INFO - Iter(train) [ 39900/160000] base_lr: 7.5773e-05 lr: 2.8015e-07 eta: 1 day, 9:16:51 time: 0.9988 data_time: 0.0042 memory: 8462 loss: 0.0231 decode.loss_ce: 0.0121 decode.acc_seg: 99.7026 aux.loss_ce: 0.0110 aux.acc_seg: 99.2476 +04/16 19:16:39 - mmengine - INFO - Iter(train) [ 39950/160000] base_lr: 7.5742e-05 lr: 2.8003e-07 eta: 1 day, 9:16:01 time: 1.0016 data_time: 0.0045 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.5977 aux.loss_ce: 0.0078 aux.acc_seg: 99.3048 +04/16 19:17:29 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 19:17:29 - mmengine - INFO - Iter(train) [ 40000/160000] base_lr: 7.5710e-05 lr: 2.7992e-07 eta: 1 day, 9:15:12 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6803 aux.loss_ce: 0.0080 aux.acc_seg: 99.0583 +04/16 19:17:29 - mmengine - INFO - Saving checkpoint at 40000 iterations +04/16 19:17:38 - mmengine - INFO - Iter(val) [ 50/355] eta: 0:00:35 time: 0.1155 data_time: 0.0013 memory: 4004 +04/16 19:17:44 - mmengine - INFO - Iter(val) [100/355] eta: 0:00:29 time: 0.1152 data_time: 0.0013 memory: 4004 +04/16 19:17:50 - mmengine - INFO - Iter(val) [150/355] eta: 0:00:23 time: 0.1157 data_time: 0.0014 memory: 4004 +04/16 19:17:56 - mmengine - INFO - Iter(val) [200/355] eta: 0:00:17 time: 0.1158 data_time: 0.0015 memory: 4004 +04/16 19:18:02 - mmengine - INFO - Iter(val) [250/355] eta: 0:00:12 time: 0.1160 data_time: 0.0015 memory: 4004 +04/16 19:18:07 - mmengine - INFO - Iter(val) [300/355] eta: 0:00:06 time: 0.1157 data_time: 0.0014 memory: 4004 +04/16 19:18:13 - mmengine - INFO - Iter(val) [350/355] eta: 0:00:00 time: 0.1158 data_time: 0.0014 memory: 4004 +04/16 19:18:14 - mmengine - INFO - per class results: +04/16 19:18:14 - mmengine - INFO - ++------------+-------+-------+ +| Class | IoU | Acc | ++------------+-------+-------+ +| background | 99.19 | 99.6 | +| contrast | 82.27 | 90.12 | ++------------+-------+-------+ +04/16 19:18:14 - mmengine - INFO - Iter(val) [355/355] aAcc: 99.2200 mIoU: 90.7300 mAcc: 94.8600 data_time: 0.0016 time: 0.1159 +04/16 19:19:04 - mmengine - INFO - Iter(train) [ 40050/160000] base_lr: 7.5679e-05 lr: 2.7980e-07 eta: 1 day, 9:14:23 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0095 decode.acc_seg: 99.6161 aux.loss_ce: 0.0090 aux.acc_seg: 99.1032 +04/16 19:19:54 - mmengine - INFO - Iter(train) [ 40100/160000] base_lr: 7.5647e-05 lr: 2.7968e-07 eta: 1 day, 9:13:34 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0091 decode.acc_seg: 99.5743 aux.loss_ce: 0.0096 aux.acc_seg: 98.7871 +04/16 19:20:44 - mmengine - INFO - Iter(train) [ 40150/160000] base_lr: 7.5616e-05 lr: 2.7957e-07 eta: 1 day, 9:12:44 time: 1.0006 data_time: 0.0048 memory: 8462 loss: 0.0203 decode.loss_ce: 0.0101 decode.acc_seg: 99.5077 aux.loss_ce: 0.0102 aux.acc_seg: 98.5815 +04/16 19:21:34 - mmengine - INFO - Iter(train) [ 40200/160000] base_lr: 7.5584e-05 lr: 2.7945e-07 eta: 1 day, 9:11:55 time: 0.9997 data_time: 0.0049 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0107 decode.acc_seg: 99.3263 aux.loss_ce: 0.0098 aux.acc_seg: 98.9107 +04/16 19:22:24 - mmengine - INFO - Iter(train) [ 40250/160000] base_lr: 7.5553e-05 lr: 2.7933e-07 eta: 1 day, 9:11:05 time: 0.9995 data_time: 0.0048 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0130 decode.acc_seg: 99.6286 aux.loss_ce: 0.0099 aux.acc_seg: 99.2659 +04/16 19:23:14 - mmengine - INFO - Iter(train) [ 40300/160000] base_lr: 7.5521e-05 lr: 2.7922e-07 eta: 1 day, 9:10:16 time: 1.0003 data_time: 0.0049 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.6624 aux.loss_ce: 0.0088 aux.acc_seg: 99.3376 +04/16 19:24:04 - mmengine - INFO - Iter(train) [ 40350/160000] base_lr: 7.5489e-05 lr: 2.7910e-07 eta: 1 day, 9:09:26 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7240 aux.loss_ce: 0.0072 aux.acc_seg: 99.3477 +04/16 19:24:54 - mmengine - INFO - Iter(train) [ 40400/160000] base_lr: 7.5458e-05 lr: 2.7898e-07 eta: 1 day, 9:08:36 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.6208 aux.loss_ce: 0.0081 aux.acc_seg: 98.8434 +04/16 19:25:44 - mmengine - INFO - Iter(train) [ 40450/160000] base_lr: 7.5426e-05 lr: 2.7887e-07 eta: 1 day, 9:07:47 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0100 decode.acc_seg: 99.5022 aux.loss_ce: 0.0094 aux.acc_seg: 98.7232 +04/16 19:26:34 - mmengine - INFO - Iter(train) [ 40500/160000] base_lr: 7.5395e-05 lr: 2.7875e-07 eta: 1 day, 9:06:57 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0102 decode.acc_seg: 99.5878 aux.loss_ce: 0.0090 aux.acc_seg: 98.8897 +04/16 19:27:24 - mmengine - INFO - Iter(train) [ 40550/160000] base_lr: 7.5363e-05 lr: 2.7863e-07 eta: 1 day, 9:06:08 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0103 decode.acc_seg: 99.4846 aux.loss_ce: 0.0085 aux.acc_seg: 99.0162 +04/16 19:28:14 - mmengine - INFO - Iter(train) [ 40600/160000] base_lr: 7.5332e-05 lr: 2.7852e-07 eta: 1 day, 9:05:18 time: 1.0003 data_time: 0.0043 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0093 decode.acc_seg: 99.5790 aux.loss_ce: 0.0093 aux.acc_seg: 99.0683 +04/16 19:29:04 - mmengine - INFO - Iter(train) [ 40650/160000] base_lr: 7.5300e-05 lr: 2.7840e-07 eta: 1 day, 9:04:29 time: 1.0003 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0089 decode.acc_seg: 99.6830 aux.loss_ce: 0.0091 aux.acc_seg: 99.3746 +04/16 19:29:54 - mmengine - INFO - Iter(train) [ 40700/160000] base_lr: 7.5269e-05 lr: 2.7828e-07 eta: 1 day, 9:03:39 time: 1.0007 data_time: 0.0047 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0093 decode.acc_seg: 99.6933 aux.loss_ce: 0.0088 aux.acc_seg: 99.0902 +04/16 19:30:44 - mmengine - INFO - Iter(train) [ 40750/160000] base_lr: 7.5237e-05 lr: 2.7817e-07 eta: 1 day, 9:02:50 time: 1.0000 data_time: 0.0044 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0095 decode.acc_seg: 99.6046 aux.loss_ce: 0.0085 aux.acc_seg: 99.1165 +04/16 19:31:34 - mmengine - INFO - Iter(train) [ 40800/160000] base_lr: 7.5206e-05 lr: 2.7805e-07 eta: 1 day, 9:02:00 time: 1.0013 data_time: 0.0043 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0086 decode.acc_seg: 99.6269 aux.loss_ce: 0.0084 aux.acc_seg: 99.3904 +04/16 19:32:24 - mmengine - INFO - Iter(train) [ 40850/160000] base_lr: 7.5174e-05 lr: 2.7793e-07 eta: 1 day, 9:01:11 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0098 decode.acc_seg: 99.5447 aux.loss_ce: 0.0089 aux.acc_seg: 99.0856 +04/16 19:33:14 - mmengine - INFO - Iter(train) [ 40900/160000] base_lr: 7.5142e-05 lr: 2.7782e-07 eta: 1 day, 9:00:21 time: 0.9992 data_time: 0.0043 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0093 decode.acc_seg: 99.3580 aux.loss_ce: 0.0090 aux.acc_seg: 98.1644 +04/16 19:34:04 - mmengine - INFO - Iter(train) [ 40950/160000] base_lr: 7.5111e-05 lr: 2.7770e-07 eta: 1 day, 8:59:31 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0113 decode.acc_seg: 99.5575 aux.loss_ce: 0.0098 aux.acc_seg: 98.8930 +04/16 19:34:54 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 19:34:54 - mmengine - INFO - Iter(train) [ 41000/160000] base_lr: 7.5079e-05 lr: 2.7758e-07 eta: 1 day, 8:58:42 time: 1.0010 data_time: 0.0042 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.5953 aux.loss_ce: 0.0077 aux.acc_seg: 99.0036 +04/16 19:35:44 - mmengine - INFO - Iter(train) [ 41050/160000] base_lr: 7.5048e-05 lr: 2.7747e-07 eta: 1 day, 8:57:52 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0088 decode.acc_seg: 99.6519 aux.loss_ce: 0.0084 aux.acc_seg: 99.2237 +04/16 19:36:34 - mmengine - INFO - Iter(train) [ 41100/160000] base_lr: 7.5016e-05 lr: 2.7735e-07 eta: 1 day, 8:57:02 time: 0.9982 data_time: 0.0046 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0081 decode.acc_seg: 99.7375 aux.loss_ce: 0.0076 aux.acc_seg: 99.3769 +04/16 19:37:24 - mmengine - INFO - Iter(train) [ 41150/160000] base_lr: 7.4985e-05 lr: 2.7723e-07 eta: 1 day, 8:56:13 time: 0.9989 data_time: 0.0048 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0091 decode.acc_seg: 99.6120 aux.loss_ce: 0.0087 aux.acc_seg: 99.0131 +04/16 19:38:14 - mmengine - INFO - Iter(train) [ 41200/160000] base_lr: 7.4953e-05 lr: 2.7712e-07 eta: 1 day, 8:55:23 time: 1.0006 data_time: 0.0050 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0083 decode.acc_seg: 99.6492 aux.loss_ce: 0.0083 aux.acc_seg: 99.1137 +04/16 19:39:04 - mmengine - INFO - Iter(train) [ 41250/160000] base_lr: 7.4922e-05 lr: 2.7700e-07 eta: 1 day, 8:54:34 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.5592 aux.loss_ce: 0.0086 aux.acc_seg: 99.1472 +04/16 19:39:54 - mmengine - INFO - Iter(train) [ 41300/160000] base_lr: 7.4890e-05 lr: 2.7688e-07 eta: 1 day, 8:53:44 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0090 decode.acc_seg: 99.6645 aux.loss_ce: 0.0087 aux.acc_seg: 99.1854 +04/16 19:40:44 - mmengine - INFO - Iter(train) [ 41350/160000] base_lr: 7.4859e-05 lr: 2.7677e-07 eta: 1 day, 8:52:55 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0093 decode.acc_seg: 99.7248 aux.loss_ce: 0.0088 aux.acc_seg: 99.3046 +04/16 19:41:34 - mmengine - INFO - Iter(train) [ 41400/160000] base_lr: 7.4827e-05 lr: 2.7665e-07 eta: 1 day, 8:52:05 time: 0.9995 data_time: 0.0052 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0090 decode.acc_seg: 99.7309 aux.loss_ce: 0.0096 aux.acc_seg: 99.1282 +04/16 19:42:24 - mmengine - INFO - Iter(train) [ 41450/160000] base_lr: 7.4795e-05 lr: 2.7653e-07 eta: 1 day, 8:51:15 time: 0.9994 data_time: 0.0045 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0093 decode.acc_seg: 99.6325 aux.loss_ce: 0.0099 aux.acc_seg: 98.8464 +04/16 19:43:14 - mmengine - INFO - Iter(train) [ 41500/160000] base_lr: 7.4764e-05 lr: 2.7642e-07 eta: 1 day, 8:50:26 time: 0.9989 data_time: 0.0049 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0108 decode.acc_seg: 99.5132 aux.loss_ce: 0.0109 aux.acc_seg: 98.7885 +04/16 19:44:04 - mmengine - INFO - Iter(train) [ 41550/160000] base_lr: 7.4732e-05 lr: 2.7630e-07 eta: 1 day, 8:49:36 time: 0.9990 data_time: 0.0044 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0098 decode.acc_seg: 99.6649 aux.loss_ce: 0.0090 aux.acc_seg: 99.2594 +04/16 19:44:54 - mmengine - INFO - Iter(train) [ 41600/160000] base_lr: 7.4701e-05 lr: 2.7618e-07 eta: 1 day, 8:48:47 time: 0.9991 data_time: 0.0044 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0084 decode.acc_seg: 99.7141 aux.loss_ce: 0.0091 aux.acc_seg: 99.4532 +04/16 19:45:44 - mmengine - INFO - Iter(train) [ 41650/160000] base_lr: 7.4669e-05 lr: 2.7607e-07 eta: 1 day, 8:47:57 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0089 decode.acc_seg: 99.7446 aux.loss_ce: 0.0089 aux.acc_seg: 99.2710 +04/16 19:46:34 - mmengine - INFO - Iter(train) [ 41700/160000] base_lr: 7.4638e-05 lr: 2.7595e-07 eta: 1 day, 8:47:07 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0099 decode.acc_seg: 99.6471 aux.loss_ce: 0.0096 aux.acc_seg: 99.0694 +04/16 19:47:24 - mmengine - INFO - Iter(train) [ 41750/160000] base_lr: 7.4606e-05 lr: 2.7583e-07 eta: 1 day, 8:46:18 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0079 decode.acc_seg: 99.7646 aux.loss_ce: 0.0086 aux.acc_seg: 99.1426 +04/16 19:48:14 - mmengine - INFO - Iter(train) [ 41800/160000] base_lr: 7.4575e-05 lr: 2.7572e-07 eta: 1 day, 8:45:28 time: 0.9994 data_time: 0.0046 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0089 decode.acc_seg: 99.6597 aux.loss_ce: 0.0089 aux.acc_seg: 98.9962 +04/16 19:49:04 - mmengine - INFO - Iter(train) [ 41850/160000] base_lr: 7.4543e-05 lr: 2.7560e-07 eta: 1 day, 8:44:39 time: 0.9997 data_time: 0.0051 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0093 decode.acc_seg: 99.6857 aux.loss_ce: 0.0088 aux.acc_seg: 99.4478 +04/16 19:49:54 - mmengine - INFO - Iter(train) [ 41900/160000] base_lr: 7.4512e-05 lr: 2.7548e-07 eta: 1 day, 8:43:49 time: 0.9992 data_time: 0.0046 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.6201 aux.loss_ce: 0.0072 aux.acc_seg: 99.2416 +04/16 19:50:44 - mmengine - INFO - Iter(train) [ 41950/160000] base_lr: 7.4480e-05 lr: 2.7537e-07 eta: 1 day, 8:42:59 time: 0.9993 data_time: 0.0045 memory: 8462 loss: 0.0208 decode.loss_ce: 0.0104 decode.acc_seg: 99.4757 aux.loss_ce: 0.0104 aux.acc_seg: 98.8832 +04/16 19:51:34 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 19:51:34 - mmengine - INFO - Iter(train) [ 42000/160000] base_lr: 7.4448e-05 lr: 2.7525e-07 eta: 1 day, 8:42:10 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0102 decode.acc_seg: 99.6668 aux.loss_ce: 0.0097 aux.acc_seg: 99.1030 +04/16 19:52:24 - mmengine - INFO - Iter(train) [ 42050/160000] base_lr: 7.4417e-05 lr: 2.7513e-07 eta: 1 day, 8:41:20 time: 1.0008 data_time: 0.0042 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0097 decode.acc_seg: 99.6855 aux.loss_ce: 0.0093 aux.acc_seg: 98.9084 +04/16 19:53:14 - mmengine - INFO - Iter(train) [ 42100/160000] base_lr: 7.4385e-05 lr: 2.7502e-07 eta: 1 day, 8:40:30 time: 0.9993 data_time: 0.0045 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0090 decode.acc_seg: 99.6357 aux.loss_ce: 0.0087 aux.acc_seg: 99.1886 +04/16 19:54:04 - mmengine - INFO - Iter(train) [ 42150/160000] base_lr: 7.4354e-05 lr: 2.7490e-07 eta: 1 day, 8:39:41 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0093 decode.acc_seg: 99.5745 aux.loss_ce: 0.0099 aux.acc_seg: 98.7017 +04/16 19:54:54 - mmengine - INFO - Iter(train) [ 42200/160000] base_lr: 7.4322e-05 lr: 2.7478e-07 eta: 1 day, 8:38:51 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.6887 aux.loss_ce: 0.0083 aux.acc_seg: 99.2498 +04/16 19:55:44 - mmengine - INFO - Iter(train) [ 42250/160000] base_lr: 7.4291e-05 lr: 2.7467e-07 eta: 1 day, 8:38:02 time: 0.9978 data_time: 0.0046 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0093 decode.acc_seg: 99.5541 aux.loss_ce: 0.0086 aux.acc_seg: 98.9265 +04/16 19:56:34 - mmengine - INFO - Iter(train) [ 42300/160000] base_lr: 7.4259e-05 lr: 2.7455e-07 eta: 1 day, 8:37:12 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0096 decode.acc_seg: 99.6393 aux.loss_ce: 0.0089 aux.acc_seg: 99.1846 +04/16 19:57:24 - mmengine - INFO - Iter(train) [ 42350/160000] base_lr: 7.4228e-05 lr: 2.7443e-07 eta: 1 day, 8:36:22 time: 0.9991 data_time: 0.0044 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0091 decode.acc_seg: 99.6151 aux.loss_ce: 0.0097 aux.acc_seg: 98.7541 +04/16 19:58:14 - mmengine - INFO - Iter(train) [ 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decode.acc_seg: 99.6370 aux.loss_ce: 0.0089 aux.acc_seg: 99.0051 +04/16 20:04:54 - mmengine - INFO - Iter(train) [ 42800/160000] base_lr: 7.3944e-05 lr: 2.7339e-07 eta: 1 day, 8:28:57 time: 1.0002 data_time: 0.0043 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0096 decode.acc_seg: 99.5687 aux.loss_ce: 0.0090 aux.acc_seg: 98.9897 +04/16 20:05:44 - mmengine - INFO - Iter(train) [ 42850/160000] base_lr: 7.3912e-05 lr: 2.7327e-07 eta: 1 day, 8:28:07 time: 1.0002 data_time: 0.0042 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0088 decode.acc_seg: 99.6162 aux.loss_ce: 0.0088 aux.acc_seg: 99.2292 +04/16 20:06:34 - mmengine - INFO - Iter(train) [ 42900/160000] base_lr: 7.3881e-05 lr: 2.7315e-07 eta: 1 day, 8:27:17 time: 1.0003 data_time: 0.0046 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.5518 aux.loss_ce: 0.0079 aux.acc_seg: 99.0183 +04/16 20:07:24 - mmengine - INFO - Iter(train) [ 42950/160000] base_lr: 7.3849e-05 lr: 2.7304e-07 eta: 1 day, 8:26:28 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0086 decode.acc_seg: 99.6401 aux.loss_ce: 0.0089 aux.acc_seg: 99.1362 +04/16 20:08:14 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 20:08:14 - mmengine - INFO - Iter(train) [ 43000/160000] base_lr: 7.3818e-05 lr: 2.7292e-07 eta: 1 day, 8:25:38 time: 1.0019 data_time: 0.0042 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0089 decode.acc_seg: 99.7192 aux.loss_ce: 0.0088 aux.acc_seg: 99.4164 +04/16 20:09:04 - mmengine - INFO - Iter(train) [ 43050/160000] base_lr: 7.3786e-05 lr: 2.7280e-07 eta: 1 day, 8:24:49 time: 1.0001 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0090 decode.acc_seg: 99.5909 aux.loss_ce: 0.0090 aux.acc_seg: 99.0608 +04/16 20:09:54 - mmengine - INFO - Iter(train) [ 43100/160000] base_lr: 7.3754e-05 lr: 2.7269e-07 eta: 1 day, 8:23:59 time: 1.0013 data_time: 0.0042 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0109 decode.acc_seg: 99.6798 aux.loss_ce: 0.0089 aux.acc_seg: 99.3006 +04/16 20:10:44 - mmengine - INFO - Iter(train) [ 43150/160000] base_lr: 7.3723e-05 lr: 2.7257e-07 eta: 1 day, 8:23:10 time: 1.0012 data_time: 0.0044 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0087 decode.acc_seg: 99.5703 aux.loss_ce: 0.0087 aux.acc_seg: 99.1564 +04/16 20:11:34 - mmengine - INFO - Iter(train) [ 43200/160000] base_lr: 7.3691e-05 lr: 2.7245e-07 eta: 1 day, 8:22:20 time: 0.9997 data_time: 0.0041 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6157 aux.loss_ce: 0.0074 aux.acc_seg: 99.0807 +04/16 20:12:24 - mmengine - INFO - Iter(train) [ 43250/160000] base_lr: 7.3660e-05 lr: 2.7234e-07 eta: 1 day, 8:21:31 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0091 decode.acc_seg: 99.7007 aux.loss_ce: 0.0084 aux.acc_seg: 99.2489 +04/16 20:13:14 - mmengine - INFO - Iter(train) [ 43300/160000] base_lr: 7.3628e-05 lr: 2.7222e-07 eta: 1 day, 8:20:41 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0094 decode.acc_seg: 99.6492 aux.loss_ce: 0.0095 aux.acc_seg: 99.1577 +04/16 20:14:04 - mmengine - INFO - Iter(train) [ 43350/160000] base_lr: 7.3597e-05 lr: 2.7210e-07 eta: 1 day, 8:19:51 time: 0.9996 data_time: 0.0044 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.6384 aux.loss_ce: 0.0086 aux.acc_seg: 99.2035 +04/16 20:14:54 - mmengine - INFO - Iter(train) [ 43400/160000] base_lr: 7.3565e-05 lr: 2.7199e-07 eta: 1 day, 8:19:02 time: 0.9990 data_time: 0.0044 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.6355 aux.loss_ce: 0.0084 aux.acc_seg: 99.0963 +04/16 20:15:44 - mmengine - INFO - Iter(train) [ 43450/160000] base_lr: 7.3534e-05 lr: 2.7187e-07 eta: 1 day, 8:18:12 time: 1.0000 data_time: 0.0044 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0082 decode.acc_seg: 99.6965 aux.loss_ce: 0.0086 aux.acc_seg: 98.9571 +04/16 20:16:34 - mmengine - INFO - Iter(train) [ 43500/160000] base_lr: 7.3502e-05 lr: 2.7175e-07 eta: 1 day, 8:17:22 time: 1.0007 data_time: 0.0042 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0099 decode.acc_seg: 99.6550 aux.loss_ce: 0.0096 aux.acc_seg: 99.1711 +04/16 20:17:24 - mmengine - INFO - Iter(train) [ 43550/160000] base_lr: 7.3470e-05 lr: 2.7164e-07 eta: 1 day, 8:16:33 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0102 decode.acc_seg: 99.7112 aux.loss_ce: 0.0094 aux.acc_seg: 99.1861 +04/16 20:18:14 - mmengine - INFO - Iter(train) [ 43600/160000] base_lr: 7.3439e-05 lr: 2.7152e-07 eta: 1 day, 8:15:43 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0096 decode.acc_seg: 99.5728 aux.loss_ce: 0.0098 aux.acc_seg: 98.7864 +04/16 20:19:04 - mmengine - INFO - Iter(train) [ 43650/160000] base_lr: 7.3407e-05 lr: 2.7140e-07 eta: 1 day, 8:14:54 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0081 decode.acc_seg: 99.5665 aux.loss_ce: 0.0083 aux.acc_seg: 99.0601 +04/16 20:19:54 - mmengine - INFO - Iter(train) [ 43700/160000] base_lr: 7.3376e-05 lr: 2.7129e-07 eta: 1 day, 8:14:04 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0084 decode.acc_seg: 99.6069 aux.loss_ce: 0.0091 aux.acc_seg: 99.0421 +04/16 20:20:44 - mmengine - INFO - Iter(train) [ 43750/160000] base_lr: 7.3344e-05 lr: 2.7117e-07 eta: 1 day, 8:13:14 time: 1.0006 data_time: 0.0049 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0088 decode.acc_seg: 99.4873 aux.loss_ce: 0.0087 aux.acc_seg: 98.5909 +04/16 20:21:34 - mmengine - INFO - Iter(train) [ 43800/160000] base_lr: 7.3313e-05 lr: 2.7105e-07 eta: 1 day, 8:12:25 time: 1.0010 data_time: 0.0056 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0079 decode.acc_seg: 99.7057 aux.loss_ce: 0.0085 aux.acc_seg: 99.2668 +04/16 20:22:24 - mmengine - INFO - Iter(train) [ 43850/160000] base_lr: 7.3281e-05 lr: 2.7094e-07 eta: 1 day, 8:11:35 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0090 decode.acc_seg: 99.7219 aux.loss_ce: 0.0086 aux.acc_seg: 99.3162 +04/16 20:23:14 - mmengine - INFO - Iter(train) [ 43900/160000] base_lr: 7.3250e-05 lr: 2.7082e-07 eta: 1 day, 8:10:46 time: 1.0003 data_time: 0.0047 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.7856 aux.loss_ce: 0.0076 aux.acc_seg: 99.4522 +04/16 20:24:04 - mmengine - INFO - Iter(train) [ 43950/160000] base_lr: 7.3218e-05 lr: 2.7070e-07 eta: 1 day, 8:09:56 time: 1.0004 data_time: 0.0050 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.7627 aux.loss_ce: 0.0081 aux.acc_seg: 99.3587 +04/16 20:24:54 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 20:24:54 - mmengine - INFO - Iter(train) [ 44000/160000] base_lr: 7.3187e-05 lr: 2.7059e-07 eta: 1 day, 8:09:07 time: 0.9995 data_time: 0.0042 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0086 decode.acc_seg: 99.6723 aux.loss_ce: 0.0090 aux.acc_seg: 99.0702 +04/16 20:25:44 - mmengine - INFO - Iter(train) [ 44050/160000] base_lr: 7.3155e-05 lr: 2.7047e-07 eta: 1 day, 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decode.acc_seg: 99.5745 aux.loss_ce: 0.0089 aux.acc_seg: 98.9307 +04/16 20:35:44 - mmengine - INFO - Iter(train) [ 44650/160000] base_lr: 7.2776e-05 lr: 2.6907e-07 eta: 1 day, 7:58:22 time: 0.9989 data_time: 0.0045 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0076 decode.acc_seg: 99.6227 aux.loss_ce: 0.0081 aux.acc_seg: 98.8358 +04/16 20:36:34 - mmengine - INFO - Iter(train) [ 44700/160000] base_lr: 7.2745e-05 lr: 2.6895e-07 eta: 1 day, 7:57:32 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.0187 aux.loss_ce: 0.0085 aux.acc_seg: 98.4308 +04/16 20:37:24 - mmengine - INFO - Iter(train) [ 44750/160000] base_lr: 7.2713e-05 lr: 2.6884e-07 eta: 1 day, 7:56:43 time: 1.0005 data_time: 0.0045 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.5255 aux.loss_ce: 0.0082 aux.acc_seg: 98.7513 +04/16 20:38:14 - mmengine - INFO - Iter(train) [ 44800/160000] base_lr: 7.2682e-05 lr: 2.6872e-07 eta: 1 day, 7:55:53 time: 1.0002 data_time: 0.0050 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.6208 aux.loss_ce: 0.0089 aux.acc_seg: 99.0129 +04/16 20:39:04 - mmengine - INFO - Iter(train) [ 44850/160000] base_lr: 7.2650e-05 lr: 2.6860e-07 eta: 1 day, 7:55:04 time: 1.0005 data_time: 0.0048 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0086 decode.acc_seg: 99.7356 aux.loss_ce: 0.0095 aux.acc_seg: 99.1257 +04/16 20:39:54 - mmengine - INFO - Iter(train) [ 44900/160000] base_lr: 7.2619e-05 lr: 2.6849e-07 eta: 1 day, 7:54:14 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0208 decode.loss_ce: 0.0106 decode.acc_seg: 99.5712 aux.loss_ce: 0.0103 aux.acc_seg: 99.1388 +04/16 20:40:44 - mmengine - INFO - Iter(train) [ 44950/160000] base_lr: 7.2587e-05 lr: 2.6837e-07 eta: 1 day, 7:53:25 time: 1.0008 data_time: 0.0045 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0100 decode.acc_seg: 99.7240 aux.loss_ce: 0.0097 aux.acc_seg: 99.2456 +04/16 20:41:34 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 20:41:34 - mmengine - INFO - Iter(train) [ 45000/160000] base_lr: 7.2556e-05 lr: 2.6825e-07 eta: 1 day, 7:52:35 time: 1.0009 data_time: 0.0043 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0097 decode.acc_seg: 99.5369 aux.loss_ce: 0.0088 aux.acc_seg: 99.2157 +04/16 20:42:24 - mmengine - INFO - Iter(train) [ 45050/160000] base_lr: 7.2524e-05 lr: 2.6814e-07 eta: 1 day, 7:51:45 time: 0.9995 data_time: 0.0046 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0084 decode.acc_seg: 99.7570 aux.loss_ce: 0.0083 aux.acc_seg: 99.4604 +04/16 20:43:14 - mmengine - INFO - Iter(train) [ 45100/160000] base_lr: 7.2493e-05 lr: 2.6802e-07 eta: 1 day, 7:50:56 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0082 decode.acc_seg: 99.6527 aux.loss_ce: 0.0080 aux.acc_seg: 99.4343 +04/16 20:44:04 - mmengine - INFO - Iter(train) [ 45150/160000] base_lr: 7.2461e-05 lr: 2.6790e-07 eta: 1 day, 7:50:06 time: 0.9982 data_time: 0.0044 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6601 aux.loss_ce: 0.0075 aux.acc_seg: 99.0047 +04/16 20:44:55 - mmengine - INFO - Iter(train) [ 45200/160000] base_lr: 7.2429e-05 lr: 2.6779e-07 eta: 1 day, 7:49:17 time: 1.0013 data_time: 0.0045 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.7080 aux.loss_ce: 0.0080 aux.acc_seg: 99.2273 +04/16 20:45:45 - mmengine - INFO - Iter(train) [ 45250/160000] base_lr: 7.2398e-05 lr: 2.6767e-07 eta: 1 day, 7:48:27 time: 0.9992 data_time: 0.0048 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0080 decode.acc_seg: 99.6346 aux.loss_ce: 0.0087 aux.acc_seg: 99.0280 +04/16 20:46:35 - mmengine - INFO - Iter(train) [ 45300/160000] base_lr: 7.2366e-05 lr: 2.6755e-07 eta: 1 day, 7:47:37 time: 1.0023 data_time: 0.0042 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.6607 aux.loss_ce: 0.0085 aux.acc_seg: 98.9693 +04/16 20:47:25 - mmengine - INFO - Iter(train) [ 45350/160000] base_lr: 7.2335e-05 lr: 2.6744e-07 eta: 1 day, 7:46:48 time: 1.0015 data_time: 0.0047 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0095 decode.acc_seg: 99.6508 aux.loss_ce: 0.0090 aux.acc_seg: 98.9176 +04/16 20:48:15 - mmengine - INFO - Iter(train) [ 45400/160000] base_lr: 7.2303e-05 lr: 2.6732e-07 eta: 1 day, 7:45:58 time: 1.0004 data_time: 0.0049 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0089 decode.acc_seg: 99.5935 aux.loss_ce: 0.0090 aux.acc_seg: 99.2535 +04/16 20:49:05 - mmengine - INFO - Iter(train) [ 45450/160000] base_lr: 7.2272e-05 lr: 2.6720e-07 eta: 1 day, 7:45:09 time: 1.0003 data_time: 0.0046 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0087 decode.acc_seg: 99.7452 aux.loss_ce: 0.0086 aux.acc_seg: 99.3311 +04/16 20:49:55 - mmengine - INFO - Iter(train) [ 45500/160000] base_lr: 7.2240e-05 lr: 2.6709e-07 eta: 1 day, 7:44:19 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0081 decode.acc_seg: 99.5834 aux.loss_ce: 0.0087 aux.acc_seg: 98.7307 +04/16 20:50:45 - mmengine - INFO - Iter(train) [ 45550/160000] base_lr: 7.2209e-05 lr: 2.6697e-07 eta: 1 day, 7:43:29 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0088 decode.acc_seg: 99.4583 aux.loss_ce: 0.0088 aux.acc_seg: 98.8039 +04/16 20:51:35 - mmengine - INFO - Iter(train) [ 45600/160000] base_lr: 7.2177e-05 lr: 2.6685e-07 eta: 1 day, 7:42:40 time: 1.0012 data_time: 0.0048 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.5573 aux.loss_ce: 0.0079 aux.acc_seg: 98.9632 +04/16 20:52:25 - mmengine - INFO - Iter(train) [ 45650/160000] base_lr: 7.2146e-05 lr: 2.6674e-07 eta: 1 day, 7:41:50 time: 0.9990 data_time: 0.0044 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.6689 aux.loss_ce: 0.0070 aux.acc_seg: 99.1955 +04/16 20:53:15 - mmengine - INFO - Iter(train) [ 45700/160000] base_lr: 7.2114e-05 lr: 2.6662e-07 eta: 1 day, 7:41:00 time: 1.0007 data_time: 0.0044 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0091 decode.acc_seg: 99.7379 aux.loss_ce: 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decode.loss_ce: 0.0084 decode.acc_seg: 99.5726 aux.loss_ce: 0.0087 aux.acc_seg: 98.9002 +04/16 20:57:25 - mmengine - INFO - Iter(train) [ 45950/160000] base_lr: 7.1956e-05 lr: 2.6604e-07 eta: 1 day, 7:36:52 time: 1.0001 data_time: 0.0049 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0081 decode.acc_seg: 99.4663 aux.loss_ce: 0.0082 aux.acc_seg: 98.8310 +04/16 20:58:15 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 20:58:15 - mmengine - INFO - Iter(train) [ 46000/160000] base_lr: 7.1925e-05 lr: 2.6592e-07 eta: 1 day, 7:36:03 time: 0.9997 data_time: 0.0047 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0082 decode.acc_seg: 99.7248 aux.loss_ce: 0.0086 aux.acc_seg: 99.3486 +04/16 20:59:05 - mmengine - INFO - Iter(train) [ 46050/160000] base_lr: 7.1893e-05 lr: 2.6580e-07 eta: 1 day, 7:35:13 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6130 aux.loss_ce: 0.0080 aux.acc_seg: 99.0934 +04/16 20:59:55 - mmengine - INFO - Iter(train) [ 46100/160000] base_lr: 7.1862e-05 lr: 2.6569e-07 eta: 1 day, 7:34:23 time: 0.9987 data_time: 0.0047 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0082 decode.acc_seg: 99.7351 aux.loss_ce: 0.0085 aux.acc_seg: 99.1802 +04/16 21:00:45 - mmengine - INFO - Iter(train) [ 46150/160000] base_lr: 7.1830e-05 lr: 2.6557e-07 eta: 1 day, 7:33:34 time: 0.9996 data_time: 0.0055 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0083 decode.acc_seg: 99.5489 aux.loss_ce: 0.0079 aux.acc_seg: 99.2378 +04/16 21:01:35 - mmengine - INFO - Iter(train) [ 46200/160000] base_lr: 7.1799e-05 lr: 2.6545e-07 eta: 1 day, 7:32:44 time: 1.0006 data_time: 0.0049 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0090 decode.acc_seg: 99.5787 aux.loss_ce: 0.0087 aux.acc_seg: 99.0063 +04/16 21:02:25 - mmengine - INFO - Iter(train) [ 46250/160000] base_lr: 7.1767e-05 lr: 2.6534e-07 eta: 1 day, 7:31:54 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.5375 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loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7429 aux.loss_ce: 0.0072 aux.acc_seg: 99.1627 +04/16 21:06:35 - mmengine - INFO - Iter(train) [ 46500/160000] base_lr: 7.1609e-05 lr: 2.6475e-07 eta: 1 day, 7:27:46 time: 0.9993 data_time: 0.0044 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0094 decode.acc_seg: 99.6775 aux.loss_ce: 0.0087 aux.acc_seg: 99.2115 +04/16 21:07:25 - mmengine - INFO - Iter(train) [ 46550/160000] base_lr: 7.1578e-05 lr: 2.6464e-07 eta: 1 day, 7:26:57 time: 1.0005 data_time: 0.0048 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0094 decode.acc_seg: 99.7189 aux.loss_ce: 0.0091 aux.acc_seg: 99.2790 +04/16 21:08:15 - mmengine - INFO - Iter(train) [ 46600/160000] base_lr: 7.1546e-05 lr: 2.6452e-07 eta: 1 day, 7:26:07 time: 0.9988 data_time: 0.0049 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0088 decode.acc_seg: 99.6393 aux.loss_ce: 0.0096 aux.acc_seg: 98.8394 +04/16 21:09:05 - mmengine - INFO - Iter(train) [ 46650/160000] base_lr: 7.1515e-05 lr: 2.6440e-07 eta: 1 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memory: 8462 loss: 0.0168 decode.loss_ce: 0.0083 decode.acc_seg: 99.6246 aux.loss_ce: 0.0085 aux.acc_seg: 99.0425 +04/16 21:15:45 - mmengine - INFO - Iter(train) [ 47050/160000] base_lr: 7.1262e-05 lr: 2.6347e-07 eta: 1 day, 7:18:40 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0095 decode.acc_seg: 99.6002 aux.loss_ce: 0.0091 aux.acc_seg: 99.0852 +04/16 21:16:35 - mmengine - INFO - Iter(train) [ 47100/160000] base_lr: 7.1231e-05 lr: 2.6335e-07 eta: 1 day, 7:17:50 time: 1.0004 data_time: 0.0042 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0090 decode.acc_seg: 99.5827 aux.loss_ce: 0.0089 aux.acc_seg: 98.9855 +04/16 21:17:25 - mmengine - INFO - Iter(train) [ 47150/160000] base_lr: 7.1199e-05 lr: 2.6324e-07 eta: 1 day, 7:17:01 time: 0.9993 data_time: 0.0052 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0085 decode.acc_seg: 99.4734 aux.loss_ce: 0.0090 aux.acc_seg: 98.6307 +04/16 21:18:15 - mmengine - INFO - Iter(train) [ 47200/160000] base_lr: 7.1168e-05 lr: 2.6312e-07 eta: 1 day, 7:16:11 time: 1.0009 data_time: 0.0048 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0085 decode.acc_seg: 99.5968 aux.loss_ce: 0.0085 aux.acc_seg: 99.2611 +04/16 21:19:05 - mmengine - INFO - Iter(train) [ 47250/160000] base_lr: 7.1136e-05 lr: 2.6300e-07 eta: 1 day, 7:15:21 time: 0.9998 data_time: 0.0042 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.7351 aux.loss_ce: 0.0081 aux.acc_seg: 99.2352 +04/16 21:19:55 - mmengine - INFO - Iter(train) [ 47300/160000] base_lr: 7.1105e-05 lr: 2.6289e-07 eta: 1 day, 7:14:32 time: 1.0009 data_time: 0.0047 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0090 decode.acc_seg: 99.4915 aux.loss_ce: 0.0089 aux.acc_seg: 98.8310 +04/16 21:20:45 - mmengine - INFO - Iter(train) [ 47350/160000] base_lr: 7.1073e-05 lr: 2.6277e-07 eta: 1 day, 7:13:42 time: 0.9995 data_time: 0.0046 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.5287 aux.loss_ce: 0.0080 aux.acc_seg: 98.7986 +04/16 21:21:35 - mmengine - INFO - Iter(train) [ 47400/160000] base_lr: 7.1041e-05 lr: 2.6265e-07 eta: 1 day, 7:12:52 time: 0.9991 data_time: 0.0046 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0078 decode.acc_seg: 99.7190 aux.loss_ce: 0.0085 aux.acc_seg: 99.3147 +04/16 21:22:25 - mmengine - INFO - Iter(train) [ 47450/160000] base_lr: 7.1010e-05 lr: 2.6254e-07 eta: 1 day, 7:12:03 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6927 aux.loss_ce: 0.0079 aux.acc_seg: 99.1320 +04/16 21:23:15 - mmengine - INFO - Iter(train) [ 47500/160000] base_lr: 7.0978e-05 lr: 2.6242e-07 eta: 1 day, 7:11:13 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.8337 aux.loss_ce: 0.0083 aux.acc_seg: 99.4114 +04/16 21:24:05 - mmengine - INFO - Iter(train) [ 47550/160000] base_lr: 7.0947e-05 lr: 2.6231e-07 eta: 1 day, 7:10:24 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.6178 aux.loss_ce: 0.0080 aux.acc_seg: 98.8905 +04/16 21:24:55 - mmengine - INFO - Iter(train) [ 47600/160000] base_lr: 7.0915e-05 lr: 2.6219e-07 eta: 1 day, 7:09:34 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.7498 aux.loss_ce: 0.0076 aux.acc_seg: 99.4558 +04/16 21:25:45 - mmengine - INFO - Iter(train) [ 47650/160000] base_lr: 7.0884e-05 lr: 2.6207e-07 eta: 1 day, 7:08:44 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0084 decode.acc_seg: 99.6456 aux.loss_ce: 0.0084 aux.acc_seg: 98.7534 +04/16 21:26:35 - mmengine - INFO - Iter(train) [ 47700/160000] base_lr: 7.0852e-05 lr: 2.6196e-07 eta: 1 day, 7:07:54 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7168 aux.loss_ce: 0.0080 aux.acc_seg: 99.2168 +04/16 21:27:25 - mmengine - INFO - Iter(train) [ 47750/160000] base_lr: 7.0821e-05 lr: 2.6184e-07 eta: 1 day, 7:07:05 time: 0.9984 data_time: 0.0043 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.7892 aux.loss_ce: 0.0080 aux.acc_seg: 99.4446 +04/16 21:28:15 - mmengine - INFO - Iter(train) [ 47800/160000] base_lr: 7.0789e-05 lr: 2.6172e-07 eta: 1 day, 7:06:15 time: 0.9998 data_time: 0.0049 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7206 aux.loss_ce: 0.0079 aux.acc_seg: 99.1978 +04/16 21:29:05 - mmengine - INFO - Iter(train) [ 47850/160000] base_lr: 7.0758e-05 lr: 2.6161e-07 eta: 1 day, 7:05:25 time: 1.0007 data_time: 0.0047 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0093 decode.acc_seg: 99.6248 aux.loss_ce: 0.0097 aux.acc_seg: 98.7993 +04/16 21:29:55 - mmengine - INFO - Iter(train) [ 47900/160000] base_lr: 7.0726e-05 lr: 2.6149e-07 eta: 1 day, 7:04:36 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0072 decode.acc_seg: 99.6380 aux.loss_ce: 0.0078 aux.acc_seg: 98.9882 +04/16 21:30:45 - mmengine - INFO - Iter(train) [ 47950/160000] base_lr: 7.0694e-05 lr: 2.6137e-07 eta: 1 day, 7:03:46 time: 0.9992 data_time: 0.0047 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6914 aux.loss_ce: 0.0078 aux.acc_seg: 99.1798 +04/16 21:31:35 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 21:31:35 - mmengine - INFO - Iter(train) [ 48000/160000] base_lr: 7.0663e-05 lr: 2.6126e-07 eta: 1 day, 7:02:56 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0098 decode.acc_seg: 99.6243 aux.loss_ce: 0.0091 aux.acc_seg: 99.0707 +04/16 21:32:25 - mmengine - INFO - Iter(train) [ 48050/160000] base_lr: 7.0631e-05 lr: 2.6114e-07 eta: 1 day, 7:02:07 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0092 decode.acc_seg: 99.6897 aux.loss_ce: 0.0089 aux.acc_seg: 99.1552 +04/16 21:33:15 - mmengine - INFO - Iter(train) [ 48100/160000] base_lr: 7.0600e-05 lr: 2.6102e-07 eta: 1 day, 7:01:17 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0084 decode.acc_seg: 99.6603 aux.loss_ce: 0.0084 aux.acc_seg: 99.1549 +04/16 21:34:05 - mmengine - INFO - Iter(train) [ 48150/160000] base_lr: 7.0568e-05 lr: 2.6091e-07 eta: 1 day, 7:00:27 time: 0.9995 data_time: 0.0047 memory: 8462 loss: 0.0203 decode.loss_ce: 0.0103 decode.acc_seg: 99.4989 aux.loss_ce: 0.0101 aux.acc_seg: 98.7778 +04/16 21:34:55 - mmengine - INFO - Iter(train) [ 48200/160000] base_lr: 7.0537e-05 lr: 2.6079e-07 eta: 1 day, 6:59:37 time: 0.9996 data_time: 0.0046 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0098 decode.acc_seg: 99.7543 aux.loss_ce: 0.0091 aux.acc_seg: 99.4373 +04/16 21:35:45 - mmengine - INFO - Iter(train) [ 48250/160000] base_lr: 7.0505e-05 lr: 2.6067e-07 eta: 1 day, 6:58:48 time: 0.9992 data_time: 0.0051 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0081 decode.acc_seg: 99.6651 aux.loss_ce: 0.0086 aux.acc_seg: 99.2311 +04/16 21:36:35 - mmengine - INFO - Iter(train) [ 48300/160000] base_lr: 7.0474e-05 lr: 2.6056e-07 eta: 1 day, 6:57:58 time: 0.9994 data_time: 0.0043 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6485 aux.loss_ce: 0.0078 aux.acc_seg: 99.0545 +04/16 21:37:25 - mmengine - INFO - Iter(train) [ 48350/160000] base_lr: 7.0442e-05 lr: 2.6044e-07 eta: 1 day, 6:57:08 time: 1.0002 data_time: 0.0043 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.7498 aux.loss_ce: 0.0078 aux.acc_seg: 99.1947 +04/16 21:38:15 - mmengine - INFO - Iter(train) [ 48400/160000] base_lr: 7.0411e-05 lr: 2.6032e-07 eta: 1 day, 6:56:19 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0091 decode.acc_seg: 99.5787 aux.loss_ce: 0.0094 aux.acc_seg: 98.7988 +04/16 21:39:05 - mmengine - INFO - Iter(train) [ 48450/160000] base_lr: 7.0379e-05 lr: 2.6021e-07 eta: 1 day, 6:55:29 time: 1.0026 data_time: 0.0048 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7229 aux.loss_ce: 0.0078 aux.acc_seg: 99.3111 +04/16 21:39:55 - mmengine - INFO - Iter(train) [ 48500/160000] base_lr: 7.0347e-05 lr: 2.6009e-07 eta: 1 day, 6:54:39 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0083 decode.acc_seg: 99.8358 aux.loss_ce: 0.0083 aux.acc_seg: 99.4041 +04/16 21:40:45 - mmengine - INFO - Iter(train) [ 48550/160000] base_lr: 7.0316e-05 lr: 2.5997e-07 eta: 1 day, 6:53:50 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0082 decode.acc_seg: 99.7225 aux.loss_ce: 0.0090 aux.acc_seg: 99.2270 +04/16 21:41:35 - mmengine - INFO - Iter(train) [ 48600/160000] base_lr: 7.0284e-05 lr: 2.5986e-07 eta: 1 day, 6:53:00 time: 1.0002 data_time: 0.0049 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0098 decode.acc_seg: 99.4205 aux.loss_ce: 0.0094 aux.acc_seg: 98.5340 +04/16 21:42:25 - mmengine - INFO - Iter(train) [ 48650/160000] base_lr: 7.0253e-05 lr: 2.5974e-07 eta: 1 day, 6:52:10 time: 0.9992 data_time: 0.0046 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.6180 aux.loss_ce: 0.0078 aux.acc_seg: 98.8667 +04/16 21:43:15 - mmengine - INFO - Iter(train) [ 48700/160000] base_lr: 7.0221e-05 lr: 2.5962e-07 eta: 1 day, 6:51:21 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.6328 aux.loss_ce: 0.0085 aux.acc_seg: 98.6403 +04/16 21:44:05 - mmengine - INFO - Iter(train) [ 48750/160000] base_lr: 7.0190e-05 lr: 2.5951e-07 eta: 1 day, 6:50:31 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.7383 aux.loss_ce: 0.0081 aux.acc_seg: 99.1625 +04/16 21:44:55 - mmengine - INFO - Iter(train) [ 48800/160000] base_lr: 7.0158e-05 lr: 2.5939e-07 eta: 1 day, 6:49:41 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0079 decode.acc_seg: 99.7572 aux.loss_ce: 0.0074 aux.acc_seg: 99.3153 +04/16 21:45:45 - mmengine - INFO - Iter(train) [ 48850/160000] base_lr: 7.0127e-05 lr: 2.5927e-07 eta: 1 day, 6:48:51 time: 0.9990 data_time: 0.0046 memory: 8462 loss: 0.0134 decode.loss_ce: 0.0067 decode.acc_seg: 99.6578 aux.loss_ce: 0.0066 aux.acc_seg: 99.3183 +04/16 21:46:35 - mmengine - INFO - Iter(train) [ 48900/160000] base_lr: 7.0095e-05 lr: 2.5916e-07 eta: 1 day, 6:48:02 time: 1.0000 data_time: 0.0044 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6305 aux.loss_ce: 0.0080 aux.acc_seg: 98.9502 +04/16 21:47:25 - mmengine - INFO - Iter(train) [ 48950/160000] base_lr: 7.0064e-05 lr: 2.5904e-07 eta: 1 day, 6:47:12 time: 0.9987 data_time: 0.0046 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0088 decode.acc_seg: 99.4066 aux.loss_ce: 0.0089 aux.acc_seg: 98.9624 +04/16 21:48:15 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 21:48:15 - mmengine - INFO - Iter(train) [ 49000/160000] base_lr: 7.0032e-05 lr: 2.5892e-07 eta: 1 day, 6:46:22 time: 0.9999 data_time: 0.0049 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0089 decode.acc_seg: 99.5052 aux.loss_ce: 0.0092 aux.acc_seg: 99.0047 +04/16 21:49:05 - mmengine - INFO - Iter(train) [ 49050/160000] base_lr: 7.0000e-05 lr: 2.5881e-07 eta: 1 day, 6:45:33 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0089 decode.acc_seg: 99.7383 aux.loss_ce: 0.0088 aux.acc_seg: 99.0404 +04/16 21:49:55 - mmengine - INFO - Iter(train) [ 49100/160000] base_lr: 6.9969e-05 lr: 2.5869e-07 eta: 1 day, 6:44:43 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0095 decode.acc_seg: 99.6180 aux.loss_ce: 0.0102 aux.acc_seg: 98.9094 +04/16 21:50:45 - mmengine - INFO - Iter(train) [ 49150/160000] base_lr: 6.9937e-05 lr: 2.5857e-07 eta: 1 day, 6:43:53 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0090 decode.acc_seg: 99.7953 aux.loss_ce: 0.0087 aux.acc_seg: 99.5382 +04/16 21:51:35 - mmengine - INFO - Iter(train) [ 49200/160000] base_lr: 6.9906e-05 lr: 2.5846e-07 eta: 1 day, 6:43:04 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7887 aux.loss_ce: 0.0082 aux.acc_seg: 99.2756 +04/16 21:52:25 - mmengine - INFO - Iter(train) [ 49250/160000] base_lr: 6.9874e-05 lr: 2.5834e-07 eta: 1 day, 6:42:14 time: 0.9990 data_time: 0.0049 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0063 decode.acc_seg: 99.7440 aux.loss_ce: 0.0067 aux.acc_seg: 99.3977 +04/16 21:53:15 - mmengine - INFO - Iter(train) [ 49300/160000] base_lr: 6.9843e-05 lr: 2.5822e-07 eta: 1 day, 6:41:24 time: 0.9993 data_time: 0.0045 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0095 decode.acc_seg: 99.5224 aux.loss_ce: 0.0101 aux.acc_seg: 98.4156 +04/16 21:54:05 - mmengine - INFO - Iter(train) [ 49350/160000] base_lr: 6.9811e-05 lr: 2.5811e-07 eta: 1 day, 6:40:34 time: 0.9999 data_time: 0.0044 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7000 aux.loss_ce: 0.0082 aux.acc_seg: 99.0486 +04/16 21:54:55 - mmengine - INFO - Iter(train) [ 49400/160000] base_lr: 6.9780e-05 lr: 2.5799e-07 eta: 1 day, 6:39:45 time: 0.9992 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.7208 aux.loss_ce: 0.0081 aux.acc_seg: 99.2958 +04/16 21:55:45 - mmengine - INFO - Iter(train) [ 49450/160000] base_lr: 6.9748e-05 lr: 2.5787e-07 eta: 1 day, 6:38:55 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6529 aux.loss_ce: 0.0081 aux.acc_seg: 98.9460 +04/16 21:56:35 - mmengine - INFO - Iter(train) [ 49500/160000] base_lr: 6.9717e-05 lr: 2.5776e-07 eta: 1 day, 6:38:05 time: 0.9994 data_time: 0.0045 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0097 decode.acc_seg: 99.6525 aux.loss_ce: 0.0088 aux.acc_seg: 99.2466 +04/16 21:57:25 - mmengine - INFO - Iter(train) [ 49550/160000] base_lr: 6.9685e-05 lr: 2.5764e-07 eta: 1 day, 6:37:15 time: 0.9997 data_time: 0.0051 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.6832 aux.loss_ce: 0.0080 aux.acc_seg: 98.9904 +04/16 21:58:15 - mmengine - INFO - Iter(train) [ 49600/160000] base_lr: 6.9653e-05 lr: 2.5752e-07 eta: 1 day, 6:36:26 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0087 decode.acc_seg: 99.5953 aux.loss_ce: 0.0088 aux.acc_seg: 98.9237 +04/16 21:59:05 - mmengine - INFO - Iter(train) [ 49650/160000] base_lr: 6.9622e-05 lr: 2.5741e-07 eta: 1 day, 6:35:36 time: 1.0004 data_time: 0.0047 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0087 decode.acc_seg: 99.5466 aux.loss_ce: 0.0086 aux.acc_seg: 99.1205 +04/16 21:59:54 - mmengine - INFO - Iter(train) [ 49700/160000] base_lr: 6.9590e-05 lr: 2.5729e-07 eta: 1 day, 6:34:46 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0082 decode.acc_seg: 99.5956 aux.loss_ce: 0.0083 aux.acc_seg: 99.2729 +04/16 22:00:44 - mmengine - INFO - Iter(train) [ 49750/160000] base_lr: 6.9559e-05 lr: 2.5717e-07 eta: 1 day, 6:33:57 time: 0.9998 data_time: 0.0055 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0079 decode.acc_seg: 99.6555 aux.loss_ce: 0.0086 aux.acc_seg: 99.3425 +04/16 22:01:35 - mmengine - INFO - Iter(train) [ 49800/160000] base_lr: 6.9527e-05 lr: 2.5706e-07 eta: 1 day, 6:33:07 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.5544 aux.loss_ce: 0.0072 aux.acc_seg: 98.7452 +04/16 22:02:24 - mmengine - INFO - Iter(train) [ 49850/160000] base_lr: 6.9496e-05 lr: 2.5694e-07 eta: 1 day, 6:32:17 time: 0.9992 data_time: 0.0046 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6910 aux.loss_ce: 0.0078 aux.acc_seg: 99.1886 +04/16 22:03:14 - mmengine - INFO - Iter(train) [ 49900/160000] base_lr: 6.9464e-05 lr: 2.5682e-07 eta: 1 day, 6:31:27 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0081 decode.acc_seg: 99.6103 aux.loss_ce: 0.0085 aux.acc_seg: 99.0919 +04/16 22:04:04 - mmengine - INFO - Iter(train) [ 49950/160000] base_lr: 6.9433e-05 lr: 2.5671e-07 eta: 1 day, 6:30:38 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0094 decode.acc_seg: 99.4986 aux.loss_ce: 0.0095 aux.acc_seg: 98.8159 +04/16 22:04:54 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 22:04:54 - mmengine - INFO - Iter(train) [ 50000/160000] base_lr: 6.9401e-05 lr: 2.5659e-07 eta: 1 day, 6:29:48 time: 0.9995 data_time: 0.0047 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0079 decode.acc_seg: 99.7543 aux.loss_ce: 0.0085 aux.acc_seg: 99.2180 +04/16 22:04:54 - mmengine - INFO - Saving checkpoint at 50000 iterations +04/16 22:05:04 - mmengine - INFO - Iter(val) [ 50/355] eta: 0:00:35 time: 0.1157 data_time: 0.0016 memory: 4004 +04/16 22:05:10 - mmengine - INFO - Iter(val) [100/355] eta: 0:00:29 time: 0.1155 data_time: 0.0014 memory: 4004 +04/16 22:05:16 - mmengine - INFO - Iter(val) [150/355] eta: 0:00:23 time: 0.1161 data_time: 0.0017 memory: 4004 +04/16 22:05:22 - mmengine - INFO - Iter(val) [200/355] eta: 0:00:17 time: 0.1161 data_time: 0.0017 memory: 4004 +04/16 22:05:28 - mmengine - INFO - Iter(val) [250/355] eta: 0:00:12 time: 0.1158 data_time: 0.0015 memory: 4004 +04/16 22:05:33 - mmengine - INFO - Iter(val) [300/355] eta: 0:00:06 time: 0.1158 data_time: 0.0014 memory: 4004 +04/16 22:05:39 - mmengine - INFO - Iter(val) [350/355] eta: 0:00:00 time: 0.1155 data_time: 0.0012 memory: 4004 +04/16 22:05:40 - mmengine - INFO - per class results: +04/16 22:05:40 - mmengine - INFO - ++------------+-------+-------+ +| Class | IoU | Acc | ++------------+-------+-------+ +| background | 99.2 | 99.59 | +| contrast | 82.51 | 90.52 | ++------------+-------+-------+ +04/16 22:05:40 - mmengine - INFO - Iter(val) [355/355] aAcc: 99.2300 mIoU: 90.8500 mAcc: 95.0600 data_time: 0.0017 time: 0.1160 +04/16 22:06:30 - mmengine - INFO - Iter(train) [ 50050/160000] base_lr: 6.9370e-05 lr: 2.5647e-07 eta: 1 day, 6:28:59 time: 0.9975 data_time: 0.0047 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0071 decode.acc_seg: 99.7061 aux.loss_ce: 0.0070 aux.acc_seg: 99.3778 +04/16 22:07:20 - mmengine - INFO - Iter(train) [ 50100/160000] base_lr: 6.9338e-05 lr: 2.5636e-07 eta: 1 day, 6:28:09 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0084 decode.acc_seg: 99.7219 aux.loss_ce: 0.0086 aux.acc_seg: 99.2453 +04/16 22:08:10 - mmengine - INFO - Iter(train) [ 50150/160000] base_lr: 6.9306e-05 lr: 2.5624e-07 eta: 1 day, 6:27:19 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6168 aux.loss_ce: 0.0081 aux.acc_seg: 98.7686 +04/16 22:09:00 - mmengine - INFO - Iter(train) [ 50200/160000] base_lr: 6.9275e-05 lr: 2.5612e-07 eta: 1 day, 6:26:30 time: 0.9978 data_time: 0.0044 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6302 aux.loss_ce: 0.0082 aux.acc_seg: 99.0292 +04/16 22:09:50 - mmengine - INFO - Iter(train) [ 50250/160000] base_lr: 6.9243e-05 lr: 2.5601e-07 eta: 1 day, 6:25:40 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0078 decode.acc_seg: 99.5680 aux.loss_ce: 0.0090 aux.acc_seg: 98.6553 +04/16 22:10:40 - mmengine - INFO - Iter(train) [ 50300/160000] base_lr: 6.9212e-05 lr: 2.5589e-07 eta: 1 day, 6:24:50 time: 0.9994 data_time: 0.0046 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0085 decode.acc_seg: 99.6376 aux.loss_ce: 0.0094 aux.acc_seg: 98.9412 +04/16 22:11:30 - mmengine - INFO - Iter(train) [ 50350/160000] base_lr: 6.9180e-05 lr: 2.5577e-07 eta: 1 day, 6:24:00 time: 0.9992 data_time: 0.0046 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0087 decode.acc_seg: 99.6986 aux.loss_ce: 0.0092 aux.acc_seg: 99.2458 +04/16 22:12:20 - mmengine - INFO - Iter(train) [ 50400/160000] base_lr: 6.9149e-05 lr: 2.5566e-07 eta: 1 day, 6:23:11 time: 1.0010 data_time: 0.0047 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0080 decode.acc_seg: 99.7988 aux.loss_ce: 0.0085 aux.acc_seg: 99.4024 +04/16 22:13:10 - mmengine - INFO - Iter(train) [ 50450/160000] base_lr: 6.9117e-05 lr: 2.5554e-07 eta: 1 day, 6:22:21 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6393 aux.loss_ce: 0.0076 aux.acc_seg: 99.0263 +04/16 22:14:00 - mmengine - INFO - Iter(train) [ 50500/160000] base_lr: 6.9086e-05 lr: 2.5542e-07 eta: 1 day, 6:21:31 time: 0.9992 data_time: 0.0047 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0085 decode.acc_seg: 99.5884 aux.loss_ce: 0.0089 aux.acc_seg: 99.0612 +04/16 22:14:50 - mmengine - INFO - Iter(train) [ 50550/160000] base_lr: 6.9054e-05 lr: 2.5531e-07 eta: 1 day, 6:20:41 time: 0.9989 data_time: 0.0045 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.7112 aux.loss_ce: 0.0079 aux.acc_seg: 99.3021 +04/16 22:15:40 - mmengine - INFO - Iter(train) [ 50600/160000] base_lr: 6.9023e-05 lr: 2.5519e-07 eta: 1 day, 6:19:51 time: 1.0007 data_time: 0.0049 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.5321 aux.loss_ce: 0.0076 aux.acc_seg: 98.6328 +04/16 22:16:30 - mmengine - INFO - Iter(train) [ 50650/160000] base_lr: 6.8991e-05 lr: 2.5507e-07 eta: 1 day, 6:19:02 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0081 decode.acc_seg: 99.5033 aux.loss_ce: 0.0083 aux.acc_seg: 98.3282 +04/16 22:17:20 - mmengine - INFO - Iter(train) [ 50700/160000] base_lr: 6.8959e-05 lr: 2.5496e-07 eta: 1 day, 6:18:12 time: 0.9985 data_time: 0.0047 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.7309 aux.loss_ce: 0.0077 aux.acc_seg: 99.2088 +04/16 22:18:09 - mmengine - INFO - Iter(train) [ 50750/160000] base_lr: 6.8928e-05 lr: 2.5484e-07 eta: 1 day, 6:17:22 time: 0.9993 data_time: 0.0050 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.6696 aux.loss_ce: 0.0078 aux.acc_seg: 99.0030 +04/16 22:18:59 - mmengine - INFO - Iter(train) [ 50800/160000] base_lr: 6.8896e-05 lr: 2.5472e-07 eta: 1 day, 6:16:32 time: 0.9997 data_time: 0.0050 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0075 decode.acc_seg: 99.7080 aux.loss_ce: 0.0080 aux.acc_seg: 98.9895 +04/16 22:19:49 - mmengine - INFO - Iter(train) [ 50850/160000] base_lr: 6.8865e-05 lr: 2.5461e-07 eta: 1 day, 6:15:43 time: 0.9996 data_time: 0.0047 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.5073 aux.loss_ce: 0.0082 aux.acc_seg: 98.5756 +04/16 22:20:39 - mmengine - INFO - Iter(train) [ 50900/160000] base_lr: 6.8833e-05 lr: 2.5449e-07 eta: 1 day, 6:14:53 time: 0.9996 data_time: 0.0054 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6412 aux.loss_ce: 0.0078 aux.acc_seg: 99.0788 +04/16 22:21:29 - mmengine - INFO - Iter(train) [ 50950/160000] base_lr: 6.8802e-05 lr: 2.5437e-07 eta: 1 day, 6:14:03 time: 1.0000 data_time: 0.0050 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0084 decode.acc_seg: 99.7515 aux.loss_ce: 0.0093 aux.acc_seg: 99.1920 +04/16 22:22:19 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 22:22:19 - mmengine - INFO - Iter(train) [ 51000/160000] base_lr: 6.8770e-05 lr: 2.5426e-07 eta: 1 day, 6:13:13 time: 0.9996 data_time: 0.0052 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0075 decode.acc_seg: 99.5951 aux.loss_ce: 0.0086 aux.acc_seg: 99.1821 +04/16 22:23:09 - mmengine - INFO - Iter(train) [ 51050/160000] base_lr: 6.8739e-05 lr: 2.5414e-07 eta: 1 day, 6:12:23 time: 0.9980 data_time: 0.0049 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0082 decode.acc_seg: 99.5388 aux.loss_ce: 0.0087 aux.acc_seg: 98.9073 +04/16 22:23:59 - mmengine - INFO - Iter(train) [ 51100/160000] base_lr: 6.8707e-05 lr: 2.5402e-07 eta: 1 day, 6:11:34 time: 0.9982 data_time: 0.0046 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0088 decode.acc_seg: 99.5554 aux.loss_ce: 0.0092 aux.acc_seg: 98.8064 +04/16 22:24:49 - mmengine - INFO - Iter(train) [ 51150/160000] base_lr: 6.8676e-05 lr: 2.5391e-07 eta: 1 day, 6:10:44 time: 1.0000 data_time: 0.0050 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0089 decode.acc_seg: 99.7726 aux.loss_ce: 0.0091 aux.acc_seg: 99.3719 +04/16 22:25:39 - mmengine - INFO - Iter(train) [ 51200/160000] base_lr: 6.8644e-05 lr: 2.5379e-07 eta: 1 day, 6:09:54 time: 0.9995 data_time: 0.0053 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0079 decode.acc_seg: 99.7620 aux.loss_ce: 0.0092 aux.acc_seg: 99.0164 +04/16 22:26:29 - mmengine - INFO - Iter(train) [ 51250/160000] base_lr: 6.8612e-05 lr: 2.5367e-07 eta: 1 day, 6:09:04 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0099 decode.acc_seg: 99.6565 aux.loss_ce: 0.0092 aux.acc_seg: 99.2025 +04/16 22:27:19 - mmengine - INFO - Iter(train) [ 51300/160000] base_lr: 6.8581e-05 lr: 2.5356e-07 eta: 1 day, 6:08:14 time: 0.9980 data_time: 0.0045 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0085 decode.acc_seg: 99.6807 aux.loss_ce: 0.0088 aux.acc_seg: 99.3999 +04/16 22:28:09 - mmengine - INFO - Iter(train) [ 51350/160000] base_lr: 6.8549e-05 lr: 2.5344e-07 eta: 1 day, 6:07:25 time: 0.9983 data_time: 0.0048 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0091 decode.acc_seg: 99.6429 aux.loss_ce: 0.0092 aux.acc_seg: 99.0431 +04/16 22:28:59 - mmengine - INFO - Iter(train) [ 51400/160000] base_lr: 6.8518e-05 lr: 2.5332e-07 eta: 1 day, 6:06:35 time: 0.9991 data_time: 0.0046 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0087 decode.acc_seg: 99.4207 aux.loss_ce: 0.0090 aux.acc_seg: 98.5525 +04/16 22:29:49 - mmengine - INFO - Iter(train) [ 51450/160000] base_lr: 6.8486e-05 lr: 2.5321e-07 eta: 1 day, 6:05:45 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.6510 aux.loss_ce: 0.0079 aux.acc_seg: 99.2872 +04/16 22:30:39 - mmengine - INFO - Iter(train) [ 51500/160000] base_lr: 6.8455e-05 lr: 2.5309e-07 eta: 1 day, 6:04:55 time: 0.9987 data_time: 0.0052 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.4526 aux.loss_ce: 0.0080 aux.acc_seg: 98.9122 +04/16 22:31:29 - mmengine - INFO - Iter(train) [ 51550/160000] base_lr: 6.8423e-05 lr: 2.5297e-07 eta: 1 day, 6:04:06 time: 0.9998 data_time: 0.0051 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0074 decode.acc_seg: 99.8579 aux.loss_ce: 0.0083 aux.acc_seg: 99.5220 +04/16 22:32:19 - mmengine - INFO - Iter(train) [ 51600/160000] base_lr: 6.8392e-05 lr: 2.5286e-07 eta: 1 day, 6:03:16 time: 1.0012 data_time: 0.0047 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0077 decode.acc_seg: 99.5605 aux.loss_ce: 0.0082 aux.acc_seg: 98.6015 +04/16 22:33:09 - mmengine - INFO - Iter(train) [ 51650/160000] base_lr: 6.8360e-05 lr: 2.5274e-07 eta: 1 day, 6:02:26 time: 0.9984 data_time: 0.0046 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0078 decode.acc_seg: 99.7519 aux.loss_ce: 0.0079 aux.acc_seg: 99.1133 +04/16 22:33:59 - mmengine - INFO - Iter(train) [ 51700/160000] base_lr: 6.8329e-05 lr: 2.5262e-07 eta: 1 day, 6:01:36 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0092 decode.acc_seg: 99.6235 aux.loss_ce: 0.0099 aux.acc_seg: 98.6456 +04/16 22:34:49 - mmengine - INFO - Iter(train) [ 51750/160000] base_lr: 6.8297e-05 lr: 2.5251e-07 eta: 1 day, 6:00:46 time: 0.9982 data_time: 0.0049 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.4482 aux.loss_ce: 0.0073 aux.acc_seg: 99.1529 +04/16 22:35:39 - mmengine - INFO - Iter(train) [ 51800/160000] base_lr: 6.8265e-05 lr: 2.5239e-07 eta: 1 day, 5:59:57 time: 0.9980 data_time: 0.0046 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.7250 aux.loss_ce: 0.0077 aux.acc_seg: 99.1871 +04/16 22:36:29 - mmengine - INFO - Iter(train) [ 51850/160000] base_lr: 6.8234e-05 lr: 2.5227e-07 eta: 1 day, 5:59:07 time: 0.9995 data_time: 0.0047 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0085 decode.acc_seg: 99.4257 aux.loss_ce: 0.0082 aux.acc_seg: 98.5600 +04/16 22:37:19 - mmengine - INFO - Iter(train) [ 51900/160000] base_lr: 6.8202e-05 lr: 2.5216e-07 eta: 1 day, 5:58:17 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0083 decode.acc_seg: 99.7498 aux.loss_ce: 0.0085 aux.acc_seg: 99.4606 +04/16 22:38:09 - mmengine - INFO - Iter(train) [ 51950/160000] base_lr: 6.8171e-05 lr: 2.5204e-07 eta: 1 day, 5:57:27 time: 0.9980 data_time: 0.0044 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.6624 aux.loss_ce: 0.0077 aux.acc_seg: 99.2783 +04/16 22:38:59 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 22:38:59 - mmengine - INFO - Iter(train) [ 52000/160000] base_lr: 6.8139e-05 lr: 2.5192e-07 eta: 1 day, 5:56:37 time: 0.9982 data_time: 0.0048 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0087 decode.acc_seg: 99.7227 aux.loss_ce: 0.0087 aux.acc_seg: 99.4970 +04/16 22:39:49 - mmengine - INFO - Iter(train) [ 52050/160000] base_lr: 6.8108e-05 lr: 2.5181e-07 eta: 1 day, 5:55:47 time: 0.9975 data_time: 0.0048 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.6786 aux.loss_ce: 0.0088 aux.acc_seg: 99.0822 +04/16 22:40:38 - mmengine - INFO - Iter(train) [ 52100/160000] base_lr: 6.8076e-05 lr: 2.5169e-07 eta: 1 day, 5:54:57 time: 0.9991 data_time: 0.0046 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0082 decode.acc_seg: 99.5260 aux.loss_ce: 0.0085 aux.acc_seg: 98.5950 +04/16 22:41:28 - mmengine - INFO - Iter(train) [ 52150/160000] base_lr: 6.8045e-05 lr: 2.5157e-07 eta: 1 day, 5:54:08 time: 0.9993 data_time: 0.0044 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7528 aux.loss_ce: 0.0074 aux.acc_seg: 99.5098 +04/16 22:42:18 - mmengine - INFO - Iter(train) [ 52200/160000] base_lr: 6.8013e-05 lr: 2.5146e-07 eta: 1 day, 5:53:18 time: 0.9987 data_time: 0.0047 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6742 aux.loss_ce: 0.0073 aux.acc_seg: 99.1848 +04/16 22:43:08 - mmengine - INFO - Iter(train) [ 52250/160000] base_lr: 6.7982e-05 lr: 2.5134e-07 eta: 1 day, 5:52:28 time: 0.9990 data_time: 0.0046 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0098 decode.acc_seg: 99.3906 aux.loss_ce: 0.0100 aux.acc_seg: 98.8962 +04/16 22:43:58 - mmengine - INFO - Iter(train) [ 52300/160000] base_lr: 6.7950e-05 lr: 2.5122e-07 eta: 1 day, 5:51:38 time: 0.9986 data_time: 0.0046 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7282 aux.loss_ce: 0.0074 aux.acc_seg: 99.0484 +04/16 22:44:48 - mmengine - INFO - Iter(train) [ 52350/160000] base_lr: 6.7918e-05 lr: 2.5111e-07 eta: 1 day, 5:50:48 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0085 decode.acc_seg: 99.6134 aux.loss_ce: 0.0094 aux.acc_seg: 99.1953 +04/16 22:45:38 - mmengine - INFO - Iter(train) [ 52400/160000] base_lr: 6.7887e-05 lr: 2.5099e-07 eta: 1 day, 5:49:58 time: 0.9989 data_time: 0.0045 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.6756 aux.loss_ce: 0.0081 aux.acc_seg: 99.2476 +04/16 22:46:28 - mmengine - INFO - Iter(train) [ 52450/160000] base_lr: 6.7855e-05 lr: 2.5088e-07 eta: 1 day, 5:49:08 time: 0.9982 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0082 decode.acc_seg: 99.6368 aux.loss_ce: 0.0084 aux.acc_seg: 99.0398 +04/16 22:47:18 - mmengine - INFO - Iter(train) [ 52500/160000] base_lr: 6.7824e-05 lr: 2.5076e-07 eta: 1 day, 5:48:18 time: 0.9989 data_time: 0.0045 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.6225 aux.loss_ce: 0.0078 aux.acc_seg: 98.9750 +04/16 22:48:08 - mmengine - INFO - Iter(train) [ 52550/160000] base_lr: 6.7792e-05 lr: 2.5064e-07 eta: 1 day, 5:47:29 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0084 decode.acc_seg: 99.6349 aux.loss_ce: 0.0088 aux.acc_seg: 99.2867 +04/16 22:48:58 - mmengine - INFO - Iter(train) [ 52600/160000] base_lr: 6.7761e-05 lr: 2.5053e-07 eta: 1 day, 5:46:39 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0092 decode.acc_seg: 99.5438 aux.loss_ce: 0.0093 aux.acc_seg: 98.9717 +04/16 22:49:48 - mmengine - INFO - Iter(train) [ 52650/160000] base_lr: 6.7729e-05 lr: 2.5041e-07 eta: 1 day, 5:45:49 time: 0.9980 data_time: 0.0050 memory: 8462 loss: 0.0134 decode.loss_ce: 0.0065 decode.acc_seg: 99.7768 aux.loss_ce: 0.0068 aux.acc_seg: 99.2828 +04/16 22:50:38 - mmengine - INFO - Iter(train) [ 52700/160000] base_lr: 6.7698e-05 lr: 2.5029e-07 eta: 1 day, 5:44:59 time: 0.9978 data_time: 0.0046 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0093 decode.acc_seg: 99.6967 aux.loss_ce: 0.0086 aux.acc_seg: 99.1804 +04/16 22:51:27 - mmengine - INFO - Iter(train) [ 52750/160000] base_lr: 6.7666e-05 lr: 2.5018e-07 eta: 1 day, 5:44:09 time: 0.9980 data_time: 0.0046 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0089 decode.acc_seg: 99.5892 aux.loss_ce: 0.0093 aux.acc_seg: 98.9882 +04/16 22:52:17 - mmengine - INFO - Iter(train) [ 52800/160000] base_lr: 6.7634e-05 lr: 2.5006e-07 eta: 1 day, 5:43:19 time: 0.9984 data_time: 0.0046 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.5016 aux.loss_ce: 0.0082 aux.acc_seg: 98.7511 +04/16 22:53:07 - mmengine - INFO - Iter(train) [ 52850/160000] base_lr: 6.7603e-05 lr: 2.4994e-07 eta: 1 day, 5:42:29 time: 0.9977 data_time: 0.0051 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0083 decode.acc_seg: 99.7194 aux.loss_ce: 0.0088 aux.acc_seg: 99.2336 +04/16 22:53:57 - mmengine - INFO - Iter(train) [ 52900/160000] base_lr: 6.7571e-05 lr: 2.4983e-07 eta: 1 day, 5:41:39 time: 0.9979 data_time: 0.0048 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0076 decode.acc_seg: 99.6332 aux.loss_ce: 0.0084 aux.acc_seg: 99.3027 +04/16 22:54:47 - mmengine - INFO - Iter(train) [ 52950/160000] base_lr: 6.7540e-05 lr: 2.4971e-07 eta: 1 day, 5:40:49 time: 0.9977 data_time: 0.0052 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0075 decode.acc_seg: 99.6027 aux.loss_ce: 0.0082 aux.acc_seg: 98.8695 +04/16 22:55:37 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 22:55:37 - mmengine - INFO - Iter(train) [ 53000/160000] base_lr: 6.7508e-05 lr: 2.4959e-07 eta: 1 day, 5:40:00 time: 0.9995 data_time: 0.0050 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0098 decode.acc_seg: 99.7593 aux.loss_ce: 0.0085 aux.acc_seg: 99.2371 +04/16 22:56:27 - mmengine - INFO - Iter(train) [ 53050/160000] base_lr: 6.7477e-05 lr: 2.4948e-07 eta: 1 day, 5:39:10 time: 0.9956 data_time: 0.0045 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0089 decode.acc_seg: 99.7759 aux.loss_ce: 0.0088 aux.acc_seg: 99.2552 +04/16 22:57:17 - mmengine - INFO - Iter(train) [ 53100/160000] base_lr: 6.7445e-05 lr: 2.4936e-07 eta: 1 day, 5:38:20 time: 0.9984 data_time: 0.0046 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0078 decode.acc_seg: 99.7066 aux.loss_ce: 0.0085 aux.acc_seg: 99.1474 +04/16 22:58:07 - mmengine - INFO - Iter(train) [ 53150/160000] base_lr: 6.7414e-05 lr: 2.4924e-07 eta: 1 day, 5:37:30 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0092 decode.acc_seg: 99.6326 aux.loss_ce: 0.0103 aux.acc_seg: 98.9431 +04/16 22:58:57 - mmengine - INFO - Iter(train) [ 53200/160000] base_lr: 6.7382e-05 lr: 2.4913e-07 eta: 1 day, 5:36:40 time: 0.9965 data_time: 0.0045 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.7290 aux.loss_ce: 0.0081 aux.acc_seg: 99.2523 +04/16 22:59:46 - mmengine - INFO - Iter(train) [ 53250/160000] base_lr: 6.7351e-05 lr: 2.4901e-07 eta: 1 day, 5:35:50 time: 0.9971 data_time: 0.0044 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0085 decode.acc_seg: 99.4743 aux.loss_ce: 0.0088 aux.acc_seg: 98.8598 +04/16 23:00:36 - mmengine - INFO - Iter(train) [ 53300/160000] base_lr: 6.7319e-05 lr: 2.4889e-07 eta: 1 day, 5:35:00 time: 0.9971 data_time: 0.0046 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0081 decode.acc_seg: 99.7292 aux.loss_ce: 0.0085 aux.acc_seg: 99.1329 +04/16 23:01:26 - mmengine - INFO - Iter(train) [ 53350/160000] base_lr: 6.7287e-05 lr: 2.4878e-07 eta: 1 day, 5:34:10 time: 0.9985 data_time: 0.0055 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0085 decode.acc_seg: 99.3555 aux.loss_ce: 0.0089 aux.acc_seg: 98.5073 +04/16 23:02:16 - mmengine - INFO - Iter(train) [ 53400/160000] base_lr: 6.7256e-05 lr: 2.4866e-07 eta: 1 day, 5:33:20 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0080 decode.acc_seg: 99.5564 aux.loss_ce: 0.0089 aux.acc_seg: 98.7888 +04/16 23:03:06 - mmengine - INFO - Iter(train) [ 53450/160000] base_lr: 6.7224e-05 lr: 2.4854e-07 eta: 1 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INFO - Iter(train) [ 54200/160000] base_lr: 6.6751e-05 lr: 2.4679e-07 eta: 1 day, 5:19:59 time: 0.9958 data_time: 0.0045 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.6202 aux.loss_ce: 0.0081 aux.acc_seg: 98.9937 +04/16 23:16:23 - mmengine - INFO - Iter(train) [ 54250/160000] base_lr: 6.6720e-05 lr: 2.4668e-07 eta: 1 day, 5:19:09 time: 0.9970 data_time: 0.0045 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.6475 aux.loss_ce: 0.0080 aux.acc_seg: 98.8007 +04/16 23:17:13 - mmengine - INFO - Iter(train) [ 54300/160000] base_lr: 6.6688e-05 lr: 2.4656e-07 eta: 1 day, 5:18:19 time: 0.9948 data_time: 0.0044 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0083 decode.acc_seg: 99.6634 aux.loss_ce: 0.0084 aux.acc_seg: 98.9866 +04/16 23:18:03 - mmengine - INFO - Iter(train) [ 54350/160000] base_lr: 6.6657e-05 lr: 2.4644e-07 eta: 1 day, 5:17:29 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0084 decode.acc_seg: 99.7025 aux.loss_ce: 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decode.loss_ce: 0.0085 decode.acc_seg: 99.5005 aux.loss_ce: 0.0090 aux.acc_seg: 99.0097 +04/16 23:22:12 - mmengine - INFO - Iter(train) [ 54600/160000] base_lr: 6.6499e-05 lr: 2.4586e-07 eta: 1 day, 5:13:19 time: 0.9958 data_time: 0.0048 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0090 decode.acc_seg: 99.7503 aux.loss_ce: 0.0090 aux.acc_seg: 99.2399 +04/16 23:23:02 - mmengine - INFO - Iter(train) [ 54650/160000] base_lr: 6.6467e-05 lr: 2.4574e-07 eta: 1 day, 5:12:29 time: 0.9963 data_time: 0.0050 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0076 decode.acc_seg: 99.6700 aux.loss_ce: 0.0072 aux.acc_seg: 99.1671 +04/16 23:23:52 - mmengine - INFO - Iter(train) [ 54700/160000] base_lr: 6.6436e-05 lr: 2.4563e-07 eta: 1 day, 5:11:39 time: 0.9970 data_time: 0.0044 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0088 decode.acc_seg: 99.6424 aux.loss_ce: 0.0086 aux.acc_seg: 98.8768 +04/16 23:24:42 - mmengine - INFO - Iter(train) [ 54750/160000] base_lr: 6.6404e-05 lr: 2.4551e-07 eta: 1 day, 5:10:48 time: 0.9971 data_time: 0.0048 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0071 decode.acc_seg: 99.7747 aux.loss_ce: 0.0080 aux.acc_seg: 99.3519 +04/16 23:25:32 - mmengine - INFO - Iter(train) [ 54800/160000] base_lr: 6.6373e-05 lr: 2.4539e-07 eta: 1 day, 5:09:58 time: 0.9972 data_time: 0.0043 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0082 decode.acc_seg: 99.5329 aux.loss_ce: 0.0085 aux.acc_seg: 98.6334 +04/16 23:26:21 - mmengine - INFO - Iter(train) [ 54850/160000] base_lr: 6.6341e-05 lr: 2.4528e-07 eta: 1 day, 5:09:08 time: 0.9965 data_time: 0.0048 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0082 decode.acc_seg: 99.6662 aux.loss_ce: 0.0085 aux.acc_seg: 99.0597 +04/16 23:27:11 - mmengine - INFO - Iter(train) [ 54900/160000] base_lr: 6.6310e-05 lr: 2.4516e-07 eta: 1 day, 5:08:18 time: 0.9964 data_time: 0.0046 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0081 decode.acc_seg: 99.8238 aux.loss_ce: 0.0078 aux.acc_seg: 99.3221 +04/16 23:28:01 - mmengine - INFO - Iter(train) [ 54950/160000] 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day, 5:01:38 time: 0.9961 data_time: 0.0053 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.6387 aux.loss_ce: 0.0078 aux.acc_seg: 99.0450 +04/16 23:34:40 - mmengine - INFO - Iter(train) [ 55350/160000] base_lr: 6.6026e-05 lr: 2.4411e-07 eta: 1 day, 5:00:48 time: 0.9964 data_time: 0.0050 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.6101 aux.loss_ce: 0.0076 aux.acc_seg: 98.9889 +04/16 23:35:29 - mmengine - INFO - Iter(train) [ 55400/160000] base_lr: 6.5994e-05 lr: 2.4399e-07 eta: 1 day, 4:59:58 time: 0.9974 data_time: 0.0050 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0078 decode.acc_seg: 99.6679 aux.loss_ce: 0.0084 aux.acc_seg: 99.0776 +04/16 23:36:19 - mmengine - INFO - Iter(train) [ 55450/160000] base_lr: 6.5963e-05 lr: 2.4388e-07 eta: 1 day, 4:59:08 time: 0.9961 data_time: 0.0049 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0076 decode.acc_seg: 99.6929 aux.loss_ce: 0.0084 aux.acc_seg: 99.0248 +04/16 23:37:09 - mmengine - INFO - Iter(train) [ 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decode.acc_seg: 99.6983 aux.loss_ce: 0.0090 aux.acc_seg: 99.0719 +04/16 23:43:48 - mmengine - INFO - Iter(train) [ 55900/160000] base_lr: 6.5679e-05 lr: 2.4283e-07 eta: 1 day, 4:51:37 time: 0.9956 data_time: 0.0045 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0092 decode.acc_seg: 99.6111 aux.loss_ce: 0.0089 aux.acc_seg: 99.0194 +04/16 23:44:37 - mmengine - INFO - Iter(train) [ 55950/160000] base_lr: 6.5647e-05 lr: 2.4271e-07 eta: 1 day, 4:50:47 time: 0.9981 data_time: 0.0049 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6408 aux.loss_ce: 0.0078 aux.acc_seg: 98.8640 +04/16 23:45:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/16 23:45:27 - mmengine - INFO - Iter(train) [ 56000/160000] base_lr: 6.5616e-05 lr: 2.4259e-07 eta: 1 day, 4:49:57 time: 0.9962 data_time: 0.0048 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0077 decode.acc_seg: 99.8175 aux.loss_ce: 0.0086 aux.acc_seg: 99.3641 +04/16 23:46:17 - mmengine - INFO - Iter(train) [ 56050/160000] base_lr: 6.5584e-05 lr: 2.4248e-07 eta: 1 day, 4:49:06 time: 0.9973 data_time: 0.0057 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0079 decode.acc_seg: 99.6599 aux.loss_ce: 0.0084 aux.acc_seg: 99.1657 +04/16 23:47:07 - mmengine - INFO - Iter(train) [ 56100/160000] base_lr: 6.5552e-05 lr: 2.4236e-07 eta: 1 day, 4:48:16 time: 0.9959 data_time: 0.0048 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0082 decode.acc_seg: 99.6529 aux.loss_ce: 0.0096 aux.acc_seg: 98.9407 +04/16 23:47:57 - mmengine - INFO - Iter(train) [ 56150/160000] base_lr: 6.5521e-05 lr: 2.4224e-07 eta: 1 day, 4:47:26 time: 0.9966 data_time: 0.0049 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.7486 aux.loss_ce: 0.0080 aux.acc_seg: 99.3921 +04/16 23:48:46 - mmengine - INFO - Iter(train) [ 56200/160000] base_lr: 6.5489e-05 lr: 2.4213e-07 eta: 1 day, 4:46:36 time: 0.9985 data_time: 0.0051 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0090 decode.acc_seg: 99.6469 aux.loss_ce: 0.0081 aux.acc_seg: 99.1430 +04/16 23:49:36 - mmengine - INFO - Iter(train) [ 56250/160000] base_lr: 6.5458e-05 lr: 2.4201e-07 eta: 1 day, 4:45:46 time: 0.9964 data_time: 0.0045 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6906 aux.loss_ce: 0.0079 aux.acc_seg: 99.3254 +04/16 23:50:26 - mmengine - INFO - Iter(train) [ 56300/160000] base_lr: 6.5426e-05 lr: 2.4189e-07 eta: 1 day, 4:44:56 time: 0.9963 data_time: 0.0048 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0077 decode.acc_seg: 99.6508 aux.loss_ce: 0.0083 aux.acc_seg: 99.0313 +04/16 23:51:16 - mmengine - INFO - Iter(train) [ 56350/160000] base_lr: 6.5395e-05 lr: 2.4178e-07 eta: 1 day, 4:44:06 time: 0.9965 data_time: 0.0045 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0086 decode.acc_seg: 99.6262 aux.loss_ce: 0.0087 aux.acc_seg: 99.1371 +04/16 23:52:06 - mmengine - INFO - Iter(train) [ 56400/160000] base_lr: 6.5363e-05 lr: 2.4166e-07 eta: 1 day, 4:43:16 time: 0.9954 data_time: 0.0045 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.7545 aux.loss_ce: 0.0081 aux.acc_seg: 99.4267 +04/16 23:52:55 - mmengine - INFO - Iter(train) [ 56450/160000] base_lr: 6.5332e-05 lr: 2.4154e-07 eta: 1 day, 4:42:26 time: 0.9967 data_time: 0.0050 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0086 decode.acc_seg: 99.5520 aux.loss_ce: 0.0082 aux.acc_seg: 99.0448 +04/16 23:53:45 - mmengine - INFO - Iter(train) [ 56500/160000] base_lr: 6.5300e-05 lr: 2.4143e-07 eta: 1 day, 4:41:36 time: 0.9960 data_time: 0.0047 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.7601 aux.loss_ce: 0.0084 aux.acc_seg: 99.1243 +04/16 23:54:35 - mmengine - INFO - Iter(train) [ 56550/160000] base_lr: 6.5269e-05 lr: 2.4131e-07 eta: 1 day, 4:40:46 time: 0.9969 data_time: 0.0051 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0085 decode.acc_seg: 99.2119 aux.loss_ce: 0.0077 aux.acc_seg: 98.3730 +04/16 23:55:25 - mmengine - INFO - Iter(train) [ 56600/160000] base_lr: 6.5237e-05 lr: 2.4119e-07 eta: 1 day, 4:39:56 time: 0.9976 data_time: 0.0049 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.7047 aux.loss_ce: 0.0078 aux.acc_seg: 99.2519 +04/16 23:56:15 - mmengine - INFO - Iter(train) [ 56650/160000] base_lr: 6.5205e-05 lr: 2.4108e-07 eta: 1 day, 4:39:06 time: 0.9964 data_time: 0.0049 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0084 decode.acc_seg: 99.4806 aux.loss_ce: 0.0089 aux.acc_seg: 98.9159 +04/16 23:57:05 - mmengine - INFO - Iter(train) [ 56700/160000] base_lr: 6.5174e-05 lr: 2.4096e-07 eta: 1 day, 4:38:16 time: 0.9960 data_time: 0.0052 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0074 decode.acc_seg: 99.7871 aux.loss_ce: 0.0080 aux.acc_seg: 99.4078 +04/16 23:57:54 - mmengine - INFO - Iter(train) [ 56750/160000] base_lr: 6.5142e-05 lr: 2.4084e-07 eta: 1 day, 4:37:26 time: 0.9954 data_time: 0.0047 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0086 decode.acc_seg: 99.5737 aux.loss_ce: 0.0095 aux.acc_seg: 99.0189 +04/16 23:58:44 - mmengine - INFO - Iter(train) [ 56800/160000] base_lr: 6.5111e-05 lr: 2.4073e-07 eta: 1 day, 4:36:36 time: 0.9968 data_time: 0.0047 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0076 decode.acc_seg: 99.6952 aux.loss_ce: 0.0083 aux.acc_seg: 99.1755 +04/16 23:59:34 - mmengine - INFO - Iter(train) [ 56850/160000] base_lr: 6.5079e-05 lr: 2.4061e-07 eta: 1 day, 4:35:45 time: 0.9952 data_time: 0.0046 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.5892 aux.loss_ce: 0.0079 aux.acc_seg: 98.5762 +04/17 00:00:24 - mmengine - INFO - Iter(train) [ 56900/160000] base_lr: 6.5048e-05 lr: 2.4049e-07 eta: 1 day, 4:34:55 time: 0.9960 data_time: 0.0048 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.7309 aux.loss_ce: 0.0082 aux.acc_seg: 99.1968 +04/17 00:01:14 - mmengine - INFO - Iter(train) [ 56950/160000] base_lr: 6.5016e-05 lr: 2.4038e-07 eta: 1 day, 4:34:05 time: 0.9963 data_time: 0.0048 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6555 aux.loss_ce: 0.0080 aux.acc_seg: 99.2270 +04/17 00:02:03 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/17 00:02:03 - mmengine - INFO - Iter(train) [ 57000/160000] base_lr: 6.4985e-05 lr: 2.4026e-07 eta: 1 day, 4:33:15 time: 0.9961 data_time: 0.0047 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0087 decode.acc_seg: 99.5787 aux.loss_ce: 0.0082 aux.acc_seg: 99.1844 +04/17 00:02:53 - mmengine - INFO - Iter(train) [ 57050/160000] base_lr: 6.4953e-05 lr: 2.4014e-07 eta: 1 day, 4:32:25 time: 0.9963 data_time: 0.0046 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0075 decode.acc_seg: 99.6214 aux.loss_ce: 0.0084 aux.acc_seg: 98.8850 +04/17 00:03:43 - mmengine - INFO - Iter(train) [ 57100/160000] base_lr: 6.4922e-05 lr: 2.4003e-07 eta: 1 day, 4:31:35 time: 0.9960 data_time: 0.0048 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0085 decode.acc_seg: 99.7746 aux.loss_ce: 0.0090 aux.acc_seg: 99.2683 +04/17 00:04:33 - mmengine - INFO - Iter(train) [ 57150/160000] base_lr: 6.4890e-05 lr: 2.3991e-07 eta: 1 day, 4:30:45 time: 0.9953 data_time: 0.0048 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0079 decode.acc_seg: 99.5529 aux.loss_ce: 0.0090 aux.acc_seg: 98.8056 +04/17 00:05:23 - mmengine - INFO - Iter(train) [ 57200/160000] base_lr: 6.4858e-05 lr: 2.3980e-07 eta: 1 day, 4:29:55 time: 0.9963 data_time: 0.0047 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0081 decode.acc_seg: 99.6891 aux.loss_ce: 0.0085 aux.acc_seg: 99.3155 +04/17 00:06:13 - mmengine - INFO - Iter(train) [ 57250/160000] base_lr: 6.4827e-05 lr: 2.3968e-07 eta: 1 day, 4:29:05 time: 0.9957 data_time: 0.0048 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.6056 aux.loss_ce: 0.0079 aux.acc_seg: 98.8895 +04/17 00:07:02 - mmengine - INFO - Iter(train) [ 57300/160000] base_lr: 6.4795e-05 lr: 2.3956e-07 eta: 1 day, 4:28:15 time: 0.9955 data_time: 0.0044 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.7478 aux.loss_ce: 0.0081 aux.acc_seg: 99.4514 +04/17 00:07:52 - mmengine - INFO - Iter(train) [ 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0.0050 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0076 decode.acc_seg: 99.6788 aux.loss_ce: 0.0085 aux.acc_seg: 99.1293 +04/17 00:17:50 - mmengine - INFO - Iter(train) [ 57950/160000] base_lr: 6.4385e-05 lr: 2.3805e-07 eta: 1 day, 4:17:24 time: 0.9963 data_time: 0.0046 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6782 aux.loss_ce: 0.0076 aux.acc_seg: 99.3078 +04/17 00:18:40 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/17 00:18:40 - mmengine - INFO - Iter(train) [ 58000/160000] base_lr: 6.4354e-05 lr: 2.3793e-07 eta: 1 day, 4:16:34 time: 0.9965 data_time: 0.0046 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6408 aux.loss_ce: 0.0080 aux.acc_seg: 98.8129 +04/17 00:19:30 - mmengine - INFO - Iter(train) [ 58050/160000] base_lr: 6.4322e-05 lr: 2.3781e-07 eta: 1 day, 4:15:44 time: 0.9969 data_time: 0.0045 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0069 decode.acc_seg: 99.7543 aux.loss_ce: 0.0080 aux.acc_seg: 99.2516 +04/17 00:20:20 - mmengine - INFO - Iter(train) [ 58100/160000] base_lr: 6.4291e-05 lr: 2.3770e-07 eta: 1 day, 4:14:54 time: 0.9957 data_time: 0.0044 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.7808 aux.loss_ce: 0.0074 aux.acc_seg: 99.3057 +04/17 00:21:09 - mmengine - INFO - Iter(train) [ 58150/160000] base_lr: 6.4259e-05 lr: 2.3758e-07 eta: 1 day, 4:14:04 time: 0.9984 data_time: 0.0047 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7034 aux.loss_ce: 0.0083 aux.acc_seg: 99.1354 +04/17 00:21:59 - mmengine - INFO - Iter(train) [ 58200/160000] base_lr: 6.4228e-05 lr: 2.3746e-07 eta: 1 day, 4:13:14 time: 0.9967 data_time: 0.0044 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.6964 aux.loss_ce: 0.0075 aux.acc_seg: 99.0650 +04/17 00:22:49 - mmengine - INFO - Iter(train) [ 58250/160000] base_lr: 6.4196e-05 lr: 2.3735e-07 eta: 1 day, 4:12:24 time: 0.9960 data_time: 0.0049 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.7053 aux.loss_ce: 0.0073 aux.acc_seg: 99.3534 +04/17 00:23:39 - mmengine - INFO - Iter(train) [ 58300/160000] base_lr: 6.4164e-05 lr: 2.3723e-07 eta: 1 day, 4:11:34 time: 0.9957 data_time: 0.0044 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0074 decode.acc_seg: 99.6477 aux.loss_ce: 0.0087 aux.acc_seg: 99.0463 +04/17 00:24:29 - mmengine - INFO - Iter(train) [ 58350/160000] base_lr: 6.4133e-05 lr: 2.3711e-07 eta: 1 day, 4:10:44 time: 0.9968 data_time: 0.0048 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6548 aux.loss_ce: 0.0078 aux.acc_seg: 99.2752 +04/17 00:25:19 - mmengine - INFO - Iter(train) [ 58400/160000] base_lr: 6.4101e-05 lr: 2.3700e-07 eta: 1 day, 4:09:54 time: 0.9961 data_time: 0.0050 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.6813 aux.loss_ce: 0.0078 aux.acc_seg: 98.8253 +04/17 00:26:08 - mmengine - INFO - Iter(train) [ 58450/160000] base_lr: 6.4070e-05 lr: 2.3688e-07 eta: 1 day, 4:09:04 time: 0.9945 data_time: 0.0044 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0085 decode.acc_seg: 99.6655 aux.loss_ce: 0.0091 aux.acc_seg: 99.2115 +04/17 00:26:58 - mmengine - INFO - Iter(train) [ 58500/160000] base_lr: 6.4038e-05 lr: 2.3676e-07 eta: 1 day, 4:08:14 time: 0.9963 data_time: 0.0044 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.6471 aux.loss_ce: 0.0073 aux.acc_seg: 99.4682 +04/17 00:27:48 - mmengine - INFO - Iter(train) [ 58550/160000] base_lr: 6.4007e-05 lr: 2.3665e-07 eta: 1 day, 4:07:24 time: 0.9973 data_time: 0.0045 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0074 decode.acc_seg: 99.7215 aux.loss_ce: 0.0083 aux.acc_seg: 99.2233 +04/17 00:28:38 - mmengine - INFO - Iter(train) [ 58600/160000] base_lr: 6.3975e-05 lr: 2.3653e-07 eta: 1 day, 4:06:34 time: 0.9957 data_time: 0.0046 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0085 decode.acc_seg: 99.6063 aux.loss_ce: 0.0088 aux.acc_seg: 99.2188 +04/17 00:29:28 - mmengine - INFO - Iter(train) [ 58650/160000] base_lr: 6.3944e-05 lr: 2.3641e-07 eta: 1 day, 4:05:44 time: 0.9977 data_time: 0.0047 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0069 decode.acc_seg: 99.5914 aux.loss_ce: 0.0080 aux.acc_seg: 98.8672 +04/17 00:30:18 - mmengine - INFO - Iter(train) [ 58700/160000] base_lr: 6.3912e-05 lr: 2.3630e-07 eta: 1 day, 4:04:54 time: 0.9965 data_time: 0.0046 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0082 decode.acc_seg: 99.6300 aux.loss_ce: 0.0085 aux.acc_seg: 99.0694 +04/17 00:31:07 - mmengine - INFO - Iter(train) [ 58750/160000] base_lr: 6.3881e-05 lr: 2.3618e-07 eta: 1 day, 4:04:04 time: 0.9957 data_time: 0.0047 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0082 decode.acc_seg: 99.5741 aux.loss_ce: 0.0083 aux.acc_seg: 98.8823 +04/17 00:31:57 - mmengine - INFO - Iter(train) [ 58800/160000] base_lr: 6.3849e-05 lr: 2.3606e-07 eta: 1 day, 4:03:14 time: 0.9965 data_time: 0.0047 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0080 decode.acc_seg: 99.6418 aux.loss_ce: 0.0085 aux.acc_seg: 99.1936 +04/17 00:32:47 - mmengine - INFO - Iter(train) [ 58850/160000] base_lr: 6.3817e-05 lr: 2.3595e-07 eta: 1 day, 4:02:24 time: 0.9979 data_time: 0.0047 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0096 decode.acc_seg: 99.5193 aux.loss_ce: 0.0098 aux.acc_seg: 98.9685 +04/17 00:33:37 - mmengine - INFO - Iter(train) [ 58900/160000] base_lr: 6.3786e-05 lr: 2.3583e-07 eta: 1 day, 4:01:34 time: 0.9960 data_time: 0.0050 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0073 decode.acc_seg: 99.6208 aux.loss_ce: 0.0082 aux.acc_seg: 99.2405 +04/17 00:34:27 - mmengine - INFO - Iter(train) [ 58950/160000] base_lr: 6.3754e-05 lr: 2.3571e-07 eta: 1 day, 4:00:44 time: 0.9971 data_time: 0.0047 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0091 decode.acc_seg: 99.7253 aux.loss_ce: 0.0087 aux.acc_seg: 99.2334 +04/17 00:35:17 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/17 00:35:17 - mmengine - INFO - Iter(train) [ 59000/160000] base_lr: 6.3723e-05 lr: 2.3560e-07 eta: 1 day, 3:59:54 time: 0.9960 data_time: 0.0047 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0089 decode.acc_seg: 99.6449 aux.loss_ce: 0.0090 aux.acc_seg: 99.1611 +04/17 00:36:06 - mmengine - INFO - Iter(train) [ 59050/160000] base_lr: 6.3691e-05 lr: 2.3548e-07 eta: 1 day, 3:59:04 time: 0.9965 data_time: 0.0047 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0080 decode.acc_seg: 99.6113 aux.loss_ce: 0.0092 aux.acc_seg: 99.0860 +04/17 00:36:56 - mmengine - INFO - Iter(train) [ 59100/160000] base_lr: 6.3660e-05 lr: 2.3536e-07 eta: 1 day, 3:58:14 time: 0.9971 data_time: 0.0048 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7328 aux.loss_ce: 0.0077 aux.acc_seg: 99.1354 +04/17 00:37:46 - mmengine - INFO - Iter(train) [ 59150/160000] base_lr: 6.3628e-05 lr: 2.3525e-07 eta: 1 day, 3:57:24 time: 0.9970 data_time: 0.0048 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0080 decode.acc_seg: 99.4686 aux.loss_ce: 0.0085 aux.acc_seg: 98.6643 +04/17 00:38:36 - mmengine - INFO - Iter(train) [ 59200/160000] base_lr: 6.3597e-05 lr: 2.3513e-07 eta: 1 day, 3:56:34 time: 0.9957 data_time: 0.0044 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0074 decode.acc_seg: 99.6296 aux.loss_ce: 0.0084 aux.acc_seg: 99.1884 +04/17 00:39:26 - mmengine - INFO - Iter(train) [ 59250/160000] base_lr: 6.3565e-05 lr: 2.3501e-07 eta: 1 day, 3:55:44 time: 0.9964 data_time: 0.0046 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0081 decode.acc_seg: 99.5554 aux.loss_ce: 0.0089 aux.acc_seg: 98.7679 +04/17 00:40:16 - mmengine - INFO - Iter(train) [ 59300/160000] base_lr: 6.3534e-05 lr: 2.3490e-07 eta: 1 day, 3:54:54 time: 0.9971 data_time: 0.0047 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0075 decode.acc_seg: 99.6595 aux.loss_ce: 0.0084 aux.acc_seg: 99.4104 +04/17 00:41:06 - mmengine - INFO - Iter(train) [ 59350/160000] base_lr: 6.3502e-05 lr: 2.3478e-07 eta: 1 day, 3:54:04 time: 0.9968 data_time: 0.0050 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6786 aux.loss_ce: 0.0072 aux.acc_seg: 99.0585 +04/17 00:41:55 - mmengine - INFO - Iter(train) [ 59400/160000] base_lr: 6.3470e-05 lr: 2.3466e-07 eta: 1 day, 3:53:14 time: 0.9970 data_time: 0.0047 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0081 decode.acc_seg: 99.4442 aux.loss_ce: 0.0083 aux.acc_seg: 98.7026 +04/17 00:42:45 - mmengine - INFO - Iter(train) [ 59450/160000] base_lr: 6.3439e-05 lr: 2.3455e-07 eta: 1 day, 3:52:24 time: 0.9978 data_time: 0.0047 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0078 decode.acc_seg: 99.6820 aux.loss_ce: 0.0081 aux.acc_seg: 99.5205 +04/17 00:43:35 - mmengine - INFO - Iter(train) [ 59500/160000] base_lr: 6.3407e-05 lr: 2.3443e-07 eta: 1 day, 3:51:34 time: 0.9974 data_time: 0.0046 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0078 decode.acc_seg: 99.6511 aux.loss_ce: 0.0086 aux.acc_seg: 98.9683 +04/17 00:44:25 - mmengine - INFO - Iter(train) [ 59550/160000] base_lr: 6.3376e-05 lr: 2.3431e-07 eta: 1 day, 3:50:44 time: 0.9973 data_time: 0.0046 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0083 decode.acc_seg: 99.6803 aux.loss_ce: 0.0089 aux.acc_seg: 99.1005 +04/17 00:45:15 - mmengine - INFO - Iter(train) [ 59600/160000] base_lr: 6.3344e-05 lr: 2.3420e-07 eta: 1 day, 3:49:54 time: 0.9972 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0082 decode.acc_seg: 99.7608 aux.loss_ce: 0.0084 aux.acc_seg: 99.3568 +04/17 00:46:05 - mmengine - INFO - Iter(train) [ 59650/160000] base_lr: 6.3313e-05 lr: 2.3408e-07 eta: 1 day, 3:49:04 time: 0.9969 data_time: 0.0043 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0078 decode.acc_seg: 99.6710 aux.loss_ce: 0.0086 aux.acc_seg: 99.2632 +04/17 00:46:55 - mmengine - INFO - Iter(train) [ 59700/160000] base_lr: 6.3281e-05 lr: 2.3396e-07 eta: 1 day, 3:48:14 time: 0.9961 data_time: 0.0045 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0085 decode.acc_seg: 99.6861 aux.loss_ce: 0.0070 aux.acc_seg: 99.3183 +04/17 00:47:45 - mmengine - INFO - Iter(train) [ 59750/160000] base_lr: 6.3250e-05 lr: 2.3385e-07 eta: 1 day, 3:47:24 time: 0.9978 data_time: 0.0044 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0081 decode.acc_seg: 99.5653 aux.loss_ce: 0.0081 aux.acc_seg: 98.7288 +04/17 00:48:34 - mmengine - INFO - Iter(train) [ 59800/160000] base_lr: 6.3218e-05 lr: 2.3373e-07 eta: 1 day, 3:46:34 time: 0.9973 data_time: 0.0048 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.8343 aux.loss_ce: 0.0074 aux.acc_seg: 99.6162 +04/17 00:49:24 - mmengine - INFO - Iter(train) [ 59850/160000] base_lr: 6.3187e-05 lr: 2.3361e-07 eta: 1 day, 3:45:44 time: 0.9963 data_time: 0.0045 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0089 decode.acc_seg: 99.6489 aux.loss_ce: 0.0092 aux.acc_seg: 98.8594 +04/17 00:50:14 - mmengine - INFO - Iter(train) [ 59900/160000] base_lr: 6.3155e-05 lr: 2.3350e-07 eta: 1 day, 3:44:55 time: 0.9980 data_time: 0.0047 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6519 aux.loss_ce: 0.0081 aux.acc_seg: 99.2622 +04/17 00:51:04 - mmengine - INFO - Iter(train) [ 59950/160000] base_lr: 6.3123e-05 lr: 2.3338e-07 eta: 1 day, 3:44:05 time: 0.9964 data_time: 0.0045 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0089 decode.acc_seg: 99.4581 aux.loss_ce: 0.0084 aux.acc_seg: 98.9388 +04/17 00:51:54 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/17 00:51:54 - mmengine - INFO - Iter(train) [ 60000/160000] base_lr: 6.3092e-05 lr: 2.3326e-07 eta: 1 day, 3:43:15 time: 0.9968 data_time: 0.0048 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0087 decode.acc_seg: 99.7086 aux.loss_ce: 0.0086 aux.acc_seg: 99.1188 +04/17 00:51:54 - mmengine - INFO - Saving checkpoint at 60000 iterations +04/17 00:52:04 - mmengine - INFO - Iter(val) [ 50/355] eta: 0:00:35 time: 0.1150 data_time: 0.0014 memory: 4004 +04/17 00:52:10 - mmengine - INFO - Iter(val) [100/355] eta: 0:00:29 time: 0.1152 data_time: 0.0015 memory: 4004 +04/17 00:52:15 - mmengine - INFO - Iter(val) [150/355] eta: 0:00:23 time: 0.1155 data_time: 0.0016 memory: 4004 +04/17 00:52:21 - mmengine - INFO - Iter(val) [200/355] eta: 0:00:17 time: 0.1155 data_time: 0.0014 memory: 4004 +04/17 00:52:27 - mmengine - INFO - Iter(val) [250/355] eta: 0:00:12 time: 0.1157 data_time: 0.0014 memory: 4004 +04/17 00:52:33 - mmengine - INFO - Iter(val) [300/355] eta: 0:00:06 time: 0.1160 data_time: 0.0020 memory: 4004 +04/17 00:52:39 - mmengine - INFO - Iter(val) [350/355] eta: 0:00:00 time: 0.1156 data_time: 0.0015 memory: 4004 +04/17 00:52:40 - mmengine - INFO - per class results: +04/17 00:52:40 - mmengine - INFO - ++------------+-------+-------+ +| Class | IoU | Acc | ++------------+-------+-------+ +| background | 99.19 | 99.61 | +| contrast | 82.27 | 89.84 | ++------------+-------+-------+ +04/17 00:52:40 - mmengine - INFO - Iter(val) [355/355] aAcc: 99.2200 mIoU: 90.7300 mAcc: 94.7200 data_time: 0.0015 time: 0.1157 +04/17 00:53:29 - mmengine - INFO - Iter(train) [ 60050/160000] base_lr: 6.3060e-05 lr: 2.3315e-07 eta: 1 day, 3:42:25 time: 0.9983 data_time: 0.0053 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0087 decode.acc_seg: 99.7343 aux.loss_ce: 0.0095 aux.acc_seg: 99.2016 +04/17 00:54:19 - mmengine - INFO - Iter(train) [ 60100/160000] base_lr: 6.3029e-05 lr: 2.3303e-07 eta: 1 day, 3:41:35 time: 0.9990 data_time: 0.0048 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.5863 aux.loss_ce: 0.0071 aux.acc_seg: 99.0122 +04/17 00:55:09 - mmengine - INFO - Iter(train) [ 60150/160000] base_lr: 6.2997e-05 lr: 2.3291e-07 eta: 1 day, 3:40:45 time: 0.9980 data_time: 0.0048 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0079 decode.acc_seg: 99.7379 aux.loss_ce: 0.0085 aux.acc_seg: 99.2889 +04/17 00:55:59 - mmengine - INFO - Iter(train) [ 60200/160000] base_lr: 6.2966e-05 lr: 2.3280e-07 eta: 1 day, 3:39:56 time: 0.9978 data_time: 0.0049 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.6826 aux.loss_ce: 0.0080 aux.acc_seg: 99.0971 +04/17 00:56:49 - mmengine - INFO - Iter(train) [ 60250/160000] base_lr: 6.2934e-05 lr: 2.3268e-07 eta: 1 day, 3:39:06 time: 0.9988 data_time: 0.0053 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0074 decode.acc_seg: 99.6826 aux.loss_ce: 0.0084 aux.acc_seg: 98.9386 +04/17 00:57:39 - mmengine - INFO - Iter(train) [ 60300/160000] base_lr: 6.2903e-05 lr: 2.3256e-07 eta: 1 day, 3:38:16 time: 0.9983 data_time: 0.0051 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.7467 aux.loss_ce: 0.0084 aux.acc_seg: 99.3000 +04/17 00:58:29 - mmengine - INFO - Iter(train) [ 60350/160000] base_lr: 6.2871e-05 lr: 2.3245e-07 eta: 1 day, 3:37:26 time: 0.9971 data_time: 0.0047 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0079 decode.acc_seg: 99.7631 aux.loss_ce: 0.0085 aux.acc_seg: 99.4083 +04/17 00:59:19 - mmengine - INFO - Iter(train) [ 60400/160000] base_lr: 6.2840e-05 lr: 2.3233e-07 eta: 1 day, 3:36:36 time: 0.9978 data_time: 0.0050 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0080 decode.acc_seg: 99.6956 aux.loss_ce: 0.0084 aux.acc_seg: 99.2638 +04/17 01:00:09 - mmengine - INFO - Iter(train) [ 60450/160000] base_lr: 6.2808e-05 lr: 2.3221e-07 eta: 1 day, 3:35:46 time: 0.9978 data_time: 0.0046 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0083 decode.acc_seg: 99.6384 aux.loss_ce: 0.0091 aux.acc_seg: 98.8962 +04/17 01:00:59 - mmengine - INFO - Iter(train) [ 60500/160000] base_lr: 6.2776e-05 lr: 2.3210e-07 eta: 1 day, 3:34:56 time: 0.9980 data_time: 0.0051 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0086 decode.acc_seg: 99.6979 aux.loss_ce: 0.0088 aux.acc_seg: 99.1194 +04/17 01:01:49 - mmengine - INFO - Iter(train) [ 60550/160000] base_lr: 6.2745e-05 lr: 2.3198e-07 eta: 1 day, 3:34:07 time: 0.9988 data_time: 0.0051 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0087 decode.acc_seg: 99.5541 aux.loss_ce: 0.0092 aux.acc_seg: 98.9962 +04/17 01:02:39 - mmengine - INFO - Iter(train) [ 60600/160000] base_lr: 6.2713e-05 lr: 2.3186e-07 eta: 1 day, 3:33:17 time: 0.9983 data_time: 0.0046 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6687 aux.loss_ce: 0.0078 aux.acc_seg: 99.1209 +04/17 01:03:29 - mmengine - INFO - Iter(train) [ 60650/160000] base_lr: 6.2682e-05 lr: 2.3175e-07 eta: 1 day, 3:32:27 time: 0.9982 data_time: 0.0050 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.6916 aux.loss_ce: 0.0082 aux.acc_seg: 99.3086 +04/17 01:04:18 - mmengine - INFO - Iter(train) [ 60700/160000] base_lr: 6.2650e-05 lr: 2.3163e-07 eta: 1 day, 3:31:37 time: 0.9976 data_time: 0.0046 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0077 decode.acc_seg: 99.6794 aux.loss_ce: 0.0083 aux.acc_seg: 99.2924 +04/17 01:05:08 - mmengine - INFO - Iter(train) [ 60750/160000] base_lr: 6.2619e-05 lr: 2.3151e-07 eta: 1 day, 3:30:47 time: 0.9983 data_time: 0.0050 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0093 decode.acc_seg: 99.6153 aux.loss_ce: 0.0094 aux.acc_seg: 99.0814 +04/17 01:05:58 - mmengine - INFO - Iter(train) [ 60800/160000] base_lr: 6.2587e-05 lr: 2.3140e-07 eta: 1 day, 3:29:57 time: 0.9979 data_time: 0.0051 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0081 decode.acc_seg: 99.6361 aux.loss_ce: 0.0085 aux.acc_seg: 99.0313 +04/17 01:06:48 - mmengine - INFO - Iter(train) [ 60850/160000] base_lr: 6.2556e-05 lr: 2.3128e-07 eta: 1 day, 3:29:07 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7324 aux.loss_ce: 0.0068 aux.acc_seg: 99.2758 +04/17 01:07:38 - mmengine - INFO - Iter(train) [ 60900/160000] base_lr: 6.2524e-05 lr: 2.3116e-07 eta: 1 day, 3:28:18 time: 0.9987 data_time: 0.0052 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0081 decode.acc_seg: 99.5985 aux.loss_ce: 0.0089 aux.acc_seg: 98.8792 +04/17 01:08:28 - mmengine - INFO - Iter(train) [ 60950/160000] base_lr: 6.2493e-05 lr: 2.3105e-07 eta: 1 day, 3:27:28 time: 0.9985 data_time: 0.0049 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0073 decode.acc_seg: 99.6412 aux.loss_ce: 0.0080 aux.acc_seg: 98.9189 +04/17 01:09:18 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/17 01:09:18 - mmengine - INFO - Iter(train) [ 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98.9201 +04/17 01:12:38 - mmengine - INFO - Iter(train) [ 61200/160000] base_lr: 6.2335e-05 lr: 2.3046e-07 eta: 1 day, 3:23:19 time: 0.9984 data_time: 0.0052 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0081 decode.acc_seg: 99.5611 aux.loss_ce: 0.0088 aux.acc_seg: 99.2458 +04/17 01:13:28 - mmengine - INFO - Iter(train) [ 61250/160000] base_lr: 6.2303e-05 lr: 2.3035e-07 eta: 1 day, 3:22:29 time: 0.9987 data_time: 0.0047 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0073 decode.acc_seg: 99.6452 aux.loss_ce: 0.0072 aux.acc_seg: 98.8508 +04/17 01:14:18 - mmengine - INFO - Iter(train) [ 61300/160000] base_lr: 6.2272e-05 lr: 2.3023e-07 eta: 1 day, 3:21:39 time: 0.9986 data_time: 0.0048 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7452 aux.loss_ce: 0.0072 aux.acc_seg: 99.3576 +04/17 01:15:08 - mmengine - INFO - Iter(train) [ 61350/160000] base_lr: 6.2240e-05 lr: 2.3011e-07 eta: 1 day, 3:20:49 time: 0.9990 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0079 decode.acc_seg: 99.6916 aux.loss_ce: 0.0082 aux.acc_seg: 99.2538 +04/17 01:15:58 - mmengine - INFO - Iter(train) [ 61400/160000] base_lr: 6.2209e-05 lr: 2.3000e-07 eta: 1 day, 3:20:00 time: 0.9994 data_time: 0.0052 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0078 decode.acc_seg: 99.7406 aux.loss_ce: 0.0083 aux.acc_seg: 99.2584 +04/17 01:16:48 - mmengine - INFO - Iter(train) [ 61450/160000] base_lr: 6.2177e-05 lr: 2.2988e-07 eta: 1 day, 3:19:10 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0075 decode.acc_seg: 99.8035 aux.loss_ce: 0.0078 aux.acc_seg: 99.3460 +04/17 01:17:38 - mmengine - INFO - Iter(train) [ 61500/160000] base_lr: 6.2146e-05 lr: 2.2976e-07 eta: 1 day, 3:18:20 time: 0.9998 data_time: 0.0047 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0084 decode.acc_seg: 99.4026 aux.loss_ce: 0.0084 aux.acc_seg: 98.6305 +04/17 01:18:28 - mmengine - INFO - Iter(train) [ 61550/160000] base_lr: 6.2114e-05 lr: 2.2965e-07 eta: 1 day, 3:17:30 time: 1.0007 data_time: 0.0052 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0071 decode.acc_seg: 99.5100 aux.loss_ce: 0.0084 aux.acc_seg: 98.5268 +04/17 01:19:18 - mmengine - INFO - Iter(train) [ 61600/160000] base_lr: 6.2082e-05 lr: 2.2953e-07 eta: 1 day, 3:16:40 time: 0.9999 data_time: 0.0050 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.7517 aux.loss_ce: 0.0076 aux.acc_seg: 99.1806 +04/17 01:20:08 - mmengine - INFO - Iter(train) [ 61650/160000] base_lr: 6.2051e-05 lr: 2.2941e-07 eta: 1 day, 3:15:51 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6758 aux.loss_ce: 0.0082 aux.acc_seg: 98.9677 +04/17 01:20:58 - mmengine - INFO - Iter(train) [ 61700/160000] base_lr: 6.2019e-05 lr: 2.2930e-07 eta: 1 day, 3:15:01 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0069 decode.acc_seg: 99.6979 aux.loss_ce: 0.0080 aux.acc_seg: 99.2336 +04/17 01:21:48 - mmengine - INFO - Iter(train) [ 61750/160000] base_lr: 6.1988e-05 lr: 2.2918e-07 eta: 1 day, 3:14:11 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0066 decode.acc_seg: 99.6271 aux.loss_ce: 0.0073 aux.acc_seg: 98.8829 +04/17 01:22:38 - mmengine - INFO - Iter(train) [ 61800/160000] base_lr: 6.1956e-05 lr: 2.2906e-07 eta: 1 day, 3:13:21 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0092 decode.acc_seg: 99.6683 aux.loss_ce: 0.0093 aux.acc_seg: 99.0225 +04/17 01:23:28 - mmengine - INFO - Iter(train) [ 61850/160000] base_lr: 6.1925e-05 lr: 2.2895e-07 eta: 1 day, 3:12:32 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0081 decode.acc_seg: 99.6580 aux.loss_ce: 0.0090 aux.acc_seg: 99.1571 +04/17 01:24:18 - mmengine - INFO - Iter(train) [ 61900/160000] base_lr: 6.1893e-05 lr: 2.2883e-07 eta: 1 day, 3:11:42 time: 0.9984 data_time: 0.0046 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0075 decode.acc_seg: 99.6153 aux.loss_ce: 0.0083 aux.acc_seg: 99.0864 +04/17 01:25:08 - mmengine - INFO - Iter(train) [ 61950/160000] base_lr: 6.1862e-05 lr: 2.2872e-07 eta: 1 day, 3:10:52 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0081 decode.acc_seg: 99.7885 aux.loss_ce: 0.0087 aux.acc_seg: 99.2813 +04/17 01:25:58 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/17 01:25:58 - mmengine - INFO - Iter(train) [ 62000/160000] base_lr: 6.1830e-05 lr: 2.2860e-07 eta: 1 day, 3:10:02 time: 0.9986 data_time: 0.0049 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0075 decode.acc_seg: 99.6367 aux.loss_ce: 0.0083 aux.acc_seg: 99.0023 +04/17 01:26:48 - mmengine - INFO - Iter(train) [ 62050/160000] base_lr: 6.1798e-05 lr: 2.2848e-07 eta: 1 day, 3:09:13 time: 0.9991 data_time: 0.0045 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.5485 aux.loss_ce: 0.0076 aux.acc_seg: 98.6851 +04/17 01:27:38 - mmengine - INFO - Iter(train) [ 62100/160000] base_lr: 6.1767e-05 lr: 2.2837e-07 eta: 1 day, 3:08:23 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6651 aux.loss_ce: 0.0074 aux.acc_seg: 99.2819 +04/17 01:28:28 - mmengine - INFO - Iter(train) [ 62150/160000] base_lr: 6.1735e-05 lr: 2.2825e-07 eta: 1 day, 3:07:33 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0070 decode.acc_seg: 99.7620 aux.loss_ce: 0.0078 aux.acc_seg: 99.2718 +04/17 01:29:18 - mmengine - INFO - Iter(train) [ 62200/160000] base_lr: 6.1704e-05 lr: 2.2813e-07 eta: 1 day, 3:06:43 time: 0.9992 data_time: 0.0045 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0073 decode.acc_seg: 99.5941 aux.loss_ce: 0.0081 aux.acc_seg: 98.6378 +04/17 01:30:08 - mmengine - INFO - Iter(train) [ 62250/160000] base_lr: 6.1672e-05 lr: 2.2802e-07 eta: 1 day, 3:05:53 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0075 decode.acc_seg: 99.4978 aux.loss_ce: 0.0082 aux.acc_seg: 98.8611 +04/17 01:30:57 - mmengine - INFO - Iter(train) [ 62300/160000] base_lr: 6.1641e-05 lr: 2.2790e-07 eta: 1 day, 3:05:04 time: 1.0008 data_time: 0.0043 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0075 decode.acc_seg: 99.5865 aux.loss_ce: 0.0085 aux.acc_seg: 99.0210 +04/17 01:31:48 - mmengine - INFO - Iter(train) [ 62350/160000] base_lr: 6.1609e-05 lr: 2.2778e-07 eta: 1 day, 3:04:14 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.8123 aux.loss_ce: 0.0076 aux.acc_seg: 99.4509 +04/17 01:32:38 - mmengine - INFO - Iter(train) [ 62400/160000] base_lr: 6.1578e-05 lr: 2.2767e-07 eta: 1 day, 3:03:24 time: 0.9989 data_time: 0.0043 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0071 decode.acc_seg: 99.7084 aux.loss_ce: 0.0082 aux.acc_seg: 99.1547 +04/17 01:33:28 - mmengine - INFO - Iter(train) [ 62450/160000] base_lr: 6.1546e-05 lr: 2.2755e-07 eta: 1 day, 3:02:34 time: 0.9991 data_time: 0.0047 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0077 decode.acc_seg: 99.7868 aux.loss_ce: 0.0088 aux.acc_seg: 99.2298 +04/17 01:34:18 - mmengine - INFO - Iter(train) [ 62500/160000] base_lr: 6.1515e-05 lr: 2.2743e-07 eta: 1 day, 3:01:45 time: 1.0020 data_time: 0.0046 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7049 aux.loss_ce: 0.0073 aux.acc_seg: 99.2956 +04/17 01:35:08 - mmengine - INFO - Iter(train) [ 62550/160000] base_lr: 6.1483e-05 lr: 2.2732e-07 eta: 1 day, 3:00:55 time: 0.9994 data_time: 0.0049 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0067 decode.acc_seg: 99.6576 aux.loss_ce: 0.0065 aux.acc_seg: 99.1966 +04/17 01:35:58 - mmengine - INFO - Iter(train) [ 62600/160000] base_lr: 6.1451e-05 lr: 2.2720e-07 eta: 1 day, 3:00:05 time: 1.0003 data_time: 0.0049 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.7746 aux.loss_ce: 0.0076 aux.acc_seg: 99.3265 +04/17 01:36:48 - mmengine - INFO - Iter(train) [ 62650/160000] base_lr: 6.1420e-05 lr: 2.2708e-07 eta: 1 day, 2:59:16 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.8293 aux.loss_ce: 0.0071 aux.acc_seg: 99.4669 +04/17 01:37:38 - mmengine - INFO - Iter(train) [ 62700/160000] base_lr: 6.1388e-05 lr: 2.2697e-07 eta: 1 day, 2:58:26 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0070 decode.acc_seg: 99.5947 aux.loss_ce: 0.0080 aux.acc_seg: 99.0145 +04/17 01:38:28 - mmengine - INFO - Iter(train) [ 62750/160000] base_lr: 6.1357e-05 lr: 2.2685e-07 eta: 1 day, 2:57:36 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.6117 aux.loss_ce: 0.0076 aux.acc_seg: 99.1707 +04/17 01:39:18 - mmengine - INFO - Iter(train) [ 62800/160000] base_lr: 6.1325e-05 lr: 2.2673e-07 eta: 1 day, 2:56:46 time: 1.0013 data_time: 0.0047 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.6681 aux.loss_ce: 0.0078 aux.acc_seg: 99.3587 +04/17 01:40:08 - mmengine - INFO - Iter(train) [ 62850/160000] base_lr: 6.1294e-05 lr: 2.2662e-07 eta: 1 day, 2:55:57 time: 0.9993 data_time: 0.0045 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0086 decode.acc_seg: 99.5710 aux.loss_ce: 0.0102 aux.acc_seg: 98.3358 +04/17 01:40:58 - mmengine - INFO - Iter(train) [ 62900/160000] base_lr: 6.1262e-05 lr: 2.2650e-07 eta: 1 day, 2:55:07 time: 0.9990 data_time: 0.0047 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0072 decode.acc_seg: 99.6313 aux.loss_ce: 0.0080 aux.acc_seg: 99.2073 +04/17 01:41:48 - mmengine - INFO - Iter(train) [ 62950/160000] base_lr: 6.1231e-05 lr: 2.2638e-07 eta: 1 day, 2:54:17 time: 0.9996 data_time: 0.0046 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0071 decode.acc_seg: 99.7908 aux.loss_ce: 0.0069 aux.acc_seg: 99.6157 +04/17 01:42:38 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240416_080959 +04/17 01:42:38 - mmengine - INFO - Iter(train) [ 63000/160000] base_lr: 6.1199e-05 lr: 2.2627e-07 eta: 1 day, 2:53:27 time: 0.9990 data_time: 0.0047 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.6899 aux.loss_ce: 0.0073 aux.acc_seg: 99.3097 +04/17 01:43:28 - mmengine - INFO - Iter(train) [ 63050/160000] base_lr: 6.1168e-05 lr: 2.2615e-07 eta: 1 day, 2:52:38 time: 0.9994 data_time: 0.0049 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.6851 aux.loss_ce: 0.0069 aux.acc_seg: 99.4026 +04/17 01:44:18 - mmengine - INFO - Iter(train) [ 63100/160000] base_lr: 6.1136e-05 lr: 2.2603e-07 eta: 1 day, 2:51:48 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.6614 aux.loss_ce: 0.0080 aux.acc_seg: 99.0128 +04/17 01:45:08 - mmengine - INFO - Iter(train) [ 63150/160000] base_lr: 6.1104e-05 lr: 2.2592e-07 eta: 1 day, 2:50:58 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0072 decode.acc_seg: 99.7526 aux.loss_ce: 0.0086 aux.acc_seg: 99.2950 +04/17 01:45:58 - mmengine - INFO - Iter(train) [ 63200/160000] base_lr: 6.1073e-05 lr: 2.2580e-07 eta: 1 day, 2:50:08 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.7175 aux.loss_ce: 0.0079 aux.acc_seg: 99.3681 +04/17 01:46:48 - mmengine - INFO - Iter(train) [ 63250/160000] base_lr: 6.1041e-05 lr: 2.2568e-07 eta: 1 day, 2:49:18 time: 1.0005 data_time: 0.0047 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0070 decode.acc_seg: 99.7387 aux.loss_ce: 0.0086 aux.acc_seg: 99.3063 +04/17 01:47:38 - mmengine - INFO - Iter(train) [ 63300/160000] base_lr: 6.1010e-05 lr: 2.2557e-07 eta: 1 day, 2:48:29 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.6525 aux.loss_ce: 0.0077 aux.acc_seg: 99.2231 +04/17 01:48:28 - mmengine - INFO - Iter(train) [ 63350/160000] base_lr: 6.0978e-05 lr: 2.2545e-07 eta: 1 day, 2:47:39 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0078 decode.acc_seg: 99.7160 aux.loss_ce: 0.0091 aux.acc_seg: 98.8708 +04/17 01:49:18 - mmengine - INFO - Iter(train) [ 63400/160000] base_lr: 6.0947e-05 lr: 2.2533e-07 eta: 1 day, 2:46:49 time: 1.0000 data_time: 0.0048 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7473 aux.loss_ce: 0.0071 aux.acc_seg: 99.4810 +04/17 01:50:08 - mmengine - INFO - Iter(train) [ 63450/160000] base_lr: 6.0915e-05 lr: 2.2522e-07 eta: 1 day, 2:45:59 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6769 aux.loss_ce: 0.0080 aux.acc_seg: 99.2762 +04/17 01:50:58 - mmengine - INFO - Iter(train) [ 63500/160000] base_lr: 6.0884e-05 lr: 2.2510e-07 eta: 1 day, 2:45:10 time: 1.0004 data_time: 0.0050 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0071 decode.acc_seg: 99.8365 aux.loss_ce: 0.0080 aux.acc_seg: 99.3488 +04/17 01:51:48 - mmengine - INFO - Iter(train) [ 63550/160000] base_lr: 6.0852e-05 lr: 2.2498e-07 eta: 1 day, 2:44:20 time: 1.0020 data_time: 0.0046 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.8329 aux.loss_ce: 0.0073 aux.acc_seg: 99.4867 +04/17 01:52:38 - mmengine - INFO - Iter(train) [ 63600/160000] base_lr: 6.0821e-05 lr: 2.2487e-07 eta: 1 day, 2:43:30 time: 0.9993 data_time: 0.0047 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7368 aux.loss_ce: 0.0072 aux.acc_seg: 99.3559 +04/17 01:53:28 - mmengine - INFO - Iter(train) [ 63650/160000] base_lr: 6.0789e-05 lr: 2.2475e-07 eta: 1 day, 2:42:40 time: 0.9991 data_time: 0.0050 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.6851 aux.loss_ce: 0.0082 aux.acc_seg: 99.2630 +04/17 01:54:18 - mmengine - INFO - Iter(train) [ 63700/160000] base_lr: 6.0757e-05 lr: 2.2463e-07 eta: 1 day, 2:41:51 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0070 decode.acc_seg: 99.5785 aux.loss_ce: 0.0080 aux.acc_seg: 99.0826 +04/17 01:55:08 - mmengine - INFO - Iter(train) [ 63750/160000] base_lr: 6.0726e-05 lr: 2.2452e-07 eta: 1 day, 2:41:01 time: 1.0010 data_time: 0.0049 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0083 decode.acc_seg: 99.6666 aux.loss_ce: 0.0088 aux.acc_seg: 98.8733 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 366197 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 366198 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 366199 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 366200 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 366197 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 366198 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 366199 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 366200 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 366166 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 366166 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 366166 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/17 01:58:49 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1256867340 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1256867340 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/17 01:58:50 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=101, + dilations=( + 1, + 1, + 1, + 1, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dropout_ratio=0.1, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + pretrained='open-mmlab://resnet101_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/upernet_r101_4xb4-160k_cag-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/17 01:58:52 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +04/17 01:58:53 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +04/17 01:58:53 - mmengine - INFO - load model from: open-mmlab://resnet101_v1c +04/17 01:58:53 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet101_v1c +Downloading: "https://download.openmmlab.com/pretrain/third_party/resnet101_v1c-e67eebb6.pth" to /root/.cache/torch/hub/checkpoints/resnet101_v1c-e67eebb6.pth +04/17 01:59:09 - mmengine - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: fc.weight, fc.bias + +04/17 01:59:09 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/17 01:59:09 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/17 01:59:09 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/upernet_r101_4xb4-160k_cag-512x512. +04/17 01:59:52 - mmengine - INFO - Iter(train) [ 50/160000] lr: 9.9973e-03 eta: 1 day, 14:09:24 time: 0.7661 data_time: 0.0057 memory: 8166 loss: 0.2475 decode.loss_ce: 0.1574 decode.acc_seg: 96.1514 aux.loss_ce: 0.0902 aux.acc_seg: 96.1514 +04/17 02:00:31 - mmengine - INFO - Iter(train) [ 100/160000] lr: 9.9945e-03 eta: 1 day, 12:25:46 time: 0.7943 data_time: 0.0054 memory: 8166 loss: 0.1151 decode.loss_ce: 0.0504 decode.acc_seg: 98.2868 aux.loss_ce: 0.0647 aux.acc_seg: 96.7345 +04/17 02:01:11 - mmengine - INFO - Iter(train) [ 150/160000] lr: 9.9917e-03 eta: 1 day, 12:04:58 time: 0.7971 data_time: 0.0059 memory: 8166 loss: 0.0849 decode.loss_ce: 0.0463 decode.acc_seg: 98.0924 aux.loss_ce: 0.0387 aux.acc_seg: 97.1909 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 789665 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 789666 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 789667 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 789668 closing signal SIGINT +Traceback (most recent call last): +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in +Traceback (most recent call last): +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + main() +main() + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + runner.train()runner.train() + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + outputs = self.runner.model.train_step(model = self.train_loop.run() # type: ignoremodel = self.train_loop.run() # type: ignore + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss) +self.run_iter(data_batch)self.run_iter(data_batch) File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter +self.backward(loss) File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + outputs = self.runner.model.train_step( +outputs = self.runner.model.train_step( File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss)torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)optim_wrapper.update_params(parsed_loss)torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + self.backward(loss) self.backward(loss)KeyboardInterrupt +Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward +KeyboardInterrupt + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward +loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) +torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward passVariable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +KeyboardInterruptKeyboardInterrupt + +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 789665 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 789666 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 789667 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 789668 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 789635 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 789635 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 789635 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/17 02:01:24 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 259084046 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 259084046 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/17 02:01:24 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=101, + dilations=( + 1, + 1, + 1, + 1, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dropout_ratio=0.1, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + pretrained='open-mmlab://resnet101_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/upernet_r101_4xb4-160k_cag-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/17 02:01:26 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +04/17 02:01:27 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +04/17 02:01:27 - mmengine - INFO - load model from: open-mmlab://resnet101_v1c +04/17 02:01:27 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet101_v1c +04/17 02:01:27 - mmengine - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: fc.weight, fc.bias + +04/17 02:01:27 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/17 02:01:27 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/17 02:01:27 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/upernet_r101_4xb4-160k_cag-512x512. +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 792179 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 792180 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 792181 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 792182 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main +Traceback (most recent call last): + runner.train() File "tools/train.py", line 104, in + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + main() +main() File "tools/train.py", line 100, in main + + File "tools/train.py", line 100, in main +runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + runner.train()model = self.train_loop.run() # type: ignore + +runner.train() File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + model = self.train_loop.run() # type: ignoreoptim_wrapper.update_params(parsed_loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward +self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + self.run_iter(data_batch)loss.backward(**kwargs) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward +outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + outputs = self.runner.model.train_step( +optim_wrapper.update_params(parsed_loss) File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + +outputs = self.runner.model.train_step( File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + +torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss)optim_wrapper.update_params(parsed_loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +self.backward(loss) +KeyboardInterrupt File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + + loss.backward(**kwargs) +self.backward(loss) File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward +Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 792179 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 792180 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 792181 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 792182 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 792148 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 792148 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 792148 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/17 02:02:09 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1622490299 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1622490299 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/17 02:02:10 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=101, + dilations=( + 1, + 1, + 1, + 1, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dropout_ratio=0.1, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + pretrained='open-mmlab://resnet101_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/upernet_r101_4xb4-160k_cag-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/17 02:02:12 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +04/17 02:02:13 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +04/17 02:02:13 - mmengine - INFO - load model from: open-mmlab://resnet101_v1c +04/17 02:02:13 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet101_v1c +04/17 02:02:13 - mmengine - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: fc.weight, fc.bias + +04/17 02:02:13 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/17 02:02:13 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/17 02:02:13 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/upernet_r101_4xb4-160k_cag-512x512. +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 793328 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 793329 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 793330 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 793331 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 793328 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 793329 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 793330 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 793331 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 793297 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 793297 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 793297 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/17 02:02:22 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 968316655 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 968316655 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/17 02:02:23 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=101, + dilations=( + 1, + 1, + 1, + 1, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dropout_ratio=0.1, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + pretrained='open-mmlab://resnet101_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/upernet_r101_4xb4-160k_cag-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/17 02:02:24 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +04/17 02:02:26 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +04/17 02:02:26 - mmengine - INFO - load model from: open-mmlab://resnet101_v1c +04/17 02:02:26 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet101_v1c +04/17 02:02:26 - mmengine - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: fc.weight, fc.bias + +04/17 02:02:26 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/17 02:02:26 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/17 02:02:26 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/upernet_r101_4xb4-160k_cag-512x512. +04/17 02:02:58 - mmengine - INFO - Iter(train) [ 50/160000] lr: 9.9973e-03 eta: 1 day, 4:45:42 time: 0.5454 data_time: 0.0064 memory: 7635 loss: 0.1422 decode.loss_ce: 0.0900 decode.acc_seg: 96.6448 aux.loss_ce: 0.0522 aux.acc_seg: 96.6413 +04/17 02:03:26 - mmengine - INFO - Iter(train) [ 100/160000] lr: 9.9945e-03 eta: 1 day, 2:31:41 time: 0.5494 data_time: 0.0055 memory: 7635 loss: 0.0984 decode.loss_ce: 0.0598 decode.acc_seg: 97.3445 aux.loss_ce: 0.0386 aux.acc_seg: 96.4732 +04/17 02:03:53 - mmengine - INFO - Iter(train) [ 150/160000] lr: 9.9917e-03 eta: 1 day, 1:47:48 time: 0.5473 data_time: 0.0064 memory: 7635 loss: 0.0804 decode.loss_ce: 0.0477 decode.acc_seg: 98.2474 aux.loss_ce: 0.0327 aux.acc_seg: 96.4436 +04/17 02:04:21 - mmengine - INFO - Iter(train) [ 200/160000] lr: 9.9889e-03 eta: 1 day, 1:26:04 time: 0.5494 data_time: 0.0062 memory: 7635 loss: 0.0758 decode.loss_ce: 0.0457 decode.acc_seg: 97.5277 aux.loss_ce: 0.0302 aux.acc_seg: 95.7481 +04/17 02:04:48 - mmengine - INFO - Iter(train) [ 250/160000] lr: 9.9861e-03 eta: 1 day, 1:12:38 time: 0.5488 data_time: 0.0062 memory: 7635 loss: 0.0767 decode.loss_ce: 0.0468 decode.acc_seg: 98.4130 aux.loss_ce: 0.0299 aux.acc_seg: 97.0962 +04/17 02:05:15 - mmengine - INFO - Iter(train) [ 300/160000] lr: 9.9833e-03 eta: 1 day, 1:04:02 time: 0.5501 data_time: 0.0056 memory: 7635 loss: 0.0743 decode.loss_ce: 0.0468 decode.acc_seg: 98.1305 aux.loss_ce: 0.0275 aux.acc_seg: 97.3926 +04/17 02:05:43 - mmengine - INFO - Iter(train) [ 350/160000] lr: 9.9806e-03 eta: 1 day, 0:57:55 time: 0.5519 data_time: 0.0056 memory: 7635 loss: 0.0737 decode.loss_ce: 0.0474 decode.acc_seg: 97.6963 aux.loss_ce: 0.0263 aux.acc_seg: 97.4849 +04/17 02:06:11 - mmengine - INFO - Iter(train) [ 400/160000] lr: 9.9778e-03 eta: 1 day, 0:53:18 time: 0.5512 data_time: 0.0060 memory: 7635 loss: 0.0647 decode.loss_ce: 0.0413 decode.acc_seg: 98.7028 aux.loss_ce: 0.0234 aux.acc_seg: 98.0618 +04/17 02:06:38 - mmengine - INFO - Iter(train) [ 450/160000] lr: 9.9750e-03 eta: 1 day, 0:49:44 time: 0.5509 data_time: 0.0060 memory: 7635 loss: 0.0556 decode.loss_ce: 0.0345 decode.acc_seg: 98.9654 aux.loss_ce: 0.0210 aux.acc_seg: 98.1362 +04/17 02:07:06 - mmengine - INFO - Iter(train) [ 500/160000] lr: 9.9722e-03 eta: 1 day, 0:47:28 time: 0.5506 data_time: 0.0060 memory: 7635 loss: 0.0546 decode.loss_ce: 0.0342 decode.acc_seg: 98.3095 aux.loss_ce: 0.0204 aux.acc_seg: 97.5809 +04/17 02:07:33 - mmengine - INFO - Iter(train) [ 550/160000] lr: 9.9694e-03 eta: 1 day, 0:44:58 time: 0.5514 data_time: 0.0059 memory: 7635 loss: 0.0577 decode.loss_ce: 0.0372 decode.acc_seg: 99.0083 aux.loss_ce: 0.0205 aux.acc_seg: 98.3421 +04/17 02:08:01 - mmengine - INFO - Iter(train) [ 600/160000] lr: 9.9666e-03 eta: 1 day, 0:42:50 time: 0.5509 data_time: 0.0055 memory: 7635 loss: 0.0616 decode.loss_ce: 0.0397 decode.acc_seg: 98.0560 aux.loss_ce: 0.0219 aux.acc_seg: 97.6258 +04/17 02:08:28 - mmengine - INFO - Iter(train) [ 650/160000] lr: 9.9639e-03 eta: 1 day, 0:41:06 time: 0.5517 data_time: 0.0053 memory: 7635 loss: 0.0578 decode.loss_ce: 0.0370 decode.acc_seg: 98.5957 aux.loss_ce: 0.0208 aux.acc_seg: 97.8125 +04/17 02:08:56 - mmengine - INFO - Iter(train) [ 700/160000] lr: 9.9611e-03 eta: 1 day, 0:39:15 time: 0.5503 data_time: 0.0059 memory: 7635 loss: 0.0532 decode.loss_ce: 0.0340 decode.acc_seg: 98.7547 aux.loss_ce: 0.0192 aux.acc_seg: 98.1804 +04/17 02:09:24 - mmengine - INFO - Iter(train) [ 750/160000] lr: 9.9583e-03 eta: 1 day, 0:37:42 time: 0.5514 data_time: 0.0061 memory: 7635 loss: 0.0522 decode.loss_ce: 0.0336 decode.acc_seg: 99.0025 aux.loss_ce: 0.0186 aux.acc_seg: 98.7077 +WARNING:torch.distributed.elastic.agent.server.api:Received 1 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 794125 closing signal SIGHUP +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 794126 closing signal SIGHUP +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 794127 closing signal SIGHUP +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 794128 closing signal SIGHUP +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/17 02:22:25 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 536263187 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 536263187 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/17 02:22:25 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=101, + dilations=( + 1, + 1, + 1, + 1, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dropout_ratio=0.1, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + pretrained='open-mmlab://resnet101_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/upernet_r101_4xb4-160k_cag-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/17 02:22:27 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +04/17 02:22:28 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +04/17 02:22:28 - mmengine - INFO - load model from: open-mmlab://resnet101_v1c +04/17 02:22:28 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet101_v1c +04/17 02:22:28 - mmengine - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: fc.weight, fc.bias + +04/17 02:22:28 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/17 02:22:28 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/17 02:22:28 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/upernet_r101_4xb4-160k_cag-512x512. +04/17 02:23:00 - mmengine - INFO - Iter(train) [ 50/160000] lr: 9.9973e-03 eta: 1 day, 4:26:13 time: 0.5398 data_time: 0.0063 memory: 7635 loss: 0.1440 decode.loss_ce: 0.0952 decode.acc_seg: 97.1099 aux.loss_ce: 0.0488 aux.acc_seg: 97.1099 +04/17 02:23:28 - mmengine - INFO - Iter(train) [ 100/160000] lr: 9.9945e-03 eta: 1 day, 2:15:52 time: 0.5444 data_time: 0.0059 memory: 7635 loss: 0.0943 decode.loss_ce: 0.0589 decode.acc_seg: 97.2680 aux.loss_ce: 0.0355 aux.acc_seg: 96.6311 +04/17 02:23:55 - mmengine - INFO - Iter(train) [ 150/160000] lr: 9.9917e-03 eta: 1 day, 1:33:56 time: 0.5451 data_time: 0.0062 memory: 7635 loss: 0.0767 decode.loss_ce: 0.0459 decode.acc_seg: 98.7021 aux.loss_ce: 0.0307 aux.acc_seg: 96.7049 +04/17 02:24:22 - mmengine - INFO - Iter(train) [ 200/160000] lr: 9.9889e-03 eta: 1 day, 1:13:36 time: 0.5473 data_time: 0.0056 memory: 7635 loss: 0.0753 decode.loss_ce: 0.0463 decode.acc_seg: 98.7144 aux.loss_ce: 0.0291 aux.acc_seg: 97.6154 +04/17 02:24:49 - mmengine - INFO - Iter(train) [ 250/160000] lr: 9.9861e-03 eta: 1 day, 1:01:50 time: 0.5468 data_time: 0.0057 memory: 7635 loss: 0.0751 decode.loss_ce: 0.0457 decode.acc_seg: 98.6154 aux.loss_ce: 0.0294 aux.acc_seg: 97.7401 +04/17 02:25:17 - mmengine - INFO - Iter(train) [ 300/160000] lr: 9.9833e-03 eta: 1 day, 0:54:02 time: 0.5479 data_time: 0.0060 memory: 7635 loss: 0.0692 decode.loss_ce: 0.0432 decode.acc_seg: 98.8323 aux.loss_ce: 0.0260 aux.acc_seg: 98.2899 +04/17 02:25:44 - mmengine - INFO - Iter(train) [ 350/160000] lr: 9.9806e-03 eta: 1 day, 0:48:39 time: 0.5470 data_time: 0.0057 memory: 7635 loss: 0.0672 decode.loss_ce: 0.0424 decode.acc_seg: 98.5642 aux.loss_ce: 0.0249 aux.acc_seg: 97.9949 +04/17 02:26:12 - mmengine - INFO - Iter(train) [ 400/160000] lr: 9.9778e-03 eta: 1 day, 0:44:29 time: 0.5486 data_time: 0.0065 memory: 7635 loss: 0.0723 decode.loss_ce: 0.0463 decode.acc_seg: 97.5659 aux.loss_ce: 0.0260 aux.acc_seg: 97.4424 +04/17 02:26:39 - mmengine - INFO - Iter(train) [ 450/160000] lr: 9.9750e-03 eta: 1 day, 0:41:24 time: 0.5492 data_time: 0.0058 memory: 7635 loss: 0.0604 decode.loss_ce: 0.0377 decode.acc_seg: 98.4617 aux.loss_ce: 0.0228 aux.acc_seg: 97.6000 +04/17 02:27:07 - mmengine - INFO - Iter(train) [ 500/160000] lr: 9.9722e-03 eta: 1 day, 0:39:25 time: 0.5489 data_time: 0.0061 memory: 7635 loss: 0.0621 decode.loss_ce: 0.0397 decode.acc_seg: 98.8098 aux.loss_ce: 0.0224 aux.acc_seg: 98.3232 +04/17 02:27:34 - mmengine - INFO - Iter(train) [ 550/160000] lr: 9.9694e-03 eta: 1 day, 0:37:21 time: 0.5496 data_time: 0.0061 memory: 7635 loss: 0.0637 decode.loss_ce: 0.0407 decode.acc_seg: 99.2899 aux.loss_ce: 0.0230 aux.acc_seg: 98.3852 +04/17 02:28:02 - mmengine - INFO - Iter(train) [ 600/160000] lr: 9.9666e-03 eta: 1 day, 0:35:40 time: 0.5503 data_time: 0.0058 memory: 7635 loss: 0.0580 decode.loss_ce: 0.0368 decode.acc_seg: 98.2452 aux.loss_ce: 0.0213 aux.acc_seg: 96.9857 +04/17 02:28:29 - mmengine - INFO - Iter(train) [ 650/160000] lr: 9.9639e-03 eta: 1 day, 0:34:01 time: 0.5498 data_time: 0.0057 memory: 7635 loss: 0.0550 decode.loss_ce: 0.0349 decode.acc_seg: 98.5855 aux.loss_ce: 0.0201 aux.acc_seg: 98.1388 +04/17 02:28:57 - mmengine - INFO - Iter(train) [ 700/160000] lr: 9.9611e-03 eta: 1 day, 0:32:33 time: 0.5508 data_time: 0.0066 memory: 7635 loss: 0.0603 decode.loss_ce: 0.0393 decode.acc_seg: 98.4143 aux.loss_ce: 0.0210 aux.acc_seg: 97.8218 +04/17 02:29:24 - mmengine - INFO - Iter(train) [ 750/160000] lr: 9.9583e-03 eta: 1 day, 0:31:20 time: 0.5510 data_time: 0.0058 memory: 7635 loss: 0.0479 decode.loss_ce: 0.0299 decode.acc_seg: 98.2230 aux.loss_ce: 0.0180 aux.acc_seg: 97.5971 +04/17 02:29:52 - mmengine - INFO - Iter(train) [ 800/160000] lr: 9.9555e-03 eta: 1 day, 0:30:12 time: 0.5502 data_time: 0.0056 memory: 7635 loss: 0.0497 decode.loss_ce: 0.0315 decode.acc_seg: 99.0558 aux.loss_ce: 0.0182 aux.acc_seg: 98.1543 +04/17 02:30:19 - mmengine - INFO - Iter(train) [ 850/160000] lr: 9.9527e-03 eta: 1 day, 0:29:05 time: 0.5504 data_time: 0.0065 memory: 7635 loss: 0.0490 decode.loss_ce: 0.0306 decode.acc_seg: 99.3666 aux.loss_ce: 0.0184 aux.acc_seg: 98.7853 +04/17 02:30:47 - mmengine - INFO - Iter(train) [ 900/160000] lr: 9.9499e-03 eta: 1 day, 0:28:09 time: 0.5507 data_time: 0.0059 memory: 7635 loss: 0.0533 decode.loss_ce: 0.0340 decode.acc_seg: 98.0306 aux.loss_ce: 0.0193 aux.acc_seg: 97.3399 +04/17 02:31:14 - mmengine - INFO - Iter(train) [ 950/160000] lr: 9.9471e-03 eta: 1 day, 0:27:18 time: 0.5504 data_time: 0.0066 memory: 7635 loss: 0.0532 decode.loss_ce: 0.0340 decode.acc_seg: 98.8028 aux.loss_ce: 0.0191 aux.acc_seg: 98.3513 +04/17 02:31:42 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 02:31:42 - mmengine - INFO - Iter(train) [ 1000/160000] lr: 9.9444e-03 eta: 1 day, 0:26:27 time: 0.5500 data_time: 0.0064 memory: 7635 loss: 0.0465 decode.loss_ce: 0.0290 decode.acc_seg: 99.1015 aux.loss_ce: 0.0175 aux.acc_seg: 98.5930 +04/17 02:32:09 - mmengine - INFO - Iter(train) [ 1050/160000] lr: 9.9416e-03 eta: 1 day, 0:25:40 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0456 decode.loss_ce: 0.0287 decode.acc_seg: 98.5889 aux.loss_ce: 0.0168 aux.acc_seg: 98.2008 +04/17 02:32:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 02:32:37 - mmengine - INFO - Iter(train) [ 1100/160000] lr: 9.9388e-03 eta: 1 day, 0:24:54 time: 0.5515 data_time: 0.0055 memory: 7635 loss: 0.0503 decode.loss_ce: 0.0322 decode.acc_seg: 99.0838 aux.loss_ce: 0.0181 aux.acc_seg: 98.5331 +04/17 02:33:04 - mmengine - INFO - Iter(train) [ 1150/160000] lr: 9.9360e-03 eta: 1 day, 0:24:12 time: 0.5511 data_time: 0.0052 memory: 7635 loss: 0.0447 decode.loss_ce: 0.0280 decode.acc_seg: 99.0372 aux.loss_ce: 0.0166 aux.acc_seg: 98.5503 +04/17 02:33:32 - mmengine - INFO - Iter(train) [ 1200/160000] lr: 9.9332e-03 eta: 1 day, 0:23:34 time: 0.5521 data_time: 0.0065 memory: 7635 loss: 0.0595 decode.loss_ce: 0.0387 decode.acc_seg: 99.2927 aux.loss_ce: 0.0208 aux.acc_seg: 98.7716 +04/17 02:34:00 - mmengine - INFO - Iter(train) [ 1250/160000] lr: 9.9304e-03 eta: 1 day, 0:22:54 time: 0.5505 data_time: 0.0067 memory: 7635 loss: 0.0443 decode.loss_ce: 0.0279 decode.acc_seg: 98.7502 aux.loss_ce: 0.0164 aux.acc_seg: 98.2639 +04/17 02:34:27 - mmengine - INFO - Iter(train) [ 1300/160000] lr: 9.9276e-03 eta: 1 day, 0:22:16 time: 0.5506 data_time: 0.0056 memory: 7635 loss: 0.0481 decode.loss_ce: 0.0303 decode.acc_seg: 98.9054 aux.loss_ce: 0.0178 aux.acc_seg: 98.3751 +04/17 02:34:55 - mmengine - INFO - Iter(train) [ 1350/160000] lr: 9.9248e-03 eta: 1 day, 0:21:40 time: 0.5507 data_time: 0.0061 memory: 7635 loss: 0.0418 decode.loss_ce: 0.0262 decode.acc_seg: 98.9240 aux.loss_ce: 0.0156 aux.acc_seg: 98.3135 +04/17 02:35:22 - mmengine - INFO - Iter(train) [ 1400/160000] lr: 9.9221e-03 eta: 1 day, 0:21:02 time: 0.5507 data_time: 0.0059 memory: 7635 loss: 0.0399 decode.loss_ce: 0.0246 decode.acc_seg: 99.2918 aux.loss_ce: 0.0153 aux.acc_seg: 98.6363 +04/17 02:35:50 - mmengine - INFO - Iter(train) [ 1450/160000] lr: 9.9193e-03 eta: 1 day, 0:20:24 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0392 decode.loss_ce: 0.0243 decode.acc_seg: 98.7610 aux.loss_ce: 0.0149 aux.acc_seg: 98.0757 +04/17 02:36:17 - mmengine - INFO - Iter(train) [ 1500/160000] lr: 9.9165e-03 eta: 1 day, 0:19:50 time: 0.5509 data_time: 0.0056 memory: 7635 loss: 0.0439 decode.loss_ce: 0.0272 decode.acc_seg: 99.0294 aux.loss_ce: 0.0168 aux.acc_seg: 98.4800 +04/17 02:36:45 - mmengine - INFO - Iter(train) [ 1550/160000] lr: 9.9137e-03 eta: 1 day, 0:19:17 time: 0.5527 data_time: 0.0060 memory: 7635 loss: 0.0410 decode.loss_ce: 0.0255 decode.acc_seg: 97.9154 aux.loss_ce: 0.0156 aux.acc_seg: 97.3303 +04/17 02:37:13 - mmengine - INFO - Iter(train) [ 1600/160000] lr: 9.9109e-03 eta: 1 day, 0:18:54 time: 0.5505 data_time: 0.0061 memory: 7635 loss: 0.0396 decode.loss_ce: 0.0247 decode.acc_seg: 99.0260 aux.loss_ce: 0.0149 aux.acc_seg: 98.5363 +04/17 02:37:40 - mmengine - INFO - Iter(train) [ 1650/160000] lr: 9.9081e-03 eta: 1 day, 0:18:22 time: 0.5512 data_time: 0.0055 memory: 7635 loss: 0.0462 decode.loss_ce: 0.0291 decode.acc_seg: 99.0538 aux.loss_ce: 0.0170 aux.acc_seg: 98.4682 +04/17 02:38:08 - mmengine - INFO - Iter(train) [ 1700/160000] lr: 9.9053e-03 eta: 1 day, 0:17:50 time: 0.5519 data_time: 0.0068 memory: 7635 loss: 0.0442 decode.loss_ce: 0.0286 decode.acc_seg: 99.2168 aux.loss_ce: 0.0157 aux.acc_seg: 98.7749 +04/17 02:38:35 - mmengine - INFO - Iter(train) [ 1750/160000] lr: 9.9025e-03 eta: 1 day, 0:17:19 time: 0.5530 data_time: 0.0058 memory: 7635 loss: 0.0415 decode.loss_ce: 0.0259 decode.acc_seg: 99.2253 aux.loss_ce: 0.0156 aux.acc_seg: 98.6609 +04/17 02:39:03 - mmengine - INFO - Iter(train) [ 1800/160000] lr: 9.8998e-03 eta: 1 day, 0:16:46 time: 0.5506 data_time: 0.0059 memory: 7635 loss: 0.0408 decode.loss_ce: 0.0256 decode.acc_seg: 98.8564 aux.loss_ce: 0.0152 aux.acc_seg: 98.4221 +04/17 02:39:30 - mmengine - INFO - Iter(train) [ 1850/160000] lr: 9.8970e-03 eta: 1 day, 0:16:13 time: 0.5518 data_time: 0.0054 memory: 7635 loss: 0.0420 decode.loss_ce: 0.0264 decode.acc_seg: 98.8280 aux.loss_ce: 0.0156 aux.acc_seg: 98.5531 +04/17 02:39:58 - mmengine - INFO - Iter(train) [ 1900/160000] lr: 9.8942e-03 eta: 1 day, 0:15:43 time: 0.5517 data_time: 0.0054 memory: 7635 loss: 0.0354 decode.loss_ce: 0.0217 decode.acc_seg: 98.9604 aux.loss_ce: 0.0137 aux.acc_seg: 98.6360 +04/17 02:40:26 - mmengine - INFO - Iter(train) [ 1950/160000] lr: 9.8914e-03 eta: 1 day, 0:15:12 time: 0.5521 data_time: 0.0056 memory: 7635 loss: 0.0415 decode.loss_ce: 0.0258 decode.acc_seg: 98.5333 aux.loss_ce: 0.0157 aux.acc_seg: 97.9955 +04/17 02:40:53 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 02:40:53 - mmengine - INFO - Iter(train) [ 2000/160000] lr: 9.8886e-03 eta: 1 day, 0:14:40 time: 0.5519 data_time: 0.0055 memory: 7635 loss: 0.0353 decode.loss_ce: 0.0216 decode.acc_seg: 99.3915 aux.loss_ce: 0.0138 aux.acc_seg: 98.9317 +04/17 02:41:21 - mmengine - INFO - Iter(train) [ 2050/160000] lr: 9.8858e-03 eta: 1 day, 0:14:11 time: 0.5522 data_time: 0.0059 memory: 7635 loss: 0.0385 decode.loss_ce: 0.0235 decode.acc_seg: 99.2126 aux.loss_ce: 0.0149 aux.acc_seg: 98.5108 +04/17 02:41:48 - mmengine - INFO - Iter(train) [ 2100/160000] lr: 9.8830e-03 eta: 1 day, 0:13:44 time: 0.5515 data_time: 0.0056 memory: 7635 loss: 0.0391 decode.loss_ce: 0.0241 decode.acc_seg: 99.0423 aux.loss_ce: 0.0150 aux.acc_seg: 98.5409 +04/17 02:42:16 - mmengine - INFO - Iter(train) [ 2150/160000] lr: 9.8802e-03 eta: 1 day, 0:13:17 time: 0.5526 data_time: 0.0057 memory: 7635 loss: 0.0359 decode.loss_ce: 0.0218 decode.acc_seg: 99.0562 aux.loss_ce: 0.0141 aux.acc_seg: 98.5904 +04/17 02:42:44 - mmengine - INFO - Iter(train) [ 2200/160000] lr: 9.8775e-03 eta: 1 day, 0:12:47 time: 0.5511 data_time: 0.0059 memory: 7635 loss: 0.0396 decode.loss_ce: 0.0248 decode.acc_seg: 99.3354 aux.loss_ce: 0.0148 aux.acc_seg: 98.8929 +04/17 02:43:11 - mmengine - INFO - Iter(train) [ 2250/160000] lr: 9.8747e-03 eta: 1 day, 0:12:18 time: 0.5520 data_time: 0.0053 memory: 7635 loss: 0.0348 decode.loss_ce: 0.0217 decode.acc_seg: 99.0128 aux.loss_ce: 0.0131 aux.acc_seg: 98.8256 +04/17 02:43:39 - mmengine - INFO - Iter(train) [ 2300/160000] lr: 9.8719e-03 eta: 1 day, 0:11:49 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0336 decode.loss_ce: 0.0204 decode.acc_seg: 99.2620 aux.loss_ce: 0.0132 aux.acc_seg: 98.8499 +04/17 02:44:06 - mmengine - INFO - Iter(train) [ 2350/160000] lr: 9.8691e-03 eta: 1 day, 0:11:20 time: 0.5517 data_time: 0.0060 memory: 7635 loss: 0.0422 decode.loss_ce: 0.0266 decode.acc_seg: 99.2365 aux.loss_ce: 0.0156 aux.acc_seg: 98.7563 +04/17 02:44:34 - mmengine - INFO - Iter(train) [ 2400/160000] lr: 9.8663e-03 eta: 1 day, 0:10:52 time: 0.5512 data_time: 0.0055 memory: 7635 loss: 0.0345 decode.loss_ce: 0.0212 decode.acc_seg: 99.4081 aux.loss_ce: 0.0133 aux.acc_seg: 98.9314 +04/17 02:45:02 - mmengine - INFO - Iter(train) [ 2450/160000] lr: 9.8635e-03 eta: 1 day, 0:10:22 time: 0.5509 data_time: 0.0058 memory: 7635 loss: 0.0352 decode.loss_ce: 0.0220 decode.acc_seg: 99.0789 aux.loss_ce: 0.0132 aux.acc_seg: 98.7149 +04/17 02:45:29 - mmengine - INFO - Iter(train) [ 2500/160000] lr: 9.8607e-03 eta: 1 day, 0:09:52 time: 0.5530 data_time: 0.0055 memory: 7635 loss: 0.0353 decode.loss_ce: 0.0216 decode.acc_seg: 99.0977 aux.loss_ce: 0.0137 aux.acc_seg: 98.6426 +04/17 02:45:57 - mmengine - INFO - Iter(train) [ 2550/160000] lr: 9.8579e-03 eta: 1 day, 0:09:22 time: 0.5518 data_time: 0.0061 memory: 7635 loss: 0.0361 decode.loss_ce: 0.0225 decode.acc_seg: 99.1934 aux.loss_ce: 0.0136 aux.acc_seg: 98.7804 +04/17 02:46:24 - mmengine - INFO - Iter(train) [ 2600/160000] lr: 9.8551e-03 eta: 1 day, 0:08:52 time: 0.5525 data_time: 0.0057 memory: 7635 loss: 0.0341 decode.loss_ce: 0.0213 decode.acc_seg: 99.3256 aux.loss_ce: 0.0128 aux.acc_seg: 98.8477 +04/17 02:46:52 - mmengine - INFO - Iter(train) [ 2650/160000] lr: 9.8524e-03 eta: 1 day, 0:08:31 time: 0.5618 data_time: 0.0058 memory: 7635 loss: 0.0315 decode.loss_ce: 0.0190 decode.acc_seg: 99.3283 aux.loss_ce: 0.0125 aux.acc_seg: 98.7658 +04/17 02:47:20 - mmengine - INFO - Iter(train) [ 2700/160000] lr: 9.8496e-03 eta: 1 day, 0:08:03 time: 0.5523 data_time: 0.0055 memory: 7635 loss: 0.0339 decode.loss_ce: 0.0208 decode.acc_seg: 99.1889 aux.loss_ce: 0.0131 aux.acc_seg: 98.8381 +04/17 02:47:47 - mmengine - INFO - Iter(train) [ 2750/160000] lr: 9.8468e-03 eta: 1 day, 0:07:34 time: 0.5512 data_time: 0.0057 memory: 7635 loss: 0.0327 decode.loss_ce: 0.0198 decode.acc_seg: 99.3170 aux.loss_ce: 0.0129 aux.acc_seg: 98.8644 +04/17 02:48:15 - mmengine - INFO - Iter(train) [ 2800/160000] lr: 9.8440e-03 eta: 1 day, 0:07:07 time: 0.5529 data_time: 0.0056 memory: 7635 loss: 0.0387 decode.loss_ce: 0.0238 decode.acc_seg: 99.2949 aux.loss_ce: 0.0149 aux.acc_seg: 98.6554 +04/17 02:48:43 - mmengine - INFO - Iter(train) [ 2850/160000] lr: 9.8412e-03 eta: 1 day, 0:06:40 time: 0.5528 data_time: 0.0058 memory: 7635 loss: 0.0401 decode.loss_ce: 0.0253 decode.acc_seg: 99.1680 aux.loss_ce: 0.0148 aux.acc_seg: 98.7084 +04/17 02:49:10 - mmengine - INFO - Iter(train) [ 2900/160000] lr: 9.8384e-03 eta: 1 day, 0:06:12 time: 0.5518 data_time: 0.0068 memory: 7635 loss: 0.0348 decode.loss_ce: 0.0211 decode.acc_seg: 99.0792 aux.loss_ce: 0.0136 aux.acc_seg: 98.3262 +04/17 02:49:38 - mmengine - INFO - Iter(train) [ 2950/160000] lr: 9.8356e-03 eta: 1 day, 0:05:42 time: 0.5506 data_time: 0.0062 memory: 7635 loss: 0.0316 decode.loss_ce: 0.0192 decode.acc_seg: 99.1305 aux.loss_ce: 0.0124 aux.acc_seg: 98.6819 +04/17 02:50:05 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 02:50:05 - mmengine - INFO - Iter(train) [ 3000/160000] lr: 9.8328e-03 eta: 1 day, 0:05:14 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0376 decode.loss_ce: 0.0230 decode.acc_seg: 99.1773 aux.loss_ce: 0.0145 aux.acc_seg: 98.6571 +04/17 02:50:33 - mmengine - INFO - Iter(train) [ 3050/160000] lr: 9.8300e-03 eta: 1 day, 0:04:44 time: 0.5519 data_time: 0.0060 memory: 7635 loss: 0.0339 decode.loss_ce: 0.0208 decode.acc_seg: 99.2224 aux.loss_ce: 0.0131 aux.acc_seg: 98.7208 +04/17 02:51:01 - mmengine - INFO - Iter(train) [ 3100/160000] lr: 9.8273e-03 eta: 1 day, 0:04:16 time: 0.5527 data_time: 0.0060 memory: 7635 loss: 0.0426 decode.loss_ce: 0.0267 decode.acc_seg: 99.3018 aux.loss_ce: 0.0159 aux.acc_seg: 98.7821 +04/17 02:51:28 - mmengine - INFO - Iter(train) [ 3150/160000] lr: 9.8245e-03 eta: 1 day, 0:03:48 time: 0.5506 data_time: 0.0056 memory: 7635 loss: 0.0350 decode.loss_ce: 0.0212 decode.acc_seg: 99.3638 aux.loss_ce: 0.0138 aux.acc_seg: 98.8532 +04/17 02:51:56 - mmengine - INFO - Iter(train) [ 3200/160000] lr: 9.8217e-03 eta: 1 day, 0:03:19 time: 0.5509 data_time: 0.0067 memory: 7635 loss: 0.0366 decode.loss_ce: 0.0225 decode.acc_seg: 99.2182 aux.loss_ce: 0.0141 aux.acc_seg: 98.4915 +04/17 02:52:23 - mmengine - INFO - Iter(train) [ 3250/160000] lr: 9.8189e-03 eta: 1 day, 0:02:51 time: 0.5520 data_time: 0.0066 memory: 7635 loss: 0.0358 decode.loss_ce: 0.0221 decode.acc_seg: 98.7513 aux.loss_ce: 0.0137 aux.acc_seg: 98.3668 +04/17 02:52:51 - mmengine - INFO - Iter(train) [ 3300/160000] lr: 9.8161e-03 eta: 1 day, 0:02:23 time: 0.5529 data_time: 0.0059 memory: 7635 loss: 0.0363 decode.loss_ce: 0.0224 decode.acc_seg: 99.3017 aux.loss_ce: 0.0139 aux.acc_seg: 98.7112 +04/17 02:53:19 - mmengine - INFO - Iter(train) [ 3350/160000] lr: 9.8133e-03 eta: 1 day, 0:01:56 time: 0.5523 data_time: 0.0058 memory: 7635 loss: 0.0313 decode.loss_ce: 0.0188 decode.acc_seg: 99.4477 aux.loss_ce: 0.0125 aux.acc_seg: 98.9939 +04/17 02:53:46 - mmengine - INFO - Iter(train) [ 3400/160000] lr: 9.8105e-03 eta: 1 day, 0:01:28 time: 0.5535 data_time: 0.0059 memory: 7635 loss: 0.0349 decode.loss_ce: 0.0220 decode.acc_seg: 99.0543 aux.loss_ce: 0.0129 aux.acc_seg: 98.5743 +04/17 02:54:14 - mmengine - INFO - Iter(train) [ 3450/160000] lr: 9.8077e-03 eta: 1 day, 0:01:00 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0341 decode.loss_ce: 0.0209 decode.acc_seg: 99.4157 aux.loss_ce: 0.0132 aux.acc_seg: 98.9601 +04/17 02:54:41 - mmengine - INFO - Iter(train) [ 3500/160000] lr: 9.8049e-03 eta: 1 day, 0:00:32 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0352 decode.loss_ce: 0.0215 decode.acc_seg: 99.2278 aux.loss_ce: 0.0137 aux.acc_seg: 98.4088 +04/17 02:55:09 - mmengine - INFO - Iter(train) [ 3550/160000] lr: 9.8021e-03 eta: 1 day, 0:00:05 time: 0.5530 data_time: 0.0058 memory: 7635 loss: 0.0360 decode.loss_ce: 0.0221 decode.acc_seg: 99.0523 aux.loss_ce: 0.0139 aux.acc_seg: 98.5312 +04/17 02:55:37 - mmengine - INFO - Iter(train) [ 3600/160000] lr: 9.7994e-03 eta: 23:59:37 time: 0.5511 data_time: 0.0054 memory: 7635 loss: 0.0382 decode.loss_ce: 0.0237 decode.acc_seg: 98.8614 aux.loss_ce: 0.0145 aux.acc_seg: 98.4686 +04/17 02:56:04 - mmengine - INFO - Iter(train) [ 3650/160000] lr: 9.7966e-03 eta: 23:59:09 time: 0.5531 data_time: 0.0066 memory: 7635 loss: 0.0331 decode.loss_ce: 0.0204 decode.acc_seg: 99.1750 aux.loss_ce: 0.0127 aux.acc_seg: 98.7031 +04/17 02:56:32 - mmengine - INFO - Iter(train) [ 3700/160000] lr: 9.7938e-03 eta: 23:58:41 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0381 decode.loss_ce: 0.0238 decode.acc_seg: 98.8438 aux.loss_ce: 0.0143 aux.acc_seg: 98.4982 +04/17 02:57:00 - mmengine - INFO - Iter(train) [ 3750/160000] lr: 9.7910e-03 eta: 23:58:17 time: 0.5513 data_time: 0.0060 memory: 7635 loss: 0.0341 decode.loss_ce: 0.0204 decode.acc_seg: 99.2230 aux.loss_ce: 0.0137 aux.acc_seg: 98.6295 +04/17 02:57:27 - mmengine - INFO - Iter(train) [ 3800/160000] lr: 9.7882e-03 eta: 23:57:48 time: 0.5516 data_time: 0.0067 memory: 7635 loss: 0.0292 decode.loss_ce: 0.0178 decode.acc_seg: 99.3898 aux.loss_ce: 0.0115 aux.acc_seg: 98.7861 +04/17 02:57:55 - mmengine - INFO - Iter(train) [ 3850/160000] lr: 9.7854e-03 eta: 23:57:20 time: 0.5525 data_time: 0.0069 memory: 7635 loss: 0.0350 decode.loss_ce: 0.0216 decode.acc_seg: 99.2817 aux.loss_ce: 0.0134 aux.acc_seg: 98.6565 +04/17 02:58:22 - mmengine - INFO - Iter(train) [ 3900/160000] lr: 9.7826e-03 eta: 23:56:52 time: 0.5518 data_time: 0.0065 memory: 7635 loss: 0.0318 decode.loss_ce: 0.0194 decode.acc_seg: 99.3762 aux.loss_ce: 0.0125 aux.acc_seg: 98.8199 +04/17 02:58:50 - mmengine - INFO - Iter(train) [ 3950/160000] lr: 9.7798e-03 eta: 23:56:23 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0335 decode.loss_ce: 0.0205 decode.acc_seg: 99.2721 aux.loss_ce: 0.0130 aux.acc_seg: 98.8877 +04/17 02:59:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 02:59:18 - mmengine - INFO - Iter(train) [ 4000/160000] lr: 9.7770e-03 eta: 23:55:56 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0334 decode.loss_ce: 0.0204 decode.acc_seg: 98.9753 aux.loss_ce: 0.0129 aux.acc_seg: 98.5274 +04/17 02:59:45 - mmengine - INFO - Iter(train) [ 4050/160000] lr: 9.7742e-03 eta: 23:55:27 time: 0.5510 data_time: 0.0054 memory: 7635 loss: 0.0334 decode.loss_ce: 0.0201 decode.acc_seg: 99.1365 aux.loss_ce: 0.0133 aux.acc_seg: 98.4941 +04/17 03:00:13 - mmengine - INFO - Iter(train) [ 4100/160000] lr: 9.7714e-03 eta: 23:54:59 time: 0.5514 data_time: 0.0059 memory: 7635 loss: 0.0341 decode.loss_ce: 0.0207 decode.acc_seg: 99.2615 aux.loss_ce: 0.0134 aux.acc_seg: 98.8306 +04/17 03:00:40 - mmengine - INFO - Iter(train) [ 4150/160000] lr: 9.7686e-03 eta: 23:54:30 time: 0.5524 data_time: 0.0066 memory: 7635 loss: 0.0373 decode.loss_ce: 0.0232 decode.acc_seg: 99.0534 aux.loss_ce: 0.0141 aux.acc_seg: 98.2186 +04/17 03:01:08 - mmengine - INFO - Iter(train) [ 4200/160000] lr: 9.7659e-03 eta: 23:54:02 time: 0.5517 data_time: 0.0061 memory: 7635 loss: 0.0330 decode.loss_ce: 0.0200 decode.acc_seg: 98.9285 aux.loss_ce: 0.0130 aux.acc_seg: 98.4211 +04/17 03:01:36 - mmengine - INFO - Iter(train) [ 4250/160000] lr: 9.7631e-03 eta: 23:53:33 time: 0.5509 data_time: 0.0059 memory: 7635 loss: 0.0309 decode.loss_ce: 0.0187 decode.acc_seg: 99.2372 aux.loss_ce: 0.0122 aux.acc_seg: 98.9645 +04/17 03:02:03 - mmengine - INFO - Iter(train) [ 4300/160000] lr: 9.7603e-03 eta: 23:53:05 time: 0.5517 data_time: 0.0056 memory: 7635 loss: 0.0338 decode.loss_ce: 0.0206 decode.acc_seg: 99.0699 aux.loss_ce: 0.0133 aux.acc_seg: 98.4479 +04/17 03:02:31 - mmengine - INFO - Iter(train) [ 4350/160000] lr: 9.7575e-03 eta: 23:52:36 time: 0.5519 data_time: 0.0059 memory: 7635 loss: 0.0325 decode.loss_ce: 0.0198 decode.acc_seg: 99.1191 aux.loss_ce: 0.0127 aux.acc_seg: 98.4472 +04/17 03:02:58 - mmengine - INFO - Iter(train) [ 4400/160000] lr: 9.7547e-03 eta: 23:52:07 time: 0.5521 data_time: 0.0057 memory: 7635 loss: 0.0337 decode.loss_ce: 0.0209 decode.acc_seg: 99.3491 aux.loss_ce: 0.0128 aux.acc_seg: 98.8690 +04/17 03:03:26 - mmengine - INFO - Iter(train) [ 4450/160000] lr: 9.7519e-03 eta: 23:51:39 time: 0.5513 data_time: 0.0069 memory: 7635 loss: 0.0325 decode.loss_ce: 0.0197 decode.acc_seg: 98.8806 aux.loss_ce: 0.0127 aux.acc_seg: 98.5091 +04/17 03:03:53 - mmengine - INFO - Iter(train) [ 4500/160000] lr: 9.7491e-03 eta: 23:51:11 time: 0.5511 data_time: 0.0057 memory: 7635 loss: 0.0321 decode.loss_ce: 0.0196 decode.acc_seg: 99.3941 aux.loss_ce: 0.0125 aux.acc_seg: 98.9326 +04/17 03:04:21 - mmengine - INFO - Iter(train) [ 4550/160000] lr: 9.7463e-03 eta: 23:50:43 time: 0.5530 data_time: 0.0068 memory: 7635 loss: 0.0306 decode.loss_ce: 0.0186 decode.acc_seg: 99.2918 aux.loss_ce: 0.0120 aux.acc_seg: 98.9423 +04/17 03:04:49 - mmengine - INFO - Iter(train) [ 4600/160000] lr: 9.7435e-03 eta: 23:50:16 time: 0.5526 data_time: 0.0058 memory: 7635 loss: 0.0372 decode.loss_ce: 0.0230 decode.acc_seg: 99.0516 aux.loss_ce: 0.0142 aux.acc_seg: 98.4576 +04/17 03:05:16 - mmengine - INFO - Iter(train) [ 4650/160000] lr: 9.7407e-03 eta: 23:49:48 time: 0.5522 data_time: 0.0056 memory: 7635 loss: 0.0345 decode.loss_ce: 0.0215 decode.acc_seg: 99.6372 aux.loss_ce: 0.0130 aux.acc_seg: 99.1232 +04/17 03:05:44 - mmengine - INFO - Iter(train) [ 4700/160000] lr: 9.7379e-03 eta: 23:49:20 time: 0.5523 data_time: 0.0063 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0178 decode.acc_seg: 98.9411 aux.loss_ce: 0.0117 aux.acc_seg: 98.4021 +04/17 03:06:12 - mmengine - INFO - Iter(train) [ 4750/160000] lr: 9.7351e-03 eta: 23:48:53 time: 0.5528 data_time: 0.0054 memory: 7635 loss: 0.0304 decode.loss_ce: 0.0187 decode.acc_seg: 99.2499 aux.loss_ce: 0.0117 aux.acc_seg: 98.7282 +04/17 03:06:39 - mmengine - INFO - Iter(train) [ 4800/160000] lr: 9.7323e-03 eta: 23:48:29 time: 0.5627 data_time: 0.0060 memory: 7635 loss: 0.0325 decode.loss_ce: 0.0196 decode.acc_seg: 99.1263 aux.loss_ce: 0.0129 aux.acc_seg: 98.6772 +04/17 03:07:07 - mmengine - INFO - Iter(train) [ 4850/160000] lr: 9.7296e-03 eta: 23:48:00 time: 0.5506 data_time: 0.0064 memory: 7635 loss: 0.0281 decode.loss_ce: 0.0165 decode.acc_seg: 99.5572 aux.loss_ce: 0.0117 aux.acc_seg: 99.1561 +04/17 03:07:34 - mmengine - INFO - Iter(train) [ 4900/160000] lr: 9.7268e-03 eta: 23:47:31 time: 0.5515 data_time: 0.0066 memory: 7635 loss: 0.0314 decode.loss_ce: 0.0194 decode.acc_seg: 99.3351 aux.loss_ce: 0.0119 aux.acc_seg: 98.7594 +04/17 03:08:02 - mmengine - INFO - Iter(train) [ 4950/160000] lr: 9.7240e-03 eta: 23:47:03 time: 0.5512 data_time: 0.0055 memory: 7635 loss: 0.0288 decode.loss_ce: 0.0180 decode.acc_seg: 99.1686 aux.loss_ce: 0.0108 aux.acc_seg: 98.8390 +04/17 03:08:30 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 03:08:30 - mmengine - INFO - Iter(train) [ 5000/160000] lr: 9.7212e-03 eta: 23:46:35 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0286 decode.loss_ce: 0.0173 decode.acc_seg: 99.5490 aux.loss_ce: 0.0114 aux.acc_seg: 99.1513 +04/17 03:08:57 - mmengine - INFO - Iter(train) [ 5050/160000] lr: 9.7184e-03 eta: 23:46:07 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0337 decode.loss_ce: 0.0212 decode.acc_seg: 99.1278 aux.loss_ce: 0.0125 aux.acc_seg: 98.8377 +04/17 03:09:25 - mmengine - INFO - Iter(train) [ 5100/160000] lr: 9.7156e-03 eta: 23:45:39 time: 0.5514 data_time: 0.0059 memory: 7635 loss: 0.0282 decode.loss_ce: 0.0168 decode.acc_seg: 99.4632 aux.loss_ce: 0.0114 aux.acc_seg: 98.9523 +04/17 03:09:52 - mmengine - INFO - Iter(train) [ 5150/160000] lr: 9.7128e-03 eta: 23:45:12 time: 0.5514 data_time: 0.0054 memory: 7635 loss: 0.0318 decode.loss_ce: 0.0193 decode.acc_seg: 99.0087 aux.loss_ce: 0.0125 aux.acc_seg: 98.6403 +04/17 03:10:20 - mmengine - INFO - Iter(train) [ 5200/160000] lr: 9.7100e-03 eta: 23:44:44 time: 0.5509 data_time: 0.0053 memory: 7635 loss: 0.0306 decode.loss_ce: 0.0185 decode.acc_seg: 99.2336 aux.loss_ce: 0.0121 aux.acc_seg: 98.7778 +04/17 03:10:48 - mmengine - INFO - Iter(train) [ 5250/160000] lr: 9.7072e-03 eta: 23:44:16 time: 0.5518 data_time: 0.0056 memory: 7635 loss: 0.0302 decode.loss_ce: 0.0185 decode.acc_seg: 99.0332 aux.loss_ce: 0.0118 aux.acc_seg: 98.3864 +04/17 03:11:15 - mmengine - INFO - Iter(train) [ 5300/160000] lr: 9.7044e-03 eta: 23:43:46 time: 0.5511 data_time: 0.0065 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0179 decode.acc_seg: 99.0035 aux.loss_ce: 0.0115 aux.acc_seg: 98.5707 +04/17 03:11:43 - mmengine - INFO - Iter(train) [ 5350/160000] lr: 9.7016e-03 eta: 23:43:19 time: 0.5541 data_time: 0.0057 memory: 7635 loss: 0.0297 decode.loss_ce: 0.0180 decode.acc_seg: 99.4402 aux.loss_ce: 0.0118 aux.acc_seg: 98.9441 +04/17 03:12:10 - mmengine - INFO - Iter(train) [ 5400/160000] lr: 9.6988e-03 eta: 23:42:52 time: 0.5518 data_time: 0.0070 memory: 7635 loss: 0.0278 decode.loss_ce: 0.0167 decode.acc_seg: 99.0986 aux.loss_ce: 0.0111 aux.acc_seg: 98.6318 +04/17 03:12:38 - mmengine - INFO - Iter(train) [ 5450/160000] lr: 9.6960e-03 eta: 23:42:24 time: 0.5511 data_time: 0.0056 memory: 7635 loss: 0.0297 decode.loss_ce: 0.0182 decode.acc_seg: 99.2891 aux.loss_ce: 0.0115 aux.acc_seg: 98.8083 +04/17 03:13:06 - mmengine - INFO - Iter(train) [ 5500/160000] lr: 9.6932e-03 eta: 23:41:56 time: 0.5529 data_time: 0.0058 memory: 7635 loss: 0.0324 decode.loss_ce: 0.0201 decode.acc_seg: 99.2442 aux.loss_ce: 0.0123 aux.acc_seg: 98.7219 +04/17 03:13:33 - mmengine - INFO - Iter(train) [ 5550/160000] lr: 9.6904e-03 eta: 23:41:28 time: 0.5511 data_time: 0.0057 memory: 7635 loss: 0.0346 decode.loss_ce: 0.0215 decode.acc_seg: 99.3938 aux.loss_ce: 0.0131 aux.acc_seg: 98.7620 +04/17 03:14:01 - mmengine - INFO - Iter(train) [ 5600/160000] lr: 9.6877e-03 eta: 23:41:01 time: 0.5527 data_time: 0.0060 memory: 7635 loss: 0.0288 decode.loss_ce: 0.0173 decode.acc_seg: 99.5282 aux.loss_ce: 0.0115 aux.acc_seg: 99.1069 +04/17 03:14:28 - mmengine - INFO - Iter(train) [ 5650/160000] lr: 9.6849e-03 eta: 23:40:33 time: 0.5496 data_time: 0.0059 memory: 7635 loss: 0.0275 decode.loss_ce: 0.0164 decode.acc_seg: 99.3816 aux.loss_ce: 0.0111 aux.acc_seg: 99.0400 +04/17 03:14:56 - mmengine - INFO - Iter(train) [ 5700/160000] lr: 9.6821e-03 eta: 23:40:06 time: 0.5523 data_time: 0.0056 memory: 7635 loss: 0.0277 decode.loss_ce: 0.0166 decode.acc_seg: 99.2375 aux.loss_ce: 0.0111 aux.acc_seg: 98.7439 +04/17 03:15:24 - mmengine - INFO - Iter(train) [ 5750/160000] lr: 9.6793e-03 eta: 23:39:39 time: 0.5535 data_time: 0.0062 memory: 7635 loss: 0.0312 decode.loss_ce: 0.0186 decode.acc_seg: 98.9881 aux.loss_ce: 0.0126 aux.acc_seg: 98.3813 +04/17 03:15:51 - mmengine - INFO - Iter(train) [ 5800/160000] lr: 9.6765e-03 eta: 23:39:12 time: 0.5531 data_time: 0.0058 memory: 7635 loss: 0.0297 decode.loss_ce: 0.0179 decode.acc_seg: 99.3886 aux.loss_ce: 0.0118 aux.acc_seg: 98.9565 +04/17 03:16:19 - mmengine - INFO - Iter(train) [ 5850/160000] lr: 9.6737e-03 eta: 23:38:44 time: 0.5518 data_time: 0.0070 memory: 7635 loss: 0.0289 decode.loss_ce: 0.0171 decode.acc_seg: 99.4504 aux.loss_ce: 0.0119 aux.acc_seg: 98.9906 +04/17 03:16:47 - mmengine - INFO - Iter(train) [ 5900/160000] lr: 9.6709e-03 eta: 23:38:20 time: 0.5522 data_time: 0.0061 memory: 7635 loss: 0.0364 decode.loss_ce: 0.0226 decode.acc_seg: 98.7685 aux.loss_ce: 0.0138 aux.acc_seg: 98.1620 +04/17 03:17:14 - mmengine - INFO - Iter(train) [ 5950/160000] lr: 9.6681e-03 eta: 23:37:53 time: 0.5535 data_time: 0.0061 memory: 7635 loss: 0.0271 decode.loss_ce: 0.0166 decode.acc_seg: 99.4514 aux.loss_ce: 0.0104 aux.acc_seg: 99.1319 +04/17 03:17:42 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 03:17:42 - mmengine - INFO - Iter(train) [ 6000/160000] lr: 9.6653e-03 eta: 23:37:26 time: 0.5519 data_time: 0.0057 memory: 7635 loss: 0.0291 decode.loss_ce: 0.0174 decode.acc_seg: 99.1006 aux.loss_ce: 0.0118 aux.acc_seg: 98.7028 +04/17 03:18:10 - mmengine - INFO - Iter(train) [ 6050/160000] lr: 9.6625e-03 eta: 23:36:58 time: 0.5511 data_time: 0.0062 memory: 7635 loss: 0.0312 decode.loss_ce: 0.0186 decode.acc_seg: 99.3483 aux.loss_ce: 0.0126 aux.acc_seg: 98.9719 +04/17 03:18:37 - mmengine - INFO - Iter(train) [ 6100/160000] lr: 9.6597e-03 eta: 23:36:31 time: 0.5528 data_time: 0.0056 memory: 7635 loss: 0.0310 decode.loss_ce: 0.0192 decode.acc_seg: 99.4406 aux.loss_ce: 0.0118 aux.acc_seg: 99.0075 +04/17 03:19:05 - mmengine - INFO - Iter(train) [ 6150/160000] lr: 9.6569e-03 eta: 23:36:03 time: 0.5513 data_time: 0.0055 memory: 7635 loss: 0.0270 decode.loss_ce: 0.0163 decode.acc_seg: 98.9075 aux.loss_ce: 0.0106 aux.acc_seg: 98.4277 +04/17 03:19:32 - mmengine - INFO - Iter(train) [ 6200/160000] lr: 9.6541e-03 eta: 23:35:36 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0322 decode.loss_ce: 0.0196 decode.acc_seg: 98.6740 aux.loss_ce: 0.0126 aux.acc_seg: 98.2050 +04/17 03:20:00 - mmengine - INFO - Iter(train) [ 6250/160000] lr: 9.6513e-03 eta: 23:35:08 time: 0.5516 data_time: 0.0060 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0154 decode.acc_seg: 99.5450 aux.loss_ce: 0.0106 aux.acc_seg: 99.1028 +04/17 03:20:28 - mmengine - INFO - Iter(train) [ 6300/160000] lr: 9.6485e-03 eta: 23:34:41 time: 0.5524 data_time: 0.0061 memory: 7635 loss: 0.0299 decode.loss_ce: 0.0178 decode.acc_seg: 99.3025 aux.loss_ce: 0.0121 aux.acc_seg: 98.9420 +04/17 03:20:55 - mmengine - INFO - Iter(train) [ 6350/160000] lr: 9.6457e-03 eta: 23:34:14 time: 0.5526 data_time: 0.0057 memory: 7635 loss: 0.0296 decode.loss_ce: 0.0176 decode.acc_seg: 99.2557 aux.loss_ce: 0.0120 aux.acc_seg: 98.8164 +04/17 03:21:23 - mmengine - INFO - Iter(train) [ 6400/160000] lr: 9.6429e-03 eta: 23:33:46 time: 0.5516 data_time: 0.0057 memory: 7635 loss: 0.0338 decode.loss_ce: 0.0206 decode.acc_seg: 99.1667 aux.loss_ce: 0.0132 aux.acc_seg: 98.7056 +04/17 03:21:51 - mmengine - INFO - Iter(train) [ 6450/160000] lr: 9.6401e-03 eta: 23:33:19 time: 0.5544 data_time: 0.0062 memory: 7635 loss: 0.0317 decode.loss_ce: 0.0193 decode.acc_seg: 99.4510 aux.loss_ce: 0.0124 aux.acc_seg: 98.9374 +04/17 03:22:18 - mmengine - INFO - Iter(train) [ 6500/160000] lr: 9.6373e-03 eta: 23:32:52 time: 0.5529 data_time: 0.0069 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0177 decode.acc_seg: 98.8971 aux.loss_ce: 0.0117 aux.acc_seg: 98.3251 +04/17 03:22:46 - mmengine - INFO - Iter(train) [ 6550/160000] lr: 9.6345e-03 eta: 23:32:25 time: 0.5551 data_time: 0.0058 memory: 7635 loss: 0.0325 decode.loss_ce: 0.0202 decode.acc_seg: 99.1228 aux.loss_ce: 0.0123 aux.acc_seg: 98.6965 +04/17 03:23:13 - mmengine - INFO - Iter(train) [ 6600/160000] lr: 9.6317e-03 eta: 23:31:58 time: 0.5529 data_time: 0.0055 memory: 7635 loss: 0.0299 decode.loss_ce: 0.0184 decode.acc_seg: 99.4324 aux.loss_ce: 0.0115 aux.acc_seg: 98.8950 +04/17 03:23:41 - mmengine - INFO - Iter(train) [ 6650/160000] lr: 9.6290e-03 eta: 23:31:30 time: 0.5528 data_time: 0.0058 memory: 7635 loss: 0.0275 decode.loss_ce: 0.0167 decode.acc_seg: 99.4091 aux.loss_ce: 0.0109 aux.acc_seg: 98.9687 +04/17 03:24:09 - mmengine - INFO - Iter(train) [ 6700/160000] lr: 9.6262e-03 eta: 23:31:03 time: 0.5526 data_time: 0.0059 memory: 7635 loss: 0.0307 decode.loss_ce: 0.0186 decode.acc_seg: 99.3761 aux.loss_ce: 0.0121 aux.acc_seg: 99.0348 +04/17 03:24:36 - mmengine - INFO - Iter(train) [ 6750/160000] lr: 9.6234e-03 eta: 23:30:35 time: 0.5523 data_time: 0.0055 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0175 decode.acc_seg: 99.4396 aux.loss_ce: 0.0119 aux.acc_seg: 98.8461 +04/17 03:25:04 - mmengine - INFO - Iter(train) [ 6800/160000] lr: 9.6206e-03 eta: 23:30:07 time: 0.5510 data_time: 0.0060 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0145 decode.acc_seg: 99.3783 aux.loss_ce: 0.0101 aux.acc_seg: 98.7988 +04/17 03:25:32 - mmengine - INFO - Iter(train) [ 6850/160000] lr: 9.6178e-03 eta: 23:29:40 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0257 decode.loss_ce: 0.0152 decode.acc_seg: 99.1746 aux.loss_ce: 0.0105 aux.acc_seg: 98.7113 +04/17 03:25:59 - mmengine - INFO - Iter(train) [ 6900/160000] lr: 9.6150e-03 eta: 23:29:12 time: 0.5517 data_time: 0.0059 memory: 7635 loss: 0.0277 decode.loss_ce: 0.0165 decode.acc_seg: 99.0493 aux.loss_ce: 0.0112 aux.acc_seg: 98.6212 +04/17 03:26:27 - mmengine - INFO - Iter(train) [ 6950/160000] lr: 9.6122e-03 eta: 23:28:46 time: 0.5517 data_time: 0.0056 memory: 7635 loss: 0.0263 decode.loss_ce: 0.0153 decode.acc_seg: 99.5555 aux.loss_ce: 0.0110 aux.acc_seg: 99.0334 +04/17 03:26:54 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 03:26:54 - mmengine - INFO - Iter(train) [ 7000/160000] lr: 9.6094e-03 eta: 23:28:18 time: 0.5529 data_time: 0.0063 memory: 7635 loss: 0.0264 decode.loss_ce: 0.0157 decode.acc_seg: 99.3204 aux.loss_ce: 0.0107 aux.acc_seg: 98.9942 +04/17 03:27:22 - mmengine - INFO - Iter(train) [ 7050/160000] lr: 9.6066e-03 eta: 23:27:51 time: 0.5532 data_time: 0.0065 memory: 7635 loss: 0.0311 decode.loss_ce: 0.0192 decode.acc_seg: 99.1666 aux.loss_ce: 0.0119 aux.acc_seg: 98.8470 +04/17 03:27:50 - mmengine - INFO - Iter(train) [ 7100/160000] lr: 9.6038e-03 eta: 23:27:24 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0267 decode.loss_ce: 0.0158 decode.acc_seg: 99.3400 aux.loss_ce: 0.0109 aux.acc_seg: 98.8838 +04/17 03:28:17 - mmengine - INFO - Iter(train) [ 7150/160000] lr: 9.6010e-03 eta: 23:26:57 time: 0.5530 data_time: 0.0054 memory: 7635 loss: 0.0306 decode.loss_ce: 0.0189 decode.acc_seg: 99.6149 aux.loss_ce: 0.0117 aux.acc_seg: 99.1050 +04/17 03:28:45 - mmengine - INFO - Iter(train) [ 7200/160000] lr: 9.5982e-03 eta: 23:26:29 time: 0.5523 data_time: 0.0061 memory: 7635 loss: 0.0276 decode.loss_ce: 0.0165 decode.acc_seg: 98.9423 aux.loss_ce: 0.0111 aux.acc_seg: 98.6954 +04/17 03:29:13 - mmengine - INFO - Iter(train) [ 7250/160000] lr: 9.5954e-03 eta: 23:26:01 time: 0.5539 data_time: 0.0060 memory: 7635 loss: 0.0280 decode.loss_ce: 0.0169 decode.acc_seg: 99.4061 aux.loss_ce: 0.0111 aux.acc_seg: 98.7851 +04/17 03:29:40 - mmengine - INFO - Iter(train) [ 7300/160000] lr: 9.5926e-03 eta: 23:25:34 time: 0.5526 data_time: 0.0061 memory: 7635 loss: 0.0241 decode.loss_ce: 0.0140 decode.acc_seg: 99.5446 aux.loss_ce: 0.0101 aux.acc_seg: 99.1001 +04/17 03:30:08 - mmengine - INFO - Iter(train) [ 7350/160000] lr: 9.5898e-03 eta: 23:25:06 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0252 decode.loss_ce: 0.0150 decode.acc_seg: 99.4038 aux.loss_ce: 0.0103 aux.acc_seg: 98.9410 +04/17 03:30:35 - mmengine - INFO - Iter(train) [ 7400/160000] lr: 9.5870e-03 eta: 23:24:38 time: 0.5507 data_time: 0.0061 memory: 7635 loss: 0.0272 decode.loss_ce: 0.0165 decode.acc_seg: 99.5064 aux.loss_ce: 0.0107 aux.acc_seg: 99.0931 +04/17 03:31:03 - mmengine - INFO - Iter(train) [ 7450/160000] lr: 9.5842e-03 eta: 23:24:11 time: 0.5527 data_time: 0.0063 memory: 7635 loss: 0.0241 decode.loss_ce: 0.0142 decode.acc_seg: 99.5798 aux.loss_ce: 0.0099 aux.acc_seg: 99.0539 +04/17 03:31:31 - mmengine - INFO - Iter(train) [ 7500/160000] lr: 9.5814e-03 eta: 23:23:43 time: 0.5523 data_time: 0.0059 memory: 7635 loss: 0.0274 decode.loss_ce: 0.0161 decode.acc_seg: 99.2188 aux.loss_ce: 0.0113 aux.acc_seg: 98.9532 +04/17 03:31:58 - mmengine - INFO - Iter(train) [ 7550/160000] lr: 9.5786e-03 eta: 23:23:16 time: 0.5516 data_time: 0.0061 memory: 7635 loss: 0.0270 decode.loss_ce: 0.0161 decode.acc_seg: 99.1962 aux.loss_ce: 0.0109 aux.acc_seg: 98.7874 +04/17 03:32:26 - mmengine - INFO - Iter(train) [ 7600/160000] lr: 9.5758e-03 eta: 23:22:49 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0253 decode.loss_ce: 0.0148 decode.acc_seg: 99.4922 aux.loss_ce: 0.0105 aux.acc_seg: 98.8668 +04/17 03:32:54 - mmengine - INFO - Iter(train) [ 7650/160000] lr: 9.5730e-03 eta: 23:22:22 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0279 decode.loss_ce: 0.0166 decode.acc_seg: 99.3082 aux.loss_ce: 0.0113 aux.acc_seg: 98.6896 +04/17 03:33:21 - mmengine - INFO - Iter(train) [ 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0.5533 data_time: 0.0060 memory: 7635 loss: 0.0266 decode.loss_ce: 0.0162 decode.acc_seg: 99.3392 aux.loss_ce: 0.0104 aux.acc_seg: 98.9814 +04/17 03:35:39 - mmengine - INFO - Iter(train) [ 7950/160000] lr: 9.5562e-03 eta: 23:19:37 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0267 decode.loss_ce: 0.0160 decode.acc_seg: 99.5321 aux.loss_ce: 0.0107 aux.acc_seg: 99.1002 +04/17 03:36:07 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 03:36:07 - mmengine - INFO - Iter(train) [ 8000/160000] lr: 9.5534e-03 eta: 23:19:10 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0295 decode.loss_ce: 0.0178 decode.acc_seg: 99.3558 aux.loss_ce: 0.0117 aux.acc_seg: 98.8987 +04/17 03:36:35 - mmengine - INFO - Iter(train) [ 8050/160000] lr: 9.5506e-03 eta: 23:18:44 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0251 decode.loss_ce: 0.0147 decode.acc_seg: 99.4716 aux.loss_ce: 0.0104 aux.acc_seg: 98.8608 +04/17 03:37:02 - mmengine - INFO - Iter(train) [ 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0.5532 data_time: 0.0057 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0134 decode.acc_seg: 99.5517 aux.loss_ce: 0.0100 aux.acc_seg: 99.0553 +04/17 03:39:20 - mmengine - INFO - Iter(train) [ 8350/160000] lr: 9.5338e-03 eta: 23:15:59 time: 0.5520 data_time: 0.0060 memory: 7635 loss: 0.0293 decode.loss_ce: 0.0174 decode.acc_seg: 99.0095 aux.loss_ce: 0.0119 aux.acc_seg: 98.5011 +04/17 03:39:48 - mmengine - INFO - Iter(train) [ 8400/160000] lr: 9.5310e-03 eta: 23:15:31 time: 0.5519 data_time: 0.0060 memory: 7635 loss: 0.0258 decode.loss_ce: 0.0151 decode.acc_seg: 99.3837 aux.loss_ce: 0.0107 aux.acc_seg: 98.9029 +04/17 03:40:16 - mmengine - INFO - Iter(train) [ 8450/160000] lr: 9.5282e-03 eta: 23:15:03 time: 0.5530 data_time: 0.0062 memory: 7635 loss: 0.0244 decode.loss_ce: 0.0144 decode.acc_seg: 99.1686 aux.loss_ce: 0.0100 aux.acc_seg: 98.6112 +04/17 03:40:43 - mmengine - INFO - Iter(train) [ 8500/160000] lr: 9.5254e-03 eta: 23:14:36 time: 0.5515 data_time: 0.0056 memory: 7635 loss: 0.0298 decode.loss_ce: 0.0180 decode.acc_seg: 99.2611 aux.loss_ce: 0.0118 aux.acc_seg: 98.9025 +04/17 03:41:11 - mmengine - INFO - Iter(train) [ 8550/160000] lr: 9.5226e-03 eta: 23:14:08 time: 0.5522 data_time: 0.0058 memory: 7635 loss: 0.0266 decode.loss_ce: 0.0157 decode.acc_seg: 99.4311 aux.loss_ce: 0.0109 aux.acc_seg: 99.0005 +04/17 03:41:38 - mmengine - INFO - Iter(train) [ 8600/160000] lr: 9.5198e-03 eta: 23:13:40 time: 0.5507 data_time: 0.0071 memory: 7635 loss: 0.0259 decode.loss_ce: 0.0153 decode.acc_seg: 99.2587 aux.loss_ce: 0.0106 aux.acc_seg: 98.9434 +04/17 03:42:06 - mmengine - INFO - Iter(train) [ 8650/160000] lr: 9.5170e-03 eta: 23:13:12 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0249 decode.loss_ce: 0.0145 decode.acc_seg: 99.4470 aux.loss_ce: 0.0104 aux.acc_seg: 98.9309 +04/17 03:42:34 - mmengine - INFO - Iter(train) [ 8700/160000] lr: 9.5142e-03 eta: 23:12:45 time: 0.5530 data_time: 0.0064 memory: 7635 loss: 0.0278 decode.loss_ce: 0.0165 decode.acc_seg: 99.1575 aux.loss_ce: 0.0113 aux.acc_seg: 98.7628 +04/17 03:43:01 - mmengine - INFO - Iter(train) [ 8750/160000] lr: 9.5114e-03 eta: 23:12:18 time: 0.5524 data_time: 0.0057 memory: 7635 loss: 0.0281 decode.loss_ce: 0.0168 decode.acc_seg: 99.1311 aux.loss_ce: 0.0113 aux.acc_seg: 98.7585 +04/17 03:43:29 - mmengine - INFO - Iter(train) [ 8800/160000] lr: 9.5086e-03 eta: 23:11:51 time: 0.5521 data_time: 0.0059 memory: 7635 loss: 0.0232 decode.loss_ce: 0.0135 decode.acc_seg: 99.5893 aux.loss_ce: 0.0096 aux.acc_seg: 99.2972 +04/17 03:43:57 - mmengine - INFO - Iter(train) [ 8850/160000] lr: 9.5058e-03 eta: 23:11:23 time: 0.5520 data_time: 0.0059 memory: 7635 loss: 0.0263 decode.loss_ce: 0.0157 decode.acc_seg: 99.4049 aux.loss_ce: 0.0106 aux.acc_seg: 98.8473 +04/17 03:44:24 - mmengine - INFO - Iter(train) [ 8900/160000] lr: 9.5030e-03 eta: 23:10:56 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0241 decode.loss_ce: 0.0139 decode.acc_seg: 99.5001 aux.loss_ce: 0.0102 aux.acc_seg: 99.1658 +04/17 03:44:52 - mmengine - INFO - Iter(train) [ 8950/160000] lr: 9.5002e-03 eta: 23:10:28 time: 0.5510 data_time: 0.0063 memory: 7635 loss: 0.0274 decode.loss_ce: 0.0162 decode.acc_seg: 99.3296 aux.loss_ce: 0.0112 aux.acc_seg: 98.7326 +04/17 03:45:19 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 03:45:19 - mmengine - INFO - Iter(train) [ 9000/160000] lr: 9.4974e-03 eta: 23:10:01 time: 0.5531 data_time: 0.0057 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0137 decode.acc_seg: 99.5480 aux.loss_ce: 0.0098 aux.acc_seg: 99.1562 +04/17 03:45:47 - mmengine - INFO - Iter(train) [ 9050/160000] lr: 9.4946e-03 eta: 23:09:34 time: 0.5523 data_time: 0.0058 memory: 7635 loss: 0.0311 decode.loss_ce: 0.0187 decode.acc_seg: 99.3399 aux.loss_ce: 0.0124 aux.acc_seg: 98.9336 +04/17 03:46:15 - mmengine - INFO - Iter(train) [ 9100/160000] lr: 9.4918e-03 eta: 23:09:08 time: 0.5631 data_time: 0.0062 memory: 7635 loss: 0.0253 decode.loss_ce: 0.0149 decode.acc_seg: 99.4165 aux.loss_ce: 0.0104 aux.acc_seg: 98.9483 +04/17 03:46:42 - mmengine - INFO - Iter(train) [ 9150/160000] lr: 9.4890e-03 eta: 23:08:41 time: 0.5510 data_time: 0.0054 memory: 7635 loss: 0.0254 decode.loss_ce: 0.0148 decode.acc_seg: 99.1962 aux.loss_ce: 0.0106 aux.acc_seg: 98.4969 +04/17 03:47:10 - mmengine - INFO - Iter(train) [ 9200/160000] lr: 9.4862e-03 eta: 23:08:13 time: 0.5518 data_time: 0.0058 memory: 7635 loss: 0.0254 decode.loss_ce: 0.0148 decode.acc_seg: 99.3896 aux.loss_ce: 0.0106 aux.acc_seg: 98.8047 +04/17 03:47:38 - mmengine - INFO - Iter(train) [ 9250/160000] lr: 9.4834e-03 eta: 23:07:45 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0248 decode.loss_ce: 0.0146 decode.acc_seg: 99.2444 aux.loss_ce: 0.0102 aux.acc_seg: 98.7537 +04/17 03:48:05 - mmengine - INFO - Iter(train) [ 9300/160000] lr: 9.4806e-03 eta: 23:07:18 time: 0.5514 data_time: 0.0058 memory: 7635 loss: 0.0232 decode.loss_ce: 0.0137 decode.acc_seg: 99.3546 aux.loss_ce: 0.0095 aux.acc_seg: 98.9738 +04/17 03:48:33 - mmengine - INFO - Iter(train) [ 9350/160000] lr: 9.4778e-03 eta: 23:06:50 time: 0.5502 data_time: 0.0061 memory: 7635 loss: 0.0275 decode.loss_ce: 0.0161 decode.acc_seg: 99.5212 aux.loss_ce: 0.0114 aux.acc_seg: 99.1037 +04/17 03:49:01 - mmengine - INFO - Iter(train) [ 9400/160000] lr: 9.4750e-03 eta: 23:06:22 time: 0.5527 data_time: 0.0059 memory: 7635 loss: 0.0268 decode.loss_ce: 0.0158 decode.acc_seg: 99.4898 aux.loss_ce: 0.0109 aux.acc_seg: 98.9123 +04/17 03:49:28 - mmengine - INFO - Iter(train) [ 9450/160000] lr: 9.4722e-03 eta: 23:05:55 time: 0.5518 data_time: 0.0060 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0143 decode.acc_seg: 99.2866 aux.loss_ce: 0.0103 aux.acc_seg: 98.7805 +04/17 03:49:56 - mmengine - INFO - Iter(train) [ 9500/160000] lr: 9.4694e-03 eta: 23:05:26 time: 0.5505 data_time: 0.0059 memory: 7635 loss: 0.0258 decode.loss_ce: 0.0152 decode.acc_seg: 99.2098 aux.loss_ce: 0.0106 aux.acc_seg: 98.6047 +04/17 03:50:23 - mmengine - INFO - Iter(train) [ 9550/160000] lr: 9.4666e-03 eta: 23:04:58 time: 0.5512 data_time: 0.0059 memory: 7635 loss: 0.0269 decode.loss_ce: 0.0160 decode.acc_seg: 99.4492 aux.loss_ce: 0.0108 aux.acc_seg: 98.9509 +04/17 03:50:51 - mmengine - INFO - Iter(train) [ 9600/160000] lr: 9.4638e-03 eta: 23:04:30 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0250 decode.loss_ce: 0.0148 decode.acc_seg: 99.5263 aux.loss_ce: 0.0102 aux.acc_seg: 99.0936 +04/17 03:51:19 - mmengine - INFO - Iter(train) [ 9650/160000] lr: 9.4610e-03 eta: 23:04:02 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0279 decode.loss_ce: 0.0165 decode.acc_seg: 99.4065 aux.loss_ce: 0.0114 aux.acc_seg: 98.9489 +04/17 03:51:46 - mmengine - INFO - Iter(train) [ 9700/160000] lr: 9.4582e-03 eta: 23:03:34 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0279 decode.loss_ce: 0.0166 decode.acc_seg: 99.4917 aux.loss_ce: 0.0113 aux.acc_seg: 98.9057 +04/17 03:52:14 - mmengine - INFO - Iter(train) [ 9750/160000] lr: 9.4554e-03 eta: 23:03:06 time: 0.5510 data_time: 0.0060 memory: 7635 loss: 0.0278 decode.loss_ce: 0.0166 decode.acc_seg: 99.3966 aux.loss_ce: 0.0112 aux.acc_seg: 98.7986 +04/17 03:52:41 - mmengine - INFO - Iter(train) [ 9800/160000] lr: 9.4526e-03 eta: 23:02:37 time: 0.5512 data_time: 0.0061 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0153 decode.acc_seg: 99.3086 aux.loss_ce: 0.0107 aux.acc_seg: 98.8056 +04/17 03:53:09 - mmengine - INFO - Iter(train) [ 9850/160000] lr: 9.4498e-03 eta: 23:02:09 time: 0.5513 data_time: 0.0062 memory: 7635 loss: 0.0280 decode.loss_ce: 0.0166 decode.acc_seg: 99.5903 aux.loss_ce: 0.0114 aux.acc_seg: 99.0402 +04/17 03:53:36 - mmengine - INFO - Iter(train) [ 9900/160000] lr: 9.4470e-03 eta: 23:01:41 time: 0.5510 data_time: 0.0054 memory: 7635 loss: 0.0258 decode.loss_ce: 0.0151 decode.acc_seg: 99.4496 aux.loss_ce: 0.0106 aux.acc_seg: 99.0826 +04/17 03:54:04 - mmengine - INFO - Iter(train) [ 9950/160000] lr: 9.4442e-03 eta: 23:01:14 time: 0.5533 data_time: 0.0069 memory: 7635 loss: 0.0241 decode.loss_ce: 0.0145 decode.acc_seg: 99.4751 aux.loss_ce: 0.0096 aux.acc_seg: 99.0772 +04/17 03:54:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 03:54:32 - mmengine - INFO - Iter(train) [ 10000/160000] lr: 9.4414e-03 eta: 23:00:46 time: 0.5528 data_time: 0.0062 memory: 7635 loss: 0.0244 decode.loss_ce: 0.0144 decode.acc_seg: 99.4905 aux.loss_ce: 0.0100 aux.acc_seg: 99.2376 +04/17 03:54:32 - mmengine - INFO - Saving checkpoint at 10000 iterations +04/17 03:54:37 - mmengine - INFO - Iter(val) [ 50/355] eta: 0:00:20 time: 0.0465 data_time: 0.0014 memory: 5542 +04/17 03:54:39 - mmengine - INFO - Iter(val) [100/355] eta: 0:00:14 time: 0.0467 data_time: 0.0015 memory: 1657 +04/17 03:54:41 - mmengine - INFO - Iter(val) [150/355] eta: 0:00:10 time: 0.0465 data_time: 0.0014 memory: 1657 +04/17 03:54:44 - mmengine - INFO - Iter(val) [200/355] eta: 0:00:08 time: 0.0468 data_time: 0.0014 memory: 1657 +04/17 03:54:46 - mmengine - INFO - Iter(val) [250/355] eta: 0:00:05 time: 0.0469 data_time: 0.0015 memory: 1657 +04/17 03:54:48 - mmengine - INFO - Iter(val) [300/355] eta: 0:00:02 time: 0.0466 data_time: 0.0015 memory: 1657 +04/17 03:54:51 - mmengine - INFO - Iter(val) [350/355] eta: 0:00:00 time: 0.0454 data_time: 0.0013 memory: 1657 +04/17 03:54:51 - mmengine - INFO - per class results: +04/17 03:54:51 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.08 | 99.52 | 99.54 | 99.55 | 99.52 | +| contrast | 80.15 | 89.32 | 88.98 | 88.64 | 89.32 | ++------------+-------+-------+--------+-----------+--------+ +04/17 03:54:51 - mmengine - INFO - Iter(val) [355/355] aAcc: 99.1100 mIoU: 89.6100 mAcc: 94.4200 mFscore: 94.2600 mPrecision: 94.0900 mRecall: 94.4200 data_time: 0.0019 time: 0.0495 +04/17 03:55:19 - mmengine - INFO - Iter(train) [ 10050/160000] lr: 9.4386e-03 eta: 23:00:22 time: 0.5518 data_time: 0.0058 memory: 7635 loss: 0.0284 decode.loss_ce: 0.0174 decode.acc_seg: 98.8690 aux.loss_ce: 0.0111 aux.acc_seg: 98.5484 +04/17 03:55:46 - mmengine - INFO - Iter(train) [ 10100/160000] lr: 9.4358e-03 eta: 22:59:56 time: 0.5513 data_time: 0.0059 memory: 7635 loss: 0.0265 decode.loss_ce: 0.0157 decode.acc_seg: 99.4200 aux.loss_ce: 0.0107 aux.acc_seg: 98.9168 +04/17 03:56:14 - mmengine - INFO - Iter(train) [ 10150/160000] lr: 9.4330e-03 eta: 22:59:27 time: 0.5519 data_time: 0.0069 memory: 7635 loss: 0.0231 decode.loss_ce: 0.0135 decode.acc_seg: 99.4089 aux.loss_ce: 0.0096 aux.acc_seg: 99.0520 +04/17 03:56:42 - mmengine - INFO - Iter(train) [ 10200/160000] lr: 9.4302e-03 eta: 22:58:59 time: 0.5508 data_time: 0.0060 memory: 7635 loss: 0.0247 decode.loss_ce: 0.0144 decode.acc_seg: 99.5257 aux.loss_ce: 0.0104 aux.acc_seg: 99.1284 +04/17 03:57:09 - mmengine - INFO - Iter(train) [ 10250/160000] lr: 9.4274e-03 eta: 22:58:32 time: 0.5504 data_time: 0.0059 memory: 7635 loss: 0.0238 decode.loss_ce: 0.0136 decode.acc_seg: 99.5479 aux.loss_ce: 0.0101 aux.acc_seg: 99.0498 +04/17 03:57:37 - mmengine - INFO - Iter(train) [ 10300/160000] lr: 9.4246e-03 eta: 22:58:04 time: 0.5519 data_time: 0.0062 memory: 7635 loss: 0.0271 decode.loss_ce: 0.0162 decode.acc_seg: 99.2863 aux.loss_ce: 0.0109 aux.acc_seg: 98.8252 +04/17 03:58:05 - mmengine - INFO - Iter(train) [ 10350/160000] lr: 9.4218e-03 eta: 22:57:36 time: 0.5526 data_time: 0.0063 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0142 decode.acc_seg: 99.6452 aux.loss_ce: 0.0104 aux.acc_seg: 99.1855 +04/17 03:58:32 - mmengine - INFO - Iter(train) [ 10400/160000] lr: 9.4190e-03 eta: 22:57:08 time: 0.5511 data_time: 0.0059 memory: 7635 loss: 0.0267 decode.loss_ce: 0.0160 decode.acc_seg: 98.8905 aux.loss_ce: 0.0107 aux.acc_seg: 98.4540 +04/17 03:59:00 - mmengine - INFO - Iter(train) [ 10450/160000] lr: 9.4162e-03 eta: 22:56:40 time: 0.5530 data_time: 0.0076 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0151 decode.acc_seg: 99.4430 aux.loss_ce: 0.0109 aux.acc_seg: 98.9142 +04/17 03:59:27 - mmengine - INFO - Iter(train) [ 10500/160000] lr: 9.4134e-03 eta: 22:56:12 time: 0.5516 data_time: 0.0060 memory: 7635 loss: 0.0256 decode.loss_ce: 0.0151 decode.acc_seg: 99.1611 aux.loss_ce: 0.0105 aux.acc_seg: 98.7815 +04/17 03:59:55 - mmengine - INFO - Iter(train) [ 10550/160000] lr: 9.4106e-03 eta: 22:55:45 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0242 decode.loss_ce: 0.0142 decode.acc_seg: 99.1981 aux.loss_ce: 0.0100 aux.acc_seg: 98.7757 +04/17 04:00:22 - mmengine - INFO - Iter(train) [ 10600/160000] lr: 9.4078e-03 eta: 22:55:17 time: 0.5527 data_time: 0.0060 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0154 decode.acc_seg: 99.2702 aux.loss_ce: 0.0106 aux.acc_seg: 98.8341 +04/17 04:00:50 - mmengine - INFO - Iter(train) [ 10650/160000] lr: 9.4050e-03 eta: 22:54:49 time: 0.5524 data_time: 0.0061 memory: 7635 loss: 0.0251 decode.loss_ce: 0.0149 decode.acc_seg: 99.3166 aux.loss_ce: 0.0102 aux.acc_seg: 98.8374 +04/17 04:01:18 - mmengine - INFO - Iter(train) [ 10700/160000] lr: 9.4022e-03 eta: 22:54:21 time: 0.5526 data_time: 0.0061 memory: 7635 loss: 0.0258 decode.loss_ce: 0.0156 decode.acc_seg: 99.3894 aux.loss_ce: 0.0103 aux.acc_seg: 98.9603 +04/17 04:01:45 - mmengine - INFO - Iter(train) [ 10750/160000] lr: 9.3993e-03 eta: 22:53:54 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0258 decode.loss_ce: 0.0154 decode.acc_seg: 99.3429 aux.loss_ce: 0.0105 aux.acc_seg: 98.8722 +04/17 04:02:13 - mmengine - INFO - Iter(train) [ 10800/160000] lr: 9.3965e-03 eta: 22:53:25 time: 0.5509 data_time: 0.0061 memory: 7635 loss: 0.0266 decode.loss_ce: 0.0158 decode.acc_seg: 99.4295 aux.loss_ce: 0.0107 aux.acc_seg: 98.9844 +04/17 04:02:41 - mmengine - INFO - Iter(train) [ 10850/160000] lr: 9.3937e-03 eta: 22:52:58 time: 0.5520 data_time: 0.0058 memory: 7635 loss: 0.0242 decode.loss_ce: 0.0144 decode.acc_seg: 99.2278 aux.loss_ce: 0.0099 aux.acc_seg: 98.6084 +04/17 04:03:08 - mmengine - INFO - Iter(train) [ 10900/160000] lr: 9.3909e-03 eta: 22:52:30 time: 0.5526 data_time: 0.0063 memory: 7635 loss: 0.0256 decode.loss_ce: 0.0153 decode.acc_seg: 99.3282 aux.loss_ce: 0.0102 aux.acc_seg: 98.9741 +04/17 04:03:36 - mmengine - INFO - Iter(train) [ 10950/160000] lr: 9.3881e-03 eta: 22:52:03 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0275 decode.loss_ce: 0.0163 decode.acc_seg: 99.2203 aux.loss_ce: 0.0113 aux.acc_seg: 98.8931 +04/17 04:04:03 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 04:04:03 - mmengine - INFO - Iter(train) [ 11000/160000] lr: 9.3853e-03 eta: 22:51:35 time: 0.5522 data_time: 0.0059 memory: 7635 loss: 0.0231 decode.loss_ce: 0.0133 decode.acc_seg: 99.4202 aux.loss_ce: 0.0098 aux.acc_seg: 98.9544 +04/17 04:04:31 - mmengine - INFO - Iter(train) [ 11050/160000] lr: 9.3825e-03 eta: 22:51:08 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0237 decode.loss_ce: 0.0138 decode.acc_seg: 99.5981 aux.loss_ce: 0.0099 aux.acc_seg: 99.0320 +04/17 04:04:59 - mmengine - INFO - Iter(train) [ 11100/160000] lr: 9.3797e-03 eta: 22:50:40 time: 0.5518 data_time: 0.0057 memory: 7635 loss: 0.0283 decode.loss_ce: 0.0170 decode.acc_seg: 99.3212 aux.loss_ce: 0.0113 aux.acc_seg: 98.7568 +04/17 04:05:26 - mmengine - INFO - Iter(train) [ 11150/160000] lr: 9.3769e-03 eta: 22:50:13 time: 0.5516 data_time: 0.0061 memory: 7635 loss: 0.0263 decode.loss_ce: 0.0154 decode.acc_seg: 99.5035 aux.loss_ce: 0.0108 aux.acc_seg: 99.0602 +04/17 04:05:54 - mmengine - INFO - Iter(train) [ 11200/160000] lr: 9.3741e-03 eta: 22:49:46 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0271 decode.loss_ce: 0.0162 decode.acc_seg: 99.4478 aux.loss_ce: 0.0109 aux.acc_seg: 98.8791 +04/17 04:06:22 - mmengine - INFO - Iter(train) [ 11250/160000] lr: 9.3713e-03 eta: 22:49:19 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0231 decode.loss_ce: 0.0132 decode.acc_seg: 99.4971 aux.loss_ce: 0.0099 aux.acc_seg: 99.0219 +04/17 04:06:49 - mmengine - INFO - Iter(train) [ 11300/160000] lr: 9.3685e-03 eta: 22:48:53 time: 0.5528 data_time: 0.0060 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0120 decode.acc_seg: 99.4635 aux.loss_ce: 0.0087 aux.acc_seg: 99.0871 +04/17 04:07:17 - mmengine - INFO - Iter(train) [ 11350/160000] lr: 9.3657e-03 eta: 22:48:25 time: 0.5520 data_time: 0.0061 memory: 7635 loss: 0.0241 decode.loss_ce: 0.0139 decode.acc_seg: 99.3774 aux.loss_ce: 0.0102 aux.acc_seg: 98.7114 +04/17 04:07:45 - mmengine - INFO - Iter(train) [ 11400/160000] lr: 9.3629e-03 eta: 22:47:57 time: 0.5510 data_time: 0.0054 memory: 7635 loss: 0.0266 decode.loss_ce: 0.0156 decode.acc_seg: 99.5908 aux.loss_ce: 0.0110 aux.acc_seg: 99.2347 +04/17 04:08:12 - mmengine - INFO - Iter(train) [ 11450/160000] lr: 9.3601e-03 eta: 22:47:30 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0136 decode.acc_seg: 99.3744 aux.loss_ce: 0.0098 aux.acc_seg: 99.0390 +04/17 04:08:40 - mmengine - INFO - Iter(train) [ 11500/160000] lr: 9.3573e-03 eta: 22:47:02 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0227 decode.loss_ce: 0.0130 decode.acc_seg: 99.5013 aux.loss_ce: 0.0096 aux.acc_seg: 99.0967 +04/17 04:09:07 - mmengine - INFO - Iter(train) [ 11550/160000] lr: 9.3545e-03 eta: 22:46:35 time: 0.5534 data_time: 0.0055 memory: 7635 loss: 0.0236 decode.loss_ce: 0.0137 decode.acc_seg: 99.4680 aux.loss_ce: 0.0099 aux.acc_seg: 99.0490 +04/17 04:09:35 - mmengine - INFO - Iter(train) [ 11600/160000] lr: 9.3517e-03 eta: 22:46:07 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0129 decode.acc_seg: 99.4855 aux.loss_ce: 0.0094 aux.acc_seg: 98.9380 +04/17 04:10:03 - mmengine - INFO - Iter(train) [ 11650/160000] lr: 9.3489e-03 eta: 22:45:40 time: 0.5530 data_time: 0.0064 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0132 decode.acc_seg: 99.4992 aux.loss_ce: 0.0093 aux.acc_seg: 99.1189 +04/17 04:10:30 - mmengine - INFO - Iter(train) [ 11700/160000] lr: 9.3461e-03 eta: 22:45:13 time: 0.5544 data_time: 0.0057 memory: 7635 loss: 0.0256 decode.loss_ce: 0.0151 decode.acc_seg: 99.4854 aux.loss_ce: 0.0105 aux.acc_seg: 98.9978 +04/17 04:10:58 - mmengine - INFO - Iter(train) [ 11750/160000] lr: 9.3433e-03 eta: 22:44:46 time: 0.5533 data_time: 0.0058 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0131 decode.acc_seg: 99.5871 aux.loss_ce: 0.0094 aux.acc_seg: 99.2082 +04/17 04:11:26 - mmengine - INFO - Iter(train) [ 11800/160000] lr: 9.3404e-03 eta: 22:44:19 time: 0.5526 data_time: 0.0060 memory: 7635 loss: 0.0250 decode.loss_ce: 0.0147 decode.acc_seg: 99.5094 aux.loss_ce: 0.0103 aux.acc_seg: 99.0664 +04/17 04:11:53 - mmengine - INFO - Iter(train) [ 11850/160000] lr: 9.3376e-03 eta: 22:43:52 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0266 decode.loss_ce: 0.0155 decode.acc_seg: 99.3142 aux.loss_ce: 0.0111 aux.acc_seg: 98.9658 +04/17 04:12:21 - mmengine - INFO - Iter(train) [ 11900/160000] lr: 9.3348e-03 eta: 22:43:25 time: 0.5512 data_time: 0.0060 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0149 decode.acc_seg: 99.0739 aux.loss_ce: 0.0110 aux.acc_seg: 98.6077 +04/17 04:12:49 - mmengine - INFO - Iter(train) [ 11950/160000] lr: 9.3320e-03 eta: 22:42:57 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0237 decode.loss_ce: 0.0138 decode.acc_seg: 99.3942 aux.loss_ce: 0.0100 aux.acc_seg: 98.9405 +04/17 04:13:16 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 04:13:16 - mmengine - INFO - Iter(train) [ 12000/160000] lr: 9.3292e-03 eta: 22:42:31 time: 0.5550 data_time: 0.0068 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0136 decode.acc_seg: 99.3548 aux.loss_ce: 0.0099 aux.acc_seg: 98.9933 +04/17 04:13:44 - mmengine - INFO - Iter(train) [ 12050/160000] lr: 9.3264e-03 eta: 22:42:03 time: 0.5519 data_time: 0.0059 memory: 7635 loss: 0.0238 decode.loss_ce: 0.0138 decode.acc_seg: 99.5077 aux.loss_ce: 0.0099 aux.acc_seg: 99.0158 +04/17 04:14:12 - mmengine - INFO - Iter(train) [ 12100/160000] lr: 9.3236e-03 eta: 22:41:36 time: 0.5543 data_time: 0.0069 memory: 7635 loss: 0.0262 decode.loss_ce: 0.0156 decode.acc_seg: 99.2000 aux.loss_ce: 0.0106 aux.acc_seg: 98.8114 +04/17 04:14:39 - mmengine - INFO - Iter(train) [ 12150/160000] lr: 9.3208e-03 eta: 22:41:09 time: 0.5535 data_time: 0.0055 memory: 7635 loss: 0.0266 decode.loss_ce: 0.0157 decode.acc_seg: 99.3168 aux.loss_ce: 0.0109 aux.acc_seg: 98.7861 +04/17 04:15:07 - mmengine - INFO - Iter(train) [ 12200/160000] lr: 9.3180e-03 eta: 22:40:42 time: 0.5557 data_time: 0.0064 memory: 7635 loss: 0.0275 decode.loss_ce: 0.0165 decode.acc_seg: 99.1772 aux.loss_ce: 0.0111 aux.acc_seg: 98.8053 +04/17 04:15:35 - mmengine - INFO - Iter(train) [ 12250/160000] lr: 9.3152e-03 eta: 22:40:16 time: 0.5527 data_time: 0.0065 memory: 7635 loss: 0.0251 decode.loss_ce: 0.0147 decode.acc_seg: 99.2440 aux.loss_ce: 0.0104 aux.acc_seg: 98.8281 +04/17 04:16:02 - mmengine - INFO - Iter(train) [ 12300/160000] lr: 9.3124e-03 eta: 22:39:49 time: 0.5536 data_time: 0.0062 memory: 7635 loss: 0.0269 decode.loss_ce: 0.0155 decode.acc_seg: 99.4492 aux.loss_ce: 0.0114 aux.acc_seg: 98.9661 +04/17 04:16:30 - mmengine - INFO - Iter(train) [ 12350/160000] lr: 9.3096e-03 eta: 22:39:22 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0266 decode.loss_ce: 0.0160 decode.acc_seg: 99.3697 aux.loss_ce: 0.0106 aux.acc_seg: 98.9136 +04/17 04:16:58 - mmengine - INFO - Iter(train) [ 12400/160000] lr: 9.3068e-03 eta: 22:38:56 time: 0.5531 data_time: 0.0059 memory: 7635 loss: 0.0240 decode.loss_ce: 0.0140 decode.acc_seg: 99.4109 aux.loss_ce: 0.0100 aux.acc_seg: 98.8147 +04/17 04:17:26 - mmengine - INFO - Iter(train) [ 12450/160000] lr: 9.3040e-03 eta: 22:38:29 time: 0.5546 data_time: 0.0068 memory: 7635 loss: 0.0238 decode.loss_ce: 0.0137 decode.acc_seg: 99.3942 aux.loss_ce: 0.0101 aux.acc_seg: 98.8612 +04/17 04:17:53 - mmengine - INFO - Iter(train) [ 12500/160000] lr: 9.3012e-03 eta: 22:38:02 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0238 decode.loss_ce: 0.0138 decode.acc_seg: 99.4101 aux.loss_ce: 0.0100 aux.acc_seg: 98.9651 +04/17 04:18:21 - mmengine - INFO - Iter(train) [ 12550/160000] lr: 9.2984e-03 eta: 22:37:34 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0273 decode.loss_ce: 0.0162 decode.acc_seg: 99.2426 aux.loss_ce: 0.0111 aux.acc_seg: 98.7826 +04/17 04:18:49 - mmengine - INFO - Iter(train) [ 12600/160000] lr: 9.2955e-03 eta: 22:37:08 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0241 decode.loss_ce: 0.0141 decode.acc_seg: 99.5333 aux.loss_ce: 0.0099 aux.acc_seg: 98.8665 +04/17 04:19:16 - mmengine - INFO - Iter(train) [ 12650/160000] lr: 9.2927e-03 eta: 22:36:41 time: 0.5535 data_time: 0.0060 memory: 7635 loss: 0.0245 decode.loss_ce: 0.0145 decode.acc_seg: 99.2301 aux.loss_ce: 0.0100 aux.acc_seg: 98.4837 +04/17 04:19:44 - mmengine - INFO - Iter(train) [ 12700/160000] lr: 9.2899e-03 eta: 22:36:13 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0129 decode.acc_seg: 99.3186 aux.loss_ce: 0.0095 aux.acc_seg: 98.6949 +04/17 04:20:12 - mmengine - INFO - Iter(train) [ 12750/160000] lr: 9.2871e-03 eta: 22:35:46 time: 0.5537 data_time: 0.0074 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0130 decode.acc_seg: 99.4762 aux.loss_ce: 0.0093 aux.acc_seg: 98.9079 +04/17 04:20:39 - mmengine - INFO - Iter(train) [ 12800/160000] lr: 9.2843e-03 eta: 22:35:20 time: 0.5530 data_time: 0.0058 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0129 decode.acc_seg: 99.3520 aux.loss_ce: 0.0095 aux.acc_seg: 98.9733 +04/17 04:21:07 - mmengine - INFO - Iter(train) [ 12850/160000] lr: 9.2815e-03 eta: 22:34:53 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0137 decode.acc_seg: 99.3434 aux.loss_ce: 0.0097 aux.acc_seg: 98.7396 +04/17 04:21:35 - mmengine - INFO - Iter(train) [ 12900/160000] lr: 9.2787e-03 eta: 22:34:26 time: 0.5535 data_time: 0.0068 memory: 7635 loss: 0.0224 decode.loss_ce: 0.0130 decode.acc_seg: 99.5496 aux.loss_ce: 0.0094 aux.acc_seg: 99.1914 +04/17 04:22:02 - mmengine - INFO - Iter(train) [ 12950/160000] lr: 9.2759e-03 eta: 22:33:58 time: 0.5540 data_time: 0.0056 memory: 7635 loss: 0.0242 decode.loss_ce: 0.0139 decode.acc_seg: 99.4299 aux.loss_ce: 0.0103 aux.acc_seg: 98.9958 +04/17 04:22:30 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 04:22:30 - mmengine - INFO - Iter(train) [ 13000/160000] lr: 9.2731e-03 eta: 22:33:31 time: 0.5526 data_time: 0.0057 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0154 decode.acc_seg: 99.3240 aux.loss_ce: 0.0106 aux.acc_seg: 98.8968 +04/17 04:22:58 - mmengine - INFO - Iter(train) [ 13050/160000] lr: 9.2703e-03 eta: 22:33:04 time: 0.5518 data_time: 0.0060 memory: 7635 loss: 0.0254 decode.loss_ce: 0.0151 decode.acc_seg: 99.1981 aux.loss_ce: 0.0102 aux.acc_seg: 98.7233 +04/17 04:23:25 - mmengine - INFO - Iter(train) [ 13100/160000] lr: 9.2675e-03 eta: 22:32:37 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0234 decode.loss_ce: 0.0137 decode.acc_seg: 99.5710 aux.loss_ce: 0.0097 aux.acc_seg: 99.2008 +04/17 04:23:53 - mmengine - INFO - Iter(train) [ 13150/160000] lr: 9.2647e-03 eta: 22:32:10 time: 0.5532 data_time: 0.0056 memory: 7635 loss: 0.0224 decode.loss_ce: 0.0129 decode.acc_seg: 99.4144 aux.loss_ce: 0.0096 aux.acc_seg: 98.8643 +04/17 04:24:21 - mmengine - INFO - Iter(train) [ 13200/160000] lr: 9.2618e-03 eta: 22:31:43 time: 0.5535 data_time: 0.0062 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0134 decode.acc_seg: 99.4789 aux.loss_ce: 0.0099 aux.acc_seg: 98.7773 +04/17 04:24:48 - mmengine - INFO - Iter(train) [ 13250/160000] lr: 9.2590e-03 eta: 22:31:16 time: 0.5537 data_time: 0.0060 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0121 decode.acc_seg: 99.3909 aux.loss_ce: 0.0092 aux.acc_seg: 98.9341 +04/17 04:25:16 - mmengine - INFO - Iter(train) [ 13300/160000] lr: 9.2562e-03 eta: 22:30:49 time: 0.5623 data_time: 0.0060 memory: 7635 loss: 0.0240 decode.loss_ce: 0.0140 decode.acc_seg: 99.4434 aux.loss_ce: 0.0100 aux.acc_seg: 99.0723 +04/17 04:25:44 - mmengine - INFO - Iter(train) [ 13350/160000] lr: 9.2534e-03 eta: 22:30:22 time: 0.5538 data_time: 0.0059 memory: 7635 loss: 0.0234 decode.loss_ce: 0.0136 decode.acc_seg: 99.3601 aux.loss_ce: 0.0098 aux.acc_seg: 98.8899 +04/17 04:26:11 - mmengine - INFO - Iter(train) [ 13400/160000] lr: 9.2506e-03 eta: 22:29:55 time: 0.5526 data_time: 0.0059 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0121 decode.acc_seg: 99.3878 aux.loss_ce: 0.0095 aux.acc_seg: 98.7974 +04/17 04:26:39 - mmengine - INFO - Iter(train) [ 13450/160000] lr: 9.2478e-03 eta: 22:29:29 time: 0.5622 data_time: 0.0067 memory: 7635 loss: 0.0247 decode.loss_ce: 0.0143 decode.acc_seg: 99.2961 aux.loss_ce: 0.0104 aux.acc_seg: 98.8160 +04/17 04:27:07 - mmengine - INFO - Iter(train) [ 13500/160000] lr: 9.2450e-03 eta: 22:29:02 time: 0.5531 data_time: 0.0057 memory: 7635 loss: 0.0239 decode.loss_ce: 0.0138 decode.acc_seg: 99.5115 aux.loss_ce: 0.0101 aux.acc_seg: 99.0416 +04/17 04:27:34 - mmengine - INFO - Iter(train) [ 13550/160000] lr: 9.2422e-03 eta: 22:28:34 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0281 decode.loss_ce: 0.0168 decode.acc_seg: 99.4470 aux.loss_ce: 0.0113 aux.acc_seg: 99.1710 +04/17 04:28:02 - mmengine - INFO - Iter(train) [ 13600/160000] lr: 9.2394e-03 eta: 22:28:07 time: 0.5526 data_time: 0.0067 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0123 decode.acc_seg: 99.4963 aux.loss_ce: 0.0092 aux.acc_seg: 99.0114 +04/17 04:28:30 - mmengine - INFO - Iter(train) [ 13650/160000] lr: 9.2366e-03 eta: 22:27:40 time: 0.5525 data_time: 0.0061 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0113 decode.acc_seg: 99.5402 aux.loss_ce: 0.0087 aux.acc_seg: 99.2253 +04/17 04:28:57 - mmengine - INFO - Iter(train) [ 13700/160000] lr: 9.2338e-03 eta: 22:27:12 time: 0.5516 data_time: 0.0058 memory: 7635 loss: 0.0236 decode.loss_ce: 0.0137 decode.acc_seg: 99.5404 aux.loss_ce: 0.0099 aux.acc_seg: 99.0393 +04/17 04:29:25 - mmengine - INFO - Iter(train) [ 13750/160000] lr: 9.2309e-03 eta: 22:26:45 time: 0.5539 data_time: 0.0062 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0121 decode.acc_seg: 99.5745 aux.loss_ce: 0.0093 aux.acc_seg: 98.9949 +04/17 04:29:53 - mmengine - INFO - Iter(train) [ 13800/160000] lr: 9.2281e-03 eta: 22:26:17 time: 0.5522 data_time: 0.0057 memory: 7635 loss: 0.0228 decode.loss_ce: 0.0132 decode.acc_seg: 99.3312 aux.loss_ce: 0.0095 aux.acc_seg: 98.9589 +04/17 04:30:20 - mmengine - INFO - Iter(train) [ 13850/160000] lr: 9.2253e-03 eta: 22:25:49 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0130 decode.acc_seg: 99.5625 aux.loss_ce: 0.0096 aux.acc_seg: 99.0304 +04/17 04:30:48 - mmengine - INFO - Iter(train) [ 13900/160000] lr: 9.2225e-03 eta: 22:25:21 time: 0.5518 data_time: 0.0057 memory: 7635 loss: 0.0250 decode.loss_ce: 0.0144 decode.acc_seg: 99.4550 aux.loss_ce: 0.0106 aux.acc_seg: 99.1648 +04/17 04:31:15 - mmengine - INFO - Iter(train) [ 13950/160000] lr: 9.2197e-03 eta: 22:24:54 time: 0.5525 data_time: 0.0054 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0119 decode.acc_seg: 99.4488 aux.loss_ce: 0.0091 aux.acc_seg: 98.7908 +04/17 04:31:43 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 04:31:43 - mmengine - INFO - Iter(train) [ 14000/160000] lr: 9.2169e-03 eta: 22:24:26 time: 0.5523 data_time: 0.0056 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0124 decode.acc_seg: 99.5605 aux.loss_ce: 0.0092 aux.acc_seg: 99.1129 +04/17 04:32:11 - mmengine - INFO - Iter(train) [ 14050/160000] lr: 9.2141e-03 eta: 22:23:58 time: 0.5515 data_time: 0.0059 memory: 7635 loss: 0.0229 decode.loss_ce: 0.0131 decode.acc_seg: 99.1985 aux.loss_ce: 0.0098 aux.acc_seg: 98.6768 +04/17 04:32:38 - mmengine - INFO - Iter(train) [ 14100/160000] lr: 9.2113e-03 eta: 22:23:30 time: 0.5543 data_time: 0.0073 memory: 7635 loss: 0.0261 decode.loss_ce: 0.0155 decode.acc_seg: 99.6572 aux.loss_ce: 0.0106 aux.acc_seg: 99.3086 +04/17 04:33:06 - mmengine - INFO - Iter(train) [ 14150/160000] lr: 9.2085e-03 eta: 22:23:03 time: 0.5526 data_time: 0.0064 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0128 decode.acc_seg: 99.3685 aux.loss_ce: 0.0097 aux.acc_seg: 98.7271 +04/17 04:33:34 - mmengine - INFO - Iter(train) [ 14200/160000] lr: 9.2057e-03 eta: 22:22:35 time: 0.5519 data_time: 0.0061 memory: 7635 loss: 0.0242 decode.loss_ce: 0.0141 decode.acc_seg: 99.3918 aux.loss_ce: 0.0101 aux.acc_seg: 98.8184 +04/17 04:34:01 - mmengine - INFO - Iter(train) [ 14250/160000] lr: 9.2028e-03 eta: 22:22:07 time: 0.5522 data_time: 0.0065 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0136 decode.acc_seg: 99.4950 aux.loss_ce: 0.0100 aux.acc_seg: 99.2186 +04/17 04:34:29 - mmengine - INFO - Iter(train) [ 14300/160000] lr: 9.2000e-03 eta: 22:21:40 time: 0.5525 data_time: 0.0067 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0125 decode.acc_seg: 99.5231 aux.loss_ce: 0.0093 aux.acc_seg: 99.0948 +04/17 04:34:56 - mmengine - INFO - Iter(train) [ 14350/160000] lr: 9.1972e-03 eta: 22:21:12 time: 0.5535 data_time: 0.0075 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0150 decode.acc_seg: 99.4243 aux.loss_ce: 0.0109 aux.acc_seg: 98.8042 +04/17 04:35:24 - mmengine - INFO - Iter(train) [ 14400/160000] lr: 9.1944e-03 eta: 22:20:45 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0124 decode.acc_seg: 99.4529 aux.loss_ce: 0.0095 aux.acc_seg: 98.9574 +04/17 04:35:52 - mmengine - INFO - Iter(train) [ 14450/160000] lr: 9.1916e-03 eta: 22:20:18 time: 0.5523 data_time: 0.0061 memory: 7635 loss: 0.0237 decode.loss_ce: 0.0137 decode.acc_seg: 99.3893 aux.loss_ce: 0.0100 aux.acc_seg: 98.9051 +04/17 04:36:19 - mmengine - INFO - Iter(train) [ 14500/160000] lr: 9.1888e-03 eta: 22:19:50 time: 0.5514 data_time: 0.0062 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0135 decode.acc_seg: 99.4831 aux.loss_ce: 0.0100 aux.acc_seg: 99.0737 +04/17 04:36:47 - mmengine - INFO - Iter(train) [ 14550/160000] lr: 9.1860e-03 eta: 22:19:24 time: 0.5534 data_time: 0.0068 memory: 7635 loss: 0.0228 decode.loss_ce: 0.0131 decode.acc_seg: 99.5494 aux.loss_ce: 0.0097 aux.acc_seg: 99.2378 +04/17 04:37:15 - mmengine - INFO - Iter(train) [ 14600/160000] lr: 9.1832e-03 eta: 22:18:56 time: 0.5527 data_time: 0.0057 memory: 7635 loss: 0.0245 decode.loss_ce: 0.0142 decode.acc_seg: 99.4138 aux.loss_ce: 0.0103 aux.acc_seg: 98.9269 +04/17 04:37:42 - mmengine - INFO - Iter(train) [ 14650/160000] lr: 9.1804e-03 eta: 22:18:28 time: 0.5526 data_time: 0.0066 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0132 decode.acc_seg: 99.5334 aux.loss_ce: 0.0094 aux.acc_seg: 99.0604 +04/17 04:38:10 - mmengine - INFO - Iter(train) [ 14700/160000] lr: 9.1776e-03 eta: 22:18:01 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0239 decode.loss_ce: 0.0138 decode.acc_seg: 99.4104 aux.loss_ce: 0.0101 aux.acc_seg: 99.0147 +04/17 04:38:38 - mmengine - INFO - Iter(train) [ 14750/160000] lr: 9.1747e-03 eta: 22:17:33 time: 0.5523 data_time: 0.0061 memory: 7635 loss: 0.0257 decode.loss_ce: 0.0150 decode.acc_seg: 99.2423 aux.loss_ce: 0.0107 aux.acc_seg: 98.8490 +04/17 04:39:05 - mmengine - INFO - Iter(train) [ 14800/160000] lr: 9.1719e-03 eta: 22:17:06 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0129 decode.acc_seg: 99.5416 aux.loss_ce: 0.0097 aux.acc_seg: 99.0267 +04/17 04:39:33 - mmengine - INFO - Iter(train) [ 14850/160000] lr: 9.1691e-03 eta: 22:16:38 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0248 decode.loss_ce: 0.0143 decode.acc_seg: 99.3867 aux.loss_ce: 0.0105 aux.acc_seg: 98.8303 +04/17 04:40:01 - mmengine - INFO - Iter(train) [ 14900/160000] lr: 9.1663e-03 eta: 22:16:11 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0261 decode.loss_ce: 0.0153 decode.acc_seg: 99.5447 aux.loss_ce: 0.0108 aux.acc_seg: 99.1027 +04/17 04:40:28 - mmengine - INFO - Iter(train) [ 14950/160000] lr: 9.1635e-03 eta: 22:15:43 time: 0.5531 data_time: 0.0057 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0127 decode.acc_seg: 99.5181 aux.loss_ce: 0.0099 aux.acc_seg: 99.0941 +04/17 04:40:56 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 04:40:56 - mmengine - INFO - Iter(train) [ 15000/160000] lr: 9.1607e-03 eta: 22:15:16 time: 0.5532 data_time: 0.0055 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0126 decode.acc_seg: 99.4672 aux.loss_ce: 0.0094 aux.acc_seg: 98.8308 +04/17 04:41:24 - mmengine - INFO - Iter(train) [ 15050/160000] lr: 9.1579e-03 eta: 22:14:49 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0232 decode.loss_ce: 0.0132 decode.acc_seg: 99.4109 aux.loss_ce: 0.0100 aux.acc_seg: 98.9364 +04/17 04:41:51 - mmengine - INFO - Iter(train) [ 15100/160000] lr: 9.1551e-03 eta: 22:14:21 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0109 decode.acc_seg: 99.6185 aux.loss_ce: 0.0085 aux.acc_seg: 99.2871 +04/17 04:42:19 - mmengine - INFO - Iter(train) [ 15150/160000] lr: 9.1522e-03 eta: 22:13:54 time: 0.5525 data_time: 0.0058 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0121 decode.acc_seg: 99.6285 aux.loss_ce: 0.0093 aux.acc_seg: 99.2195 +04/17 04:42:46 - mmengine - INFO - Iter(train) [ 15200/160000] lr: 9.1494e-03 eta: 22:13:26 time: 0.5535 data_time: 0.0069 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0121 decode.acc_seg: 99.4781 aux.loss_ce: 0.0091 aux.acc_seg: 99.0991 +04/17 04:43:14 - mmengine - INFO - Iter(train) [ 15250/160000] lr: 9.1466e-03 eta: 22:12:59 time: 0.5535 data_time: 0.0059 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0130 decode.acc_seg: 99.5077 aux.loss_ce: 0.0093 aux.acc_seg: 99.0988 +04/17 04:43:42 - mmengine - INFO - Iter(train) [ 15300/160000] lr: 9.1438e-03 eta: 22:12:31 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0234 decode.loss_ce: 0.0133 decode.acc_seg: 99.3849 aux.loss_ce: 0.0101 aux.acc_seg: 98.8997 +04/17 04:44:09 - mmengine - INFO - Iter(train) [ 15350/160000] lr: 9.1410e-03 eta: 22:12:04 time: 0.5533 data_time: 0.0059 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0124 decode.acc_seg: 99.3773 aux.loss_ce: 0.0094 aux.acc_seg: 99.0790 +04/17 04:44:37 - mmengine - INFO - Iter(train) [ 15400/160000] lr: 9.1382e-03 eta: 22:11:36 time: 0.5523 data_time: 0.0063 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0136 decode.acc_seg: 99.4472 aux.loss_ce: 0.0098 aux.acc_seg: 99.0348 +04/17 04:45:05 - mmengine - INFO - Iter(train) [ 15450/160000] lr: 9.1354e-03 eta: 22:11:09 time: 0.5526 data_time: 0.0058 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0132 decode.acc_seg: 99.3195 aux.loss_ce: 0.0100 aux.acc_seg: 98.7923 +04/17 04:45:32 - mmengine - INFO - Iter(train) [ 15500/160000] lr: 9.1326e-03 eta: 22:10:42 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0124 decode.acc_seg: 99.4955 aux.loss_ce: 0.0093 aux.acc_seg: 99.1011 +04/17 04:46:00 - mmengine - INFO - Iter(train) [ 15550/160000] lr: 9.1297e-03 eta: 22:10:15 time: 0.5527 data_time: 0.0058 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0113 decode.acc_seg: 99.4737 aux.loss_ce: 0.0088 aux.acc_seg: 98.9878 +04/17 04:46:28 - mmengine - INFO - Iter(train) [ 15600/160000] lr: 9.1269e-03 eta: 22:09:48 time: 0.5610 data_time: 0.0068 memory: 7635 loss: 0.0238 decode.loss_ce: 0.0136 decode.acc_seg: 99.3832 aux.loss_ce: 0.0102 aux.acc_seg: 98.8857 +04/17 04:46:55 - mmengine - INFO - Iter(train) [ 15650/160000] lr: 9.1241e-03 eta: 22:09:20 time: 0.5533 data_time: 0.0059 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0111 decode.acc_seg: 99.5057 aux.loss_ce: 0.0089 aux.acc_seg: 99.0979 +04/17 04:47:23 - mmengine - INFO - Iter(train) [ 15700/160000] lr: 9.1213e-03 eta: 22:08:53 time: 0.5538 data_time: 0.0070 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0125 decode.acc_seg: 99.4911 aux.loss_ce: 0.0089 aux.acc_seg: 99.1747 +04/17 04:47:51 - mmengine - INFO - Iter(train) [ 15750/160000] lr: 9.1185e-03 eta: 22:08:26 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0224 decode.loss_ce: 0.0128 decode.acc_seg: 99.4130 aux.loss_ce: 0.0095 aux.acc_seg: 99.0441 +04/17 04:48:18 - mmengine - INFO - Iter(train) [ 15800/160000] lr: 9.1157e-03 eta: 22:07:58 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0121 decode.acc_seg: 99.5008 aux.loss_ce: 0.0093 aux.acc_seg: 99.0557 +04/17 04:48:46 - mmengine - INFO - Iter(train) [ 15850/160000] lr: 9.1129e-03 eta: 22:07:31 time: 0.5522 data_time: 0.0057 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0125 decode.acc_seg: 99.4353 aux.loss_ce: 0.0093 aux.acc_seg: 99.0086 +04/17 04:49:14 - mmengine - INFO - Iter(train) [ 15900/160000] lr: 9.1101e-03 eta: 22:07:04 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0127 decode.acc_seg: 99.5936 aux.loss_ce: 0.0095 aux.acc_seg: 99.1505 +04/17 04:49:41 - mmengine - INFO - Iter(train) [ 15950/160000] lr: 9.1072e-03 eta: 22:06:37 time: 0.5549 data_time: 0.0077 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0117 decode.acc_seg: 99.6064 aux.loss_ce: 0.0087 aux.acc_seg: 99.2549 +04/17 04:50:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 04:50:09 - mmengine - INFO - Iter(train) [ 16000/160000] lr: 9.1044e-03 eta: 22:06:10 time: 0.5529 data_time: 0.0060 memory: 7635 loss: 0.0261 decode.loss_ce: 0.0152 decode.acc_seg: 99.3852 aux.loss_ce: 0.0109 aux.acc_seg: 98.5144 +04/17 04:50:37 - mmengine - INFO - Iter(train) [ 16050/160000] lr: 9.1016e-03 eta: 22:05:43 time: 0.5534 data_time: 0.0059 memory: 7635 loss: 0.0229 decode.loss_ce: 0.0133 decode.acc_seg: 99.7216 aux.loss_ce: 0.0096 aux.acc_seg: 99.4108 +04/17 04:51:05 - mmengine - INFO - Iter(train) [ 16100/160000] lr: 9.0988e-03 eta: 22:05:15 time: 0.5529 data_time: 0.0060 memory: 7635 loss: 0.0236 decode.loss_ce: 0.0135 decode.acc_seg: 99.6138 aux.loss_ce: 0.0101 aux.acc_seg: 99.1704 +04/17 04:51:32 - mmengine - INFO - Iter(train) [ 16150/160000] lr: 9.0960e-03 eta: 22:04:48 time: 0.5531 data_time: 0.0062 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0134 decode.acc_seg: 99.1458 aux.loss_ce: 0.0098 aux.acc_seg: 98.5909 +04/17 04:52:00 - mmengine - INFO - Iter(train) [ 16200/160000] lr: 9.0932e-03 eta: 22:04:21 time: 0.5528 data_time: 0.0070 memory: 7635 loss: 0.0229 decode.loss_ce: 0.0133 decode.acc_seg: 99.5446 aux.loss_ce: 0.0095 aux.acc_seg: 99.0372 +04/17 04:52:27 - mmengine - INFO - Iter(train) [ 16250/160000] lr: 9.0904e-03 eta: 22:03:53 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0261 decode.loss_ce: 0.0153 decode.acc_seg: 99.5235 aux.loss_ce: 0.0108 aux.acc_seg: 98.8697 +04/17 04:52:55 - mmengine - INFO - Iter(train) [ 16300/160000] lr: 9.0875e-03 eta: 22:03:26 time: 0.5541 data_time: 0.0056 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0124 decode.acc_seg: 99.3850 aux.loss_ce: 0.0098 aux.acc_seg: 98.9579 +04/17 04:53:23 - mmengine - INFO - Iter(train) [ 16350/160000] lr: 9.0847e-03 eta: 22:02:58 time: 0.5550 data_time: 0.0056 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0138 decode.acc_seg: 99.4367 aux.loss_ce: 0.0097 aux.acc_seg: 98.9577 +04/17 04:53:50 - mmengine - INFO - Iter(train) [ 16400/160000] lr: 9.0819e-03 eta: 22:02:31 time: 0.5536 data_time: 0.0066 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0124 decode.acc_seg: 99.6416 aux.loss_ce: 0.0091 aux.acc_seg: 99.4053 +04/17 04:54:18 - mmengine - INFO - Iter(train) [ 16450/160000] lr: 9.0791e-03 eta: 22:02:04 time: 0.5535 data_time: 0.0065 memory: 7635 loss: 0.0228 decode.loss_ce: 0.0132 decode.acc_seg: 99.5540 aux.loss_ce: 0.0095 aux.acc_seg: 98.9800 +04/17 04:54:46 - mmengine - INFO - Iter(train) [ 16500/160000] lr: 9.0763e-03 eta: 22:01:36 time: 0.5531 data_time: 0.0063 memory: 7635 loss: 0.0261 decode.loss_ce: 0.0155 decode.acc_seg: 99.5005 aux.loss_ce: 0.0106 aux.acc_seg: 99.0659 +04/17 04:55:14 - mmengine - INFO - Iter(train) [ 16550/160000] lr: 9.0735e-03 eta: 22:01:10 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0116 decode.acc_seg: 99.6051 aux.loss_ce: 0.0093 aux.acc_seg: 99.1837 +04/17 04:55:41 - mmengine - INFO - Iter(train) [ 16600/160000] lr: 9.0706e-03 eta: 22:00:42 time: 0.5527 data_time: 0.0065 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0125 decode.acc_seg: 99.5650 aux.loss_ce: 0.0092 aux.acc_seg: 99.2495 +04/17 04:56:09 - mmengine - INFO - Iter(train) [ 16650/160000] lr: 9.0678e-03 eta: 22:00:15 time: 0.5541 data_time: 0.0069 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0118 decode.acc_seg: 99.5330 aux.loss_ce: 0.0091 aux.acc_seg: 99.2336 +04/17 04:56:37 - mmengine - INFO - Iter(train) [ 16700/160000] lr: 9.0650e-03 eta: 21:59:48 time: 0.5529 data_time: 0.0069 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0138 decode.acc_seg: 99.2662 aux.loss_ce: 0.0096 aux.acc_seg: 98.9785 +04/17 04:57:04 - mmengine - INFO - Iter(train) [ 16750/160000] lr: 9.0622e-03 eta: 21:59:21 time: 0.5536 data_time: 0.0069 memory: 7635 loss: 0.0252 decode.loss_ce: 0.0147 decode.acc_seg: 99.5746 aux.loss_ce: 0.0105 aux.acc_seg: 99.1149 +04/17 04:57:32 - mmengine - INFO - Iter(train) [ 16800/160000] lr: 9.0594e-03 eta: 21:58:53 time: 0.5535 data_time: 0.0058 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0133 decode.acc_seg: 99.4496 aux.loss_ce: 0.0093 aux.acc_seg: 99.1562 +04/17 04:58:00 - mmengine - INFO - Iter(train) [ 16850/160000] lr: 9.0566e-03 eta: 21:58:26 time: 0.5536 data_time: 0.0057 memory: 7635 loss: 0.0228 decode.loss_ce: 0.0129 decode.acc_seg: 99.3956 aux.loss_ce: 0.0098 aux.acc_seg: 98.8151 +04/17 04:58:27 - mmengine - INFO - Iter(train) [ 16900/160000] lr: 9.0538e-03 eta: 21:57:58 time: 0.5533 data_time: 0.0060 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0128 decode.acc_seg: 99.6158 aux.loss_ce: 0.0095 aux.acc_seg: 99.3298 +04/17 04:58:55 - mmengine - INFO - Iter(train) [ 16950/160000] lr: 9.0509e-03 eta: 21:57:31 time: 0.5533 data_time: 0.0060 memory: 7635 loss: 0.0249 decode.loss_ce: 0.0146 decode.acc_seg: 99.5616 aux.loss_ce: 0.0103 aux.acc_seg: 99.1063 +04/17 04:59:23 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 04:59:23 - mmengine - INFO - Iter(train) [ 17000/160000] lr: 9.0481e-03 eta: 21:57:04 time: 0.5523 data_time: 0.0061 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0125 decode.acc_seg: 99.3622 aux.loss_ce: 0.0093 aux.acc_seg: 99.0306 +04/17 04:59:50 - mmengine - INFO - Iter(train) [ 17050/160000] lr: 9.0453e-03 eta: 21:56:36 time: 0.5529 data_time: 0.0073 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0118 decode.acc_seg: 99.5401 aux.loss_ce: 0.0093 aux.acc_seg: 99.0092 +04/17 05:00:18 - mmengine - INFO - Iter(train) [ 17100/160000] lr: 9.0425e-03 eta: 21:56:09 time: 0.5518 data_time: 0.0062 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0122 decode.acc_seg: 99.4996 aux.loss_ce: 0.0093 aux.acc_seg: 99.1595 +04/17 05:00:46 - mmengine - INFO - Iter(train) [ 17150/160000] lr: 9.0397e-03 eta: 21:55:41 time: 0.5529 data_time: 0.0058 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0131 decode.acc_seg: 99.5560 aux.loss_ce: 0.0094 aux.acc_seg: 99.0885 +04/17 05:01:13 - mmengine - INFO - Iter(train) [ 17200/160000] lr: 9.0369e-03 eta: 21:55:14 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0122 decode.acc_seg: 99.5878 aux.loss_ce: 0.0092 aux.acc_seg: 99.2035 +04/17 05:01:41 - mmengine - INFO - Iter(train) [ 17250/160000] lr: 9.0340e-03 eta: 21:54:46 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0114 decode.acc_seg: 99.6227 aux.loss_ce: 0.0089 aux.acc_seg: 99.1842 +04/17 05:02:08 - mmengine - INFO - Iter(train) [ 17300/160000] lr: 9.0312e-03 eta: 21:54:19 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0121 decode.acc_seg: 99.5550 aux.loss_ce: 0.0093 aux.acc_seg: 99.2855 +04/17 05:02:36 - mmengine - INFO - Iter(train) [ 17350/160000] lr: 9.0284e-03 eta: 21:53:51 time: 0.5523 data_time: 0.0066 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0132 decode.acc_seg: 99.5240 aux.loss_ce: 0.0098 aux.acc_seg: 98.9947 +04/17 05:03:04 - mmengine - INFO - Iter(train) [ 17400/160000] lr: 9.0256e-03 eta: 21:53:23 time: 0.5515 data_time: 0.0066 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0143 decode.acc_seg: 99.4363 aux.loss_ce: 0.0103 aux.acc_seg: 99.0086 +04/17 05:03:31 - mmengine - INFO - Iter(train) [ 17450/160000] lr: 9.0228e-03 eta: 21:52:56 time: 0.5523 data_time: 0.0055 memory: 7635 loss: 0.0239 decode.loss_ce: 0.0139 decode.acc_seg: 99.1714 aux.loss_ce: 0.0101 aux.acc_seg: 98.6675 +04/17 05:03:59 - mmengine - INFO - Iter(train) [ 17500/160000] lr: 9.0200e-03 eta: 21:52:28 time: 0.5528 data_time: 0.0071 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0116 decode.acc_seg: 99.6930 aux.loss_ce: 0.0091 aux.acc_seg: 99.3424 +04/17 05:04:27 - mmengine - INFO - Iter(train) [ 17550/160000] lr: 9.0171e-03 eta: 21:52:01 time: 0.5524 data_time: 0.0062 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0110 decode.acc_seg: 99.5161 aux.loss_ce: 0.0090 aux.acc_seg: 98.9089 +04/17 05:04:54 - mmengine - INFO - Iter(train) [ 17600/160000] lr: 9.0143e-03 eta: 21:51:33 time: 0.5539 data_time: 0.0071 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0125 decode.acc_seg: 99.5656 aux.loss_ce: 0.0098 aux.acc_seg: 98.9678 +04/17 05:05:22 - mmengine - INFO - Iter(train) [ 17650/160000] lr: 9.0115e-03 eta: 21:51:06 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0121 decode.acc_seg: 99.5657 aux.loss_ce: 0.0095 aux.acc_seg: 99.1462 +04/17 05:05:50 - mmengine - INFO - Iter(train) [ 17700/160000] lr: 9.0087e-03 eta: 21:50:39 time: 0.5512 data_time: 0.0066 memory: 7635 loss: 0.0224 decode.loss_ce: 0.0129 decode.acc_seg: 99.3938 aux.loss_ce: 0.0095 aux.acc_seg: 98.9109 +04/17 05:06:17 - mmengine - INFO - Iter(train) [ 17750/160000] lr: 9.0059e-03 eta: 21:50:12 time: 0.5525 data_time: 0.0058 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0112 decode.acc_seg: 99.6460 aux.loss_ce: 0.0086 aux.acc_seg: 99.1364 +04/17 05:06:45 - mmengine - INFO - Iter(train) [ 17800/160000] lr: 9.0031e-03 eta: 21:49:44 time: 0.5530 data_time: 0.0061 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0125 decode.acc_seg: 99.6550 aux.loss_ce: 0.0094 aux.acc_seg: 99.1895 +04/17 05:07:13 - mmengine - INFO - Iter(train) [ 17850/160000] lr: 9.0002e-03 eta: 21:49:17 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0136 decode.acc_seg: 99.5639 aux.loss_ce: 0.0097 aux.acc_seg: 99.1932 +04/17 05:07:40 - mmengine - INFO - Iter(train) [ 17900/160000] lr: 8.9974e-03 eta: 21:48:49 time: 0.5521 data_time: 0.0060 memory: 7635 loss: 0.0227 decode.loss_ce: 0.0129 decode.acc_seg: 99.4536 aux.loss_ce: 0.0098 aux.acc_seg: 99.0761 +04/17 05:08:08 - mmengine - INFO - Iter(train) [ 17950/160000] lr: 8.9946e-03 eta: 21:48:22 time: 0.5533 data_time: 0.0058 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0111 decode.acc_seg: 99.6744 aux.loss_ce: 0.0087 aux.acc_seg: 99.3177 +04/17 05:08:36 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 05:08:36 - mmengine - INFO - Iter(train) [ 18000/160000] lr: 8.9918e-03 eta: 21:47:54 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0135 decode.acc_seg: 99.3295 aux.loss_ce: 0.0095 aux.acc_seg: 98.8405 +04/17 05:09:03 - mmengine - INFO - Iter(train) [ 18050/160000] lr: 8.9890e-03 eta: 21:47:27 time: 0.5532 data_time: 0.0067 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0126 decode.acc_seg: 99.3703 aux.loss_ce: 0.0097 aux.acc_seg: 98.7511 +04/17 05:09:31 - mmengine - INFO - Iter(train) [ 18100/160000] lr: 8.9862e-03 eta: 21:46:59 time: 0.5531 data_time: 0.0062 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0118 decode.acc_seg: 99.4115 aux.loss_ce: 0.0092 aux.acc_seg: 99.0157 +04/17 05:09:59 - mmengine - INFO - Iter(train) [ 18150/160000] lr: 8.9833e-03 eta: 21:46:32 time: 0.5542 data_time: 0.0057 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0123 decode.acc_seg: 99.4532 aux.loss_ce: 0.0096 aux.acc_seg: 99.0580 +04/17 05:10:26 - mmengine - INFO - Iter(train) [ 18200/160000] lr: 8.9805e-03 eta: 21:46:05 time: 0.5543 data_time: 0.0063 memory: 7635 loss: 0.0250 decode.loss_ce: 0.0146 decode.acc_seg: 99.4089 aux.loss_ce: 0.0104 aux.acc_seg: 99.1213 +04/17 05:10:54 - mmengine - INFO - Iter(train) [ 18250/160000] lr: 8.9777e-03 eta: 21:45:38 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0229 decode.loss_ce: 0.0134 decode.acc_seg: 99.4775 aux.loss_ce: 0.0096 aux.acc_seg: 98.9356 +04/17 05:11:22 - mmengine - INFO - Iter(train) [ 18300/160000] lr: 8.9749e-03 eta: 21:45:10 time: 0.5528 data_time: 0.0062 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0119 decode.acc_seg: 99.4629 aux.loss_ce: 0.0094 aux.acc_seg: 98.8179 +04/17 05:11:49 - mmengine - INFO - Iter(train) [ 18350/160000] lr: 8.9721e-03 eta: 21:44:43 time: 0.5520 data_time: 0.0060 memory: 7635 loss: 0.0251 decode.loss_ce: 0.0148 decode.acc_seg: 99.3949 aux.loss_ce: 0.0103 aux.acc_seg: 98.8255 +04/17 05:12:17 - mmengine - INFO - Iter(train) [ 18400/160000] lr: 8.9692e-03 eta: 21:44:15 time: 0.5529 data_time: 0.0064 memory: 7635 loss: 0.0241 decode.loss_ce: 0.0139 decode.acc_seg: 99.2954 aux.loss_ce: 0.0102 aux.acc_seg: 99.0137 +04/17 05:12:45 - mmengine - INFO - Iter(train) [ 18450/160000] lr: 8.9664e-03 eta: 21:43:48 time: 0.5535 data_time: 0.0061 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0133 decode.acc_seg: 99.6344 aux.loss_ce: 0.0097 aux.acc_seg: 99.0255 +04/17 05:13:12 - mmengine - INFO - Iter(train) [ 18500/160000] lr: 8.9636e-03 eta: 21:43:20 time: 0.5546 data_time: 0.0063 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0130 decode.acc_seg: 99.5232 aux.loss_ce: 0.0094 aux.acc_seg: 99.1300 +04/17 05:13:40 - mmengine - INFO - Iter(train) [ 18550/160000] lr: 8.9608e-03 eta: 21:42:53 time: 0.5543 data_time: 0.0063 memory: 7635 loss: 0.0221 decode.loss_ce: 0.0126 decode.acc_seg: 99.5961 aux.loss_ce: 0.0095 aux.acc_seg: 99.1305 +04/17 05:14:08 - mmengine - INFO - Iter(train) [ 18600/160000] lr: 8.9580e-03 eta: 21:42:25 time: 0.5524 data_time: 0.0066 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0117 decode.acc_seg: 99.5456 aux.loss_ce: 0.0091 aux.acc_seg: 99.1299 +04/17 05:14:35 - mmengine - INFO - Iter(train) [ 18650/160000] lr: 8.9551e-03 eta: 21:41:58 time: 0.5537 data_time: 0.0069 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0126 decode.acc_seg: 99.0963 aux.loss_ce: 0.0097 aux.acc_seg: 98.5669 +04/17 05:15:03 - mmengine - INFO - Iter(train) [ 18700/160000] lr: 8.9523e-03 eta: 21:41:31 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0130 decode.acc_seg: 99.2403 aux.loss_ce: 0.0096 aux.acc_seg: 98.9312 +04/17 05:15:31 - mmengine - INFO - Iter(train) [ 18750/160000] lr: 8.9495e-03 eta: 21:41:04 time: 0.5528 data_time: 0.0056 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0114 decode.acc_seg: 99.1571 aux.loss_ce: 0.0085 aux.acc_seg: 98.7487 +04/17 05:15:58 - mmengine - INFO - Iter(train) [ 18800/160000] lr: 8.9467e-03 eta: 21:40:37 time: 0.5532 data_time: 0.0059 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0122 decode.acc_seg: 99.4122 aux.loss_ce: 0.0093 aux.acc_seg: 99.0150 +04/17 05:16:26 - mmengine - INFO - Iter(train) [ 18850/160000] lr: 8.9439e-03 eta: 21:40:10 time: 0.5552 data_time: 0.0060 memory: 7635 loss: 0.0227 decode.loss_ce: 0.0133 decode.acc_seg: 99.2135 aux.loss_ce: 0.0095 aux.acc_seg: 98.6745 +04/17 05:16:54 - mmengine - INFO - Iter(train) [ 18900/160000] lr: 8.9411e-03 eta: 21:39:42 time: 0.5523 data_time: 0.0063 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0126 decode.acc_seg: 99.4956 aux.loss_ce: 0.0094 aux.acc_seg: 99.0646 +04/17 05:17:21 - mmengine - INFO - Iter(train) [ 18950/160000] lr: 8.9382e-03 eta: 21:39:15 time: 0.5538 data_time: 0.0062 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.3465 aux.loss_ce: 0.0090 aux.acc_seg: 98.9604 +04/17 05:17:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 05:17:49 - mmengine - INFO - Iter(train) [ 19000/160000] lr: 8.9354e-03 eta: 21:38:47 time: 0.5536 data_time: 0.0056 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0120 decode.acc_seg: 99.5575 aux.loss_ce: 0.0093 aux.acc_seg: 99.0992 +04/17 05:18:17 - mmengine - INFO - Iter(train) [ 19050/160000] lr: 8.9326e-03 eta: 21:38:20 time: 0.5552 data_time: 0.0063 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0119 decode.acc_seg: 99.3151 aux.loss_ce: 0.0090 aux.acc_seg: 98.9167 +04/17 05:18:44 - mmengine - INFO - Iter(train) [ 19100/160000] lr: 8.9298e-03 eta: 21:37:53 time: 0.5525 data_time: 0.0066 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0128 decode.acc_seg: 99.2525 aux.loss_ce: 0.0095 aux.acc_seg: 98.7122 +04/17 05:19:12 - mmengine - INFO - Iter(train) [ 19150/160000] lr: 8.9270e-03 eta: 21:37:26 time: 0.5525 data_time: 0.0061 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0119 decode.acc_seg: 99.6479 aux.loss_ce: 0.0092 aux.acc_seg: 99.2475 +04/17 05:19:40 - mmengine - INFO - Iter(train) [ 19200/160000] lr: 8.9241e-03 eta: 21:36:58 time: 0.5531 data_time: 0.0063 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0125 decode.acc_seg: 99.4622 aux.loss_ce: 0.0095 aux.acc_seg: 98.9523 +04/17 05:20:07 - mmengine - INFO - Iter(train) [ 19250/160000] lr: 8.9213e-03 eta: 21:36:31 time: 0.5537 data_time: 0.0054 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0117 decode.acc_seg: 99.7088 aux.loss_ce: 0.0086 aux.acc_seg: 99.4136 +04/17 05:20:35 - mmengine - INFO - Iter(train) [ 19300/160000] lr: 8.9185e-03 eta: 21:36:03 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0118 decode.acc_seg: 99.5994 aux.loss_ce: 0.0093 aux.acc_seg: 99.1597 +04/17 05:21:03 - mmengine - INFO - Iter(train) [ 19350/160000] lr: 8.9157e-03 eta: 21:35:36 time: 0.5546 data_time: 0.0063 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0118 decode.acc_seg: 99.5967 aux.loss_ce: 0.0093 aux.acc_seg: 98.9788 +04/17 05:21:30 - mmengine - INFO - Iter(train) [ 19400/160000] lr: 8.9129e-03 eta: 21:35:09 time: 0.5541 data_time: 0.0061 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0117 decode.acc_seg: 99.4917 aux.loss_ce: 0.0090 aux.acc_seg: 98.9080 +04/17 05:21:58 - mmengine - INFO - Iter(train) [ 19450/160000] lr: 8.9100e-03 eta: 21:34:41 time: 0.5529 data_time: 0.0060 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0115 decode.acc_seg: 99.5297 aux.loss_ce: 0.0090 aux.acc_seg: 98.9814 +04/17 05:22:26 - mmengine - INFO - Iter(train) [ 19500/160000] lr: 8.9072e-03 eta: 21:34:14 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0124 decode.acc_seg: 99.0890 aux.loss_ce: 0.0098 aux.acc_seg: 98.7421 +04/17 05:22:54 - mmengine - INFO - Iter(train) [ 19550/160000] lr: 8.9044e-03 eta: 21:33:47 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0127 decode.acc_seg: 99.6622 aux.loss_ce: 0.0090 aux.acc_seg: 99.3778 +04/17 05:23:21 - mmengine - INFO - Iter(train) [ 19600/160000] lr: 8.9016e-03 eta: 21:33:19 time: 0.5531 data_time: 0.0057 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0110 decode.acc_seg: 99.5071 aux.loss_ce: 0.0087 aux.acc_seg: 98.8554 +04/17 05:23:49 - mmengine - INFO - Iter(train) [ 19650/160000] lr: 8.8987e-03 eta: 21:32:52 time: 0.5537 data_time: 0.0057 memory: 7635 loss: 0.0237 decode.loss_ce: 0.0137 decode.acc_seg: 99.3431 aux.loss_ce: 0.0101 aux.acc_seg: 98.9584 +04/17 05:24:17 - mmengine - INFO - Iter(train) [ 19700/160000] lr: 8.8959e-03 eta: 21:32:24 time: 0.5539 data_time: 0.0059 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0116 decode.acc_seg: 99.5342 aux.loss_ce: 0.0095 aux.acc_seg: 98.7595 +04/17 05:24:44 - mmengine - INFO - Iter(train) [ 19750/160000] lr: 8.8931e-03 eta: 21:31:57 time: 0.5522 data_time: 0.0066 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0130 decode.acc_seg: 99.5636 aux.loss_ce: 0.0100 aux.acc_seg: 99.1068 +04/17 05:25:12 - mmengine - INFO - Iter(train) [ 19800/160000] lr: 8.8903e-03 eta: 21:31:30 time: 0.5531 data_time: 0.0059 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0120 decode.acc_seg: 99.3897 aux.loss_ce: 0.0090 aux.acc_seg: 98.8853 +04/17 05:25:40 - mmengine - INFO - Iter(train) [ 19850/160000] lr: 8.8875e-03 eta: 21:31:03 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0228 decode.loss_ce: 0.0129 decode.acc_seg: 99.5498 aux.loss_ce: 0.0099 aux.acc_seg: 99.0361 +04/17 05:26:07 - mmengine - INFO - Iter(train) [ 19900/160000] lr: 8.8846e-03 eta: 21:30:36 time: 0.5614 data_time: 0.0059 memory: 7635 loss: 0.0224 decode.loss_ce: 0.0127 decode.acc_seg: 99.6275 aux.loss_ce: 0.0097 aux.acc_seg: 99.1510 +04/17 05:26:35 - mmengine - INFO - Iter(train) [ 19950/160000] lr: 8.8818e-03 eta: 21:30:08 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.4675 aux.loss_ce: 0.0090 aux.acc_seg: 98.8947 +04/17 05:27:03 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 05:27:03 - mmengine - INFO - Iter(train) [ 20000/160000] lr: 8.8790e-03 eta: 21:29:41 time: 0.5544 data_time: 0.0064 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0106 decode.acc_seg: 99.4899 aux.loss_ce: 0.0086 aux.acc_seg: 98.9120 +04/17 05:27:03 - mmengine - INFO - Saving checkpoint at 20000 iterations +04/17 05:27:07 - mmengine - INFO - Iter(val) [ 50/355] eta: 0:00:14 time: 0.0475 data_time: 0.0015 memory: 1657 +04/17 05:27:09 - mmengine - INFO - Iter(val) [100/355] eta: 0:00:12 time: 0.0466 data_time: 0.0015 memory: 1657 +04/17 05:27:11 - mmengine - INFO - Iter(val) [150/355] eta: 0:00:09 time: 0.0463 data_time: 0.0013 memory: 1657 +04/17 05:27:14 - mmengine - INFO - Iter(val) [200/355] eta: 0:00:07 time: 0.0462 data_time: 0.0014 memory: 1657 +04/17 05:27:16 - mmengine - INFO - Iter(val) [250/355] eta: 0:00:04 time: 0.0473 data_time: 0.0013 memory: 1657 +04/17 05:27:18 - mmengine - INFO - Iter(val) [300/355] eta: 0:00:02 time: 0.0478 data_time: 0.0014 memory: 1657 +04/17 05:27:21 - mmengine - INFO - Iter(val) [350/355] eta: 0:00:00 time: 0.0462 data_time: 0.0015 memory: 1657 +04/17 05:27:21 - mmengine - INFO - per class results: +04/17 05:27:21 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.12 | 99.64 | 99.56 | 99.48 | 99.64 | +| contrast | 80.59 | 87.49 | 89.25 | 91.08 | 87.49 | ++------------+-------+-------+--------+-----------+--------+ +04/17 05:27:21 - mmengine - INFO - Iter(val) [355/355] aAcc: 99.1500 mIoU: 89.8500 mAcc: 93.5700 mFscore: 94.4000 mPrecision: 95.2800 mRecall: 93.5700 data_time: 0.0015 time: 0.0467 +04/17 05:27:49 - mmengine - INFO - Iter(train) [ 20050/160000] lr: 8.8762e-03 eta: 21:29:17 time: 0.5527 data_time: 0.0057 memory: 7635 loss: 0.0255 decode.loss_ce: 0.0148 decode.acc_seg: 99.4461 aux.loss_ce: 0.0107 aux.acc_seg: 98.9660 +04/17 05:28:17 - mmengine - INFO - Iter(train) [ 20100/160000] lr: 8.8734e-03 eta: 21:28:49 time: 0.5534 data_time: 0.0060 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0126 decode.acc_seg: 99.5745 aux.loss_ce: 0.0096 aux.acc_seg: 98.9091 +04/17 05:28:44 - mmengine - INFO - Iter(train) [ 20150/160000] lr: 8.8705e-03 eta: 21:28:22 time: 0.5543 data_time: 0.0061 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0109 decode.acc_seg: 99.4581 aux.loss_ce: 0.0086 aux.acc_seg: 99.0719 +04/17 05:29:12 - mmengine - INFO - Iter(train) [ 20200/160000] lr: 8.8677e-03 eta: 21:27:54 time: 0.5538 data_time: 0.0075 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0126 decode.acc_seg: 99.5244 aux.loss_ce: 0.0096 aux.acc_seg: 98.9345 +04/17 05:29:40 - mmengine - INFO - Iter(train) [ 20250/160000] lr: 8.8649e-03 eta: 21:27:27 time: 0.5527 data_time: 0.0057 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0123 decode.acc_seg: 99.5229 aux.loss_ce: 0.0092 aux.acc_seg: 99.0468 +04/17 05:30:07 - mmengine - INFO - Iter(train) [ 20300/160000] lr: 8.8621e-03 eta: 21:27:00 time: 0.5527 data_time: 0.0076 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0121 decode.acc_seg: 99.6001 aux.loss_ce: 0.0097 aux.acc_seg: 99.0586 +04/17 05:30:35 - mmengine - INFO - Iter(train) [ 20350/160000] lr: 8.8592e-03 eta: 21:26:32 time: 0.5543 data_time: 0.0057 memory: 7635 loss: 0.0227 decode.loss_ce: 0.0129 decode.acc_seg: 99.4566 aux.loss_ce: 0.0097 aux.acc_seg: 98.9085 +04/17 05:31:03 - mmengine - INFO - Iter(train) [ 20400/160000] lr: 8.8564e-03 eta: 21:26:05 time: 0.5544 data_time: 0.0066 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0113 decode.acc_seg: 99.6126 aux.loss_ce: 0.0090 aux.acc_seg: 99.3439 +04/17 05:31:30 - mmengine - INFO - Iter(train) [ 20450/160000] lr: 8.8536e-03 eta: 21:25:38 time: 0.5535 data_time: 0.0066 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0152 decode.acc_seg: 99.1918 aux.loss_ce: 0.0108 aux.acc_seg: 98.7544 +04/17 05:31:58 - mmengine - INFO - Iter(train) [ 20500/160000] lr: 8.8508e-03 eta: 21:25:10 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0123 decode.acc_seg: 99.6285 aux.loss_ce: 0.0092 aux.acc_seg: 99.0986 +04/17 05:32:26 - mmengine - INFO - Iter(train) [ 20550/160000] lr: 8.8480e-03 eta: 21:24:43 time: 0.5534 data_time: 0.0073 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0117 decode.acc_seg: 99.6758 aux.loss_ce: 0.0089 aux.acc_seg: 99.2632 +04/17 05:32:54 - mmengine - INFO - Iter(train) [ 20600/160000] lr: 8.8451e-03 eta: 21:24:16 time: 0.5535 data_time: 0.0067 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0124 decode.acc_seg: 99.2835 aux.loss_ce: 0.0090 aux.acc_seg: 98.8653 +04/17 05:33:21 - mmengine - INFO - Iter(train) [ 20650/160000] lr: 8.8423e-03 eta: 21:23:48 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0113 decode.acc_seg: 99.5019 aux.loss_ce: 0.0089 aux.acc_seg: 99.1848 +04/17 05:33:49 - mmengine - INFO - Iter(train) [ 20700/160000] lr: 8.8395e-03 eta: 21:23:21 time: 0.5525 data_time: 0.0060 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0110 decode.acc_seg: 99.5225 aux.loss_ce: 0.0090 aux.acc_seg: 98.9757 +04/17 05:34:17 - mmengine - INFO - Iter(train) [ 20750/160000] lr: 8.8367e-03 eta: 21:22:54 time: 0.5647 data_time: 0.0066 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0122 decode.acc_seg: 99.4334 aux.loss_ce: 0.0093 aux.acc_seg: 98.9375 +04/17 05:34:44 - mmengine - INFO - Iter(train) [ 20800/160000] lr: 8.8338e-03 eta: 21:22:27 time: 0.5537 data_time: 0.0068 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0120 decode.acc_seg: 99.4299 aux.loss_ce: 0.0090 aux.acc_seg: 98.9567 +04/17 05:35:12 - mmengine - INFO - Iter(train) [ 20850/160000] lr: 8.8310e-03 eta: 21:22:00 time: 0.5523 data_time: 0.0063 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0122 decode.acc_seg: 99.6354 aux.loss_ce: 0.0091 aux.acc_seg: 99.2721 +04/17 05:35:40 - mmengine - INFO - Iter(train) [ 20900/160000] lr: 8.8282e-03 eta: 21:21:32 time: 0.5539 data_time: 0.0062 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0119 decode.acc_seg: 99.5650 aux.loss_ce: 0.0091 aux.acc_seg: 99.1774 +04/17 05:36:07 - mmengine - INFO - Iter(train) [ 20950/160000] lr: 8.8254e-03 eta: 21:21:05 time: 0.5537 data_time: 0.0059 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0109 decode.acc_seg: 99.6063 aux.loss_ce: 0.0087 aux.acc_seg: 99.1423 +04/17 05:36:35 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 05:36:35 - mmengine - INFO - Iter(train) [ 21000/160000] lr: 8.8225e-03 eta: 21:20:38 time: 0.5542 data_time: 0.0067 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0122 decode.acc_seg: 99.5399 aux.loss_ce: 0.0093 aux.acc_seg: 99.0988 +04/17 05:37:03 - mmengine - INFO - Iter(train) [ 21050/160000] lr: 8.8197e-03 eta: 21:20:11 time: 0.5540 data_time: 0.0061 memory: 7635 loss: 0.0228 decode.loss_ce: 0.0126 decode.acc_seg: 99.6324 aux.loss_ce: 0.0102 aux.acc_seg: 99.1165 +04/17 05:37:31 - mmengine - INFO - Iter(train) [ 21100/160000] lr: 8.8169e-03 eta: 21:19:44 time: 0.5532 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0107 decode.acc_seg: 99.5358 aux.loss_ce: 0.0085 aux.acc_seg: 99.0521 +04/17 05:37:58 - mmengine - INFO - Iter(train) [ 21150/160000] lr: 8.8141e-03 eta: 21:19:17 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0117 decode.acc_seg: 99.4616 aux.loss_ce: 0.0090 aux.acc_seg: 98.9782 +04/17 05:38:26 - mmengine - INFO - Iter(train) [ 21200/160000] lr: 8.8112e-03 eta: 21:18:49 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0270 decode.loss_ce: 0.0159 decode.acc_seg: 99.1541 aux.loss_ce: 0.0110 aux.acc_seg: 98.4802 +04/17 05:38:54 - mmengine - INFO - Iter(train) [ 21250/160000] lr: 8.8084e-03 eta: 21:18:22 time: 0.5553 data_time: 0.0061 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0121 decode.acc_seg: 99.5953 aux.loss_ce: 0.0095 aux.acc_seg: 99.0539 +04/17 05:39:21 - mmengine - INFO - Iter(train) [ 21300/160000] lr: 8.8056e-03 eta: 21:17:55 time: 0.5553 data_time: 0.0061 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.6044 aux.loss_ce: 0.0090 aux.acc_seg: 99.2778 +04/17 05:39:49 - mmengine - INFO - Iter(train) [ 21350/160000] lr: 8.8028e-03 eta: 21:17:28 time: 0.5537 data_time: 0.0060 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.4109 aux.loss_ce: 0.0089 aux.acc_seg: 98.8693 +04/17 05:40:17 - mmengine - INFO - Iter(train) [ 21400/160000] lr: 8.7999e-03 eta: 21:17:00 time: 0.5537 data_time: 0.0059 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0110 decode.acc_seg: 99.3356 aux.loss_ce: 0.0083 aux.acc_seg: 99.0127 +04/17 05:40:44 - mmengine - INFO - Iter(train) [ 21450/160000] lr: 8.7971e-03 eta: 21:16:33 time: 0.5529 data_time: 0.0061 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0123 decode.acc_seg: 99.6156 aux.loss_ce: 0.0097 aux.acc_seg: 99.1965 +04/17 05:41:12 - mmengine - INFO - Iter(train) [ 21500/160000] lr: 8.7943e-03 eta: 21:16:06 time: 0.5549 data_time: 0.0063 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0117 decode.acc_seg: 99.3603 aux.loss_ce: 0.0090 aux.acc_seg: 98.9705 +04/17 05:41:40 - mmengine - INFO - Iter(train) [ 21550/160000] lr: 8.7915e-03 eta: 21:15:38 time: 0.5531 data_time: 0.0071 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0107 decode.acc_seg: 99.6437 aux.loss_ce: 0.0082 aux.acc_seg: 99.3950 +04/17 05:42:08 - mmengine - INFO - Iter(train) [ 21600/160000] lr: 8.7886e-03 eta: 21:15:11 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0112 decode.acc_seg: 99.5418 aux.loss_ce: 0.0087 aux.acc_seg: 98.9607 +04/17 05:42:35 - mmengine - INFO - Iter(train) [ 21650/160000] lr: 8.7858e-03 eta: 21:14:44 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0113 decode.acc_seg: 99.5805 aux.loss_ce: 0.0088 aux.acc_seg: 99.2621 +04/17 05:43:03 - mmengine - INFO - Iter(train) [ 21700/160000] lr: 8.7830e-03 eta: 21:14:17 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0123 decode.acc_seg: 99.3257 aux.loss_ce: 0.0092 aux.acc_seg: 99.0567 +04/17 05:43:31 - mmengine - INFO - Iter(train) [ 21750/160000] lr: 8.7802e-03 eta: 21:13:49 time: 0.5529 data_time: 0.0064 memory: 7635 loss: 0.0232 decode.loss_ce: 0.0135 decode.acc_seg: 99.5781 aux.loss_ce: 0.0098 aux.acc_seg: 99.1523 +04/17 05:43:58 - mmengine - INFO - Iter(train) [ 21800/160000] lr: 8.7773e-03 eta: 21:13:22 time: 0.5537 data_time: 0.0060 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0123 decode.acc_seg: 99.3830 aux.loss_ce: 0.0089 aux.acc_seg: 99.0145 +04/17 05:44:26 - mmengine - INFO - Iter(train) [ 21850/160000] lr: 8.7745e-03 eta: 21:12:55 time: 0.5650 data_time: 0.0060 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.4530 aux.loss_ce: 0.0090 aux.acc_seg: 98.9358 +04/17 05:44:54 - mmengine - INFO - Iter(train) [ 21900/160000] lr: 8.7717e-03 eta: 21:12:28 time: 0.5548 data_time: 0.0068 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0112 decode.acc_seg: 99.6578 aux.loss_ce: 0.0088 aux.acc_seg: 99.1710 +04/17 05:45:22 - mmengine - INFO - Iter(train) [ 21950/160000] lr: 8.7689e-03 eta: 21:12:01 time: 0.5546 data_time: 0.0064 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0121 decode.acc_seg: 99.4195 aux.loss_ce: 0.0092 aux.acc_seg: 99.0110 +04/17 05:45:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 05:45:49 - mmengine - INFO - Iter(train) [ 22000/160000] lr: 8.7660e-03 eta: 21:11:34 time: 0.5539 data_time: 0.0056 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0111 decode.acc_seg: 99.5405 aux.loss_ce: 0.0087 aux.acc_seg: 99.0419 +04/17 05:46:17 - mmengine - INFO - Iter(train) [ 22050/160000] lr: 8.7632e-03 eta: 21:11:07 time: 0.5546 data_time: 0.0057 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.6042 aux.loss_ce: 0.0080 aux.acc_seg: 99.0553 +04/17 05:46:45 - mmengine - INFO - Iter(train) [ 22100/160000] lr: 8.7604e-03 eta: 21:10:40 time: 0.5547 data_time: 0.0064 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0124 decode.acc_seg: 99.5714 aux.loss_ce: 0.0095 aux.acc_seg: 99.1062 +04/17 05:47:13 - mmengine - INFO - Iter(train) [ 22150/160000] lr: 8.7576e-03 eta: 21:10:14 time: 0.5547 data_time: 0.0056 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0115 decode.acc_seg: 99.6792 aux.loss_ce: 0.0088 aux.acc_seg: 99.2495 +04/17 05:47:40 - mmengine - INFO - Iter(train) [ 22200/160000] lr: 8.7547e-03 eta: 21:09:46 time: 0.5549 data_time: 0.0063 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0103 decode.acc_seg: 99.4948 aux.loss_ce: 0.0088 aux.acc_seg: 98.9191 +04/17 05:48:08 - mmengine - INFO - Iter(train) [ 22250/160000] lr: 8.7519e-03 eta: 21:09:19 time: 0.5556 data_time: 0.0056 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0112 decode.acc_seg: 99.6146 aux.loss_ce: 0.0086 aux.acc_seg: 99.2919 +04/17 05:48:36 - mmengine - INFO - Iter(train) [ 22300/160000] lr: 8.7491e-03 eta: 21:08:52 time: 0.5558 data_time: 0.0060 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0122 decode.acc_seg: 99.5606 aux.loss_ce: 0.0096 aux.acc_seg: 99.0362 +04/17 05:49:04 - mmengine - INFO - Iter(train) [ 22350/160000] lr: 8.7463e-03 eta: 21:08:25 time: 0.5543 data_time: 0.0058 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0103 decode.acc_seg: 99.5120 aux.loss_ce: 0.0086 aux.acc_seg: 98.9896 +04/17 05:49:31 - mmengine - INFO - Iter(train) [ 22400/160000] lr: 8.7434e-03 eta: 21:07:58 time: 0.5532 data_time: 0.0065 memory: 7635 loss: 0.0224 decode.loss_ce: 0.0127 decode.acc_seg: 99.5862 aux.loss_ce: 0.0098 aux.acc_seg: 99.1762 +04/17 05:49:59 - mmengine - INFO - Iter(train) [ 22450/160000] lr: 8.7406e-03 eta: 21:07:31 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0102 decode.acc_seg: 99.6047 aux.loss_ce: 0.0083 aux.acc_seg: 99.0493 +04/17 05:50:27 - mmengine - INFO - Iter(train) [ 22500/160000] lr: 8.7378e-03 eta: 21:07:03 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0120 decode.acc_seg: 99.4661 aux.loss_ce: 0.0087 aux.acc_seg: 98.9499 +04/17 05:50:54 - mmengine - INFO - Iter(train) [ 22550/160000] lr: 8.7350e-03 eta: 21:06:36 time: 0.5536 data_time: 0.0059 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0099 decode.acc_seg: 99.5499 aux.loss_ce: 0.0080 aux.acc_seg: 99.1887 +04/17 05:51:22 - mmengine - INFO - Iter(train) [ 22600/160000] lr: 8.7321e-03 eta: 21:06:09 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0111 decode.acc_seg: 99.5870 aux.loss_ce: 0.0086 aux.acc_seg: 99.2036 +04/17 05:51:50 - mmengine - INFO - Iter(train) [ 22650/160000] lr: 8.7293e-03 eta: 21:05:41 time: 0.5551 data_time: 0.0064 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0111 decode.acc_seg: 99.6707 aux.loss_ce: 0.0086 aux.acc_seg: 99.2799 +04/17 05:52:18 - mmengine - INFO - Iter(train) [ 22700/160000] lr: 8.7265e-03 eta: 21:05:14 time: 0.5533 data_time: 0.0059 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0124 decode.acc_seg: 99.7109 aux.loss_ce: 0.0094 aux.acc_seg: 99.4370 +04/17 05:52:45 - mmengine - INFO - Iter(train) [ 22750/160000] lr: 8.7236e-03 eta: 21:04:46 time: 0.5532 data_time: 0.0059 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0112 decode.acc_seg: 99.5122 aux.loss_ce: 0.0094 aux.acc_seg: 99.0606 +04/17 05:53:13 - mmengine - INFO - Iter(train) [ 22800/160000] lr: 8.7208e-03 eta: 21:04:19 time: 0.5537 data_time: 0.0066 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0115 decode.acc_seg: 99.4382 aux.loss_ce: 0.0092 aux.acc_seg: 99.0163 +04/17 05:53:41 - mmengine - INFO - Iter(train) [ 22850/160000] lr: 8.7180e-03 eta: 21:03:52 time: 0.5538 data_time: 0.0064 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0120 decode.acc_seg: 99.6519 aux.loss_ce: 0.0095 aux.acc_seg: 99.1600 +04/17 05:54:08 - mmengine - INFO - Iter(train) [ 22900/160000] lr: 8.7152e-03 eta: 21:03:24 time: 0.5537 data_time: 0.0058 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0110 decode.acc_seg: 99.4405 aux.loss_ce: 0.0090 aux.acc_seg: 98.8873 +04/17 05:54:36 - mmengine - INFO - Iter(train) [ 22950/160000] lr: 8.7123e-03 eta: 21:02:57 time: 0.5542 data_time: 0.0071 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0121 decode.acc_seg: 99.5530 aux.loss_ce: 0.0095 aux.acc_seg: 98.8122 +04/17 05:55:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 05:55:04 - mmengine - INFO - Iter(train) [ 23000/160000] lr: 8.7095e-03 eta: 21:02:30 time: 0.5547 data_time: 0.0071 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.4958 aux.loss_ce: 0.0087 aux.acc_seg: 99.2250 +04/17 05:55:31 - mmengine - INFO - Iter(train) [ 23050/160000] lr: 8.7067e-03 eta: 21:02:02 time: 0.5531 data_time: 0.0063 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0109 decode.acc_seg: 99.6135 aux.loss_ce: 0.0088 aux.acc_seg: 99.1367 +04/17 05:55:59 - mmengine - INFO - Iter(train) [ 23100/160000] lr: 8.7038e-03 eta: 21:01:35 time: 0.5545 data_time: 0.0063 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0101 decode.acc_seg: 99.5773 aux.loss_ce: 0.0084 aux.acc_seg: 99.1000 +04/17 05:56:27 - mmengine - INFO - Iter(train) [ 23150/160000] lr: 8.7010e-03 eta: 21:01:08 time: 0.5630 data_time: 0.0062 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0106 decode.acc_seg: 99.6355 aux.loss_ce: 0.0087 aux.acc_seg: 99.1062 +04/17 05:56:55 - mmengine - INFO - Iter(train) [ 23200/160000] lr: 8.6982e-03 eta: 21:00:41 time: 0.5541 data_time: 0.0068 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0109 decode.acc_seg: 99.6552 aux.loss_ce: 0.0089 aux.acc_seg: 99.1572 +04/17 05:57:22 - mmengine - INFO - Iter(train) [ 23250/160000] lr: 8.6954e-03 eta: 21:00:14 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0116 decode.acc_seg: 99.3690 aux.loss_ce: 0.0093 aux.acc_seg: 99.0586 +04/17 05:57:50 - mmengine - INFO - Iter(train) [ 23300/160000] lr: 8.6925e-03 eta: 20:59:46 time: 0.5526 data_time: 0.0058 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0120 decode.acc_seg: 99.5623 aux.loss_ce: 0.0086 aux.acc_seg: 99.2438 +04/17 05:58:18 - mmengine - INFO - Iter(train) [ 23350/160000] lr: 8.6897e-03 eta: 20:59:19 time: 0.5544 data_time: 0.0068 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0101 decode.acc_seg: 99.4234 aux.loss_ce: 0.0083 aux.acc_seg: 98.9252 +04/17 05:58:45 - mmengine - INFO - Iter(train) [ 23400/160000] lr: 8.6869e-03 eta: 20:58:51 time: 0.5530 data_time: 0.0062 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0115 decode.acc_seg: 99.4738 aux.loss_ce: 0.0093 aux.acc_seg: 99.0345 +04/17 05:59:13 - mmengine - INFO - Iter(train) [ 23450/160000] lr: 8.6840e-03 eta: 20:58:24 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0113 decode.acc_seg: 99.6345 aux.loss_ce: 0.0085 aux.acc_seg: 99.2324 +04/17 05:59:41 - mmengine - INFO - Iter(train) [ 23500/160000] lr: 8.6812e-03 eta: 20:57:56 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0103 decode.acc_seg: 99.5246 aux.loss_ce: 0.0084 aux.acc_seg: 99.1241 +04/17 06:00:08 - mmengine - INFO - Iter(train) [ 23550/160000] lr: 8.6784e-03 eta: 20:57:29 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0108 decode.acc_seg: 99.6054 aux.loss_ce: 0.0088 aux.acc_seg: 99.2346 +04/17 06:00:36 - mmengine - INFO - Iter(train) [ 23600/160000] lr: 8.6756e-03 eta: 20:57:02 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0121 decode.acc_seg: 99.5667 aux.loss_ce: 0.0092 aux.acc_seg: 99.2044 +04/17 06:01:04 - mmengine - INFO - Iter(train) [ 23650/160000] lr: 8.6727e-03 eta: 20:56:34 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0121 decode.acc_seg: 99.3974 aux.loss_ce: 0.0092 aux.acc_seg: 98.9027 +04/17 06:01:32 - mmengine - INFO - Iter(train) [ 23700/160000] lr: 8.6699e-03 eta: 20:56:07 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0122 decode.acc_seg: 99.4102 aux.loss_ce: 0.0094 aux.acc_seg: 98.7565 +04/17 06:01:59 - mmengine - INFO - Iter(train) [ 23750/160000] lr: 8.6671e-03 eta: 20:55:40 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0095 decode.acc_seg: 99.7561 aux.loss_ce: 0.0077 aux.acc_seg: 99.5364 +04/17 06:02:27 - mmengine - INFO - Iter(train) [ 23800/160000] lr: 8.6642e-03 eta: 20:55:13 time: 0.5564 data_time: 0.0062 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.7054 aux.loss_ce: 0.0081 aux.acc_seg: 99.1726 +04/17 06:02:55 - mmengine - INFO - Iter(train) [ 23850/160000] lr: 8.6614e-03 eta: 20:54:45 time: 0.5557 data_time: 0.0070 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0106 decode.acc_seg: 99.5234 aux.loss_ce: 0.0084 aux.acc_seg: 99.0002 +04/17 06:03:22 - mmengine - INFO - Iter(train) [ 23900/160000] lr: 8.6586e-03 eta: 20:54:18 time: 0.5536 data_time: 0.0067 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0127 decode.acc_seg: 99.5132 aux.loss_ce: 0.0095 aux.acc_seg: 98.9788 +04/17 06:03:50 - mmengine - INFO - Iter(train) [ 23950/160000] lr: 8.6558e-03 eta: 20:53:51 time: 0.5545 data_time: 0.0057 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0115 decode.acc_seg: 99.4175 aux.loss_ce: 0.0090 aux.acc_seg: 98.9756 +04/17 06:04:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 06:04:18 - mmengine - INFO - Iter(train) [ 24000/160000] lr: 8.6529e-03 eta: 20:53:24 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0121 decode.acc_seg: 99.4178 aux.loss_ce: 0.0094 aux.acc_seg: 98.9408 +04/17 06:04:46 - mmengine - INFO - Iter(train) [ 24050/160000] lr: 8.6501e-03 eta: 20:52:56 time: 0.5557 data_time: 0.0064 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0116 decode.acc_seg: 99.5782 aux.loss_ce: 0.0091 aux.acc_seg: 99.3015 +04/17 06:05:13 - mmengine - INFO - Iter(train) [ 24100/160000] lr: 8.6473e-03 eta: 20:52:29 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0113 decode.acc_seg: 99.6398 aux.loss_ce: 0.0091 aux.acc_seg: 99.2044 +04/17 06:05:41 - mmengine - INFO - Iter(train) [ 24150/160000] lr: 8.6444e-03 eta: 20:52:02 time: 0.5551 data_time: 0.0065 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0110 decode.acc_seg: 99.5223 aux.loss_ce: 0.0088 aux.acc_seg: 99.1008 +04/17 06:06:09 - mmengine - INFO - Iter(train) [ 24200/160000] lr: 8.6416e-03 eta: 20:51:34 time: 0.5539 data_time: 0.0060 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0109 decode.acc_seg: 99.4917 aux.loss_ce: 0.0086 aux.acc_seg: 99.1086 +04/17 06:06:36 - mmengine - INFO - Iter(train) [ 24250/160000] lr: 8.6388e-03 eta: 20:51:07 time: 0.5524 data_time: 0.0058 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.6735 aux.loss_ce: 0.0082 aux.acc_seg: 99.2729 +04/17 06:07:04 - mmengine - INFO - Iter(train) [ 24300/160000] lr: 8.6359e-03 eta: 20:50:40 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0126 decode.acc_seg: 99.5812 aux.loss_ce: 0.0097 aux.acc_seg: 99.1949 +04/17 06:07:32 - mmengine - INFO - Iter(train) [ 24350/160000] lr: 8.6331e-03 eta: 20:50:12 time: 0.5541 data_time: 0.0060 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0113 decode.acc_seg: 99.5622 aux.loss_ce: 0.0090 aux.acc_seg: 99.1330 +04/17 06:08:00 - mmengine - INFO - Iter(train) [ 24400/160000] lr: 8.6303e-03 eta: 20:49:45 time: 0.5535 data_time: 0.0059 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0115 decode.acc_seg: 99.6341 aux.loss_ce: 0.0093 aux.acc_seg: 99.1335 +04/17 06:08:27 - mmengine - INFO - Iter(train) [ 24450/160000] lr: 8.6275e-03 eta: 20:49:17 time: 0.5531 data_time: 0.0063 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0108 decode.acc_seg: 99.5450 aux.loss_ce: 0.0087 aux.acc_seg: 98.9058 +04/17 06:08:55 - mmengine - INFO - Iter(train) [ 24500/160000] lr: 8.6246e-03 eta: 20:48:50 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.5281 aux.loss_ce: 0.0087 aux.acc_seg: 99.0461 +04/17 06:09:23 - mmengine - INFO - Iter(train) [ 24550/160000] lr: 8.6218e-03 eta: 20:48:22 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0106 decode.acc_seg: 99.5327 aux.loss_ce: 0.0087 aux.acc_seg: 98.9229 +04/17 06:09:50 - mmengine - INFO - Iter(train) [ 24600/160000] lr: 8.6190e-03 eta: 20:47:55 time: 0.5550 data_time: 0.0068 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.4426 aux.loss_ce: 0.0084 aux.acc_seg: 98.8665 +04/17 06:10:18 - mmengine - INFO - Iter(train) [ 24650/160000] lr: 8.6161e-03 eta: 20:47:27 time: 0.5545 data_time: 0.0057 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0107 decode.acc_seg: 99.5864 aux.loss_ce: 0.0085 aux.acc_seg: 99.0477 +04/17 06:10:46 - mmengine - INFO - Iter(train) [ 24700/160000] lr: 8.6133e-03 eta: 20:47:00 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.6146 aux.loss_ce: 0.0090 aux.acc_seg: 99.0986 +04/17 06:11:13 - mmengine - INFO - Iter(train) [ 24750/160000] lr: 8.6105e-03 eta: 20:46:33 time: 0.5554 data_time: 0.0066 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0093 decode.acc_seg: 99.5455 aux.loss_ce: 0.0078 aux.acc_seg: 99.0612 +04/17 06:11:41 - mmengine - INFO - Iter(train) [ 24800/160000] lr: 8.6076e-03 eta: 20:46:06 time: 0.5547 data_time: 0.0059 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0116 decode.acc_seg: 99.6256 aux.loss_ce: 0.0093 aux.acc_seg: 99.1723 +04/17 06:12:09 - mmengine - INFO - Iter(train) [ 24850/160000] lr: 8.6048e-03 eta: 20:45:38 time: 0.5528 data_time: 0.0058 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0107 decode.acc_seg: 99.5779 aux.loss_ce: 0.0085 aux.acc_seg: 98.9428 +04/17 06:12:36 - mmengine - INFO - Iter(train) [ 24900/160000] lr: 8.6020e-03 eta: 20:45:11 time: 0.5531 data_time: 0.0060 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0108 decode.acc_seg: 99.2304 aux.loss_ce: 0.0086 aux.acc_seg: 98.9576 +04/17 06:13:04 - mmengine - INFO - Iter(train) [ 24950/160000] lr: 8.5991e-03 eta: 20:44:43 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0117 decode.acc_seg: 99.5942 aux.loss_ce: 0.0092 aux.acc_seg: 99.1375 +04/17 06:13:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 06:13:32 - mmengine - INFO - Iter(train) [ 25000/160000] lr: 8.5963e-03 eta: 20:44:15 time: 0.5556 data_time: 0.0065 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0120 decode.acc_seg: 99.3312 aux.loss_ce: 0.0088 aux.acc_seg: 99.1397 +04/17 06:13:59 - mmengine - INFO - Iter(train) [ 25050/160000] lr: 8.5935e-03 eta: 20:43:48 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0107 decode.acc_seg: 99.4274 aux.loss_ce: 0.0089 aux.acc_seg: 98.9287 +04/17 06:14:27 - mmengine - INFO - Iter(train) [ 25100/160000] lr: 8.5906e-03 eta: 20:43:21 time: 0.5545 data_time: 0.0065 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0110 decode.acc_seg: 99.4225 aux.loss_ce: 0.0086 aux.acc_seg: 98.9243 +04/17 06:14:55 - mmengine - INFO - Iter(train) [ 25150/160000] lr: 8.5878e-03 eta: 20:42:53 time: 0.5540 data_time: 0.0063 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0101 decode.acc_seg: 99.5281 aux.loss_ce: 0.0083 aux.acc_seg: 99.1858 +04/17 06:15:23 - mmengine - INFO - Iter(train) [ 25200/160000] lr: 8.5850e-03 eta: 20:42:26 time: 0.5523 data_time: 0.0063 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0106 decode.acc_seg: 99.6846 aux.loss_ce: 0.0087 aux.acc_seg: 99.2772 +04/17 06:15:50 - mmengine - INFO - Iter(train) [ 25250/160000] lr: 8.5821e-03 eta: 20:41:58 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0118 decode.acc_seg: 99.4268 aux.loss_ce: 0.0091 aux.acc_seg: 99.0958 +04/17 06:16:18 - mmengine - INFO - Iter(train) [ 25300/160000] lr: 8.5793e-03 eta: 20:41:31 time: 0.5526 data_time: 0.0061 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0115 decode.acc_seg: 99.4090 aux.loss_ce: 0.0092 aux.acc_seg: 98.9176 +04/17 06:16:46 - mmengine - INFO - Iter(train) [ 25350/160000] lr: 8.5765e-03 eta: 20:41:04 time: 0.5528 data_time: 0.0065 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0104 decode.acc_seg: 99.6112 aux.loss_ce: 0.0084 aux.acc_seg: 99.1398 +04/17 06:17:13 - mmengine - INFO - Iter(train) [ 25400/160000] lr: 8.5736e-03 eta: 20:40:36 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.6025 aux.loss_ce: 0.0086 aux.acc_seg: 99.0537 +04/17 06:17:41 - mmengine - INFO - Iter(train) [ 25450/160000] lr: 8.5708e-03 eta: 20:40:09 time: 0.5544 data_time: 0.0061 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0114 decode.acc_seg: 99.5256 aux.loss_ce: 0.0092 aux.acc_seg: 98.8937 +04/17 06:18:09 - mmengine - INFO - Iter(train) [ 25500/160000] lr: 8.5680e-03 eta: 20:39:41 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0103 decode.acc_seg: 99.5145 aux.loss_ce: 0.0088 aux.acc_seg: 98.8540 +04/17 06:18:36 - mmengine - INFO - Iter(train) [ 25550/160000] lr: 8.5651e-03 eta: 20:39:14 time: 0.5524 data_time: 0.0066 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0096 decode.acc_seg: 99.6950 aux.loss_ce: 0.0080 aux.acc_seg: 99.2313 +04/17 06:19:04 - mmengine - INFO - Iter(train) [ 25600/160000] lr: 8.5623e-03 eta: 20:38:46 time: 0.5530 data_time: 0.0057 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0101 decode.acc_seg: 99.5444 aux.loss_ce: 0.0082 aux.acc_seg: 99.2862 +04/17 06:19:32 - mmengine - INFO - Iter(train) [ 25650/160000] lr: 8.5595e-03 eta: 20:38:19 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0101 decode.acc_seg: 99.7309 aux.loss_ce: 0.0086 aux.acc_seg: 99.2523 +04/17 06:19:59 - mmengine - INFO - Iter(train) [ 25700/160000] lr: 8.5566e-03 eta: 20:37:51 time: 0.5544 data_time: 0.0076 memory: 7635 loss: 0.0254 decode.loss_ce: 0.0151 decode.acc_seg: 99.5668 aux.loss_ce: 0.0102 aux.acc_seg: 98.9284 +04/17 06:20:27 - mmengine - INFO - Iter(train) [ 25750/160000] lr: 8.5538e-03 eta: 20:37:24 time: 0.5540 data_time: 0.0059 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0110 decode.acc_seg: 99.4843 aux.loss_ce: 0.0089 aux.acc_seg: 98.9662 +04/17 06:20:55 - mmengine - INFO - Iter(train) [ 25800/160000] lr: 8.5510e-03 eta: 20:36:56 time: 0.5533 data_time: 0.0060 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0112 decode.acc_seg: 99.5192 aux.loss_ce: 0.0094 aux.acc_seg: 98.9622 +04/17 06:21:22 - mmengine - INFO - Iter(train) [ 25850/160000] lr: 8.5481e-03 eta: 20:36:28 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0118 decode.acc_seg: 99.5270 aux.loss_ce: 0.0091 aux.acc_seg: 99.0009 +04/17 06:21:50 - mmengine - INFO - Iter(train) [ 25900/160000] lr: 8.5453e-03 eta: 20:36:01 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0118 decode.acc_seg: 99.5065 aux.loss_ce: 0.0096 aux.acc_seg: 99.0421 +04/17 06:22:18 - mmengine - INFO - Iter(train) [ 25950/160000] lr: 8.5425e-03 eta: 20:35:33 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0119 decode.acc_seg: 99.4792 aux.loss_ce: 0.0093 aux.acc_seg: 98.8772 +04/17 06:22:45 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 06:22:45 - mmengine - INFO - Iter(train) [ 26000/160000] lr: 8.5396e-03 eta: 20:35:05 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0112 decode.acc_seg: 99.6405 aux.loss_ce: 0.0090 aux.acc_seg: 99.1919 +04/17 06:23:13 - mmengine - INFO - Iter(train) [ 26050/160000] lr: 8.5368e-03 eta: 20:34:38 time: 0.5534 data_time: 0.0057 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0111 decode.acc_seg: 99.5835 aux.loss_ce: 0.0084 aux.acc_seg: 99.1900 +04/17 06:23:41 - mmengine - INFO - Iter(train) [ 26100/160000] lr: 8.5340e-03 eta: 20:34:10 time: 0.5538 data_time: 0.0060 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0116 decode.acc_seg: 99.5837 aux.loss_ce: 0.0091 aux.acc_seg: 99.0800 +04/17 06:24:09 - mmengine - INFO - Iter(train) [ 26150/160000] lr: 8.5311e-03 eta: 20:33:43 time: 0.5633 data_time: 0.0062 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0124 decode.acc_seg: 99.5199 aux.loss_ce: 0.0094 aux.acc_seg: 98.9968 +04/17 06:24:36 - mmengine - INFO - Iter(train) [ 26200/160000] lr: 8.5283e-03 eta: 20:33:16 time: 0.5547 data_time: 0.0062 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0117 decode.acc_seg: 99.6347 aux.loss_ce: 0.0090 aux.acc_seg: 99.1317 +04/17 06:25:04 - mmengine - INFO - Iter(train) [ 26250/160000] lr: 8.5255e-03 eta: 20:32:48 time: 0.5541 data_time: 0.0064 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0119 decode.acc_seg: 99.5460 aux.loss_ce: 0.0098 aux.acc_seg: 99.1327 +04/17 06:25:32 - mmengine - INFO - Iter(train) [ 26300/160000] lr: 8.5226e-03 eta: 20:32:21 time: 0.5540 data_time: 0.0070 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0107 decode.acc_seg: 99.5099 aux.loss_ce: 0.0088 aux.acc_seg: 98.8997 +04/17 06:25:59 - mmengine - INFO - Iter(train) [ 26350/160000] lr: 8.5198e-03 eta: 20:31:53 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0117 decode.acc_seg: 99.4177 aux.loss_ce: 0.0094 aux.acc_seg: 99.0552 +04/17 06:26:27 - mmengine - INFO - Iter(train) [ 26400/160000] lr: 8.5170e-03 eta: 20:31:26 time: 0.5544 data_time: 0.0063 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0107 decode.acc_seg: 99.5645 aux.loss_ce: 0.0089 aux.acc_seg: 99.2006 +04/17 06:26:55 - mmengine - INFO - Iter(train) [ 26450/160000] lr: 8.5141e-03 eta: 20:30:59 time: 0.5534 data_time: 0.0060 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0111 decode.acc_seg: 99.5743 aux.loss_ce: 0.0091 aux.acc_seg: 99.2047 +04/17 06:27:22 - mmengine - INFO - Iter(train) [ 26500/160000] lr: 8.5113e-03 eta: 20:30:31 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0113 decode.acc_seg: 99.6210 aux.loss_ce: 0.0085 aux.acc_seg: 99.0860 +04/17 06:27:50 - mmengine - INFO - Iter(train) [ 26550/160000] lr: 8.5085e-03 eta: 20:30:04 time: 0.5527 data_time: 0.0059 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0113 decode.acc_seg: 99.4643 aux.loss_ce: 0.0091 aux.acc_seg: 98.9594 +04/17 06:28:18 - mmengine - INFO - Iter(train) [ 26600/160000] lr: 8.5056e-03 eta: 20:29:36 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0116 decode.acc_seg: 99.3349 aux.loss_ce: 0.0090 aux.acc_seg: 99.0543 +04/17 06:28:45 - mmengine - INFO - Iter(train) [ 26650/160000] lr: 8.5028e-03 eta: 20:29:08 time: 0.5529 data_time: 0.0061 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0110 decode.acc_seg: 99.2491 aux.loss_ce: 0.0087 aux.acc_seg: 98.7731 +04/17 06:29:13 - mmengine - INFO - Iter(train) [ 26700/160000] lr: 8.5000e-03 eta: 20:28:41 time: 0.5536 data_time: 0.0070 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0102 decode.acc_seg: 99.4468 aux.loss_ce: 0.0081 aux.acc_seg: 99.0105 +04/17 06:29:41 - mmengine - INFO - Iter(train) [ 26750/160000] lr: 8.4971e-03 eta: 20:28:13 time: 0.5538 data_time: 0.0060 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0115 decode.acc_seg: 99.6280 aux.loss_ce: 0.0092 aux.acc_seg: 99.2716 +04/17 06:30:08 - mmengine - INFO - Iter(train) [ 26800/160000] lr: 8.4943e-03 eta: 20:27:46 time: 0.5540 data_time: 0.0070 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0100 decode.acc_seg: 99.6223 aux.loss_ce: 0.0087 aux.acc_seg: 99.2036 +04/17 06:30:36 - mmengine - INFO - Iter(train) [ 26850/160000] lr: 8.4914e-03 eta: 20:27:18 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0111 decode.acc_seg: 99.6864 aux.loss_ce: 0.0085 aux.acc_seg: 99.2263 +04/17 06:31:04 - mmengine - INFO - Iter(train) [ 26900/160000] lr: 8.4886e-03 eta: 20:26:51 time: 0.5534 data_time: 0.0067 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0113 decode.acc_seg: 99.5808 aux.loss_ce: 0.0092 aux.acc_seg: 99.0973 +04/17 06:31:31 - mmengine - INFO - Iter(train) [ 26950/160000] lr: 8.4858e-03 eta: 20:26:23 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0110 decode.acc_seg: 99.6390 aux.loss_ce: 0.0091 aux.acc_seg: 99.2087 +04/17 06:31:59 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 06:31:59 - mmengine - INFO - Iter(train) [ 27000/160000] lr: 8.4829e-03 eta: 20:25:55 time: 0.5525 data_time: 0.0058 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0101 decode.acc_seg: 99.5855 aux.loss_ce: 0.0080 aux.acc_seg: 99.1312 +04/17 06:32:27 - mmengine - INFO - Iter(train) [ 27050/160000] lr: 8.4801e-03 eta: 20:25:28 time: 0.5531 data_time: 0.0059 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.6612 aux.loss_ce: 0.0082 aux.acc_seg: 99.3042 +04/17 06:32:54 - mmengine - INFO - Iter(train) [ 27100/160000] lr: 8.4773e-03 eta: 20:25:00 time: 0.5542 data_time: 0.0060 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0117 decode.acc_seg: 99.5104 aux.loss_ce: 0.0090 aux.acc_seg: 99.0363 +04/17 06:33:22 - mmengine - INFO - Iter(train) [ 27150/160000] lr: 8.4744e-03 eta: 20:24:32 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0106 decode.acc_seg: 99.5104 aux.loss_ce: 0.0087 aux.acc_seg: 98.9560 +04/17 06:33:50 - mmengine - INFO - Iter(train) [ 27200/160000] lr: 8.4716e-03 eta: 20:24:06 time: 0.5650 data_time: 0.0064 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0111 decode.acc_seg: 99.5119 aux.loss_ce: 0.0084 aux.acc_seg: 99.2408 +04/17 06:34:18 - mmengine - INFO - Iter(train) [ 27250/160000] lr: 8.4688e-03 eta: 20:23:38 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0113 decode.acc_seg: 99.5870 aux.loss_ce: 0.0088 aux.acc_seg: 99.2472 +04/17 06:34:45 - mmengine - INFO - Iter(train) [ 27300/160000] lr: 8.4659e-03 eta: 20:23:10 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0243 decode.loss_ce: 0.0145 decode.acc_seg: 99.5888 aux.loss_ce: 0.0098 aux.acc_seg: 99.1717 +04/17 06:35:13 - mmengine - INFO - Iter(train) [ 27350/160000] lr: 8.4631e-03 eta: 20:22:43 time: 0.5542 data_time: 0.0064 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.4240 aux.loss_ce: 0.0089 aux.acc_seg: 98.7696 +04/17 06:35:41 - mmengine - INFO - Iter(train) [ 27400/160000] lr: 8.4602e-03 eta: 20:22:16 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.5619 aux.loss_ce: 0.0087 aux.acc_seg: 99.0899 +04/17 06:36:08 - mmengine - INFO - Iter(train) [ 27450/160000] lr: 8.4574e-03 eta: 20:21:48 time: 0.5526 data_time: 0.0062 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0111 decode.acc_seg: 99.4605 aux.loss_ce: 0.0087 aux.acc_seg: 99.0443 +04/17 06:36:36 - mmengine - INFO - Iter(train) [ 27500/160000] lr: 8.4546e-03 eta: 20:21:21 time: 0.5548 data_time: 0.0059 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0103 decode.acc_seg: 99.5278 aux.loss_ce: 0.0082 aux.acc_seg: 99.1844 +04/17 06:37:04 - mmengine - INFO - Iter(train) [ 27550/160000] lr: 8.4517e-03 eta: 20:20:54 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0107 decode.acc_seg: 99.6335 aux.loss_ce: 0.0086 aux.acc_seg: 99.1250 +04/17 06:37:32 - mmengine - INFO - Iter(train) [ 27600/160000] lr: 8.4489e-03 eta: 20:20:26 time: 0.5542 data_time: 0.0056 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0106 decode.acc_seg: 99.6546 aux.loss_ce: 0.0090 aux.acc_seg: 99.0662 +04/17 06:37:59 - mmengine - INFO - Iter(train) [ 27650/160000] lr: 8.4461e-03 eta: 20:19:58 time: 0.5531 data_time: 0.0063 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.5378 aux.loss_ce: 0.0085 aux.acc_seg: 99.1428 +04/17 06:38:27 - mmengine - INFO - Iter(train) [ 27700/160000] lr: 8.4432e-03 eta: 20:19:31 time: 0.5529 data_time: 0.0064 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0095 decode.acc_seg: 99.5130 aux.loss_ce: 0.0080 aux.acc_seg: 99.1068 +04/17 06:38:54 - mmengine - INFO - Iter(train) [ 27750/160000] lr: 8.4404e-03 eta: 20:19:03 time: 0.5516 data_time: 0.0058 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0110 decode.acc_seg: 99.6272 aux.loss_ce: 0.0089 aux.acc_seg: 99.2136 +04/17 06:39:22 - mmengine - INFO - Iter(train) [ 27800/160000] lr: 8.4375e-03 eta: 20:18:36 time: 0.5553 data_time: 0.0067 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0109 decode.acc_seg: 99.6243 aux.loss_ce: 0.0084 aux.acc_seg: 99.2695 +04/17 06:39:50 - mmengine - INFO - Iter(train) [ 27850/160000] lr: 8.4347e-03 eta: 20:18:08 time: 0.5550 data_time: 0.0058 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0099 decode.acc_seg: 99.5437 aux.loss_ce: 0.0082 aux.acc_seg: 98.9361 +04/17 06:40:18 - mmengine - INFO - Iter(train) [ 27900/160000] lr: 8.4319e-03 eta: 20:17:41 time: 0.5543 data_time: 0.0062 memory: 7635 loss: 0.0231 decode.loss_ce: 0.0128 decode.acc_seg: 99.5472 aux.loss_ce: 0.0103 aux.acc_seg: 98.9493 +04/17 06:40:45 - mmengine - INFO - Iter(train) [ 27950/160000] lr: 8.4290e-03 eta: 20:17:13 time: 0.5528 data_time: 0.0062 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0119 decode.acc_seg: 99.5675 aux.loss_ce: 0.0094 aux.acc_seg: 99.0839 +04/17 06:41:13 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 06:41:13 - mmengine - INFO - Iter(train) [ 28000/160000] lr: 8.4262e-03 eta: 20:16:46 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0100 decode.acc_seg: 99.6709 aux.loss_ce: 0.0083 aux.acc_seg: 99.2907 +04/17 06:41:41 - mmengine - INFO - Iter(train) [ 28050/160000] lr: 8.4233e-03 eta: 20:16:18 time: 0.5533 data_time: 0.0057 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0109 decode.acc_seg: 99.4734 aux.loss_ce: 0.0087 aux.acc_seg: 99.1693 +04/17 06:42:08 - mmengine - INFO - Iter(train) [ 28100/160000] lr: 8.4205e-03 eta: 20:15:50 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0112 decode.acc_seg: 99.4831 aux.loss_ce: 0.0089 aux.acc_seg: 99.2082 +04/17 06:42:36 - mmengine - INFO - Iter(train) [ 28150/160000] lr: 8.4177e-03 eta: 20:15:23 time: 0.5535 data_time: 0.0071 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0109 decode.acc_seg: 99.6026 aux.loss_ce: 0.0087 aux.acc_seg: 99.0334 +04/17 06:43:04 - mmengine - INFO - Iter(train) [ 28200/160000] lr: 8.4148e-03 eta: 20:14:55 time: 0.5538 data_time: 0.0064 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0110 decode.acc_seg: 99.6615 aux.loss_ce: 0.0088 aux.acc_seg: 99.2807 +04/17 06:43:31 - mmengine - INFO - Iter(train) [ 28250/160000] lr: 8.4120e-03 eta: 20:14:28 time: 0.5542 data_time: 0.0056 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.4784 aux.loss_ce: 0.0091 aux.acc_seg: 98.9578 +04/17 06:43:59 - mmengine - INFO - Iter(train) [ 28300/160000] lr: 8.4092e-03 eta: 20:14:00 time: 0.5630 data_time: 0.0059 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0122 decode.acc_seg: 99.3261 aux.loss_ce: 0.0095 aux.acc_seg: 98.7772 +04/17 06:44:27 - mmengine - INFO - Iter(train) [ 28350/160000] lr: 8.4063e-03 eta: 20:13:33 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0128 decode.acc_seg: 99.5937 aux.loss_ce: 0.0098 aux.acc_seg: 99.0811 +04/17 06:44:54 - mmengine - INFO - Iter(train) [ 28400/160000] lr: 8.4035e-03 eta: 20:13:05 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.6407 aux.loss_ce: 0.0086 aux.acc_seg: 99.3244 +04/17 06:45:22 - mmengine - INFO - Iter(train) [ 28450/160000] lr: 8.4006e-03 eta: 20:12:38 time: 0.5531 data_time: 0.0057 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0114 decode.acc_seg: 99.5809 aux.loss_ce: 0.0092 aux.acc_seg: 99.1844 +04/17 06:45:50 - mmengine - INFO - Iter(train) [ 28500/160000] lr: 8.3978e-03 eta: 20:12:10 time: 0.5526 data_time: 0.0062 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0111 decode.acc_seg: 99.4272 aux.loss_ce: 0.0088 aux.acc_seg: 98.8027 +04/17 06:46:17 - mmengine - INFO - Iter(train) [ 28550/160000] lr: 8.3950e-03 eta: 20:11:43 time: 0.5533 data_time: 0.0069 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.6221 aux.loss_ce: 0.0087 aux.acc_seg: 99.0142 +04/17 06:46:45 - mmengine - INFO - Iter(train) [ 28600/160000] lr: 8.3921e-03 eta: 20:11:15 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.6454 aux.loss_ce: 0.0081 aux.acc_seg: 99.0096 +04/17 06:47:13 - mmengine - INFO - Iter(train) [ 28650/160000] lr: 8.3893e-03 eta: 20:10:47 time: 0.5518 data_time: 0.0060 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.5618 aux.loss_ce: 0.0084 aux.acc_seg: 99.2650 +04/17 06:47:40 - mmengine - INFO - Iter(train) [ 28700/160000] lr: 8.3864e-03 eta: 20:10:20 time: 0.5548 data_time: 0.0058 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0126 decode.acc_seg: 99.3545 aux.loss_ce: 0.0096 aux.acc_seg: 98.7711 +04/17 06:48:08 - mmengine - INFO - Iter(train) [ 28750/160000] lr: 8.3836e-03 eta: 20:09:52 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0120 decode.acc_seg: 99.5036 aux.loss_ce: 0.0092 aux.acc_seg: 99.0538 +04/17 06:48:36 - mmengine - INFO - Iter(train) [ 28800/160000] lr: 8.3808e-03 eta: 20:09:25 time: 0.5541 data_time: 0.0058 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.5505 aux.loss_ce: 0.0087 aux.acc_seg: 98.8218 +04/17 06:49:04 - mmengine - INFO - Iter(train) [ 28850/160000] lr: 8.3779e-03 eta: 20:08:57 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0098 decode.acc_seg: 99.6283 aux.loss_ce: 0.0080 aux.acc_seg: 99.1926 +04/17 06:49:31 - mmengine - INFO - Iter(train) [ 28900/160000] lr: 8.3751e-03 eta: 20:08:30 time: 0.5540 data_time: 0.0060 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0101 decode.acc_seg: 99.4685 aux.loss_ce: 0.0083 aux.acc_seg: 99.0213 +04/17 06:49:59 - mmengine - INFO - Iter(train) [ 28950/160000] lr: 8.3722e-03 eta: 20:08:03 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0112 decode.acc_seg: 99.6550 aux.loss_ce: 0.0090 aux.acc_seg: 99.1830 +04/17 06:50:27 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 06:50:27 - mmengine - INFO - Iter(train) [ 29000/160000] lr: 8.3694e-03 eta: 20:07:35 time: 0.5523 data_time: 0.0064 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0122 decode.acc_seg: 99.6282 aux.loss_ce: 0.0092 aux.acc_seg: 99.1642 +04/17 06:50:54 - mmengine - INFO - Iter(train) [ 29050/160000] lr: 8.3666e-03 eta: 20:07:07 time: 0.5521 data_time: 0.0058 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0120 decode.acc_seg: 99.3945 aux.loss_ce: 0.0099 aux.acc_seg: 98.7250 +04/17 06:51:22 - mmengine - INFO - Iter(train) [ 29100/160000] lr: 8.3637e-03 eta: 20:06:40 time: 0.5542 data_time: 0.0065 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0116 decode.acc_seg: 99.4136 aux.loss_ce: 0.0091 aux.acc_seg: 98.8048 +04/17 06:51:50 - mmengine - INFO - Iter(train) [ 29150/160000] lr: 8.3609e-03 eta: 20:06:12 time: 0.5545 data_time: 0.0061 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0108 decode.acc_seg: 99.6570 aux.loss_ce: 0.0089 aux.acc_seg: 99.1039 +04/17 06:52:17 - mmengine - INFO - Iter(train) [ 29200/160000] lr: 8.3580e-03 eta: 20:05:45 time: 0.5534 data_time: 0.0057 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0103 decode.acc_seg: 99.5222 aux.loss_ce: 0.0080 aux.acc_seg: 99.0696 +04/17 06:52:45 - mmengine - INFO - Iter(train) [ 29250/160000] lr: 8.3552e-03 eta: 20:05:17 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0113 decode.acc_seg: 99.5973 aux.loss_ce: 0.0090 aux.acc_seg: 99.3248 +04/17 06:53:13 - mmengine - INFO - Iter(train) [ 29300/160000] lr: 8.3524e-03 eta: 20:04:50 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0125 decode.acc_seg: 99.4563 aux.loss_ce: 0.0094 aux.acc_seg: 98.9904 +04/17 06:53:40 - mmengine - INFO - Iter(train) [ 29350/160000] lr: 8.3495e-03 eta: 20:04:22 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0107 decode.acc_seg: 99.5584 aux.loss_ce: 0.0088 aux.acc_seg: 98.9595 +04/17 06:54:08 - mmengine - INFO - Iter(train) [ 29400/160000] lr: 8.3467e-03 eta: 20:03:55 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0111 decode.acc_seg: 99.5743 aux.loss_ce: 0.0095 aux.acc_seg: 99.1711 +04/17 06:54:36 - mmengine - INFO - Iter(train) [ 29450/160000] lr: 8.3438e-03 eta: 20:03:27 time: 0.5526 data_time: 0.0074 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0108 decode.acc_seg: 99.5565 aux.loss_ce: 0.0092 aux.acc_seg: 99.0637 +04/17 06:55:03 - mmengine - INFO - Iter(train) [ 29500/160000] lr: 8.3410e-03 eta: 20:02:59 time: 0.5532 data_time: 0.0066 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0116 decode.acc_seg: 99.6478 aux.loss_ce: 0.0094 aux.acc_seg: 99.1285 +04/17 06:55:31 - mmengine - INFO - Iter(train) [ 29550/160000] lr: 8.3381e-03 eta: 20:02:32 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0102 decode.acc_seg: 99.7054 aux.loss_ce: 0.0083 aux.acc_seg: 99.2275 +04/17 06:55:59 - mmengine - INFO - Iter(train) [ 29600/160000] lr: 8.3353e-03 eta: 20:02:05 time: 0.5619 data_time: 0.0062 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0108 decode.acc_seg: 99.5362 aux.loss_ce: 0.0090 aux.acc_seg: 99.1015 +04/17 06:56:27 - mmengine - INFO - Iter(train) [ 29650/160000] lr: 8.3325e-03 eta: 20:01:37 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0120 decode.acc_seg: 99.3860 aux.loss_ce: 0.0092 aux.acc_seg: 98.9589 +04/17 06:56:54 - mmengine - INFO - Iter(train) [ 29700/160000] lr: 8.3296e-03 eta: 20:01:10 time: 0.5535 data_time: 0.0058 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0104 decode.acc_seg: 99.6583 aux.loss_ce: 0.0088 aux.acc_seg: 99.1363 +04/17 06:57:22 - mmengine - INFO - Iter(train) [ 29750/160000] lr: 8.3268e-03 eta: 20:00:42 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0123 decode.acc_seg: 99.5695 aux.loss_ce: 0.0094 aux.acc_seg: 98.8542 +04/17 06:57:50 - mmengine - INFO - Iter(train) [ 29800/160000] lr: 8.3239e-03 eta: 20:00:14 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0113 decode.acc_seg: 99.6489 aux.loss_ce: 0.0094 aux.acc_seg: 99.1776 +04/17 06:58:17 - mmengine - INFO - Iter(train) [ 29850/160000] lr: 8.3211e-03 eta: 19:59:47 time: 0.5542 data_time: 0.0071 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0114 decode.acc_seg: 99.2705 aux.loss_ce: 0.0092 aux.acc_seg: 98.8076 +04/17 06:58:45 - mmengine - INFO - Iter(train) [ 29900/160000] lr: 8.3182e-03 eta: 19:59:19 time: 0.5512 data_time: 0.0062 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0100 decode.acc_seg: 99.5226 aux.loss_ce: 0.0088 aux.acc_seg: 98.6992 +04/17 06:59:12 - mmengine - INFO - Iter(train) [ 29950/160000] lr: 8.3154e-03 eta: 19:58:51 time: 0.5522 data_time: 0.0062 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0119 decode.acc_seg: 99.4985 aux.loss_ce: 0.0095 aux.acc_seg: 98.7783 +04/17 06:59:40 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 06:59:40 - mmengine - INFO - Iter(train) [ 30000/160000] lr: 8.3126e-03 eta: 19:58:24 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0106 decode.acc_seg: 99.5883 aux.loss_ce: 0.0087 aux.acc_seg: 99.2011 +04/17 06:59:40 - mmengine - INFO - Saving checkpoint at 30000 iterations +04/17 06:59:44 - mmengine - INFO - Iter(val) [ 50/355] eta: 0:00:14 time: 0.0467 data_time: 0.0015 memory: 1657 +04/17 06:59:46 - mmengine - INFO - Iter(val) [100/355] eta: 0:00:11 time: 0.0464 data_time: 0.0014 memory: 1657 +04/17 06:59:49 - mmengine - INFO - Iter(val) [150/355] eta: 0:00:09 time: 0.0462 data_time: 0.0013 memory: 1657 +04/17 06:59:51 - mmengine - INFO - Iter(val) [200/355] eta: 0:00:07 time: 0.0463 data_time: 0.0015 memory: 1657 +04/17 06:59:53 - mmengine - INFO - Iter(val) [250/355] eta: 0:00:04 time: 0.0461 data_time: 0.0014 memory: 1657 +04/17 06:59:56 - mmengine - INFO - Iter(val) [300/355] eta: 0:00:02 time: 0.0466 data_time: 0.0015 memory: 1657 +04/17 06:59:58 - mmengine - INFO - Iter(val) [350/355] eta: 0:00:00 time: 0.0458 data_time: 0.0013 memory: 1657 +04/17 06:59:59 - mmengine - INFO - per class results: +04/17 06:59:59 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.14 | 99.64 | 99.57 | 99.49 | 99.64 | +| contrast | 80.93 | 87.83 | 89.46 | 91.15 | 87.83 | ++------------+-------+-------+--------+-----------+--------+ +04/17 06:59:59 - mmengine - INFO - Iter(val) [355/355] aAcc: 99.1700 mIoU: 90.0300 mAcc: 93.7400 mFscore: 94.5100 mPrecision: 95.3200 mRecall: 93.7400 data_time: 0.0015 time: 0.0465 +04/17 07:00:26 - mmengine - INFO - Iter(train) [ 30050/160000] lr: 8.3097e-03 eta: 19:57:58 time: 0.5519 data_time: 0.0058 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5041 aux.loss_ce: 0.0085 aux.acc_seg: 99.1146 +04/17 07:00:54 - mmengine - INFO - Iter(train) [ 30100/160000] lr: 8.3069e-03 eta: 19:57:30 time: 0.5518 data_time: 0.0061 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0109 decode.acc_seg: 99.6054 aux.loss_ce: 0.0088 aux.acc_seg: 99.0529 +04/17 07:01:21 - mmengine - INFO - Iter(train) [ 30150/160000] lr: 8.3040e-03 eta: 19:57:02 time: 0.5528 data_time: 0.0059 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.6139 aux.loss_ce: 0.0085 aux.acc_seg: 99.1058 +04/17 07:01:49 - mmengine - INFO - Iter(train) [ 30200/160000] lr: 8.3012e-03 eta: 19:56:34 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0116 decode.acc_seg: 99.4350 aux.loss_ce: 0.0096 aux.acc_seg: 98.9349 +04/17 07:02:17 - mmengine - INFO - Iter(train) [ 30250/160000] lr: 8.2983e-03 eta: 19:56:06 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0114 decode.acc_seg: 99.5555 aux.loss_ce: 0.0094 aux.acc_seg: 99.0211 +04/17 07:02:44 - mmengine - INFO - Iter(train) [ 30300/160000] lr: 8.2955e-03 eta: 19:55:39 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0118 decode.acc_seg: 99.6118 aux.loss_ce: 0.0093 aux.acc_seg: 99.1990 +04/17 07:03:12 - mmengine - INFO - Iter(train) [ 30350/160000] lr: 8.2927e-03 eta: 19:55:11 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0114 decode.acc_seg: 99.6575 aux.loss_ce: 0.0089 aux.acc_seg: 99.1538 +04/17 07:03:40 - mmengine - INFO - Iter(train) [ 30400/160000] lr: 8.2898e-03 eta: 19:54:44 time: 0.5511 data_time: 0.0057 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0109 decode.acc_seg: 99.5982 aux.loss_ce: 0.0089 aux.acc_seg: 99.0856 +04/17 07:04:07 - mmengine - INFO - Iter(train) [ 30450/160000] lr: 8.2870e-03 eta: 19:54:16 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0106 decode.acc_seg: 99.3167 aux.loss_ce: 0.0087 aux.acc_seg: 98.8218 +04/17 07:04:35 - mmengine - INFO - Iter(train) [ 30500/160000] lr: 8.2841e-03 eta: 19:53:48 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0105 decode.acc_seg: 99.5469 aux.loss_ce: 0.0084 aux.acc_seg: 99.2051 +04/17 07:05:03 - mmengine - INFO - Iter(train) [ 30550/160000] lr: 8.2813e-03 eta: 19:53:20 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0114 decode.acc_seg: 99.5904 aux.loss_ce: 0.0089 aux.acc_seg: 99.2641 +04/17 07:05:30 - mmengine - INFO - Iter(train) [ 30600/160000] lr: 8.2784e-03 eta: 19:52:53 time: 0.5529 data_time: 0.0063 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0120 decode.acc_seg: 99.6280 aux.loss_ce: 0.0089 aux.acc_seg: 99.2269 +04/17 07:05:58 - mmengine - INFO - Iter(train) [ 30650/160000] lr: 8.2756e-03 eta: 19:52:25 time: 0.5528 data_time: 0.0073 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.6181 aux.loss_ce: 0.0086 aux.acc_seg: 99.2119 +04/17 07:06:26 - mmengine - INFO - Iter(train) [ 30700/160000] lr: 8.2728e-03 eta: 19:51:57 time: 0.5528 data_time: 0.0062 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0103 decode.acc_seg: 99.6109 aux.loss_ce: 0.0086 aux.acc_seg: 99.1459 +04/17 07:06:53 - mmengine - INFO - Iter(train) [ 30750/160000] lr: 8.2699e-03 eta: 19:51:30 time: 0.5534 data_time: 0.0059 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0105 decode.acc_seg: 99.6828 aux.loss_ce: 0.0085 aux.acc_seg: 99.3529 +04/17 07:07:21 - mmengine - INFO - Iter(train) [ 30800/160000] lr: 8.2671e-03 eta: 19:51:02 time: 0.5528 data_time: 0.0066 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0110 decode.acc_seg: 99.5640 aux.loss_ce: 0.0087 aux.acc_seg: 99.0981 +04/17 07:07:49 - mmengine - INFO - Iter(train) [ 30850/160000] lr: 8.2642e-03 eta: 19:50:35 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0117 decode.acc_seg: 99.4967 aux.loss_ce: 0.0092 aux.acc_seg: 99.1055 +04/17 07:08:16 - mmengine - INFO - Iter(train) [ 30900/160000] lr: 8.2614e-03 eta: 19:50:07 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.5605 aux.loss_ce: 0.0089 aux.acc_seg: 99.1894 +04/17 07:08:44 - mmengine - INFO - Iter(train) [ 30950/160000] lr: 8.2585e-03 eta: 19:49:39 time: 0.5524 data_time: 0.0056 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0119 decode.acc_seg: 99.4685 aux.loss_ce: 0.0088 aux.acc_seg: 99.0562 +04/17 07:09:12 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240417_022222 +04/17 07:09:12 - mmengine - INFO - Iter(train) [ 31000/160000] lr: 8.2557e-03 eta: 19:49:11 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.5527 aux.loss_ce: 0.0081 aux.acc_seg: 99.0878 +04/17 07:09:39 - mmengine - INFO - Iter(train) [ 31050/160000] lr: 8.2528e-03 eta: 19:48:44 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0224 decode.loss_ce: 0.0126 decode.acc_seg: 99.5718 aux.loss_ce: 0.0097 aux.acc_seg: 98.9946 +04/17 07:10:07 - mmengine - INFO - Iter(train) [ 31100/160000] lr: 8.2500e-03 eta: 19:48:16 time: 0.5521 data_time: 0.0060 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0112 decode.acc_seg: 99.7195 aux.loss_ce: 0.0096 aux.acc_seg: 99.1817 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 1259 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 1260 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 1261 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 1262 closing signal SIGINT +Traceback (most recent call last): +Traceback (most recent call last): +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + main()main()main()main() + + + + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + runner.train() + runner.train()runner.train() File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +runner.train() + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignoremodel = self.train_loop.run() # type: ignore + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + model = self.train_loop.run() # type: ignoremodel = self.train_loop.run() # type: ignore + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + self.run_iter(data_batch)self.run_iter(data_batch) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + outputs = self.runner.model.train_step(outputs = self.runner.model.train_step( + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step +losses = self._run_forward(data, mode='loss') +losses = self._run_forward(data, mode='loss') File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + losses = self._run_forward(data, mode='loss') + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + losses = self._run_forward(data, mode='loss') + results = self(**data, mode=mode) File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward +results = self(**data, mode=mode) + + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + results = self(**data, mode=mode) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + results = self(**data, mode=mode) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + return forward_call(*input, **kwargs)return forward_call(*input, **kwargs) + + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward +return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward +return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + output = self.module(*inputs[0], **kwargs[0])output = self.module(*inputs[0], **kwargs[0]) + +output = self.module(*inputs[0], **kwargs[0]) File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl +output = self.module(*inputs[0], **kwargs[0]) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward +return forward_call(*input, **kwargs) +return forward_call(*input, **kwargs) File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward +return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss +return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss +return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train +loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss +loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss + loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss + loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss + losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy + loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy +loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy + loss['acc_seg'] = accuracy(correct = correct[:, target != ignore_index] + + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy + KeyboardInterruptcorrect = correct[:, target != ignore_index] + +correct = correct[:, target != ignore_index]KeyboardInterrupt + +KeyboardInterrupt correct = correct[:, target != ignore_index] + +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 1259 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 1260 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 1261 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 1262 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 1218 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 1218 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 1218 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/17 08:31:33 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1692964102 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1692964102 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/17 08:31:33 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/17 08:31:36 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.pos_embed as id 0 + +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1set param backbone.cls_token as id 0 + +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.pos_embed as id 0 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1set param backbone.layers.0.ln1.bias as id 1 + +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.0.attn.proj.bias as id 1set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1set param backbone.layers.1.ffn.layers.0.0.weight as id 2 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1set param backbone.layers.1.ffn.layers.0.0.bias as id 2 + +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.1.ln1.bias as id 2set param backbone.layers.2.attn.qkv.bias as id 3 + +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.1.attn.proj.bias as id 2set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.2.ffn.layers.1.weight as id 3set param backbone.layers.1.ln2.weight as id 2 + +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.2.ln1.bias as id 3set param backbone.layers.3.ln2.bias as id 4 + +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.2.attn.qkv.weight as id 3set param backbone.layers.3.ffn.layers.0.0.bias as id 4 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.4.gamma_1 as id 5 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.4.ln1.weight as id 5set param backbone.layers.2.ln2.bias as id 3 + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.4.attn.proj.bias as id 5 + +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.3.ln1.bias as id 4set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.5.gamma_1 as id 6set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.3.attn.qkv.bias as id 4 + +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.5.ln1.bias as id 6 + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.3.ln2.weight as id 4set param backbone.layers.5.attn.qkv.weight as id 6 + +set param backbone.layers.3.ln2.bias as id 4set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4set param backbone.layers.5.attn.proj.bias as id 6 + +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4set param backbone.layers.5.ffn.layers.0.0.weight as id 6 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.4.gamma_1 as id 5 + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.6.attn.relative_position_bias_table as id 7set param backbone.layers.4.attn.qkv.weight as id 5 + +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.4.attn.qkv.bias as id 5 + +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.6.attn.proj.bias as id 7 + +set param backbone.layers.6.ln2.weight as id 7set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.5.ln1.bias as id 6 + +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.5.attn.qkv.weight as id 6set param backbone.layers.6.ffn.layers.1.bias as id 7 + +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.7.ln1.bias as id 8 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.5.ffn.layers.1.weight as id 6 + +set param backbone.layers.5.ffn.layers.1.bias as id 6set param backbone.layers.7.ln2.weight as id 8 + +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.6.gamma_2 as id 7set param backbone.layers.7.ffn.layers.0.0.bias as id 8 + +set param backbone.layers.6.ln1.weight as id 7set param backbone.layers.7.ffn.layers.1.weight as id 8 + +set param backbone.layers.6.ln1.bias as id 7set param backbone.layers.7.ffn.layers.1.bias as id 8 + +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.8.gamma_2 as id 9 + +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.8.ln1.weight as id 9 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.6.ffn.layers.1.weight as id 7set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.10.gamma_1 as id 11 + +set param backbone.layers.10.gamma_2 as id 11set param backbone.layers.7.gamma_2 as id 8 + +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.7.ln1.weight as id 8set param backbone.layers.10.ln1.bias as id 11 + +set param backbone.layers.7.ln1.bias as id 8set param backbone.layers.10.attn.relative_position_bias_table as id 11 + +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.7.attn.relative_position_bias_table as id 8 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.7.attn.qkv.weight as id 8set param backbone.layers.10.attn.proj.weight as id 11 + +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.10.ln2.bias as id 11 + +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.7.ln2.weight as id 8set param backbone.layers.10.ffn.layers.0.0.bias as id 11 + +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.8.gamma_1 as id 9set param backbone.layers.11.attn.qkv.bias as id 12 + +set param backbone.layers.8.gamma_2 as id 9set param backbone.layers.11.attn.proj.weight as id 12 + +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.11.ln2.weight as id 12set param backbone.layers.8.ln1.bias as id 9 + +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9set param neck.upsample_4x.0.weight as id 13 + +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param neck.upsample_4x.1.weight as id 13set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param neck.upsample_4x.1.bias as id 13 +set param backbone.layers.8.ffn.layers.1.weight as id 9set param neck.upsample_4x.3.weight as id 13 + +set param neck.upsample_4x.3.bias as id 13set param backbone.layers.8.ffn.layers.1.bias as id 9 + +set param neck.upsample_2x.0.weight as id 13 +set param backbone.layers.9.gamma_1 as id 10set param neck.upsample_2x.0.bias as id 13 + +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10set param decode_head.conv_seg.weight as id 13 + +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param backbone.layers.9.ln2.weight as id 10set param decode_head.psp_modules.0.1.bn.bias as id 13 + +set param backbone.layers.9.ln2.bias as id 10 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10set param decode_head.psp_modules.1.1.bn.bias as id 13 + +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param backbone.layers.10.gamma_1 as id 11 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param backbone.layers.10.gamma_2 as id 11 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13set param backbone.layers.10.ln1.weight as id 11 + +set param backbone.layers.10.ln1.bias as id 11set param decode_head.bottleneck.conv.weight as id 13 + +set param decode_head.bottleneck.bn.weight as id 13set param backbone.layers.10.attn.relative_position_bias_table as id 11 + +set param decode_head.bottleneck.bn.bias as id 13 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param decode_head.lateral_convs.0.conv.weight as id 13set param backbone.layers.10.attn.qkv.bias as id 11 + +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param backbone.layers.10.attn.proj.weight as id 11set param decode_head.lateral_convs.0.bn.bias as id 13 + +set param backbone.layers.10.attn.proj.bias as id 11 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13set param backbone.layers.10.ln2.weight as id 11 + +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param backbone.layers.10.ln2.bias as id 11 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param backbone.layers.10.ffn.layers.1.weight as id 11set param decode_head.fpn_convs.0.bn.weight as id 13 + +set param decode_head.fpn_convs.0.bn.bias as id 13set param backbone.layers.10.ffn.layers.1.bias as id 11 + +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13set param backbone.layers.11.gamma_1 as id 12 + +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param backbone.layers.11.gamma_2 as id 12 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param backbone.layers.11.ln1.weight as id 12 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param backbone.layers.11.ln1.bias as id 12set param decode_head.fpn_convs.2.bn.bias as id 13 + +set param backbone.layers.11.attn.relative_position_bias_table as id 12set param decode_head.fpn_bottleneck.conv.weight as id 13 + +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12set param auxiliary_head.conv_seg.weight as id 13 + +set param auxiliary_head.conv_seg.bias as id 13 +set param backbone.layers.11.ln2.weight as id 12 +set param auxiliary_head.convs.0.conv.weight as id 13set param backbone.layers.11.ln2.bias as id 12 + +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/17 08:31:37 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +04/17 08:31:37 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/17 08:31:37 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/17 08:31:37 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/MAE4000. +04/17 08:32:34 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:06:51 time: 1.0239 data_time: 0.0042 memory: 8935 loss: 7.0101 decode.loss_ce: 5.0132 decode.acc_seg: 1.2255 aux.loss_ce: 1.9970 aux.acc_seg: 0.1034 +04/17 08:33:25 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 1 day, 23:47:20 time: 1.0232 data_time: 0.0044 memory: 8462 loss: 6.7600 decode.loss_ce: 4.7872 decode.acc_seg: 22.1193 aux.loss_ce: 1.9728 aux.acc_seg: 13.3171 From eb51fc44670dbd8738a62861d0016e882fee7879 Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Wed, 17 Apr 2024 23:50:58 +0000 Subject: [PATCH 15/24] 2024.04.18 --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index ce225418b5..88f4e10013 100644 --- a/.gitignore +++ b/.gitignore @@ -120,4 +120,5 @@ mmseg/.mim *.pth logs/ -*.png \ No newline at end of file +*.png +nohup.out \ No newline at end of file From 1cf79a9e7a6263ba67d3adf5547d7f2bfcbaa73a Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Wed, 17 Apr 2024 23:51:29 +0000 Subject: [PATCH 16/24] 2024.04.18 --- nohup.out | 1225 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1225 insertions(+) diff --git a/nohup.out b/nohup.out index 353438b038..1bfb25ccd6 100644 --- a/nohup.out +++ b/nohup.out @@ -9427,3 +9427,1228 @@ Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/conver 04/17 08:31:37 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/MAE4000. 04/17 08:32:34 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:06:51 time: 1.0239 data_time: 0.0042 memory: 8935 loss: 7.0101 decode.loss_ce: 5.0132 decode.acc_seg: 1.2255 aux.loss_ce: 1.9970 aux.acc_seg: 0.1034 04/17 08:33:25 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 1 day, 23:47:20 time: 1.0232 data_time: 0.0044 memory: 8462 loss: 6.7600 decode.loss_ce: 4.7872 decode.acc_seg: 22.1193 aux.loss_ce: 1.9728 aux.acc_seg: 13.3171 +04/17 08:34:16 - mmengine - INFO - Iter(train) [ 150/160000] base_lr: 9.9401e-06 lr: 3.6750e-08 eta: 1 day, 22:59:29 time: 1.0228 data_time: 0.0041 memory: 8462 loss: 6.2894 decode.loss_ce: 4.3502 decode.acc_seg: 42.1999 aux.loss_ce: 1.9392 aux.acc_seg: 23.7051 +04/17 08:35:07 - mmengine - INFO - Iter(train) [ 200/160000] base_lr: 1.3276e-05 lr: 4.9083e-08 eta: 1 day, 22:33:58 time: 1.0184 data_time: 0.0040 memory: 8462 loss: 5.7614 decode.loss_ce: 3.8749 decode.acc_seg: 58.2272 aux.loss_ce: 1.8865 aux.acc_seg: 34.2005 +04/17 08:35:58 - mmengine - INFO - Iter(train) [ 250/160000] base_lr: 1.6611e-05 lr: 6.1415e-08 eta: 1 day, 22:15:08 time: 1.0145 data_time: 0.0039 memory: 8462 loss: 5.0487 decode.loss_ce: 3.2347 decode.acc_seg: 78.0340 aux.loss_ce: 1.8141 aux.acc_seg: 43.1890 +04/17 08:36:49 - mmengine - INFO - Iter(train) [ 300/160000] base_lr: 1.9947e-05 lr: 7.3747e-08 eta: 1 day, 22:00:43 time: 1.0089 data_time: 0.0040 memory: 8462 loss: 4.3936 decode.loss_ce: 2.6770 decode.acc_seg: 85.6838 aux.loss_ce: 1.7167 aux.acc_seg: 64.1836 +04/17 08:37:39 - mmengine - INFO - Iter(train) [ 350/160000] base_lr: 2.3282e-05 lr: 8.6079e-08 eta: 1 day, 21:48:21 time: 1.0050 data_time: 0.0044 memory: 8462 loss: 3.7508 decode.loss_ce: 2.1300 decode.acc_seg: 87.5259 aux.loss_ce: 1.6208 aux.acc_seg: 62.4052 +04/17 08:38:29 - mmengine - INFO - Iter(train) [ 400/160000] base_lr: 2.6618e-05 lr: 9.8412e-08 eta: 1 day, 21:38:10 time: 1.0046 data_time: 0.0045 memory: 8462 loss: 3.1693 decode.loss_ce: 1.6545 decode.acc_seg: 95.9000 aux.loss_ce: 1.5148 aux.acc_seg: 74.7307 +04/17 08:39:19 - mmengine - INFO - Iter(train) [ 450/160000] base_lr: 2.9953e-05 lr: 1.1074e-07 eta: 1 day, 21:29:51 time: 1.0036 data_time: 0.0041 memory: 8462 loss: 2.5232 decode.loss_ce: 1.1461 decode.acc_seg: 96.0243 aux.loss_ce: 1.3771 aux.acc_seg: 65.2546 +04/17 08:40:10 - mmengine - INFO - Iter(train) [ 500/160000] base_lr: 3.3289e-05 lr: 1.2308e-07 eta: 1 day, 21:23:02 time: 1.0035 data_time: 0.0040 memory: 8462 loss: 2.0180 decode.loss_ce: 0.7713 decode.acc_seg: 95.5585 aux.loss_ce: 1.2467 aux.acc_seg: 72.2454 +04/17 08:41:00 - mmengine - INFO - Iter(train) [ 550/160000] base_lr: 3.6624e-05 lr: 1.3541e-07 eta: 1 day, 21:17:08 time: 1.0037 data_time: 0.0041 memory: 8462 loss: 1.6391 decode.loss_ce: 0.5415 decode.acc_seg: 97.0821 aux.loss_ce: 1.0976 aux.acc_seg: 81.3814 +04/17 08:41:50 - mmengine - INFO - Iter(train) [ 600/160000] base_lr: 3.9960e-05 lr: 1.4774e-07 eta: 1 day, 21:11:56 time: 1.0025 data_time: 0.0044 memory: 8462 loss: 1.2957 decode.loss_ce: 0.3839 decode.acc_seg: 96.9400 aux.loss_ce: 0.9119 aux.acc_seg: 88.7630 +04/17 08:42:40 - mmengine - INFO - Iter(train) [ 650/160000] base_lr: 4.3296e-05 lr: 1.6007e-07 eta: 1 day, 21:07:17 time: 1.0012 data_time: 0.0042 memory: 8462 loss: 1.0201 decode.loss_ce: 0.2598 decode.acc_seg: 99.0055 aux.loss_ce: 0.7604 aux.acc_seg: 94.6356 +04/17 08:43:30 - mmengine - INFO - Iter(train) [ 700/160000] base_lr: 4.6631e-05 lr: 1.7240e-07 eta: 1 day, 21:02:57 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.8337 decode.loss_ce: 0.2176 decode.acc_seg: 96.4949 aux.loss_ce: 0.6161 aux.acc_seg: 95.5870 +04/17 08:44:20 - mmengine - INFO - Iter(train) [ 750/160000] base_lr: 4.9967e-05 lr: 1.8474e-07 eta: 1 day, 20:58:43 time: 0.9966 data_time: 0.0043 memory: 8462 loss: 0.6015 decode.loss_ce: 0.1586 decode.acc_seg: 97.6515 aux.loss_ce: 0.4429 aux.acc_seg: 97.1811 +04/17 08:45:10 - mmengine - INFO - Iter(train) [ 800/160000] base_lr: 5.3302e-05 lr: 1.9707e-07 eta: 1 day, 20:54:39 time: 0.9973 data_time: 0.0043 memory: 8462 loss: 0.4766 decode.loss_ce: 0.1337 decode.acc_seg: 98.6265 aux.loss_ce: 0.3429 aux.acc_seg: 97.3650 +04/17 08:46:00 - mmengine - INFO - Iter(train) [ 850/160000] base_lr: 5.6638e-05 lr: 2.0940e-07 eta: 1 day, 20:50:56 time: 0.9974 data_time: 0.0039 memory: 8462 loss: 0.3757 decode.loss_ce: 0.1175 decode.acc_seg: 98.1531 aux.loss_ce: 0.2582 aux.acc_seg: 96.8962 +04/17 08:46:50 - mmengine - INFO - Iter(train) [ 900/160000] base_lr: 5.9973e-05 lr: 2.2173e-07 eta: 1 day, 20:47:32 time: 0.9970 data_time: 0.0040 memory: 8462 loss: 0.3494 decode.loss_ce: 0.1149 decode.acc_seg: 97.5227 aux.loss_ce: 0.2345 aux.acc_seg: 96.7276 +04/17 08:47:39 - mmengine - INFO - Iter(train) [ 950/160000] base_lr: 6.3309e-05 lr: 2.3407e-07 eta: 1 day, 20:44:24 time: 0.9968 data_time: 0.0040 memory: 8462 loss: 0.2647 decode.loss_ce: 0.0933 decode.acc_seg: 98.1697 aux.loss_ce: 0.1714 aux.acc_seg: 97.2790 +04/17 08:48:29 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 08:48:29 - mmengine - INFO - Iter(train) [ 1000/160000] base_lr: 6.6644e-05 lr: 2.4640e-07 eta: 1 day, 20:41:36 time: 0.9979 data_time: 0.0043 memory: 8462 loss: 0.2273 decode.loss_ce: 0.0908 decode.acc_seg: 98.6879 aux.loss_ce: 0.1365 aux.acc_seg: 97.6452 +04/17 08:49:19 - mmengine - INFO - Iter(train) [ 1050/160000] base_lr: 6.9980e-05 lr: 2.5873e-07 eta: 1 day, 20:38:59 time: 0.9989 data_time: 0.0049 memory: 8462 loss: 0.2224 decode.loss_ce: 0.0969 decode.acc_seg: 97.7600 aux.loss_ce: 0.1255 aux.acc_seg: 96.2608 +04/17 08:50:09 - mmengine - INFO - Iter(train) [ 1100/160000] base_lr: 7.3316e-05 lr: 2.7106e-07 eta: 1 day, 20:36:31 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.1755 decode.loss_ce: 0.0823 decode.acc_seg: 97.8956 aux.loss_ce: 0.0933 aux.acc_seg: 96.8731 +04/17 08:50:59 - mmengine - INFO - Iter(train) [ 1150/160000] base_lr: 7.6651e-05 lr: 2.8339e-07 eta: 1 day, 20:34:13 time: 0.9981 data_time: 0.0045 memory: 8462 loss: 0.1492 decode.loss_ce: 0.0711 decode.acc_seg: 97.3215 aux.loss_ce: 0.0781 aux.acc_seg: 95.2385 +04/17 08:51:49 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 08:51:49 - mmengine - INFO - Iter(train) [ 1200/160000] base_lr: 7.9987e-05 lr: 2.9573e-07 eta: 1 day, 20:32:04 time: 0.9979 data_time: 0.0046 memory: 8462 loss: 0.1411 decode.loss_ce: 0.0710 decode.acc_seg: 98.7997 aux.loss_ce: 0.0700 aux.acc_seg: 97.9790 +04/17 08:52:39 - mmengine - INFO - Iter(train) [ 1250/160000] base_lr: 8.3322e-05 lr: 3.0806e-07 eta: 1 day, 20:30:02 time: 0.9994 data_time: 0.0047 memory: 8462 loss: 0.1245 decode.loss_ce: 0.0608 decode.acc_seg: 98.9174 aux.loss_ce: 0.0637 aux.acc_seg: 98.4625 +04/17 08:53:29 - mmengine - INFO - Iter(train) [ 1300/160000] base_lr: 8.6658e-05 lr: 3.2039e-07 eta: 1 day, 20:28:03 time: 0.9979 data_time: 0.0041 memory: 8462 loss: 0.1184 decode.loss_ce: 0.0597 decode.acc_seg: 97.2700 aux.loss_ce: 0.0587 aux.acc_seg: 96.9965 +04/17 08:54:19 - mmengine - INFO - Iter(train) [ 1350/160000] base_lr: 8.9993e-05 lr: 3.3272e-07 eta: 1 day, 20:26:10 time: 0.9970 data_time: 0.0039 memory: 8462 loss: 0.1053 decode.loss_ce: 0.0539 decode.acc_seg: 98.1895 aux.loss_ce: 0.0514 aux.acc_seg: 97.5515 +04/17 08:55:09 - mmengine - INFO - Iter(train) [ 1400/160000] base_lr: 9.3329e-05 lr: 3.4506e-07 eta: 1 day, 20:24:20 time: 0.9986 data_time: 0.0044 memory: 8462 loss: 0.0978 decode.loss_ce: 0.0511 decode.acc_seg: 98.8268 aux.loss_ce: 0.0467 aux.acc_seg: 98.3084 +04/17 08:55:58 - mmengine - INFO - Iter(train) [ 1450/160000] base_lr: 9.6664e-05 lr: 3.5739e-07 eta: 1 day, 20:22:34 time: 0.9980 data_time: 0.0045 memory: 8462 loss: 0.1061 decode.loss_ce: 0.0579 decode.acc_seg: 97.1462 aux.loss_ce: 0.0482 aux.acc_seg: 95.9591 +04/17 08:56:48 - mmengine - INFO - Iter(train) [ 1500/160000] base_lr: 1.0000e-04 lr: 3.6972e-07 eta: 1 day, 20:20:56 time: 0.9979 data_time: 0.0038 memory: 8462 loss: 0.1053 decode.loss_ce: 0.0602 decode.acc_seg: 97.4928 aux.loss_ce: 0.0451 aux.acc_seg: 97.0434 +04/17 08:57:38 - mmengine - INFO - Iter(train) [ 1550/160000] base_lr: 9.9969e-05 lr: 3.6961e-07 eta: 1 day, 20:19:24 time: 0.9988 data_time: 0.0041 memory: 8462 loss: 0.0964 decode.loss_ce: 0.0526 decode.acc_seg: 98.3452 aux.loss_ce: 0.0438 aux.acc_seg: 97.5035 +04/17 08:58:28 - mmengine - INFO - Iter(train) [ 1600/160000] base_lr: 9.9938e-05 lr: 3.6949e-07 eta: 1 day, 20:17:50 time: 0.9984 data_time: 0.0041 memory: 8462 loss: 0.0870 decode.loss_ce: 0.0482 decode.acc_seg: 97.9443 aux.loss_ce: 0.0388 aux.acc_seg: 97.1024 +04/17 08:59:18 - mmengine - INFO - Iter(train) [ 1650/160000] base_lr: 9.9906e-05 lr: 3.6937e-07 eta: 1 day, 20:16:19 time: 0.9976 data_time: 0.0046 memory: 8462 loss: 0.0931 decode.loss_ce: 0.0529 decode.acc_seg: 98.5298 aux.loss_ce: 0.0402 aux.acc_seg: 98.1544 +04/17 09:00:08 - mmengine - INFO - Iter(train) [ 1700/160000] base_lr: 9.9874e-05 lr: 3.6926e-07 eta: 1 day, 20:14:52 time: 0.9971 data_time: 0.0042 memory: 8462 loss: 0.0831 decode.loss_ce: 0.0484 decode.acc_seg: 97.0835 aux.loss_ce: 0.0347 aux.acc_seg: 96.7350 +04/17 09:00:58 - mmengine - INFO - Iter(train) [ 1750/160000] base_lr: 9.9843e-05 lr: 3.6914e-07 eta: 1 day, 20:13:28 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.0656 decode.loss_ce: 0.0356 decode.acc_seg: 98.7150 aux.loss_ce: 0.0301 aux.acc_seg: 97.9824 +04/17 09:01:48 - mmengine - INFO - Iter(train) [ 1800/160000] base_lr: 9.9811e-05 lr: 3.6902e-07 eta: 1 day, 20:12:06 time: 0.9982 data_time: 0.0041 memory: 8462 loss: 0.0693 decode.loss_ce: 0.0407 decode.acc_seg: 99.0469 aux.loss_ce: 0.0285 aux.acc_seg: 98.2542 +04/17 09:02:38 - mmengine - INFO - Iter(train) [ 1850/160000] base_lr: 9.9780e-05 lr: 3.6891e-07 eta: 1 day, 20:10:46 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0593 decode.loss_ce: 0.0350 decode.acc_seg: 98.9889 aux.loss_ce: 0.0243 aux.acc_seg: 98.6946 +04/17 09:03:28 - mmengine - INFO - Iter(train) [ 1900/160000] base_lr: 9.9748e-05 lr: 3.6879e-07 eta: 1 day, 20:09:26 time: 0.9981 data_time: 0.0044 memory: 8462 loss: 0.0596 decode.loss_ce: 0.0332 decode.acc_seg: 98.8941 aux.loss_ce: 0.0264 aux.acc_seg: 98.0751 +04/17 09:04:18 - mmengine - INFO - Iter(train) [ 1950/160000] base_lr: 9.9717e-05 lr: 3.6867e-07 eta: 1 day, 20:08:11 time: 0.9993 data_time: 0.0047 memory: 8462 loss: 0.0573 decode.loss_ce: 0.0316 decode.acc_seg: 99.2884 aux.loss_ce: 0.0257 aux.acc_seg: 98.5992 +04/17 09:05:08 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 09:05:08 - mmengine - INFO - Iter(train) [ 2000/160000] base_lr: 9.9685e-05 lr: 3.6856e-07 eta: 1 day, 20:06:58 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0635 decode.loss_ce: 0.0379 decode.acc_seg: 99.3744 aux.loss_ce: 0.0256 aux.acc_seg: 98.4859 +04/17 09:05:58 - mmengine - INFO - Iter(train) [ 2050/160000] base_lr: 9.9654e-05 lr: 3.6844e-07 eta: 1 day, 20:05:42 time: 0.9965 data_time: 0.0040 memory: 8462 loss: 0.0711 decode.loss_ce: 0.0426 decode.acc_seg: 98.8665 aux.loss_ce: 0.0285 aux.acc_seg: 98.4669 +04/17 09:06:48 - mmengine - INFO - Iter(train) [ 2100/160000] base_lr: 9.9622e-05 lr: 3.6832e-07 eta: 1 day, 20:04:29 time: 0.9983 data_time: 0.0042 memory: 8462 loss: 0.0604 decode.loss_ce: 0.0348 decode.acc_seg: 98.1258 aux.loss_ce: 0.0257 aux.acc_seg: 97.6543 +04/17 09:07:38 - mmengine - INFO - Iter(train) [ 2150/160000] base_lr: 9.9591e-05 lr: 3.6821e-07 eta: 1 day, 20:03:15 time: 0.9988 data_time: 0.0042 memory: 8462 loss: 0.0644 decode.loss_ce: 0.0382 decode.acc_seg: 97.8350 aux.loss_ce: 0.0261 aux.acc_seg: 97.0947 +04/17 09:08:28 - mmengine - INFO - Iter(train) [ 2200/160000] base_lr: 9.9559e-05 lr: 3.6809e-07 eta: 1 day, 20:02:05 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0556 decode.loss_ce: 0.0321 decode.acc_seg: 98.8342 aux.loss_ce: 0.0235 aux.acc_seg: 98.3250 +04/17 09:09:18 - mmengine - INFO - Iter(train) [ 2250/160000] base_lr: 9.9527e-05 lr: 3.6797e-07 eta: 1 day, 20:00:56 time: 0.9994 data_time: 0.0040 memory: 8462 loss: 0.0595 decode.loss_ce: 0.0345 decode.acc_seg: 99.4347 aux.loss_ce: 0.0250 aux.acc_seg: 98.9706 +04/17 09:10:07 - mmengine - INFO - Iter(train) [ 2300/160000] base_lr: 9.9496e-05 lr: 3.6786e-07 eta: 1 day, 19:59:48 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0528 decode.loss_ce: 0.0305 decode.acc_seg: 99.5253 aux.loss_ce: 0.0223 aux.acc_seg: 98.7959 +04/17 09:10:57 - mmengine - INFO - Iter(train) [ 2350/160000] base_lr: 9.9464e-05 lr: 3.6774e-07 eta: 1 day, 19:58:40 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0578 decode.loss_ce: 0.0343 decode.acc_seg: 97.5035 aux.loss_ce: 0.0235 aux.acc_seg: 97.0406 +04/17 09:11:47 - mmengine - INFO - Iter(train) [ 2400/160000] base_lr: 9.9433e-05 lr: 3.6762e-07 eta: 1 day, 19:57:34 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0488 decode.loss_ce: 0.0280 decode.acc_seg: 98.8338 aux.loss_ce: 0.0208 aux.acc_seg: 98.2491 +04/17 09:12:37 - mmengine - INFO - Iter(train) [ 2450/160000] base_lr: 9.9401e-05 lr: 3.6751e-07 eta: 1 day, 19:56:30 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0573 decode.loss_ce: 0.0339 decode.acc_seg: 98.7181 aux.loss_ce: 0.0234 aux.acc_seg: 98.1361 +04/17 09:13:27 - mmengine - INFO - Iter(train) [ 2500/160000] base_lr: 9.9370e-05 lr: 3.6739e-07 eta: 1 day, 19:55:24 time: 0.9984 data_time: 0.0040 memory: 8462 loss: 0.0536 decode.loss_ce: 0.0317 decode.acc_seg: 98.9445 aux.loss_ce: 0.0219 aux.acc_seg: 98.4241 +04/17 09:14:17 - mmengine - INFO - Iter(train) [ 2550/160000] base_lr: 9.9338e-05 lr: 3.6727e-07 eta: 1 day, 19:54:20 time: 0.9994 data_time: 0.0041 memory: 8462 loss: 0.0553 decode.loss_ce: 0.0330 decode.acc_seg: 98.1611 aux.loss_ce: 0.0222 aux.acc_seg: 97.6421 +04/17 09:15:07 - mmengine - INFO - Iter(train) [ 2600/160000] base_lr: 9.9307e-05 lr: 3.6716e-07 eta: 1 day, 19:53:18 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0635 decode.loss_ce: 0.0380 decode.acc_seg: 99.0538 aux.loss_ce: 0.0255 aux.acc_seg: 98.2327 +04/17 09:15:57 - mmengine - INFO - Iter(train) [ 2650/160000] base_lr: 9.9275e-05 lr: 3.6704e-07 eta: 1 day, 19:52:16 time: 0.9996 data_time: 0.0042 memory: 8462 loss: 0.0500 decode.loss_ce: 0.0291 decode.acc_seg: 98.7988 aux.loss_ce: 0.0209 aux.acc_seg: 98.3414 +04/17 09:16:47 - mmengine - INFO - Iter(train) [ 2700/160000] base_lr: 9.9244e-05 lr: 3.6692e-07 eta: 1 day, 19:51:14 time: 0.9999 data_time: 0.0050 memory: 8462 loss: 0.0618 decode.loss_ce: 0.0368 decode.acc_seg: 98.3723 aux.loss_ce: 0.0249 aux.acc_seg: 97.3526 +04/17 09:17:37 - mmengine - INFO - Iter(train) [ 2750/160000] base_lr: 9.9212e-05 lr: 3.6681e-07 eta: 1 day, 19:50:11 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0538 decode.loss_ce: 0.0319 decode.acc_seg: 98.5094 aux.loss_ce: 0.0220 aux.acc_seg: 98.0539 +04/17 09:18:27 - mmengine - INFO - Iter(train) [ 2800/160000] base_lr: 9.9180e-05 lr: 3.6669e-07 eta: 1 day, 19:49:10 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0552 decode.loss_ce: 0.0334 decode.acc_seg: 98.9201 aux.loss_ce: 0.0217 aux.acc_seg: 98.0875 +04/17 09:19:17 - mmengine - INFO - Iter(train) [ 2850/160000] base_lr: 9.9149e-05 lr: 3.6657e-07 eta: 1 day, 19:48:11 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0417 decode.loss_ce: 0.0238 decode.acc_seg: 99.2218 aux.loss_ce: 0.0179 aux.acc_seg: 99.0133 +04/17 09:20:07 - mmengine - INFO - Iter(train) [ 2900/160000] base_lr: 9.9117e-05 lr: 3.6646e-07 eta: 1 day, 19:47:10 time: 0.9996 data_time: 0.0044 memory: 8462 loss: 0.0500 decode.loss_ce: 0.0298 decode.acc_seg: 99.1209 aux.loss_ce: 0.0201 aux.acc_seg: 98.5458 +04/17 09:20:57 - mmengine - INFO - Iter(train) [ 2950/160000] base_lr: 9.9086e-05 lr: 3.6634e-07 eta: 1 day, 19:46:10 time: 0.9993 data_time: 0.0043 memory: 8462 loss: 0.0529 decode.loss_ce: 0.0313 decode.acc_seg: 99.5939 aux.loss_ce: 0.0216 aux.acc_seg: 99.2641 +04/17 09:21:47 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 09:21:47 - mmengine - INFO - Iter(train) [ 3000/160000] base_lr: 9.9054e-05 lr: 3.6622e-07 eta: 1 day, 19:45:09 time: 0.9983 data_time: 0.0041 memory: 8462 loss: 0.0457 decode.loss_ce: 0.0268 decode.acc_seg: 98.7343 aux.loss_ce: 0.0190 aux.acc_seg: 98.1049 +04/17 09:22:37 - mmengine - INFO - Iter(train) [ 3050/160000] base_lr: 9.9023e-05 lr: 3.6611e-07 eta: 1 day, 19:44:11 time: 1.0002 data_time: 0.0042 memory: 8462 loss: 0.0476 decode.loss_ce: 0.0275 decode.acc_seg: 99.1837 aux.loss_ce: 0.0200 aux.acc_seg: 98.4442 +04/17 09:23:27 - mmengine - INFO - Iter(train) [ 3100/160000] base_lr: 9.8991e-05 lr: 3.6599e-07 eta: 1 day, 19:43:12 time: 0.9995 data_time: 0.0047 memory: 8462 loss: 0.0473 decode.loss_ce: 0.0283 decode.acc_seg: 98.4861 aux.loss_ce: 0.0190 aux.acc_seg: 97.4447 +04/17 09:24:17 - mmengine - INFO - Iter(train) [ 3150/160000] base_lr: 9.8960e-05 lr: 3.6587e-07 eta: 1 day, 19:42:14 time: 1.0001 data_time: 0.0043 memory: 8462 loss: 0.0475 decode.loss_ce: 0.0282 decode.acc_seg: 98.9435 aux.loss_ce: 0.0193 aux.acc_seg: 98.5840 +04/17 09:25:07 - mmengine - INFO - Iter(train) [ 3200/160000] base_lr: 9.8928e-05 lr: 3.6576e-07 eta: 1 day, 19:41:16 time: 0.9992 data_time: 0.0039 memory: 8462 loss: 0.0488 decode.loss_ce: 0.0294 decode.acc_seg: 98.9069 aux.loss_ce: 0.0194 aux.acc_seg: 98.5401 +04/17 09:25:57 - mmengine - INFO - Iter(train) [ 3250/160000] base_lr: 9.8897e-05 lr: 3.6564e-07 eta: 1 day, 19:40:18 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0468 decode.loss_ce: 0.0273 decode.acc_seg: 99.3895 aux.loss_ce: 0.0195 aux.acc_seg: 99.0913 +04/17 09:26:47 - mmengine - INFO - Iter(train) [ 3300/160000] base_lr: 9.8865e-05 lr: 3.6552e-07 eta: 1 day, 19:39:20 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0460 decode.loss_ce: 0.0271 decode.acc_seg: 97.9965 aux.loss_ce: 0.0189 aux.acc_seg: 97.4051 +04/17 09:27:37 - mmengine - INFO - Iter(train) [ 3350/160000] base_lr: 9.8833e-05 lr: 3.6541e-07 eta: 1 day, 19:38:21 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0462 decode.loss_ce: 0.0276 decode.acc_seg: 99.5575 aux.loss_ce: 0.0186 aux.acc_seg: 99.0801 +04/17 09:28:27 - mmengine - INFO - Iter(train) [ 3400/160000] base_lr: 9.8802e-05 lr: 3.6529e-07 eta: 1 day, 19:37:25 time: 1.0004 data_time: 0.0042 memory: 8462 loss: 0.0479 decode.loss_ce: 0.0294 decode.acc_seg: 99.4705 aux.loss_ce: 0.0185 aux.acc_seg: 98.8924 +04/17 09:29:17 - mmengine - INFO - Iter(train) [ 3450/160000] base_lr: 9.8770e-05 lr: 3.6517e-07 eta: 1 day, 19:36:28 time: 0.9993 data_time: 0.0047 memory: 8462 loss: 0.0443 decode.loss_ce: 0.0267 decode.acc_seg: 99.0513 aux.loss_ce: 0.0176 aux.acc_seg: 98.5983 +04/17 09:30:07 - mmengine - INFO - Iter(train) [ 3500/160000] base_lr: 9.8739e-05 lr: 3.6506e-07 eta: 1 day, 19:35:31 time: 1.0001 data_time: 0.0043 memory: 8462 loss: 0.0527 decode.loss_ce: 0.0326 decode.acc_seg: 99.3343 aux.loss_ce: 0.0202 aux.acc_seg: 98.6254 +04/17 09:30:57 - mmengine - INFO - Iter(train) [ 3550/160000] base_lr: 9.8707e-05 lr: 3.6494e-07 eta: 1 day, 19:34:35 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0407 decode.loss_ce: 0.0241 decode.acc_seg: 99.0374 aux.loss_ce: 0.0166 aux.acc_seg: 98.3089 +04/17 09:31:47 - mmengine - INFO - Iter(train) [ 3600/160000] base_lr: 9.8676e-05 lr: 3.6482e-07 eta: 1 day, 19:33:38 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0448 decode.loss_ce: 0.0264 decode.acc_seg: 98.7450 aux.loss_ce: 0.0184 aux.acc_seg: 98.2246 +04/17 09:32:37 - mmengine - INFO - Iter(train) [ 3650/160000] base_lr: 9.8644e-05 lr: 3.6471e-07 eta: 1 day, 19:32:43 time: 1.0006 data_time: 0.0043 memory: 8462 loss: 0.0418 decode.loss_ce: 0.0246 decode.acc_seg: 99.4370 aux.loss_ce: 0.0172 aux.acc_seg: 98.7982 +04/17 09:33:27 - mmengine - INFO - Iter(train) [ 3700/160000] base_lr: 9.8613e-05 lr: 3.6459e-07 eta: 1 day, 19:31:47 time: 1.0014 data_time: 0.0041 memory: 8462 loss: 0.0480 decode.loss_ce: 0.0291 decode.acc_seg: 98.6782 aux.loss_ce: 0.0189 aux.acc_seg: 98.5292 +04/17 09:34:17 - mmengine - INFO - Iter(train) [ 3750/160000] base_lr: 9.8581e-05 lr: 3.6447e-07 eta: 1 day, 19:30:50 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0477 decode.loss_ce: 0.0289 decode.acc_seg: 99.2256 aux.loss_ce: 0.0188 aux.acc_seg: 98.6662 +04/17 09:35:07 - mmengine - INFO - Iter(train) [ 3800/160000] base_lr: 9.8550e-05 lr: 3.6436e-07 eta: 1 day, 19:29:53 time: 0.9986 data_time: 0.0039 memory: 8462 loss: 0.0446 decode.loss_ce: 0.0276 decode.acc_seg: 99.1703 aux.loss_ce: 0.0170 aux.acc_seg: 98.7638 +04/17 09:35:57 - mmengine - INFO - Iter(train) [ 3850/160000] base_lr: 9.8518e-05 lr: 3.6424e-07 eta: 1 day, 19:28:56 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0389 decode.loss_ce: 0.0235 decode.acc_seg: 99.4406 aux.loss_ce: 0.0154 aux.acc_seg: 98.9601 +04/17 09:36:47 - mmengine - INFO - Iter(train) [ 3900/160000] base_lr: 9.8486e-05 lr: 3.6412e-07 eta: 1 day, 19:28:01 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0435 decode.loss_ce: 0.0262 decode.acc_seg: 98.1987 aux.loss_ce: 0.0173 aux.acc_seg: 97.4186 +04/17 09:37:37 - mmengine - INFO - Iter(train) [ 3950/160000] base_lr: 9.8455e-05 lr: 3.6401e-07 eta: 1 day, 19:27:06 time: 1.0012 data_time: 0.0041 memory: 8462 loss: 0.0486 decode.loss_ce: 0.0304 decode.acc_seg: 99.3372 aux.loss_ce: 0.0182 aux.acc_seg: 99.0168 +04/17 09:38:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 09:38:27 - mmengine - INFO - Iter(train) [ 4000/160000] base_lr: 9.8423e-05 lr: 3.6389e-07 eta: 1 day, 19:26:10 time: 0.9985 data_time: 0.0041 memory: 8462 loss: 0.0411 decode.loss_ce: 0.0260 decode.acc_seg: 99.3818 aux.loss_ce: 0.0151 aux.acc_seg: 98.6591 +04/17 09:39:17 - mmengine - INFO - Iter(train) [ 4050/160000] base_lr: 9.8392e-05 lr: 3.6377e-07 eta: 1 day, 19:25:15 time: 0.9987 data_time: 0.0040 memory: 8462 loss: 0.0447 decode.loss_ce: 0.0270 decode.acc_seg: 99.2233 aux.loss_ce: 0.0177 aux.acc_seg: 98.2870 +04/17 09:40:07 - mmengine - INFO - Iter(train) [ 4100/160000] base_lr: 9.8360e-05 lr: 3.6366e-07 eta: 1 day, 19:24:21 time: 1.0004 data_time: 0.0040 memory: 8462 loss: 0.0434 decode.loss_ce: 0.0265 decode.acc_seg: 99.3273 aux.loss_ce: 0.0169 aux.acc_seg: 98.5081 +04/17 09:40:57 - mmengine - INFO - Iter(train) [ 4150/160000] base_lr: 9.8329e-05 lr: 3.6354e-07 eta: 1 day, 19:23:25 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0374 decode.loss_ce: 0.0228 decode.acc_seg: 98.9891 aux.loss_ce: 0.0146 aux.acc_seg: 98.5933 +04/17 09:41:47 - mmengine - INFO - Iter(train) [ 4200/160000] base_lr: 9.8297e-05 lr: 3.6342e-07 eta: 1 day, 19:22:31 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0402 decode.loss_ce: 0.0240 decode.acc_seg: 98.2637 aux.loss_ce: 0.0162 aux.acc_seg: 98.2533 +04/17 09:42:37 - mmengine - INFO - Iter(train) [ 4250/160000] base_lr: 9.8266e-05 lr: 3.6331e-07 eta: 1 day, 19:21:36 time: 0.9989 data_time: 0.0041 memory: 8462 loss: 0.0347 decode.loss_ce: 0.0202 decode.acc_seg: 99.0572 aux.loss_ce: 0.0145 aux.acc_seg: 98.3082 +04/17 09:43:27 - mmengine - INFO - Iter(train) [ 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decode.acc_seg: 99.0816 aux.loss_ce: 0.0172 aux.acc_seg: 98.5561 +04/17 09:50:07 - mmengine - INFO - Iter(train) [ 4700/160000] base_lr: 9.7982e-05 lr: 3.6226e-07 eta: 1 day, 19:13:29 time: 0.9994 data_time: 0.0047 memory: 8462 loss: 0.0339 decode.loss_ce: 0.0196 decode.acc_seg: 99.4043 aux.loss_ce: 0.0144 aux.acc_seg: 98.8764 +04/17 09:50:57 - mmengine - INFO - Iter(train) [ 4750/160000] base_lr: 9.7950e-05 lr: 3.6214e-07 eta: 1 day, 19:12:33 time: 0.9985 data_time: 0.0049 memory: 8462 loss: 0.0379 decode.loss_ce: 0.0226 decode.acc_seg: 98.9700 aux.loss_ce: 0.0153 aux.acc_seg: 98.1937 +04/17 09:51:47 - mmengine - INFO - Iter(train) [ 4800/160000] base_lr: 9.7919e-05 lr: 3.6203e-07 eta: 1 day, 19:11:40 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0353 decode.loss_ce: 0.0215 decode.acc_seg: 99.1190 aux.loss_ce: 0.0139 aux.acc_seg: 98.4636 +04/17 09:52:37 - mmengine - INFO - Iter(train) [ 4850/160000] base_lr: 9.7887e-05 lr: 3.6191e-07 eta: 1 day, 19:10:46 time: 0.9983 data_time: 0.0041 memory: 8462 loss: 0.0354 decode.loss_ce: 0.0206 decode.acc_seg: 99.3814 aux.loss_ce: 0.0148 aux.acc_seg: 98.8951 +04/17 09:53:27 - mmengine - INFO - Iter(train) [ 4900/160000] base_lr: 9.7856e-05 lr: 3.6179e-07 eta: 1 day, 19:09:53 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0361 decode.loss_ce: 0.0214 decode.acc_seg: 98.6313 aux.loss_ce: 0.0147 aux.acc_seg: 98.2702 +04/17 09:54:17 - mmengine - INFO - Iter(train) [ 4950/160000] base_lr: 9.7824e-05 lr: 3.6168e-07 eta: 1 day, 19:08:59 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0347 decode.loss_ce: 0.0205 decode.acc_seg: 99.5302 aux.loss_ce: 0.0142 aux.acc_seg: 99.1716 +04/17 09:55:07 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 09:55:07 - mmengine - INFO - Iter(train) [ 5000/160000] base_lr: 9.7792e-05 lr: 3.6156e-07 eta: 1 day, 19:08:07 time: 1.0003 data_time: 0.0046 memory: 8462 loss: 0.0372 decode.loss_ce: 0.0226 decode.acc_seg: 99.1566 aux.loss_ce: 0.0146 aux.acc_seg: 98.8195 +04/17 09:55:57 - mmengine - INFO - Iter(train) [ 5050/160000] base_lr: 9.7761e-05 lr: 3.6144e-07 eta: 1 day, 19:07:15 time: 1.0014 data_time: 0.0046 memory: 8462 loss: 0.0359 decode.loss_ce: 0.0222 decode.acc_seg: 99.2836 aux.loss_ce: 0.0138 aux.acc_seg: 98.8277 +04/17 09:56:47 - mmengine - INFO - Iter(train) [ 5100/160000] base_lr: 9.7729e-05 lr: 3.6133e-07 eta: 1 day, 19:06:21 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0186 decode.acc_seg: 99.5804 aux.loss_ce: 0.0127 aux.acc_seg: 99.2104 +04/17 09:57:37 - mmengine - INFO - Iter(train) [ 5150/160000] base_lr: 9.7698e-05 lr: 3.6121e-07 eta: 1 day, 19:05:29 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0433 decode.loss_ce: 0.0268 decode.acc_seg: 99.1711 aux.loss_ce: 0.0166 aux.acc_seg: 98.8379 +04/17 09:58:27 - mmengine - INFO - Iter(train) [ 5200/160000] base_lr: 9.7666e-05 lr: 3.6109e-07 eta: 1 day, 19:04:35 time: 0.9999 data_time: 0.0041 memory: 8462 loss: 0.0383 decode.loss_ce: 0.0227 decode.acc_seg: 99.3835 aux.loss_ce: 0.0156 aux.acc_seg: 98.8422 +04/17 09:59:17 - mmengine - INFO - Iter(train) [ 5250/160000] base_lr: 9.7635e-05 lr: 3.6098e-07 eta: 1 day, 19:03:44 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0380 decode.loss_ce: 0.0231 decode.acc_seg: 99.4253 aux.loss_ce: 0.0149 aux.acc_seg: 98.9494 +04/17 10:00:07 - mmengine - INFO - Iter(train) [ 5300/160000] base_lr: 9.7603e-05 lr: 3.6086e-07 eta: 1 day, 19:02:51 time: 1.0010 data_time: 0.0041 memory: 8462 loss: 0.0373 decode.loss_ce: 0.0229 decode.acc_seg: 98.6557 aux.loss_ce: 0.0145 aux.acc_seg: 98.1289 +04/17 10:00:57 - mmengine - INFO - Iter(train) [ 5350/160000] base_lr: 9.7572e-05 lr: 3.6074e-07 eta: 1 day, 19:01:58 time: 1.0000 data_time: 0.0040 memory: 8462 loss: 0.0357 decode.loss_ce: 0.0208 decode.acc_seg: 98.8726 aux.loss_ce: 0.0148 aux.acc_seg: 98.3179 +04/17 10:01:47 - mmengine - INFO - Iter(train) [ 5400/160000] base_lr: 9.7540e-05 lr: 3.6063e-07 eta: 1 day, 19:01:06 time: 1.0000 data_time: 0.0042 memory: 8462 loss: 0.0377 decode.loss_ce: 0.0226 decode.acc_seg: 99.2432 aux.loss_ce: 0.0151 aux.acc_seg: 98.8401 +04/17 10:02:37 - mmengine - INFO - Iter(train) [ 5450/160000] base_lr: 9.7509e-05 lr: 3.6051e-07 eta: 1 day, 19:00:14 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0334 decode.loss_ce: 0.0194 decode.acc_seg: 99.0490 aux.loss_ce: 0.0141 aux.acc_seg: 98.4489 +04/17 10:03:27 - mmengine - INFO - Iter(train) [ 5500/160000] base_lr: 9.7477e-05 lr: 3.6039e-07 eta: 1 day, 18:59:21 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0403 decode.loss_ce: 0.0243 decode.acc_seg: 99.5821 aux.loss_ce: 0.0160 aux.acc_seg: 98.7394 +04/17 10:04:17 - mmengine - INFO - Iter(train) [ 5550/160000] base_lr: 9.7445e-05 lr: 3.6028e-07 eta: 1 day, 18:58:29 time: 1.0009 data_time: 0.0042 memory: 8462 loss: 0.0361 decode.loss_ce: 0.0219 decode.acc_seg: 99.4492 aux.loss_ce: 0.0143 aux.acc_seg: 98.9521 +04/17 10:05:07 - mmengine - INFO - Iter(train) [ 5600/160000] base_lr: 9.7414e-05 lr: 3.6016e-07 eta: 1 day, 18:57:36 time: 1.0006 data_time: 0.0040 memory: 8462 loss: 0.0342 decode.loss_ce: 0.0203 decode.acc_seg: 99.4709 aux.loss_ce: 0.0139 aux.acc_seg: 99.1426 +04/17 10:05:57 - mmengine - INFO - Iter(train) [ 5650/160000] base_lr: 9.7382e-05 lr: 3.6004e-07 eta: 1 day, 18:56:45 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0376 decode.loss_ce: 0.0223 decode.acc_seg: 98.4533 aux.loss_ce: 0.0153 aux.acc_seg: 97.8525 +04/17 10:06:47 - mmengine - INFO - Iter(train) [ 5700/160000] base_lr: 9.7351e-05 lr: 3.5993e-07 eta: 1 day, 18:55:53 time: 1.0012 data_time: 0.0042 memory: 8462 loss: 0.0358 decode.loss_ce: 0.0211 decode.acc_seg: 99.3319 aux.loss_ce: 0.0148 aux.acc_seg: 98.8008 +04/17 10:07:37 - mmengine - INFO - Iter(train) [ 5750/160000] base_lr: 9.7319e-05 lr: 3.5981e-07 eta: 1 day, 18:55:01 time: 0.9993 data_time: 0.0043 memory: 8462 loss: 0.0328 decode.loss_ce: 0.0194 decode.acc_seg: 99.6233 aux.loss_ce: 0.0133 aux.acc_seg: 98.9773 +04/17 10:08:27 - mmengine - INFO - Iter(train) [ 5800/160000] base_lr: 9.7288e-05 lr: 3.5969e-07 eta: 1 day, 18:54:10 time: 1.0010 data_time: 0.0042 memory: 8462 loss: 0.0342 decode.loss_ce: 0.0208 decode.acc_seg: 99.3694 aux.loss_ce: 0.0133 aux.acc_seg: 99.0971 +04/17 10:09:17 - mmengine - INFO - Iter(train) [ 5850/160000] base_lr: 9.7256e-05 lr: 3.5958e-07 eta: 1 day, 18:53:18 time: 0.9987 data_time: 0.0044 memory: 8462 loss: 0.0315 decode.loss_ce: 0.0187 decode.acc_seg: 99.3813 aux.loss_ce: 0.0128 aux.acc_seg: 99.0602 +04/17 10:10:07 - mmengine - INFO - Iter(train) [ 5900/160000] base_lr: 9.7225e-05 lr: 3.5946e-07 eta: 1 day, 18:52:26 time: 1.0002 data_time: 0.0042 memory: 8462 loss: 0.0377 decode.loss_ce: 0.0230 decode.acc_seg: 99.0547 aux.loss_ce: 0.0146 aux.acc_seg: 99.2113 +04/17 10:10:57 - mmengine - INFO - Iter(train) [ 5950/160000] base_lr: 9.7193e-05 lr: 3.5934e-07 eta: 1 day, 18:51:35 time: 1.0009 data_time: 0.0045 memory: 8462 loss: 0.0346 decode.loss_ce: 0.0212 decode.acc_seg: 99.1407 aux.loss_ce: 0.0134 aux.acc_seg: 98.7675 +04/17 10:11:47 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 10:11:47 - mmengine - INFO - Iter(train) [ 6000/160000] base_lr: 9.7161e-05 lr: 3.5923e-07 eta: 1 day, 18:50:43 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0308 decode.loss_ce: 0.0179 decode.acc_seg: 99.2178 aux.loss_ce: 0.0129 aux.acc_seg: 98.6378 +04/17 10:12:37 - mmengine - INFO - Iter(train) [ 6050/160000] base_lr: 9.7130e-05 lr: 3.5911e-07 eta: 1 day, 18:49:51 time: 1.0007 data_time: 0.0044 memory: 8462 loss: 0.0298 decode.loss_ce: 0.0170 decode.acc_seg: 99.5426 aux.loss_ce: 0.0128 aux.acc_seg: 98.9246 +04/17 10:13:27 - mmengine - INFO - Iter(train) [ 6100/160000] base_lr: 9.7098e-05 lr: 3.5899e-07 eta: 1 day, 18:49:00 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0350 decode.loss_ce: 0.0213 decode.acc_seg: 99.6117 aux.loss_ce: 0.0137 aux.acc_seg: 99.2361 +04/17 10:14:17 - mmengine - INFO - Iter(train) [ 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decode.acc_seg: 99.1266 aux.loss_ce: 0.0138 aux.acc_seg: 98.5552 +04/17 10:20:57 - mmengine - INFO - Iter(train) [ 6550/160000] base_lr: 9.6814e-05 lr: 3.5794e-07 eta: 1 day, 18:41:16 time: 0.9999 data_time: 0.0044 memory: 8462 loss: 0.0331 decode.loss_ce: 0.0196 decode.acc_seg: 98.7511 aux.loss_ce: 0.0135 aux.acc_seg: 98.2067 +04/17 10:21:47 - mmengine - INFO - Iter(train) [ 6600/160000] base_lr: 9.6783e-05 lr: 3.5783e-07 eta: 1 day, 18:40:24 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0330 decode.loss_ce: 0.0203 decode.acc_seg: 99.3629 aux.loss_ce: 0.0127 aux.acc_seg: 98.8394 +04/17 10:22:37 - mmengine - INFO - Iter(train) [ 6650/160000] base_lr: 9.6751e-05 lr: 3.5771e-07 eta: 1 day, 18:39:32 time: 0.9991 data_time: 0.0041 memory: 8462 loss: 0.0314 decode.loss_ce: 0.0184 decode.acc_seg: 99.6651 aux.loss_ce: 0.0130 aux.acc_seg: 99.2207 +04/17 10:23:27 - mmengine - INFO - Iter(train) [ 6700/160000] base_lr: 9.6720e-05 lr: 3.5759e-07 eta: 1 day, 18:38:41 time: 1.0009 data_time: 0.0047 memory: 8462 loss: 0.0288 decode.loss_ce: 0.0163 decode.acc_seg: 99.4678 aux.loss_ce: 0.0125 aux.acc_seg: 98.8937 +04/17 10:24:17 - mmengine - INFO - Iter(train) [ 6750/160000] base_lr: 9.6688e-05 lr: 3.5748e-07 eta: 1 day, 18:37:50 time: 1.0009 data_time: 0.0044 memory: 8462 loss: 0.0323 decode.loss_ce: 0.0190 decode.acc_seg: 99.3738 aux.loss_ce: 0.0133 aux.acc_seg: 98.7343 +04/17 10:25:07 - mmengine - INFO - Iter(train) [ 6800/160000] base_lr: 9.6657e-05 lr: 3.5736e-07 eta: 1 day, 18:36:59 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0381 decode.loss_ce: 0.0228 decode.acc_seg: 99.1611 aux.loss_ce: 0.0153 aux.acc_seg: 98.4055 +04/17 10:25:57 - mmengine - INFO - Iter(train) [ 6850/160000] base_lr: 9.6625e-05 lr: 3.5724e-07 eta: 1 day, 18:36:07 time: 0.9994 data_time: 0.0043 memory: 8462 loss: 0.0337 decode.loss_ce: 0.0199 decode.acc_seg: 99.4860 aux.loss_ce: 0.0138 aux.acc_seg: 98.8110 +04/17 10:26:47 - mmengine - INFO - Iter(train) [ 6900/160000] base_lr: 9.6594e-05 lr: 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decode.loss_ce: 0.0205 decode.acc_seg: 99.5481 aux.loss_ce: 0.0132 aux.acc_seg: 99.1684 +04/17 10:30:07 - mmengine - INFO - Iter(train) [ 7100/160000] base_lr: 9.6467e-05 lr: 3.5666e-07 eta: 1 day, 18:31:51 time: 1.0014 data_time: 0.0043 memory: 8462 loss: 0.0319 decode.loss_ce: 0.0187 decode.acc_seg: 99.1455 aux.loss_ce: 0.0132 aux.acc_seg: 98.8676 +04/17 10:30:57 - mmengine - INFO - Iter(train) [ 7150/160000] base_lr: 9.6436e-05 lr: 3.5654e-07 eta: 1 day, 18:31:00 time: 1.0008 data_time: 0.0043 memory: 8462 loss: 0.0319 decode.loss_ce: 0.0187 decode.acc_seg: 98.9529 aux.loss_ce: 0.0131 aux.acc_seg: 98.1979 +04/17 10:31:47 - mmengine - INFO - Iter(train) [ 7200/160000] base_lr: 9.6404e-05 lr: 3.5643e-07 eta: 1 day, 18:30:09 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0187 decode.acc_seg: 99.6557 aux.loss_ce: 0.0125 aux.acc_seg: 99.3261 +04/17 10:32:37 - mmengine - INFO - Iter(train) [ 7250/160000] base_lr: 9.6373e-05 lr: 3.5631e-07 eta: 1 day, 18:29:18 time: 1.0010 data_time: 0.0047 memory: 8462 loss: 0.0336 decode.loss_ce: 0.0197 decode.acc_seg: 98.9107 aux.loss_ce: 0.0139 aux.acc_seg: 98.4201 +04/17 10:33:27 - mmengine - INFO - Iter(train) [ 7300/160000] base_lr: 9.6341e-05 lr: 3.5619e-07 eta: 1 day, 18:28:28 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0378 decode.loss_ce: 0.0227 decode.acc_seg: 98.6927 aux.loss_ce: 0.0151 aux.acc_seg: 98.0427 +04/17 10:34:18 - mmengine - INFO - Iter(train) [ 7350/160000] base_lr: 9.6310e-05 lr: 3.5608e-07 eta: 1 day, 18:27:37 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0358 decode.loss_ce: 0.0215 decode.acc_seg: 99.4621 aux.loss_ce: 0.0142 aux.acc_seg: 98.7711 +04/17 10:35:08 - mmengine - INFO - Iter(train) [ 7400/160000] base_lr: 9.6278e-05 lr: 3.5596e-07 eta: 1 day, 18:26:46 time: 1.0027 data_time: 0.0047 memory: 8462 loss: 0.0302 decode.loss_ce: 0.0181 decode.acc_seg: 99.4499 aux.loss_ce: 0.0122 aux.acc_seg: 99.2104 +04/17 10:35:58 - mmengine - INFO - Iter(train) [ 7450/160000] base_lr: 9.6247e-05 lr: 3.5584e-07 eta: 1 day, 18:25:55 time: 1.0011 data_time: 0.0042 memory: 8462 loss: 0.0323 decode.loss_ce: 0.0190 decode.acc_seg: 98.8752 aux.loss_ce: 0.0133 aux.acc_seg: 98.3652 +04/17 10:36:48 - mmengine - INFO - Iter(train) [ 7500/160000] base_lr: 9.6215e-05 lr: 3.5573e-07 eta: 1 day, 18:25:05 time: 1.0014 data_time: 0.0042 memory: 8462 loss: 0.0277 decode.loss_ce: 0.0164 decode.acc_seg: 99.1823 aux.loss_ce: 0.0114 aux.acc_seg: 98.7297 +04/17 10:37:38 - mmengine - INFO - Iter(train) [ 7550/160000] base_lr: 9.6184e-05 lr: 3.5561e-07 eta: 1 day, 18:24:15 time: 1.0003 data_time: 0.0039 memory: 8462 loss: 0.0301 decode.loss_ce: 0.0178 decode.acc_seg: 99.1760 aux.loss_ce: 0.0123 aux.acc_seg: 98.8791 +04/17 10:38:28 - mmengine - INFO - Iter(train) [ 7600/160000] base_lr: 9.6152e-05 lr: 3.5549e-07 eta: 1 day, 18:23:24 time: 0.9997 data_time: 0.0040 memory: 8462 loss: 0.0308 decode.loss_ce: 0.0181 decode.acc_seg: 99.1236 aux.loss_ce: 0.0127 aux.acc_seg: 98.4756 +04/17 10:39:18 - mmengine - INFO - Iter(train) [ 7650/160000] base_lr: 9.6120e-05 lr: 3.5538e-07 eta: 1 day, 18:22:33 time: 1.0000 data_time: 0.0039 memory: 8462 loss: 0.0333 decode.loss_ce: 0.0200 decode.acc_seg: 99.0524 aux.loss_ce: 0.0133 aux.acc_seg: 98.4222 +04/17 10:40:08 - mmengine - INFO - Iter(train) [ 7700/160000] base_lr: 9.6089e-05 lr: 3.5526e-07 eta: 1 day, 18:21:42 time: 0.9994 data_time: 0.0040 memory: 8462 loss: 0.0285 decode.loss_ce: 0.0160 decode.acc_seg: 99.4146 aux.loss_ce: 0.0125 aux.acc_seg: 98.5973 +04/17 10:40:58 - mmengine - INFO - Iter(train) [ 7750/160000] base_lr: 9.6057e-05 lr: 3.5514e-07 eta: 1 day, 18:20:51 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0324 decode.loss_ce: 0.0193 decode.acc_seg: 99.3982 aux.loss_ce: 0.0131 aux.acc_seg: 98.7888 +04/17 10:41:48 - mmengine - INFO - Iter(train) [ 7800/160000] base_lr: 9.6026e-05 lr: 3.5503e-07 eta: 1 day, 18:20:00 time: 1.0006 data_time: 0.0040 memory: 8462 loss: 0.0360 decode.loss_ce: 0.0224 decode.acc_seg: 99.0635 aux.loss_ce: 0.0136 aux.acc_seg: 98.8470 +04/17 10:42:38 - mmengine - INFO - Iter(train) [ 7850/160000] base_lr: 9.5994e-05 lr: 3.5491e-07 eta: 1 day, 18:19:10 time: 1.0003 data_time: 0.0044 memory: 8462 loss: 0.0255 decode.loss_ce: 0.0145 decode.acc_seg: 99.1734 aux.loss_ce: 0.0110 aux.acc_seg: 98.4362 +04/17 10:43:28 - mmengine - INFO - Iter(train) [ 7900/160000] base_lr: 9.5963e-05 lr: 3.5479e-07 eta: 1 day, 18:18:19 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0285 decode.loss_ce: 0.0167 decode.acc_seg: 99.1795 aux.loss_ce: 0.0118 aux.acc_seg: 98.7524 +04/17 10:44:18 - mmengine - INFO - Iter(train) [ 7950/160000] base_lr: 9.5931e-05 lr: 3.5468e-07 eta: 1 day, 18:17:27 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0262 decode.loss_ce: 0.0151 decode.acc_seg: 99.2907 aux.loss_ce: 0.0111 aux.acc_seg: 98.7049 +04/17 10:45:08 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 10:45:08 - mmengine - INFO - Iter(train) [ 8000/160000] base_lr: 9.5900e-05 lr: 3.5456e-07 eta: 1 day, 18:16:36 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0325 decode.loss_ce: 0.0191 decode.acc_seg: 99.4884 aux.loss_ce: 0.0134 aux.acc_seg: 98.7900 +04/17 10:45:58 - mmengine - INFO - Iter(train) [ 8050/160000] base_lr: 9.5868e-05 lr: 3.5444e-07 eta: 1 day, 18:15:44 time: 0.9992 data_time: 0.0045 memory: 8462 loss: 0.0257 decode.loss_ce: 0.0149 decode.acc_seg: 99.4730 aux.loss_ce: 0.0108 aux.acc_seg: 99.1138 +04/17 10:46:48 - mmengine - INFO - Iter(train) [ 8100/160000] base_lr: 9.5837e-05 lr: 3.5433e-07 eta: 1 day, 18:14:52 time: 0.9996 data_time: 0.0044 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0180 decode.acc_seg: 98.8304 aux.loss_ce: 0.0131 aux.acc_seg: 97.3690 +04/17 10:47:38 - mmengine - INFO - Iter(train) [ 8150/160000] base_lr: 9.5805e-05 lr: 3.5421e-07 eta: 1 day, 18:14:01 time: 0.9994 data_time: 0.0043 memory: 8462 loss: 0.0284 decode.loss_ce: 0.0165 decode.acc_seg: 99.4873 aux.loss_ce: 0.0118 aux.acc_seg: 99.1148 +04/17 10:48:28 - mmengine - INFO - Iter(train) [ 8200/160000] base_lr: 9.5773e-05 lr: 3.5409e-07 eta: 1 day, 18:13:10 time: 1.0002 data_time: 0.0042 memory: 8462 loss: 0.0321 decode.loss_ce: 0.0184 decode.acc_seg: 99.5493 aux.loss_ce: 0.0136 aux.acc_seg: 99.0458 +04/17 10:49:18 - mmengine - INFO - Iter(train) [ 8250/160000] base_lr: 9.5742e-05 lr: 3.5398e-07 eta: 1 day, 18:12:18 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0264 decode.loss_ce: 0.0148 decode.acc_seg: 99.6235 aux.loss_ce: 0.0116 aux.acc_seg: 99.0587 +04/17 10:50:08 - mmengine - INFO - Iter(train) [ 8300/160000] base_lr: 9.5710e-05 lr: 3.5386e-07 eta: 1 day, 18:11:27 time: 0.9996 data_time: 0.0042 memory: 8462 loss: 0.0318 decode.loss_ce: 0.0187 decode.acc_seg: 99.6046 aux.loss_ce: 0.0132 aux.acc_seg: 99.2018 +04/17 10:50:58 - mmengine - INFO - Iter(train) [ 8350/160000] base_lr: 9.5679e-05 lr: 3.5374e-07 eta: 1 day, 18:10:35 time: 1.0004 data_time: 0.0048 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0187 decode.acc_seg: 99.0238 aux.loss_ce: 0.0125 aux.acc_seg: 98.4592 +04/17 10:51:48 - mmengine - INFO - Iter(train) [ 8400/160000] base_lr: 9.5647e-05 lr: 3.5363e-07 eta: 1 day, 18:09:44 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0297 decode.loss_ce: 0.0168 decode.acc_seg: 99.4942 aux.loss_ce: 0.0129 aux.acc_seg: 99.0360 +04/17 10:52:38 - mmengine - INFO - Iter(train) [ 8450/160000] base_lr: 9.5616e-05 lr: 3.5351e-07 eta: 1 day, 18:08:53 time: 1.0005 data_time: 0.0047 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0164 decode.acc_seg: 99.4204 aux.loss_ce: 0.0121 aux.acc_seg: 98.8857 +04/17 10:53:28 - mmengine - INFO - Iter(train) [ 8500/160000] base_lr: 9.5584e-05 lr: 3.5339e-07 eta: 1 day, 18:08:02 time: 1.0005 data_time: 0.0050 memory: 8462 loss: 0.0294 decode.loss_ce: 0.0174 decode.acc_seg: 99.3834 aux.loss_ce: 0.0120 aux.acc_seg: 98.7679 +04/17 10:54:18 - mmengine - INFO - Iter(train) [ 8550/160000] base_lr: 9.5553e-05 lr: 3.5328e-07 eta: 1 day, 18:07:11 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0322 decode.loss_ce: 0.0191 decode.acc_seg: 99.6374 aux.loss_ce: 0.0131 aux.acc_seg: 99.1426 +04/17 10:55:08 - mmengine - INFO - Iter(train) [ 8600/160000] base_lr: 9.5521e-05 lr: 3.5316e-07 eta: 1 day, 18:06:20 time: 0.9991 data_time: 0.0048 memory: 8462 loss: 0.0302 decode.loss_ce: 0.0176 decode.acc_seg: 99.0908 aux.loss_ce: 0.0126 aux.acc_seg: 98.6433 +04/17 10:55:58 - mmengine - INFO - Iter(train) [ 8650/160000] base_lr: 9.5490e-05 lr: 3.5304e-07 eta: 1 day, 18:05:28 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0315 decode.loss_ce: 0.0184 decode.acc_seg: 99.4024 aux.loss_ce: 0.0130 aux.acc_seg: 98.8716 +04/17 10:56:48 - mmengine - INFO - Iter(train) [ 8700/160000] base_lr: 9.5458e-05 lr: 3.5293e-07 eta: 1 day, 18:04:36 time: 1.0000 data_time: 0.0043 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0186 decode.acc_seg: 99.1198 aux.loss_ce: 0.0126 aux.acc_seg: 98.9529 +04/17 10:57:38 - mmengine - INFO - Iter(train) [ 8750/160000] base_lr: 9.5426e-05 lr: 3.5281e-07 eta: 1 day, 18:03:45 time: 0.9992 data_time: 0.0045 memory: 8462 loss: 0.0291 decode.loss_ce: 0.0169 decode.acc_seg: 98.7726 aux.loss_ce: 0.0121 aux.acc_seg: 97.8586 +04/17 10:58:28 - mmengine - INFO - Iter(train) [ 8800/160000] base_lr: 9.5395e-05 lr: 3.5269e-07 eta: 1 day, 18:02:53 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0293 decode.loss_ce: 0.0173 decode.acc_seg: 99.5106 aux.loss_ce: 0.0119 aux.acc_seg: 99.0864 +04/17 10:59:18 - mmengine - INFO - Iter(train) [ 8850/160000] base_lr: 9.5363e-05 lr: 3.5258e-07 eta: 1 day, 18:02:02 time: 0.9989 data_time: 0.0044 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0170 decode.acc_seg: 98.9819 aux.loss_ce: 0.0117 aux.acc_seg: 98.1556 +04/17 11:00:08 - mmengine - INFO - Iter(train) [ 8900/160000] base_lr: 9.5332e-05 lr: 3.5246e-07 eta: 1 day, 18:01:11 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0326 decode.loss_ce: 0.0199 decode.acc_seg: 99.2109 aux.loss_ce: 0.0127 aux.acc_seg: 98.8785 +04/17 11:00:58 - mmengine - INFO - Iter(train) [ 8950/160000] base_lr: 9.5300e-05 lr: 3.5234e-07 eta: 1 day, 18:00:19 time: 1.0001 data_time: 0.0045 memory: 8462 loss: 0.0297 decode.loss_ce: 0.0174 decode.acc_seg: 99.3111 aux.loss_ce: 0.0123 aux.acc_seg: 98.6567 +04/17 11:01:48 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 11:01:48 - mmengine - INFO - Iter(train) [ 9000/160000] base_lr: 9.5269e-05 lr: 3.5223e-07 eta: 1 day, 17:59:29 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0280 decode.loss_ce: 0.0168 decode.acc_seg: 99.1098 aux.loss_ce: 0.0111 aux.acc_seg: 98.6355 +04/17 11:02:38 - mmengine - INFO - Iter(train) [ 9050/160000] base_lr: 9.5237e-05 lr: 3.5211e-07 eta: 1 day, 17:58:37 time: 0.9995 data_time: 0.0041 memory: 8462 loss: 0.0279 decode.loss_ce: 0.0163 decode.acc_seg: 99.2556 aux.loss_ce: 0.0117 aux.acc_seg: 98.5273 +04/17 11:03:28 - mmengine - INFO - Iter(train) [ 9100/160000] base_lr: 9.5206e-05 lr: 3.5199e-07 eta: 1 day, 17:57:46 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0171 decode.acc_seg: 99.2605 aux.loss_ce: 0.0112 aux.acc_seg: 98.7671 +04/17 11:04:18 - mmengine - INFO - Iter(train) [ 9150/160000] base_lr: 9.5174e-05 lr: 3.5188e-07 eta: 1 day, 17:56:54 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0292 decode.loss_ce: 0.0174 decode.acc_seg: 99.2044 aux.loss_ce: 0.0118 aux.acc_seg: 98.6469 +04/17 11:05:08 - mmengine - INFO - Iter(train) [ 9200/160000] base_lr: 9.5143e-05 lr: 3.5176e-07 eta: 1 day, 17:56:03 time: 0.9999 data_time: 0.0041 memory: 8462 loss: 0.0288 decode.loss_ce: 0.0166 decode.acc_seg: 99.1257 aux.loss_ce: 0.0122 aux.acc_seg: 98.5540 +04/17 11:05:58 - mmengine - INFO - Iter(train) [ 9250/160000] base_lr: 9.5111e-05 lr: 3.5164e-07 eta: 1 day, 17:55:12 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0361 decode.loss_ce: 0.0221 decode.acc_seg: 99.4940 aux.loss_ce: 0.0140 aux.acc_seg: 99.1802 +04/17 11:06:47 - mmengine - INFO - Iter(train) [ 9300/160000] base_lr: 9.5079e-05 lr: 3.5153e-07 eta: 1 day, 17:54:20 time: 0.9994 data_time: 0.0041 memory: 8462 loss: 0.0266 decode.loss_ce: 0.0153 decode.acc_seg: 99.6422 aux.loss_ce: 0.0114 aux.acc_seg: 99.3658 +04/17 11:07:37 - mmengine - INFO - Iter(train) [ 9350/160000] base_lr: 9.5048e-05 lr: 3.5141e-07 eta: 1 day, 17:53:29 time: 0.9984 data_time: 0.0042 memory: 8462 loss: 0.0290 decode.loss_ce: 0.0168 decode.acc_seg: 99.5070 aux.loss_ce: 0.0122 aux.acc_seg: 99.0034 +04/17 11:08:27 - mmengine - INFO - Iter(train) [ 9400/160000] base_lr: 9.5016e-05 lr: 3.5130e-07 eta: 1 day, 17:52:37 time: 0.9990 data_time: 0.0046 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0172 decode.acc_seg: 99.3139 aux.loss_ce: 0.0114 aux.acc_seg: 99.0477 +04/17 11:09:17 - mmengine - INFO - Iter(train) [ 9450/160000] base_lr: 9.4985e-05 lr: 3.5118e-07 eta: 1 day, 17:51:46 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0280 decode.loss_ce: 0.0161 decode.acc_seg: 99.5781 aux.loss_ce: 0.0120 aux.acc_seg: 99.3572 +04/17 11:10:07 - mmengine - INFO - Iter(train) [ 9500/160000] base_lr: 9.4953e-05 lr: 3.5106e-07 eta: 1 day, 17:50:55 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0311 decode.loss_ce: 0.0183 decode.acc_seg: 99.4741 aux.loss_ce: 0.0128 aux.acc_seg: 98.6847 +04/17 11:10:57 - mmengine - INFO - Iter(train) [ 9550/160000] base_lr: 9.4922e-05 lr: 3.5095e-07 eta: 1 day, 17:50:04 time: 0.9996 data_time: 0.0047 memory: 8462 loss: 0.0341 decode.loss_ce: 0.0208 decode.acc_seg: 99.3027 aux.loss_ce: 0.0133 aux.acc_seg: 98.7852 +04/17 11:11:47 - mmengine - INFO - Iter(train) [ 9600/160000] base_lr: 9.4890e-05 lr: 3.5083e-07 eta: 1 day, 17:49:12 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0271 decode.loss_ce: 0.0162 decode.acc_seg: 99.4505 aux.loss_ce: 0.0109 aux.acc_seg: 98.7850 +04/17 11:12:37 - mmengine - INFO - Iter(train) [ 9650/160000] base_lr: 9.4859e-05 lr: 3.5071e-07 eta: 1 day, 17:48:21 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0422 decode.loss_ce: 0.0263 decode.acc_seg: 97.1151 aux.loss_ce: 0.0158 aux.acc_seg: 97.0732 +04/17 11:13:27 - mmengine - INFO - Iter(train) [ 9700/160000] base_lr: 9.4827e-05 lr: 3.5060e-07 eta: 1 day, 17:47:29 time: 0.9986 data_time: 0.0044 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0143 decode.acc_seg: 99.3326 aux.loss_ce: 0.0102 aux.acc_seg: 98.6275 +04/17 11:14:17 - mmengine - INFO - Iter(train) [ 9750/160000] base_lr: 9.4796e-05 lr: 3.5048e-07 eta: 1 day, 17:46:38 time: 0.9988 data_time: 0.0042 memory: 8462 loss: 0.0313 decode.loss_ce: 0.0178 decode.acc_seg: 99.2683 aux.loss_ce: 0.0135 aux.acc_seg: 98.4152 +04/17 11:15:07 - mmengine - INFO - Iter(train) [ 9800/160000] base_lr: 9.4764e-05 lr: 3.5036e-07 eta: 1 day, 17:45:46 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0285 decode.loss_ce: 0.0174 decode.acc_seg: 99.5544 aux.loss_ce: 0.0111 aux.acc_seg: 99.2006 +04/17 11:15:57 - mmengine - INFO - Iter(train) [ 9850/160000] base_lr: 9.4732e-05 lr: 3.5025e-07 eta: 1 day, 17:44:55 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0149 decode.acc_seg: 99.2363 aux.loss_ce: 0.0118 aux.acc_seg: 98.6767 +04/17 11:16:47 - mmengine - INFO - Iter(train) [ 9900/160000] base_lr: 9.4701e-05 lr: 3.5013e-07 eta: 1 day, 17:44:03 time: 0.9983 data_time: 0.0044 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0154 decode.acc_seg: 98.6429 aux.loss_ce: 0.0113 aux.acc_seg: 98.1266 +04/17 11:17:37 - mmengine - INFO - Iter(train) [ 9950/160000] base_lr: 9.4669e-05 lr: 3.5001e-07 eta: 1 day, 17:43:11 time: 0.9989 data_time: 0.0045 memory: 8462 loss: 0.0329 decode.loss_ce: 0.0202 decode.acc_seg: 99.0934 aux.loss_ce: 0.0127 aux.acc_seg: 98.5346 +04/17 11:18:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 11:18:27 - mmengine - INFO - Iter(train) [ 10000/160000] base_lr: 9.4638e-05 lr: 3.4990e-07 eta: 1 day, 17:42:20 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0318 decode.loss_ce: 0.0197 decode.acc_seg: 99.1579 aux.loss_ce: 0.0122 aux.acc_seg: 98.8224 +04/17 11:18:27 - mmengine - INFO - Saving checkpoint at 10000 iterations +04/17 11:18:38 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:21 time: 0.1155 data_time: 0.0014 memory: 7125 +04/17 11:18:44 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:13 time: 0.1156 data_time: 0.0014 memory: 4004 +04/17 11:18:50 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:06 time: 0.1157 data_time: 0.0014 memory: 4004 +04/17 11:18:56 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1154 data_time: 0.0012 memory: 4004 +04/17 11:18:56 - mmengine - INFO - per class results: +04/17 11:18:56 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.08 | 99.54 | 99.54 | 99.54 | 99.54 | +| contrast | 80.1 | 89.03 | 88.95 | 88.87 | 89.03 | ++------------+-------+-------+--------+-----------+--------+ +04/17 11:18:56 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1200 mIoU: 89.5900 mAcc: 94.2800 mFscore: 94.2400 mPrecision: 94.2100 mRecall: 94.2800 data_time: 0.0025 time: 0.1230 +04/17 11:19:46 - mmengine - INFO - Iter(train) [ 10050/160000] base_lr: 9.4606e-05 lr: 3.4978e-07 eta: 1 day, 17:41:31 time: 0.9982 data_time: 0.0045 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0127 decode.acc_seg: 99.5184 aux.loss_ce: 0.0101 aux.acc_seg: 98.6654 +04/17 11:20:36 - mmengine - INFO - Iter(train) [ 10100/160000] base_lr: 9.4575e-05 lr: 3.4966e-07 eta: 1 day, 17:40:38 time: 0.9976 data_time: 0.0043 memory: 8462 loss: 0.0265 decode.loss_ce: 0.0152 decode.acc_seg: 99.2083 aux.loss_ce: 0.0113 aux.acc_seg: 98.8071 +04/17 11:21:26 - mmengine - INFO - Iter(train) [ 10150/160000] base_lr: 9.4543e-05 lr: 3.4955e-07 eta: 1 day, 17:39:46 time: 0.9978 data_time: 0.0042 memory: 8462 loss: 0.0320 decode.loss_ce: 0.0193 decode.acc_seg: 99.5743 aux.loss_ce: 0.0127 aux.acc_seg: 98.9920 +04/17 11:22:16 - mmengine - INFO - Iter(train) [ 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aux.acc_seg: 99.3280 +04/17 11:25:35 - mmengine - INFO - Iter(train) [ 10400/160000] base_lr: 9.4385e-05 lr: 3.4896e-07 eta: 1 day, 17:35:29 time: 0.9999 data_time: 0.0047 memory: 8462 loss: 0.0279 decode.loss_ce: 0.0161 decode.acc_seg: 99.0896 aux.loss_ce: 0.0118 aux.acc_seg: 98.7675 +04/17 11:26:25 - mmengine - INFO - Iter(train) [ 10450/160000] base_lr: 9.4354e-05 lr: 3.4885e-07 eta: 1 day, 17:34:38 time: 0.9985 data_time: 0.0045 memory: 8462 loss: 0.0307 decode.loss_ce: 0.0181 decode.acc_seg: 99.5108 aux.loss_ce: 0.0127 aux.acc_seg: 99.0753 +04/17 11:27:15 - mmengine - INFO - Iter(train) [ 10500/160000] base_lr: 9.4322e-05 lr: 3.4873e-07 eta: 1 day, 17:33:46 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.0291 decode.loss_ce: 0.0175 decode.acc_seg: 99.3355 aux.loss_ce: 0.0116 aux.acc_seg: 98.9653 +04/17 11:28:05 - mmengine - INFO - Iter(train) [ 10550/160000] base_lr: 9.4291e-05 lr: 3.4861e-07 eta: 1 day, 17:32:55 time: 1.0003 data_time: 0.0043 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0176 decode.acc_seg: 99.3546 aux.loss_ce: 0.0111 aux.acc_seg: 98.6609 +04/17 11:28:55 - mmengine - INFO - Iter(train) [ 10600/160000] base_lr: 9.4259e-05 lr: 3.4850e-07 eta: 1 day, 17:32:03 time: 0.9968 data_time: 0.0045 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0156 decode.acc_seg: 99.4614 aux.loss_ce: 0.0119 aux.acc_seg: 98.6715 +04/17 11:29:45 - mmengine - INFO - Iter(train) [ 10650/160000] base_lr: 9.4228e-05 lr: 3.4838e-07 eta: 1 day, 17:31:11 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0164 decode.acc_seg: 99.5975 aux.loss_ce: 0.0119 aux.acc_seg: 99.0761 +04/17 11:30:35 - mmengine - INFO - Iter(train) [ 10700/160000] base_lr: 9.4196e-05 lr: 3.4826e-07 eta: 1 day, 17:30:20 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0154 decode.acc_seg: 99.5754 aux.loss_ce: 0.0113 aux.acc_seg: 99.1217 +04/17 11:31:25 - mmengine - INFO - Iter(train) [ 10750/160000] base_lr: 9.4165e-05 lr: 3.4815e-07 eta: 1 day, 17:29:28 time: 0.9996 data_time: 0.0049 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0124 decode.acc_seg: 99.7696 aux.loss_ce: 0.0100 aux.acc_seg: 99.5125 +04/17 11:32:15 - mmengine - INFO - Iter(train) [ 10800/160000] base_lr: 9.4133e-05 lr: 3.4803e-07 eta: 1 day, 17:28:37 time: 0.9997 data_time: 0.0041 memory: 8462 loss: 0.0295 decode.loss_ce: 0.0172 decode.acc_seg: 99.4450 aux.loss_ce: 0.0124 aux.acc_seg: 98.9927 +04/17 11:33:05 - mmengine - INFO - Iter(train) [ 10850/160000] base_lr: 9.4102e-05 lr: 3.4791e-07 eta: 1 day, 17:27:46 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0328 decode.loss_ce: 0.0201 decode.acc_seg: 99.3118 aux.loss_ce: 0.0128 aux.acc_seg: 99.0999 +04/17 11:33:55 - mmengine - INFO - Iter(train) [ 10900/160000] base_lr: 9.4070e-05 lr: 3.4780e-07 eta: 1 day, 17:26:54 time: 0.9985 data_time: 0.0047 memory: 8462 loss: 0.0293 decode.loss_ce: 0.0176 decode.acc_seg: 99.5304 aux.loss_ce: 0.0117 aux.acc_seg: 99.0887 +04/17 11:34:45 - mmengine - INFO - Iter(train) [ 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memory: 8462 loss: 0.0285 decode.loss_ce: 0.0160 decode.acc_seg: 99.6382 aux.loss_ce: 0.0124 aux.acc_seg: 99.2136 +04/17 11:38:04 - mmengine - INFO - Iter(train) [ 11150/160000] base_lr: 9.3912e-05 lr: 3.4721e-07 eta: 1 day, 17:22:37 time: 0.9979 data_time: 0.0044 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0143 decode.acc_seg: 99.3307 aux.loss_ce: 0.0109 aux.acc_seg: 98.5315 +04/17 11:38:54 - mmengine - INFO - Iter(train) [ 11200/160000] base_lr: 9.3881e-05 lr: 3.4710e-07 eta: 1 day, 17:21:46 time: 0.9979 data_time: 0.0042 memory: 8462 loss: 0.0291 decode.loss_ce: 0.0178 decode.acc_seg: 99.6355 aux.loss_ce: 0.0113 aux.acc_seg: 99.2823 +04/17 11:39:44 - mmengine - INFO - Iter(train) [ 11250/160000] base_lr: 9.3849e-05 lr: 3.4698e-07 eta: 1 day, 17:20:54 time: 0.9981 data_time: 0.0045 memory: 8462 loss: 0.0241 decode.loss_ce: 0.0136 decode.acc_seg: 99.3332 aux.loss_ce: 0.0106 aux.acc_seg: 98.6628 +04/17 11:40:34 - mmengine - INFO - Iter(train) [ 11300/160000] base_lr: 9.3818e-05 lr: 3.4686e-07 eta: 1 day, 17:20:02 time: 0.9988 data_time: 0.0047 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0133 decode.acc_seg: 99.3959 aux.loss_ce: 0.0107 aux.acc_seg: 98.8110 +04/17 11:41:24 - mmengine - INFO - Iter(train) [ 11350/160000] base_lr: 9.3786e-05 lr: 3.4675e-07 eta: 1 day, 17:19:10 time: 0.9985 data_time: 0.0041 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0141 decode.acc_seg: 99.1455 aux.loss_ce: 0.0106 aux.acc_seg: 98.6660 +04/17 11:42:14 - mmengine - INFO - Iter(train) [ 11400/160000] base_lr: 9.3755e-05 lr: 3.4663e-07 eta: 1 day, 17:18:19 time: 0.9975 data_time: 0.0051 memory: 8462 loss: 0.0301 decode.loss_ce: 0.0177 decode.acc_seg: 99.1589 aux.loss_ce: 0.0124 aux.acc_seg: 98.4291 +04/17 11:43:04 - mmengine - INFO - Iter(train) [ 11450/160000] base_lr: 9.3723e-05 lr: 3.4651e-07 eta: 1 day, 17:17:27 time: 0.9990 data_time: 0.0048 memory: 8462 loss: 0.0251 decode.loss_ce: 0.0149 decode.acc_seg: 99.0963 aux.loss_ce: 0.0102 aux.acc_seg: 98.6616 +04/17 11:43:54 - mmengine - INFO - Iter(train) [ 11500/160000] base_lr: 9.3691e-05 lr: 3.4640e-07 eta: 1 day, 17:16:36 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0246 decode.loss_ce: 0.0141 decode.acc_seg: 99.2422 aux.loss_ce: 0.0105 aux.acc_seg: 98.6483 +04/17 11:44:44 - mmengine - INFO - Iter(train) [ 11550/160000] base_lr: 9.3660e-05 lr: 3.4628e-07 eta: 1 day, 17:15:45 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0138 decode.acc_seg: 99.5718 aux.loss_ce: 0.0108 aux.acc_seg: 98.8657 +04/17 11:45:34 - mmengine - INFO - Iter(train) [ 11600/160000] base_lr: 9.3628e-05 lr: 3.4616e-07 eta: 1 day, 17:14:53 time: 0.9977 data_time: 0.0041 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0157 decode.acc_seg: 99.7395 aux.loss_ce: 0.0110 aux.acc_seg: 99.3423 +04/17 11:46:24 - mmengine - INFO - Iter(train) [ 11650/160000] base_lr: 9.3597e-05 lr: 3.4605e-07 eta: 1 day, 17:14:02 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0138 decode.acc_seg: 99.1066 aux.loss_ce: 0.0108 aux.acc_seg: 98.3194 +04/17 11:47:14 - mmengine - INFO - Iter(train) [ 11700/160000] base_lr: 9.3565e-05 lr: 3.4593e-07 eta: 1 day, 17:13:11 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0288 decode.loss_ce: 0.0169 decode.acc_seg: 98.5687 aux.loss_ce: 0.0119 aux.acc_seg: 98.0122 +04/17 11:48:03 - mmengine - INFO - Iter(train) [ 11750/160000] base_lr: 9.3534e-05 lr: 3.4581e-07 eta: 1 day, 17:12:19 time: 0.9987 data_time: 0.0043 memory: 8462 loss: 0.0266 decode.loss_ce: 0.0157 decode.acc_seg: 99.4154 aux.loss_ce: 0.0109 aux.acc_seg: 98.7604 +04/17 11:48:53 - mmengine - INFO - Iter(train) [ 11800/160000] base_lr: 9.3502e-05 lr: 3.4570e-07 eta: 1 day, 17:11:28 time: 0.9971 data_time: 0.0042 memory: 8462 loss: 0.0273 decode.loss_ce: 0.0153 decode.acc_seg: 99.6025 aux.loss_ce: 0.0120 aux.acc_seg: 99.0688 +04/17 11:49:43 - mmengine - INFO - Iter(train) [ 11850/160000] base_lr: 9.3471e-05 lr: 3.4558e-07 eta: 1 day, 17:10:36 time: 0.9984 data_time: 0.0043 memory: 8462 loss: 0.0260 decode.loss_ce: 0.0149 decode.acc_seg: 99.0017 aux.loss_ce: 0.0111 aux.acc_seg: 98.8382 +04/17 11:50:33 - mmengine - INFO - Iter(train) [ 11900/160000] base_lr: 9.3439e-05 lr: 3.4546e-07 eta: 1 day, 17:09:45 time: 0.9980 data_time: 0.0044 memory: 8462 loss: 0.0258 decode.loss_ce: 0.0150 decode.acc_seg: 99.1013 aux.loss_ce: 0.0108 aux.acc_seg: 98.4045 +04/17 11:51:23 - mmengine - INFO - Iter(train) [ 11950/160000] base_lr: 9.3408e-05 lr: 3.4535e-07 eta: 1 day, 17:08:54 time: 0.9983 data_time: 0.0045 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0159 decode.acc_seg: 99.4509 aux.loss_ce: 0.0116 aux.acc_seg: 98.7490 +04/17 11:52:13 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 11:52:13 - mmengine - INFO - Iter(train) [ 12000/160000] base_lr: 9.3376e-05 lr: 3.4523e-07 eta: 1 day, 17:08:02 time: 0.9975 data_time: 0.0043 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0151 decode.acc_seg: 98.8495 aux.loss_ce: 0.0110 aux.acc_seg: 98.4133 +04/17 11:53:03 - mmengine - INFO - Iter(train) [ 12050/160000] base_lr: 9.3344e-05 lr: 3.4511e-07 eta: 1 day, 17:07:10 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0136 decode.acc_seg: 99.4600 aux.loss_ce: 0.0097 aux.acc_seg: 99.2146 +04/17 11:53:53 - mmengine - INFO - Iter(train) [ 12100/160000] base_lr: 9.3313e-05 lr: 3.4500e-07 eta: 1 day, 17:06:19 time: 0.9968 data_time: 0.0051 memory: 8462 loss: 0.0287 decode.loss_ce: 0.0161 decode.acc_seg: 99.6464 aux.loss_ce: 0.0126 aux.acc_seg: 99.0677 +04/17 11:54:43 - mmengine - INFO - Iter(train) [ 12150/160000] base_lr: 9.3281e-05 lr: 3.4488e-07 eta: 1 day, 17:05:27 time: 0.9973 data_time: 0.0045 memory: 8462 loss: 0.0291 decode.loss_ce: 0.0168 decode.acc_seg: 99.0984 aux.loss_ce: 0.0123 aux.acc_seg: 98.5958 +04/17 11:55:33 - mmengine - INFO - Iter(train) [ 12200/160000] base_lr: 9.3250e-05 lr: 3.4476e-07 eta: 1 day, 17:04:35 time: 0.9968 data_time: 0.0042 memory: 8462 loss: 0.0271 decode.loss_ce: 0.0160 decode.acc_seg: 99.2754 aux.loss_ce: 0.0111 aux.acc_seg: 98.7045 +04/17 11:56:22 - mmengine - INFO - Iter(train) [ 12250/160000] base_lr: 9.3218e-05 lr: 3.4465e-07 eta: 1 day, 17:03:43 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0274 decode.loss_ce: 0.0156 decode.acc_seg: 99.5762 aux.loss_ce: 0.0118 aux.acc_seg: 98.3437 +04/17 11:57:12 - mmengine - INFO - Iter(train) [ 12300/160000] base_lr: 9.3187e-05 lr: 3.4453e-07 eta: 1 day, 17:02:52 time: 0.9974 data_time: 0.0045 memory: 8462 loss: 0.0259 decode.loss_ce: 0.0148 decode.acc_seg: 99.4024 aux.loss_ce: 0.0111 aux.acc_seg: 98.6334 +04/17 11:58:02 - mmengine - INFO - Iter(train) [ 12350/160000] base_lr: 9.3155e-05 lr: 3.4441e-07 eta: 1 day, 17:02:01 time: 0.9980 data_time: 0.0047 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0144 decode.acc_seg: 99.4419 aux.loss_ce: 0.0107 aux.acc_seg: 98.6288 +04/17 11:58:52 - mmengine - INFO - Iter(train) [ 12400/160000] base_lr: 9.3124e-05 lr: 3.4430e-07 eta: 1 day, 17:01:09 time: 0.9977 data_time: 0.0047 memory: 8462 loss: 0.0268 decode.loss_ce: 0.0153 decode.acc_seg: 99.0204 aux.loss_ce: 0.0115 aux.acc_seg: 98.3780 +04/17 11:59:42 - mmengine - INFO - Iter(train) [ 12450/160000] base_lr: 9.3092e-05 lr: 3.4418e-07 eta: 1 day, 17:00:18 time: 0.9977 data_time: 0.0048 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0142 decode.acc_seg: 99.4844 aux.loss_ce: 0.0110 aux.acc_seg: 98.9780 +04/17 12:00:32 - mmengine - INFO - Iter(train) [ 12500/160000] base_lr: 9.3061e-05 lr: 3.4406e-07 eta: 1 day, 16:59:26 time: 0.9965 data_time: 0.0042 memory: 8462 loss: 0.0316 decode.loss_ce: 0.0184 decode.acc_seg: 99.4486 aux.loss_ce: 0.0132 aux.acc_seg: 98.5220 +04/17 12:01:22 - mmengine - INFO - Iter(train) [ 12550/160000] base_lr: 9.3029e-05 lr: 3.4395e-07 eta: 1 day, 16:58:35 time: 0.9981 data_time: 0.0044 memory: 8462 loss: 0.0269 decode.loss_ce: 0.0160 decode.acc_seg: 99.4404 aux.loss_ce: 0.0109 aux.acc_seg: 99.0669 +04/17 12:02:12 - mmengine - INFO - Iter(train) [ 12600/160000] base_lr: 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12:05:31 - mmengine - INFO - Iter(train) [ 12800/160000] base_lr: 9.2871e-05 lr: 3.4336e-07 eta: 1 day, 16:54:18 time: 0.9983 data_time: 0.0048 memory: 8462 loss: 0.0259 decode.loss_ce: 0.0146 decode.acc_seg: 99.4265 aux.loss_ce: 0.0113 aux.acc_seg: 98.6423 +04/17 12:06:21 - mmengine - INFO - Iter(train) [ 12850/160000] base_lr: 9.2840e-05 lr: 3.4325e-07 eta: 1 day, 16:53:26 time: 0.9972 data_time: 0.0046 memory: 8462 loss: 0.0269 decode.loss_ce: 0.0159 decode.acc_seg: 99.3744 aux.loss_ce: 0.0110 aux.acc_seg: 98.5023 +04/17 12:07:11 - mmengine - INFO - Iter(train) [ 12900/160000] base_lr: 9.2808e-05 lr: 3.4313e-07 eta: 1 day, 16:52:35 time: 0.9987 data_time: 0.0041 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0135 decode.acc_seg: 99.3826 aux.loss_ce: 0.0106 aux.acc_seg: 98.7181 +04/17 12:08:01 - mmengine - INFO - Iter(train) [ 12950/160000] base_lr: 9.2777e-05 lr: 3.4301e-07 eta: 1 day, 16:51:43 time: 0.9990 data_time: 0.0058 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0118 decode.acc_seg: 99.4797 aux.loss_ce: 0.0095 aux.acc_seg: 98.8075 +04/17 12:08:51 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 12:08:51 - mmengine - INFO - Iter(train) [ 13000/160000] base_lr: 9.2745e-05 lr: 3.4290e-07 eta: 1 day, 16:50:52 time: 0.9986 data_time: 0.0047 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0142 decode.acc_seg: 99.0213 aux.loss_ce: 0.0105 aux.acc_seg: 98.4758 +04/17 12:09:41 - mmengine - INFO - Iter(train) [ 13050/160000] base_lr: 9.2714e-05 lr: 3.4278e-07 eta: 1 day, 16:50:00 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0134 decode.acc_seg: 99.3568 aux.loss_ce: 0.0108 aux.acc_seg: 98.4947 +04/17 12:10:31 - mmengine - INFO - Iter(train) [ 13100/160000] base_lr: 9.2682e-05 lr: 3.4266e-07 eta: 1 day, 16:49:09 time: 0.9990 data_time: 0.0042 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0125 decode.acc_seg: 99.0801 aux.loss_ce: 0.0097 aux.acc_seg: 98.3044 +04/17 12:11:21 - mmengine - INFO - 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0.0104 aux.acc_seg: 98.6025 +04/17 12:14:40 - mmengine - INFO - Iter(train) [ 13350/160000] base_lr: 9.2524e-05 lr: 3.4208e-07 eta: 1 day, 16:44:52 time: 0.9980 data_time: 0.0041 memory: 8462 loss: 0.0238 decode.loss_ce: 0.0135 decode.acc_seg: 99.5161 aux.loss_ce: 0.0103 aux.acc_seg: 99.2218 +04/17 12:15:30 - mmengine - INFO - Iter(train) [ 13400/160000] base_lr: 9.2493e-05 lr: 3.4196e-07 eta: 1 day, 16:44:00 time: 0.9974 data_time: 0.0045 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0134 decode.acc_seg: 99.6300 aux.loss_ce: 0.0113 aux.acc_seg: 99.0601 +04/17 12:16:20 - mmengine - INFO - Iter(train) [ 13450/160000] base_lr: 9.2461e-05 lr: 3.4185e-07 eta: 1 day, 16:43:09 time: 0.9979 data_time: 0.0045 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0135 decode.acc_seg: 99.5245 aux.loss_ce: 0.0105 aux.acc_seg: 98.8018 +04/17 12:17:10 - mmengine - INFO - Iter(train) [ 13500/160000] base_lr: 9.2430e-05 lr: 3.4173e-07 eta: 1 day, 16:42:18 time: 0.9969 data_time: 0.0043 memory: 8462 loss: 0.0278 decode.loss_ce: 0.0156 decode.acc_seg: 99.4808 aux.loss_ce: 0.0123 aux.acc_seg: 98.9613 +04/17 12:18:00 - mmengine - INFO - Iter(train) [ 13550/160000] base_lr: 9.2398e-05 lr: 3.4161e-07 eta: 1 day, 16:41:26 time: 0.9970 data_time: 0.0047 memory: 8462 loss: 0.0221 decode.loss_ce: 0.0125 decode.acc_seg: 98.9977 aux.loss_ce: 0.0096 aux.acc_seg: 98.8420 +04/17 12:18:50 - mmengine - INFO - Iter(train) [ 13600/160000] base_lr: 9.2367e-05 lr: 3.4150e-07 eta: 1 day, 16:40:35 time: 0.9968 data_time: 0.0048 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0115 decode.acc_seg: 99.4091 aux.loss_ce: 0.0095 aux.acc_seg: 98.9174 +04/17 12:19:40 - mmengine - INFO - Iter(train) [ 13650/160000] base_lr: 9.2335e-05 lr: 3.4138e-07 eta: 1 day, 16:39:43 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0118 decode.acc_seg: 99.1947 aux.loss_ce: 0.0101 aux.acc_seg: 98.6771 +04/17 12:20:29 - mmengine - INFO - Iter(train) [ 13700/160000] base_lr: 9.2303e-05 lr: 3.4126e-07 eta: 1 day, 16:38:52 time: 0.9994 data_time: 0.0046 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0123 decode.acc_seg: 99.5081 aux.loss_ce: 0.0096 aux.acc_seg: 98.7627 +04/17 12:21:19 - mmengine - INFO - Iter(train) [ 13750/160000] base_lr: 9.2272e-05 lr: 3.4115e-07 eta: 1 day, 16:38:00 time: 0.9972 data_time: 0.0048 memory: 8462 loss: 0.0248 decode.loss_ce: 0.0138 decode.acc_seg: 99.3843 aux.loss_ce: 0.0110 aux.acc_seg: 98.3892 +04/17 12:22:09 - mmengine - INFO - Iter(train) [ 13800/160000] base_lr: 9.2240e-05 lr: 3.4103e-07 eta: 1 day, 16:37:09 time: 0.9985 data_time: 0.0048 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0167 decode.acc_seg: 99.5398 aux.loss_ce: 0.0116 aux.acc_seg: 99.1119 +04/17 12:22:59 - mmengine - INFO - Iter(train) [ 13850/160000] base_lr: 9.2209e-05 lr: 3.4091e-07 eta: 1 day, 16:36:18 time: 0.9953 data_time: 0.0045 memory: 8462 loss: 0.0256 decode.loss_ce: 0.0142 decode.acc_seg: 98.9668 aux.loss_ce: 0.0114 aux.acc_seg: 98.2203 +04/17 12:23:49 - mmengine - INFO - Iter(train) [ 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memory: 8462 loss: 0.0270 decode.loss_ce: 0.0141 decode.acc_seg: 99.3965 aux.loss_ce: 0.0128 aux.acc_seg: 98.5229 +04/17 12:27:09 - mmengine - INFO - Iter(train) [ 14100/160000] base_lr: 9.2051e-05 lr: 3.4033e-07 eta: 1 day, 16:32:01 time: 0.9974 data_time: 0.0043 memory: 8462 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.5802 aux.loss_ce: 0.0090 aux.acc_seg: 99.3126 +04/17 12:27:58 - mmengine - INFO - Iter(train) [ 14150/160000] base_lr: 9.2020e-05 lr: 3.4022e-07 eta: 1 day, 16:31:10 time: 0.9978 data_time: 0.0044 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0126 decode.acc_seg: 99.6016 aux.loss_ce: 0.0102 aux.acc_seg: 98.9491 +04/17 12:28:48 - mmengine - INFO - Iter(train) [ 14200/160000] base_lr: 9.1988e-05 lr: 3.4010e-07 eta: 1 day, 16:30:18 time: 0.9979 data_time: 0.0048 memory: 8462 loss: 0.0248 decode.loss_ce: 0.0138 decode.acc_seg: 99.4162 aux.loss_ce: 0.0109 aux.acc_seg: 98.5853 +04/17 12:29:38 - mmengine - INFO - Iter(train) [ 14250/160000] base_lr: 9.1956e-05 lr: 3.3998e-07 eta: 1 day, 16:29:27 time: 0.9979 data_time: 0.0042 memory: 8462 loss: 0.0226 decode.loss_ce: 0.0123 decode.acc_seg: 99.3723 aux.loss_ce: 0.0103 aux.acc_seg: 98.7762 +04/17 12:30:28 - mmengine - INFO - Iter(train) [ 14300/160000] base_lr: 9.1925e-05 lr: 3.3987e-07 eta: 1 day, 16:28:35 time: 0.9959 data_time: 0.0042 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0114 decode.acc_seg: 99.5321 aux.loss_ce: 0.0096 aux.acc_seg: 98.9382 +04/17 12:31:18 - mmengine - INFO - Iter(train) [ 14350/160000] base_lr: 9.1893e-05 lr: 3.3975e-07 eta: 1 day, 16:27:44 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0255 decode.loss_ce: 0.0144 decode.acc_seg: 99.4427 aux.loss_ce: 0.0111 aux.acc_seg: 98.8909 +04/17 12:32:08 - mmengine - INFO - Iter(train) [ 14400/160000] base_lr: 9.1862e-05 lr: 3.3963e-07 eta: 1 day, 16:26:53 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0243 decode.loss_ce: 0.0139 decode.acc_seg: 99.5283 aux.loss_ce: 0.0105 aux.acc_seg: 99.2180 +04/17 12:32:58 - mmengine - INFO - Iter(train) [ 14450/160000] base_lr: 9.1830e-05 lr: 3.3952e-07 eta: 1 day, 16:26:02 time: 0.9968 data_time: 0.0044 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0125 decode.acc_seg: 99.5022 aux.loss_ce: 0.0101 aux.acc_seg: 98.7011 +04/17 12:33:48 - mmengine - INFO - Iter(train) [ 14500/160000] base_lr: 9.1799e-05 lr: 3.3940e-07 eta: 1 day, 16:25:10 time: 0.9973 data_time: 0.0047 memory: 8462 loss: 0.0254 decode.loss_ce: 0.0139 decode.acc_seg: 99.5682 aux.loss_ce: 0.0115 aux.acc_seg: 99.0284 +04/17 12:34:37 - mmengine - INFO - Iter(train) [ 14550/160000] base_lr: 9.1767e-05 lr: 3.3928e-07 eta: 1 day, 16:24:19 time: 0.9985 data_time: 0.0046 memory: 8462 loss: 0.0246 decode.loss_ce: 0.0136 decode.acc_seg: 99.6719 aux.loss_ce: 0.0110 aux.acc_seg: 99.3185 +04/17 12:35:27 - mmengine - INFO - Iter(train) [ 14600/160000] base_lr: 9.1736e-05 lr: 3.3917e-07 eta: 1 day, 16:23:28 time: 0.9979 data_time: 0.0049 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0125 decode.acc_seg: 99.6122 aux.loss_ce: 0.0099 aux.acc_seg: 99.1463 +04/17 12:36:17 - mmengine - INFO - Iter(train) [ 14650/160000] base_lr: 9.1704e-05 lr: 3.3905e-07 eta: 1 day, 16:22:37 time: 0.9967 data_time: 0.0044 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0127 decode.acc_seg: 99.5100 aux.loss_ce: 0.0098 aux.acc_seg: 99.2855 +04/17 12:37:07 - mmengine - INFO - Iter(train) [ 14700/160000] base_lr: 9.1673e-05 lr: 3.3893e-07 eta: 1 day, 16:21:46 time: 0.9975 data_time: 0.0043 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0120 decode.acc_seg: 99.6143 aux.loss_ce: 0.0102 aux.acc_seg: 99.1068 +04/17 12:37:57 - mmengine - INFO - Iter(train) [ 14750/160000] base_lr: 9.1641e-05 lr: 3.3882e-07 eta: 1 day, 16:20:55 time: 0.9978 data_time: 0.0043 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0164 decode.acc_seg: 99.5216 aux.loss_ce: 0.0120 aux.acc_seg: 98.9532 +04/17 12:38:47 - mmengine - INFO - Iter(train) [ 14800/160000] base_lr: 9.1609e-05 lr: 3.3870e-07 eta: 1 day, 16:20:04 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0154 decode.acc_seg: 99.6338 aux.loss_ce: 0.0122 aux.acc_seg: 99.1535 +04/17 12:39:37 - mmengine - INFO - Iter(train) [ 14850/160000] base_lr: 9.1578e-05 lr: 3.3858e-07 eta: 1 day, 16:19:13 time: 0.9978 data_time: 0.0044 memory: 8462 loss: 0.0235 decode.loss_ce: 0.0129 decode.acc_seg: 99.4799 aux.loss_ce: 0.0106 aux.acc_seg: 98.4684 +04/17 12:40:27 - mmengine - INFO - Iter(train) [ 14900/160000] base_lr: 9.1546e-05 lr: 3.3847e-07 eta: 1 day, 16:18:22 time: 0.9977 data_time: 0.0044 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0132 decode.acc_seg: 99.5035 aux.loss_ce: 0.0108 aux.acc_seg: 99.1489 +04/17 12:41:17 - mmengine - INFO - Iter(train) [ 14950/160000] base_lr: 9.1515e-05 lr: 3.3835e-07 eta: 1 day, 16:17:31 time: 0.9975 data_time: 0.0046 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0135 decode.acc_seg: 99.4625 aux.loss_ce: 0.0105 aux.acc_seg: 99.0116 +04/17 12:42:06 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 12:42:06 - mmengine - INFO - Iter(train) [ 15000/160000] base_lr: 9.1483e-05 lr: 3.3823e-07 eta: 1 day, 16:16:40 time: 0.9989 data_time: 0.0046 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0151 decode.acc_seg: 99.4242 aux.loss_ce: 0.0111 aux.acc_seg: 99.0814 +04/17 12:42:56 - mmengine - INFO - Iter(train) [ 15050/160000] base_lr: 9.1452e-05 lr: 3.3812e-07 eta: 1 day, 16:15:48 time: 0.9970 data_time: 0.0042 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0142 decode.acc_seg: 99.3740 aux.loss_ce: 0.0098 aux.acc_seg: 99.0076 +04/17 12:43:46 - mmengine - INFO - Iter(train) [ 15100/160000] base_lr: 9.1420e-05 lr: 3.3800e-07 eta: 1 day, 16:14:57 time: 0.9973 data_time: 0.0043 memory: 8462 loss: 0.0253 decode.loss_ce: 0.0133 decode.acc_seg: 99.4009 aux.loss_ce: 0.0120 aux.acc_seg: 98.8981 +04/17 12:44:36 - mmengine - INFO - Iter(train) [ 15150/160000] base_lr: 9.1389e-05 lr: 3.3788e-07 eta: 1 day, 16:14:06 time: 0.9979 data_time: 0.0042 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0128 decode.acc_seg: 99.4537 aux.loss_ce: 0.0101 aux.acc_seg: 98.9344 +04/17 12:45:26 - mmengine - INFO - Iter(train) [ 15200/160000] base_lr: 9.1357e-05 lr: 3.3777e-07 eta: 1 day, 16:13:15 time: 0.9968 data_time: 0.0042 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0114 decode.acc_seg: 99.4286 aux.loss_ce: 0.0097 aux.acc_seg: 98.8831 +04/17 12:46:16 - mmengine - INFO - Iter(train) [ 15250/160000] base_lr: 9.1325e-05 lr: 3.3765e-07 eta: 1 day, 16:12:23 time: 0.9980 data_time: 0.0043 memory: 8462 loss: 0.0251 decode.loss_ce: 0.0142 decode.acc_seg: 99.4448 aux.loss_ce: 0.0109 aux.acc_seg: 98.9090 +04/17 12:47:06 - mmengine - INFO - Iter(train) [ 15300/160000] base_lr: 9.1294e-05 lr: 3.3753e-07 eta: 1 day, 16:11:33 time: 0.9967 data_time: 0.0042 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0134 decode.acc_seg: 99.2613 aux.loss_ce: 0.0111 aux.acc_seg: 98.5228 +04/17 12:47:56 - mmengine - INFO - Iter(train) [ 15350/160000] base_lr: 9.1262e-05 lr: 3.3742e-07 eta: 1 day, 16:10:41 time: 0.9972 data_time: 0.0045 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0122 decode.acc_seg: 99.6096 aux.loss_ce: 0.0101 aux.acc_seg: 99.0072 +04/17 12:48:45 - mmengine - INFO - Iter(train) [ 15400/160000] base_lr: 9.1231e-05 lr: 3.3730e-07 eta: 1 day, 16:09:50 time: 0.9982 data_time: 0.0052 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.5132 aux.loss_ce: 0.0086 aux.acc_seg: 99.0273 +04/17 12:49:35 - mmengine - INFO - Iter(train) [ 15450/160000] base_lr: 9.1199e-05 lr: 3.3718e-07 eta: 1 day, 16:08:59 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0234 decode.loss_ce: 0.0126 decode.acc_seg: 99.6105 aux.loss_ce: 0.0108 aux.acc_seg: 98.9338 +04/17 12:50:25 - mmengine - INFO - Iter(train) [ 15500/160000] base_lr: 9.1168e-05 lr: 3.3707e-07 eta: 1 day, 16:08:08 time: 0.9982 data_time: 0.0046 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0137 decode.acc_seg: 99.3944 aux.loss_ce: 0.0101 aux.acc_seg: 98.6105 +04/17 12:51:15 - mmengine - INFO - Iter(train) [ 15550/160000] base_lr: 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12:54:35 - mmengine - INFO - Iter(train) [ 15750/160000] base_lr: 9.1010e-05 lr: 3.3648e-07 eta: 1 day, 16:03:52 time: 0.9969 data_time: 0.0046 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0129 decode.acc_seg: 99.5964 aux.loss_ce: 0.0099 aux.acc_seg: 99.0538 +04/17 12:55:24 - mmengine - INFO - Iter(train) [ 15800/160000] base_lr: 9.0978e-05 lr: 3.3637e-07 eta: 1 day, 16:03:01 time: 0.9977 data_time: 0.0044 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0131 decode.acc_seg: 99.5281 aux.loss_ce: 0.0108 aux.acc_seg: 98.9275 +04/17 12:56:14 - mmengine - INFO - Iter(train) [ 15850/160000] base_lr: 9.0947e-05 lr: 3.3625e-07 eta: 1 day, 16:02:10 time: 0.9989 data_time: 0.0049 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0130 decode.acc_seg: 99.7799 aux.loss_ce: 0.0108 aux.acc_seg: 99.4410 +04/17 12:57:04 - mmengine - INFO - Iter(train) [ 15900/160000] base_lr: 9.0915e-05 lr: 3.3613e-07 eta: 1 day, 16:01:19 time: 0.9970 data_time: 0.0045 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0138 decode.acc_seg: 99.0255 aux.loss_ce: 0.0109 aux.acc_seg: 98.0291 +04/17 12:57:54 - mmengine - INFO - Iter(train) [ 15950/160000] base_lr: 9.0884e-05 lr: 3.3602e-07 eta: 1 day, 16:00:28 time: 0.9987 data_time: 0.0048 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0121 decode.acc_seg: 99.2308 aux.loss_ce: 0.0093 aux.acc_seg: 98.8634 +04/17 12:58:44 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 12:58:44 - mmengine - INFO - Iter(train) [ 16000/160000] base_lr: 9.0852e-05 lr: 3.3590e-07 eta: 1 day, 15:59:36 time: 0.9974 data_time: 0.0051 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0126 decode.acc_seg: 98.7751 aux.loss_ce: 0.0098 aux.acc_seg: 98.1810 +04/17 12:59:34 - mmengine - INFO - Iter(train) [ 16050/160000] base_lr: 9.0821e-05 lr: 3.3578e-07 eta: 1 day, 15:58:45 time: 0.9982 data_time: 0.0048 memory: 8462 loss: 0.0217 decode.loss_ce: 0.0117 decode.acc_seg: 99.1167 aux.loss_ce: 0.0099 aux.acc_seg: 98.3572 +04/17 13:00:24 - mmengine - INFO - Iter(train) [ 16100/160000] base_lr: 9.0789e-05 lr: 3.3567e-07 eta: 1 day, 15:57:54 time: 0.9979 data_time: 0.0042 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0111 decode.acc_seg: 99.3414 aux.loss_ce: 0.0094 aux.acc_seg: 98.5754 +04/17 13:01:14 - mmengine - INFO - Iter(train) [ 16150/160000] base_lr: 9.0758e-05 lr: 3.3555e-07 eta: 1 day, 15:57:03 time: 0.9976 data_time: 0.0043 memory: 8462 loss: 0.0244 decode.loss_ce: 0.0141 decode.acc_seg: 99.2956 aux.loss_ce: 0.0103 aux.acc_seg: 99.0616 +04/17 13:02:03 - mmengine - INFO - Iter(train) [ 16200/160000] base_lr: 9.0726e-05 lr: 3.3543e-07 eta: 1 day, 15:56:12 time: 0.9976 data_time: 0.0039 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0127 decode.acc_seg: 99.5726 aux.loss_ce: 0.0100 aux.acc_seg: 99.1488 +04/17 13:02:53 - mmengine - INFO - Iter(train) [ 16250/160000] base_lr: 9.0695e-05 lr: 3.3532e-07 eta: 1 day, 15:55:21 time: 0.9975 data_time: 0.0045 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0120 decode.acc_seg: 99.4024 aux.loss_ce: 0.0100 aux.acc_seg: 98.6151 +04/17 13:03:43 - mmengine - INFO - Iter(train) [ 16300/160000] base_lr: 9.0663e-05 lr: 3.3520e-07 eta: 1 day, 15:54:30 time: 0.9963 data_time: 0.0044 memory: 8462 loss: 0.0241 decode.loss_ce: 0.0135 decode.acc_seg: 99.3757 aux.loss_ce: 0.0107 aux.acc_seg: 98.7549 +04/17 13:04:33 - mmengine - INFO - Iter(train) [ 16350/160000] base_lr: 9.0631e-05 lr: 3.3508e-07 eta: 1 day, 15:53:39 time: 0.9961 data_time: 0.0043 memory: 8462 loss: 0.0276 decode.loss_ce: 0.0151 decode.acc_seg: 99.6042 aux.loss_ce: 0.0125 aux.acc_seg: 99.2022 +04/17 13:05:23 - mmengine - INFO - Iter(train) [ 16400/160000] base_lr: 9.0600e-05 lr: 3.3497e-07 eta: 1 day, 15:52:48 time: 0.9965 data_time: 0.0045 memory: 8462 loss: 0.0253 decode.loss_ce: 0.0138 decode.acc_seg: 99.4143 aux.loss_ce: 0.0114 aux.acc_seg: 98.7661 +04/17 13:06:13 - mmengine - INFO - Iter(train) [ 16450/160000] base_lr: 9.0568e-05 lr: 3.3485e-07 eta: 1 day, 15:51:57 time: 0.9980 data_time: 0.0046 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0107 decode.acc_seg: 99.5127 aux.loss_ce: 0.0087 aux.acc_seg: 98.9428 +04/17 13:07:03 - mmengine - INFO - Iter(train) [ 16500/160000] base_lr: 9.0537e-05 lr: 3.3473e-07 eta: 1 day, 15:51:06 time: 0.9970 data_time: 0.0043 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0118 decode.acc_seg: 99.8001 aux.loss_ce: 0.0096 aux.acc_seg: 99.4225 +04/17 13:07:53 - mmengine - INFO - Iter(train) [ 16550/160000] base_lr: 9.0505e-05 lr: 3.3462e-07 eta: 1 day, 15:50:15 time: 0.9988 data_time: 0.0047 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0119 decode.acc_seg: 99.6180 aux.loss_ce: 0.0100 aux.acc_seg: 99.0452 +04/17 13:08:42 - mmengine - INFO - Iter(train) [ 16600/160000] base_lr: 9.0474e-05 lr: 3.3450e-07 eta: 1 day, 15:49:25 time: 0.9987 data_time: 0.0050 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0127 decode.acc_seg: 99.4692 aux.loss_ce: 0.0104 aux.acc_seg: 99.0362 +04/17 13:09:32 - mmengine - INFO - Iter(train) [ 16650/160000] base_lr: 9.0442e-05 lr: 3.3438e-07 eta: 1 day, 15:48:34 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0117 decode.acc_seg: 99.4780 aux.loss_ce: 0.0104 aux.acc_seg: 98.8773 +04/17 13:10:22 - mmengine - INFO - Iter(train) [ 16700/160000] base_lr: 9.0411e-05 lr: 3.3427e-07 eta: 1 day, 15:47:44 time: 0.9975 data_time: 0.0045 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0137 decode.acc_seg: 99.4667 aux.loss_ce: 0.0107 aux.acc_seg: 98.9105 +04/17 13:11:12 - mmengine - INFO - Iter(train) [ 16750/160000] base_lr: 9.0379e-05 lr: 3.3415e-07 eta: 1 day, 15:46:53 time: 0.9969 data_time: 0.0043 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0121 decode.acc_seg: 99.6586 aux.loss_ce: 0.0101 aux.acc_seg: 99.2302 +04/17 13:12:02 - mmengine - INFO - Iter(train) [ 16800/160000] base_lr: 9.0348e-05 lr: 3.3403e-07 eta: 1 day, 15:46:02 time: 0.9977 data_time: 0.0043 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0122 decode.acc_seg: 99.7305 aux.loss_ce: 0.0101 aux.acc_seg: 99.3217 +04/17 13:12:52 - mmengine - INFO - Iter(train) [ 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memory: 8462 loss: 0.0238 decode.loss_ce: 0.0127 decode.acc_seg: 99.7061 aux.loss_ce: 0.0111 aux.acc_seg: 99.3080 +04/17 13:16:12 - mmengine - INFO - Iter(train) [ 17050/160000] base_lr: 9.0190e-05 lr: 3.3345e-07 eta: 1 day, 15:41:48 time: 0.9982 data_time: 0.0047 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0107 decode.acc_seg: 99.4898 aux.loss_ce: 0.0079 aux.acc_seg: 99.2535 +04/17 13:17:02 - mmengine - INFO - Iter(train) [ 17100/160000] base_lr: 9.0158e-05 lr: 3.3333e-07 eta: 1 day, 15:40:58 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0125 decode.acc_seg: 99.1219 aux.loss_ce: 0.0097 aux.acc_seg: 98.7671 +04/17 13:17:51 - mmengine - INFO - Iter(train) [ 17150/160000] base_lr: 9.0127e-05 lr: 3.3322e-07 eta: 1 day, 15:40:07 time: 0.9980 data_time: 0.0045 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0113 decode.acc_seg: 99.5983 aux.loss_ce: 0.0102 aux.acc_seg: 99.0562 +04/17 13:18:41 - mmengine - INFO - Iter(train) [ 17200/160000] base_lr: 9.0095e-05 lr: 3.3310e-07 eta: 1 day, 15:39:17 time: 0.9983 data_time: 0.0044 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0116 decode.acc_seg: 99.5598 aux.loss_ce: 0.0103 aux.acc_seg: 98.6620 +04/17 13:19:31 - mmengine - INFO - Iter(train) [ 17250/160000] base_lr: 9.0064e-05 lr: 3.3298e-07 eta: 1 day, 15:38:26 time: 0.9996 data_time: 0.0041 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0135 decode.acc_seg: 99.4719 aux.loss_ce: 0.0107 aux.acc_seg: 99.0494 +04/17 13:20:21 - mmengine - INFO - Iter(train) [ 17300/160000] base_lr: 9.0032e-05 lr: 3.3287e-07 eta: 1 day, 15:37:36 time: 0.9993 data_time: 0.0048 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0120 decode.acc_seg: 99.6330 aux.loss_ce: 0.0100 aux.acc_seg: 99.3477 +04/17 13:21:11 - mmengine - INFO - Iter(train) [ 17350/160000] base_lr: 9.0001e-05 lr: 3.3275e-07 eta: 1 day, 15:36:45 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0114 decode.acc_seg: 99.5409 aux.loss_ce: 0.0104 aux.acc_seg: 98.9855 +04/17 13:22:01 - mmengine - INFO - Iter(train) [ 17400/160000] base_lr: 8.9969e-05 lr: 3.3263e-07 eta: 1 day, 15:35:55 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0126 decode.acc_seg: 99.4459 aux.loss_ce: 0.0094 aux.acc_seg: 98.9763 +04/17 13:22:51 - mmengine - INFO - Iter(train) [ 17450/160000] base_lr: 8.9937e-05 lr: 3.3252e-07 eta: 1 day, 15:35:05 time: 0.9987 data_time: 0.0049 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0139 decode.acc_seg: 99.3793 aux.loss_ce: 0.0102 aux.acc_seg: 98.7186 +04/17 13:23:41 - mmengine - INFO - Iter(train) [ 17500/160000] base_lr: 8.9906e-05 lr: 3.3240e-07 eta: 1 day, 15:34:14 time: 0.9980 data_time: 0.0042 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0117 decode.acc_seg: 99.6786 aux.loss_ce: 0.0101 aux.acc_seg: 99.2096 +04/17 13:24:31 - mmengine - INFO - Iter(train) [ 17550/160000] base_lr: 8.9874e-05 lr: 3.3228e-07 eta: 1 day, 15:33:24 time: 0.9995 data_time: 0.0051 memory: 8462 loss: 0.0234 decode.loss_ce: 0.0125 decode.acc_seg: 99.6319 aux.loss_ce: 0.0108 aux.acc_seg: 99.2407 +04/17 13:25:21 - mmengine - INFO - Iter(train) [ 17600/160000] base_lr: 8.9843e-05 lr: 3.3217e-07 eta: 1 day, 15:32:33 time: 0.9965 data_time: 0.0044 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0117 decode.acc_seg: 99.3477 aux.loss_ce: 0.0106 aux.acc_seg: 98.6540 +04/17 13:26:11 - mmengine - INFO - Iter(train) [ 17650/160000] base_lr: 8.9811e-05 lr: 3.3205e-07 eta: 1 day, 15:31:43 time: 0.9986 data_time: 0.0049 memory: 8462 loss: 0.0271 decode.loss_ce: 0.0147 decode.acc_seg: 99.2949 aux.loss_ce: 0.0124 aux.acc_seg: 98.1636 +04/17 13:27:01 - mmengine - INFO - Iter(train) [ 17700/160000] base_lr: 8.9780e-05 lr: 3.3193e-07 eta: 1 day, 15:30:52 time: 0.9986 data_time: 0.0044 memory: 8462 loss: 0.0241 decode.loss_ce: 0.0135 decode.acc_seg: 99.1665 aux.loss_ce: 0.0106 aux.acc_seg: 98.5540 +04/17 13:27:51 - mmengine - INFO - Iter(train) [ 17750/160000] base_lr: 8.9748e-05 lr: 3.3182e-07 eta: 1 day, 15:30:02 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0111 decode.acc_seg: 99.6120 aux.loss_ce: 0.0103 aux.acc_seg: 98.6225 +04/17 13:28:41 - mmengine - INFO - Iter(train) [ 17800/160000] base_lr: 8.9717e-05 lr: 3.3170e-07 eta: 1 day, 15:29:12 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0101 decode.acc_seg: 99.6502 aux.loss_ce: 0.0085 aux.acc_seg: 99.2655 +04/17 13:29:31 - mmengine - INFO - Iter(train) [ 17850/160000] base_lr: 8.9685e-05 lr: 3.3158e-07 eta: 1 day, 15:28:21 time: 0.9986 data_time: 0.0048 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0132 decode.acc_seg: 99.6231 aux.loss_ce: 0.0113 aux.acc_seg: 99.1957 +04/17 13:30:20 - mmengine - INFO - Iter(train) [ 17900/160000] base_lr: 8.9654e-05 lr: 3.3147e-07 eta: 1 day, 15:27:31 time: 0.9986 data_time: 0.0046 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0108 decode.acc_seg: 99.6162 aux.loss_ce: 0.0091 aux.acc_seg: 99.0400 +04/17 13:31:10 - mmengine - INFO - Iter(train) [ 17950/160000] base_lr: 8.9622e-05 lr: 3.3135e-07 eta: 1 day, 15:26:40 time: 0.9975 data_time: 0.0043 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0121 decode.acc_seg: 99.8589 aux.loss_ce: 0.0097 aux.acc_seg: 99.5493 +04/17 13:32:00 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 13:32:00 - mmengine - INFO - Iter(train) [ 18000/160000] base_lr: 8.9590e-05 lr: 3.3123e-07 eta: 1 day, 15:25:50 time: 0.9972 data_time: 0.0043 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0125 decode.acc_seg: 99.6740 aux.loss_ce: 0.0104 aux.acc_seg: 99.0997 +04/17 13:32:50 - mmengine - INFO - Iter(train) [ 18050/160000] base_lr: 8.9559e-05 lr: 3.3112e-07 eta: 1 day, 15:25:00 time: 0.9987 data_time: 0.0043 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0131 decode.acc_seg: 99.5947 aux.loss_ce: 0.0105 aux.acc_seg: 99.1249 +04/17 13:33:40 - mmengine - INFO - Iter(train) [ 18100/160000] base_lr: 8.9527e-05 lr: 3.3100e-07 eta: 1 day, 15:24:09 time: 0.9989 data_time: 0.0044 memory: 8462 loss: 0.0232 decode.loss_ce: 0.0118 decode.acc_seg: 99.4345 aux.loss_ce: 0.0113 aux.acc_seg: 98.7534 +04/17 13:34:30 - mmengine - INFO - Iter(train) [ 18150/160000] base_lr: 8.9496e-05 lr: 3.3088e-07 eta: 1 day, 15:23:19 time: 0.9981 data_time: 0.0045 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0121 decode.acc_seg: 99.5770 aux.loss_ce: 0.0107 aux.acc_seg: 99.1978 +04/17 13:35:20 - mmengine - INFO - Iter(train) [ 18200/160000] base_lr: 8.9464e-05 lr: 3.3077e-07 eta: 1 day, 15:22:28 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0116 decode.acc_seg: 99.7555 aux.loss_ce: 0.0099 aux.acc_seg: 99.4345 +04/17 13:36:10 - mmengine - INFO - Iter(train) [ 18250/160000] base_lr: 8.9433e-05 lr: 3.3065e-07 eta: 1 day, 15:21:38 time: 0.9981 data_time: 0.0043 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0124 decode.acc_seg: 99.6183 aux.loss_ce: 0.0094 aux.acc_seg: 98.8300 +04/17 13:37:00 - mmengine - INFO - Iter(train) [ 18300/160000] base_lr: 8.9401e-05 lr: 3.3053e-07 eta: 1 day, 15:20:48 time: 0.9982 data_time: 0.0048 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0133 decode.acc_seg: 99.3641 aux.loss_ce: 0.0106 aux.acc_seg: 98.5109 +04/17 13:37:50 - mmengine - INFO - Iter(train) [ 18350/160000] base_lr: 8.9370e-05 lr: 3.3042e-07 eta: 1 day, 15:19:57 time: 0.9981 data_time: 0.0045 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0124 decode.acc_seg: 99.4347 aux.loss_ce: 0.0107 aux.acc_seg: 98.7696 +04/17 13:38:40 - mmengine - INFO - Iter(train) [ 18400/160000] base_lr: 8.9338e-05 lr: 3.3030e-07 eta: 1 day, 15:19:07 time: 0.9994 data_time: 0.0046 memory: 8462 loss: 0.0277 decode.loss_ce: 0.0151 decode.acc_seg: 99.4741 aux.loss_ce: 0.0126 aux.acc_seg: 99.0673 +04/17 13:39:30 - mmengine - INFO - Iter(train) [ 18450/160000] base_lr: 8.9307e-05 lr: 3.3018e-07 eta: 1 day, 15:18:17 time: 0.9995 data_time: 0.0050 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0097 decode.acc_seg: 99.5771 aux.loss_ce: 0.0091 aux.acc_seg: 98.8585 +04/17 13:40:20 - mmengine - INFO - Iter(train) [ 18500/160000] base_lr: 8.9275e-05 lr: 3.3007e-07 eta: 1 day, 15:17:27 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0111 decode.acc_seg: 99.6395 aux.loss_ce: 0.0102 aux.acc_seg: 99.1505 +04/17 13:41:10 - mmengine - INFO - Iter(train) [ 18550/160000] base_lr: 8.9243e-05 lr: 3.2995e-07 eta: 1 day, 15:16:37 time: 0.9982 data_time: 0.0044 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0100 decode.acc_seg: 99.6601 aux.loss_ce: 0.0095 aux.acc_seg: 99.2523 +04/17 13:42:00 - mmengine - INFO - Iter(train) [ 18600/160000] base_lr: 8.9212e-05 lr: 3.2983e-07 eta: 1 day, 15:15:47 time: 1.0000 data_time: 0.0051 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0120 decode.acc_seg: 99.4564 aux.loss_ce: 0.0099 aux.acc_seg: 98.5744 +04/17 13:42:50 - mmengine - INFO - Iter(train) [ 18650/160000] base_lr: 8.9180e-05 lr: 3.2972e-07 eta: 1 day, 15:14:56 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0111 decode.acc_seg: 99.4984 aux.loss_ce: 0.0102 aux.acc_seg: 98.9391 +04/17 13:43:40 - mmengine - INFO - Iter(train) [ 18700/160000] base_lr: 8.9149e-05 lr: 3.2960e-07 eta: 1 day, 15:14:06 time: 0.9989 data_time: 0.0046 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0109 decode.acc_seg: 99.7986 aux.loss_ce: 0.0092 aux.acc_seg: 99.4448 +04/17 13:44:29 - mmengine - INFO - Iter(train) [ 18750/160000] base_lr: 8.9117e-05 lr: 3.2948e-07 eta: 1 day, 15:13:16 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0116 decode.acc_seg: 99.5569 aux.loss_ce: 0.0097 aux.acc_seg: 99.1232 +04/17 13:45:19 - mmengine - INFO - Iter(train) [ 18800/160000] base_lr: 8.9086e-05 lr: 3.2937e-07 eta: 1 day, 15:12:26 time: 0.9985 data_time: 0.0042 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0092 decode.acc_seg: 99.5239 aux.loss_ce: 0.0087 aux.acc_seg: 98.6853 +04/17 13:46:09 - mmengine - INFO - Iter(train) [ 18850/160000] base_lr: 8.9054e-05 lr: 3.2925e-07 eta: 1 day, 15:11:35 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0113 decode.acc_seg: 99.4699 aux.loss_ce: 0.0103 aux.acc_seg: 98.9473 +04/17 13:46:59 - mmengine - INFO - Iter(train) [ 18900/160000] base_lr: 8.9023e-05 lr: 3.2914e-07 eta: 1 day, 15:10:45 time: 0.9991 data_time: 0.0049 memory: 8462 loss: 0.0249 decode.loss_ce: 0.0132 decode.acc_seg: 99.6851 aux.loss_ce: 0.0116 aux.acc_seg: 99.2382 +04/17 13:47:49 - mmengine - INFO - Iter(train) [ 18950/160000] base_lr: 8.8991e-05 lr: 3.2902e-07 eta: 1 day, 15:09:54 time: 0.9983 data_time: 0.0046 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0108 decode.acc_seg: 99.3652 aux.loss_ce: 0.0098 aux.acc_seg: 98.5264 +04/17 13:48:39 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 13:48:39 - mmengine - INFO - Iter(train) [ 19000/160000] base_lr: 8.8960e-05 lr: 3.2890e-07 eta: 1 day, 15:09:04 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0120 decode.acc_seg: 99.6391 aux.loss_ce: 0.0103 aux.acc_seg: 99.2020 +04/17 13:49:29 - mmengine - INFO - Iter(train) [ 19050/160000] base_lr: 8.8928e-05 lr: 3.2879e-07 eta: 1 day, 15:08:14 time: 0.9976 data_time: 0.0042 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0114 decode.acc_seg: 99.5697 aux.loss_ce: 0.0102 aux.acc_seg: 98.9531 +04/17 13:50:19 - mmengine - INFO - Iter(train) [ 19100/160000] base_lr: 8.8896e-05 lr: 3.2867e-07 eta: 1 day, 15:07:23 time: 0.9977 data_time: 0.0042 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0113 decode.acc_seg: 99.6140 aux.loss_ce: 0.0091 aux.acc_seg: 99.0669 +04/17 13:51:09 - mmengine - INFO - Iter(train) [ 19150/160000] base_lr: 8.8865e-05 lr: 3.2855e-07 eta: 1 day, 15:06:33 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0126 decode.acc_seg: 99.3668 aux.loss_ce: 0.0098 aux.acc_seg: 98.8646 +04/17 13:51:59 - mmengine - INFO - Iter(train) [ 19200/160000] base_lr: 8.8833e-05 lr: 3.2844e-07 eta: 1 day, 15:05:43 time: 0.9993 data_time: 0.0044 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0120 decode.acc_seg: 99.4331 aux.loss_ce: 0.0108 aux.acc_seg: 98.8338 +04/17 13:52:49 - mmengine - INFO - Iter(train) [ 19250/160000] base_lr: 8.8802e-05 lr: 3.2832e-07 eta: 1 day, 15:04:53 time: 0.9982 data_time: 0.0043 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0117 decode.acc_seg: 99.3053 aux.loss_ce: 0.0102 aux.acc_seg: 98.6570 +04/17 13:53:39 - mmengine - INFO - Iter(train) [ 19300/160000] base_lr: 8.8770e-05 lr: 3.2820e-07 eta: 1 day, 15:04:03 time: 0.9992 data_time: 0.0043 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0118 decode.acc_seg: 99.6469 aux.loss_ce: 0.0102 aux.acc_seg: 99.2630 +04/17 13:54:29 - mmengine - INFO - Iter(train) [ 19350/160000] base_lr: 8.8739e-05 lr: 3.2809e-07 eta: 1 day, 15:03:13 time: 0.9986 data_time: 0.0042 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0125 decode.acc_seg: 99.3605 aux.loss_ce: 0.0102 aux.acc_seg: 98.8811 +04/17 13:55:19 - mmengine - INFO - Iter(train) [ 19400/160000] base_lr: 8.8707e-05 lr: 3.2797e-07 eta: 1 day, 15:02:22 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0126 decode.acc_seg: 99.1262 aux.loss_ce: 0.0106 aux.acc_seg: 98.6568 +04/17 13:56:09 - mmengine - INFO - Iter(train) [ 19450/160000] base_lr: 8.8676e-05 lr: 3.2785e-07 eta: 1 day, 15:01:32 time: 1.0000 data_time: 0.0043 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0112 decode.acc_seg: 99.6866 aux.loss_ce: 0.0107 aux.acc_seg: 99.1751 +04/17 13:56:59 - mmengine - INFO - Iter(train) [ 19500/160000] base_lr: 8.8644e-05 lr: 3.2774e-07 eta: 1 day, 15:00:42 time: 0.9976 data_time: 0.0044 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0137 decode.acc_seg: 99.2142 aux.loss_ce: 0.0111 aux.acc_seg: 98.6185 +04/17 13:57:49 - mmengine - INFO - Iter(train) [ 19550/160000] base_lr: 8.8613e-05 lr: 3.2762e-07 eta: 1 day, 14:59:52 time: 0.9992 data_time: 0.0041 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0116 decode.acc_seg: 99.1375 aux.loss_ce: 0.0091 aux.acc_seg: 98.7877 +04/17 13:58:39 - mmengine - INFO - Iter(train) [ 19600/160000] base_lr: 8.8581e-05 lr: 3.2750e-07 eta: 1 day, 14:59:02 time: 0.9983 data_time: 0.0042 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0093 decode.acc_seg: 99.5197 aux.loss_ce: 0.0089 aux.acc_seg: 99.0948 +04/17 13:59:28 - mmengine - INFO - Iter(train) [ 19650/160000] base_lr: 8.8549e-05 lr: 3.2739e-07 eta: 1 day, 14:58:11 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0097 decode.acc_seg: 99.7725 aux.loss_ce: 0.0090 aux.acc_seg: 99.3301 +04/17 14:00:18 - mmengine - INFO - Iter(train) [ 19700/160000] base_lr: 8.8518e-05 lr: 3.2727e-07 eta: 1 day, 14:57:21 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0114 decode.acc_seg: 99.7171 aux.loss_ce: 0.0096 aux.acc_seg: 99.4770 +04/17 14:01:08 - mmengine - INFO - Iter(train) [ 19750/160000] base_lr: 8.8486e-05 lr: 3.2715e-07 eta: 1 day, 14:56:31 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0104 decode.acc_seg: 99.5567 aux.loss_ce: 0.0091 aux.acc_seg: 99.2126 +04/17 14:01:58 - mmengine - INFO - Iter(train) [ 19800/160000] base_lr: 8.8455e-05 lr: 3.2704e-07 eta: 1 day, 14:55:41 time: 0.9990 data_time: 0.0044 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0133 decode.acc_seg: 99.5886 aux.loss_ce: 0.0112 aux.acc_seg: 98.8911 +04/17 14:02:48 - mmengine - INFO - Iter(train) [ 19850/160000] base_lr: 8.8423e-05 lr: 3.2692e-07 eta: 1 day, 14:54:51 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0129 decode.acc_seg: 99.3370 aux.loss_ce: 0.0101 aux.acc_seg: 98.9658 +04/17 14:03:38 - mmengine - INFO - Iter(train) [ 19900/160000] base_lr: 8.8392e-05 lr: 3.2680e-07 eta: 1 day, 14:54:01 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0098 decode.acc_seg: 99.7274 aux.loss_ce: 0.0091 aux.acc_seg: 99.2981 +04/17 14:04:28 - mmengine - INFO - Iter(train) [ 19950/160000] base_lr: 8.8360e-05 lr: 3.2669e-07 eta: 1 day, 14:53:11 time: 0.9984 data_time: 0.0044 memory: 8462 loss: 0.0244 decode.loss_ce: 0.0134 decode.acc_seg: 98.7705 aux.loss_ce: 0.0110 aux.acc_seg: 98.3341 +04/17 14:05:18 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 14:05:18 - mmengine - INFO - Iter(train) [ 20000/160000] base_lr: 8.8329e-05 lr: 3.2657e-07 eta: 1 day, 14:52:21 time: 0.9999 data_time: 0.0046 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0089 decode.acc_seg: 99.6473 aux.loss_ce: 0.0090 aux.acc_seg: 99.0765 +04/17 14:05:18 - mmengine - INFO - Saving checkpoint at 20000 iterations +04/17 14:05:28 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1155 data_time: 0.0014 memory: 4004 +04/17 14:05:34 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1158 data_time: 0.0015 memory: 4004 +04/17 14:05:40 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1156 data_time: 0.0014 memory: 4004 +04/17 14:05:46 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1153 data_time: 0.0013 memory: 4004 +04/17 14:05:46 - mmengine - INFO - per class results: +04/17 14:05:46 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.14 | 99.55 | 99.57 | 99.58 | 99.55 | +| contrast | 81.18 | 89.96 | 89.61 | 89.28 | 89.96 | ++------------+-------+-------+--------+-----------+--------+ +04/17 14:05:46 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1700 mIoU: 90.1600 mAcc: 94.7500 mFscore: 94.5900 mPrecision: 94.4300 mRecall: 94.7500 data_time: 0.0015 time: 0.1160 +04/17 14:06:36 - mmengine - INFO - Iter(train) [ 20050/160000] base_lr: 8.8297e-05 lr: 3.2645e-07 eta: 1 day, 14:51:31 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0221 decode.loss_ce: 0.0120 decode.acc_seg: 99.3975 aux.loss_ce: 0.0101 aux.acc_seg: 98.7370 +04/17 14:07:26 - mmengine - INFO - Iter(train) [ 20100/160000] base_lr: 8.8266e-05 lr: 3.2634e-07 eta: 1 day, 14:50:41 time: 0.9985 data_time: 0.0046 memory: 8462 loss: 0.0203 decode.loss_ce: 0.0107 decode.acc_seg: 99.5319 aux.loss_ce: 0.0096 aux.acc_seg: 98.8304 +04/17 14:08:16 - mmengine - INFO - Iter(train) [ 20150/160000] base_lr: 8.8234e-05 lr: 3.2622e-07 eta: 1 day, 14:49:51 time: 0.9998 data_time: 0.0048 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0112 decode.acc_seg: 99.5188 aux.loss_ce: 0.0113 aux.acc_seg: 98.8132 +04/17 14:09:06 - mmengine - INFO - Iter(train) [ 20200/160000] base_lr: 8.8202e-05 lr: 3.2610e-07 eta: 1 day, 14:49:01 time: 1.0004 data_time: 0.0047 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0116 decode.acc_seg: 99.5768 aux.loss_ce: 0.0099 aux.acc_seg: 98.7598 +04/17 14:09:55 - mmengine - INFO - Iter(train) [ 20250/160000] base_lr: 8.8171e-05 lr: 3.2599e-07 eta: 1 day, 14:48:11 time: 0.9997 data_time: 0.0050 memory: 8462 loss: 0.0212 decode.loss_ce: 0.0112 decode.acc_seg: 99.4320 aux.loss_ce: 0.0100 aux.acc_seg: 98.8493 +04/17 14:10:45 - mmengine - INFO - Iter(train) [ 20300/160000] base_lr: 8.8139e-05 lr: 3.2587e-07 eta: 1 day, 14:47:21 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0124 decode.acc_seg: 99.3404 aux.loss_ce: 0.0112 aux.acc_seg: 98.4577 +04/17 14:11:35 - mmengine - INFO - Iter(train) [ 20350/160000] base_lr: 8.8108e-05 lr: 3.2575e-07 eta: 1 day, 14:46:31 time: 0.9991 data_time: 0.0045 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0104 decode.acc_seg: 99.5899 aux.loss_ce: 0.0093 aux.acc_seg: 99.0639 +04/17 14:12:25 - mmengine - INFO - Iter(train) [ 20400/160000] base_lr: 8.8076e-05 lr: 3.2564e-07 eta: 1 day, 14:45:41 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0110 decode.acc_seg: 99.4804 aux.loss_ce: 0.0101 aux.acc_seg: 98.7062 +04/17 14:13:15 - mmengine - INFO - Iter(train) [ 20450/160000] base_lr: 8.8045e-05 lr: 3.2552e-07 eta: 1 day, 14:44:51 time: 0.9996 data_time: 0.0051 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0105 decode.acc_seg: 99.5016 aux.loss_ce: 0.0100 aux.acc_seg: 98.5600 +04/17 14:14:05 - mmengine - INFO - Iter(train) [ 20500/160000] base_lr: 8.8013e-05 lr: 3.2540e-07 eta: 1 day, 14:44:01 time: 0.9985 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0106 decode.acc_seg: 99.5970 aux.loss_ce: 0.0094 aux.acc_seg: 98.8459 +04/17 14:14:55 - mmengine - INFO - Iter(train) [ 20550/160000] base_lr: 8.7982e-05 lr: 3.2529e-07 eta: 1 day, 14:43:11 time: 1.0003 data_time: 0.0043 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0110 decode.acc_seg: 99.0553 aux.loss_ce: 0.0095 aux.acc_seg: 98.2803 +04/17 14:15:45 - mmengine - INFO - Iter(train) [ 20600/160000] base_lr: 8.7950e-05 lr: 3.2517e-07 eta: 1 day, 14:42:21 time: 0.9983 data_time: 0.0047 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0100 decode.acc_seg: 99.6210 aux.loss_ce: 0.0100 aux.acc_seg: 99.0175 +04/17 14:16:35 - mmengine - INFO - Iter(train) [ 20650/160000] base_lr: 8.7919e-05 lr: 3.2505e-07 eta: 1 day, 14:41:31 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0135 decode.acc_seg: 98.8693 aux.loss_ce: 0.0104 aux.acc_seg: 98.3368 +04/17 14:17:25 - mmengine - INFO - Iter(train) [ 20700/160000] base_lr: 8.7887e-05 lr: 3.2494e-07 eta: 1 day, 14:40:41 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0122 decode.acc_seg: 99.4473 aux.loss_ce: 0.0103 aux.acc_seg: 98.7219 +04/17 14:18:15 - mmengine - INFO - Iter(train) [ 20750/160000] base_lr: 8.7855e-05 lr: 3.2482e-07 eta: 1 day, 14:39:51 time: 0.9991 data_time: 0.0046 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0098 decode.acc_seg: 99.6065 aux.loss_ce: 0.0086 aux.acc_seg: 99.1484 +04/17 14:19:05 - mmengine - INFO - Iter(train) [ 20800/160000] base_lr: 8.7824e-05 lr: 3.2470e-07 eta: 1 day, 14:39:01 time: 0.9999 data_time: 0.0046 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0115 decode.acc_seg: 99.6258 aux.loss_ce: 0.0105 aux.acc_seg: 99.0911 +04/17 14:19:55 - mmengine - INFO - Iter(train) [ 20850/160000] base_lr: 8.7792e-05 lr: 3.2459e-07 eta: 1 day, 14:38:11 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0121 decode.acc_seg: 99.5569 aux.loss_ce: 0.0106 aux.acc_seg: 99.1344 +04/17 14:20:45 - mmengine - INFO - Iter(train) [ 20900/160000] base_lr: 8.7761e-05 lr: 3.2447e-07 eta: 1 day, 14:37:21 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0117 decode.acc_seg: 99.2662 aux.loss_ce: 0.0106 aux.acc_seg: 98.4653 +04/17 14:21:35 - mmengine - INFO - Iter(train) [ 20950/160000] base_lr: 8.7729e-05 lr: 3.2435e-07 eta: 1 day, 14:36:31 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0111 decode.acc_seg: 99.4967 aux.loss_ce: 0.0105 aux.acc_seg: 98.8319 +04/17 14:22:25 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 14:22:25 - mmengine - INFO - Iter(train) [ 21000/160000] base_lr: 8.7698e-05 lr: 3.2424e-07 eta: 1 day, 14:35:41 time: 0.9986 data_time: 0.0046 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0109 decode.acc_seg: 99.6061 aux.loss_ce: 0.0099 aux.acc_seg: 99.0068 +04/17 14:23:15 - mmengine - INFO - Iter(train) [ 21050/160000] base_lr: 8.7666e-05 lr: 3.2412e-07 eta: 1 day, 14:34:51 time: 0.9994 data_time: 0.0047 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0108 decode.acc_seg: 99.4978 aux.loss_ce: 0.0115 aux.acc_seg: 99.0391 +04/17 14:24:05 - mmengine - INFO - Iter(train) [ 21100/160000] base_lr: 8.7635e-05 lr: 3.2400e-07 eta: 1 day, 14:34:01 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0109 decode.acc_seg: 99.3053 aux.loss_ce: 0.0095 aux.acc_seg: 98.8251 +04/17 14:24:55 - mmengine - INFO - Iter(train) [ 21150/160000] base_lr: 8.7603e-05 lr: 3.2389e-07 eta: 1 day, 14:33:11 time: 1.0008 data_time: 0.0047 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0116 decode.acc_seg: 99.6618 aux.loss_ce: 0.0101 aux.acc_seg: 99.1035 +04/17 14:25:45 - mmengine - INFO - Iter(train) [ 21200/160000] base_lr: 8.7572e-05 lr: 3.2377e-07 eta: 1 day, 14:32:21 time: 1.0010 data_time: 0.0047 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0108 decode.acc_seg: 99.4436 aux.loss_ce: 0.0094 aux.acc_seg: 98.6851 +04/17 14:26:35 - mmengine - INFO - Iter(train) [ 21250/160000] base_lr: 8.7540e-05 lr: 3.2365e-07 eta: 1 day, 14:31:31 time: 0.9999 data_time: 0.0046 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0099 decode.acc_seg: 99.5758 aux.loss_ce: 0.0095 aux.acc_seg: 99.0086 +04/17 14:27:25 - mmengine - INFO - Iter(train) [ 21300/160000] base_lr: 8.7508e-05 lr: 3.2354e-07 eta: 1 day, 14:30:41 time: 0.9984 data_time: 0.0044 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0109 decode.acc_seg: 99.5089 aux.loss_ce: 0.0102 aux.acc_seg: 98.9325 +04/17 14:28:15 - mmengine - INFO - Iter(train) [ 21350/160000] base_lr: 8.7477e-05 lr: 3.2342e-07 eta: 1 day, 14:29:50 time: 0.9979 data_time: 0.0044 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0124 decode.acc_seg: 99.1619 aux.loss_ce: 0.0117 aux.acc_seg: 97.6984 +04/17 14:29:05 - mmengine - INFO - Iter(train) [ 21400/160000] base_lr: 8.7445e-05 lr: 3.2330e-07 eta: 1 day, 14:29:00 time: 1.0005 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0106 decode.acc_seg: 99.6611 aux.loss_ce: 0.0095 aux.acc_seg: 99.1283 +04/17 14:29:55 - mmengine - INFO - Iter(train) [ 21450/160000] base_lr: 8.7414e-05 lr: 3.2319e-07 eta: 1 day, 14:28:10 time: 0.9994 data_time: 0.0043 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0095 decode.acc_seg: 99.4682 aux.loss_ce: 0.0091 aux.acc_seg: 98.8892 +04/17 14:30:45 - mmengine - INFO - Iter(train) [ 21500/160000] base_lr: 8.7382e-05 lr: 3.2307e-07 eta: 1 day, 14:27:20 time: 0.9987 data_time: 0.0045 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0114 decode.acc_seg: 99.5121 aux.loss_ce: 0.0091 aux.acc_seg: 99.0715 +04/17 14:31:35 - mmengine - INFO - Iter(train) [ 21550/160000] base_lr: 8.7351e-05 lr: 3.2295e-07 eta: 1 day, 14:26:30 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0112 decode.acc_seg: 99.4154 aux.loss_ce: 0.0099 aux.acc_seg: 99.1652 +04/17 14:32:25 - mmengine - INFO - Iter(train) [ 21600/160000] base_lr: 8.7319e-05 lr: 3.2284e-07 eta: 1 day, 14:25:40 time: 1.0001 data_time: 0.0053 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0113 decode.acc_seg: 99.2371 aux.loss_ce: 0.0102 aux.acc_seg: 98.2595 +04/17 14:33:15 - mmengine - INFO - Iter(train) [ 21650/160000] base_lr: 8.7288e-05 lr: 3.2272e-07 eta: 1 day, 14:24:50 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0088 decode.acc_seg: 99.5977 aux.loss_ce: 0.0086 aux.acc_seg: 98.9861 +04/17 14:34:05 - mmengine - INFO - Iter(train) [ 21700/160000] base_lr: 8.7256e-05 lr: 3.2260e-07 eta: 1 day, 14:24:00 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0099 decode.acc_seg: 99.7166 aux.loss_ce: 0.0093 aux.acc_seg: 99.2840 +04/17 14:34:55 - mmengine - INFO - Iter(train) [ 21750/160000] base_lr: 8.7225e-05 lr: 3.2249e-07 eta: 1 day, 14:23:10 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0120 decode.acc_seg: 99.5123 aux.loss_ce: 0.0109 aux.acc_seg: 98.7047 +04/17 14:35:45 - mmengine - INFO - Iter(train) [ 21800/160000] base_lr: 8.7193e-05 lr: 3.2237e-07 eta: 1 day, 14:22:20 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0117 decode.acc_seg: 99.4455 aux.loss_ce: 0.0101 aux.acc_seg: 98.8354 +04/17 14:36:35 - mmengine - INFO - Iter(train) [ 21850/160000] base_lr: 8.7161e-05 lr: 3.2225e-07 eta: 1 day, 14:21:30 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0099 decode.acc_seg: 99.6090 aux.loss_ce: 0.0091 aux.acc_seg: 99.1480 +04/17 14:37:25 - mmengine - INFO - Iter(train) [ 21900/160000] base_lr: 8.7130e-05 lr: 3.2214e-07 eta: 1 day, 14:20:40 time: 0.9999 data_time: 0.0048 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0112 decode.acc_seg: 99.3156 aux.loss_ce: 0.0103 aux.acc_seg: 98.5327 +04/17 14:38:15 - mmengine - INFO - Iter(train) [ 21950/160000] base_lr: 8.7098e-05 lr: 3.2202e-07 eta: 1 day, 14:19:50 time: 0.9998 data_time: 0.0047 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0095 decode.acc_seg: 99.6925 aux.loss_ce: 0.0090 aux.acc_seg: 99.2367 +04/17 14:39:05 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 14:39:05 - mmengine - INFO - Iter(train) [ 22000/160000] base_lr: 8.7067e-05 lr: 3.2190e-07 eta: 1 day, 14:19:00 time: 0.9991 data_time: 0.0045 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0087 decode.acc_seg: 99.7269 aux.loss_ce: 0.0086 aux.acc_seg: 99.2006 +04/17 14:39:55 - mmengine - INFO - Iter(train) [ 22050/160000] base_lr: 8.7035e-05 lr: 3.2179e-07 eta: 1 day, 14:18:10 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0123 decode.acc_seg: 99.4081 aux.loss_ce: 0.0116 aux.acc_seg: 98.3019 +04/17 14:40:45 - mmengine - INFO - Iter(train) [ 22100/160000] base_lr: 8.7004e-05 lr: 3.2167e-07 eta: 1 day, 14:17:21 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0126 decode.acc_seg: 99.6626 aux.loss_ce: 0.0107 aux.acc_seg: 99.1590 +04/17 14:41:35 - mmengine - INFO - Iter(train) [ 22150/160000] base_lr: 8.6972e-05 lr: 3.2155e-07 eta: 1 day, 14:16:31 time: 1.0003 data_time: 0.0047 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0105 decode.acc_seg: 99.7681 aux.loss_ce: 0.0093 aux.acc_seg: 99.4343 +04/17 14:42:25 - mmengine - INFO - Iter(train) [ 22200/160000] base_lr: 8.6941e-05 lr: 3.2144e-07 eta: 1 day, 14:15:41 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0085 decode.acc_seg: 99.6647 aux.loss_ce: 0.0089 aux.acc_seg: 99.1859 +04/17 14:43:15 - mmengine - INFO - Iter(train) [ 22250/160000] base_lr: 8.6909e-05 lr: 3.2132e-07 eta: 1 day, 14:14:51 time: 0.9980 data_time: 0.0045 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0112 decode.acc_seg: 99.6481 aux.loss_ce: 0.0101 aux.acc_seg: 99.2300 +04/17 14:44:05 - mmengine - INFO - Iter(train) [ 22300/160000] base_lr: 8.6878e-05 lr: 3.2120e-07 eta: 1 day, 14:14:01 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6572 aux.loss_ce: 0.0080 aux.acc_seg: 99.1638 +04/17 14:44:55 - mmengine - INFO - Iter(train) [ 22350/160000] base_lr: 8.6846e-05 lr: 3.2109e-07 eta: 1 day, 14:13:11 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0098 decode.acc_seg: 99.5817 aux.loss_ce: 0.0102 aux.acc_seg: 99.0046 +04/17 14:45:45 - mmengine - INFO - Iter(train) [ 22400/160000] base_lr: 8.6814e-05 lr: 3.2097e-07 eta: 1 day, 14:12:21 time: 1.0010 data_time: 0.0053 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0114 decode.acc_seg: 99.5935 aux.loss_ce: 0.0100 aux.acc_seg: 98.8609 +04/17 14:46:35 - mmengine - INFO - Iter(train) [ 22450/160000] base_lr: 8.6783e-05 lr: 3.2085e-07 eta: 1 day, 14:11:31 time: 1.0001 data_time: 0.0044 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0114 decode.acc_seg: 99.6431 aux.loss_ce: 0.0095 aux.acc_seg: 99.1547 +04/17 14:47:25 - mmengine - INFO - Iter(train) [ 22500/160000] base_lr: 8.6751e-05 lr: 3.2074e-07 eta: 1 day, 14:10:41 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0099 decode.acc_seg: 99.6481 aux.loss_ce: 0.0093 aux.acc_seg: 98.8970 +04/17 14:48:15 - mmengine - INFO - Iter(train) [ 22550/160000] base_lr: 8.6720e-05 lr: 3.2062e-07 eta: 1 day, 14:09:51 time: 1.0005 data_time: 0.0049 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0107 decode.acc_seg: 99.6973 aux.loss_ce: 0.0092 aux.acc_seg: 98.9571 +04/17 14:49:05 - mmengine - INFO - Iter(train) [ 22600/160000] base_lr: 8.6688e-05 lr: 3.2050e-07 eta: 1 day, 14:09:01 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.5554 aux.loss_ce: 0.0086 aux.acc_seg: 99.0030 +04/17 14:49:55 - mmengine - INFO - Iter(train) [ 22650/160000] base_lr: 8.6657e-05 lr: 3.2039e-07 eta: 1 day, 14:08:12 time: 1.0015 data_time: 0.0052 memory: 8462 loss: 0.0217 decode.loss_ce: 0.0113 decode.acc_seg: 99.2714 aux.loss_ce: 0.0104 aux.acc_seg: 98.3213 +04/17 14:50:45 - mmengine - INFO - Iter(train) [ 22700/160000] base_lr: 8.6625e-05 lr: 3.2027e-07 eta: 1 day, 14:07:22 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0232 decode.loss_ce: 0.0122 decode.acc_seg: 99.4961 aux.loss_ce: 0.0110 aux.acc_seg: 99.1377 +04/17 14:51:34 - mmengine - INFO - Iter(train) [ 22750/160000] base_lr: 8.6594e-05 lr: 3.2015e-07 eta: 1 day, 14:06:32 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0087 decode.acc_seg: 99.7097 aux.loss_ce: 0.0078 aux.acc_seg: 99.4293 +04/17 14:52:24 - mmengine - INFO - Iter(train) [ 22800/160000] base_lr: 8.6562e-05 lr: 3.2004e-07 eta: 1 day, 14:05:42 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0096 decode.acc_seg: 99.4398 aux.loss_ce: 0.0094 aux.acc_seg: 99.2588 +04/17 14:53:14 - mmengine - INFO - Iter(train) [ 22850/160000] base_lr: 8.6531e-05 lr: 3.1992e-07 eta: 1 day, 14:04:51 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0119 decode.acc_seg: 99.4925 aux.loss_ce: 0.0105 aux.acc_seg: 98.6650 +04/17 14:54:04 - mmengine - INFO - Iter(train) [ 22900/160000] base_lr: 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0.0197 decode.loss_ce: 0.0100 decode.acc_seg: 99.4980 aux.loss_ce: 0.0097 aux.acc_seg: 98.8644 +04/17 14:57:24 - mmengine - INFO - Iter(train) [ 23100/160000] base_lr: 8.6373e-05 lr: 3.1934e-07 eta: 1 day, 14:00:42 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.5207 aux.loss_ce: 0.0088 aux.acc_seg: 98.8394 +04/17 14:58:14 - mmengine - INFO - Iter(train) [ 23150/160000] base_lr: 8.6341e-05 lr: 3.1922e-07 eta: 1 day, 13:59:52 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0097 decode.acc_seg: 99.5272 aux.loss_ce: 0.0097 aux.acc_seg: 98.7566 +04/17 14:59:04 - mmengine - INFO - Iter(train) [ 23200/160000] base_lr: 8.6310e-05 lr: 3.1910e-07 eta: 1 day, 13:59:02 time: 0.9996 data_time: 0.0041 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0103 decode.acc_seg: 99.6855 aux.loss_ce: 0.0099 aux.acc_seg: 99.0540 +04/17 14:59:54 - mmengine - INFO - Iter(train) [ 23250/160000] base_lr: 8.6278e-05 lr: 3.1899e-07 eta: 1 day, 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aux.acc_seg: 99.1447 +04/17 15:06:34 - mmengine - INFO - Iter(train) [ 23650/160000] base_lr: 8.6026e-05 lr: 3.1806e-07 eta: 1 day, 13:51:33 time: 1.0012 data_time: 0.0044 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0116 decode.acc_seg: 99.5560 aux.loss_ce: 0.0097 aux.acc_seg: 98.9267 +04/17 15:07:24 - mmengine - INFO - Iter(train) [ 23700/160000] base_lr: 8.5994e-05 lr: 3.1794e-07 eta: 1 day, 13:50:43 time: 0.9991 data_time: 0.0044 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.6264 aux.loss_ce: 0.0081 aux.acc_seg: 98.8258 +04/17 15:08:14 - mmengine - INFO - Iter(train) [ 23750/160000] base_lr: 8.5963e-05 lr: 3.1782e-07 eta: 1 day, 13:49:53 time: 0.9989 data_time: 0.0046 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0105 decode.acc_seg: 99.6782 aux.loss_ce: 0.0094 aux.acc_seg: 98.9418 +04/17 15:09:04 - mmengine - INFO - Iter(train) [ 23800/160000] base_lr: 8.5931e-05 lr: 3.1771e-07 eta: 1 day, 13:49:03 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0103 decode.acc_seg: 99.7223 aux.loss_ce: 0.0096 aux.acc_seg: 99.2645 +04/17 15:09:54 - mmengine - INFO - Iter(train) [ 23850/160000] base_lr: 8.5900e-05 lr: 3.1759e-07 eta: 1 day, 13:48:13 time: 1.0001 data_time: 0.0049 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0113 decode.acc_seg: 99.6099 aux.loss_ce: 0.0103 aux.acc_seg: 98.5582 +04/17 15:10:44 - mmengine - INFO - Iter(train) [ 23900/160000] base_lr: 8.5868e-05 lr: 3.1747e-07 eta: 1 day, 13:47:23 time: 0.9998 data_time: 0.0048 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0097 decode.acc_seg: 99.6197 aux.loss_ce: 0.0095 aux.acc_seg: 99.0349 +04/17 15:11:34 - mmengine - INFO - Iter(train) [ 23950/160000] base_lr: 8.5837e-05 lr: 3.1736e-07 eta: 1 day, 13:46:33 time: 0.9994 data_time: 0.0050 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0117 decode.acc_seg: 99.5115 aux.loss_ce: 0.0107 aux.acc_seg: 98.7293 +04/17 15:12:24 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 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decode.acc_seg: 99.6435 aux.loss_ce: 0.0113 aux.acc_seg: 98.7827 +04/17 15:15:44 - mmengine - INFO - Iter(train) [ 24200/160000] base_lr: 8.5679e-05 lr: 3.1677e-07 eta: 1 day, 13:42:23 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0103 decode.acc_seg: 99.6401 aux.loss_ce: 0.0101 aux.acc_seg: 98.8926 +04/17 15:16:34 - mmengine - INFO - Iter(train) [ 24250/160000] base_lr: 8.5647e-05 lr: 3.1666e-07 eta: 1 day, 13:41:33 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0092 decode.acc_seg: 99.5783 aux.loss_ce: 0.0099 aux.acc_seg: 99.0211 +04/17 15:17:24 - mmengine - INFO - Iter(train) [ 24300/160000] base_lr: 8.5616e-05 lr: 3.1654e-07 eta: 1 day, 13:40:43 time: 0.9982 data_time: 0.0044 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0090 decode.acc_seg: 99.5420 aux.loss_ce: 0.0075 aux.acc_seg: 99.0318 +04/17 15:18:14 - mmengine - INFO - Iter(train) [ 24350/160000] base_lr: 8.5584e-05 lr: 3.1642e-07 eta: 1 day, 13:39:53 time: 0.9995 data_time: 0.0046 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0098 decode.acc_seg: 99.6916 aux.loss_ce: 0.0093 aux.acc_seg: 99.1362 +04/17 15:19:04 - mmengine - INFO - Iter(train) [ 24400/160000] base_lr: 8.5553e-05 lr: 3.1631e-07 eta: 1 day, 13:39:03 time: 0.9997 data_time: 0.0047 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0098 decode.acc_seg: 99.6367 aux.loss_ce: 0.0098 aux.acc_seg: 99.1823 +04/17 15:19:54 - mmengine - INFO - Iter(train) [ 24450/160000] base_lr: 8.5521e-05 lr: 3.1619e-07 eta: 1 day, 13:38:14 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0104 decode.acc_seg: 99.5043 aux.loss_ce: 0.0092 aux.acc_seg: 98.9340 +04/17 15:20:44 - mmengine - INFO - Iter(train) [ 24500/160000] base_lr: 8.5489e-05 lr: 3.1607e-07 eta: 1 day, 13:37:23 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0098 decode.acc_seg: 99.3469 aux.loss_ce: 0.0096 aux.acc_seg: 98.7484 +04/17 15:21:34 - mmengine - INFO - Iter(train) [ 24550/160000] base_lr: 8.5458e-05 lr: 3.1596e-07 eta: 1 day, 13:36:34 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0102 decode.acc_seg: 99.5737 aux.loss_ce: 0.0099 aux.acc_seg: 98.8726 +04/17 15:22:24 - mmengine - INFO - Iter(train) [ 24600/160000] base_lr: 8.5426e-05 lr: 3.1584e-07 eta: 1 day, 13:35:44 time: 1.0013 data_time: 0.0045 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0093 decode.acc_seg: 99.5886 aux.loss_ce: 0.0093 aux.acc_seg: 98.9346 +04/17 15:23:14 - mmengine - INFO - Iter(train) [ 24650/160000] base_lr: 8.5395e-05 lr: 3.1572e-07 eta: 1 day, 13:34:54 time: 0.9982 data_time: 0.0043 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0089 decode.acc_seg: 99.7517 aux.loss_ce: 0.0087 aux.acc_seg: 99.3788 +04/17 15:24:04 - mmengine - INFO - Iter(train) [ 24700/160000] base_lr: 8.5363e-05 lr: 3.1561e-07 eta: 1 day, 13:34:04 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0090 decode.acc_seg: 99.5827 aux.loss_ce: 0.0095 aux.acc_seg: 98.7396 +04/17 15:24:54 - mmengine - INFO - Iter(train) [ 24750/160000] base_lr: 8.5332e-05 lr: 3.1549e-07 eta: 1 day, 13:33:14 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0124 decode.acc_seg: 99.6279 aux.loss_ce: 0.0105 aux.acc_seg: 98.9002 +04/17 15:25:44 - mmengine - INFO - Iter(train) [ 24800/160000] base_lr: 8.5300e-05 lr: 3.1537e-07 eta: 1 day, 13:32:24 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0100 decode.acc_seg: 99.5020 aux.loss_ce: 0.0099 aux.acc_seg: 98.9683 +04/17 15:26:34 - mmengine - INFO - Iter(train) [ 24850/160000] base_lr: 8.5269e-05 lr: 3.1526e-07 eta: 1 day, 13:31:34 time: 1.0006 data_time: 0.0050 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0103 decode.acc_seg: 99.4736 aux.loss_ce: 0.0099 aux.acc_seg: 98.9622 +04/17 15:27:24 - mmengine - INFO - Iter(train) [ 24900/160000] base_lr: 8.5237e-05 lr: 3.1514e-07 eta: 1 day, 13:30:44 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0099 decode.acc_seg: 99.5302 aux.loss_ce: 0.0102 aux.acc_seg: 98.9445 +04/17 15:28:14 - mmengine - INFO - Iter(train) [ 24950/160000] base_lr: 8.5206e-05 lr: 3.1502e-07 eta: 1 day, 13:29:54 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0095 decode.acc_seg: 99.5508 aux.loss_ce: 0.0089 aux.acc_seg: 98.8819 +04/17 15:29:04 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 15:29:04 - mmengine - INFO - Iter(train) [ 25000/160000] base_lr: 8.5174e-05 lr: 3.1491e-07 eta: 1 day, 13:29:04 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0109 decode.acc_seg: 99.5039 aux.loss_ce: 0.0106 aux.acc_seg: 98.5380 +04/17 15:29:54 - mmengine - INFO - Iter(train) [ 25050/160000] base_lr: 8.5142e-05 lr: 3.1479e-07 eta: 1 day, 13:28:14 time: 1.0018 data_time: 0.0046 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0091 decode.acc_seg: 99.5274 aux.loss_ce: 0.0087 aux.acc_seg: 98.6612 +04/17 15:30:44 - mmengine - INFO - Iter(train) [ 25100/160000] base_lr: 8.5111e-05 lr: 3.1467e-07 eta: 1 day, 13:27:24 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0108 decode.acc_seg: 99.4074 aux.loss_ce: 0.0105 aux.acc_seg: 98.5809 +04/17 15:31:34 - mmengine - INFO - Iter(train) [ 25150/160000] base_lr: 8.5079e-05 lr: 3.1456e-07 eta: 1 day, 13:26:34 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0084 decode.acc_seg: 99.6000 aux.loss_ce: 0.0086 aux.acc_seg: 98.8234 +04/17 15:32:24 - mmengine - INFO - Iter(train) [ 25200/160000] base_lr: 8.5048e-05 lr: 3.1444e-07 eta: 1 day, 13:25:44 time: 0.9993 data_time: 0.0043 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0106 decode.acc_seg: 99.3948 aux.loss_ce: 0.0103 aux.acc_seg: 98.6862 +04/17 15:33:14 - mmengine - INFO - Iter(train) [ 25250/160000] base_lr: 8.5016e-05 lr: 3.1432e-07 eta: 1 day, 13:24:54 time: 0.9995 data_time: 0.0048 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0102 decode.acc_seg: 99.6029 aux.loss_ce: 0.0104 aux.acc_seg: 98.9231 +04/17 15:34:04 - mmengine - INFO - Iter(train) [ 25300/160000] base_lr: 8.4985e-05 lr: 3.1421e-07 eta: 1 day, 13:24:04 time: 1.0018 data_time: 0.0043 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0096 decode.acc_seg: 99.5554 aux.loss_ce: 0.0091 aux.acc_seg: 98.9813 +04/17 15:34:54 - mmengine - INFO - Iter(train) [ 25350/160000] base_lr: 8.4953e-05 lr: 3.1409e-07 eta: 1 day, 13:23:14 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0104 decode.acc_seg: 99.7545 aux.loss_ce: 0.0093 aux.acc_seg: 99.3500 +04/17 15:35:44 - mmengine - INFO - Iter(train) [ 25400/160000] base_lr: 8.4922e-05 lr: 3.1397e-07 eta: 1 day, 13:22:24 time: 1.0003 data_time: 0.0056 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0096 decode.acc_seg: 99.7293 aux.loss_ce: 0.0092 aux.acc_seg: 99.2653 +04/17 15:36:34 - mmengine - INFO - Iter(train) [ 25450/160000] base_lr: 8.4890e-05 lr: 3.1386e-07 eta: 1 day, 13:21:34 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0091 decode.acc_seg: 99.5956 aux.loss_ce: 0.0086 aux.acc_seg: 99.1735 +04/17 15:37:24 - mmengine - INFO - Iter(train) [ 25500/160000] base_lr: 8.4859e-05 lr: 3.1374e-07 eta: 1 day, 13:20:45 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0101 decode.acc_seg: 99.7757 aux.loss_ce: 0.0098 aux.acc_seg: 99.2811 +04/17 15:38:14 - mmengine - INFO - Iter(train) [ 25550/160000] base_lr: 8.4827e-05 lr: 3.1362e-07 eta: 1 day, 13:19:55 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0105 decode.acc_seg: 99.6950 aux.loss_ce: 0.0096 aux.acc_seg: 99.2168 +04/17 15:39:04 - mmengine - INFO - Iter(train) [ 25600/160000] base_lr: 8.4795e-05 lr: 3.1351e-07 eta: 1 day, 13:19:05 time: 0.9999 data_time: 0.0043 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.5350 aux.loss_ce: 0.0084 aux.acc_seg: 99.0034 +04/17 15:39:54 - mmengine - INFO - Iter(train) [ 25650/160000] base_lr: 8.4764e-05 lr: 3.1339e-07 eta: 1 day, 13:18:15 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0090 decode.acc_seg: 99.5932 aux.loss_ce: 0.0091 aux.acc_seg: 98.7549 +04/17 15:40:44 - mmengine - INFO - Iter(train) [ 25700/160000] base_lr: 8.4732e-05 lr: 3.1327e-07 eta: 1 day, 13:17:25 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0090 decode.acc_seg: 99.7034 aux.loss_ce: 0.0087 aux.acc_seg: 99.1829 +04/17 15:41:34 - mmengine - INFO - Iter(train) [ 25750/160000] base_lr: 8.4701e-05 lr: 3.1316e-07 eta: 1 day, 13:16:35 time: 1.0003 data_time: 0.0053 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0100 decode.acc_seg: 99.6637 aux.loss_ce: 0.0095 aux.acc_seg: 99.0105 +04/17 15:42:24 - mmengine - INFO - Iter(train) [ 25800/160000] base_lr: 8.4669e-05 lr: 3.1304e-07 eta: 1 day, 13:15:45 time: 1.0000 data_time: 0.0048 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0085 decode.acc_seg: 99.7545 aux.loss_ce: 0.0085 aux.acc_seg: 99.1781 +04/17 15:43:14 - mmengine - INFO - Iter(train) [ 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memory: 8462 loss: 0.0177 decode.loss_ce: 0.0089 decode.acc_seg: 99.5583 aux.loss_ce: 0.0088 aux.acc_seg: 99.0234 +04/17 15:46:34 - mmengine - INFO - Iter(train) [ 26050/160000] base_lr: 8.4512e-05 lr: 3.1246e-07 eta: 1 day, 13:11:35 time: 1.0002 data_time: 0.0043 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0104 decode.acc_seg: 99.3654 aux.loss_ce: 0.0095 aux.acc_seg: 98.8035 +04/17 15:47:24 - mmengine - INFO - Iter(train) [ 26100/160000] base_lr: 8.4480e-05 lr: 3.1234e-07 eta: 1 day, 13:10:45 time: 1.0006 data_time: 0.0042 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0095 decode.acc_seg: 99.3250 aux.loss_ce: 0.0105 aux.acc_seg: 98.2485 +04/17 15:48:14 - mmengine - INFO - Iter(train) [ 26150/160000] base_lr: 8.4448e-05 lr: 3.1222e-07 eta: 1 day, 13:09:55 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.6895 aux.loss_ce: 0.0086 aux.acc_seg: 99.3401 +04/17 15:49:04 - mmengine - INFO - Iter(train) [ 26200/160000] base_lr: 8.4417e-05 lr: 3.1211e-07 eta: 1 day, 13:09:05 time: 0.9983 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0078 decode.acc_seg: 99.5541 aux.loss_ce: 0.0083 aux.acc_seg: 98.9290 +04/17 15:49:54 - mmengine - INFO - Iter(train) [ 26250/160000] base_lr: 8.4385e-05 lr: 3.1199e-07 eta: 1 day, 13:08:15 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.5800 aux.loss_ce: 0.0084 aux.acc_seg: 99.3288 +04/17 15:50:44 - mmengine - INFO - Iter(train) [ 26300/160000] base_lr: 8.4354e-05 lr: 3.1187e-07 eta: 1 day, 13:07:25 time: 0.9991 data_time: 0.0048 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0084 decode.acc_seg: 99.5434 aux.loss_ce: 0.0086 aux.acc_seg: 99.2088 +04/17 15:51:34 - mmengine - INFO - Iter(train) [ 26350/160000] base_lr: 8.4322e-05 lr: 3.1176e-07 eta: 1 day, 13:06:35 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0101 decode.acc_seg: 99.6191 aux.loss_ce: 0.0114 aux.acc_seg: 99.0993 +04/17 15:52:24 - mmengine - INFO - Iter(train) [ 26400/160000] base_lr: 8.4291e-05 lr: 3.1164e-07 eta: 1 day, 13:05:45 time: 0.9989 data_time: 0.0047 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0098 decode.acc_seg: 99.5804 aux.loss_ce: 0.0103 aux.acc_seg: 98.5632 +04/17 15:53:14 - mmengine - INFO - Iter(train) [ 26450/160000] base_lr: 8.4259e-05 lr: 3.1152e-07 eta: 1 day, 13:04:55 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0096 decode.acc_seg: 99.6672 aux.loss_ce: 0.0091 aux.acc_seg: 99.0217 +04/17 15:54:04 - mmengine - INFO - Iter(train) [ 26500/160000] base_lr: 8.4228e-05 lr: 3.1141e-07 eta: 1 day, 13:04:06 time: 1.0003 data_time: 0.0049 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0103 decode.acc_seg: 99.5424 aux.loss_ce: 0.0117 aux.acc_seg: 98.9143 +04/17 15:54:54 - mmengine - INFO - Iter(train) [ 26550/160000] base_lr: 8.4196e-05 lr: 3.1129e-07 eta: 1 day, 13:03:16 time: 1.0008 data_time: 0.0050 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0094 decode.acc_seg: 99.3586 aux.loss_ce: 0.0102 aux.acc_seg: 98.1108 +04/17 15:55:44 - mmengine - INFO - Iter(train) [ 26600/160000] base_lr: 8.4165e-05 lr: 3.1117e-07 eta: 1 day, 13:02:26 time: 1.0004 data_time: 0.0048 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.6861 aux.loss_ce: 0.0083 aux.acc_seg: 99.3816 +04/17 15:56:34 - mmengine - INFO - Iter(train) [ 26650/160000] base_lr: 8.4133e-05 lr: 3.1106e-07 eta: 1 day, 13:01:36 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0097 decode.acc_seg: 99.6843 aux.loss_ce: 0.0091 aux.acc_seg: 98.8846 +04/17 15:57:24 - mmengine - INFO - Iter(train) [ 26700/160000] base_lr: 8.4101e-05 lr: 3.1094e-07 eta: 1 day, 13:00:46 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0096 decode.acc_seg: 99.5705 aux.loss_ce: 0.0097 aux.acc_seg: 98.9902 +04/17 15:58:14 - mmengine - INFO - Iter(train) [ 26750/160000] base_lr: 8.4070e-05 lr: 3.1082e-07 eta: 1 day, 12:59:56 time: 1.0011 data_time: 0.0043 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0092 decode.acc_seg: 99.7662 aux.loss_ce: 0.0090 aux.acc_seg: 99.4053 +04/17 15:59:04 - mmengine - INFO - Iter(train) [ 26800/160000] base_lr: 8.4038e-05 lr: 3.1071e-07 eta: 1 day, 12:59:06 time: 1.0011 data_time: 0.0044 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0091 decode.acc_seg: 99.5886 aux.loss_ce: 0.0089 aux.acc_seg: 99.1833 +04/17 15:59:54 - mmengine - INFO - Iter(train) [ 26850/160000] base_lr: 8.4007e-05 lr: 3.1059e-07 eta: 1 day, 12:58:16 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0100 decode.acc_seg: 99.6778 aux.loss_ce: 0.0103 aux.acc_seg: 99.0946 +04/17 16:00:44 - mmengine - INFO - Iter(train) [ 26900/160000] base_lr: 8.3975e-05 lr: 3.1047e-07 eta: 1 day, 12:57:27 time: 1.0005 data_time: 0.0048 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0086 decode.acc_seg: 99.6935 aux.loss_ce: 0.0088 aux.acc_seg: 99.0488 +04/17 16:01:34 - mmengine - INFO - Iter(train) [ 26950/160000] base_lr: 8.3944e-05 lr: 3.1036e-07 eta: 1 day, 12:56:37 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0113 decode.acc_seg: 99.6801 aux.loss_ce: 0.0101 aux.acc_seg: 98.8554 +04/17 16:02:24 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 16:02:24 - mmengine - INFO - Iter(train) [ 27000/160000] base_lr: 8.3912e-05 lr: 3.1024e-07 eta: 1 day, 12:55:47 time: 1.0002 data_time: 0.0042 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0090 decode.acc_seg: 99.6613 aux.loss_ce: 0.0093 aux.acc_seg: 99.0273 +04/17 16:03:14 - mmengine - INFO - Iter(train) [ 27050/160000] base_lr: 8.3881e-05 lr: 3.1012e-07 eta: 1 day, 12:54:57 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0095 decode.acc_seg: 99.5941 aux.loss_ce: 0.0098 aux.acc_seg: 98.9958 +04/17 16:04:04 - mmengine - INFO - Iter(train) [ 27100/160000] base_lr: 8.3849e-05 lr: 3.1001e-07 eta: 1 day, 12:54:07 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0085 decode.acc_seg: 99.6609 aux.loss_ce: 0.0084 aux.acc_seg: 99.1459 +04/17 16:04:54 - mmengine - INFO - Iter(train) [ 27150/160000] base_lr: 8.3818e-05 lr: 3.0989e-07 eta: 1 day, 12:53:17 time: 0.9974 data_time: 0.0046 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0095 decode.acc_seg: 99.3881 aux.loss_ce: 0.0093 aux.acc_seg: 98.5313 +04/17 16:05:44 - mmengine - INFO - Iter(train) [ 27200/160000] base_lr: 8.3786e-05 lr: 3.0977e-07 eta: 1 day, 12:52:27 time: 0.9983 data_time: 0.0045 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.6912 aux.loss_ce: 0.0084 aux.acc_seg: 99.0955 +04/17 16:06:34 - mmengine - INFO - Iter(train) [ 27250/160000] base_lr: 8.3754e-05 lr: 3.0966e-07 eta: 1 day, 12:51:38 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0101 decode.acc_seg: 99.5813 aux.loss_ce: 0.0093 aux.acc_seg: 98.8226 +04/17 16:07:24 - mmengine - INFO - Iter(train) [ 27300/160000] base_lr: 8.3723e-05 lr: 3.0954e-07 eta: 1 day, 12:50:48 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0098 decode.acc_seg: 99.5985 aux.loss_ce: 0.0092 aux.acc_seg: 98.8377 +04/17 16:08:14 - mmengine - INFO - Iter(train) [ 27350/160000] base_lr: 8.3691e-05 lr: 3.0942e-07 eta: 1 day, 12:49:58 time: 0.9998 data_time: 0.0051 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0100 decode.acc_seg: 99.6555 aux.loss_ce: 0.0099 aux.acc_seg: 98.7318 +04/17 16:09:04 - mmengine - INFO - Iter(train) [ 27400/160000] base_lr: 8.3660e-05 lr: 3.0931e-07 eta: 1 day, 12:49:08 time: 0.9989 data_time: 0.0041 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0084 decode.acc_seg: 99.7126 aux.loss_ce: 0.0085 aux.acc_seg: 99.1102 +04/17 16:09:54 - mmengine - INFO - Iter(train) [ 27450/160000] base_lr: 8.3628e-05 lr: 3.0919e-07 eta: 1 day, 12:48:18 time: 1.0001 data_time: 0.0043 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6378 aux.loss_ce: 0.0082 aux.acc_seg: 98.9553 +04/17 16:10:44 - mmengine - INFO - Iter(train) [ 27500/160000] base_lr: 8.3597e-05 lr: 3.0907e-07 eta: 1 day, 12:47:28 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0093 decode.acc_seg: 99.6639 aux.loss_ce: 0.0086 aux.acc_seg: 99.1831 +04/17 16:11:34 - mmengine - INFO - Iter(train) [ 27550/160000] base_lr: 8.3565e-05 lr: 3.0896e-07 eta: 1 day, 12:46:38 time: 0.9997 data_time: 0.0050 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0103 decode.acc_seg: 99.5102 aux.loss_ce: 0.0095 aux.acc_seg: 98.8453 +04/17 16:12:24 - mmengine - INFO - Iter(train) [ 27600/160000] base_lr: 8.3534e-05 lr: 3.0884e-07 eta: 1 day, 12:45:48 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0097 decode.acc_seg: 99.6437 aux.loss_ce: 0.0103 aux.acc_seg: 98.9790 +04/17 16:13:14 - mmengine - INFO - Iter(train) [ 27650/160000] base_lr: 8.3502e-05 lr: 3.0872e-07 eta: 1 day, 12:44:58 time: 0.9998 data_time: 0.0048 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0109 decode.acc_seg: 99.1842 aux.loss_ce: 0.0098 aux.acc_seg: 98.6662 +04/17 16:14:04 - mmengine - INFO - Iter(train) [ 27700/160000] base_lr: 8.3471e-05 lr: 3.0861e-07 eta: 1 day, 12:44:09 time: 1.0016 data_time: 0.0046 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0091 decode.acc_seg: 99.5279 aux.loss_ce: 0.0093 aux.acc_seg: 98.8819 +04/17 16:14:54 - mmengine - INFO - Iter(train) [ 27750/160000] base_lr: 8.3439e-05 lr: 3.0849e-07 eta: 1 day, 12:43:19 time: 1.0006 data_time: 0.0050 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0081 decode.acc_seg: 99.5584 aux.loss_ce: 0.0084 aux.acc_seg: 99.1430 +04/17 16:15:44 - mmengine - INFO - Iter(train) [ 27800/160000] base_lr: 8.3407e-05 lr: 3.0837e-07 eta: 1 day, 12:42:29 time: 1.0014 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0102 decode.acc_seg: 99.7395 aux.loss_ce: 0.0099 aux.acc_seg: 99.1167 +04/17 16:16:34 - mmengine - INFO - Iter(train) [ 27850/160000] base_lr: 8.3376e-05 lr: 3.0826e-07 eta: 1 day, 12:41:39 time: 1.0003 data_time: 0.0048 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0093 decode.acc_seg: 99.6017 aux.loss_ce: 0.0096 aux.acc_seg: 98.9437 +04/17 16:17:24 - mmengine - INFO - Iter(train) [ 27900/160000] base_lr: 8.3344e-05 lr: 3.0814e-07 eta: 1 day, 12:40:49 time: 1.0001 data_time: 0.0044 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0093 decode.acc_seg: 99.5377 aux.loss_ce: 0.0087 aux.acc_seg: 98.9826 +04/17 16:18:14 - mmengine - INFO - Iter(train) [ 27950/160000] base_lr: 8.3313e-05 lr: 3.0802e-07 eta: 1 day, 12:39:59 time: 1.0013 data_time: 0.0044 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0089 decode.acc_seg: 99.6466 aux.loss_ce: 0.0099 aux.acc_seg: 99.0154 +04/17 16:19:04 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 16:19:04 - mmengine - INFO - Iter(train) [ 28000/160000] base_lr: 8.3281e-05 lr: 3.0791e-07 eta: 1 day, 12:39:09 time: 0.9994 data_time: 0.0043 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0094 decode.acc_seg: 99.5798 aux.loss_ce: 0.0093 aux.acc_seg: 98.9143 +04/17 16:19:54 - mmengine - INFO - Iter(train) [ 28050/160000] base_lr: 8.3250e-05 lr: 3.0779e-07 eta: 1 day, 12:38:19 time: 0.9998 data_time: 0.0042 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0086 decode.acc_seg: 99.4314 aux.loss_ce: 0.0087 aux.acc_seg: 98.5657 +04/17 16:20:44 - mmengine - INFO - Iter(train) [ 28100/160000] base_lr: 8.3218e-05 lr: 3.0767e-07 eta: 1 day, 12:37:29 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.4890 aux.loss_ce: 0.0080 aux.acc_seg: 98.7194 +04/17 16:21:34 - mmengine - INFO - Iter(train) [ 28150/160000] base_lr: 8.3187e-05 lr: 3.0756e-07 eta: 1 day, 12:36:40 time: 1.0013 data_time: 0.0045 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0078 decode.acc_seg: 99.6967 aux.loss_ce: 0.0080 aux.acc_seg: 99.5455 +04/17 16:22:24 - mmengine - INFO - Iter(train) [ 28200/160000] base_lr: 8.3155e-05 lr: 3.0744e-07 eta: 1 day, 12:35:50 time: 0.9999 data_time: 0.0048 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0088 decode.acc_seg: 99.4530 aux.loss_ce: 0.0092 aux.acc_seg: 98.8672 +04/17 16:23:14 - mmengine - INFO - Iter(train) [ 28250/160000] base_lr: 8.3124e-05 lr: 3.0732e-07 eta: 1 day, 12:35:00 time: 1.0004 data_time: 0.0048 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0109 decode.acc_seg: 99.3597 aux.loss_ce: 0.0105 aux.acc_seg: 98.2992 +04/17 16:24:04 - mmengine - INFO - Iter(train) [ 28300/160000] base_lr: 8.3092e-05 lr: 3.0721e-07 eta: 1 day, 12:34:10 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0109 decode.acc_seg: 99.7747 aux.loss_ce: 0.0104 aux.acc_seg: 99.4843 +04/17 16:24:54 - mmengine - INFO - Iter(train) [ 28350/160000] base_lr: 8.3060e-05 lr: 3.0709e-07 eta: 1 day, 12:33:20 time: 0.9990 data_time: 0.0047 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0096 decode.acc_seg: 99.6977 aux.loss_ce: 0.0092 aux.acc_seg: 98.8693 +04/17 16:25:44 - mmengine - INFO - Iter(train) [ 28400/160000] base_lr: 8.3029e-05 lr: 3.0697e-07 eta: 1 day, 12:32:30 time: 1.0008 data_time: 0.0045 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0092 decode.acc_seg: 99.7694 aux.loss_ce: 0.0090 aux.acc_seg: 99.2901 +04/17 16:26:34 - mmengine - INFO - Iter(train) [ 28450/160000] base_lr: 8.2997e-05 lr: 3.0686e-07 eta: 1 day, 12:31:40 time: 1.0002 data_time: 0.0048 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0089 decode.acc_seg: 99.5073 aux.loss_ce: 0.0089 aux.acc_seg: 98.7480 +04/17 16:27:24 - mmengine - INFO - Iter(train) [ 28500/160000] base_lr: 8.2966e-05 lr: 3.0674e-07 eta: 1 day, 12:30:51 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0098 decode.acc_seg: 99.4968 aux.loss_ce: 0.0095 aux.acc_seg: 99.0419 +04/17 16:28:14 - mmengine - INFO - Iter(train) [ 28550/160000] base_lr: 8.2934e-05 lr: 3.0663e-07 eta: 1 day, 12:30:01 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0091 decode.acc_seg: 99.6040 aux.loss_ce: 0.0100 aux.acc_seg: 98.9010 +04/17 16:29:04 - mmengine - INFO - Iter(train) [ 28600/160000] base_lr: 8.2903e-05 lr: 3.0651e-07 eta: 1 day, 12:29:11 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0100 decode.acc_seg: 99.6107 aux.loss_ce: 0.0104 aux.acc_seg: 98.8239 +04/17 16:29:54 - mmengine - INFO - Iter(train) [ 28650/160000] base_lr: 8.2871e-05 lr: 3.0639e-07 eta: 1 day, 12:28:21 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.7017 aux.loss_ce: 0.0081 aux.acc_seg: 99.3147 +04/17 16:30:44 - mmengine - INFO - Iter(train) [ 28700/160000] base_lr: 8.2840e-05 lr: 3.0628e-07 eta: 1 day, 12:27:31 time: 1.0018 data_time: 0.0045 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0098 decode.acc_seg: 99.6914 aux.loss_ce: 0.0093 aux.acc_seg: 99.1346 +04/17 16:31:34 - mmengine - INFO - Iter(train) [ 28750/160000] base_lr: 8.2808e-05 lr: 3.0616e-07 eta: 1 day, 12:26:41 time: 1.0001 data_time: 0.0045 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0093 decode.acc_seg: 99.6403 aux.loss_ce: 0.0093 aux.acc_seg: 99.2172 +04/17 16:32:24 - mmengine - INFO - Iter(train) [ 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aux.acc_seg: 99.1526 +04/17 16:35:44 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 16:35:44 - mmengine - INFO - Iter(train) [ 29000/160000] base_lr: 8.2650e-05 lr: 3.0558e-07 eta: 1 day, 12:22:32 time: 1.0016 data_time: 0.0044 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0084 decode.acc_seg: 99.5972 aux.loss_ce: 0.0085 aux.acc_seg: 99.0278 +04/17 16:36:34 - mmengine - INFO - Iter(train) [ 29050/160000] base_lr: 8.2619e-05 lr: 3.0546e-07 eta: 1 day, 12:21:42 time: 1.0009 data_time: 0.0047 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0086 decode.acc_seg: 99.5779 aux.loss_ce: 0.0092 aux.acc_seg: 99.0356 +04/17 16:37:24 - mmengine - INFO - Iter(train) [ 29100/160000] base_lr: 8.2587e-05 lr: 3.0534e-07 eta: 1 day, 12:20:52 time: 1.0013 data_time: 0.0050 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0094 decode.acc_seg: 99.5415 aux.loss_ce: 0.0091 aux.acc_seg: 99.1438 +04/17 16:38:14 - mmengine - INFO - Iter(train) [ 29150/160000] base_lr: 8.2556e-05 lr: 3.0523e-07 eta: 1 day, 12:20:02 time: 1.0008 data_time: 0.0043 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0093 decode.acc_seg: 99.7322 aux.loss_ce: 0.0095 aux.acc_seg: 99.3235 +04/17 16:39:04 - mmengine - INFO - Iter(train) [ 29200/160000] base_lr: 8.2524e-05 lr: 3.0511e-07 eta: 1 day, 12:19:13 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.7124 aux.loss_ce: 0.0080 aux.acc_seg: 99.0248 +04/17 16:39:54 - mmengine - INFO - Iter(train) [ 29250/160000] base_lr: 8.2493e-05 lr: 3.0499e-07 eta: 1 day, 12:18:23 time: 1.0018 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0090 decode.acc_seg: 99.5857 aux.loss_ce: 0.0088 aux.acc_seg: 99.1066 +04/17 16:40:44 - mmengine - INFO - Iter(train) [ 29300/160000] base_lr: 8.2461e-05 lr: 3.0488e-07 eta: 1 day, 12:17:33 time: 1.0012 data_time: 0.0046 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0086 decode.acc_seg: 99.6841 aux.loss_ce: 0.0089 aux.acc_seg: 99.0004 +04/17 16:41:34 - mmengine - INFO - Iter(train) [ 29350/160000] base_lr: 8.2430e-05 lr: 3.0476e-07 eta: 1 day, 12:16:43 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0090 decode.acc_seg: 99.5378 aux.loss_ce: 0.0092 aux.acc_seg: 98.6506 +04/17 16:42:24 - mmengine - INFO - Iter(train) [ 29400/160000] base_lr: 8.2398e-05 lr: 3.0464e-07 eta: 1 day, 12:15:53 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0090 decode.acc_seg: 99.7160 aux.loss_ce: 0.0092 aux.acc_seg: 99.1240 +04/17 16:43:14 - mmengine - INFO - Iter(train) [ 29450/160000] base_lr: 8.2366e-05 lr: 3.0453e-07 eta: 1 day, 12:15:03 time: 0.9994 data_time: 0.0048 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0098 decode.acc_seg: 99.4558 aux.loss_ce: 0.0090 aux.acc_seg: 98.5292 +04/17 16:44:04 - mmengine - INFO - Iter(train) [ 29500/160000] base_lr: 8.2335e-05 lr: 3.0441e-07 eta: 1 day, 12:14:13 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0084 decode.acc_seg: 99.6563 aux.loss_ce: 0.0093 aux.acc_seg: 99.0377 +04/17 16:44:54 - mmengine - INFO - Iter(train) [ 29550/160000] base_lr: 8.2303e-05 lr: 3.0429e-07 eta: 1 day, 12:13:24 time: 1.0001 data_time: 0.0042 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.6290 aux.loss_ce: 0.0083 aux.acc_seg: 98.9460 +04/17 16:45:44 - mmengine - INFO - Iter(train) [ 29600/160000] base_lr: 8.2272e-05 lr: 3.0418e-07 eta: 1 day, 12:12:34 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0096 decode.acc_seg: 99.5298 aux.loss_ce: 0.0101 aux.acc_seg: 98.7080 +04/17 16:46:34 - mmengine - INFO - Iter(train) [ 29650/160000] base_lr: 8.2240e-05 lr: 3.0406e-07 eta: 1 day, 12:11:44 time: 1.0004 data_time: 0.0044 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0092 decode.acc_seg: 99.6342 aux.loss_ce: 0.0092 aux.acc_seg: 98.8804 +04/17 16:47:24 - mmengine - INFO - Iter(train) [ 29700/160000] base_lr: 8.2209e-05 lr: 3.0394e-07 eta: 1 day, 12:10:54 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.6902 aux.loss_ce: 0.0082 aux.acc_seg: 99.1652 +04/17 16:48:14 - mmengine - INFO - Iter(train) [ 29750/160000] base_lr: 8.2177e-05 lr: 3.0383e-07 eta: 1 day, 12:10:04 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0082 decode.acc_seg: 99.6937 aux.loss_ce: 0.0089 aux.acc_seg: 99.2622 +04/17 16:49:04 - mmengine - INFO - Iter(train) [ 29800/160000] base_lr: 8.2146e-05 lr: 3.0371e-07 eta: 1 day, 12:09:14 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0089 decode.acc_seg: 99.6332 aux.loss_ce: 0.0088 aux.acc_seg: 98.9525 +04/17 16:49:54 - mmengine - INFO - Iter(train) [ 29850/160000] base_lr: 8.2114e-05 lr: 3.0359e-07 eta: 1 day, 12:08:24 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0094 decode.acc_seg: 99.7751 aux.loss_ce: 0.0096 aux.acc_seg: 99.3799 +04/17 16:50:44 - mmengine - INFO - Iter(train) [ 29900/160000] base_lr: 8.2083e-05 lr: 3.0348e-07 eta: 1 day, 12:07:34 time: 1.0004 data_time: 0.0047 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.7232 aux.loss_ce: 0.0084 aux.acc_seg: 99.4005 +04/17 16:51:34 - mmengine - INFO - Iter(train) [ 29950/160000] base_lr: 8.2051e-05 lr: 3.0336e-07 eta: 1 day, 12:06:45 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0084 decode.acc_seg: 99.7128 aux.loss_ce: 0.0083 aux.acc_seg: 99.5285 +04/17 16:52:24 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 16:52:24 - mmengine - INFO - Iter(train) [ 30000/160000] base_lr: 8.2019e-05 lr: 3.0324e-07 eta: 1 day, 12:05:55 time: 1.0000 data_time: 0.0048 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0087 decode.acc_seg: 99.6927 aux.loss_ce: 0.0086 aux.acc_seg: 99.1001 +04/17 16:52:24 - mmengine - INFO - Saving checkpoint at 30000 iterations +04/17 16:52:34 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1160 data_time: 0.0015 memory: 4004 +04/17 16:52:40 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1161 data_time: 0.0016 memory: 4004 +04/17 16:52:46 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1158 data_time: 0.0013 memory: 4004 +04/17 16:52:51 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1155 data_time: 0.0013 memory: 4004 +04/17 16:52:52 - mmengine - INFO - per class results: +04/17 16:52:52 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.17 | 99.57 | 99.58 | 99.6 | 99.57 | +| contrast | 81.89 | 90.44 | 90.05 | 89.66 | 90.44 | ++------------+-------+-------+--------+-----------+--------+ +04/17 16:52:52 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2000 mIoU: 90.5300 mAcc: 95.0000 mFscore: 94.8200 mPrecision: 94.6300 mRecall: 95.0000 data_time: 0.0018 time: 0.1163 +04/17 16:53:42 - mmengine - INFO - Iter(train) [ 30050/160000] base_lr: 8.1988e-05 lr: 3.0313e-07 eta: 1 day, 12:05:05 time: 0.9991 data_time: 0.0046 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0093 decode.acc_seg: 99.6229 aux.loss_ce: 0.0092 aux.acc_seg: 98.9380 +04/17 16:54:32 - mmengine - INFO - Iter(train) [ 30100/160000] base_lr: 8.1956e-05 lr: 3.0301e-07 eta: 1 day, 12:04:15 time: 0.9996 data_time: 0.0051 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0094 decode.acc_seg: 99.5686 aux.loss_ce: 0.0089 aux.acc_seg: 98.9799 +04/17 16:55:22 - mmengine - INFO - Iter(train) [ 30150/160000] base_lr: 8.1925e-05 lr: 3.0289e-07 eta: 1 day, 12:03:25 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0087 decode.acc_seg: 99.5405 aux.loss_ce: 0.0086 aux.acc_seg: 98.7236 +04/17 16:56:12 - mmengine - INFO - Iter(train) [ 30200/160000] base_lr: 8.1893e-05 lr: 3.0278e-07 eta: 1 day, 12:02:35 time: 1.0004 data_time: 0.0050 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0088 decode.acc_seg: 99.7475 aux.loss_ce: 0.0087 aux.acc_seg: 99.2769 +04/17 16:57:02 - mmengine - INFO - Iter(train) [ 30250/160000] base_lr: 8.1862e-05 lr: 3.0266e-07 eta: 1 day, 12:01:46 time: 0.9994 data_time: 0.0045 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0089 decode.acc_seg: 99.4925 aux.loss_ce: 0.0094 aux.acc_seg: 98.6217 +04/17 16:57:52 - mmengine - INFO - Iter(train) [ 30300/160000] base_lr: 8.1830e-05 lr: 3.0254e-07 eta: 1 day, 12:00:56 time: 0.9999 data_time: 0.0049 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0087 decode.acc_seg: 99.6794 aux.loss_ce: 0.0085 aux.acc_seg: 99.3116 +04/17 16:58:42 - mmengine - INFO - Iter(train) [ 30350/160000] base_lr: 8.1799e-05 lr: 3.0243e-07 eta: 1 day, 12:00:06 time: 1.0018 data_time: 0.0048 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0086 decode.acc_seg: 99.7377 aux.loss_ce: 0.0087 aux.acc_seg: 99.4810 +04/17 16:59:32 - mmengine - INFO - Iter(train) [ 30400/160000] base_lr: 8.1767e-05 lr: 3.0231e-07 eta: 1 day, 11:59:16 time: 0.9999 data_time: 0.0043 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0082 decode.acc_seg: 99.5684 aux.loss_ce: 0.0086 aux.acc_seg: 98.9161 +04/17 17:00:22 - mmengine - INFO - Iter(train) [ 30450/160000] base_lr: 8.1736e-05 lr: 3.0219e-07 eta: 1 day, 11:58:26 time: 1.0013 data_time: 0.0044 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0092 decode.acc_seg: 99.7215 aux.loss_ce: 0.0092 aux.acc_seg: 99.3488 +04/17 17:01:12 - mmengine - INFO - Iter(train) [ 30500/160000] base_lr: 8.1704e-05 lr: 3.0208e-07 eta: 1 day, 11:57:36 time: 1.0001 data_time: 0.0043 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0091 decode.acc_seg: 99.6208 aux.loss_ce: 0.0088 aux.acc_seg: 98.4980 +04/17 17:02:02 - mmengine - INFO - Iter(train) [ 30550/160000] base_lr: 8.1672e-05 lr: 3.0196e-07 eta: 1 day, 11:56:46 time: 0.9996 data_time: 0.0046 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.7467 aux.loss_ce: 0.0076 aux.acc_seg: 99.0271 +04/17 17:02:52 - mmengine - INFO - Iter(train) [ 30600/160000] base_lr: 8.1641e-05 lr: 3.0184e-07 eta: 1 day, 11:55:57 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0121 decode.acc_seg: 99.5672 aux.loss_ce: 0.0108 aux.acc_seg: 98.9258 +04/17 17:03:42 - mmengine - INFO - Iter(train) [ 30650/160000] base_lr: 8.1609e-05 lr: 3.0173e-07 eta: 1 day, 11:55:07 time: 0.9990 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0086 decode.acc_seg: 99.5903 aux.loss_ce: 0.0092 aux.acc_seg: 98.6874 +04/17 17:04:32 - mmengine - INFO - Iter(train) [ 30700/160000] base_lr: 8.1578e-05 lr: 3.0161e-07 eta: 1 day, 11:54:17 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0085 decode.acc_seg: 99.6534 aux.loss_ce: 0.0083 aux.acc_seg: 99.3263 +04/17 17:05:22 - mmengine - INFO - Iter(train) [ 30750/160000] base_lr: 8.1546e-05 lr: 3.0149e-07 eta: 1 day, 11:53:27 time: 1.0014 data_time: 0.0044 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.6407 aux.loss_ce: 0.0082 aux.acc_seg: 98.9790 +04/17 17:06:12 - mmengine - INFO - Iter(train) [ 30800/160000] base_lr: 8.1515e-05 lr: 3.0138e-07 eta: 1 day, 11:52:37 time: 0.9999 data_time: 0.0047 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0099 decode.acc_seg: 99.6201 aux.loss_ce: 0.0105 aux.acc_seg: 99.0280 +04/17 17:07:02 - mmengine - INFO - Iter(train) [ 30850/160000] base_lr: 8.1483e-05 lr: 3.0126e-07 eta: 1 day, 11:51:47 time: 1.0010 data_time: 0.0048 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0082 decode.acc_seg: 99.6662 aux.loss_ce: 0.0084 aux.acc_seg: 99.2002 +04/17 17:07:52 - mmengine - INFO - Iter(train) [ 30900/160000] base_lr: 8.1452e-05 lr: 3.0114e-07 eta: 1 day, 11:50:57 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0089 decode.acc_seg: 99.5846 aux.loss_ce: 0.0087 aux.acc_seg: 99.1982 +04/17 17:08:42 - mmengine - INFO - Iter(train) [ 30950/160000] base_lr: 8.1420e-05 lr: 3.0103e-07 eta: 1 day, 11:50:07 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.6296 aux.loss_ce: 0.0081 aux.acc_seg: 99.0252 +04/17 17:09:32 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 17:09:32 - mmengine - INFO - Iter(train) [ 31000/160000] base_lr: 8.1389e-05 lr: 3.0091e-07 eta: 1 day, 11:49:17 time: 0.9979 data_time: 0.0043 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0093 decode.acc_seg: 99.6519 aux.loss_ce: 0.0086 aux.acc_seg: 99.0602 +04/17 17:10:22 - mmengine - INFO - Iter(train) [ 31050/160000] base_lr: 8.1357e-05 lr: 3.0079e-07 eta: 1 day, 11:48:28 time: 1.0004 data_time: 0.0044 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0080 decode.acc_seg: 99.6960 aux.loss_ce: 0.0083 aux.acc_seg: 99.1999 +04/17 17:11:12 - mmengine - INFO - Iter(train) [ 31100/160000] base_lr: 8.1325e-05 lr: 3.0068e-07 eta: 1 day, 11:47:38 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0094 decode.acc_seg: 99.5914 aux.loss_ce: 0.0101 aux.acc_seg: 98.2475 +04/17 17:12:02 - mmengine - INFO - Iter(train) [ 31150/160000] base_lr: 8.1294e-05 lr: 3.0056e-07 eta: 1 day, 11:46:48 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.7179 aux.loss_ce: 0.0084 aux.acc_seg: 99.2775 +04/17 17:12:52 - mmengine - INFO - Iter(train) [ 31200/160000] base_lr: 8.1262e-05 lr: 3.0044e-07 eta: 1 day, 11:45:58 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0089 decode.acc_seg: 99.5459 aux.loss_ce: 0.0086 aux.acc_seg: 98.5708 +04/17 17:13:42 - mmengine - INFO - Iter(train) [ 31250/160000] base_lr: 8.1231e-05 lr: 3.0033e-07 eta: 1 day, 11:45:08 time: 1.0007 data_time: 0.0043 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0086 decode.acc_seg: 99.5615 aux.loss_ce: 0.0088 aux.acc_seg: 98.9775 +04/17 17:14:32 - mmengine - INFO - Iter(train) [ 31300/160000] base_lr: 8.1199e-05 lr: 3.0021e-07 eta: 1 day, 11:44:18 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0081 decode.acc_seg: 99.6435 aux.loss_ce: 0.0083 aux.acc_seg: 99.1495 +04/17 17:15:22 - mmengine - INFO - Iter(train) [ 31350/160000] base_lr: 8.1168e-05 lr: 3.0009e-07 eta: 1 day, 11:43:28 time: 1.0012 data_time: 0.0050 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0106 decode.acc_seg: 99.4677 aux.loss_ce: 0.0099 aux.acc_seg: 98.7720 +04/17 17:16:12 - mmengine - INFO - Iter(train) [ 31400/160000] base_lr: 8.1136e-05 lr: 2.9998e-07 eta: 1 day, 11:42:38 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0084 decode.acc_seg: 99.6706 aux.loss_ce: 0.0094 aux.acc_seg: 98.9902 +04/17 17:17:02 - mmengine - INFO - Iter(train) [ 31450/160000] base_lr: 8.1105e-05 lr: 2.9986e-07 eta: 1 day, 11:41:49 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0087 decode.acc_seg: 99.6580 aux.loss_ce: 0.0087 aux.acc_seg: 99.0391 +04/17 17:17:52 - mmengine - INFO - Iter(train) [ 31500/160000] base_lr: 8.1073e-05 lr: 2.9974e-07 eta: 1 day, 11:40:59 time: 1.0016 data_time: 0.0048 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0087 decode.acc_seg: 99.5750 aux.loss_ce: 0.0094 aux.acc_seg: 98.9014 +04/17 17:18:42 - mmengine - INFO - Iter(train) [ 31550/160000] base_lr: 8.1042e-05 lr: 2.9963e-07 eta: 1 day, 11:40:09 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.6256 aux.loss_ce: 0.0081 aux.acc_seg: 99.2733 +04/17 17:19:32 - mmengine - INFO - Iter(train) [ 31600/160000] base_lr: 8.1010e-05 lr: 2.9951e-07 eta: 1 day, 11:39:19 time: 1.0005 data_time: 0.0043 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0088 decode.acc_seg: 99.6853 aux.loss_ce: 0.0094 aux.acc_seg: 99.0355 +04/17 17:20:22 - mmengine - INFO - Iter(train) [ 31650/160000] base_lr: 8.0978e-05 lr: 2.9939e-07 eta: 1 day, 11:38:29 time: 1.0009 data_time: 0.0042 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0089 decode.acc_seg: 99.7139 aux.loss_ce: 0.0087 aux.acc_seg: 99.0431 +04/17 17:21:12 - mmengine - INFO - Iter(train) [ 31700/160000] base_lr: 8.0947e-05 lr: 2.9928e-07 eta: 1 day, 11:37:39 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0087 decode.acc_seg: 99.5094 aux.loss_ce: 0.0090 aux.acc_seg: 98.4970 +04/17 17:22:02 - mmengine - INFO - Iter(train) [ 31750/160000] base_lr: 8.0915e-05 lr: 2.9916e-07 eta: 1 day, 11:36:50 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0093 decode.acc_seg: 99.6027 aux.loss_ce: 0.0088 aux.acc_seg: 99.1896 +04/17 17:22:52 - mmengine - INFO - Iter(train) [ 31800/160000] base_lr: 8.0884e-05 lr: 2.9904e-07 eta: 1 day, 11:36:00 time: 1.0007 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0083 decode.acc_seg: 99.6967 aux.loss_ce: 0.0095 aux.acc_seg: 99.1680 +04/17 17:23:42 - mmengine - INFO - Iter(train) [ 31850/160000] base_lr: 8.0852e-05 lr: 2.9893e-07 eta: 1 day, 11:35:10 time: 1.0009 data_time: 0.0046 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0094 decode.acc_seg: 99.6681 aux.loss_ce: 0.0093 aux.acc_seg: 99.1896 +04/17 17:24:32 - mmengine - INFO - Iter(train) [ 31900/160000] base_lr: 8.0821e-05 lr: 2.9881e-07 eta: 1 day, 11:34:20 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0083 decode.acc_seg: 99.6717 aux.loss_ce: 0.0083 aux.acc_seg: 99.2178 +04/17 17:25:22 - mmengine - INFO - Iter(train) [ 31950/160000] base_lr: 8.0789e-05 lr: 2.9869e-07 eta: 1 day, 11:33:30 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0093 decode.acc_seg: 99.6681 aux.loss_ce: 0.0093 aux.acc_seg: 99.0532 +04/17 17:26:12 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 17:26:12 - mmengine - INFO - Iter(train) [ 32000/160000] base_lr: 8.0758e-05 lr: 2.9858e-07 eta: 1 day, 11:32:40 time: 1.0000 data_time: 0.0049 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0096 decode.acc_seg: 99.5377 aux.loss_ce: 0.0099 aux.acc_seg: 98.8344 +04/17 17:27:02 - mmengine - INFO - Iter(train) [ 32050/160000] base_lr: 8.0726e-05 lr: 2.9846e-07 eta: 1 day, 11:31:51 time: 1.0012 data_time: 0.0048 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0103 decode.acc_seg: 99.5535 aux.loss_ce: 0.0112 aux.acc_seg: 98.8245 +04/17 17:27:52 - mmengine - INFO - Iter(train) [ 32100/160000] base_lr: 8.0695e-05 lr: 2.9834e-07 eta: 1 day, 11:31:01 time: 1.0007 data_time: 0.0048 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0091 decode.acc_seg: 99.5520 aux.loss_ce: 0.0096 aux.acc_seg: 98.9317 +04/17 17:28:42 - mmengine - INFO - Iter(train) [ 32150/160000] base_lr: 8.0663e-05 lr: 2.9823e-07 eta: 1 day, 11:30:11 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0085 decode.acc_seg: 99.6653 aux.loss_ce: 0.0082 aux.acc_seg: 98.9891 +04/17 17:29:32 - mmengine - INFO - Iter(train) [ 32200/160000] base_lr: 8.0631e-05 lr: 2.9811e-07 eta: 1 day, 11:29:21 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0074 decode.acc_seg: 99.6592 aux.loss_ce: 0.0084 aux.acc_seg: 98.9397 +04/17 17:30:22 - mmengine - INFO - Iter(train) [ 32250/160000] base_lr: 8.0600e-05 lr: 2.9799e-07 eta: 1 day, 11:28:31 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0079 decode.acc_seg: 99.7044 aux.loss_ce: 0.0087 aux.acc_seg: 99.1220 +04/17 17:31:12 - mmengine - INFO - Iter(train) [ 32300/160000] base_lr: 8.0568e-05 lr: 2.9788e-07 eta: 1 day, 11:27:42 time: 0.9999 data_time: 0.0046 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0083 decode.acc_seg: 99.5981 aux.loss_ce: 0.0089 aux.acc_seg: 99.0562 +04/17 17:32:02 - mmengine - INFO - Iter(train) [ 32350/160000] base_lr: 8.0537e-05 lr: 2.9776e-07 eta: 1 day, 11:26:52 time: 0.9999 data_time: 0.0047 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0072 decode.acc_seg: 99.7723 aux.loss_ce: 0.0081 aux.acc_seg: 99.3151 +04/17 17:32:52 - mmengine - INFO - Iter(train) [ 32400/160000] base_lr: 8.0505e-05 lr: 2.9764e-07 eta: 1 day, 11:26:02 time: 1.0009 data_time: 0.0047 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.6283 aux.loss_ce: 0.0080 aux.acc_seg: 99.1644 +04/17 17:33:42 - mmengine - INFO - Iter(train) [ 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aux.acc_seg: 98.9851 +04/17 17:37:03 - mmengine - INFO - Iter(train) [ 32650/160000] base_lr: 8.0348e-05 lr: 2.9706e-07 eta: 1 day, 11:21:53 time: 1.0004 data_time: 0.0047 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0081 decode.acc_seg: 99.8064 aux.loss_ce: 0.0086 aux.acc_seg: 99.3704 +04/17 17:37:52 - mmengine - INFO - Iter(train) [ 32700/160000] base_lr: 8.0316e-05 lr: 2.9694e-07 eta: 1 day, 11:21:03 time: 1.0001 data_time: 0.0044 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0092 decode.acc_seg: 99.6901 aux.loss_ce: 0.0090 aux.acc_seg: 99.2725 +04/17 17:38:43 - mmengine - INFO - Iter(train) [ 32750/160000] base_lr: 8.0284e-05 lr: 2.9683e-07 eta: 1 day, 11:20:13 time: 1.0008 data_time: 0.0051 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.5844 aux.loss_ce: 0.0074 aux.acc_seg: 98.8190 +04/17 17:39:33 - mmengine - INFO - Iter(train) [ 32800/160000] base_lr: 8.0253e-05 lr: 2.9671e-07 eta: 1 day, 11:19:23 time: 1.0012 data_time: 0.0043 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0101 decode.acc_seg: 99.6080 aux.loss_ce: 0.0098 aux.acc_seg: 99.1676 +04/17 17:40:23 - mmengine - INFO - Iter(train) [ 32850/160000] base_lr: 8.0221e-05 lr: 2.9659e-07 eta: 1 day, 11:18:34 time: 0.9995 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0079 decode.acc_seg: 99.6521 aux.loss_ce: 0.0082 aux.acc_seg: 98.9561 +04/17 17:41:13 - mmengine - INFO - Iter(train) [ 32900/160000] base_lr: 8.0190e-05 lr: 2.9648e-07 eta: 1 day, 11:17:44 time: 1.0006 data_time: 0.0048 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.7070 aux.loss_ce: 0.0081 aux.acc_seg: 99.1634 +04/17 17:42:03 - mmengine - INFO - Iter(train) [ 32950/160000] base_lr: 8.0158e-05 lr: 2.9636e-07 eta: 1 day, 11:16:54 time: 1.0020 data_time: 0.0045 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7488 aux.loss_ce: 0.0072 aux.acc_seg: 99.3393 +04/17 17:42:53 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 17:42:53 - mmengine - INFO - Iter(train) [ 33000/160000] base_lr: 8.0127e-05 lr: 2.9624e-07 eta: 1 day, 11:16:04 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0079 decode.acc_seg: 99.7297 aux.loss_ce: 0.0088 aux.acc_seg: 99.3464 +04/17 17:43:43 - mmengine - INFO - Iter(train) [ 33050/160000] base_lr: 8.0095e-05 lr: 2.9613e-07 eta: 1 day, 11:15:14 time: 1.0004 data_time: 0.0044 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0090 decode.acc_seg: 99.6449 aux.loss_ce: 0.0096 aux.acc_seg: 99.0391 +04/17 17:44:33 - mmengine - INFO - Iter(train) [ 33100/160000] base_lr: 8.0064e-05 lr: 2.9601e-07 eta: 1 day, 11:14:25 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0091 decode.acc_seg: 99.4652 aux.loss_ce: 0.0098 aux.acc_seg: 98.3170 +04/17 17:45:23 - mmengine - INFO - Iter(train) [ 33150/160000] base_lr: 8.0032e-05 lr: 2.9589e-07 eta: 1 day, 11:13:35 time: 1.0021 data_time: 0.0044 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0073 decode.acc_seg: 99.7093 aux.loss_ce: 0.0081 aux.acc_seg: 99.1882 +04/17 17:46:13 - mmengine - INFO - Iter(train) [ 33200/160000] base_lr: 8.0001e-05 lr: 2.9578e-07 eta: 1 day, 11:12:45 time: 1.0014 data_time: 0.0044 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0077 decode.acc_seg: 99.7759 aux.loss_ce: 0.0083 aux.acc_seg: 99.1198 +04/17 17:47:03 - mmengine - INFO - Iter(train) [ 33250/160000] base_lr: 7.9969e-05 lr: 2.9566e-07 eta: 1 day, 11:11:55 time: 1.0003 data_time: 0.0047 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.7992 aux.loss_ce: 0.0073 aux.acc_seg: 99.5865 +04/17 17:47:53 - mmengine - INFO - Iter(train) [ 33300/160000] base_lr: 7.9937e-05 lr: 2.9555e-07 eta: 1 day, 11:11:06 time: 1.0017 data_time: 0.0054 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0086 decode.acc_seg: 99.6231 aux.loss_ce: 0.0089 aux.acc_seg: 99.0973 +04/17 17:48:43 - mmengine - INFO - Iter(train) [ 33350/160000] base_lr: 7.9906e-05 lr: 2.9543e-07 eta: 1 day, 11:10:16 time: 1.0011 data_time: 0.0044 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0073 decode.acc_seg: 99.5970 aux.loss_ce: 0.0083 aux.acc_seg: 99.0637 +04/17 17:49:33 - mmengine - INFO - Iter(train) [ 33400/160000] base_lr: 7.9874e-05 lr: 2.9531e-07 eta: 1 day, 11:09:26 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0086 decode.acc_seg: 99.6271 aux.loss_ce: 0.0085 aux.acc_seg: 98.9088 +04/17 17:50:23 - mmengine - INFO - Iter(train) [ 33450/160000] base_lr: 7.9843e-05 lr: 2.9520e-07 eta: 1 day, 11:08:36 time: 1.0015 data_time: 0.0043 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0083 decode.acc_seg: 99.6145 aux.loss_ce: 0.0085 aux.acc_seg: 99.1199 +04/17 17:51:13 - mmengine - INFO - Iter(train) [ 33500/160000] base_lr: 7.9811e-05 lr: 2.9508e-07 eta: 1 day, 11:07:46 time: 1.0003 data_time: 0.0050 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0082 decode.acc_seg: 99.7141 aux.loss_ce: 0.0098 aux.acc_seg: 98.9956 +04/17 17:52:03 - mmengine - INFO - Iter(train) [ 33550/160000] base_lr: 7.9780e-05 lr: 2.9496e-07 eta: 1 day, 11:06:56 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.5331 aux.loss_ce: 0.0078 aux.acc_seg: 99.1173 +04/17 17:52:53 - mmengine - INFO - Iter(train) [ 33600/160000] base_lr: 7.9748e-05 lr: 2.9485e-07 eta: 1 day, 11:06:07 time: 1.0011 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0089 decode.acc_seg: 99.6397 aux.loss_ce: 0.0087 aux.acc_seg: 99.3166 +04/17 17:53:43 - mmengine - INFO - Iter(train) [ 33650/160000] base_lr: 7.9717e-05 lr: 2.9473e-07 eta: 1 day, 11:05:17 time: 1.0014 data_time: 0.0045 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0092 decode.acc_seg: 99.5447 aux.loss_ce: 0.0092 aux.acc_seg: 98.8676 +04/17 17:54:33 - mmengine - INFO - Iter(train) [ 33700/160000] base_lr: 7.9685e-05 lr: 2.9461e-07 eta: 1 day, 11:04:27 time: 1.0010 data_time: 0.0041 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0079 decode.acc_seg: 99.6780 aux.loss_ce: 0.0085 aux.acc_seg: 98.8388 +04/17 17:55:23 - mmengine - INFO - Iter(train) [ 33750/160000] base_lr: 7.9653e-05 lr: 2.9450e-07 eta: 1 day, 11:03:37 time: 1.0008 data_time: 0.0049 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0085 decode.acc_seg: 99.7696 aux.loss_ce: 0.0092 aux.acc_seg: 99.3156 +04/17 17:56:13 - mmengine - INFO - Iter(train) [ 33800/160000] base_lr: 7.9622e-05 lr: 2.9438e-07 eta: 1 day, 11:02:48 time: 1.0008 data_time: 0.0048 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0086 decode.acc_seg: 99.5960 aux.loss_ce: 0.0091 aux.acc_seg: 98.7976 +04/17 17:57:03 - mmengine - INFO - Iter(train) [ 33850/160000] base_lr: 7.9590e-05 lr: 2.9426e-07 eta: 1 day, 11:01:58 time: 1.0015 data_time: 0.0043 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0076 decode.acc_seg: 99.6857 aux.loss_ce: 0.0082 aux.acc_seg: 99.0356 +04/17 17:57:54 - mmengine - INFO - Iter(train) [ 33900/160000] base_lr: 7.9559e-05 lr: 2.9415e-07 eta: 1 day, 11:01:08 time: 1.0011 data_time: 0.0050 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0098 decode.acc_seg: 99.7295 aux.loss_ce: 0.0093 aux.acc_seg: 99.0496 +04/17 17:58:44 - mmengine - INFO - Iter(train) [ 33950/160000] base_lr: 7.9527e-05 lr: 2.9403e-07 eta: 1 day, 11:00:18 time: 1.0003 data_time: 0.0041 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0091 decode.acc_seg: 99.5611 aux.loss_ce: 0.0093 aux.acc_seg: 98.7150 +04/17 17:59:34 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 17:59:34 - mmengine - INFO - Iter(train) [ 34000/160000] base_lr: 7.9496e-05 lr: 2.9391e-07 eta: 1 day, 10:59:28 time: 1.0011 data_time: 0.0044 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.5853 aux.loss_ce: 0.0082 aux.acc_seg: 98.8203 +04/17 18:00:24 - mmengine - INFO - Iter(train) [ 34050/160000] base_lr: 7.9464e-05 lr: 2.9380e-07 eta: 1 day, 10:58:38 time: 1.0006 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0084 decode.acc_seg: 99.6889 aux.loss_ce: 0.0095 aux.acc_seg: 99.3422 +04/17 18:01:14 - mmengine - INFO - Iter(train) [ 34100/160000] base_lr: 7.9433e-05 lr: 2.9368e-07 eta: 1 day, 10:57:49 time: 1.0003 data_time: 0.0047 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0077 decode.acc_seg: 99.5663 aux.loss_ce: 0.0086 aux.acc_seg: 98.7095 +04/17 18:02:04 - mmengine - INFO - Iter(train) [ 34150/160000] base_lr: 7.9401e-05 lr: 2.9356e-07 eta: 1 day, 10:56:59 time: 1.0005 data_time: 0.0044 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0078 decode.acc_seg: 99.5920 aux.loss_ce: 0.0082 aux.acc_seg: 99.0913 +04/17 18:02:54 - mmengine - INFO - Iter(train) [ 34200/160000] base_lr: 7.9370e-05 lr: 2.9345e-07 eta: 1 day, 10:56:09 time: 0.9992 data_time: 0.0045 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.5230 aux.loss_ce: 0.0081 aux.acc_seg: 98.9002 +04/17 18:03:44 - mmengine - INFO - Iter(train) [ 34250/160000] base_lr: 7.9338e-05 lr: 2.9333e-07 eta: 1 day, 10:55:19 time: 1.0003 data_time: 0.0044 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0078 decode.acc_seg: 99.7005 aux.loss_ce: 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decode.loss_ce: 0.0094 decode.acc_seg: 99.3542 aux.loss_ce: 0.0099 aux.acc_seg: 98.7295 +04/17 18:07:54 - mmengine - INFO - Iter(train) [ 34500/160000] base_lr: 7.9180e-05 lr: 2.9275e-07 eta: 1 day, 10:51:10 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0089 decode.acc_seg: 99.7618 aux.loss_ce: 0.0091 aux.acc_seg: 99.5354 +04/17 18:08:44 - mmengine - INFO - Iter(train) [ 34550/160000] base_lr: 7.9149e-05 lr: 2.9263e-07 eta: 1 day, 10:50:20 time: 1.0006 data_time: 0.0043 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0081 decode.acc_seg: 99.4892 aux.loss_ce: 0.0091 aux.acc_seg: 98.6187 +04/17 18:09:34 - mmengine - INFO - Iter(train) [ 34600/160000] base_lr: 7.9117e-05 lr: 2.9251e-07 eta: 1 day, 10:49:30 time: 1.0007 data_time: 0.0047 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.6304 aux.loss_ce: 0.0079 aux.acc_seg: 98.9979 +04/17 18:10:24 - mmengine - INFO - Iter(train) [ 34650/160000] base_lr: 7.9086e-05 lr: 2.9240e-07 eta: 1 day, 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memory: 8462 loss: 0.0159 decode.loss_ce: 0.0074 decode.acc_seg: 99.7099 aux.loss_ce: 0.0085 aux.acc_seg: 99.0732 +04/17 18:17:05 - mmengine - INFO - Iter(train) [ 35050/160000] base_lr: 7.8833e-05 lr: 2.9146e-07 eta: 1 day, 10:42:03 time: 1.0005 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0087 decode.acc_seg: 99.5447 aux.loss_ce: 0.0092 aux.acc_seg: 99.0398 +04/17 18:17:55 - mmengine - INFO - Iter(train) [ 35100/160000] base_lr: 7.8802e-05 lr: 2.9135e-07 eta: 1 day, 10:41:13 time: 1.0006 data_time: 0.0048 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.4404 aux.loss_ce: 0.0085 aux.acc_seg: 98.6664 +04/17 18:18:45 - mmengine - INFO - Iter(train) [ 35150/160000] base_lr: 7.8770e-05 lr: 2.9123e-07 eta: 1 day, 10:40:23 time: 1.0017 data_time: 0.0050 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.7715 aux.loss_ce: 0.0073 aux.acc_seg: 99.4728 +04/17 18:19:35 - mmengine - INFO - Iter(train) [ 35200/160000] base_lr: 7.8739e-05 lr: 2.9111e-07 eta: 1 day, 10:39:33 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0093 decode.acc_seg: 99.7454 aux.loss_ce: 0.0090 aux.acc_seg: 99.4465 +04/17 18:20:25 - mmengine - INFO - Iter(train) [ 35250/160000] base_lr: 7.8707e-05 lr: 2.9100e-07 eta: 1 day, 10:38:43 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0080 decode.acc_seg: 99.6777 aux.loss_ce: 0.0087 aux.acc_seg: 99.0917 +04/17 18:21:15 - mmengine - INFO - Iter(train) [ 35300/160000] base_lr: 7.8676e-05 lr: 2.9088e-07 eta: 1 day, 10:37:54 time: 1.0014 data_time: 0.0042 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0085 decode.acc_seg: 99.7107 aux.loss_ce: 0.0088 aux.acc_seg: 99.1545 +04/17 18:22:05 - mmengine - INFO - Iter(train) [ 35350/160000] base_lr: 7.8644e-05 lr: 2.9076e-07 eta: 1 day, 10:37:04 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0077 decode.acc_seg: 99.6267 aux.loss_ce: 0.0085 aux.acc_seg: 98.8953 +04/17 18:22:55 - mmengine - INFO - Iter(train) [ 35400/160000] base_lr: 7.8612e-05 lr: 2.9065e-07 eta: 1 day, 10:36:14 time: 0.9996 data_time: 0.0042 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0068 decode.acc_seg: 99.6824 aux.loss_ce: 0.0077 aux.acc_seg: 99.0627 +04/17 18:23:45 - mmengine - INFO - Iter(train) [ 35450/160000] base_lr: 7.8581e-05 lr: 2.9053e-07 eta: 1 day, 10:35:24 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0082 decode.acc_seg: 99.5533 aux.loss_ce: 0.0087 aux.acc_seg: 98.6879 +04/17 18:24:35 - mmengine - INFO - Iter(train) [ 35500/160000] base_lr: 7.8549e-05 lr: 2.9041e-07 eta: 1 day, 10:34:34 time: 1.0011 data_time: 0.0046 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0070 decode.acc_seg: 99.7019 aux.loss_ce: 0.0079 aux.acc_seg: 99.1934 +04/17 18:25:25 - mmengine - INFO - Iter(train) [ 35550/160000] base_lr: 7.8518e-05 lr: 2.9030e-07 eta: 1 day, 10:33:44 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0092 decode.acc_seg: 99.6517 aux.loss_ce: 0.0091 aux.acc_seg: 99.1537 +04/17 18:26:15 - mmengine - INFO - Iter(train) [ 35600/160000] base_lr: 7.8486e-05 lr: 2.9018e-07 eta: 1 day, 10:32:55 time: 1.0007 data_time: 0.0047 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0083 decode.acc_seg: 99.6302 aux.loss_ce: 0.0092 aux.acc_seg: 99.0995 +04/17 18:27:05 - mmengine - INFO - Iter(train) [ 35650/160000] base_lr: 7.8455e-05 lr: 2.9006e-07 eta: 1 day, 10:32:05 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0086 decode.acc_seg: 99.5853 aux.loss_ce: 0.0087 aux.acc_seg: 99.2022 +04/17 18:27:55 - mmengine - INFO - Iter(train) [ 35700/160000] base_lr: 7.8423e-05 lr: 2.8995e-07 eta: 1 day, 10:31:15 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0081 decode.acc_seg: 99.5628 aux.loss_ce: 0.0090 aux.acc_seg: 98.5685 +04/17 18:28:45 - mmengine - INFO - Iter(train) [ 35750/160000] base_lr: 7.8392e-05 lr: 2.8983e-07 eta: 1 day, 10:30:25 time: 1.0009 data_time: 0.0052 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0075 decode.acc_seg: 99.7187 aux.loss_ce: 0.0080 aux.acc_seg: 99.4799 +04/17 18:29:35 - mmengine - INFO - Iter(train) [ 35800/160000] base_lr: 7.8360e-05 lr: 2.8971e-07 eta: 1 day, 10:29:35 time: 1.0014 data_time: 0.0046 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0076 decode.acc_seg: 99.6855 aux.loss_ce: 0.0087 aux.acc_seg: 99.1003 +04/17 18:30:25 - mmengine - INFO - Iter(train) [ 35850/160000] base_lr: 7.8329e-05 lr: 2.8960e-07 eta: 1 day, 10:28:45 time: 1.0012 data_time: 0.0047 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7375 aux.loss_ce: 0.0069 aux.acc_seg: 99.3561 +04/17 18:31:15 - mmengine - INFO - Iter(train) [ 35900/160000] base_lr: 7.8297e-05 lr: 2.8948e-07 eta: 1 day, 10:27:56 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0078 decode.acc_seg: 99.5060 aux.loss_ce: 0.0088 aux.acc_seg: 99.0116 +04/17 18:32:05 - mmengine - INFO - Iter(train) [ 35950/160000] base_lr: 7.8265e-05 lr: 2.8936e-07 eta: 1 day, 10:27:06 time: 1.0015 data_time: 0.0043 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0077 decode.acc_seg: 99.7074 aux.loss_ce: 0.0082 aux.acc_seg: 99.2037 +04/17 18:32:55 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 18:32:55 - mmengine - INFO - Iter(train) [ 36000/160000] base_lr: 7.8234e-05 lr: 2.8925e-07 eta: 1 day, 10:26:16 time: 1.0010 data_time: 0.0049 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0086 decode.acc_seg: 99.5695 aux.loss_ce: 0.0088 aux.acc_seg: 98.9298 +04/17 18:33:45 - mmengine - INFO - Iter(train) [ 36050/160000] base_lr: 7.8202e-05 lr: 2.8913e-07 eta: 1 day, 10:25:26 time: 1.0012 data_time: 0.0044 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0070 decode.acc_seg: 99.6040 aux.loss_ce: 0.0082 aux.acc_seg: 98.9573 +04/17 18:34:35 - mmengine - INFO - Iter(train) [ 36100/160000] base_lr: 7.8171e-05 lr: 2.8901e-07 eta: 1 day, 10:24:37 time: 1.0012 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.8489 aux.loss_ce: 0.0078 aux.acc_seg: 99.6672 +04/17 18:35:26 - mmengine - INFO - Iter(train) [ 36150/160000] base_lr: 7.8139e-05 lr: 2.8890e-07 eta: 1 day, 10:23:47 time: 1.0021 data_time: 0.0046 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0082 decode.acc_seg: 99.6416 aux.loss_ce: 0.0091 aux.acc_seg: 99.0370 +04/17 18:36:16 - mmengine - INFO - Iter(train) [ 36200/160000] base_lr: 7.8108e-05 lr: 2.8878e-07 eta: 1 day, 10:22:57 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0077 decode.acc_seg: 99.7070 aux.loss_ce: 0.0089 aux.acc_seg: 99.0919 +04/17 18:37:06 - mmengine - INFO - Iter(train) [ 36250/160000] base_lr: 7.8076e-05 lr: 2.8866e-07 eta: 1 day, 10:22:07 time: 1.0010 data_time: 0.0051 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.6822 aux.loss_ce: 0.0074 aux.acc_seg: 98.9012 +04/17 18:37:56 - mmengine - INFO - Iter(train) [ 36300/160000] base_lr: 7.8045e-05 lr: 2.8855e-07 eta: 1 day, 10:21:17 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0086 decode.acc_seg: 99.6410 aux.loss_ce: 0.0093 aux.acc_seg: 98.8859 +04/17 18:38:46 - mmengine - INFO - Iter(train) [ 36350/160000] base_lr: 7.8013e-05 lr: 2.8843e-07 eta: 1 day, 10:20:28 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0083 decode.acc_seg: 99.6387 aux.loss_ce: 0.0089 aux.acc_seg: 98.8016 +04/17 18:39:36 - mmengine - INFO - Iter(train) [ 36400/160000] base_lr: 7.7982e-05 lr: 2.8831e-07 eta: 1 day, 10:19:38 time: 1.0013 data_time: 0.0045 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6830 aux.loss_ce: 0.0077 aux.acc_seg: 99.3126 +04/17 18:40:26 - mmengine - INFO - Iter(train) [ 36450/160000] base_lr: 7.7950e-05 lr: 2.8820e-07 eta: 1 day, 10:18:48 time: 1.0016 data_time: 0.0048 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0082 decode.acc_seg: 99.6511 aux.loss_ce: 0.0093 aux.acc_seg: 98.9363 +04/17 18:41:16 - mmengine - INFO - Iter(train) [ 36500/160000] base_lr: 7.7918e-05 lr: 2.8808e-07 eta: 1 day, 10:17:58 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0085 decode.acc_seg: 99.7471 aux.loss_ce: 0.0093 aux.acc_seg: 99.2018 +04/17 18:42:06 - mmengine - INFO - Iter(train) [ 36550/160000] base_lr: 7.7887e-05 lr: 2.8796e-07 eta: 1 day, 10:17:08 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0073 decode.acc_seg: 99.5436 aux.loss_ce: 0.0085 aux.acc_seg: 98.8192 +04/17 18:42:56 - mmengine - INFO - Iter(train) [ 36600/160000] base_lr: 7.7855e-05 lr: 2.8785e-07 eta: 1 day, 10:16:18 time: 1.0000 data_time: 0.0043 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0078 decode.acc_seg: 99.7873 aux.loss_ce: 0.0083 aux.acc_seg: 99.2937 +04/17 18:43:46 - mmengine - INFO - Iter(train) [ 36650/160000] base_lr: 7.7824e-05 lr: 2.8773e-07 eta: 1 day, 10:15:28 time: 1.0014 data_time: 0.0049 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.7637 aux.loss_ce: 0.0077 aux.acc_seg: 99.2682 +04/17 18:44:36 - mmengine - INFO - Iter(train) [ 36700/160000] base_lr: 7.7792e-05 lr: 2.8761e-07 eta: 1 day, 10:14:39 time: 1.0011 data_time: 0.0043 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0090 decode.acc_seg: 99.5399 aux.loss_ce: 0.0090 aux.acc_seg: 99.2033 +04/17 18:45:26 - mmengine - INFO - Iter(train) [ 36750/160000] base_lr: 7.7761e-05 lr: 2.8750e-07 eta: 1 day, 10:13:49 time: 0.9999 data_time: 0.0047 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0084 decode.acc_seg: 99.6468 aux.loss_ce: 0.0090 aux.acc_seg: 98.8775 +04/17 18:46:16 - mmengine - INFO - Iter(train) [ 36800/160000] base_lr: 7.7729e-05 lr: 2.8738e-07 eta: 1 day, 10:12:59 time: 1.0014 data_time: 0.0041 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0074 decode.acc_seg: 99.7473 aux.loss_ce: 0.0083 aux.acc_seg: 99.3780 +04/17 18:47:06 - mmengine - INFO - Iter(train) [ 36850/160000] base_lr: 7.7698e-05 lr: 2.8726e-07 eta: 1 day, 10:12:09 time: 1.0005 data_time: 0.0042 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0082 decode.acc_seg: 99.5848 aux.loss_ce: 0.0096 aux.acc_seg: 98.5756 +04/17 18:47:56 - mmengine - INFO - Iter(train) [ 36900/160000] base_lr: 7.7666e-05 lr: 2.8715e-07 eta: 1 day, 10:11:19 time: 1.0010 data_time: 0.0051 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0076 decode.acc_seg: 99.7629 aux.loss_ce: 0.0081 aux.acc_seg: 99.1388 +04/17 18:48:46 - mmengine - INFO - Iter(train) [ 36950/160000] base_lr: 7.7635e-05 lr: 2.8703e-07 eta: 1 day, 10:10:29 time: 1.0018 data_time: 0.0045 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7015 aux.loss_ce: 0.0070 aux.acc_seg: 99.1478 +04/17 18:49:36 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 18:49:36 - mmengine - INFO - Iter(train) [ 37000/160000] base_lr: 7.7603e-05 lr: 2.8691e-07 eta: 1 day, 10:09:40 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0078 decode.acc_seg: 99.7145 aux.loss_ce: 0.0085 aux.acc_seg: 98.6742 +04/17 18:50:26 - mmengine - INFO - Iter(train) [ 37050/160000] base_lr: 7.7571e-05 lr: 2.8680e-07 eta: 1 day, 10:08:50 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0074 decode.acc_seg: 99.6902 aux.loss_ce: 0.0082 aux.acc_seg: 99.0879 +04/17 18:51:16 - mmengine - INFO - Iter(train) [ 37100/160000] base_lr: 7.7540e-05 lr: 2.8668e-07 eta: 1 day, 10:08:00 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0070 decode.acc_seg: 99.7778 aux.loss_ce: 0.0084 aux.acc_seg: 99.3973 +04/17 18:52:06 - mmengine - INFO - Iter(train) [ 37150/160000] base_lr: 7.7508e-05 lr: 2.8656e-07 eta: 1 day, 10:07:10 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0075 decode.acc_seg: 99.6952 aux.loss_ce: 0.0079 aux.acc_seg: 99.2620 +04/17 18:52:56 - mmengine - INFO - Iter(train) [ 37200/160000] base_lr: 7.7477e-05 lr: 2.8645e-07 eta: 1 day, 10:06:20 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0071 decode.acc_seg: 99.7860 aux.loss_ce: 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decode.loss_ce: 0.0077 decode.acc_seg: 99.6948 aux.loss_ce: 0.0087 aux.acc_seg: 99.0623 +04/17 18:57:07 - mmengine - INFO - Iter(train) [ 37450/160000] base_lr: 7.7319e-05 lr: 2.8586e-07 eta: 1 day, 10:02:11 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0080 decode.acc_seg: 99.6958 aux.loss_ce: 0.0087 aux.acc_seg: 99.1169 +04/17 18:57:57 - mmengine - INFO - Iter(train) [ 37500/160000] base_lr: 7.7288e-05 lr: 2.8575e-07 eta: 1 day, 10:01:21 time: 0.9990 data_time: 0.0042 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.7894 aux.loss_ce: 0.0079 aux.acc_seg: 99.3212 +04/17 18:58:47 - mmengine - INFO - Iter(train) [ 37550/160000] base_lr: 7.7256e-05 lr: 2.8563e-07 eta: 1 day, 10:00:31 time: 1.0024 data_time: 0.0059 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0102 decode.acc_seg: 99.4537 aux.loss_ce: 0.0094 aux.acc_seg: 99.0900 +04/17 18:59:37 - mmengine - INFO - Iter(train) [ 37600/160000] base_lr: 7.7224e-05 lr: 2.8551e-07 eta: 1 day, 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99.1543 +04/17 19:06:17 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 19:06:17 - mmengine - INFO - Iter(train) [ 38000/160000] base_lr: 7.6972e-05 lr: 2.8458e-07 eta: 1 day, 9:53:04 time: 1.0017 data_time: 0.0045 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0075 decode.acc_seg: 99.7446 aux.loss_ce: 0.0080 aux.acc_seg: 99.4513 +04/17 19:07:07 - mmengine - INFO - Iter(train) [ 38050/160000] base_lr: 7.6941e-05 lr: 2.8447e-07 eta: 1 day, 9:52:14 time: 1.0026 data_time: 0.0044 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0074 decode.acc_seg: 99.6555 aux.loss_ce: 0.0080 aux.acc_seg: 99.0978 +04/17 19:07:57 - mmengine - INFO - Iter(train) [ 38100/160000] base_lr: 7.6909e-05 lr: 2.8435e-07 eta: 1 day, 9:51:24 time: 1.0015 data_time: 0.0049 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0078 decode.acc_seg: 99.6794 aux.loss_ce: 0.0081 aux.acc_seg: 99.0656 +04/17 19:08:48 - mmengine - INFO - Iter(train) [ 38150/160000] base_lr: 7.6877e-05 lr: 2.8423e-07 eta: 1 day, 9:50:34 time: 1.0009 data_time: 0.0048 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0079 decode.acc_seg: 99.7627 aux.loss_ce: 0.0085 aux.acc_seg: 99.2317 +04/17 19:09:38 - mmengine - INFO - Iter(train) [ 38200/160000] base_lr: 7.6846e-05 lr: 2.8412e-07 eta: 1 day, 9:49:45 time: 1.0015 data_time: 0.0044 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7185 aux.loss_ce: 0.0073 aux.acc_seg: 99.0358 +04/17 19:10:28 - mmengine - INFO - Iter(train) [ 38250/160000] base_lr: 7.6814e-05 lr: 2.8400e-07 eta: 1 day, 9:48:55 time: 1.0012 data_time: 0.0044 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0079 decode.acc_seg: 99.7562 aux.loss_ce: 0.0093 aux.acc_seg: 99.3286 +04/17 19:11:18 - mmengine - INFO - Iter(train) [ 38300/160000] base_lr: 7.6783e-05 lr: 2.8388e-07 eta: 1 day, 9:48:05 time: 1.0018 data_time: 0.0052 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0074 decode.acc_seg: 99.7210 aux.loss_ce: 0.0080 aux.acc_seg: 99.2395 +04/17 19:12:08 - mmengine - INFO - Iter(train) [ 38350/160000] base_lr: 7.6751e-05 lr: 2.8377e-07 eta: 1 day, 9:47:15 time: 1.0011 data_time: 0.0043 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0093 decode.acc_seg: 99.5348 aux.loss_ce: 0.0096 aux.acc_seg: 98.8853 +04/17 19:12:58 - mmengine - INFO - Iter(train) [ 38400/160000] base_lr: 7.6720e-05 lr: 2.8365e-07 eta: 1 day, 9:46:25 time: 1.0017 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.7086 aux.loss_ce: 0.0080 aux.acc_seg: 99.3008 +04/17 19:13:48 - mmengine - INFO - Iter(train) [ 38450/160000] base_lr: 7.6688e-05 lr: 2.8353e-07 eta: 1 day, 9:45:36 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0084 decode.acc_seg: 99.4303 aux.loss_ce: 0.0089 aux.acc_seg: 98.4764 +04/17 19:14:38 - mmengine - INFO - Iter(train) [ 38500/160000] base_lr: 7.6657e-05 lr: 2.8342e-07 eta: 1 day, 9:44:46 time: 1.0006 data_time: 0.0044 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7578 aux.loss_ce: 0.0077 aux.acc_seg: 99.1474 +04/17 19:15:28 - mmengine - INFO - Iter(train) [ 38550/160000] base_lr: 7.6625e-05 lr: 2.8330e-07 eta: 1 day, 9:43:56 time: 1.0016 data_time: 0.0049 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7684 aux.loss_ce: 0.0071 aux.acc_seg: 99.2023 +04/17 19:16:18 - mmengine - INFO - Iter(train) [ 38600/160000] base_lr: 7.6594e-05 lr: 2.8318e-07 eta: 1 day, 9:43:06 time: 1.0022 data_time: 0.0044 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.2529 aux.loss_ce: 0.0075 aux.acc_seg: 98.7282 +04/17 19:17:08 - mmengine - INFO - Iter(train) [ 38650/160000] base_lr: 7.6562e-05 lr: 2.8307e-07 eta: 1 day, 9:42:16 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0077 decode.acc_seg: 99.5695 aux.loss_ce: 0.0087 aux.acc_seg: 98.8285 +04/17 19:17:58 - mmengine - INFO - Iter(train) [ 38700/160000] base_lr: 7.6530e-05 lr: 2.8295e-07 eta: 1 day, 9:41:27 time: 1.0010 data_time: 0.0044 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7311 aux.loss_ce: 0.0079 aux.acc_seg: 99.1293 +04/17 19:18:48 - mmengine - INFO - Iter(train) [ 38750/160000] base_lr: 7.6499e-05 lr: 2.8283e-07 eta: 1 day, 9:40:37 time: 1.0011 data_time: 0.0047 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.7602 aux.loss_ce: 0.0075 aux.acc_seg: 99.3006 +04/17 19:19:38 - mmengine - INFO - Iter(train) [ 38800/160000] base_lr: 7.6467e-05 lr: 2.8272e-07 eta: 1 day, 9:39:47 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0089 decode.acc_seg: 99.7873 aux.loss_ce: 0.0086 aux.acc_seg: 99.3906 +04/17 19:20:28 - mmengine - INFO - Iter(train) [ 38850/160000] base_lr: 7.6436e-05 lr: 2.8260e-07 eta: 1 day, 9:38:57 time: 1.0017 data_time: 0.0048 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0081 decode.acc_seg: 99.6780 aux.loss_ce: 0.0087 aux.acc_seg: 99.0749 +04/17 19:21:18 - mmengine - INFO - Iter(train) [ 38900/160000] base_lr: 7.6404e-05 lr: 2.8248e-07 eta: 1 day, 9:38:07 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0090 decode.acc_seg: 99.6393 aux.loss_ce: 0.0096 aux.acc_seg: 99.0166 +04/17 19:22:08 - mmengine - INFO - Iter(train) [ 38950/160000] base_lr: 7.6373e-05 lr: 2.8237e-07 eta: 1 day, 9:37:17 time: 1.0005 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0076 decode.acc_seg: 99.7993 aux.loss_ce: 0.0089 aux.acc_seg: 99.1705 +04/17 19:22:58 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 19:22:58 - mmengine - INFO - Iter(train) [ 39000/160000] base_lr: 7.6341e-05 lr: 2.8225e-07 eta: 1 day, 9:36:27 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0089 decode.acc_seg: 99.6943 aux.loss_ce: 0.0095 aux.acc_seg: 98.9830 +04/17 19:23:48 - mmengine - INFO - Iter(train) [ 39050/160000] base_lr: 7.6310e-05 lr: 2.8213e-07 eta: 1 day, 9:35:38 time: 1.0006 data_time: 0.0044 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0087 decode.acc_seg: 99.6820 aux.loss_ce: 0.0088 aux.acc_seg: 99.0828 +04/17 19:24:39 - mmengine - INFO - Iter(train) [ 39100/160000] base_lr: 7.6278e-05 lr: 2.8202e-07 eta: 1 day, 9:34:48 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.7107 aux.loss_ce: 0.0075 aux.acc_seg: 99.1867 +04/17 19:25:29 - mmengine - INFO - Iter(train) [ 39150/160000] base_lr: 7.6247e-05 lr: 2.8190e-07 eta: 1 day, 9:33:58 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0087 decode.acc_seg: 99.7093 aux.loss_ce: 0.0084 aux.acc_seg: 99.2432 +04/17 19:26:19 - mmengine - INFO - Iter(train) [ 39200/160000] base_lr: 7.6215e-05 lr: 2.8178e-07 eta: 1 day, 9:33:08 time: 1.0016 data_time: 0.0045 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0070 decode.acc_seg: 99.6433 aux.loss_ce: 0.0077 aux.acc_seg: 99.0658 +04/17 19:27:09 - mmengine - INFO - Iter(train) [ 39250/160000] base_lr: 7.6183e-05 lr: 2.8167e-07 eta: 1 day, 9:32:18 time: 1.0018 data_time: 0.0041 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0090 decode.acc_seg: 99.6525 aux.loss_ce: 0.0090 aux.acc_seg: 99.0257 +04/17 19:27:59 - mmengine - INFO - Iter(train) [ 39300/160000] base_lr: 7.6152e-05 lr: 2.8155e-07 eta: 1 day, 9:31:28 time: 1.0014 data_time: 0.0045 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0074 decode.acc_seg: 99.8295 aux.loss_ce: 0.0080 aux.acc_seg: 99.4982 +04/17 19:28:49 - mmengine - INFO - Iter(train) [ 39350/160000] base_lr: 7.6120e-05 lr: 2.8143e-07 eta: 1 day, 9:30:38 time: 1.0003 data_time: 0.0044 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0078 decode.acc_seg: 99.5817 aux.loss_ce: 0.0094 aux.acc_seg: 98.6925 +04/17 19:29:39 - mmengine - INFO - Iter(train) [ 39400/160000] base_lr: 7.6089e-05 lr: 2.8132e-07 eta: 1 day, 9:29:49 time: 1.0016 data_time: 0.0041 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0078 decode.acc_seg: 99.7734 aux.loss_ce: 0.0085 aux.acc_seg: 99.3156 +04/17 19:30:29 - mmengine - INFO - Iter(train) [ 39450/160000] base_lr: 7.6057e-05 lr: 2.8120e-07 eta: 1 day, 9:28:59 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0088 decode.acc_seg: 99.7704 aux.loss_ce: 0.0089 aux.acc_seg: 99.4074 +04/17 19:31:19 - mmengine - INFO - Iter(train) [ 39500/160000] base_lr: 7.6026e-05 lr: 2.8108e-07 eta: 1 day, 9:28:09 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7780 aux.loss_ce: 0.0074 aux.acc_seg: 99.3631 +04/17 19:32:09 - mmengine - INFO - Iter(train) [ 39550/160000] base_lr: 7.5994e-05 lr: 2.8097e-07 eta: 1 day, 9:27:19 time: 1.0022 data_time: 0.0047 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7141 aux.loss_ce: 0.0077 aux.acc_seg: 99.1346 +04/17 19:32:59 - mmengine - INFO - Iter(train) [ 39600/160000] base_lr: 7.5963e-05 lr: 2.8085e-07 eta: 1 day, 9:26:29 time: 1.0008 data_time: 0.0051 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0074 decode.acc_seg: 99.7297 aux.loss_ce: 0.0081 aux.acc_seg: 99.2731 +04/17 19:33:49 - mmengine - INFO - Iter(train) [ 39650/160000] base_lr: 7.5931e-05 lr: 2.8073e-07 eta: 1 day, 9:25:39 time: 1.0010 data_time: 0.0046 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.4810 aux.loss_ce: 0.0071 aux.acc_seg: 98.8874 +04/17 19:34:39 - mmengine - INFO - Iter(train) [ 39700/160000] base_lr: 7.5900e-05 lr: 2.8062e-07 eta: 1 day, 9:24:50 time: 1.0007 data_time: 0.0043 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0078 decode.acc_seg: 99.7000 aux.loss_ce: 0.0090 aux.acc_seg: 99.2617 +04/17 19:35:29 - mmengine - INFO - Iter(train) [ 39750/160000] base_lr: 7.5868e-05 lr: 2.8050e-07 eta: 1 day, 9:24:00 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0078 decode.acc_seg: 99.6181 aux.loss_ce: 0.0084 aux.acc_seg: 98.8501 +04/17 19:36:19 - mmengine - INFO - Iter(train) [ 39800/160000] base_lr: 7.5836e-05 lr: 2.8038e-07 eta: 1 day, 9:23:10 time: 1.0013 data_time: 0.0043 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0077 decode.acc_seg: 99.7023 aux.loss_ce: 0.0088 aux.acc_seg: 99.0917 +04/17 19:37:09 - mmengine - INFO - Iter(train) [ 39850/160000] base_lr: 7.5805e-05 lr: 2.8027e-07 eta: 1 day, 9:22:20 time: 1.0010 data_time: 0.0049 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0079 decode.acc_seg: 99.6706 aux.loss_ce: 0.0086 aux.acc_seg: 98.9368 +04/17 19:37:59 - mmengine - INFO - Iter(train) [ 39900/160000] base_lr: 7.5773e-05 lr: 2.8015e-07 eta: 1 day, 9:21:30 time: 1.0016 data_time: 0.0052 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.4333 aux.loss_ce: 0.0079 aux.acc_seg: 98.9017 +04/17 19:38:49 - mmengine - INFO - Iter(train) [ 39950/160000] base_lr: 7.5742e-05 lr: 2.8003e-07 eta: 1 day, 9:20:41 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0091 decode.acc_seg: 99.6988 aux.loss_ce: 0.0096 aux.acc_seg: 99.2237 +04/17 19:39:40 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 19:39:40 - mmengine - INFO - Iter(train) [ 40000/160000] base_lr: 7.5710e-05 lr: 2.7992e-07 eta: 1 day, 9:19:51 time: 1.0016 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0072 decode.acc_seg: 99.6504 aux.loss_ce: 0.0081 aux.acc_seg: 99.0877 +04/17 19:39:40 - mmengine - INFO - Saving checkpoint at 40000 iterations +04/17 19:39:49 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1158 data_time: 0.0016 memory: 4004 +04/17 19:39:55 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1157 data_time: 0.0015 memory: 4004 +04/17 19:40:01 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1156 data_time: 0.0014 memory: 4004 +04/17 19:40:07 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1155 data_time: 0.0012 memory: 4004 +04/17 19:40:07 - mmengine - INFO - per class results: +04/17 19:40:07 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.18 | 99.58 | 99.59 | 99.6 | 99.58 | +| contrast | 81.99 | 90.36 | 90.1 | 89.85 | 90.36 | ++------------+-------+-------+--------+-----------+--------+ +04/17 19:40:07 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2100 mIoU: 90.5800 mAcc: 94.9700 mFscore: 94.8500 mPrecision: 94.7200 mRecall: 94.9700 data_time: 0.0018 time: 0.1161 +04/17 19:40:57 - mmengine - INFO - Iter(train) [ 40050/160000] base_lr: 7.5679e-05 lr: 2.7980e-07 eta: 1 day, 9:19:02 time: 0.9999 data_time: 0.0051 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0085 decode.acc_seg: 99.7334 aux.loss_ce: 0.0089 aux.acc_seg: 99.3618 +04/17 19:41:47 - mmengine - INFO - Iter(train) [ 40100/160000] base_lr: 7.5647e-05 lr: 2.7968e-07 eta: 1 day, 9:18:12 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0078 decode.acc_seg: 99.6231 aux.loss_ce: 0.0079 aux.acc_seg: 99.2281 +04/17 19:42:37 - mmengine - INFO - Iter(train) [ 40150/160000] base_lr: 7.5616e-05 lr: 2.7957e-07 eta: 1 day, 9:17:22 time: 1.0004 data_time: 0.0049 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0102 decode.acc_seg: 99.7002 aux.loss_ce: 0.0094 aux.acc_seg: 99.0236 +04/17 19:43:27 - mmengine - INFO - Iter(train) [ 40200/160000] base_lr: 7.5584e-05 lr: 2.7945e-07 eta: 1 day, 9:16:32 time: 1.0012 data_time: 0.0054 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0066 decode.acc_seg: 99.8011 aux.loss_ce: 0.0076 aux.acc_seg: 99.4436 +04/17 19:44:17 - mmengine - INFO - Iter(train) [ 40250/160000] base_lr: 7.5553e-05 lr: 2.7933e-07 eta: 1 day, 9:15:43 time: 1.0022 data_time: 0.0048 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6887 aux.loss_ce: 0.0077 aux.acc_seg: 99.2823 +04/17 19:45:07 - mmengine - INFO - Iter(train) [ 40300/160000] base_lr: 7.5521e-05 lr: 2.7922e-07 eta: 1 day, 9:14:53 time: 1.0010 data_time: 0.0048 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7808 aux.loss_ce: 0.0072 aux.acc_seg: 99.4278 +04/17 19:45:57 - mmengine - INFO - Iter(train) [ 40350/160000] base_lr: 7.5489e-05 lr: 2.7910e-07 eta: 1 day, 9:14:03 time: 1.0020 data_time: 0.0047 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7711 aux.loss_ce: 0.0072 aux.acc_seg: 99.3299 +04/17 19:46:48 - mmengine - INFO - Iter(train) [ 40400/160000] base_lr: 7.5458e-05 lr: 2.7898e-07 eta: 1 day, 9:13:13 time: 1.0018 data_time: 0.0045 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6933 aux.loss_ce: 0.0077 aux.acc_seg: 99.2861 +04/17 19:47:38 - mmengine - INFO - Iter(train) [ 40450/160000] base_lr: 7.5426e-05 lr: 2.7887e-07 eta: 1 day, 9:12:23 time: 1.0012 data_time: 0.0046 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0074 decode.acc_seg: 99.6700 aux.loss_ce: 0.0086 aux.acc_seg: 99.1879 +04/17 19:48:28 - mmengine - INFO - Iter(train) [ 40500/160000] base_lr: 7.5395e-05 lr: 2.7875e-07 eta: 1 day, 9:11:34 time: 1.0008 data_time: 0.0045 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0085 decode.acc_seg: 99.6632 aux.loss_ce: 0.0086 aux.acc_seg: 98.9334 +04/17 19:49:18 - mmengine - INFO - Iter(train) [ 40550/160000] base_lr: 7.5363e-05 lr: 2.7863e-07 eta: 1 day, 9:10:44 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.7723 aux.loss_ce: 0.0081 aux.acc_seg: 99.3284 +04/17 19:50:08 - mmengine - INFO - Iter(train) [ 40600/160000] base_lr: 7.5332e-05 lr: 2.7852e-07 eta: 1 day, 9:09:54 time: 1.0024 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0085 decode.acc_seg: 99.6166 aux.loss_ce: 0.0094 aux.acc_seg: 98.9628 +04/17 19:50:58 - mmengine - INFO - Iter(train) [ 40650/160000] base_lr: 7.5300e-05 lr: 2.7840e-07 eta: 1 day, 9:09:04 time: 1.0010 data_time: 0.0042 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0081 decode.acc_seg: 99.6910 aux.loss_ce: 0.0086 aux.acc_seg: 99.0755 +04/17 19:51:48 - mmengine - INFO - Iter(train) [ 40700/160000] base_lr: 7.5269e-05 lr: 2.7828e-07 eta: 1 day, 9:08:14 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.5726 aux.loss_ce: 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decode.loss_ce: 0.0067 decode.acc_seg: 99.7723 aux.loss_ce: 0.0080 aux.acc_seg: 99.2199 +04/17 19:55:58 - mmengine - INFO - Iter(train) [ 40950/160000] base_lr: 7.5111e-05 lr: 2.7770e-07 eta: 1 day, 9:04:05 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.6653 aux.loss_ce: 0.0089 aux.acc_seg: 98.8714 +04/17 19:56:48 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 19:56:48 - mmengine - INFO - Iter(train) [ 41000/160000] base_lr: 7.5079e-05 lr: 2.7758e-07 eta: 1 day, 9:03:15 time: 1.0021 data_time: 0.0052 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0080 decode.acc_seg: 99.6866 aux.loss_ce: 0.0088 aux.acc_seg: 99.2544 +04/17 19:57:38 - mmengine - INFO - Iter(train) [ 41050/160000] base_lr: 7.5048e-05 lr: 2.7747e-07 eta: 1 day, 9:02:26 time: 1.0022 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7316 aux.loss_ce: 0.0075 aux.acc_seg: 99.3258 +04/17 19:58:28 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aux.loss_ce: 0.0088 aux.acc_seg: 98.7860 +04/17 20:01:49 - mmengine - INFO - Iter(train) [ 41300/160000] base_lr: 7.4890e-05 lr: 2.7688e-07 eta: 1 day, 8:58:16 time: 1.0025 data_time: 0.0049 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0083 decode.acc_seg: 99.7313 aux.loss_ce: 0.0084 aux.acc_seg: 99.1772 +04/17 20:02:39 - mmengine - INFO - Iter(train) [ 41350/160000] base_lr: 7.4859e-05 lr: 2.7677e-07 eta: 1 day, 8:57:27 time: 1.0009 data_time: 0.0046 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0079 decode.acc_seg: 99.5749 aux.loss_ce: 0.0093 aux.acc_seg: 98.8382 +04/17 20:03:29 - mmengine - INFO - Iter(train) [ 41400/160000] base_lr: 7.4827e-05 lr: 2.7665e-07 eta: 1 day, 8:56:37 time: 1.0015 data_time: 0.0044 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0106 decode.acc_seg: 99.4713 aux.loss_ce: 0.0098 aux.acc_seg: 98.8861 +04/17 20:04:19 - mmengine - INFO - Iter(train) [ 41450/160000] base_lr: 7.4795e-05 lr: 2.7653e-07 eta: 1 day, 8:55:47 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.6305 aux.loss_ce: 0.0077 aux.acc_seg: 99.0713 +04/17 20:05:09 - mmengine - INFO - Iter(train) [ 41500/160000] base_lr: 7.4764e-05 lr: 2.7642e-07 eta: 1 day, 8:54:57 time: 1.0014 data_time: 0.0042 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0065 decode.acc_seg: 99.6655 aux.loss_ce: 0.0078 aux.acc_seg: 98.9206 +04/17 20:05:59 - mmengine - INFO - Iter(train) [ 41550/160000] base_lr: 7.4732e-05 lr: 2.7630e-07 eta: 1 day, 8:54:07 time: 1.0006 data_time: 0.0044 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0073 decode.acc_seg: 99.5380 aux.loss_ce: 0.0084 aux.acc_seg: 98.7995 +04/17 20:06:49 - mmengine - INFO - Iter(train) [ 41600/160000] base_lr: 7.4701e-05 lr: 2.7618e-07 eta: 1 day, 8:53:17 time: 1.0016 data_time: 0.0048 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.6614 aux.loss_ce: 0.0076 aux.acc_seg: 98.7581 +04/17 20:07:39 - mmengine - INFO - Iter(train) [ 41650/160000] base_lr: 7.4669e-05 lr: 2.7607e-07 eta: 1 day, 8:52:28 time: 1.0018 data_time: 0.0048 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7755 aux.loss_ce: 0.0078 aux.acc_seg: 99.2527 +04/17 20:08:29 - mmengine - INFO - Iter(train) [ 41700/160000] base_lr: 7.4638e-05 lr: 2.7595e-07 eta: 1 day, 8:51:38 time: 1.0032 data_time: 0.0043 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6704 aux.loss_ce: 0.0079 aux.acc_seg: 98.9634 +04/17 20:09:19 - mmengine - INFO - Iter(train) [ 41750/160000] base_lr: 7.4606e-05 lr: 2.7583e-07 eta: 1 day, 8:50:48 time: 1.0015 data_time: 0.0041 memory: 8462 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.8398 aux.loss_ce: 0.0068 aux.acc_seg: 99.5270 +04/17 20:10:09 - mmengine - INFO - Iter(train) [ 41800/160000] base_lr: 7.4575e-05 lr: 2.7572e-07 eta: 1 day, 8:49:58 time: 1.0016 data_time: 0.0046 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0082 decode.acc_seg: 99.6449 aux.loss_ce: 0.0090 aux.acc_seg: 98.7869 +04/17 20:10:59 - mmengine - INFO - Iter(train) [ 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memory: 8462 loss: 0.0168 decode.loss_ce: 0.0083 decode.acc_seg: 99.6696 aux.loss_ce: 0.0085 aux.acc_seg: 99.3273 +04/17 20:14:20 - mmengine - INFO - Iter(train) [ 42050/160000] base_lr: 7.4417e-05 lr: 2.7513e-07 eta: 1 day, 8:45:49 time: 1.0016 data_time: 0.0045 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0099 decode.acc_seg: 99.7437 aux.loss_ce: 0.0082 aux.acc_seg: 99.4400 +04/17 20:15:10 - mmengine - INFO - Iter(train) [ 42100/160000] base_lr: 7.4385e-05 lr: 2.7502e-07 eta: 1 day, 8:44:59 time: 1.0019 data_time: 0.0045 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0090 decode.acc_seg: 99.7183 aux.loss_ce: 0.0098 aux.acc_seg: 99.2462 +04/17 20:16:00 - mmengine - INFO - Iter(train) [ 42150/160000] base_lr: 7.4354e-05 lr: 2.7490e-07 eta: 1 day, 8:44:09 time: 1.0017 data_time: 0.0044 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0077 decode.acc_seg: 99.7400 aux.loss_ce: 0.0087 aux.acc_seg: 99.2044 +04/17 20:16:50 - mmengine - INFO - Iter(train) [ 42200/160000] base_lr: 7.4322e-05 lr: 2.7478e-07 eta: 1 day, 8:43:19 time: 1.0005 data_time: 0.0045 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0080 decode.acc_seg: 99.5975 aux.loss_ce: 0.0090 aux.acc_seg: 99.0339 +04/17 20:17:40 - mmengine - INFO - Iter(train) [ 42250/160000] base_lr: 7.4291e-05 lr: 2.7467e-07 eta: 1 day, 8:42:30 time: 1.0018 data_time: 0.0054 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0081 decode.acc_seg: 99.6281 aux.loss_ce: 0.0090 aux.acc_seg: 98.9731 +04/17 20:18:30 - mmengine - INFO - Iter(train) [ 42300/160000] base_lr: 7.4259e-05 lr: 2.7455e-07 eta: 1 day, 8:41:40 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0093 decode.acc_seg: 99.3917 aux.loss_ce: 0.0095 aux.acc_seg: 98.3717 +04/17 20:19:20 - mmengine - INFO - Iter(train) [ 42350/160000] base_lr: 7.4228e-05 lr: 2.7443e-07 eta: 1 day, 8:40:50 time: 1.0015 data_time: 0.0057 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0086 decode.acc_seg: 99.5617 aux.loss_ce: 0.0090 aux.acc_seg: 99.0385 +04/17 20:20:10 - mmengine - INFO - Iter(train) [ 42400/160000] base_lr: 7.4196e-05 lr: 2.7432e-07 eta: 1 day, 8:40:00 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.8081 aux.loss_ce: 0.0079 aux.acc_seg: 99.3773 +04/17 20:21:00 - mmengine - INFO - Iter(train) [ 42450/160000] base_lr: 7.4165e-05 lr: 2.7420e-07 eta: 1 day, 8:39:10 time: 0.9989 data_time: 0.0044 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0079 decode.acc_seg: 99.6317 aux.loss_ce: 0.0085 aux.acc_seg: 98.9735 +04/17 20:21:50 - mmengine - INFO - Iter(train) [ 42500/160000] base_lr: 7.4133e-05 lr: 2.7408e-07 eta: 1 day, 8:38:20 time: 1.0010 data_time: 0.0047 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0070 decode.acc_seg: 99.6326 aux.loss_ce: 0.0080 aux.acc_seg: 98.9738 +04/17 20:22:40 - mmengine - INFO - Iter(train) [ 42550/160000] base_lr: 7.4101e-05 lr: 2.7397e-07 eta: 1 day, 8:37:30 time: 1.0008 data_time: 0.0047 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.6315 aux.loss_ce: 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decode.loss_ce: 0.0085 decode.acc_seg: 99.6593 aux.loss_ce: 0.0087 aux.acc_seg: 98.9767 +04/17 20:26:50 - mmengine - INFO - Iter(train) [ 42800/160000] base_lr: 7.3944e-05 lr: 2.7339e-07 eta: 1 day, 8:33:21 time: 1.0008 data_time: 0.0045 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6262 aux.loss_ce: 0.0078 aux.acc_seg: 99.1676 +04/17 20:27:40 - mmengine - INFO - Iter(train) [ 42850/160000] base_lr: 7.3912e-05 lr: 2.7327e-07 eta: 1 day, 8:32:31 time: 1.0015 data_time: 0.0049 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0089 decode.acc_seg: 99.6262 aux.loss_ce: 0.0092 aux.acc_seg: 99.1257 +04/17 20:28:31 - mmengine - INFO - Iter(train) [ 42900/160000] base_lr: 7.3881e-05 lr: 2.7315e-07 eta: 1 day, 8:31:41 time: 1.0011 data_time: 0.0047 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0078 decode.acc_seg: 99.6033 aux.loss_ce: 0.0090 aux.acc_seg: 98.9317 +04/17 20:29:21 - mmengine - INFO - Iter(train) [ 42950/160000] base_lr: 7.3849e-05 lr: 2.7304e-07 eta: 1 day, 8:30:51 time: 1.0000 data_time: 0.0053 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0073 decode.acc_seg: 99.6120 aux.loss_ce: 0.0080 aux.acc_seg: 99.0517 +04/17 20:30:11 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 20:30:11 - mmengine - INFO - Iter(train) [ 43000/160000] base_lr: 7.3818e-05 lr: 2.7292e-07 eta: 1 day, 8:30:02 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0080 decode.acc_seg: 99.7398 aux.loss_ce: 0.0089 aux.acc_seg: 99.1747 +04/17 20:31:01 - mmengine - INFO - Iter(train) [ 43050/160000] base_lr: 7.3786e-05 lr: 2.7280e-07 eta: 1 day, 8:29:12 time: 1.0021 data_time: 0.0047 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0073 decode.acc_seg: 99.6790 aux.loss_ce: 0.0084 aux.acc_seg: 98.9561 +04/17 20:31:51 - mmengine - INFO - Iter(train) [ 43100/160000] base_lr: 7.3754e-05 lr: 2.7269e-07 eta: 1 day, 8:28:22 time: 1.0011 data_time: 0.0053 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7549 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loss: 0.0175 decode.loss_ce: 0.0104 decode.acc_seg: 99.7032 aux.loss_ce: 0.0070 aux.acc_seg: 99.2306 +04/17 20:36:01 - mmengine - INFO - Iter(train) [ 43350/160000] base_lr: 7.3597e-05 lr: 2.7210e-07 eta: 1 day, 8:24:13 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7654 aux.loss_ce: 0.0074 aux.acc_seg: 99.4520 +04/17 20:36:51 - mmengine - INFO - Iter(train) [ 43400/160000] base_lr: 7.3565e-05 lr: 2.7199e-07 eta: 1 day, 8:23:23 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6899 aux.loss_ce: 0.0078 aux.acc_seg: 99.1871 +04/17 20:37:41 - mmengine - INFO - Iter(train) [ 43450/160000] base_lr: 7.3534e-05 lr: 2.7187e-07 eta: 1 day, 8:22:33 time: 1.0005 data_time: 0.0044 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.6826 aux.loss_ce: 0.0076 aux.acc_seg: 98.9618 +04/17 20:38:31 - mmengine - INFO - Iter(train) [ 43500/160000] base_lr: 7.3502e-05 lr: 2.7175e-07 eta: 1 day, 8:21:43 time: 1.0016 data_time: 0.0045 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0069 decode.acc_seg: 99.7271 aux.loss_ce: 0.0078 aux.acc_seg: 99.2912 +04/17 20:39:21 - mmengine - INFO - Iter(train) [ 43550/160000] base_lr: 7.3470e-05 lr: 2.7164e-07 eta: 1 day, 8:20:53 time: 1.0016 data_time: 0.0043 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0085 decode.acc_seg: 99.5556 aux.loss_ce: 0.0092 aux.acc_seg: 98.8022 +04/17 20:40:11 - mmengine - INFO - Iter(train) [ 43600/160000] base_lr: 7.3439e-05 lr: 2.7152e-07 eta: 1 day, 8:20:03 time: 1.0012 data_time: 0.0048 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7625 aux.loss_ce: 0.0079 aux.acc_seg: 99.1348 +04/17 20:41:01 - mmengine - INFO - Iter(train) [ 43650/160000] base_lr: 7.3407e-05 lr: 2.7140e-07 eta: 1 day, 8:19:13 time: 1.0017 data_time: 0.0049 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0068 decode.acc_seg: 99.6870 aux.loss_ce: 0.0077 aux.acc_seg: 99.0499 +04/17 20:41:51 - mmengine - INFO - Iter(train) [ 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2.7047e-07 eta: 1 day, 8:12:34 time: 0.9998 data_time: 0.0048 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0075 decode.acc_seg: 99.6170 aux.loss_ce: 0.0083 aux.acc_seg: 99.1631 +04/17 20:48:32 - mmengine - INFO - Iter(train) [ 44100/160000] base_lr: 7.3123e-05 lr: 2.7035e-07 eta: 1 day, 8:11:45 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.6635 aux.loss_ce: 0.0077 aux.acc_seg: 99.0484 +04/17 20:49:22 - mmengine - INFO - Iter(train) [ 44150/160000] base_lr: 7.3092e-05 lr: 2.7024e-07 eta: 1 day, 8:10:55 time: 1.0011 data_time: 0.0049 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0082 decode.acc_seg: 99.6815 aux.loss_ce: 0.0089 aux.acc_seg: 99.2743 +04/17 20:50:12 - mmengine - INFO - Iter(train) [ 44200/160000] base_lr: 7.3060e-05 lr: 2.7012e-07 eta: 1 day, 8:10:05 time: 1.0009 data_time: 0.0048 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0079 decode.acc_seg: 99.7707 aux.loss_ce: 0.0086 aux.acc_seg: 99.3401 +04/17 20:51:02 - mmengine - INFO - Iter(train) [ 44250/160000] base_lr: 7.3029e-05 lr: 2.7000e-07 eta: 1 day, 8:09:15 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.6984 aux.loss_ce: 0.0081 aux.acc_seg: 99.1928 +04/17 20:51:52 - mmengine - INFO - Iter(train) [ 44300/160000] base_lr: 7.2997e-05 lr: 2.6989e-07 eta: 1 day, 8:08:25 time: 1.0013 data_time: 0.0048 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0098 decode.acc_seg: 99.5548 aux.loss_ce: 0.0105 aux.acc_seg: 98.5489 +04/17 20:52:42 - mmengine - INFO - Iter(train) [ 44350/160000] base_lr: 7.2966e-05 lr: 2.6977e-07 eta: 1 day, 8:07:35 time: 1.0020 data_time: 0.0043 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0072 decode.acc_seg: 99.6668 aux.loss_ce: 0.0080 aux.acc_seg: 98.9964 +04/17 20:53:32 - mmengine - INFO - Iter(train) [ 44400/160000] base_lr: 7.2934e-05 lr: 2.6965e-07 eta: 1 day, 8:06:45 time: 1.0017 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0086 decode.acc_seg: 99.6059 aux.loss_ce: 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decode.loss_ce: 0.0063 decode.acc_seg: 99.7654 aux.loss_ce: 0.0074 aux.acc_seg: 99.2189 +04/17 20:57:43 - mmengine - INFO - Iter(train) [ 44650/160000] base_lr: 7.2776e-05 lr: 2.6907e-07 eta: 1 day, 8:02:36 time: 1.0019 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0085 decode.acc_seg: 99.7700 aux.loss_ce: 0.0092 aux.acc_seg: 99.3835 +04/17 20:58:33 - mmengine - INFO - Iter(train) [ 44700/160000] base_lr: 7.2745e-05 lr: 2.6895e-07 eta: 1 day, 8:01:46 time: 1.0019 data_time: 0.0052 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0080 decode.acc_seg: 99.6042 aux.loss_ce: 0.0088 aux.acc_seg: 99.1199 +04/17 20:59:23 - mmengine - INFO - Iter(train) [ 44750/160000] base_lr: 7.2713e-05 lr: 2.6884e-07 eta: 1 day, 8:00:56 time: 1.0007 data_time: 0.0044 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0080 decode.acc_seg: 99.7177 aux.loss_ce: 0.0087 aux.acc_seg: 99.2779 +04/17 21:00:13 - mmengine - INFO - Iter(train) [ 44800/160000] base_lr: 7.2682e-05 lr: 2.6872e-07 eta: 1 day, 8:00:06 time: 1.0030 data_time: 0.0047 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0071 decode.acc_seg: 99.7200 aux.loss_ce: 0.0086 aux.acc_seg: 99.2632 +04/17 21:01:03 - mmengine - INFO - Iter(train) [ 44850/160000] base_lr: 7.2650e-05 lr: 2.6860e-07 eta: 1 day, 7:59:16 time: 1.0022 data_time: 0.0044 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0080 decode.acc_seg: 99.7496 aux.loss_ce: 0.0091 aux.acc_seg: 99.2411 +04/17 21:01:53 - mmengine - INFO - Iter(train) [ 44900/160000] base_lr: 7.2619e-05 lr: 2.6849e-07 eta: 1 day, 7:58:27 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0077 decode.acc_seg: 99.5272 aux.loss_ce: 0.0086 aux.acc_seg: 98.4587 +04/17 21:02:43 - mmengine - INFO - Iter(train) [ 44950/160000] base_lr: 7.2587e-05 lr: 2.6837e-07 eta: 1 day, 7:57:37 time: 1.0009 data_time: 0.0051 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0070 decode.acc_seg: 99.7227 aux.loss_ce: 0.0081 aux.acc_seg: 99.2695 +04/17 21:03:33 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 21:03:33 - mmengine - INFO - Iter(train) [ 45000/160000] base_lr: 7.2556e-05 lr: 2.6825e-07 eta: 1 day, 7:56:47 time: 1.0002 data_time: 0.0048 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.7528 aux.loss_ce: 0.0079 aux.acc_seg: 99.4102 +04/17 21:04:23 - mmengine - INFO - Iter(train) [ 45050/160000] base_lr: 7.2524e-05 lr: 2.6814e-07 eta: 1 day, 7:55:57 time: 1.0014 data_time: 0.0044 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0076 decode.acc_seg: 99.7311 aux.loss_ce: 0.0092 aux.acc_seg: 98.9716 +04/17 21:05:13 - mmengine - INFO - Iter(train) [ 45100/160000] base_lr: 7.2493e-05 lr: 2.6802e-07 eta: 1 day, 7:55:07 time: 1.0025 data_time: 0.0046 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7829 aux.loss_ce: 0.0076 aux.acc_seg: 99.1774 +04/17 21:06:03 - mmengine - INFO - Iter(train) [ 45150/160000] base_lr: 7.2461e-05 lr: 2.6790e-07 eta: 1 day, 7:54:17 time: 1.0001 data_time: 0.0042 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0075 decode.acc_seg: 99.6052 aux.loss_ce: 0.0083 aux.acc_seg: 98.9243 +04/17 21:06:53 - mmengine - INFO - Iter(train) [ 45200/160000] base_lr: 7.2429e-05 lr: 2.6779e-07 eta: 1 day, 7:53:27 time: 1.0000 data_time: 0.0050 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.7332 aux.loss_ce: 0.0078 aux.acc_seg: 99.3267 +04/17 21:07:43 - mmengine - INFO - Iter(train) [ 45250/160000] base_lr: 7.2398e-05 lr: 2.6767e-07 eta: 1 day, 7:52:37 time: 1.0008 data_time: 0.0051 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7673 aux.loss_ce: 0.0077 aux.acc_seg: 99.4791 +04/17 21:08:33 - mmengine - INFO - Iter(train) [ 45300/160000] base_lr: 7.2366e-05 lr: 2.6755e-07 eta: 1 day, 7:51:48 time: 1.0018 data_time: 0.0044 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7829 aux.loss_ce: 0.0077 aux.acc_seg: 99.3141 +04/17 21:09:23 - mmengine - INFO - Iter(train) [ 45350/160000] base_lr: 7.2335e-05 lr: 2.6744e-07 eta: 1 day, 7:50:58 time: 1.0025 data_time: 0.0053 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0071 decode.acc_seg: 99.6622 aux.loss_ce: 0.0079 aux.acc_seg: 99.2636 +04/17 21:10:13 - mmengine - INFO - Iter(train) [ 45400/160000] base_lr: 7.2303e-05 lr: 2.6732e-07 eta: 1 day, 7:50:08 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0081 decode.acc_seg: 99.6243 aux.loss_ce: 0.0083 aux.acc_seg: 99.2947 +04/17 21:11:03 - mmengine - INFO - Iter(train) [ 45450/160000] base_lr: 7.2272e-05 lr: 2.6720e-07 eta: 1 day, 7:49:18 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0079 decode.acc_seg: 99.6975 aux.loss_ce: 0.0088 aux.acc_seg: 99.0618 +04/17 21:11:54 - mmengine - INFO - Iter(train) [ 45500/160000] base_lr: 7.2240e-05 lr: 2.6709e-07 eta: 1 day, 7:48:28 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7581 aux.loss_ce: 0.0077 aux.acc_seg: 99.3103 +04/17 21:12:44 - mmengine - INFO - Iter(train) [ 45550/160000] base_lr: 7.2209e-05 lr: 2.6697e-07 eta: 1 day, 7:47:38 time: 1.0012 data_time: 0.0048 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0068 decode.acc_seg: 99.7274 aux.loss_ce: 0.0083 aux.acc_seg: 99.1192 +04/17 21:13:34 - mmengine - INFO - Iter(train) [ 45600/160000] base_lr: 7.2177e-05 lr: 2.6685e-07 eta: 1 day, 7:46:48 time: 1.0006 data_time: 0.0049 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.7150 aux.loss_ce: 0.0081 aux.acc_seg: 99.1716 +04/17 21:14:24 - mmengine - INFO - Iter(train) [ 45650/160000] base_lr: 7.2146e-05 lr: 2.6674e-07 eta: 1 day, 7:45:59 time: 1.0009 data_time: 0.0045 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0073 decode.acc_seg: 99.6828 aux.loss_ce: 0.0081 aux.acc_seg: 99.1716 +04/17 21:15:14 - mmengine - INFO - Iter(train) [ 45700/160000] base_lr: 7.2114e-05 lr: 2.6662e-07 eta: 1 day, 7:45:09 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0083 decode.acc_seg: 99.6675 aux.loss_ce: 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decode.loss_ce: 0.0071 decode.acc_seg: 99.7004 aux.loss_ce: 0.0081 aux.acc_seg: 99.2018 +04/17 21:19:24 - mmengine - INFO - Iter(train) [ 45950/160000] base_lr: 7.1956e-05 lr: 2.6604e-07 eta: 1 day, 7:40:59 time: 1.0010 data_time: 0.0056 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0065 decode.acc_seg: 99.7581 aux.loss_ce: 0.0079 aux.acc_seg: 99.2363 +04/17 21:20:14 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 21:20:14 - mmengine - INFO - Iter(train) [ 46000/160000] base_lr: 7.1925e-05 lr: 2.6592e-07 eta: 1 day, 7:40:09 time: 1.0018 data_time: 0.0045 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0077 decode.acc_seg: 99.5554 aux.loss_ce: 0.0091 aux.acc_seg: 98.8052 +04/17 21:21:04 - mmengine - INFO - Iter(train) [ 46050/160000] base_lr: 7.1893e-05 lr: 2.6580e-07 eta: 1 day, 7:39:19 time: 1.0008 data_time: 0.0043 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0082 decode.acc_seg: 99.7744 aux.loss_ce: 0.0090 aux.acc_seg: 99.3090 +04/17 21:21:54 - mmengine - INFO - Iter(train) [ 46100/160000] base_lr: 7.1862e-05 lr: 2.6569e-07 eta: 1 day, 7:38:29 time: 1.0013 data_time: 0.0048 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.7307 aux.loss_ce: 0.0075 aux.acc_seg: 99.2559 +04/17 21:22:44 - mmengine - INFO - Iter(train) [ 46150/160000] base_lr: 7.1830e-05 lr: 2.6557e-07 eta: 1 day, 7:37:39 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0075 decode.acc_seg: 99.7231 aux.loss_ce: 0.0085 aux.acc_seg: 99.2443 +04/17 21:23:34 - mmengine - INFO - Iter(train) [ 46200/160000] base_lr: 7.1799e-05 lr: 2.6545e-07 eta: 1 day, 7:36:49 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0068 decode.acc_seg: 99.7627 aux.loss_ce: 0.0079 aux.acc_seg: 99.3732 +04/17 21:24:24 - mmengine - INFO - Iter(train) [ 46250/160000] base_lr: 7.1767e-05 lr: 2.6534e-07 eta: 1 day, 7:35:59 time: 1.0017 data_time: 0.0050 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.7726 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memory: 8462 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6403 aux.loss_ce: 0.0079 aux.acc_seg: 99.0517 +04/17 21:37:45 - mmengine - INFO - Iter(train) [ 47050/160000] base_lr: 7.1262e-05 lr: 2.6347e-07 eta: 1 day, 7:22:41 time: 1.0009 data_time: 0.0050 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0073 decode.acc_seg: 99.7501 aux.loss_ce: 0.0085 aux.acc_seg: 99.1697 +04/17 21:38:35 - mmengine - INFO - Iter(train) [ 47100/160000] base_lr: 7.1231e-05 lr: 2.6335e-07 eta: 1 day, 7:21:51 time: 1.0017 data_time: 0.0044 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0077 decode.acc_seg: 99.6986 aux.loss_ce: 0.0087 aux.acc_seg: 99.1751 +04/17 21:39:25 - mmengine - INFO - Iter(train) [ 47150/160000] base_lr: 7.1199e-05 lr: 2.6324e-07 eta: 1 day, 7:21:01 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7280 aux.loss_ce: 0.0075 aux.acc_seg: 99.3244 +04/17 21:40:15 - mmengine - INFO - Iter(train) [ 47200/160000] base_lr: 7.1168e-05 lr: 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INFO - Iter(train) [ 47400/160000] base_lr: 7.1041e-05 lr: 2.6265e-07 eta: 1 day, 7:16:52 time: 1.0012 data_time: 0.0048 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7017 aux.loss_ce: 0.0076 aux.acc_seg: 98.8943 +04/17 21:44:25 - mmengine - INFO - Iter(train) [ 47450/160000] base_lr: 7.1010e-05 lr: 2.6254e-07 eta: 1 day, 7:16:02 time: 1.0012 data_time: 0.0050 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0075 decode.acc_seg: 99.7063 aux.loss_ce: 0.0084 aux.acc_seg: 98.9916 +04/17 21:45:16 - mmengine - INFO - Iter(train) [ 47500/160000] base_lr: 7.0978e-05 lr: 2.6242e-07 eta: 1 day, 7:15:12 time: 1.0005 data_time: 0.0044 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0068 decode.acc_seg: 99.7053 aux.loss_ce: 0.0083 aux.acc_seg: 99.2805 +04/17 21:46:06 - mmengine - INFO - Iter(train) [ 47550/160000] base_lr: 7.0947e-05 lr: 2.6231e-07 eta: 1 day, 7:14:22 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.7715 aux.loss_ce: 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decode.loss_ce: 0.0068 decode.acc_seg: 99.7084 aux.loss_ce: 0.0078 aux.acc_seg: 99.1014 +04/17 21:50:16 - mmengine - INFO - Iter(train) [ 47800/160000] base_lr: 7.0789e-05 lr: 2.6172e-07 eta: 1 day, 7:10:12 time: 1.0012 data_time: 0.0050 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.6197 aux.loss_ce: 0.0073 aux.acc_seg: 98.8844 +04/17 21:51:06 - mmengine - INFO - Iter(train) [ 47850/160000] base_lr: 7.0758e-05 lr: 2.6161e-07 eta: 1 day, 7:09:23 time: 1.0020 data_time: 0.0044 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0068 decode.acc_seg: 99.6948 aux.loss_ce: 0.0080 aux.acc_seg: 99.1024 +04/17 21:51:56 - mmengine - INFO - Iter(train) [ 47900/160000] base_lr: 7.0726e-05 lr: 2.6149e-07 eta: 1 day, 7:08:33 time: 1.0014 data_time: 0.0049 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0075 decode.acc_seg: 99.8003 aux.loss_ce: 0.0087 aux.acc_seg: 99.3961 +04/17 21:52:46 - mmengine - INFO - Iter(train) [ 47950/160000] base_lr: 7.0694e-05 lr: 2.6137e-07 eta: 1 day, 7:07:43 time: 1.0014 data_time: 0.0045 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0074 decode.acc_seg: 99.5560 aux.loss_ce: 0.0084 aux.acc_seg: 98.7616 +04/17 21:53:36 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 21:53:36 - mmengine - INFO - Iter(train) [ 48000/160000] base_lr: 7.0663e-05 lr: 2.6126e-07 eta: 1 day, 7:06:53 time: 1.0011 data_time: 0.0052 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0074 decode.acc_seg: 99.7181 aux.loss_ce: 0.0086 aux.acc_seg: 99.3011 +04/17 21:54:26 - mmengine - INFO - Iter(train) [ 48050/160000] base_lr: 7.0631e-05 lr: 2.6114e-07 eta: 1 day, 7:06:03 time: 1.0004 data_time: 0.0050 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0075 decode.acc_seg: 99.5834 aux.loss_ce: 0.0084 aux.acc_seg: 98.8811 +04/17 21:55:16 - mmengine - INFO - Iter(train) [ 48100/160000] base_lr: 7.0600e-05 lr: 2.6102e-07 eta: 1 day, 7:05:13 time: 0.9993 data_time: 0.0044 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.6038 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2.5881e-07 eta: 1 day, 6:49:26 time: 1.0013 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0074 decode.acc_seg: 99.5754 aux.loss_ce: 0.0087 aux.acc_seg: 98.7215 +04/17 22:11:58 - mmengine - INFO - Iter(train) [ 49100/160000] base_lr: 6.9969e-05 lr: 2.5869e-07 eta: 1 day, 6:48:36 time: 1.0005 data_time: 0.0044 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0082 decode.acc_seg: 99.7231 aux.loss_ce: 0.0092 aux.acc_seg: 99.1968 +04/17 22:12:48 - mmengine - INFO - Iter(train) [ 49150/160000] base_lr: 6.9937e-05 lr: 2.5857e-07 eta: 1 day, 6:47:46 time: 1.0009 data_time: 0.0047 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0072 decode.acc_seg: 99.6813 aux.loss_ce: 0.0084 aux.acc_seg: 99.0835 +04/17 22:13:38 - mmengine - INFO - Iter(train) [ 49200/160000] base_lr: 6.9906e-05 lr: 2.5846e-07 eta: 1 day, 6:46:56 time: 1.0013 data_time: 0.0051 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0067 decode.acc_seg: 99.7419 aux.loss_ce: 0.0084 aux.acc_seg: 98.9325 +04/17 22:14:28 - mmengine - INFO - Iter(train) [ 49250/160000] base_lr: 6.9874e-05 lr: 2.5834e-07 eta: 1 day, 6:46:06 time: 1.0021 data_time: 0.0046 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.6363 aux.loss_ce: 0.0075 aux.acc_seg: 99.0494 +04/17 22:15:18 - mmengine - INFO - Iter(train) [ 49300/160000] base_lr: 6.9843e-05 lr: 2.5822e-07 eta: 1 day, 6:45:16 time: 1.0011 data_time: 0.0044 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0073 decode.acc_seg: 99.6635 aux.loss_ce: 0.0081 aux.acc_seg: 99.0404 +04/17 22:16:08 - mmengine - INFO - Iter(train) [ 49350/160000] base_lr: 6.9811e-05 lr: 2.5811e-07 eta: 1 day, 6:44:27 time: 1.0020 data_time: 0.0049 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0064 decode.acc_seg: 99.7753 aux.loss_ce: 0.0083 aux.acc_seg: 99.3837 +04/17 22:16:58 - mmengine - INFO - Iter(train) [ 49400/160000] base_lr: 6.9780e-05 lr: 2.5799e-07 eta: 1 day, 6:43:37 time: 0.9983 data_time: 0.0047 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7105 aux.loss_ce: 0.0070 aux.acc_seg: 99.2142 +04/17 22:17:48 - mmengine - INFO - Iter(train) [ 49450/160000] base_lr: 6.9748e-05 lr: 2.5787e-07 eta: 1 day, 6:42:47 time: 1.0017 data_time: 0.0047 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0079 decode.acc_seg: 99.7072 aux.loss_ce: 0.0089 aux.acc_seg: 99.4307 +04/17 22:18:38 - mmengine - INFO - Iter(train) [ 49500/160000] base_lr: 6.9717e-05 lr: 2.5776e-07 eta: 1 day, 6:41:57 time: 1.0010 data_time: 0.0049 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.6824 aux.loss_ce: 0.0076 aux.acc_seg: 98.8033 +04/17 22:19:28 - mmengine - INFO - Iter(train) [ 49550/160000] base_lr: 6.9685e-05 lr: 2.5764e-07 eta: 1 day, 6:41:07 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0066 decode.acc_seg: 99.7353 aux.loss_ce: 0.0082 aux.acc_seg: 99.1476 +04/17 22:20:18 - mmengine - INFO - Iter(train) [ 49600/160000] base_lr: 6.9653e-05 lr: 2.5752e-07 eta: 1 day, 6:40:17 time: 1.0010 data_time: 0.0046 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0075 decode.acc_seg: 99.7128 aux.loss_ce: 0.0082 aux.acc_seg: 99.0692 +04/17 22:21:08 - mmengine - INFO - Iter(train) [ 49650/160000] base_lr: 6.9622e-05 lr: 2.5741e-07 eta: 1 day, 6:39:27 time: 1.0017 data_time: 0.0049 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0072 decode.acc_seg: 99.7137 aux.loss_ce: 0.0080 aux.acc_seg: 99.2208 +04/17 22:21:58 - mmengine - INFO - Iter(train) [ 49700/160000] base_lr: 6.9590e-05 lr: 2.5729e-07 eta: 1 day, 6:38:37 time: 1.0017 data_time: 0.0046 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0063 decode.acc_seg: 99.6155 aux.loss_ce: 0.0081 aux.acc_seg: 98.8371 +04/17 22:22:49 - mmengine - INFO - Iter(train) [ 49750/160000] base_lr: 6.9559e-05 lr: 2.5717e-07 eta: 1 day, 6:37:47 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.7698 aux.loss_ce: 0.0076 aux.acc_seg: 99.4476 +04/17 22:23:39 - mmengine - INFO - Iter(train) [ 49800/160000] base_lr: 6.9527e-05 lr: 2.5706e-07 eta: 1 day, 6:36:57 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0068 decode.acc_seg: 99.6786 aux.loss_ce: 0.0081 aux.acc_seg: 99.0566 +04/17 22:24:29 - mmengine - INFO - Iter(train) [ 49850/160000] base_lr: 6.9496e-05 lr: 2.5694e-07 eta: 1 day, 6:36:08 time: 1.0015 data_time: 0.0048 memory: 8462 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7276 aux.loss_ce: 0.0068 aux.acc_seg: 99.3731 +04/17 22:25:19 - mmengine - INFO - Iter(train) [ 49900/160000] base_lr: 6.9464e-05 lr: 2.5682e-07 eta: 1 day, 6:35:18 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0078 decode.acc_seg: 99.7438 aux.loss_ce: 0.0086 aux.acc_seg: 99.4097 +04/17 22:26:09 - mmengine - INFO - Iter(train) [ 49950/160000] base_lr: 6.9433e-05 lr: 2.5671e-07 eta: 1 day, 6:34:28 time: 1.0024 data_time: 0.0047 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.7375 aux.loss_ce: 0.0075 aux.acc_seg: 99.2811 +04/17 22:26:59 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 22:26:59 - mmengine - INFO - Iter(train) [ 50000/160000] base_lr: 6.9401e-05 lr: 2.5659e-07 eta: 1 day, 6:33:38 time: 1.0030 data_time: 0.0045 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.6641 aux.loss_ce: 0.0074 aux.acc_seg: 99.1444 +04/17 22:26:59 - mmengine - INFO - Saving checkpoint at 50000 iterations +04/17 22:27:09 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1159 data_time: 0.0014 memory: 4004 +04/17 22:27:15 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1158 data_time: 0.0014 memory: 4004 +04/17 22:27:20 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1160 data_time: 0.0015 memory: 4004 +04/17 22:27:26 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1161 data_time: 0.0016 memory: 4004 +04/17 22:27:26 - mmengine - INFO - per class results: +04/17 22:27:26 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.17 | 99.59 | 99.58 | 99.58 | 99.59 | +| contrast | 81.84 | 89.95 | 90.01 | 90.08 | 89.95 | ++------------+-------+-------+--------+-----------+--------+ +04/17 22:27:26 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2000 mIoU: 90.5100 mAcc: 94.7700 mFscore: 94.8000 mPrecision: 94.8300 mRecall: 94.7700 data_time: 0.0016 time: 0.1162 +04/17 22:28:17 - mmengine - INFO - Iter(train) [ 50050/160000] base_lr: 6.9370e-05 lr: 2.5647e-07 eta: 1 day, 6:32:49 time: 1.0014 data_time: 0.0044 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0072 decode.acc_seg: 99.6246 aux.loss_ce: 0.0081 aux.acc_seg: 99.0986 +04/17 22:29:07 - mmengine - INFO - Iter(train) [ 50100/160000] base_lr: 6.9338e-05 lr: 2.5636e-07 eta: 1 day, 6:31:59 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.6515 aux.loss_ce: 0.0077 aux.acc_seg: 98.9935 +04/17 22:29:57 - mmengine - INFO - Iter(train) [ 50150/160000] base_lr: 6.9306e-05 lr: 2.5624e-07 eta: 1 day, 6:31:09 time: 1.0027 data_time: 0.0048 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0060 decode.acc_seg: 99.7379 aux.loss_ce: 0.0070 aux.acc_seg: 98.9954 +04/17 22:30:47 - mmengine - INFO - Iter(train) [ 50200/160000] base_lr: 6.9275e-05 lr: 2.5612e-07 eta: 1 day, 6:30:19 time: 1.0015 data_time: 0.0045 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0076 decode.acc_seg: 99.6439 aux.loss_ce: 0.0093 aux.acc_seg: 99.0238 +04/17 22:31:37 - mmengine - INFO - Iter(train) [ 50250/160000] base_lr: 6.9243e-05 lr: 2.5601e-07 eta: 1 day, 6:29:29 time: 1.0022 data_time: 0.0044 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.6662 aux.loss_ce: 0.0077 aux.acc_seg: 99.1777 +04/17 22:32:27 - mmengine - INFO - Iter(train) [ 50300/160000] base_lr: 6.9212e-05 lr: 2.5589e-07 eta: 1 day, 6:28:39 time: 1.0010 data_time: 0.0045 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0060 decode.acc_seg: 99.7833 aux.loss_ce: 0.0076 aux.acc_seg: 99.2540 +04/17 22:33:17 - mmengine - INFO - Iter(train) [ 50350/160000] base_lr: 6.9180e-05 lr: 2.5577e-07 eta: 1 day, 6:27:50 time: 1.0016 data_time: 0.0046 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7509 aux.loss_ce: 0.0070 aux.acc_seg: 99.2617 +04/17 22:34:07 - mmengine - INFO - Iter(train) [ 50400/160000] base_lr: 6.9149e-05 lr: 2.5566e-07 eta: 1 day, 6:27:00 time: 1.0009 data_time: 0.0046 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.6490 aux.loss_ce: 0.0071 aux.acc_seg: 98.7896 +04/17 22:34:57 - mmengine - INFO - Iter(train) [ 50450/160000] base_lr: 6.9117e-05 lr: 2.5554e-07 eta: 1 day, 6:26:10 time: 1.0003 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0071 decode.acc_seg: 99.5760 aux.loss_ce: 0.0083 aux.acc_seg: 99.0036 +04/17 22:35:47 - mmengine - INFO - Iter(train) [ 50500/160000] base_lr: 6.9086e-05 lr: 2.5542e-07 eta: 1 day, 6:25:20 time: 1.0015 data_time: 0.0043 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0062 decode.acc_seg: 99.7562 aux.loss_ce: 0.0074 aux.acc_seg: 99.2680 +04/17 22:36:37 - mmengine - INFO - Iter(train) [ 50550/160000] base_lr: 6.9054e-05 lr: 2.5531e-07 eta: 1 day, 6:24:30 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.6731 aux.loss_ce: 0.0070 aux.acc_seg: 99.0002 +04/17 22:37:27 - mmengine - INFO - Iter(train) [ 50600/160000] base_lr: 6.9023e-05 lr: 2.5519e-07 eta: 1 day, 6:23:40 time: 1.0015 data_time: 0.0046 memory: 8462 loss: 0.0118 decode.loss_ce: 0.0055 decode.acc_seg: 99.8014 aux.loss_ce: 0.0062 aux.acc_seg: 99.4436 +04/17 22:38:17 - mmengine - INFO - Iter(train) [ 50650/160000] base_lr: 6.8991e-05 lr: 2.5507e-07 eta: 1 day, 6:22:50 time: 1.0010 data_time: 0.0048 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7143 aux.loss_ce: 0.0074 aux.acc_seg: 99.1955 +04/17 22:39:07 - mmengine - INFO - Iter(train) [ 50700/160000] base_lr: 6.8959e-05 lr: 2.5496e-07 eta: 1 day, 6:22:00 time: 1.0021 data_time: 0.0047 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.7755 aux.loss_ce: 0.0067 aux.acc_seg: 99.3412 +04/17 22:39:57 - mmengine - INFO - Iter(train) [ 50750/160000] base_lr: 6.8928e-05 lr: 2.5484e-07 eta: 1 day, 6:21:10 time: 1.0021 data_time: 0.0046 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0069 decode.acc_seg: 99.7391 aux.loss_ce: 0.0085 aux.acc_seg: 99.3994 +04/17 22:40:47 - mmengine - INFO - Iter(train) [ 50800/160000] base_lr: 6.8896e-05 lr: 2.5472e-07 eta: 1 day, 6:20:20 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0073 decode.acc_seg: 99.6870 aux.loss_ce: 0.0080 aux.acc_seg: 99.1617 +04/17 22:41:37 - mmengine - INFO - Iter(train) [ 50850/160000] base_lr: 6.8865e-05 lr: 2.5461e-07 eta: 1 day, 6:19:30 time: 1.0007 data_time: 0.0047 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0078 decode.acc_seg: 99.5440 aux.loss_ce: 0.0089 aux.acc_seg: 98.8024 +04/17 22:42:27 - mmengine - INFO - Iter(train) [ 50900/160000] base_lr: 6.8833e-05 lr: 2.5449e-07 eta: 1 day, 6:18:40 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0072 decode.acc_seg: 99.7692 aux.loss_ce: 0.0081 aux.acc_seg: 99.2157 +04/17 22:43:18 - mmengine - INFO - Iter(train) [ 50950/160000] base_lr: 6.8802e-05 lr: 2.5437e-07 eta: 1 day, 6:17:50 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0080 decode.acc_seg: 99.6532 aux.loss_ce: 0.0088 aux.acc_seg: 98.7093 +04/17 22:44:08 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 22:44:08 - mmengine - INFO - Iter(train) [ 51000/160000] base_lr: 6.8770e-05 lr: 2.5426e-07 eta: 1 day, 6:17:00 time: 1.0009 data_time: 0.0049 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.6750 aux.loss_ce: 0.0079 aux.acc_seg: 98.8974 +04/17 22:44:58 - mmengine - INFO - Iter(train) [ 51050/160000] base_lr: 6.8739e-05 lr: 2.5414e-07 eta: 1 day, 6:16:10 time: 1.0019 data_time: 0.0048 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0065 decode.acc_seg: 99.6536 aux.loss_ce: 0.0078 aux.acc_seg: 99.1037 +04/17 22:45:48 - mmengine - INFO - Iter(train) [ 51100/160000] base_lr: 6.8707e-05 lr: 2.5402e-07 eta: 1 day, 6:15:20 time: 1.0011 data_time: 0.0044 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0068 decode.acc_seg: 99.7547 aux.loss_ce: 0.0089 aux.acc_seg: 99.1705 +04/17 22:46:38 - mmengine - INFO - Iter(train) [ 51150/160000] base_lr: 6.8676e-05 lr: 2.5391e-07 eta: 1 day, 6:14:30 time: 1.0000 data_time: 0.0048 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0072 decode.acc_seg: 99.5274 aux.loss_ce: 0.0080 aux.acc_seg: 99.0046 +04/17 22:47:28 - mmengine - INFO - Iter(train) [ 51200/160000] base_lr: 6.8644e-05 lr: 2.5379e-07 eta: 1 day, 6:13:40 time: 0.9996 data_time: 0.0048 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0071 decode.acc_seg: 99.5813 aux.loss_ce: 0.0081 aux.acc_seg: 99.1209 +04/17 22:48:18 - mmengine - INFO - Iter(train) [ 51250/160000] base_lr: 6.8612e-05 lr: 2.5367e-07 eta: 1 day, 6:12:50 time: 0.9982 data_time: 0.0046 memory: 8462 loss: 0.0126 decode.loss_ce: 0.0056 decode.acc_seg: 99.7341 aux.loss_ce: 0.0070 aux.acc_seg: 99.1972 +04/17 22:49:08 - mmengine - INFO - Iter(train) [ 51300/160000] base_lr: 6.8581e-05 lr: 2.5356e-07 eta: 1 day, 6:12:00 time: 0.9989 data_time: 0.0048 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.6454 aux.loss_ce: 0.0079 aux.acc_seg: 99.1423 +04/17 22:49:58 - mmengine - INFO - Iter(train) [ 51350/160000] base_lr: 6.8549e-05 lr: 2.5344e-07 eta: 1 day, 6:11:10 time: 0.9982 data_time: 0.0052 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0063 decode.acc_seg: 99.7147 aux.loss_ce: 0.0083 aux.acc_seg: 98.9279 +04/17 22:50:47 - mmengine - INFO - Iter(train) [ 51400/160000] base_lr: 6.8518e-05 lr: 2.5332e-07 eta: 1 day, 6:10:20 time: 0.9981 data_time: 0.0043 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0076 decode.acc_seg: 99.6479 aux.loss_ce: 0.0092 aux.acc_seg: 98.9697 +04/17 22:51:37 - mmengine - INFO - Iter(train) [ 51450/160000] base_lr: 6.8486e-05 lr: 2.5321e-07 eta: 1 day, 6:09:30 time: 0.9994 data_time: 0.0053 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0108 decode.acc_seg: 99.7135 aux.loss_ce: 0.0086 aux.acc_seg: 99.3416 +04/17 22:52:27 - mmengine - INFO - Iter(train) [ 51500/160000] base_lr: 6.8455e-05 lr: 2.5309e-07 eta: 1 day, 6:08:39 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0124 decode.loss_ce: 0.0060 decode.acc_seg: 99.7625 aux.loss_ce: 0.0064 aux.acc_seg: 99.3465 +04/17 22:53:17 - mmengine - INFO - Iter(train) [ 51550/160000] base_lr: 6.8423e-05 lr: 2.5297e-07 eta: 1 day, 6:07:49 time: 0.9995 data_time: 0.0046 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.6857 aux.loss_ce: 0.0076 aux.acc_seg: 98.9225 +04/17 22:54:07 - mmengine - INFO - Iter(train) [ 51600/160000] base_lr: 6.8392e-05 lr: 2.5286e-07 eta: 1 day, 6:06:59 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0074 decode.acc_seg: 99.5653 aux.loss_ce: 0.0085 aux.acc_seg: 98.5556 +04/17 22:54:57 - mmengine - INFO - Iter(train) [ 51650/160000] base_lr: 6.8360e-05 lr: 2.5274e-07 eta: 1 day, 6:06:09 time: 0.9984 data_time: 0.0052 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.7093 aux.loss_ce: 0.0076 aux.acc_seg: 99.0751 +04/17 22:55:47 - mmengine - INFO - Iter(train) [ 51700/160000] base_lr: 6.8329e-05 lr: 2.5262e-07 eta: 1 day, 6:05:19 time: 0.9996 data_time: 0.0050 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.7099 aux.loss_ce: 0.0070 aux.acc_seg: 99.1999 +04/17 22:56:37 - mmengine - INFO - Iter(train) [ 51750/160000] base_lr: 6.8297e-05 lr: 2.5251e-07 eta: 1 day, 6:04:29 time: 0.9982 data_time: 0.0051 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7078 aux.loss_ce: 0.0076 aux.acc_seg: 99.0191 +04/17 22:57:27 - mmengine - INFO - Iter(train) [ 51800/160000] base_lr: 6.8265e-05 lr: 2.5239e-07 eta: 1 day, 6:03:38 time: 0.9976 data_time: 0.0046 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.7412 aux.loss_ce: 0.0076 aux.acc_seg: 99.3313 +04/17 22:58:17 - mmengine - INFO - Iter(train) [ 51850/160000] base_lr: 6.8234e-05 lr: 2.5227e-07 eta: 1 day, 6:02:48 time: 0.9991 data_time: 0.0050 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.7326 aux.loss_ce: 0.0068 aux.acc_seg: 99.1741 +04/17 22:59:07 - mmengine - INFO - Iter(train) [ 51900/160000] base_lr: 6.8202e-05 lr: 2.5216e-07 eta: 1 day, 6:01:58 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0067 decode.acc_seg: 99.6784 aux.loss_ce: 0.0079 aux.acc_seg: 99.0480 +04/17 22:59:57 - mmengine - INFO - Iter(train) [ 51950/160000] base_lr: 6.8171e-05 lr: 2.5204e-07 eta: 1 day, 6:01:08 time: 0.9980 data_time: 0.0051 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0078 decode.acc_seg: 99.6328 aux.loss_ce: 0.0094 aux.acc_seg: 99.1133 +04/17 23:00:47 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 23:00:47 - mmengine - INFO - Iter(train) [ 52000/160000] base_lr: 6.8139e-05 lr: 2.5192e-07 eta: 1 day, 6:00:17 time: 0.9974 data_time: 0.0045 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6460 aux.loss_ce: 0.0072 aux.acc_seg: 98.9784 +04/17 23:01:36 - mmengine - INFO - Iter(train) [ 52050/160000] base_lr: 6.8108e-05 lr: 2.5181e-07 eta: 1 day, 5:59:27 time: 0.9974 data_time: 0.0047 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0075 decode.acc_seg: 99.7982 aux.loss_ce: 0.0082 aux.acc_seg: 99.4713 +04/17 23:02:26 - mmengine - INFO - Iter(train) [ 52100/160000] base_lr: 6.8076e-05 lr: 2.5169e-07 eta: 1 day, 5:58:37 time: 0.9978 data_time: 0.0051 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0069 decode.acc_seg: 99.6073 aux.loss_ce: 0.0089 aux.acc_seg: 98.6242 +04/17 23:03:16 - mmengine - INFO - Iter(train) [ 52150/160000] base_lr: 6.8045e-05 lr: 2.5157e-07 eta: 1 day, 5:57:47 time: 0.9961 data_time: 0.0046 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0068 decode.acc_seg: 99.7011 aux.loss_ce: 0.0083 aux.acc_seg: 98.9805 +04/17 23:04:06 - mmengine - INFO - Iter(train) [ 52200/160000] base_lr: 6.8013e-05 lr: 2.5146e-07 eta: 1 day, 5:56:56 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0063 decode.acc_seg: 99.8224 aux.loss_ce: 0.0077 aux.acc_seg: 99.3618 +04/17 23:04:56 - mmengine - INFO - Iter(train) [ 52250/160000] base_lr: 6.7982e-05 lr: 2.5134e-07 eta: 1 day, 5:56:06 time: 0.9972 data_time: 0.0046 memory: 8462 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.7196 aux.loss_ce: 0.0066 aux.acc_seg: 99.0690 +04/17 23:05:46 - mmengine - INFO - Iter(train) [ 52300/160000] base_lr: 6.7950e-05 lr: 2.5122e-07 eta: 1 day, 5:55:16 time: 0.9983 data_time: 0.0044 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0070 decode.acc_seg: 99.6262 aux.loss_ce: 0.0084 aux.acc_seg: 99.0067 +04/17 23:06:36 - mmengine - INFO - Iter(train) [ 52350/160000] base_lr: 6.7918e-05 lr: 2.5111e-07 eta: 1 day, 5:54:25 time: 0.9967 data_time: 0.0046 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0079 decode.acc_seg: 99.6027 aux.loss_ce: 0.0088 aux.acc_seg: 99.0757 +04/17 23:07:26 - mmengine - INFO - Iter(train) [ 52400/160000] base_lr: 6.7887e-05 lr: 2.5099e-07 eta: 1 day, 5:53:35 time: 0.9973 data_time: 0.0043 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.7801 aux.loss_ce: 0.0081 aux.acc_seg: 99.2386 +04/17 23:08:16 - mmengine - INFO - Iter(train) [ 52450/160000] base_lr: 6.7855e-05 lr: 2.5088e-07 eta: 1 day, 5:52:45 time: 0.9972 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0083 decode.acc_seg: 99.4164 aux.loss_ce: 0.0083 aux.acc_seg: 98.9697 +04/17 23:09:05 - mmengine - INFO - Iter(train) [ 52500/160000] base_lr: 6.7824e-05 lr: 2.5076e-07 eta: 1 day, 5:51:55 time: 0.9969 data_time: 0.0047 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0083 decode.acc_seg: 99.6887 aux.loss_ce: 0.0095 aux.acc_seg: 98.9767 +04/17 23:09:55 - mmengine - INFO - Iter(train) [ 52550/160000] base_lr: 6.7792e-05 lr: 2.5064e-07 eta: 1 day, 5:51:04 time: 0.9958 data_time: 0.0049 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.8032 aux.loss_ce: 0.0078 aux.acc_seg: 99.3263 +04/17 23:10:45 - mmengine - INFO - Iter(train) [ 52600/160000] base_lr: 6.7761e-05 lr: 2.5053e-07 eta: 1 day, 5:50:14 time: 0.9976 data_time: 0.0046 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.6906 aux.loss_ce: 0.0077 aux.acc_seg: 99.2094 +04/17 23:11:35 - mmengine - INFO - Iter(train) [ 52650/160000] base_lr: 6.7729e-05 lr: 2.5041e-07 eta: 1 day, 5:49:24 time: 0.9961 data_time: 0.0051 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0069 decode.acc_seg: 99.7404 aux.loss_ce: 0.0078 aux.acc_seg: 99.2817 +04/17 23:12:25 - mmengine - INFO - Iter(train) [ 52700/160000] base_lr: 6.7698e-05 lr: 2.5029e-07 eta: 1 day, 5:48:33 time: 0.9960 data_time: 0.0045 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0066 decode.acc_seg: 99.7202 aux.loss_ce: 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decode.loss_ce: 0.0065 decode.acc_seg: 99.7639 aux.loss_ce: 0.0076 aux.acc_seg: 99.3019 +04/17 23:16:34 - mmengine - INFO - Iter(train) [ 52950/160000] base_lr: 6.7540e-05 lr: 2.4971e-07 eta: 1 day, 5:44:22 time: 0.9974 data_time: 0.0044 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0066 decode.acc_seg: 99.6859 aux.loss_ce: 0.0082 aux.acc_seg: 99.3515 +04/17 23:17:24 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 23:17:24 - mmengine - INFO - Iter(train) [ 53000/160000] base_lr: 6.7508e-05 lr: 2.4959e-07 eta: 1 day, 5:43:31 time: 0.9948 data_time: 0.0046 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0057 decode.acc_seg: 99.8381 aux.loss_ce: 0.0072 aux.acc_seg: 99.4926 +04/17 23:18:14 - mmengine - INFO - Iter(train) [ 53050/160000] base_lr: 6.7477e-05 lr: 2.4948e-07 eta: 1 day, 5:42:41 time: 0.9959 data_time: 0.0048 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7179 aux.loss_ce: 0.0068 aux.acc_seg: 99.3046 +04/17 23:19:04 - mmengine - INFO - Iter(train) [ 53100/160000] base_lr: 6.7445e-05 lr: 2.4936e-07 eta: 1 day, 5:41:50 time: 0.9959 data_time: 0.0046 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0073 decode.acc_seg: 99.5453 aux.loss_ce: 0.0085 aux.acc_seg: 98.7598 +04/17 23:19:53 - mmengine - INFO - Iter(train) [ 53150/160000] base_lr: 6.7414e-05 lr: 2.4924e-07 eta: 1 day, 5:41:00 time: 0.9960 data_time: 0.0047 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0064 decode.acc_seg: 99.7801 aux.loss_ce: 0.0076 aux.acc_seg: 99.2554 +04/17 23:20:43 - mmengine - INFO - Iter(train) [ 53200/160000] base_lr: 6.7382e-05 lr: 2.4913e-07 eta: 1 day, 5:40:10 time: 0.9960 data_time: 0.0049 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0074 decode.acc_seg: 99.6466 aux.loss_ce: 0.0082 aux.acc_seg: 99.1354 +04/17 23:21:33 - mmengine - INFO - Iter(train) [ 53250/160000] base_lr: 6.7351e-05 lr: 2.4901e-07 eta: 1 day, 5:39:19 time: 0.9958 data_time: 0.0050 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0064 decode.acc_seg: 99.7814 aux.loss_ce: 0.0078 aux.acc_seg: 99.4997 +04/17 23:22:23 - mmengine - INFO - Iter(train) [ 53300/160000] base_lr: 6.7319e-05 lr: 2.4889e-07 eta: 1 day, 5:38:29 time: 0.9975 data_time: 0.0049 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0070 decode.acc_seg: 99.6582 aux.loss_ce: 0.0080 aux.acc_seg: 98.8876 +04/17 23:23:13 - mmengine - INFO - Iter(train) [ 53350/160000] base_lr: 6.7287e-05 lr: 2.4878e-07 eta: 1 day, 5:37:39 time: 0.9974 data_time: 0.0055 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0074 decode.acc_seg: 99.5537 aux.loss_ce: 0.0083 aux.acc_seg: 98.9580 +04/17 23:24:02 - mmengine - INFO - Iter(train) [ 53400/160000] base_lr: 6.7256e-05 lr: 2.4866e-07 eta: 1 day, 5:36:48 time: 0.9964 data_time: 0.0046 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0062 decode.acc_seg: 99.8365 aux.loss_ce: 0.0074 aux.acc_seg: 99.4684 +04/17 23:24:52 - mmengine - INFO - Iter(train) [ 53450/160000] base_lr: 6.7224e-05 lr: 2.4854e-07 eta: 1 day, 5:35:58 time: 0.9970 data_time: 0.0045 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0066 decode.acc_seg: 99.6565 aux.loss_ce: 0.0081 aux.acc_seg: 99.1913 +04/17 23:25:42 - mmengine - INFO - Iter(train) [ 53500/160000] base_lr: 6.7193e-05 lr: 2.4843e-07 eta: 1 day, 5:35:07 time: 0.9953 data_time: 0.0050 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0068 decode.acc_seg: 99.6204 aux.loss_ce: 0.0080 aux.acc_seg: 99.0913 +04/17 23:26:32 - mmengine - INFO - Iter(train) [ 53550/160000] base_lr: 6.7161e-05 lr: 2.4831e-07 eta: 1 day, 5:34:17 time: 0.9965 data_time: 0.0046 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.7936 aux.loss_ce: 0.0075 aux.acc_seg: 99.4209 +04/17 23:27:22 - mmengine - INFO - Iter(train) [ 53600/160000] base_lr: 6.7130e-05 lr: 2.4819e-07 eta: 1 day, 5:33:27 time: 0.9958 data_time: 0.0044 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0073 decode.acc_seg: 99.5892 aux.loss_ce: 0.0083 aux.acc_seg: 98.8543 +04/17 23:28:11 - mmengine - INFO - Iter(train) [ 53650/160000] base_lr: 6.7098e-05 lr: 2.4808e-07 eta: 1 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memory: 8462 loss: 0.0149 decode.loss_ce: 0.0070 decode.acc_seg: 99.8268 aux.loss_ce: 0.0079 aux.acc_seg: 99.4104 +04/17 23:34:50 - mmengine - INFO - Iter(train) [ 54050/160000] base_lr: 6.6846e-05 lr: 2.4714e-07 eta: 1 day, 5:25:53 time: 0.9960 data_time: 0.0047 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7950 aux.loss_ce: 0.0075 aux.acc_seg: 99.4024 +04/17 23:35:40 - mmengine - INFO - Iter(train) [ 54100/160000] base_lr: 6.6814e-05 lr: 2.4703e-07 eta: 1 day, 5:25:03 time: 0.9975 data_time: 0.0050 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0066 decode.acc_seg: 99.6904 aux.loss_ce: 0.0078 aux.acc_seg: 99.1680 +04/17 23:36:30 - mmengine - INFO - Iter(train) [ 54150/160000] base_lr: 6.6783e-05 lr: 2.4691e-07 eta: 1 day, 5:24:12 time: 0.9958 data_time: 0.0046 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0060 decode.acc_seg: 99.6975 aux.loss_ce: 0.0073 aux.acc_seg: 98.7625 +04/17 23:37:20 - mmengine - INFO - Iter(train) [ 54200/160000] base_lr: 6.6751e-05 lr: 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INFO - Iter(train) [ 54400/160000] base_lr: 6.6625e-05 lr: 2.4633e-07 eta: 1 day, 5:20:01 time: 0.9955 data_time: 0.0046 memory: 8462 loss: 0.0118 decode.loss_ce: 0.0055 decode.acc_seg: 99.8287 aux.loss_ce: 0.0063 aux.acc_seg: 99.6265 +04/17 23:41:29 - mmengine - INFO - Iter(train) [ 54450/160000] base_lr: 6.6593e-05 lr: 2.4621e-07 eta: 1 day, 5:19:10 time: 0.9959 data_time: 0.0048 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.7332 aux.loss_ce: 0.0076 aux.acc_seg: 99.1829 +04/17 23:42:18 - mmengine - INFO - Iter(train) [ 54500/160000] base_lr: 6.6562e-05 lr: 2.4609e-07 eta: 1 day, 5:18:20 time: 0.9965 data_time: 0.0050 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0068 decode.acc_seg: 99.6954 aux.loss_ce: 0.0080 aux.acc_seg: 98.9834 +04/17 23:43:08 - mmengine - INFO - Iter(train) [ 54550/160000] base_lr: 6.6530e-05 lr: 2.4598e-07 eta: 1 day, 5:17:30 time: 0.9974 data_time: 0.0053 memory: 8462 loss: 0.0124 decode.loss_ce: 0.0056 decode.acc_seg: 99.7581 aux.loss_ce: 0.0068 aux.acc_seg: 98.9933 +04/17 23:43:58 - mmengine - INFO - Iter(train) [ 54600/160000] base_lr: 6.6499e-05 lr: 2.4586e-07 eta: 1 day, 5:16:39 time: 0.9973 data_time: 0.0047 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0077 decode.acc_seg: 99.6363 aux.loss_ce: 0.0093 aux.acc_seg: 98.8234 +04/17 23:44:48 - mmengine - INFO - Iter(train) [ 54650/160000] base_lr: 6.6467e-05 lr: 2.4574e-07 eta: 1 day, 5:15:49 time: 0.9965 data_time: 0.0046 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0070 decode.acc_seg: 99.7231 aux.loss_ce: 0.0081 aux.acc_seg: 99.3288 +04/17 23:45:38 - mmengine - INFO - Iter(train) [ 54700/160000] base_lr: 6.6436e-05 lr: 2.4563e-07 eta: 1 day, 5:14:59 time: 0.9957 data_time: 0.0045 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0079 decode.acc_seg: 99.6387 aux.loss_ce: 0.0106 aux.acc_seg: 99.0807 +04/17 23:46:28 - mmengine - INFO - Iter(train) [ 54750/160000] base_lr: 6.6404e-05 lr: 2.4551e-07 eta: 1 day, 5:14:08 time: 0.9973 data_time: 0.0047 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0064 decode.acc_seg: 99.6508 aux.loss_ce: 0.0082 aux.acc_seg: 98.9162 +04/17 23:47:17 - mmengine - INFO - Iter(train) [ 54800/160000] base_lr: 6.6373e-05 lr: 2.4539e-07 eta: 1 day, 5:13:18 time: 0.9961 data_time: 0.0046 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.6748 aux.loss_ce: 0.0078 aux.acc_seg: 98.9868 +04/17 23:48:07 - mmengine - INFO - Iter(train) [ 54850/160000] base_lr: 6.6341e-05 lr: 2.4528e-07 eta: 1 day, 5:12:28 time: 0.9965 data_time: 0.0042 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0074 decode.acc_seg: 99.6634 aux.loss_ce: 0.0082 aux.acc_seg: 99.1253 +04/17 23:48:57 - mmengine - INFO - Iter(train) [ 54900/160000] base_lr: 6.6310e-05 lr: 2.4516e-07 eta: 1 day, 5:11:37 time: 0.9962 data_time: 0.0047 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0071 decode.acc_seg: 99.7988 aux.loss_ce: 0.0080 aux.acc_seg: 99.3633 +04/17 23:49:47 - mmengine - INFO - Iter(train) [ 54950/160000] base_lr: 6.6278e-05 lr: 2.4504e-07 eta: 1 day, 5:10:47 time: 0.9969 data_time: 0.0044 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.7210 aux.loss_ce: 0.0077 aux.acc_seg: 99.2727 +04/17 23:50:37 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 23:50:37 - mmengine - INFO - Iter(train) [ 55000/160000] base_lr: 6.6246e-05 lr: 2.4493e-07 eta: 1 day, 5:09:57 time: 0.9945 data_time: 0.0045 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0073 decode.acc_seg: 99.7316 aux.loss_ce: 0.0083 aux.acc_seg: 98.9096 +04/17 23:51:27 - mmengine - INFO - Iter(train) [ 55050/160000] base_lr: 6.6215e-05 lr: 2.4481e-07 eta: 1 day, 5:09:06 time: 0.9956 data_time: 0.0046 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0071 decode.acc_seg: 99.8264 aux.loss_ce: 0.0088 aux.acc_seg: 99.1026 From c8ca7c079ab6cde019da062d4617d04386f4c76d Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Thu, 18 Apr 2024 08:24:30 +0000 Subject: [PATCH 17/24] 2024.04.18 --- a.ipynb | 164 ++-- nohup.out | 2345 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 2450 insertions(+), 59 deletions(-) diff --git a/a.ipynb b/a.ipynb index 817ad0b913..fa322284ef 100644 --- a/a.ipynb +++ b/a.ipynb @@ -60,23 +60,23 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "00285.png\n", - "IoU: 94.63\n", - "F1 Score: 97.24\n", - "Precision: 96.41\n", - "Recall: 98.09\n" + "00000.png\n", + "IoU: 88.77\n", + "F1 Score: 94.05\n", + "Precision: 92.45\n", + "Recall: 95.71\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -90,16 +90,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "00316.png\n", - "IoU: 94.75\n", - "F1 Score: 97.31\n", - "Precision: 97.20\n", - "Recall: 97.41\n" + "00001.png\n", + "IoU: 83.04\n", + "F1 Score: 90.73\n", + "Precision: 92.86\n", + "Recall: 88.71\n" ] }, { "data": { - "image/png": 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RTTP0pfPa1f72+6UJmDJSOlp4ASYnJyshOKqy+DYk4ITzKJ7ryIZhYOhEmzIilISUhYtQk1Fx28aZpNZ3ai9JbY18ASO3UbBAzMFeHGW3YSADjMmMstk/ly6syDgtEzieu6Gooa7XkGfYIPm9c1kzuyZacN6uczN35FCmI0ZIzmHLXNMR2JHVaZKNkIumCC47RGToNKNcC5fTDFmRq/NKry/PoC4KKjG7XZI/u6Vztd/v+ZukJkhDPwvOuReL5wZph3HDPZdJSmUo2brhcFh5OedKjBHYUeZjGCS9nDgbNiIbeg+Hw6yvr1fJuu/hDpXBYFAz7lI5nDMxfhszckHBk9R6W1lkRwdHf5dL8MClQ+Dfu0FqNjvze2TLnE26MSazu9RYgZ5EA683czNJxbxwBna4jIVyVTLq0jJU575ESORqObqWbGKKZ1tHie7A5jKgOE0xFC1JPOaBvlm2DmY2TnMQe11P3DJW3ny/PBJlSb4IczxoBG2FKyMABuiFBtszHmqY/gxRhZ5SlI3flx7em2eNBszIoZhusGeMyMWw1kqxW2vgxsZGVbO0MjF2w86NjY0vOLZer5f19fVaxOb+rv2iuKQcm5ubtWI5hgmRYjSBvMp8uNkc7cMlCgGHDcvd5eT0xA7UZJRRgCH8YDAqf7g8BtuLHLkf6+q81mtoWIrjoITF2N1gz/hM6DgFcfknqRNAtqFWq1Vr1CjJvd2uJ5ZSMAaiIYqCV/ODmJA9ieGIjZlwz2LgEctuEQY/NTVVi5DcD+PCi9uTs8AsVsnKOqrbgAyBMB7nNfyOzzpCOZrZm/Kdra2tqqXNxss4LAt7f7PbnGBAicilj0ajUTWRA79QQvgAjNLKCJpgzN71Qh5nYoT6HySP+2NLpfROFnQKR4fjoAHeTDLP9DoCsUEA6CZOxA0icAt2OtwfvbbOovPl/xlfvz/a5A134Q3oRk1OZ6zvTiesp1/aOG0EZWRCoVBOh3EreIm1+YyZRMM0EyCuAxk2IzQrJWNBkQ0hMQwLAs9uKE0NjdzHY7Y3Rw7ch/nzTMbFMRaMl8uwivtYaRqNRlZXV2t9pC7jJKPaLX8zZpQIB8A9QRhmbDGY9fX1amzMwxC61WpVR5i4w8tEitODMv8m8pkscXRxRPK4nGObBLOjAI5jKEBj9BJ5luUVQ1NDcn++5AesvwSDra2tateJA4ADDXMwjLes97qeCGutEMCaMpfxYjJAhFtCIyZI4o2QjckNmw2L8ZY+0QAY5+jHwhMNmIcJDxeneZY7kUqiy8aHYfM9Kx4RiY21fM7zZ3EdVXEUQFmODTFCQbmIVjyLCAvkZC1oaURWhu8muLyWzl9xhL1er9qowOWc0kQW6YeV2PCW8TNedAFl9/3cJ+yoS9BwbmfdKdMung3hY3hc9nBzmZPA2JrNZqWDk5OT1QFqHPwFL0KN1+UwE1jMC7Sw1/UzRU4L1NjaSa2jpskMjARhGfIhbH+fRTYjhoKY9TMxY6jt3NeRn+fzrKTeVcTPWUQUxVvNEKxJLHIu54fck7KB50rTg3M2R3ig2NzcXGXwVjYTU3wWGRtWEtWAp84Fve3JsNxN4M69gMHAY5QcXQBqopCUiZg3Ds/lq42NjYqMQXldHiNlseNFqcvNDF5brpJsQW7oFs91jbZsHkAX2f86NTVVQwmMgWhppw6sdiAz/8H/nzrnXF9fr0UMQ1gmgMez0Zp9K9kv/m9Py4CBCf5OyUga8vo+binkc1bsZMTs2jC4P4rDOMvGg1KYRHPnRVYAL4KNkD/ssGcOKLxzbkcR7yhhzlaAEuWg7MjB+ZbXw061lAHR0gSPHTD/N/lh4zBZluQL93AEc1qS1Flvdy0Z/exGuuzWKOH8j7VHzsgEgzcsdb7ogOG1Rc+999e6x32QmY03GaUlu11PJIRQVHsYEzteqGTk3SzEUmBAFrxVSTTZ45ZJdUnqMGGEZ+qdqGYI5vFxD8bEwlXCUdcIC+NclQV2bkMEM7vH1ev10u12q0bv8fHx+Fzg0mj4DmvBs52H+ZkYAnAVeG2ZOtJYhm4C4CiR8vRBvketjzU1CZTsMNLk8IzPa+FcriTmcCLkfF5/R/+k3tVU8gQ2WDtMZISOONCsr6/XiKrt7e0aGihTNcvfe2b9WdbQp2/wvBK5lde+J75b6Zzsc2MriYWMEPw58igMkwVEGX0mrIXI/czKMh4n4HyO6ItQDEHtNe0sUG5KAigi8AwBlqUBlMNK0mw2KyLHTOjKyko1x8nJydrpBD7BAKNxLdmKYCfm8gCR1xDNEd9RzqUF0I0dGjCOP6wRYzWaQDbIAadDfmZEZQdkxtIGbIYagzPiQqfYQWPnXKIkzwt5ICfrrkkhjIecnRTAYzTJaG7DOmWuhXnwTPSCsex1PfH0PQZqSOHIWWJoh3aH8b3IFCf+GJujgT08BmvGD4FAjgAX3Rdq3O9Fds6BY3Aeh9HxDOaPAvFdfs7nUGba35aXl9Nq7RxaTcGf8RKVy0Uy6eFUgXmYXSTKsFYoDtHNhmS4RYRHxh6/ZZvUz5NyZDe77T2O4+PjtfOLcLA4HpNhJr3KoymdMnHZQJifiTfW1RsdymhnRMW8jBIgflxbx0jd+I6eoteWm0/G4DnOmR1IdrueSAjZyu157cV4gKMfC4IXtfAwGI5IBIoZfrKAGEK5iBiwiSGUyvlnySay6CY0HAnwyO42KQ3VY0JxreAmVjh/BniJfLhMJJToo6TiS8QCImH3h6O3SSArB4SG13O3z6J4QFzngzg2Ow46hpCveQfuWzZ+k9PyPObk/bU2QncEufWz2+3Wms/RXSMLvm/m1frmsdKwAdGFTL3m/HtsbCzdbrdm6F5DEADr6Z5d555f2jj9vggvvOEtyub3k9izl3DV0cYTcl6BwTiaQpxgKAidhUUhKLKXrJ7heZkbOQIxV+cqzuf4vT2hZWE4hSF4EzMOxzm054lSOiIzX+SPgRhJ7AYxMSrmBSrgOSALRwWvC6lEkgrqcm5QCeGpgVIHpZEBx7O6ulrpkEsKJRuN7MrymOEhnyvTGpNNJr5cckGOPJ91Ng9BxCyjqnXCqREycGNEMtrR4kht0s2pyZc2zhLHG66y2MAA6kJMjsXkPoaLCGu34yiT0dlF7uxIUtWFzLqxQCwci5eMcjF7Sjw0Skv5g0VD4fGkXhAbTa/XqxFdhu6gg16vV+2ON0HGopg9Jpq73a98FobcbrdrZ9OQJ0JKlPI0d4BcnELgYEqm3E4nSdVuyJg7nU7VRmiYiPG4xDAzM1ONB1k7wvMsopUjkPXRZJudLdGPZxp627B4bqMxOmXQqZlTB3SR+XM/cxkm6RxQmKNhsJ22O6L2up64n9ODL3NGnxzuNjdPCqWkU8b5mskfC805lheaMVA+MAQuvZMZNxYHGOznYqQYmMkfGxzjwzgYM5E6GZUi/F2iI3PxW7a2t7erU8MhgKwMyM4OyRCXCxkwJhNX/MzGjpMkJ3SeSiOFIyjjI72hdurxMlZeboSuGJlAjgEfYUFLaInBmUG2w6W0xM9NvDAfohboxQbFZ5w+2Njs3NBb1oMUzoHDdVCvlff+es2T1NbiqYyzJHVKoTnP4mF+OJ8p6WwL1FEAATnHY8HwmCiEu3HK8dDCxQIyxjJ6oIRm0IhohtKuiaFMKJH/DAaDKgfkaA1DVS+yIbANxErnaGtnhkwdiQxxPVfkSTQHWfi8H2SM3BqNRi3PnJ2dreZn9tKOExhL+yCRicjqdAdDoQOKSOw5lywnzprISNQxHHcK4lSJ9XLOXKYlyWjjN46c+VnmOAnGxu/daFPqmskzR1VzD1/aOFdXV6sHMVEXhMuoYkaVAdhzO3eyAA1xWRwmZe9rYVLmsGLyGRspCmaDcA5iYqDf79deJ1f21zIWQ2TQAxfvMGGnBB6UfxvqJ6N9rs61De9LtGJUgRzW19e/0N3iqMkzgVcoCnAZ71/WK4nGzglBD2a0y5yvRCLkbxgajtUdQDYqvmdj8DMxNlKHsjPHhKDzv1L3fPF51zbLUhRO3A6Pe+IEMEBkUB79QjDabQzlta9x0t9Zkg0mJZiYjdWezCEe5SjbmgxlDGMcFX0AcQnTOFEAhaDzBuNDMGWHjPNRL5yhWJKqLru9vV2RO4ydxfLce73RSeeMyXmoITxKgHI6EhA9DDvtea3sZVmE//t4F0ctfu9WNtbcDSIomeXd6XRqh2eZoDOpRpRm7qyJ18o1WjtAs72loqNTJp+YA3N1LsvcjXDsBHbLhbmYh/Ndv8SK59HeyCl8zBdS1YjRRmnH/qWM0yWUUnGdZJuEMb5GufBswEjjdgsJZS4hMApgRWRiGJ9bBTFE8iSXNqzwjIP5MTaXMAzdOSfW5QGe78Xrdru1vZNELhNi1GK9SHZ2wCuMwovKWBkHcnKpws7JZSkbInLgcyY1YFqT1Iy1hHGUW4xscJB2orvlxDglO02XuNAD56Z2/uYebPR8100GZSupuQ100TqFEwbG22gnJydrsjcCMyIjdSr1FLk5ADyVcTpfcIRgIs5ZbFTG026rGw6HtXd+sLjcL6nDSQyABcBLrq2tVbXDZAR7Go1G5dlYpEajUYMSzj2SUa3RCoYCUe/k94ZXQCucBrkQc3WBGdTQarW+8GIbFMCRHO+PAhtyWhZmN3kOEcbEDgrOz1kn5DAcDquN4D7lAAfD90um1KUQE0eWFZAbGG3IiU6sra3VDnh2CcLRxTqIvEwEAhvNHqNTZamqjKY2Mn7vXJLPMnZ00ftZ0Z+ylZA5oW/7RUyuL1VKYXGBI56M4Qbf9eIYTlrJrHxOzu1trAA+yjIZMZrOd1AC52Y4AYQ0GAyqbT/eIeGEntIECML3s+fkclsizzGTaoIMmI8iWCYoBkaOQSAXIw9DMuTjsoEhIuPlJANHqGSUSjhKGhbyfdYJ8oc1tEG7a8lRx2y9u4Fwnu415t/dbrcan3XDegoy43kYq1MUG4WbGJiTCTQcmscPpMV5W8fRPQJa2XaI/exGVO11/UzGyYP4mRWJCaMoZvmshK5dWRm4er3RAc4IH6VFGLCp3hSNMrvYn6R2+LAXAkOH8nekQ+GZMwyuDcYwxzmy2cgy6rKwbIAu82bLG0fAwnNPk2+sB/ey4QPl3E5nB+VSD4bkvK3T6VQwGGhKRDRRxN/dbreKisi/zP2YA+/gNLFkVOI/hu3WN8bgcgr3RN/8XJfDzAo7QlqX0UtHSeRlyA46w0GZfMRguUAAhude86cyTibsRNaeBWX24hpzQxw4R7RSlzjdta6SELIHsvE5Ny13idDDSn5jWMu4DAkdCc1sMm+EbKGS0xIRMBzekubFoVvKkcQ5VMkA7iYr58GGdigpMjSks2JZ3kBcHAiMLT3BOEPm4DzOebRZcNbDRsmaGgW5waMkEY2o0AvDUGC7n+eoiuOxk8OpM0eeicNApzwOk1Ow7yAsE25eD55rvUIedqSuqT+VcfrdhVZWFKRsHrZXKAmYUpjkbBgIuQJGgnCYHJETJU9SnY9r54EyMUbDPAzUObJhCTAL40Z4hkQ8y72kRIrNzc3qyA+znMkIjpPb+SWshls2QJQN43PZAXkko8K2jYA/hppOU0x4YYiQLOYLUGiiEXDVelFGDKIkYzSERZG9tsjJ7LY3LuzmcMwdME4QEc8dDoe1iN/v92vv2XQ+azKJq0ydbOy2Dee6PMN6joMy0cf9jB7L64mR03kghubODT6HwAxpXLsi8qFAW1tbef/99/ODH/wghw8fzrlz59LpdCqyhChoiNvtdnP16tW8++67OX78eL75zW9WQnXjO+MD+qLYnGnqnIExMzfniGUUc0Q3zEIp1tbWsrW1lfn5+ZqDwUCcW2Eczpmdi5SOArkzDsNVvl/yA1zkmYzHDClMqg3chkMJCUVHJ5Az33OUoznEDtnjxaExFjd5OPIR0a1fJiCNHrjK55SEJnwJ+okznJqaqtbFvyeIONLxPBNMOFi+hwFjpCakWB87y92uJ24ZswdnwfAGGIMF4/8zULfQcY/V1dU8ePAgi4uLuXLlSr797W9XhjE7O5v5+fkafMK47t69m7m5uWxtbeXu3bs5cuRILXczU9ZutzM7O1uD1a6pOidhrkQbL1S/38/a2tquBJWNq9/vZ3Z2trZgNnravJxPl+1dOBoW08paRkQzsSita3J8v4ygFNldqkGByTlRcMPAkpixbvCHKGlG05EeZWVN7Cg8Vhsuc+HzRD6XeMocHthuA0YGJeJwnyvrgBN1Pm9j4rkcLWpCsjQ4O2fswo51r+uJB3w5cS4ZKJSuZLfKCIqwNjc3s7y8nOXl5dy+fTu3bt36QmRdW1vL8vJyrl+/nmZzVJwmZ4B0uHv3bmWgzWazOuFtamoq09PT1Wvy2u127WU+FrDZShQDeOeFx4C9eMyNXI3czVDMeQvkinM2PoszIer4VQvIBXk7f2EM7oE2JKbbhVQAg6TcQXpAzozR2rDd3eJ5e8eFFZifNZs7WwKdOyJHHBbfs7GSXiALz9+OM0ltz6XJK5xMiUZYSyMBdIP1Q1eTkePk+zh+1qBkgPkcPdO2H3/fDsO17i9lnEQLBGPvmNTbu/yHAXS73Tx+/Dj379/P4uJi7t69m5WVlSQ7DdIU652jJCM4bSXEuwOzJicn8/nnn6fb7WZqaioHDx5Mv9/P48ePK3YRJZycnMzCwkIOHjyYmZmZ2gKboMHDoXxcOCEuzoUFtkBQUJoxEcCCoIgotRs8WFjmy/jJ14BFGKgVyP82cQJcLVsHrWD8fHNzs9pb65PkvBYQRThJl6r4HOMDIm5vb2d2drYW5eEBgJcYAS+bMpoxUsDoHaVMIGEsPiHBxg8J6OM1XeJzHkhubRhrbqLME80Mc+4W+myyzMQR6/bUm62BNMbTeA1PDCFwOtn9+/dz/fr13Lp1K0tLS1lcXKxg0czMTNXpz/csUBbPnth518bGRubn5yuDO3ToUKX0x44dy4EDB7KxsZHV1dUsLi5mZWUlN2/ezGeffZYDBw7kxIkTOXbsWJXncISlyRsEyfzsXfG2eF+ijYkHjNwRDDk5kuAkMEoTZ+Q5GH75c+dfGJhLVoZ3jvhESfJxXkFBiYDIxfh40VGrNXqVOmM2/C75AcNNHDakCUpebkywswG6Mh+eDZTEaFzKczMC43GZJUnlrC1/pwIYD2toxOix8fuSSDKsd3oF4vDYjDa/tHHiwewVUSSEQ7fP1tZWrl27lnfeeSc/+clP0u12s7CwkJMnT+b555/P6dOns7S0VDGaV65cqQyeaIcHtUdBEfzujPn5+Zw6dSoLCwsVBN3e3s6dO3cyOTmZw4cP5/jx4+n3d86B+eyzz3Lnzp08fPgwjx8/zq1bt3L27NmcOHEiyag53RHP3pHoRfQ2IeQclh30XESqwWC0tYzcg1cycDFf5GAviyEYtuK4DCNZJ9cvHUGSHYabN1Q7ErDWsJkciG3EQPRzyYoxE+0Zu8kUw01DPDtfX27aMFrg/0R39BA0YnjNs1xDxaicn5aQmbXmmXwG+TsFMqvtwHXo0KEam8v3mXfZ/vlUxokycnMWCC/IhO7fv5+3334777//fra2tnLq1Kk8++yzOXv2bHX+ar/fz4kTJ7K5uVmdo/PWW2/l/v37VUNAo9HIgQMHcvjw4coTekN2t9vNnTt3Ku907NixLC4uptFoVG1nfNZn2xw+fDizs7Ppdru5e/duPv/886ysrGR5eTnnzp1Lq9WqjQHF9uZjRwZyTQgLPKUNy4ZmZTCkArbiPVEM/jZ5AnvrvMikE89j/CZicBBmtv1mLzs+G3rpDFzqIu3AQbvB3sQHa0CZxspspeVn3NfIwCUk/rYczDyjt4zTXVSOWo6YTtXMDruEQ65qfXDENamFs3P6slvJ0Y78Sxun4YvDL4swHA7zzjvv5M0338ytW7dy4cKFfPOb38zp06e/QGrwZ3p6OuPj43n22Wfz0Ucf5cyZM7l8+XJmZmYqwofTA0q40O/3c//+/XzyySc5fPhwDh06lPn5+Qous1PfSoDwYVIPHDiQ48eP5/33388777yTpaWlvPTSS5mfn68tGjmlczgES+RYWVnJ7OxsrQ0PRccLEz2AxysrKxkOhxVhxcJCfqDMViSXGDBCoo7RjdfMbXLb29tVIzsnP1ipHTmS1EoAzr3dmeR2R5wUzx8MRmdHOWLwXXZuWFEd+U0i7nYaPPqHTpAHG44a3bhrCFRkRMHvSA3smNyOWDoC0ghkwwn76AjOyYQSF7Jhw/uXNk5Dh5LKb7fbee+99/KHf/iHSZK//Jf/cl5//fXMzs5WQkhS0fI20MFgkMOHD+fv/t2/W8szUbyyvujC94kTJ3Lq1Klannbw4MEvNM/jvVBeNv62Wq2cPHkyMzMz+fDDD3Pnzp00Go1q7Dgf5252FFa0brdbORsckRv5LT8vDgrlUxTod3VZhPmVb9ECGuMAqJ8yb+ddLm+RwxF1MAjnjf4OCoaSsialEzHvYOWnIQADdr68vr5ec34YV0nYYBAm8Uq43u/3qzIPY/JYmRPRu8wxMVBy7vX19RqB12g0KsLMz3f+7FTMkBXdd82dgMfzn9o4MRBHjYmJidy8eTN/9Ed/lM3Nzfz2b/92Ll26VPOCzpfs1bmf257Kbg17POcIRHAglBXZY7RRoCwsPkY7NzeXV155JRsbG7lz504OHjyY559/vgbv/GyUkfGZaXQOglF5AVgoyIi1tbVqjB4fuTs5KgaP83K+450lJhiI/IbljuweL2vixgxHOwyf+fsNW64dM04bqqEbMkdufNalN5OAyNmogPkAzYGMOByiLGjFXUnWRf7NfE0GIhPn00mqVMzEElcJV73+BDJsCXnxcx+g9qWNE8GQ7DPZ7e3tvPfee3n8+HH+2l/7a7l06VLN45b5mT0c0cwLDKQrcwLDNSuX8xf3f0KXG46UibvP8Jmens7LL7+c73//+/n4449z5MiRzM3NVaUSbxfD87IYND7bKKz8XnQU3s3RdmJ4T9foHLUbjUbtbWVElcFgUHl0jrBkfpQNSvhKsR2ZE3UdrU3C2Hmg0LCUzrX8DIyA32PUjN9obHp6uqbEwFJHeBwTTsqOFz11ZMVwkSFR32tkPYNtx4k4197e3s7y8nIN7npHkkknYDHtmUZu1h102QjzSxsnBIsnOTExkdXV1dy9ezeTk5O5fPlyrWBv+MAEqU2Wk0dZTY748sCZlL2tyQ3T3RhtWfZh/ycKs7W1lSNHjuTZZ5/N22+/ndu3b2d+fr6CpSYDWERHRGCfDc51OFKBJFXuh3Ia7vpZ9uzIgM+T16FIGOnWVv1N3aurq9UxILDfzKnX62V5ebk6Aa4kOBin4TFjZiwuW7DeGAWbDVqtVlVa6/V6lTMzOWNHA3RnrkZOzJtnEZUYt9fdLHW3260ZJwbumiakz/b2drrdbqUX3IOT+r2rBGfLnNH3RqNRISCfpYyBmqhjTPy927WvcXr7l5N1lHRqaqoSCJFhfX29OuSKwdo77dYza5iRjI7KKA3RsIufm9YnSkDCGG6ZyCiV/MyZM3nnnXfy8OHDGuOK4eC5KV3Qi4ozMqtInsXFPUgHQALIqCzh8PyyjGWq3hGXuUCKuFMJI+W+vBLDsNoGMzY2Vh0XSdSykwVuo/CGtEQQk1bdbrdWA8UB0ITAPCCXOF4GnXA91sSUx4PeYLBcpSE6R/ZOHMNz8kaiPjxAo9Go+rJL6G522e/pLEk6/lhXS/j8pYwTRfegMDgEy+TW19ezvLxcy3umpqYqgeOFHQFYXEorGDEwCpgGzESZiBKwXSYWzOIBJZyTAOt4Fko9MzNTeUlyFwuY7o+pqakqKs3Pz9f2d5IPO5p6cfxsvC2dMexk4F48G6jvkoTv49wNedsgUR73BnNKHp8hCpJXIVsikpW+rKvilL2edjSGrEQMnjkcDmu7eCBicPomZlxPZm0Zq/NixktzCQ4hqR80DbR3TmsozrNN+pT8gmEpP+c+GC7rWcJnl7meyjidMyIYhHfs2LFcv349d+7cyenTpyuDcQ7hiFV6EucePIcIbXKFOqKjLTDZnhqFREDOEUoShOcQufr9nf16GIqbrR0Zm81mlpaWsrW1lYWFhVrUs3LwXObnfYJEDnJ4nA/H/7sNrVx0O0TmyvNQfhMq3tyM8ZGjk6d2u93q804RzDA68tkpGKHsltsxRqIvczQfkSQrKyvZ2tqq0g6iKPeyI8WQGaPhN06POSB/EJNbAJEp8ve+YwzfKIkc0WchOZXw1jp0FK7GCBF9cW/yUxknCo6FU7tqNBo5depU7ty5k6tXr+bIkSMZHx/P7OxsZVhELZc8jNn5uReRhXQHv5k0wyDyHrZCJTuednV1Nb1er3o/CYaTjF66Y0rcxItLMjwPyAIkbLV2znGdnp7+QmQp2UYTADwfj+otbhsbG1lcXKxInEZjpy95YmKi6gUG5tlRIkOXuSCDklFtmLUD5mK0Vi4Uj5PjyK3ZsWHZoRPO+b3OOE0Mh3UE1pus4nLqYh6i5BaszDiFEl2UJCSfRe4YBQ7YMNMMOfc3jCa1Yb3L11EY/bl/mvtwLyPSpzZOJoOSwpIeOXIkhw8fzv379/Po0aOcO3eu+gzC9kKiQCisJ29G1krmxXP0ZVwQCfyOhep2uxURAaVONOTFQq3W6MTzbreb9fX1HDt2rHZqN4qxvr5e5WkoFkrnck9JUIAauA8/5/44FhaUyDYxMVE5QkcqM34Ytkk0nJ+fi7PBMEwWoVykEfybP0BD7uXuJ9IE562Gj6wr0M7RFtlwHxNAICBkY4dnEs0IC1mYwHJExdE652SMJaQ3KYdRYfzooWEt9/HBaDY8y4WgVea2T2WcLiMgaG569OjRnD9/Pu+++24VPel4QVguDVihklS1TXtXe2cmwf3ct+ld7ywWCzEzM1NjMGEK+SzCpAlga2srt2/fTpKqod49qwh0YmKigih4cDsEYDKK5/kit07nQcbGlvLgQSOrq6tZWFhIsmPg09PTWVtbq3IdlBWCyPQ7huFTHpyC8HmUjrSAvyGuILUc4e0Ekb9ZdcZhhSOqGdYzdyAe30WP2FSBXGE9HU1M6HGhU87tkJfHDMQ0LMUJJqNGE+s2qZINHUcBgjCRY4fp70N4Ab2RM7KxvvLzL22cNsbhcFjR8kSXCxcupNvt5t69e/n0009z+fLlmtdhcCySoSoDNcxku01ZazI04N/OdfBQLme02+3aJtrt7e2srq5WEIzPLC4u5tq1a5mbm8vs7Gy2tnZedEtk5u3TZlJR5pLxrCj94SD35j9L5847aSx+mn/nn+wY+PHjn+VPzif/183XMrd6KEeOHKna+MbGxjI3N5dOp1MdBGZHR66KHIgwyBijRX7O3zEKnBufWV1drYzCUZ+1M2HGOJzDouQ2Sq+VmwnsOJyLoazOCc1BIAcbBlAeVOTUy+uDTOxY/HsbLJ8jdTG5Uzaxl4QPDpbvMBfQFp9F92mmKBnmL2WcCGt7ezuPHz9Ou93OgQMHqofNzMzkpZdeyubmZq5evZrZ2dmcP3++GgT5h+FcMkq4USoue0A+Z4Hg7Si8462AbCiKoTOemSjqt581m818/PHHefDgQQ4fPpx+v5/V1dU0GiNanM8RQb0wVs7BYJCp/mZO3H87Z37n7WxtvJuLH2+ls51UWdK7yS8cTrb+t7cy9smJCmbj7cfHx6sX7Pp5jvbJqAxTkmqgFJQNmXIPntNq7ezswTlxH8pjrJ1rxzhADIo5sy5mItEZ/vh75g5YP+7hsTuKGnbaSHkuDoooWZKYhuLefM69uXD2oLpk1CCDk0G3fNCX50BwwH6c/ti5ElSeGtbCxtG3ODc3l4mJiSoh7nQ6VRH//fffz4cffpiJiYkcPXq0+r4hXpkrsEh4Qk+Q7yNcd3WYhh4MdvYkcuw9giyfS2Qi4mxububu3bu5cuVK5ubmcujQoQoCHzlypCptkJ9xX6IPY02S8dXVnPzOd/LiH/xe5hfvpNnd+/iJA0l+K8mfb2/XlNvUvLc5mTl2qjA+Pp5ut1udNgCsNDzF8aGkXPy/0+lkbm4ui4uLlUKzLklqc+T/jlDIA0NKRsdU+pmsC3CzZGsxstKJuAvMTtB5HZ+j+cN6W6ZWGDbriRMxv4HskIPb8Fxx4AVNfMe657KPKxat1ugMZKeJe11P3JXCoVULCwtVDc2M22AwyNmzZ7OxsZEPP/ww77//fsbGxnLo0KEaBe7o44U3Lc3kzAaS35UEkRWHZglv0fF9WPjJycmqa+X27dt5//33MxwO89xzz+XkyZMZGxvL4uJi7t+/n4WFhWrcLvCjSM1mM2Pdbk7/yZ/k+d/7vczfuZPmcG+j5GotJ7+++SjfHexEcson1B7tlTmhL6mfz8Siw8r6zc7IE0Ml+nAfyxFkMTU1lbW1tYrJJQo5QpbND871kvo7VRuNUfO4Ixvrwr1x+qQYdgw8z6ww60hPLXIxO+30wg6Pe2P4EJ0Yu/NSw2I7FnJHthe6xkupyOtj4xwMRr3J3kD/1LCWJoDJyclKICTxLADe5cKFC1lfX8/169fz9ttv54033sjCwkK1gO6XBOq6LuZJ8Me1UkdhN8DzGQwIBwDchRQgCrJt69q1a+l2uzl37lwOHjxYefKDBw9WZxzNzMzkxIkTVSQjqm1lNe1PvpO/+l/9buZu387elapdrq3k2Y9XMje+lHbnaEUw8XJZe2iUmsXHEFAWxjQ+Pl6tBZ+3V09GJA2QDWOwE7AB87c5ASMd8wEYMX+zRrQBVsqmjhobPBHO73DFMM2wGmUlqVIPIy+TYRgAaYJzc5NDzNOyw6m43OQSTdn55LmYrTaaod7rEyUsn/La1ziXl5czMTFReykNC46nhJnqdDq5dOlSBoNBPv3003znO9/JL//yL+fgwYNJRiyta0d4rhKCspBEECuJFZCFoLC9vLxcO0ZkbW0tc3NzlRcDCbz//vt58OBBzp49m/Pnz9ccQqMx6tZhYXEOnUYj63f+IM//9/9DXvvOrUytPTlS7nYd+hfb+av/wU/y+73zmZmZyeHDhytZUviempqqHUxtVtAk22AwqLbD0TJZ5jRmB8mH/PNms5kDBw6k2WzWYCf3ceHdmwAw0iQ1WMraNBqNakyMvTRiDMv/t7MGInJuEbVbjAVoyFvPXMdEzzBSynDmC1wmAgnCAyArtxsmI0TmmiX3oLuIAMYckMfMzExl0M5Jd7ueeDQmCtpsNquCOIuLFzRev3TpUpLk3XffzXe/+9288cYbOXLkSAXXYHy5hw8xLvM6apEsCgbmMgD5Bd7WzC2wCaN7+PBh3n333dy/fz/PPvtszp07V92Pg8JYzJmZmUqZ2p12VtuPMv/tf5R/979+O52t3peLlsXV+MXka//+Yr73T1s5dOhQlS5AXrnZw+wo83DtLBmdWzMq2dRrr8jAzK6N15CNNMIsr5vVDftQ7qSe19p4DSkZE4bjn7ntkHE6mqPMGJNJMt+P+9OgYmbZ+aoNjSiOXNAznzFMmQyZgViIrtZP7sd+XdbECAzZPbVxUhczzkcJuCneymWMF198MUly5cqVvPXWW3n99ddz4sSJCoa6VuUFt3dDKbz1xySQFcMMHYs9NTVV5QZbW1u5detW3n///SwuLubMmTN57rnn0m6Pzix6+PBhFbGIFv1+P61GI7fW/ii//v/4b3P5+w8ztnf+/uRrLNl8rZkH/9lEPlk5moMHR6fjucheEjluLHBN1Zt7XZcl+jhfI99K6mcmGcq6pu3PkhsTSeAHXBf0/lyvr3d54MzLDibYdMbAGhoi2vhtPMyfv/me81fGbNjJZRk4rzdyQP9L2O4aKbI2K01Vgc/Stmn0Zx6lvH6mE98RJnkbhmDIBRTBQJ9//vkkydWrV/P2229na2srp0+frrB+2ZdqofAsDJZ/8ywiChfPJpfBm9IYf/v27Xz44YdZWlrKmTNncvbs2cqTwSB2u92srq5mfn6+QgOdjY2c+/M/z9/55/9tZh9vfKloWYn8TLKco+l/q5NPn3sx35mdzPf/h0aazQN55pnDNeizGyvtxcNoyk4c7mHFhpxwZElG0c+/dzsahu46H46KtedyZPahXN5HSlQZDAbVoWE8h5quc+dmc7SxHN1gTjhs10QZBzJBdswVx2Z9AaK6tAFiwllwOTcnGPi0SPd2T01N1RwUTg3UiBzsHJ4658RbcxMimw2Fn3O5eH7u3Lk0Go18+OGH+e53v5tHjx7l0qVLlbdF2RggBoaX5FnANeCFt4OB7fme4cXm5mauX7+ea9euZXNzM2fOnKkcBM+dnJysGs+Xl5erhvaDrVt54x/9s5x7+0c/EwubJMNmMphuZfHSmdz6tefz2daLabzSyOrEi1ldb6Q/HGa4tpEXXlitGErkaHnYKF1rNKxlTVA8wydgqvN8votxW1Y4Yf7tGh6Ok2fwXPMGGDzknPeDsg44AU54SFLVQJmP4bPHU0Ysnm0uhNTHBsM9kjqJhhzsaBzN7Jww1jJCln3iJjG5F3PlM8ibz1KX3ev6mXJOQ1h3jHiyKA8eCs/8zDPPpN1u5+OPP85HH32UVquVF198sSoh0IliYTk3IaoYcpQEkhWSxV1ZWckHH3xQnRH0wgsv5OjRo7VyCIZv5q7XW80b597J6//Vv8z0D5aeJJ4kSXeqkR9961imX3ojn730i1k6cCyLSz/NC3tJc23k0MbHx2tnAgFR3S1ipbJioqyQJNyDq9FoVOSF5YQTMOzn86UROFc1nDS0Bvm4EZ3x47BZMxyOySLujxHzXTsUopN/bxTltjwiKfrqq2SMXc8tmxUcoclXW61WjWjku1QdmAfkHePG+SM3b/h24HnqnNMh2FHUTBlGRbEe74C3bLfbOXv2bA4cOJAPP/ww7777brrdbl5++eXqQC23wWGsKGkJ91xbslI5At+7dy8ffvhhHj58mNnZ2Zw9ezaHDh2qHAae0FuBms1mFhYm8isn/mW+9o/+NM3/cf9oOUzSHxvLD//SqXz3r/5ituZey4GZYxUMGgyWqzE6vyBXMxnDApEXoZA4OiCRoSYNE8iGNUDpkCERy7nbbojHBsQcWF9y93J/LzrB57zRnXVFwTEC1y9p9Pch4yXhxDOAh9yXy0x8UmdWnROSxpTlExx72VnFmOxsQAb8fGVlpZIHu3nMobAWJrCQkx3mXtcTIyfCB4paqTAWBuNIZNiUJIcOHco3vvGNXLlyJR9//HGWl5dz+fLlnDx5svLGFrLzUNPvbClzy14yIhc++uij3L59O1tbWzl+/HhOnjxZkRLJKG9jLpBUM5Pt/LX2v87X/vPvp3lvf8Pc6iTf/7WTufrX/1aGh7+V3tJyWoP6i3MRvqNHqdzNZrPaTwkcQ2EGg51TB+izdS5u6J+MDqS2YlsxkafzJiubYSRGgIwc8cyGOjUwoWKWlu+weXp5ebnWEMGcqZ/bOfjkAxyamV7LkIsxm7jC2Xs8wE9yc/JPnuW1azR2Smscacrc/apBR0FDdAyV9fJeVnQCPme364mHSgOL8IAlXi7brlyvMsRl58Ubb7yR6enp/PjHP86bb76Z559/Ps8//3yNUXSOY0XAE7nnFUV79OhRPvjgg9y7dy8HDx7MpUuXcvDgwayurtZqXTCMRPft7e0caT7Ir/z5n+b1t95Nc3l/wxy0Wvmzf+/X86Pf/K1Mrc5n8fMH2djYyOzsbK2pGmXxwjkfBxqiRERNCCrkv1tZhMVGqbzDx6SRUxI7zWSEfoxM+J0bRuiZLQ2kLK+4xuj2TsYCg45iI5/SeZRHfHpzuMkynsPG6RKeMjb01g6Nn/Ns8ko7tDIH5T6MlT/Ov/ksa2Eoy9owb8ax38HST9zPiVCcm7DwwE57Wbdh4QFZZKLvSy+9lMnJybz99tv54Q9/mEePHuUb3/hGZmdnK2VCcVlA1+MQKEpy7dq1XL16Nf1+Py+99FLtFHfmwDxY4MXFxdy6dyvTJ67nf/Pf/DjH7j1KY2/4nyS5fq6VH/wv/242n/+bWdgaZq23ls8//zyNxs5J9c7BTWYhI0MzFtlkj+EY3p06M7k+Rsln2Lt64MCBqoiOvEtOwErF78vtTCAJfkZUpfXMDoB1Zy2oWTPOspeVmiD3wsnzOZ7JmMwF8FwObi4dii+jBFcSLDdHey5+B+Pc643eP2rU4ECVjFKxUUpT3/Rt8o+UJRm1G+51PRHWYnTeuW+vZm9sJWDAJTyFwDl//nwmJiby3nvv5fr161lZWck3vvGNnDhxouZhYPsYh5uhHz58mI8++igPHz7MgQMH8uyzz+bEiRM1Cp9658rKSrrdbubm5tLr9fL+o/dz5fL38o//6zs5dme4b5lkmKR79FA++D/99azP/Y0MtnY+zy4SZEHh2S/acT6GDB3FiArT09O1z/r1efbgXlRkzCkUXnzWAEPCwJEhqYGPgDHHYCSEE3HHllMQHInzP+f0jhSGq4bVjkreF2xYTYM7ukHNHYfjPLhkee3ky5psMtq7bKYfJ4ETaDZHL90ywmHNyuiOXNwJ5N6B/Qwz+RnqnGawDHHJWQwPyr5Gvgv2h1jgOwsLC/mFX/iFzMzMVC1/ly9fzoULFyovjqIDLaiNXbt2LZ999lkGg0HOnTtXvZeFz+EtqT2tr69Xwu4Ne/n477+b/+R3bufyh9m/ftlMumeP5g//w/9dHo9fSHO77kXn5uZy7969Ksdw7WwwGFQLSD+lCSgU27CVRSwjFgqH06GBYnt7O3NzczVCDEXFCJ2WkMN6HVi3kpV1eQLFLskY1tYMLQqLDgGL7bSIKKwp/y+PQ7FMTECaNCqbUFzbtVyAtj7BgbX0XB3FmSc/x+htG9zHny3LZE4ZWEfPbbfribtSDGVYdMMNBmTqHKFzD3tUaGX+PTExkVdeeSXT09P54IMP8qd/+qd59OhRXnrppQqmGZ/fvXs3n376aZaXl3Po0KGcP38+R48ereUK5Bim+tka1ev10j3czf/i92/mP/u/J619oOyg0ci1X/+lfPgf/U/zcOtIBj/NcRxhaBMDrkHwgBBg/QzBSmjpEoSjyNbWVrU1CbhpAmVtbS2Tk5NVKcE7N5wTuWBf5lLcm7Xg36Xyel8u64qTsQGwDqAtR36X4PieU4Cy+cGssOXD85GlSRXzHsnovGDeo8MzGQPPpFOMSOl1MmzHkEsEYW7AXI35AdsOb3DbDZZz/UwdQoYWTN6REcHbA1cPENZGITAaBt/r9apX+r377rt57733cuvWrbzyyis5fPhwms1mVlZW8tFHH+XTTz9Nu93OCy+8kLNnz1aFXu5rKJOk1hOJsizcfzf/6/9ma1/D3JiZyX/3n/9GcvbfSTKVVnuQjZ++Oq/RaFQn112/fr2WX+EVl5eXMz8/X+tE4neWJy2DeFIIHmS5urqaJLXWR5cg/K5JnjExMVGxiaUCNJvNWgum19CGYJbRxA0XObKfbyOzfjgiO19zSuRjTmyYSf21iOiQkQd6ZoaUMXOfslPKzoR5gBBwmv6c65V0OdlZueaJzhtJ2vEwJxOEu11PPEOI19s54iEAF4YRqiEYwnM+kaRGW6Ns/X4/8/Pzef3113Po0KG89957+e53v5uLFy+m3W7no48+yv3793PmzJm8+OKLOXbsWKU8ZmNRcH7GGJrNZubm5tJfepjf+P0/y7E9yiXDJJ+fms0H/95/mrz4StY3NtL4KRwnWqEon332Wfr9fs6ePVsRHxBg7P+kcZ88ygtqYsSdJSgXm6EbjUatKL6xsREO6GZuyBZSAuiL49je3q69LIrfW0bkYvyf+dqZuIwGTDZbb6Xm36urq5menq415QNRiV4Y6/j4eDU/R3xvL+RZOGTXxsuSnOHnbkxuSThxGQX2+zsnZExOTlZnVDkft0EzbxpNQJUmOI0OHMi+YH97/uanymNmzgQQkwae2SsY6rhPEeWxArJI3Ht6ejoXLlxIp9PJlStX8u1vfzvNZjNHjhzJG2+8kVOnTlV7H82+mT2m4bjValVnw/Z6vbQajfzWv/rXufzWoz3n/PBbp/Lf/Sf/cebGnk2zNdq5Tq5CXrK6upqlpaWcPn068/Pz1fEnHAYFcYCX944LF6Bd4sFQrRzIxoQLskpSKwGYAALiOr/l97ymgOc4PyuhGc9CCXHSfh6OCJ1xOYW6oyMKBmF2n40V/Bt0YOIGSOngAJpAniaHzFmgl+S4ODWcDWkQ9/a7TtBLM8AgMZwEa0NwAgbjABzk7Dj+QichOI/DYziP8OLC6DlJ5ufeLMvn/Q4V7ktk4ITysbGxrKysVMpvr+N7EdWJXCZZWJTnV3+cF959Z08CaONXZ/L2f/r30+mezubW6OT2ZOS5Ufrl5eWMj49nYWGh5mVhimnpQg5lGuAIaiIFJUFR8cDkVY5a7DtEFkB4jMC1YjPHZja9s4RICGQzr0Ck8D1wNkQw2FSMju/YOFHaZMTW8izuaxbWfAdy8IuOycG5F8HAEZT5msDCGTo6u+7pSGv9pDwHcjGMRm42VHQG/acWvB+c5Xri6XvG/2akDCk9IQ+URU3qfZwuGQCvENCjR49y5cqV3Lx5M/Pz8/nmN7+ZtbW13Lx5Mz/84Q8zHA7z7LPPZnp6uhonntC0tfOnTqeTdpIX/scfZGxzd0+1Pj2dP/zF/zB31i5VHUVAFd7B6XY/mFSzonZMg8GgMmAM2yxhCWeoZ/LziYmJrK+vV1DQ/aV4e6KHISRrY6UujdGGZxSEgwMdsC48i799cpxZUxsmciB62Fip93K8DFDXhzIbbvtvDNF1SMbPnHH66IbzcTspR3M7MP7vUiCRFtkRPJyXm311XuwSjZng/Zja5Geoc9rru1/TmNneCS+KoniSKDe1Ju7F569du5ZPPvkkKysrOX78eF588cXqNLpjx47lrbfeyo9//OPcu3cvr732WnViHmPAs9lJVPT/+g9y5t0ru85x0Eje+7VfzY3zL6fR66fZHHXvwMZ6i5v3mxrOl57WC8uC7Ra9+E75MzyuYTByTVLtykBhgFqQQd6xY7IFmWBEKJi7W5z/MnYrqLtmWF+cgss3fNbbDp2v4Uidqxp5eS1xTD5lwPL19jS+78sEkaEtn7eRIWc3c5Tw2RHdcJbPOEAhG5wLCI9S227XE2EtiuYakL0Vl0sZGK9pb/A5B4aB/albfvjhh9Xxmq+//npOnTpVO+rw6NGj+eVf/uW8++67uXnzZt58881cvnw5p0+frpEVwCgLKsvL+fv/5R9mYn13KPHeawv5s7/7lzPeb9UMwck7JZISEWAcLLoPQHMJiaYCFsnsrrcNsfBlg4e9rMsl7rxKRt01nJnq/N8w3/Iq0xDOjYKEKg2mjISO1Mno3TBJqiYQzwVl5378zfjcrMF8yUkxZMZgA8PgcTzojh0TRuRuMdYIJt2klO9tvsBEkvNOIndpvA5mZR691/VEQojL3R7G1YYwHgADJSl31OE+vV4vN27cqKLlxYsX8+yzz1ZtfM5Btra2Mjc3l2984xs5fPhw3nnnnfzpn/5pXn311Vy8eLFqNuAdI3jRycnJXPjRj3Jkn4O4fud/fiRH+wu1Vq2k/q4TlxgMc4C0LLyVlIXodDrVghNtSvhm4gbFLPNUvxWLz/A6Cu4BtMZgHXmcC1nhcQZcGICdM7mv4XP5fyKNv+9OJhjhMk0C0jNuFNdklFlp1w6JptzHaRYy4bNuoMAYzaLy93A4rI4gMVvt0oflZZhvx8qY+YxTLkPgva4nbhkjnKOADLSs8bDILIyL6wjU3mZpaSkffvhhbt26lYWFhXzzm9/M6dOnq+8T9s2k8XLT559/PtPT03n33Xfz1ltvZXFxMZcuXcqBAweqxWUh22tr+dq3v73vhunz9y5ndX4UxezxzM4h1K2trWxsbFSFZPKtsmxkwgrjcL9yt9ut8jwUwzIzMdFqtarWQJMJjsRsLSMfZqycQpiMjhgxwwqCgbgDdaB0hor8DTJw7ktEB2UwF9aRz/HKC+vYboSZdc7liN2INebHmGjHc9tiaTCkJvzMTiwZvYGAe6JXdkCgBLgFHKjLieiwIb2Rzl7XE098t8cyeYDgMVzDHhQGAfMZlOWzzz7LJ598ksFgkJdeeinnz5+vNb1zXyaP1yFK9Pv9qoTx9ttv59q1a1lcXMxLL72Us2fPpt1uV50zl5feyrG7d/acY7+ZzG0eyuL26GhFEyksmg0h2Yli09PTNWjF3J13oDD2vCiOoZrzUZMxyJHxWKFZeJervBUM8obP4Ujcowq5sra2VsvfMVQUMdkxClCJjZHxoR8YrhXd5ZGyfGCCCznhPFz+MERFZs7/cMygmWQEsY0iDI0Nt9FfN8f41YAlGnCKZxTgKgU/c2TlmRj1XtcTCSF7fcM8BgrrxkTphnFhlkk/ePAgH3zwQRYXF3Pw4MG89NJLOXLkSKWsvV6vdkaRa0MIDUG0Wq3Mzc3lW9/6VtVZ9Gd/9mdZXV3N5cuXf7o9aSsXfvf9NPbZBfbw8FSuXDqWqYycjo0AxdqW8S4vL2c4HGZ2drZS6N2K30l9V42NhnoonydHKhXO0A1EAUPs5zAGM8ZAMozdczLc9Bo5kpdG52M1XHR3voaMeC4R2QrpaM/lNIZWSHI8Rx0TWfzO+a9JH0dEWu/QT4zMjtOG6Tom+uByltfCBo4zxQnZ8FkndKGE2uX1RFjrUgfJrpUMxSKfsCdBmNvb2/nkk0/yySefpNfbOYD60qVLmZ+fr5QXBbCnLPsVvciwshMTE3nttddy8ODBvPXWW3nnnXeysbGRr33taznS3MzpDz/cb4q5f3Iqs8MjaY6NCBhHPDpgnHs+ePCgWjxerQ4ZxAI6b0V2LsUko137dgIofRlNHM2toCixIyz5Gz9zcb1kSVE28meMns8xF5TZl+F4t9ut+oiNInaTgzkBIxSU10iAE+swPKcWZUOCO5DQK2TgmrFJMOfAZmXRcTfV2/gdqJAPDha5uWONzxhVOe/e7XqicYLzHRkYlI0Tz4kxs/DLy8u5cuVKrl27lvn5+bzxxhu5ePFi5VEZLAtPLZAiL14N5bDH9oTPnj2bmZmZvPfee/n444/T7XbzH51bztjdjf2mmHe+eSLDybG0Uoc6CJC8Cwizvb1dRT0n+ORRjjosGBDNhu9CNApakjD8zotqRpMohbwNaWkzdDTHWCxzpyZra2s1mOWOKP54g4MjEk6CMhmGYIRAx5nzUOdyhs98hvmWJ+4ZlltHfD8TS84jkTc/d8rAGrlOaqfAc8j/LX+cG87EaMHsPLpv1nq364mlFCsgpIMhiKEQXo8I++mnn+YnP/lJVlZWcunSpbzwwgs5dOhQWq2d7hYL34sDu1lS/igFC+McYnx8PEePHs2RI0cyNzeXK+/+KC999MM0Nvee/PpE8uDixUw3xqvyC07AwnV+R4Q0UcSFsZGnME4Umqjm6Fl2XWHQROOSAXW5xs+2o/N6eK1wMoarVkinFzzX5B8wzV04jkysHY7TRX47dUNPnIqJOB+tyf3QF0dv573WBwyPn4P20CfrFt/zJm5+ZvbXTpGuJBsqMsUwfVSKUyWnO/u17iU/45YxHooxmWlL6rvJW61W7t+/n6tXr+b69euZmZnJL/3SL+XcuXNV7gYJgQDKnfMWmqMkntA7GBin26ouX76cl87fy4n/2/5R886psQyOvpJOu1PBj9L7JqOoztxnZ2fz2WefZXJyMsePH69kYPgFoTI5Obkn42mSJqk3MfBzxoJhmBzBYMvX2aE8GAcRinVDppR2DFlROL/w1g3mrBlGynNhKk3cYBC8ksFMtPNdDAp9I9qS87mJxTyEda5EH95nasKmZK6NbtAxIxfWku8TQd1YYV0lWDgqcs8yupf6Xl5PPEMIYbvDw/DDA97a2srVq1fzwQcfZH19PWfOnMnLL7+cAwcOVF4Wz4MXcjJv4iUZRRzXj8oeV4gnBLQDW5K/8eInmVjbv4709tfHsz02l/52HSaXdSgb09bWVo4dO5Zms5l79+6l0WhUJ/uNjY1lfX292kZFVLHyoUAYIJ8xC2kyJdnxtjTzIxtyKCsNBoB8nYqQGyejoj6OkrX0OqBMRDXnukYTzMk8hHM2xm9EAnPrWiZjLXNG7s2/6avlYv7ASUdt5uUcm3Xh2cgSPUJHMUATaXYkdCmRDnA0JqSmx04+z/8tt6dufPfknB852ebP48ePqyNHpqam8vWvfz3PPPPMF46SwFMxUCfVKKgbHsxamkDg526iXl9fz/j4eA4dXM/xf3ozebj3vAaN5NaF5zK+OJ7M1LfysFg2Gj+z0+nk5MmTmZiYyJ07d9Lv93Ps2LFqTu5MKftVUQbDJwyVCOt0wZufUR5v1HV0ox6MMZgXcHqCUvF8eAKXOZA3nTImjzBQR0ijGUdIl4dM+uDwndZgnI66rIOdgElCvgdUxZl4zHzeTgkEwPrYUSAbE0X8QRcZExwEJZt+f9Qcb6LUztqIwwjgSxmn4RbCRUA+ke3atWt55513srq6mlOnTuXy5cu1Lh/nFyTQKBCDtkculdHKAHxyrjY1NZXPP/88a2trOXDgQBYefJ6p39n/QOjFA42sHHwt7e5a+r3Rqd5Jav2sLhmVudv8/HyazWZu376dJFlYWKjqssiGBcP4MCITFSaSkjqMRkExGJ/5k9RzOX/eY7WBmLCy4qEkICCU2L3PzhedN/NvR1bG7HzWSMSIxySJx4dDA5pjsMwNvSQa+4hNl2DMnjpAOOB4a6AjHI0ILrMYknJvylYem+eHLuFEPP+9rp8p57QimTZeXV3Nu+++mw8++CBJ8sorr+TixYu13QmGEkSI0sPasxp2WAj2WigTTcO9Xi9Hjx6tFuqFzg/TWN4f0t4+fSKNxsXMzMxUC4sz6PVGr6d3PoKA+ZPsnA106tSp3Lt3L/fu3aua8WlKJyeE0TR54oiBM3KJisteGQVjLIamSWrOEwVw/sWzcT5WaF98l7mXnUfOIa3UlhMMJvPDeD1PIxVkAYnChb5sbGxUO1r8effI7pbD4yhtHHxmbGystvXOhI1zfCK1CSh0NEmVg/r7ML22BeaCse93/UwHfKEsZgjv3buX733ve7l161aOHTuW119/PUeOHKkmgTdy5CwJj2S0f9GsJEIBNpiwYOKu9TknbjZXcuqffZjGPtvlhkne+UuvZCz1JgciBceG8IdIWjZkJ6nqe88880y175RdIY3GiJ11uxykRLm52ERQyZia1OBzZa7GHEqFdGRFfmaLnbZgwGtra0lSlUiIssyfezgSlAQXDGuZt3ltnSOjzCiux8s6udPGEY75Uf8GYZQR1y2Lg8GgdnaQZWkojaPi/5B9yIv1KA8o8z5Ty7rUob2uJ0ZO4/pkx0N89NFH1XmzL7zwQl5//fXqLB8bDCysKXOzYHgls2Om+5k8MAsPx2FNfN4dKaenfpAD372576TXZmezefyXK/LB+StjxBB9eDasI4l/ktoCwDAiJz7D8Zl2WOwHdA6HUZawnosxmlXkHiZjGINhpNGLnZ5TDBQfFDE7O5tWq1XVP03GAbFLA2JtvTPFeWOZe9KWyedAMR4TusEcbZCWnfN45G2CipzPRo2j8f5LOy4cAuNFt3E0HgucgZvid0vVkDl6ste1r3F6QslOLvbOO+/kBz/4QQaDQb71rW/lxRdfrEEgL5QTdzB+SbDgpZgshAT5mXG8IdVgMKh6bRFMo5FMf3g/zVv7Q9qNN2ayOj1eLSjfN1LwKQIoBWwsEc9bmJLUOlaIPobLKAPygjQgwrm2iaGWvb7IFyUh8phE4e92u11FEpcVkDHGYQKLqOCIipPleE8rq2ujzgHN8Jp1T0YtboaFzmeRv52Pa57IANlZ79xwwBiRjxFIiRyQNfpJYw3rhS7gKOx4uBcyZOysDffz/JHLftD2Z36RUaPRyJtvvpnvfe97abfb+St/5a/k4sWLNVyPgMpalOEXikUu44mjRGZlXZNyMu7ySrO5U0vb3lzNM1feTPYpbw6bybXnXs9ab5jJzoiV5H6bm5uVcvI7nmsjw7j4PZ8FWjo/hCxgvPa2Zm7ZVYKisHAlw+oIbAIFeRoW4lSAW8wRRcaYnOeyTqXXN9vc6/WqQ6mJwk5p7DBQWOYOV0DUxAiQR8kvcOFEWBue64hJFHTeiRNzRDUrbrLPTCw6aQLJu3EGg52zpEAJZqjLFARHAlpEx/aDt0+MnFw//vGP83u/93uZmprKb/7mb+bZZ5+twSjX2XgoSoTgPHn+z2JbuNzLNSAiJ56a/GJra6s6XPnA43cy98/u7zel9KY6+eSFlzPsjyIdG4uZM4c5eVO4lTtJdfykj9bgPZ+OOsiARoH19fXKowKDm81mZTAuDZgpBkkkI7iFIWDEyNdlGxusIR71OOqDrAF5Nu1pROES6vX7Oyee45AcWSBHuC/OmLmUzfA2BMN45uRolqRCA+gStU/IN+e9OIiSpS6N0sZcciwmtIh4XBim+3q5V0lsgQpwvsn/B94y1mq18r3vfS83btzI3/7bfztHjx6tWD5P2AtpNqpsUTNDZ8G12+1KeZ1PEIlgaDc2NrK0tJT19fXKMGdmZnJp8Z20P9+fnn746vHcb8+l0xwdBOaFw9k4qrBQhtn8zN7XdTWu9fX1jI2N1U4BbDQamZ2drRXgDblQNB9GZdiFMdP7ynfsoZGfT8VDkc3YElWJQii+czFHTW/BYh0dadwhtLq6WoPozAGjsLK6fRKldb5nB8HPWb+VlZUab4H83ctrfXSaxd+sS6nDzlfLdj7Ga+fHZVTk3xtFGEbvdv1Mdc5eb+e0uX6/n08//TRHjhzJhQsXMjc3Vxkmny+hVonNDT2YKLXLXq+XlZWV9Hq9aiPy5uZmVldXa4c2cY2NjWV6enqnn3Z2Ns/9vxb3m06GSR7/xrFs9iYyOTleE5gdhAXunBmlKIvy7mrxApETY7hzc3PVayGAz8mozmcYRyscBszfTjNKz+7yggkI52w2FiKNoyjQCwUqYb8JD57vPY8mxwxxjUwcJQ3/0RV6VR1hDEVp08SYWCMjMuYPd+DPISvGgCFiKK1Wq5Z+AGeNhLwW6LZZfea1vb1dnVm82wFoJo/K64mbrRnsr/7qr+bRo0e5ceNGPv/8851i/8JCBV/YRULeZLbOuZvJD5TDuY1LJkBMIs/8/HwOHTqUubm5TExMZHJyMlNTU2m1Wpm4dSunbu69qTpJhoeb+fjwN9LojSAMV0laubTgRWOxWXzneJRDMFYWxATM1NRUVfdE+aw4QCGea9iJQphY43PeR8tYG42dNjMc2+rqajVns8lJam9A49xdv4GbcbDePBvuwCkA47dhgzw8Nhs9czALz++YM8bI893hw9oxJtCHYT7wmrXwv5Et2974Dp1PZZrA91qtViVfkIu5CY+FcZKvl/peXk8spTDo1157LUeOHMmnn36apaWlrK2tZW1tLd1uN91ut5YveVBjY2NV6aPdbleN4Pyu3W5Xb8miVolys1j8QTHMUCLYY/kgY5t7n2SWJA+fOZnb4y+l0xhBKjsQjBKPai+LkpCfkkOZQXZXkZlTFLLMhYbD+tu/TJQZ0rOA3JcF5jNmm03Ro1CTk5O1pgTDUpNvJj9oEcS5UDpx4zjzgjkv8yiXPqgf+0VP5PJ2KBiCCS8jBJ5tqO0cHN3wVkf3hZcIxWyySZter1dtA7SDcDXBNpKkmptr4ug6a87xNnbme11PNE4E22g0cvTo0Rw7dqxSNCuZqXFgjT1oMtoFjvCcIwDBgCIsrD0WhsLPRx61lxPffieN/j5HHownV3/rlSyvblYRHo9G/ZT5OIfyz/l3p9OptXUBg8xO+rvUgMs6G3mgu3xQRmTEM6xY9bnX98MS7XAu/gxOrmTFGQMGPDMzU9sGVdakuRfjBWbirFyrNty30/HRqHaUJWtcOg2vBzm5o1Kpc+U8klHvNnPg88hhYmKihn4YO7oIkiD6QfSVzKvJMu4BQsF5PvXLc1lgIKhbvRjoxMREDdogHEMG/mYgwENgHMJ3tLIhmqBxdOJe42uLOf6D9/abSraOjeX28y8m91IxjTgevDIekl0FFrYJH55rthHlMtnCvA3bMTxqXLC+eFu3mXEvOzKXdlgjl06QjY3OhuGuIV45kCQrKysVyWR4bSRhZ+mo5+jDZbIKQ8IgrewuOyBTjNtkIU7PNVYMzxDWh5IxDjswDLUsI1mOjMcN6+g3c5qamqqlCH45FI7CuTS6ZeRkO9nt2tc46a63Vy/3YzI4M7Tu+mHBHKGIClY+d4KgHI64VkQzuYPBIMduvJWx+939ppLNvz6Ve6vHqnKM4SAGZFLCLCYwizm56G2DY4y7wUdkwDPcyuVjEyFSTOygxGW3TBlRvejOkz0vFDkZRYBkx5n4FRgm7hqNRq37yfMwfEMhXY+1jEpmstPp1M7XLUtAGCdQs0QMLjV53GXphM/aWLgH4wNJOD+048DJmfz0PGHjYcAdlNCZ4XBYzXfnjKsvvr3N1xNhLQbKAMn5MForOvDGykoHSuk1UVyXVBAagmSRMFb+j4G32+20GsOceP+7aWzsD2nvfOOlDLYmMj09Xd3PhFBZk8KZIGDGywJhKG4UMLyihELdLRmdz+qSBz93pw4em4hpcso7ZqysMLKsDV6ey0aODHmmr83Nzayvr1fOivuhXMBbZFPyA05zWCfGiAMqySJk6tIDY7UusBbIwkgDAytTqJIzMMz3Wropge+XPATO0jpv+M4zgavk50Yi/EE21sHy2tc4IWxYyLGxserISaIkA8aIytfM4Q2dZ9qzOT/ACWA4eFEUq8zZhsNhOrfu5MC/uLbfNDKcTJYPHk3n4eQXnmGlYDyus5LTYGD01zpyAPuBPt4qRpQ0WQARZMIJWOS8zMaOwri84oXFeyc7xs44LTuzpaUjYo7OXRmnldpRmPWAdOH/ECk8F9iLojN2HBy6wj3dxOHyG+sCJGUu5LCGrUYw6JCjLOttCOw6sptO7Jy5l/NqnoX8+Cw2gSMhOnMP2kH3up5Y57SH6HQ6tWSZxWCALCSKhxdBoAiDBTNEBWI592LhUFpPnu9cuPv7mVjaf+9m/1fGcqf1S1UjNzU858jOqfg3i1S+YMfePhkdnclcGR/PMRQFDnF/fu6WMx8I7TZHap82lGR0/CZMONCOMQBJQQMmi4g8bvh3xKCWbC/PM7jnYDCoCDLn6nYKOEM+b3RgRXaEsjEko9KeSxVcyMs5JQbkHNJNGui4o175ljyTnC7JgC48fh8XA7R1fg36Q/ecw+92PZEQMpSipujJePOwDw02tCVHc4mhZESZlGtICIIcF2MBYs1ML+b8m3+852sWuO4vns/a5rHKUJxDlrkcZJHzYZ5XsonAfkNwRzU8vL20YR0ysBMDpSRffPEql7uFXN9zDsZ68PuHDx/W4CMQ0FvyHNkhzBiPnSTzcnN6q9WqpUBOa4ygTDBC3jAeHC73RtcwqDJfdnM9c7CD9Jqy7sjadUajHKcajtzmWHhFJTrE9zxGBy4/m3WbmJio6d1u177GSYSkEQC4gUCJdiYsTGZU0LMzOl7QJRJ7cNfv6FoBwvFM7o0hb998nLG3VvY1zGGS98dP5cGDbubnR32hS0tL2djYqObS7++86m9qaqpWeJ6cnKwgCZAViA/kxIHZS5vBpRuICOs6GEYNoWCyqCxwW3YscJLaxm5T/xAQeGw7SZcV7MGRMVCViIEhYzB2vq1Wq2JOkQ/EFmM182nkYxmtra1VDSyMzRAVR20yzMiOz3H5OThkIh2NANZhfu6gw7W6ulozJGqVXBg648eJ8Tt0hjFi2E/N1loQhj6wrsADokSZh1rR3PLm/MORBgPHAPmZmTp31LQ/+rNkZf9Nq9vtdt49eTJLDx5k6afwlwVIRnsku91uNjY2srq6mk6nUwnfHnl9fb1qhnDuTARL6tu5cCoQRO4UMrzFyxumml31hdPCCHAaGL/Xx9BrfHy8ymsN04G6yN6tZ/AHPAfngSPlPhgqzqpMRxxlyYXLORIpB4NBut0d5t0kk7uSiE7In3wbWfukwLLkg4G5NGf4WdZoS1Z6Y2OjOqvKyI7dNTj0pP6aCY8ZvfAe0t2uJxoni0bvJQvgxNdFeJ+fgzc2oeILr2hGkkkaljj3RXn7k71sP3w7rf23bubapUtZfe65jHVGeydpNaRGu729naWlpSwvL1eHMbPTwdCFWh0KQrHeJANyKTt2KPDbkTmyMNfhcFg1Ldgx8nsTVWVE4vtmQtmDaSfDGMmJk5HRdzqdLCws1BhyE0ZEMDOcjUajOhGCMbkXFfYSx2B4imyQtevYJlaQA3KzQXr8hpAYkBlYO0OnU9Y1dN8phtcB4+P+llMyenEuaYVLkeZf0JO9rn2Nkx5QexIuhI8XNdvJAiYjBtbKw8VkUBzT9QgY4/XLcba3t9PeXMsv/ev9G923x8fz4W/+ZuYWFqpoiOC90XU43HnvycTERFZWVmoREYaaMZCPrq2tZXV1ter5ZRHsHZm3z6ixcRC5iISNRqN6VT3eHyPHOaJYGKfJH/4N/CaqlihkamqqckKu89Kex9qRd1Fwt0GhAxipoyk1vGRHUSm7sObD4bDaC4pcGKOjm5EXsrSzc65oB+R6rL9jNpyUwbppVpi1JMVhHIa7TrNAFTDmW1tb1dZDE4yG4ujhXtcTXzuPMTjxt6CcY5YPLssCZS7KfTxwlMzMIiyrI8lPhm/l11eX9xt+Vv7S4Sw+eyb9br3hGu8FDDIkJ5JiuGVBeWFh5z2ea2trWV9fz9LSUh49epROp1Pt0vFCY0CGiciCOigKYRjH51yYx5joZQay+jvmA8xWkvcY/hq6TU9P12q/jN+5HPJHFnzf688eV+6BwQLj+G6/368dRwKyMMPtxhaTSc4jkRPjMXmXjPJWdM4Iws9Ar82YexO45VKiPWTr3N/Ig1zTYyih/W7XzwRr7VkcoomYXjx+ZijKojhxR5hMHmOAPHG5AaNiMo1WI8//yc0c2CdwDhqNfHDqf5KtjKXTSXUI8OrqaqampmoNzvauKEOpRBguwu50OpmZmami6fr6eu7fv5+xsbGK1UZOJlU2NjZqpJPLJm7ABknwfFKLkvDw+D0HjMb1VIwZhQYSs9b83LkcOajvWRbR+ePxJqOjPnBQ09PTFYx0uaJU1t3gphsc/EyjB0Nf/5sAwbOS0SHUzNl6iCPh/8wVZ0egMGS2PMyEt9vt2vtKS5va73riZmsmw4MpHRj7I2TKDngacjTyNzc0uB2OQbs/E7hnSDaqXfXTufGjfUsog5l27rz6Ui03TuqN4UmqaOZc2tDTHtU1RuCN2VwYx263Wy2Oi87ch9c0cG8WDLIERSRfNKQsjQSl4m9kCrmFcdiBouTONzFe1zshlYjqPM85dplvM0dIKl/IE4dmuTAG9MdMJgbpvJL78bfLSY7qTjUcZHxwG1vhMEocngOI0w9+hz7YuEEprsWaJEWHnSvvdT3ROJMRvEU5EBxeiYdjfAiLBQfacC+8iOt+hhemzJ1QV+PZ3M7Xv7s/E/TopVPpHprN+qP1Wu7G698ZF2xeWYdCiCgAUDGp51HMGcdE9ABCojAmD0wsmcwxyQFr2WzubFND5hhmMiIkvMDIyqUnN0iUEZ0LpfTZq/yeHlicBQqY1E8ftHyQG9GevJ1xmLRy/zbrBGx0M4HHjoxZA9bIMNsRnjE7AhslWV+ZS3niIOMDWqP3Tvm8nozDV9nGut/1M+WcTKLRaNSKp3h1exOTQQiLqIKhodAsHnWxZITpicTcn7Hs5FQ3s9W7u+e4h0luH3gh65vt9HrdSomJwmZOUSzDJPKWpN7g7KNZ8MiG83ye3R0+ra7Z3CnZzM7O1pyD5wvp5dx4ZWWlVmOGpDAJhFNhDhjJ1NRUHj16VJ1thMH6hUOsk9lX5GPewM7Ufc7OxeyUnSe6D7fX61V1c/SIPNkoycy8ERrOaLfSHbJ0zso9TIwZAeEkDIeB1ugd6QHG5/2h2AdR3uUynJSjvQkwHOde1xONE8Pz4ldGUMAgFtgQxXS0I6thI8ozPT1dPReYhxEQhcbGxvLce8s5dnv/16d9dulSBckMbbjwpE7KMWDyO8MSvlMyiMAz53dl2YTF4Vmzs7OVYtAgDzxORrtUMEAijMeKEjr/MbQCok1PT1flqY2NjdqrF5mjW8ncCME62slifC4HmCXFIDc3NyvW1k6KezAH72Utc2PP2akGn2FNrUvomF/yXJYuXPZznmj5YcwEFueUSWpjw0BdJ0ZmNkoM0nn3ftHziUdjlnklN+TBpvBN3gDdnL8AU0wSDIfDzM3NZXV1taozci8misJSm3zmj/94X8NcOXAgD44cqfIJM3NWEhbSOZkp8zIamWhAeZ3ou2Ok3+9nZWWlBp9RJJMwREW+j8Lx2cnJyaoxomQzXXP02vR6X+xTJjJ7q9LS0tIX0JEJEMZjiG84Z+PAye7GOgPLTZ4AtUuCBdmi4HweyM/PHPVMJmE0lKTQtSS1NcOBOK/kng4g6AcdT8vLy1WvMgYMdHdNlHGXrLMjpfPSL22cKLcfVn1RdbayWdqe3nASRbI3xivNzMxUMILPu9Oiglhraxm/e3dfMmjt1dmsTE5kc7VbjcHsowUPueWcB+Vynk0u5kic1EstoAiUa3p6ulJCFIEo4m4SjBNHhrKRH1t2ZjlZG7f3meFE0WBNMS7nVOUhY8jeMklSvW7QKY2NA6KHezFew30MmvQI1MGpAozV8NiOz86g5AUYA7Igehr1OA1Ap22AzJkjV/mONzZglDhLHE2rNTpi1Y6KtbRT4c9fKOeE2eRCEGUj9mAwekcFE7TBmvBw3Q/Is7y8XE0cUsXeEAENBoO0Hz7M/I0bew+6lVx5/vWsrK1XzzQji0DtJZ37Ufop4ZujHxHL3h4vmYxeP5B88bXoyIDnclnRHTF4ppvhQTSO7nzXZzoZSvEs5Mz6GlVgsL5nWWIxYqIcMTExUYPhLseUhsxniILAz/L+zuWJoswB3sMydW7qlIUxOhcvIacJKCK8c1lyRz/H5cEyUrJDx3Zih0MA+wvBWkdHBsji8TMmyGCBnyXjZ/oYYSOEubm5qkHbibSFxHV88G6a2Zt+Hh5O1n6lmc2rm1UNkWcRwXyYs5lioqi9nJ9voeKpDf1brVYFJycnJ6veVucw3Bcn5vv7//bw5O+rq6u1SLS1tVWdJk9EZIMvDg8FskLxM7qmOp1Otcl6a2vrC1vPUGbk5LXDMTNOdqYYBvM9EyNmz92J5rXmc87vyNH5PFGvLHMl9c4fzxt9RdY4MacbSWpnS/EdnuV1t+Oz8zN6RP8cOVnDva59jXN5ebmWU3mXiBXMcJGBkeegsAwMoXFfPOLMzEzVgE0dFMU2NT75h/fS2N57m836X57MB/dOVc+ycJzD2qvj1cj/EHT5GS8UiksEcF3WpSUMGqTAyX2MATkCTzFM5EQvsAkt52M+Wc8KxJpx3Mpu+Q3wstVqVTXRZJSnmZlGBp3OzikIdqAwuHbKlj3jJtoS0ZwLOp8nxzOfYZkwNr/l2p8ruQXmALnj4MDam7UtDchR2mlHWdopiSHGi6zNYdhp7XU9sc4JLCqVFWVwrsRiuMSA0HfzHl5gogPvYOR+9tzNbGW+8cneg20kg7OtbGxPVIppAy83OTN+N+tDuqA0jNFMnuEwi4DSuY8UpUNhUCLDGTOgkC0uV2AY6+vrlWLhaCYnJ6vOJxS11+tVr6j3nMuXQKEwKF+n08nU1FQ1Vozba5988fwg1yoNP31v1tH5naEuyMIlrJIHcMRGPh6LSyWsMXCd+cLeO+dkPcrITekpyRcckfkIPous+BxowpscyE/L3Tx7XT/Tie8MnujiQVjY0PclZmdgeDeM3AuOEEw9u8jbbrfTf7iUuT++tfeAO8nNb55PPpqpDAuIidJhiPzc5I6NiLkxV0fPkuSy8qJMJSFWKqTZWH7uqG5qn3twQDSsdbPZrGC6ZQ5ba4bUzoWxOfci/2c7FJeji+G2CRF0hZ/ZkVuGSf19oIzBDG2Z6+Ek7CBtwN40gA6xloyr3+/XjjL195EJBs6a8W/Gj8xMgGHMPgyOOXJ/Pm9CEadZkm7l9TMbJxHFuxZQMCfvzr+8QM7b/BkiFguF8aOUKPtgMMjq2P1sD/c+53OYZHH5YFqtyarxwAIm13Tua2jpXMIwhd+7F5P8mJ+j5K7joXjO45znoIRWAH5XMrMYn3NUdwwZOmIkJbNq52L4B2NuAhCHUJJ6/NzRg1weMo/uKOZl4gudsWNDrv4cMmD9zN7CmAJtHVVZO6cAOGPmgEGvr6/X4CYO3esEmrLOmCjzpvt+v1+9y9TO2acrYj+M046wvJ4Ia53MU8hGKXwObQlXHFkQvH+Gh/eC2YjtLfnd89//SWaW9j4Qqf9sM+/cfykTkxO1iMF9USigbAlvbRg4DxQUxtS7HDCsMjqilM47uC//RrlQXhNNzkMd1ZMv9pOiyCZ/gE/8m/my3c1Q3zk96YnPGTLp4hwJY+Re7MjZzRjKVkfkjRGbjXc6xL1Rbi4grJFWSUCZZ8CRonPOTfkbUgxHQCBi/ZgvLX04Su5fsvvcB9uwU0HXTELtdj1xV4oJhsFgUOUzMzMzNe/HQtiYTRCVTJUxN8ZDzjYzM/MFONZoNJLu5r6bq4fPNLM13UoGqaIH3tjHXOIMaINjUXmeFRPD9Dy90CYALA/gpx2Ac25yam/U9c+ck0IKYXRGNFZi508YJLkoOT0KaeILeSSpvL5fumsjMTRGdqQzLinZqEyilLkmObYbOrx2zlud86GfRlgl4nEpw80fwFanZt1ut4qG3oljMo+xOHd2tAfiWmcxcGyHZgZk99RsbaX0w9HbmmyECAilbDab1XsdobzN9hrmAvksNOCNcxwWpNVp5fsXvp9f22ectx6dzurq4bTbOxtdiTwIzQqMciAo5snvHWl3UywMJEnV1eRFctSxHO2kkhGln4wgGHLlZ26YKMeD7MvIzR+3wQ2Hw+qoFTsYRyyUEqUy9LajxVG4/ESE4PJ9fYKE0yIfBG252nBBGTyTcZUw1qcpAr17vV7V4FCiKObrn5UIxcej8HMfTLa2tlZbc+99hpxzauTfJ6NdUbtdT2zfQ6lYBArA3vZkA0tSIws8WSt/ydQ6irp2VwlmMMzX39z7VPdhksXfns/mZjIYbFX4HsiRjKA1C48iGV6YYUUGzv+4F907jijIyopug+KygaNww+GwxuI5MpeQsszjTVC5ZkhOY7k7+jN/FK40DH6HgRqqGlInqUXVklCxbJg3ZA/foQECWTgylaURZGu+w+vtiIdB2IGhz8iurGWXaRc64v2vloOdO0iH9XFq1GyO3sZGffepIyftc/aIVhSEaGInSQXnSgPFKE0u2IugXCUd3263k8Egl9/bp2lvNvlo7Y20ZkZKOjMzU2M8GSsG4Ehmr+n2MOd9jniGOcyF/5d5kxUzGVHzZR3OuxhYeJ4DcYCielyQESw67WeOIjg+oLNPZUhSewMYCgU8B2qi6I7Ehm5mutvtdtU0YWfIPIG6NmI6h1gnjKqcJ06NNWEe/nnJwnttuL+JuzJFQM50P3nNBoNB7SwkHCLHkni8jMc/S1LV9MsuPF/7GieFZbxXGbLX19drNUn3ZjIIDMBe394b4fM3uaAj2HA4TLu5d7E2SYanm2m9PpPWtZ1F9Z7JEv5xlSUF55n+juETl+EpioVyQm4YauJsyH2JLiZlXM/j3owXWRChHEkZl5ULxUEWODzyY0Mt5uj9oY7sNA9gpHa2NlQ7WtbaDRfIgvwZZ2AC0c4tSU0GQHJ6ch0YcERuG3UKxfjQK35eNjo4vwVN0FYKmUQTRmn8pGrMyWkdjggHhz7MzMzsqdNPPFSaaMfk+Tlh3vsCfX6LWUlHG/4GziEUoq5fHWiH0Ol00mzsHTmXlo5mefnFTExsVkedUFtCqCV0Y3EdWUtHgrd2bo1yljU77oVh2BuzaL6Pa15mPktoxXcsu7LpnctcAMoIqnFbm+uDeH+3wDnXdd7GGEyAGZo5z3Z0woghqGBAk1GN1GiGNQAiTk1NVe8A3W1zPBGI/NTwGPk5Z8ZJlsiI+znXtgMr5W4mmHvxGTsq1sXOwc/e7XrirhQu2sTM9vlIB3t+50AMBPYPIXgRuZ+Fws8reNnau1ibJMNznbTHJjLeS3UMxsTERLVHFMMw+YCwWaSknkvhSRkLc7EXTEaRsmTq+B7zxAPbm/NvL7wdF79zKmCn5zKWkYGZVp4P/OKeSSoHhhHa8XgNjCKSensmaYRzPOYDgqHnGmdhOIuxOsIxF5wAa1iSZkRT8xMmz9DjsoOJV8Eb1XB/yxq54RB7vV5taxxr7sBl/gV9QgasH7ZhAy6vJ7523gNw8g0MMhR0rRBhtts7r5bDOH1cIMSSo4nJJxau1Wql3+tlMNy7JvTps19PWjvvu1xaWqopCfsJDeNM8JjE8OKblEBxmJejjA2QBcJxmRiwMzJkpDGiJGpMNPC3owCw1U7HYy+ZQa8Tz/Jn3R/rfHo4rL+ewZHGRuWxc79ktHmcy6ymjwC18SMjRxmeDaJivhg7CMTphnM9H5DGmpsMcqQk18SomOf29naFIspcmTGbVzFKarVa1TE0ltNe1xNfx8DADJcQkMN9MuoxNBtrbM9AzSqiBCZr7KX58/n8/TyYfpBDe4y115tKv78jhAMHDuTBgwfp9/vVUY02iL2ID8bKs820ooDONdi1MDU1VWMyUTSipHMZ36/T2XlDNjkIUZwygD9bGpMJHj7jbVulUyDX41gY1pfLZx85Elh5k9TWEgdkyOgmDe9uaTQaFQvvMdIc4WjJhUGUrX0YIWiLNUV+JvYg0xgHsjCs5N4mrobDUceU0RRn+5Im9Hq96hBucwXMxzl1szliayGV9rue2L6HMHiwi8corOGvDZIFdp2Ty8JBEGYHXerY2tpKe+lxxrqruw90PMnZ+uFjQJDl5eXq3FuQgAkf50cYEgvMvzc2NvL48eNqIVkwGza9rnhSlKssGdlzG0r5aBM+704UHAbenoU2BAauO7+1zB1pbAyMl7dsG3ozV9YbBYdJJX+lrFS26pktt5y5t3XHqRFrz1xNpvnnLm2ZRPJao29lkwMGu1tqYIdnHXdpCqfgfuRGo1Grq+II+TfOlLXa73pinRMoQL5gAcDSesERpPE+QsdgDcNccjDxgodhos9dGebcT3Yf5/bkWB6/dCr97dEmZ473oAhM83syeo27654sFlGC1jKint/6bNLK5AkLgAGxVQsDQC4gB8sCBeJvG4bzRmSd1F8RYHLHuRuK0O12a6UOxsXWLO/8N1R3exosLr3CjAn9wBgNqbkPzypZbIw3GeW5jmKlIZMGME87KfQHY3Pk9TrxHa6ypIWROu81ojOktQNA/zFmdIK0hWf7FRnM/UsbpxcbYdkASwaLyFqyj+vr6zWK2rkRwilPV/AilNG5vHq9sTx8eDKDmUE1FnZwtNs7zdHz8/NptVqVwdkjG3p625hzLggxSkcol/eIwgKykMzBiojC7rZh2IuMDMzGeh3wyDiU0vH5nJuKVGvWtzyxXigIxrO2tla9R8Zb4lB8K7HJjd2K9DCadsLlH8bmTdolskHPTLZx8QzneSVqYC122zmCjhPZLGd0gVzTkQ/5m3DyM5NUe6AZF2vE8x8/frynXj+xlILilEQPXhKhm6Vygdm1PzyUBcyiulTDojtP+Hx8n61i55NM1Fve2u129e4T7slWK8aDk+h2u1W3xurqas2b8U4P3hdSRjWXkjB4nunGd0fH4XB0fAZjc6O1Iyif9XiRiyGZWVqnFZaJ0YvXFWPF4FyiMClXMrf8zeXcGEdHXlmWpMz2mnC0LpnMYW5GCyAA57HOJz0uI7iJiYnqeZSQWK8SPZg0csDBKVENsIPFCH0GFs6G9cKZlEHJ1xMjJ+16TNqtSQiSBbUBO2RbGbwAKAsRxwvBpFDgN/7ogz3H+ei5U+lPTWTYG1aRkcXqdDpZWlrK4uJi5ubmKk89HO60CQ6Hw+rIyCTVhuNkJ5K4iI6HtJGgcMwThXPu5oiIwdmZkbvYmJ2/EtUdSWFAdyPldivzmMxC0fiu4S5rYAeAEjuvNdrh/y6vYdSsJycIIrder1ex6GbsmbfZcH6OfvkVFsgP9MAYgfkEAcYBOYThG3Ia5Tg4AMmZOw7NbL0JOusDFQlewYGBmjjb63oiW2vIxmWyx7Q6SueDdb0H0pM1oVKeb8vC2jNPLw/2PHHv8eNT6fXGarDKnTBJsri4WO0KcG6DgMfHxyvo6gsFLcdkhWHMFjhygLHkc95e5DINuRxMoGGhlaXM340ynGvhBFyicFNHSfKw1svLy5VsuIf3aeI4kDFz51lEPAgy1g+CzruVvDm7hLA2YGTP7y0fZIRsubw+ZX7H30ZvNmIHjTL94W/GQ75O8wPf4SpRpfNt/3y362dia/EYLIIhCMK2Qpjw4fOOvo4IDA4FsgAqIqSZbDf2zjmbEyPhAwEZ06FDh6ojJ8jD3MGBkjgich97RsaF1yxJDUdkZGcK3pGMCIMy2XjZUIDcLItS6ZrNUadKCff4fdkt41IBBu1am+uEhqhAO5Rxbm6uKst4O55JPH+vjBBlQ4uV34ZhVrRMh5JRnRCoWpJAJYQmn+b33Jfv2QGxnjiZ0kkiH8Zpx+5IzXdBErvlzbtdP9OWMZMGGF/ZtsQDSapZLBQCRfNEmICNmvs5sg43H6a19NGuYxu0Grn5y6drW4msDGNjYzl79myVHxqqA9mSUbtbMoqWLnTzGedqXggTO3ZWg8GgluM4JaC26TE7QphF5HtJ/VQ5K7XLADybnzn3d1cQzgJZlE0nzltN1qC0kDSMBYjKubQ4GTs3lBTG2kaM/jjHBco2m80vdJrxqj5aAc3kktezxqyV32GDEwD1OOoZFpeBBYdH6uTardMVozme5f7q/a4nNr4bGgETyXn4N4vnjcAYqHMUe89eb3RgtaOtI071rHu9nPnJ7pFzmGYWN89UUAmB47Fc9jCjyKI7Z3a0dn5kFpBxWS5uAcT4YYZhjO2gkIshMt1WNkZHRDsJE0XIy/Jm4RkDzsERHMdDpBgfH69enMRnHY3sVFxGcl2Svyknkc85SnIPy8uODRiJk2J+7oRqNBo1QoU8EnnsVvoAUlu+OCyMBYdi8scOkjHjCHDgRGoTSRgwzgfd5OhQ7Gu/64lsrXMlfsbC83vnoIZ8yWg7jg0EgQwGg2pztvNLFoB7bzW2srNjc5drOCqIQ/2zSPZkRCoXlu3N/X+usuDMohp6m+QyUmCh6AAy5LETsOE70iFDH2tR5k48C5kh72Rk/CVhZ8Xf2trK1NRUBY/LFMMsJw7ZNVGXDgzDuV/Z5ZSMTiRwhDdE5PN+wRVOAGMpERun5jvFcO4NzEa+oBWcj9MYDNsEj+fo9cPwsAvknozyYpcEmTObMoDre9rfnr/JyLvgBV205/cIiYGUEBhhOcLyWSukSYLyunHkx+k19+hBbDRqhpCM8D4Qir/tofmc9066jIDyMd6ynGCl4nvOUfkeXt5Oivv7e7sZ2m7e32sDscb3zPbipYfDYbW/0w6PqEMND0Z2dXW1Wh+jDN732e12ay1+JtRcAzQLa8deRt6G1g9jAoG5U8moB2NNUjWImCCzgzWXwdoNBoPqAG2+xztkXBbDuThoAJeZB2kAOgQ5NBwOKyfg11/igEzI7XU9sfGdQVnhESy7DkxCOBpYMCiUO2ZMXPBvGwCQ4rd+ZzsTG7uPEaXkHq7RGT6ZnLHCMM8SCTi/4DMoLUphz+mDn+y1O51ObQub63Le94lS+ER6EIEjOGtgB2CZ2QCSUc8s42Ut3chgKOadK8A2DMgpCASbUwN2a6AXzBOW2OuELBmzmwKQI/J13sc9yxqoAwW6Nz4+Xsk8Sc1R82yjBEdmE5oYU3murufuphJ0nPfcMDYcIXPz+He7nrhljIdDFbv2xERsgEn9dQtMDMEYYiSpQdDBYPRaOnvHRm/vMgoL4bzLHs95hZ2Hozr3cDRLRm1+jNUHBVsRfA/DNhu8n2/0gdLasJybuLaGETrd4N9EOqMTRwpDeLe78czt7e3q8DZHBuRAp5HRDpHZaQhjxVBdVishO7Ir04KSdLKTsGEawrsk5M4s9za7S61ME8yN0OdNtEaeTodw1hzqzfNxNNwPR8DfjA/YTulqt2tf46ShF0v3QiajaFEyjigAZABMm/NNR1iUEONiklDe+3VRdKeT/nS75hUxVPJLjwslKU83Z4FQJLch4lQc+aw8kCCekxWBlzVRjEYx7cF5LtGa6GMHaKfDZ1HSMg+2ozJx1Gw2vxCJMTYbEfdCDjRVGJ34oCtk7tTAhoRs7fhwcjZCy5D5ufTjsRKtTEQiKwcIcwBOFexgy/GXTpE5eVcSek5e7PsbXXLKI45vbW2t2tViCF5eT2xCQNGgrIkmTnKBK3hBitCunzkfM+tWdtEYNprA2eu6c+F0uqePZ3qw00zsLhgziCy2F8b5iYkLE0A+SoTPG+LzM7yuIzA5OPmHHQjzZRsXCsicTZJhlKAAGyP3cteLoZJrkKxpOSfqwO7uQVntdIgayAtZcfG51dXVikMgD8NAjMRQbjeLGPbhQMo3cfMsl2K4h1s+7fx8Ch7OCwiPjEtDNruODJ0CsQa+QAu+l6E9Gyocffe69jVOFgID8uZZlKfT6eTatWtZWVnJhQsXanvgDNNgqXq9Xu7fv5/vfe976ff7+ZVf+ZUcPny4RtpgACX03O2a6c1mpjmbQXaUxAXwpH4ciet2PI+xGgbzWSAYC+q8FS9pRXcUZPwoSDkPd+sk9c3FdmI4Q0O3ErIyB0eP3fbWOlIZ3nW73YpV5vvD4c4G68FgUJWGnIt7F4y/w5rNzMyk1WpVSMEoBDmg6Pwfx8H6WaalEzCfgRNxtPLaeL0Zi9eJOaDfpBqdTqd20gJpDQwrqJLvUz+HPUYuW1s7h7G7TdC5717XEyMnN7cSManNzc0sLi7mj/7oj/KTn/wkBw4cyMWLF3P8+PHMzc1VSkaReGlpKVevXs2tW7fS7/dz5syZXLlypRIE9/ZeweFwmI31vU95dxJvQ2L8KAyCMfQzy+Z80dGBfxveYAhEQ+7hMRjqtlqt6j0kHJ9iKGdWGAPYLWrZ2XW73VqjvJ2hXyfPnMycmiFkfYgelh8RyM4OxwnJhDzNeiejlwYxR+eZyajHFflR1mHdgaxEcOqYGLmRieXkiGfDMcphg3cZoc2Wk/PjZHFURlWkbSbQyrZFUJCjKfr+F2JrHX3wvExgeXk5jx49yo0bN6pI8tFHH+Xq1au1JJjFA0JAKx84cCDb29u5evVq5VUQgOum09ON/NbRH+45xn/yt/o58FPY7TyB57jLh4V3FEDYXkQzhCgOFwtsj0/fqWEOSs2CohRJql0whoooE7mLobHzfeA+bxhzKQUIiAwxTO4J4eNanB2Tt4i5zxkDpq2QqAk8NEtJTus+asN47mndAspi0EkqZEae1263aw0lyYigc6qR1F9ebAfm57pc5oYTHIGJN2TsVMsOxyQfezXZVIHTQB/n5uYquF8e31JeT9xs7VwLo1tdXc2tW7eyuLiYxcXFrKysZHt7uxoYymH4x5arfn/08hai19zcXObn52vfRXAT41s5dOP39hzj+63lbP+bf5PTp0/n6NGjmZ+frx26ZHIDxWKxWBC8G57Nr09PRiSDcyDn28kIZVBrLaE50cCwDoiFsmDAzntBESZ0Zmdnq5xpONypYxIZDU8N13bz0u12O91utzrKxeUA8qPJycmas8ToDf+ZS+kU3MaZjFokyzTBhX8+jwxs+HzeJ+fxbAykLL04upvoo/mCZ9pJY6TAV0N4AhTra4cPmgSJmFdwGkUOzXj2up7Y+F4KNElmZmZy7ty5HDx4sML3q6urFZRjQq77QfM7WrneOT09nYWFhcpIK8JhaSnt/3LvYT73vefzJxOPcvPmzUxNTeXEiRN55plncvr06aqFD+ETQVAI/k+kdV8ohAUkjeEMczDL6yiKIpk04Ll02TjvcZ+mz/JpNBrVGam0qeEAkGtSJ9uAZC638DmUh8gAOYE8xsbGqu/yM5dmzPqy3csMuyOnDYsDqzEyCDJHepyHyULkg7NwuYTLv+O5oIySQcVQDVVBVURwxoABLi8vV+to0sgEJgbNupX95W6g59Ud09PTf7H2vXJxzAqurKzkwYMHuX37du7evVuFcS/C9vZ2VldXa7BtamqqNqmtra3cuXMnDx48yJkzZ3L58uUaudFutdLYO/Ln8nMvZ/LFF3P79u3cvn07169fz7Vr13Ly5Mm8+uqrOXHiRC0HQLA4EhbHxuR+WoRaNtaj3EAdE1/+jJsA/BzDZqITC0vpiUja7XarPHNmZqb2El0fzIyRu3ncrCxoxixys7mzORyHBLNJbucGgiQVqjCU6/VGpyg6n2I8yJM52cmZTANa+wgX5sk9afskn/YWQBwaa0UkxZnwPGCqiUqgJjAdYsrNGiAQ5OQ9u+5yQk8gkqheJKkQpN8O/lTGae+8sbGRxcXF3LlzJ59//nmWlpaqkwZceMazcQiwPRatX5yUNjs7m2PHjuXChQvVa+fX19czMzMzgpDNzfQbe3Tvt5Px+fHMzs7mhRdeyPPPP59Hjx7lypUr+fjjj3Pz5s1cunQpr732WmZmZr5AxxuWucBsZ+KmAuAcikXU9BEebl8saXIbqA2Zz9tjE+VarVZVT15fX69OsKOh3crOPSCTiHQosAmh8tXxKBy5P0ra6/UqCIhy42DL4j/PcT6djDrMMGQjCqce3kHCuJ2XE4UZN6wo0c6oCKdlviPZcS4lnMZ5ou8mGRk/bYLN5qjxALLUMJo5tdvtzM7O1th81sSk0FOXUoBe169fz5UrV3Lnzp20Wq0cOXIkFy5cyPHjx/Pw4cN88MEHWV1dzeHDh/Pqq6/m1KlTmZ+f/8LugG63mwcPHuTevXu5ceNGtra2KniMITx69Kga+HA4zHvH3stfmVzK3C7j653tZPiLs2ksjvpPDx8+nOPHj+fixYv5/ve/nytXrqTZbOZrX/tarc7GAjmCmFjBc9vz2zMaPmGkZW5mWOXc158xaWACAqV00TsZsaA4Aj7HvXbrS/Va9vv9qq+UYzT4t6MVio9Rcy9+5shZ1roxKqc2OOnSSDFI56jI13CdHBfnakfmPZOsEZEwGW3GMNKxc3Dut7GxUaUxREx+NzMzU2OMXRLBufN2u+np6UouJkYZM3N46sjZ7Xbz53/+5/n4448zHA5z/vz5vPTSSzl79mwFxU6cOJFGo5HTp0/n1VdfzcGDByvv69wF73nkyJFcunQpq6s7x1weOHCg1ii8tLSUbrdbCfzcxzdy5PM9xvfXDudh92KazdHLbJKdiHXmzJlMTEzkD//wD/PRRx/l0KFDeemllyr4XbKHLCK5EtAGAUISIFQUCKXygrJgKPVueSvPNQzCuGj14zMwe2Ue1mjUd1aYaPGZR2ZjWY/Jycl0Op3qfaicTojhUNs0erDxcC9yVOaGwWEERBbgOnLAMIjgZoqnp6crqOzN52awkYFzRmQEOvArJNykYEdlfoB83Kc2eEM8B7wNh8PqADSTRIwJ1EgLI/PHkbHe+5FByROM8/d///dz7dq1HD9+PN/4xjdy8eLFTE9P1+Du1NRUfuEXfqFG20NH22MY5mCIJOVMDJi8traWsbGxfJabufDpdzK+R97cOziZNOsbixFkkhw6dCjf+ta38q/+1b/Khx9+mPPnz2dmZqZaHMaDpzVxRV3NjQUoQFI/PNkQlyiA0bHgPIO2PJTDNV13ADkdqPLv9ug1Atvb2+l2u7uSdoagNgSfh+RTAil9MA+UlR5TnKuhJp+DiLGDMtuMrsDk73ZW0l5jRpcwItbWNUKQhhtYcCpwCuVpBC4/YVzeoQLy4PPk2TgQQ3n02KjKpBbyspFa9546cn7wwQe5fPlyfv3Xfz2HDh2qWTs3NcXNZMvknyTcuQPGRI6GQGj7665186Pz387f+97S7oObTq6d/Ho2NvoZDEZOAdiBUJ955pmcPXs2169fz/3792vNEV5YPBr38G55nAmRwErgaG2v6HmyuGaAvZsH5SPyojit1ujlsiUEcx5jyIgciXgoO8bBOPmb/L/ValUliunp6epAKta3zO2Yl/M3E19EKjsE/s/64PR8CqFzfDtzum9wCGaFCQRu0Lcs3BLYbDYrWO+cm7FZbxuNnbeAGYVwzpMhqclBE4k+DmcwGNRSEjdKPJVxzs3N5Wtf+1oVot2uhPXjKY2/XaNyNLMxIBTugQdiX9291t1c+OD7eWmPQ/f6F1v5/OCB9Nb6lZIACcl7eNYzzzyT999/P/fv38+zzz5beTyUHoYZJSfXQIGJihgL+QVzd07l75itNDnhYrdJERMThmQogHNSG3oJw3u9XlZWVmolDUdATil/9OhRpehEZbqZkCcOh3kD7/gM815eXq4hDFCRO79wGmyGsIyQMTAZp8gWOgweVhmD5fNuuXPrH44DMobIRXpDlPXmC7aaJakgd7LzBnPrP6dcDAaDquQyNzeXlZWVzM3NVd/HefhUeJzVU0fOEydOZH5+vlpYooFhBkaKEjp/c0LP5PmsvUeZi3U6nWwMf5T/w/95afetYs1k/T+ey42ls+l2H2d6erq2b6/MASYnJ7O9vV0xyyy+oY77T1kcYAhjc/8mSuvivks0yANvT3QyaUSO6b2PnIMK/DP8YmFZi+SLGwRoSPDZPjwLRdrY2MjKykpNHkSOI0eOZHZ2tvqZywes73A4rJh3xu6Iidzcl+o0AYIGx9ZsNrO0tFSx9OS3/r0NkIiUjNAF7aGtVqt6dw0GnqTqPjP5R24/OTmZmZmZWv7vlAP5s0aw5qAD8lL0ECdiO3FaZxj+1B1CZYHWTKKhIfQ5EZBFNOSiHGBPBZQzYdRut7Pd3Mrzb76TA4/2GFgjuXPi1QwfHEi7vV29xQzDcq6bfLHlDkFj0GZgWRzXd83u4uUxVLOqdjCGZcDFUp5mJIn0KLmdBRDSxmFixU6Rv3m+ia/t7Z39g0tLS1VuTxpBxH38+HGmpqYyNTWV9fX1anzMm/Nn+T+RjW1ojgqzs7NVlCfymyj0OUCG7W5mQZntbI0EzKJPT09X60SUNMlGHglygdDhBbasP10+wPvSuVImGQ53mm8mJiaqPyBA8np0hPubZWb99rr2Nc7Hjx+n2+1mbm6u8p4sPEJDWKbWUSyiB4YIOWCjxctjBJ1OJ+1eL7/2Bw/33mA9TKY/fpCp6ZUsDbay3Uk6Y520pn66obsxUSljr9fL4uJiWq1WBTU6nU766WfYXc5Y/6e9ntvbaTSbGQ4GSaORRpLlqWG2O8OM98bTaYw2RKNAjJ+fe0M1OQfzZkFwDkBfIgTe3I0CdgJEKH6GYgHjeQYytkJgsN1uN0tLS1XOBaQ3HF9dXc3q6mqmpqZqdUSUnbN/UVQTMRgJMmGtMUDPySUi2OGVlZWsr69nbm6uduoAuR46Yp1ijZEbTsKOjJ/TBTYxMVGdUQwR5Lx1amoqBw4cqJ6dpBZQmB9phJl09GJxcbGWczJH5PQXhrXLy8u5efNmXn755epnjp54MBTCMCT5YsO4IyhF4t3Y3PQGOfhg721iGSQn/ov38/fG/ov8/q8lt06NCsBHFo/n3MNzO0ozNczw3x3m+Nlb+cbpxzn/x3+c+R/9KMMkqxMrOfXDd3LwUZLhaPeJJpo//I3k9snk6P2zOdU9lXdeTb77i40cWj6UhccXstqeyHxjvlIw8jETQIPB6K3LzNWvfsOwiaru16TMYJkCt1FWooPTDrPGjUajgnT379/P6upqFQl6vV663W5lYDgM8tXZ2dkqulPWcLcQUBjHxxrAT3Cejh0Ahka+hhFBwBlRMC9kQxMFMgR5IS+zyskoFWB9aPw/ePBg+v1+VbKzo8SJmr13lHULo8s1BBuf7FHW1dfW1qp6cllZ2O3a1zjHx8fz4x//OEePHs3Ro0drLGzpNXkgEMlQiX87H3PrHMZdwccn1H+SpLE+zNT6av7Wvyh/s5xEZ9z+i+TVRpJekv5nT7yvr9/+p/zrcZIf5YXfTf52O2kMG7lxtpXlhWOZXT+Qd197Lf1vnMjnndNptkfn8xhKG7Z5rs7jvU3MpQRkZCNCdsBhYJRzWvJP4KgPtcL47EiSVGtEzy25OfA22UEkMJazs7NVzoZz9l5SdIL7wMhTBiLq2bmVZQ9kx75I58BGBmVKwGn7nOgBOnGHDs9mDUzo0SY5NTVVg94cn+mNHu68wnEQjTkNwUigrHrsdu1rnM8//3w+/vjjfPe73823vvWtHD16tArdREwXdpPRThMUzAk90XNycrLWu4gScK9+O/nkuU6+/r0vYUl7Xfv3Fn+pq93f+ZMM89zHvSS3MsytPPfuu+n9QSdLjcO5d/ZsPj9zJp+99FJ6hxayMTWR1qBVKajJGxQCZcRogU8utSBTOzpfKEilFI3tDB8u5+DURCYPPsz379Vrayj/gQMHqhwLmMb4qHOiQD6f18TM9vZ27t+/X62xd9+YLbZjIWcDJpplZZ4+CRBE0W63q8OqkQn3x1hci5ycnKxyR76PA3BdmXkhS96fw3N9vtL6+nrVCYQzMNFoZ2QbSFI1JvA8cwJf0Lf9lPGFF17I5uZmPv3003z/+9/PG2+8UUXQZPTeD876IVTz4LIUYLbLUcUU/GAwyKDRyuLZb2X45/8yjRJu/pxd+L3O3e0czp0cvnMnwzffzObUVJbmW7ny+oVMDA/md367k+dvv56N45O5f2zHy89szWRqZapyeCwWkddHuLi1MPlpN9PEICtzKzlwcysHFodZeubH+Rv/3XI6q72cOLCUM+88TKPXyKNvDfLt515O5yc77wXv9XpVe5nZcqKMc2v6aIGMJfxGyemJNquJkaH8RGCU2Pd0kzgQmtIKRuIoZUNC/0BzRHXySgzZLL0NA4O3Y8GQme/m5mY2NjYqI7VzZfyUVBYWFpLssLfefO1obtS01/XEQ6Vfe+21jI+P5/r16/nBD36QV155JcePH68Vb9fX12uTd6HY5RMnwCXR4Zxp0B/k+q/8YpZ6f5b5f7oPMfRzejWSTKytZWItOXZnJ8d9/Q+S1uAPcvOZ5MoLO5/7yYWFXHnlaJKfbjqeejatmdNptVp5/sf9nLw3IlmQzdbmZp5/++1Mrq5mbWItnx35LM9/NMwzN5JBa5DOdr4gr7E3kzO/cCM/+XgmjYyatJMR4cFa2ICc2/KdMhfGsKempipyyjkwUdn6QpkF40R3SkVut9uZnp6ujZWo43ZKnBdtfkBQIr2didOp0gk5wgHrCUAYNc9JUmP7l5aWqs+6sYOxY8CM/0lMbfIE46Tf8pVXXsn4+Hg++eSTvP3227l48WLOnz9fK/TjHckzvZ+NhSFHQNAQCv4Z9Pjy2jD/8mv/Qf7Gg3+che/fT3P95zuC7nc1koxtJ0k/536S6g3dwzxM8rD63J0T7+Xxws6/T95O5vdojvKSvuxf7LF5Z2Iz+aUbK3n7Vx5l4U8OVEpoKEuqAapBeRqNRsXWu01wamqqqumNj49XMM9KZ2bWDgYo6Lyb1AidwHCHw2H10lozp0RQDJDcjgiLXiajw569od5bxIiWLhf2+zsbBMghCTTuqfWxn6R61msIJsbBGEqkuNf1xAO+IA+ee+65zM3N5dNPP80nn3ySx48f54UXXqgo536/X71awflS9SAVo70gGLULvijFg9mz+Sf/s/99Xvgrb+Xlu3+exj/vZ725lJO3umnIVv//LbJyleM+eWfnz/83rl/78Vb+j/+XD9O5/WoO3jhYQVIMk5MQHj58WDnRZrOZmZmZHDhwoGZYJqrofHE7Ig4W47My4rxdF92tnMbvDPlheLnKLq9kVE3A2DA0IhddUIzDDCsEDjCd0lhZ6zWySEbnNGO0vGzZtWZqntzf5OBe177G6YJuq9XKqVOnMjs7mxs3buTu3bt56623cvHixZw9e7aqGQEnPJky6YUIKne0mwxAWOlM593Dv5H3Dv9aBs8PstK6n2c/XMxYs53NqY2cvPKDnPpsO58c/ST94XZ+8U+HmV6rvyGq9uzRA6ufPQlejD6YNBu9VB/fzp7R6ufteulK8gtvdtKdmq6VPYg07XY7CwsLmZubq22LojaXjNbIOeT6+nqtTtpoNLK6ulrrqXW3WFLfuE/EgyX2afeUbZrNZvV6dkcnoqBzWJc4MHTgclI/3QODx1hNdiITNre7nAO8xbjpmcVJuYyFMTu9wyDtuHa7nrifEyMD4s7Pz+fSpUtZWFjItWvX8sknn2RpaSnPPfdcDh4ceWQzgyXGN7Zn8bwrwGyv2a92u52JHM2NiztHaU5MTGTpuV/Ih+1G7k7czbvvv5P/Z/9uzh85lbNnz1aK5VKN55SM2t+IBhSizajx+VZrK6dP/1maP31vy9ifreT4o7cz/vsraT4eJnt1NP1bvjaPtfPw78zkN669nA9WL2bs8Kgx3jm/31sJZCNHxEhcPoDFZK1QcmAgTt3lFKAn605fs6HfyspKrexGZKS9z4eU4+T5uaGxA4NbKdGr4XBYdRVR92VeloWdi/NYjKvcs0kpypupjST5HI5kr+uJB3yRT5rubzabOXv2bA4ePJhPP/00d+7cyb179/LMM8/kwoULVfG69B4usJMzMEkG7VY0/m+WEOEbFq0urebTb3+ax7eXcurMs1l49dUM5+ayNhydEo5xEtW5vxuiodmh3V1P5POfr/29qkc0L/fTbv29nPib72Sqey/nf/J2GklmvvMgYzfXkttJ7v//FnaDBwaHmrnXPpgfXriQ93/heG72D6b7XiuNRr92QBbOyftIybcmJycrGbAmkH8ub0D8rK2tZWZmpjIMRxccr/t/0Q2jLtIjnHWZ/7IXlYjrxnleaEQnEGPAAdCq6L5ljG9tbS1ra2tJRsfzYEDoPLpCDRQiDP3mD85te3v7C2/mLue03/XEozGBGiyiPcHc3FwuXbqUY8eO5erVq7l+/XoePnyYM2fO5OTJk7VJ2pNCGDnvSUaH+RraJvUTA9y50Ww28+DBg7z//vtZWVnJmTNnqiNJyiIvz8P4zaK56AwpkqQqHtvRlF0y4+Pjuf/5y2m3X8uPDv+NDAaDHPr3P8/UZDedW2s533kzza1Bmv+4kYnhag7Nfpr8cZKtpDXczj4v7P7i1UgynsraewutDL8J5Gzk6tUTeXBpKn++9WyGJ8fyWedYNreH2VyGhR3tZGGHRavVqkE7ICo7kMq9uY6Q5KQodZIqktrhIv/hsN4wzysuQEHuyWZsECuukaIvdDehq5RcDHExQFAC9zF0pi2R+foMIyOuXm90ZAv6ip6CBsozsugKorvJ+a+h/m7XE2EtMGZHAepvbk52vO+JEycyNTWV+/fv5/bt27l69Wru3r1bHVcJ5jcbhmHivZNUmJ2Fd27B5Mzs3rx5M1evXk2z2czFixfz4osvZmJiIqurqxVxQFRA+Mb4vV6v2oGB8jEeogHN3xioWelWq5Vut5vBYPDT3fs7i3ln62AajUNZX1/PB+PPp91uZ/x/NZ52eysTnUdp/9VOGv1Bzq78IJ0fdNPb3s5gOEwfIk3ztyPJbDL4O4MMmztR7Ort81mcmvtpa9p67p7vZH39p+NLK9trW1leXq7VE+0kIeXcVtlsNqsN9awxzrksXdjw5uZ2xkFDvRlZlB5CxpsUiDzlTo6ZmZkKXQEzca6ukboTB90CmWEA6KybLJaXl7O8vJxWa2cn0PT0dFWnpdWv2+3W9sEyZ56DbtH0jmzdAYRc7aAwyv1a955onHiDpM6Cscg8fDDY2TXBeUA3b97Mw4cPqz2UZ86cyeHDhyvBeJe5yQG8imEMRox3SnZ6fm/cuJFbt25lamoqL7/8ck6ePFnLZQxHEALtXVYcv83LEYFtZsAvlNK1MpTQW5S4J0ZM5O12u0mS1dZCGpM7srw3+xvpHa2f3oeMmTuOKNlxlus3RiePb29vZ/vBdra3G9ncHLWb+XLUMYPKfdlVkqSKGKwn7Gi7vXOWL3VMoi3Rr9PppNvt7qzP8eT9X3g/jw8+ztjDsRz+3uHc+tVb6U308szVZ3Lpzy9ldna24gO8LY7SDmtNFHfjBcynW+4wBvaZukMIuVoP3MgwNzeXmZmZL0Rcv5CXziu4EW9ts8MD2XnDNw4E7sLr+hcqpbhH1B4Lb44xuHY1NzeXl19+OcvLy7l+/XoWFxfz+PHjLCws5Jlnnsnhw4drxV/6ERm46WaMEkNqNBr5/PPPc+PGjSwtLeXAgQN58cUXc+zYsSSpyCNDZyh2Gz0OAaGyaBipa2fuwcTDA7mokeE9gTEU2VEMFnl6erpWnHehm3sC05zrshfVR4uUu0m8VY/vMtbV1dXK4JAThI/7eb2pGANwo4BZSKDacDjMo8Gj3HnxTsb6Yzl341wu/emlfOe3vpP3fvu9DP/msILhU8OprP/xeraaW+mt9dLb6lUR1DqE8btX1mN1vZx1nJiYqMoXzJ3xlkd8bm5uZm5urlobHBVygcixM/YZyG6qgNFG95IRl2HHh6yMQv33UxknX7YQnXfyMBSfrTELCwuZnp7O559/Xp1L++jRo5w4cSJnz56tHX8JZHAdCSGgTBsbG7l7927u3r2bra2tnDp1KufOncvc3FxtLyVjQsAosAXEWF0SMFuIoXgx+H7pkEyLO18z80d0RaG3t7erg5ZRQgyV+4MyvLODcbXb7epYfxQBSh+FBer5HCQrqImUsbGxzMzMVHmft6HxDOuBi/j9fj9j/bGcWjyV/mY/rWErnUed/Oo//9WcvHYyjw883jGQ9lgufPdCkuTGMzcy9/FcxtbGKjIItpX0odwIDrGztbVVfQ5ngkHgZPk8MsT5u47KBX/h0hJy98/c0IDTMieCbAxd0SEMl7UFlYEin8o48QRETCKBk2SgBv2JLCRGevbs2Rw7dix3797N7du3c+fOnTx69CgnT57MwYMHq/NZvIuD5B0FX15ezv379/P5559neno6Fy9ezMmTJ6tT2jBGjMbssKGcd65jsMAPiAhvJDZLVzoQe1Yfc2JSxXDGi8Y9GB8GCmFAnsuFs4M8saHABqK0yIPNxexjBC6zZYz5Uc+cm5urHIbXpDzQy7VOxjLsDTP5cLLm8Ka3p3Ppe5eqcTabO22evU4vx987vgP/t0Yw2ZyEUwnQCQ4G9OFggBxxKM4LWRcivysQGBhHrpCmMDccgh28a7Gs49bW6GjRsbGxyg6IyugF+mZHi419aeNMRl0dWD8dICgXnsb5HAIDviXJqVOnMjc3l4cPH+bu3bv58MMPc+jQoRw5cqTKRxmoWbtbt27l7t272dzczMmTJ3Pu3LksLCzUNim7jsrfw+GwOuTa+Zybmb1hNxnlz8yPfJSFd1mH+znqYnw811AdA6YTB3LCXt1N2ii+T3YAdvNsl5SQA84UA2u1djYaPH78uJovCkI9EQNfXl6ulBammueiZDgQooRJHwyKExDQH/7Q9I3j9YHkKPP4+HgFE8vjJ5NRXowBkbsSLLzrw44XQ7Izx3i5Go1G7fgRO1enHaw7TDKyQjbYBWvB+tNsgdNxK9+XNk4GgpLg5Zg4E3WdB2/k/G4wGFR1s/Pnz+fIkSO5f/9+7t69mytXruT69es5ceJEFR22trYqI15bW8v09HTOnz9fMb8oP6QFrV+QAowPIy29KT93o0Gv16u9Vo9xs8DJiDo3s8sfnul6GjDTBJpb0Ny+6LqX36ZFPunn8W8cJgQEUIlnJTsweWJiotb7ajYRFtsK6FozOuBxmRvg81NTU1VnEJ+xcuPYk53DmVkLv32O+0EGJan6Z5kvztYdN2VjCd+zgRCNna4k9WNPQCaMfTcn6wjvBgggqp0/0BYZGYEZmT2VcXpQ/J8J8QdPZiUkIpBcE0mJsmNjYzlx4kRmZ2crI7169Wq1xYdtQq1WKydOnMixY8cq5bLQiT6M0eNrNBo1AsQMIEJzbrBbKQWU4Chgr8g8Xfbwwtso+b0jriEZUdENECamfEwJimhSjnzMpSevDwQWhuJzc5wn2xkgz9JRYDieC2O2MbPeZnqJ0siKEgR64rVxyceOxz2wJsA8Fp5BPsu8mRs6geEyBpw/6+10xbyCZcSYidysbZJq3yrOE97BNvZUxumCKYoE3EP4LDjMFA/mO45e9lLAgvPnz2dqaio3btzI7du3qwOljx49mrNnz+bIkSNf8LwskBsSyrIPisf/DWsxFoge75pghz7HT2CkJbuGd7VBG+6jUMwVBOLN0tD+znHtaDA2v/eDZ3tcpvWRD4aL7Ni8TB7K/Bkzisd3WIdkFL243JhQ7tAw7EPWfg09zDZ5mUsxXE4n7ASY+14En5/N703aOErReIEczafwPOTtcTAvjM/vq3FE9bpjQ+ikc/P9ap1PzDmZZCkklzzKsE+EMdQFY6OAKOODBw9y586dNBqNnDhxomoiHg6HuXPnTnq9Xg4ePFjzuHhYR0gScxMtJlUQ+vj4eCYnJ6uuFkcfvKdJDxS2rD+y8E76UQovLh6TnAnjcI7GvVECFt+no7PgzrOZF4uMV8fZWImBae7MIleC9OCz8AQm10wG4RTcyuc0AEUlzUBu3Btoj5HbIXl+JSOOkpcIwuScZYn8cKRGIJYD32UtvJ6Oji6xYIiQoi7vmA0vTz6A38CxPXXO6eMAubm9tUkcGKyylshA/WrAtbW1LC0t5fPPP8/jx48zGAxy/PjxKqfc3NzM/fv389lnn+XBgweZnJzM8ePHc+LEiUqxyek8ccbDGFEUIivQjVfqlfmiPTGKgcET/TESIJIJGuBfSTJ5n6Hhs3M8kxp+HsrjqObLUZk58B0/hzIJysafubm5qoEABfO6IRM4g0ajUSEnnBeGbUPHoKwPGBPHSeJUqBXzqozdUAKG5Z/boNxEgHOw4zREBXZSFsKQnJNb7kZQJuO4LzrHzw2FcYJ2lMnorXZPbZxMxF4mqXtUhGzPioL59L1er1e9BfvBgwfVgcNHjhzJwYMHs7CwUHnTycnJHDhwIKdPn85PfvKT3LlzJx9//HFu376d06dP5/Dhw5VhOD8BVrtOh3ImqaC3IRf3sGEalvIZM3xlbsRn/HtyC0MgjMPEgckTEz1OI1AkEyImW3CeOAdyTdfRxsfHq2YErxVGZYOAAOS+JuFQOqAuzCzQme9D1CFTIqbHz2UUZhTj5hMQUqfTqcpEoAoM2oQOjpq5Os/FCDnms0R+ZuDp+GFcyMJQlnm6Awm0xBo6tUJXsLGnMk63weENbLDcGHhnaIFH7fV6WV1dzeLiYtX5Pz4+nlOnTuXAgQMVMcHkzKxOTU3l1VdfzTPPPJObN2/m9u3b+eSTT3L79u0cPXo0p06dqrb8MIby33hd53ssPE3MQDCzaBgOlyNqyQgjA4zH+wdplCe/5LPAbLyuy0ilPEqWku/byyNrlzXMcKJgGAZyYi1Nspg4cccMkYFSSKs1amG0w7NBOW8zErCh8Hu+m3zx9YisBfVzDIlDrm1Aw+GwKnGYncbB4tSsz+Vl6Gvj4+cQgcyJq3TYyJR7GvqabNzV/vaxzSwuLla5D7DUyoVHd/2MUN7r9arTxfHiBw8ezIEDB6pNvYZpCNR5HJ5zdnY2zz77bI4dO5bPP/88d+/ezaeffppbt27l1KlTuXDhwhfeJGVoxb8bjUbtmMSSxHA0YlwYjY3EnhjDarVGB0s538XQzSYbRhE1TehYBiisNwRgZNyfMWxublbOymvCMxxhUSITZPbiw+HODgvWHwUDLrpziDVHPia/IAtRaBsZfaqlzL1+OEM7TCu0TwgEKTjiGoJyf5oGmCfGUtZsIbrcBOGdL86J+Y6Zfp7ltTabXq5zee1rnL/7u79bTciFWdPt7s5gF8fa2lrt6Mvx8fEsLCxUsGZ5eblmlFDTzWazaiKHrGAxgQKnTp3KzMxM1RL4zjvv5NNPP82FCxdy8eLFqhTBmFhoY363sDlnxZsT4VAmohzfN5EAyeSXBgGnXDw3IkBpzeZhhDzLUYAxWLG5H5AeOdoRcS8UAaKH4j7wn+c57012nATdMyacUFQf7AZ8J4rZ8VAmImpAygAp3RFkwo/1NwHoi0hmZIfjI5fluYa7XKRBOJ6SRGS+3BsyD8iepBa0cEDJaLsheuRqgtf2qSPniRMnqu58YIKFyCG/6+vrlYCnp6czOztblQro+LcnAi7hkYm0/X4/Dx8+rDGGTNDehn2ANC8/ePAgd+/ezccff5xvfvObOXfuXBXFDXW5b2ko0OouxiNkK7jZQPIJd6xwYWQcP2kWtYT/ZhNRXhsKYzAzyeJiNCVJYuY1qbPBzgE9fiMBR1Puj2KaRYZBp2XNTKsjkYkwM/YgF57Lszc2NqqTCEg5+L7/ADuRHeiCgIB8DK+JvobhfAZozP9BKsiWC1jd7XarMUAc4nBKYrRERnbET2WcX//616uHUGBdX1/P6upqBVephzWbzRw8eDCHDh3K3NxctU/SwkHZGKybi/l9t9utmr37/X51VqgPPnaTOFFoa2sr9+7dy3vvvZezZ8/WyikuQeCRS0aPHJTIajjqXI45IJck1QuBbMzOv/jbMJOFM7VflqxwMEQh7oHCoFhEThupf070dsR3JGPOzJGoWinJT8krn2BHruxoZcfL2GFPyWVdluF7PvqEA6NdSmm32xUiMioCrfG53d4uxho7xzYysU46n3d5BZmzRhBDW1tb6Xa7mZ6errXnEQTI10uW3bzIU7O1/+bf/JtaIdo5F7Dk4MGDOX78ePVmKrfvcVSDE2OSdBJtd8H0+/2K/WMCVlSOt0Dx6EIit221Wrl48WIt30GYjLnZbFZMsXOEJFW5wHkU0NxR1UZfwhPDF+AnRsviA/tMqnmRkI+ZYxTGkTYZ5UpAUTw10c0Ei+GxoxIGyXY3NwC4NsjlSGcjwygYG80OGBTOaGNjo3o5L2vEZ8xuIl/yQyu05UUv7nA4rJhUnJphN86E9kqTf47wOCu6oTxXoiM5OQGrhM9l7bckUF1leCrj7Pf7tXcssg2MNjsfxIsSOody6aBkAxkgjKVf0WCm0nkZz7YHRJmB3mZf7RRcOuE7JkbM3KF8fkGsIQn3cQ7G94FTRBiMn+hleOVIx9/cz2UJ5smcUCicH78v/3ZZBkeHAgEBcRhEOe7fbDarpgXGxh7PVqtV2yFTdoeZfOH/KLbrwUYjGLg3GuDYTdSxlkRDI5QSwtphklJ55w7yRZ7oIt9HVugcuozj4ihY59Im/mwPyA19Yu382fLa1zi/9a1vVRPAa5umJ4pZ4REg7CXK5ZyChQcatVqj3RlgfqAUxrK1tfO+RGAPcMUeCK9mz2rWFIO05yohzvr6eu1IDJTLeaebLVgUs7TIy3UtkzZWGntkDND5ChHF7CU/88JyHzcalHkPF59zuYjcsUQdJp9YT9IKZOP8uDxzyWSOZcb8rbBljbM0IH5fwlP33RrCOyqZ6TU0LmXlSJqMyBwOrWON0BHaFwlkRiLJ6OwqvssmfJ711Mbp4/ERBlibCWEkpvdRQAwAz1cKJ0nVRuc3M3nwyagvEmHjvYjIKCALaUbOZYsyd3M3iefJ4tpwuT+MHB7bJISjlCMaC4BCYATMi3FbyZm3lY3PlewzcrPT8f5FNzMwZuRrY3Q6AhKxDJwKuDEC2bEeKKqL8MwBHfCm6RLxWO/I2/g+jrI0fMbGelv5/cd7ee2wzMw758W4hsNhtWPK8JwgRM7JuOAh0CN0Ixk5JTvH3a4nHo1p40MBURIWg44NJoySu+zCvYhczkOS1BTOEIzn8zMMDMMy42bjcw5RLrLJFjOHjM3UOnPjPjyHBUMBDRlZEO5jhtdsq+drmfFZlM2wkO9h8Ga7WRPutRtMdAM73zWb6LdwW8nL+WPAjMMphp0Va1aWhvi9yR0rqtlZBwf+70gIHCePRD/RDZyhYakRn9MAvuto2mq1srS0lPn5+cohuQnBY8H4WA/4APgW8wf7MbVPNE4eVDYJO6rR0G5vzN8M0qwgE3adDgHxHW+SNjQ22YIRGfo4EtnIWEDnO1z2wuRNRCdHvpLB4zKpxJzYxwm54u+yOMjDxmav77nhXFAA5sGiW4mIQMAn/m9CzM+0Qvb7/WpHf7u9c/YQ87KT4JkmlBgT84V0SUYbpPm+iSATSYalfh6/895dzwn0gIxMhDk1Yj0dIJCx0zUbD89nXN7JxJZEjsN0qgbCcmXBu4FsvHva3p6/yYiAIOm1QPE6TNRvjXIuweLZOzv6umjrGpQjCx7Rns4QwkpmyMrPTeLYmKwU9vQshmEfsNz3s6HwfOdPPM+1L5M0QPN2u13lzEQL7o23RQ7IyrDZ5E4y2jJmJXXEd05XOhzXUFkzz9OGXUYYIhJox69ZZ9wcHmaozJp7Fw5jcQQHpdgZouyOil53ggrOzPtGS9RDREffcUBAdi6ChqOvdR5nymsF7fSxD+vYUxmnBetE3ZjcUANlNtT1K9aJZDDAvg8LiqKZjXWEdRQCzhBpHR3woDgHFsTUtpXLZAsKD0VPos983IzB/Fk0nkMagFE5OjI35oSyMS/P2wwnz7Dc7cRcRnBuz7iIDjgNz9dydpTh9/58s9ms1pWxl+kEMiWVwfCQDWtXrq2Vl5MpcH40QNjB7pY/IiMu65eJMBN5jUa9QQIZ0npKyYpxOLVCD+z8kTe/RxcMy73Wu11PLKXYm5VeiIViokzcYX04HFYlCRTSDFVp7MkIKvN7RyZHF6JNMmIJESLfHQwGFTQuc14Wgfu6VNHr9arDqVECwxDfxzmR8+MkVa7re/h3OBMbJvPDGbKoRF1HPyIiio3siMoem5UDJfPPjIj4GZDWR4yWqQWRnMjFWIhA8A5AQu5NZMcQvM7oELL0xgDOGWKdQEqlfHEi1CJ5EwCO12uAjHYrgWFYPN9rw/0xcKCsjZFxmjcxL7DX9URCCNiQpGoYR0kNbVAcbxNi0jBczkNQLHIQn/K2vb1dY9WYJM+kvauEmI1Go3odAEwZXttJu5U/+SIqwGhd3DfxguLwWWCq62yl8fu5yJZnG+6ayOB3JopwhqyNO2/4v+XC4rufdTisHy+zW95mpwJMZx4YKX/zb2RkR2Q0xJoyL1hbnARyMfHoXN0QH+eMXpqXMBGFbHxqPRs5HHSGw2EN5bnvGjTBDpySoPT8k9RSIuaLPF2SMx+z2/XEN1sjCLxXye45kllB7Y1h96wshjAWiqOqIYMP9XXemoxOCeDf5U5+Fs4RrCRCmKNhiD0x3rGcA0riziHG1uv1ao3jRAsUBJnxPRfLnd85tymJKpcTSvbbCIF7kgeaJTUM516OoP7b38NZusCOfjAG3kxtHgC9wfF5fT1/HEar1aqaO3BArAGOiN+hpxgN+a0dvWuTNpiSQwAGExh8JCZjx+EYNThg2VFjvLahp4a1nPNSQiqiHJGSbgcGxmJawVl4PJbzUzwcXhSShOclqb0QxgrN315019swtNKgUQpyAxu+DY/5+rvIZXp6Okn9fRiG5nyf+5K/2ohcJyUK2tCIxO6WMdlT5rwoqlENZInH46jvxg8z8nh3xm8nzR/kVSo56AIZIyeebeLJUREZMCecFJ/lvj410Lk182UuPNO5rFMIkElSf5W9oTZjNtFGOytyQ58MWcv0DRm6RfKpYa0jIF4Gb4S3wCgI3UQHvg+EarVaFaPrBBzjdpSzYSFY/rZ3QgBWNDN1JQHAfcs8BsjnDhQvDN6csaO0dBOheCYeGNtuxE1JItgIMDIuM9tENJxbCXl5Lvc0VLWTheTwIWZGRZazqX7L1eM3+UGEYb5lPdDMu+EnZBbH0Lhcg3y5MCL37lr2ZQRjLYiojJ/1RK9xFLvpWclNGLKi46wPsi6fia2YS3hq42QwJWPI9jFDQmCEE+Ayqk1NTdVgH96OBcLTQEEjNLOjeFUbwWAwqI64574k88noeBLnex4vY0BYXhzgiNsCW62dEwBMGpSRjr9RJuYI/HE6kIzqZ2U0MEzk75LFLuEvciEikfezDq4xA/EcLf18ckaaz01k8LnBYFAZV8kr+AiU3XJuHAljJiVpNHZ2mTBuUqbd0hHrHPe0jhFYnGaxviVRg/GQg7LGJvxMxNlZOW0iKjNv5uw0wUTTbtfeJ9r+9EKIKIoXl/+j4F4IBoknMwQD1tjASyOHQieh598oGJEXoTo6mNAxs+m8F8EZMhHBTYlbDqUzYgtbmcNx8XPYRRbNntfRg2e6ocNOovw/ykI05bISc0yKz2tljs5XUWofaGZ2FWVD0dlgjqz5YwNEV+w8bAAwoVZul+icVjHXJDWlRmbATc/RMBdHhHzQA2TH9xmz67DsLS2dr+fs32EzTjkwXOTouude1xNLKeRwPMCTtSEwaRSNyTukoySmkLmnYYEp/t2ICUcpYKW9Yumx7OHx0nwHaFfWoQxzh8NhdbyJvSxzWllZyczMzBd6dZPUlJI82ukCl6M346D/FOdkx2FUggxNfDgftwzJl2j6YD7Iww6OMRPVXLZhzJOTk1XEc2SAgTfZ5G1aHif6BLnCuEq2lGd6/s7xzJDbmIzoLHcTNuiA83r0jEiMfjtAuWzisVhnmWOJbMyY73Y98YAvb5w1HOT3ZthYLARgWAhLyWBRBpg66pP2fMnI47gcYphtB2II6OjNOLwYjuZltOP3XiTG4VoccxgOdwrmtHEloyjEPR2x+L9zYENYFA+FZj7Od7gM3xwtIJaA5Biv5VAaLgpaOjOQB7/jXs6zDbWdQzuaGcrxeUgjZMnl7zFOR2WTTXYm6IflU9Zg0QcTYDg/BxyOJGHtgOxef+bLeJ2j4gjNmZTko9HZF+xvz9+kXouz1eOBS4/pSGPCxsrohcdgXR9zHRF2l03Zw+Gw2mNoWA3ENEFgT2lDtWMxVHPUZaHdYuftRzDKfr9mozE68tLR25HBDgf58Rw3Y9gAuL9JiZKoQAFtaO12u7ap3Kci8Pkyj8bIWV+MBuMDZcBOe9sen0cOOBFyRWTu6IahueaIgyiRkB0+Sl4iNBTehslzHZn5v1MbOzQMfGVlpQZHDV+dC1O9sINAJ3kGdXenEDbUL22cbljGk3Q6ndy+fTuLi4tVJJuamqq98wIFptRC5GPxHUGBJiiOd4cAvcriM5d3qiNsFB7BeUcC8zA54sVlodyWBZnivM0K7RwC6OtXEzpPtlExF0MfHz6N08A5IBN7YZevyqjoI0OIwqQOhmHMl/shd6+LHW+/3682XZtY4SKSWqaG4DgqPsvfbl5hfnZWKDTRjO+xxugchl4+000z3JN54xx5rlGG81WjFP6PXGhQcImEuZcR2w7qqSPnbu8LefDgQd58883Mzc1Vh3/dv38/SbKwsJDjx49nYWGhmgBwlWiBJ0QhfciX4Q2GQ2uYhVnmncmoaaBk38oknj84DyfnpcBspCiPIY3lwvhNPKFQJr/KHs9kdAocPafMwRHH0NB5YqlQOA6iHbLjM6WRIDM7pZKwQqGd97Gvl7H64rNu+C+RgteHnyFTRy8ir52zIbIV340q3MPpGGgHlOF0h3kZdTlKkzq52sC6UmazfsFnlCmaEZnTsN2uJ5ZSDHfwuhMTE5mYmMjly5ergd+7dy/3799Po9H4wivnDHnK3AuvBswsqX6Mx0ed2FP72AiP19DIrJkjq+FekmrTrBWY6IhzwHOXgmXBgXsopp9lp+O6maMqBsxiuj/WeR7HUjJ25st2MTdzGGq50O4UAMVHpt4lgxITKTz3sq5n54ne4CDIoQ1RQVJ2Er4n8jEUdrRGb4CryNQoA+ht6GnjIAVgPOgBMmHs6KF1mTkC7e2szB3YsZsXsJMtr4YV/avrq+ur6+fnemKd86vrq+ur69/O9ZVxfnV9df2cXl8Z51fXV9fP6fWVcX51fXX9nF5fGedX11fXz+n1lXF+dX11/Zxe/2+jLmvewkVgPwAAAABJRU5ErkJggg==", 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", 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" ] @@ -113,16 +113,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "00365.png\n", - "IoU: 94.87\n", - "F1 Score: 97.37\n", - "Precision: 97.84\n", - "Recall: 96.90\n" + "00002.png\n", + "IoU: 76.92\n", + "F1 Score: 86.96\n", + "Precision: 81.38\n", + "Recall: 93.35\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -136,16 +136,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "00452.png\n", - "IoU: 94.78\n", - "F1 Score: 97.32\n", - "Precision: 96.21\n", - "Recall: 98.46\n" + "00003.png\n", + "IoU: 73.53\n", + "F1 Score: 84.75\n", + "Precision: 80.49\n", + "Recall: 89.48\n" ] }, { "data": { - "image/png": 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gMj+/h5AzfuJl19kCiR3v4Il2dnYKG5qkxLyMC6FfWVkZ2AniVA4yQV/53sbGxkDRAF4LIU4ysA0NQUbJQSVAfeYO71bXC5v7wJFY9vixMbLXxpmYWTbhZwdkFMIcUcBAcb5DDEIAIDBrdFh7XsqJsFvw8QqGXyzW+Ph4JicnB/aC1pM0zGs+n1jSRMi1es0kpV6TBDP9hgQyiWDL6x0bJksscDVz1+/3MzExMRDvuO9chwXjXjUxg4A4LnNVEArOViS8LkbBBE+jsZ8qaLfbBUon+5APCFfD5V6vV9Zzenp6IG5zjO1+Mj/0Z3d3tzDIvV6vwGNgNePlb8aGB+TaSQZkzQYPb2YyzY25wlgkGYDDdkKQf2bam81mKXiwF6SvLi5A+Zhr0Je3GR7WXpByWlixJEyo21Fs1FH34V6HvVe/bu95lJL6s0ws+SiaK2oQQKdEkkHoWu9SYfy24HguBC858GZGDQg7n2FR3Tdbb65HmmFiYqIoCtc2xPOeR0ghK3Q9/5ubm8XIcDIGfUQxuE+j0Shej/esqOxv9HWIe4GC9ANldizIeCmwT1JCJ+aO+cDA2CAhr05rsS2OvtZssHkFDC8en/vTV/rN/UlhMZf0mbk7rL3g8j0awmurzATbk34j17/a/7xW/xzVzLQZkppoMeRMUsr37Llqz+140gwm8zIMbptgMpGAACUZ8Az+DVwiPqICyHCrTsR7twpeFYXa2toqsTGCBtML2UetqWEg6+C8HgqYJBsbGyUenJ2dLRvN6RexHApdeyM7gbGxsRKCGFlY6Zh3xmAIamITg9Rut0tJX3LAALsYhXUhJMCg46lBVmNjY1lbWyuchR2BibGaEa7bC1JOu3OsMAKNtWeyYAWfz3Xd+P9qSlp7zWu9FwuE98NrWkHMeC4uLmZ9fX3AWwzL43oR6hgzyQAsM3y1h+C+ZjARODwLtZ7cz+SW43/nROm7jQ+Qd3R0tMSByUG52cbGRjY2NorCskODa2AQnM9ELpxL7PX2T42g3zWb7NiQ9eB95A2Fc9zMnNpzM78gGtaV91jr2mBCluGVbSiNViyr7XY7a2trZRzz8/MDpxxwDYgkoC/6cVj7hjwn0LaGk1iQOtY86lr2Qod9Ztj/tbe8lnti9QnKDWVYaKBJp7O/I391dbXARnuqZrM5sIWrhk3OWZK2ManR6/XKwtE3kxuuFMKST01NlT2OyWARhWMklNJeitddpI4CjI+PZ3t7eyAOp0LK82vDhJISazF3GCbYU5TW3suGhXt4Xe0xkQ/myFkCYlZ7WvrlsATjUWcYuJ4JSuaVz+NlkxTPzflHm5ubWV5ezvj4eCHB8LJcG8VkzN/UmNNtmKciuY2FNCPGZ48ie466zzCFHAZpr0VBIQ9YDLNwyUG9JZQ7hsfwigV27MDCm0msYb/TDpBIKKyvwyIibAgEVt3ewJ+tYbLjHZNVKIyvw5qtra2l3W5nfX29XB9IaxbT64wx4qQ6M58k6A0JDfO8lsw788EYamSAkalJI8uP58oGEKPBPW0MMdCwxMk+1N3a2hqIk0kXzc/PD9Qys76kWEwYmhm/mj68oFRKzbDaEtTE0NUwtTt2LUp7NcWs3z/sPn7NTCA0uHd5GIaOjIxkeno6u7u72djYKHk2H8np+zMXrtJJMkA6AblM0QM9+U7N1hpuk/IA/sEiIlywziirU1r2JvZezu11u92sr68PpDTm5uaK8oEAEFALvz0jn0GpvC3LQsr7oA7mBZjrQgAUi88T93mczBGenHl3wTtEkI2W18eliIzF5Zas7+joaObm5kpsTg20x0LI4LLWb0oRQi3oTBiWpy5nAkpZUWqlrq/r1w8jdobFms8X0vK5Xq9Xis5JQeAFQAGOf/AYKPPGxkaWl5czNzc3kHZh3PYCjq9QUOe5Go1G2TTsFIG/YyWDncTLM3dmh20kkgO4jCdDSJIMeDJIpsnJyayurhaPcvbs2czNzRViiHiyjitNeBgt1GvFPFqRUSr6U5M4jLXeeDAMPRnyM2/kUzEEeHbWhFDGEN1rhqFhbp1KgwjknozFOXMMg1n+w9oLgrUM1oLn81AdvyF4FqBhzfFH/ZnDIO0LgbGm9/v9g7NFKTzY29vL008/nVOnTmV6erpYXr6Dwuzs7BTlIBfmz9XEQZ3ucCEB13Q6xV7OeTn6gHAwdnv7ZrOZmZmZJCnjMkHk7zmOMukyPj4+YBgmJydz+vTpzMzMlLGQQzQByPWZW2pVEUIjLPrknSl8pp4LYKfZWPo+LKyw0KO8GF2MhaG5GV4KIczW830MGvcArTQa+xVU1N+67ho5IPc/MTFRxvtNizmtPFgMLzLvY4GfbxrlKMUc9rc/wzWGNSfnSaRjGYkNZ2Zm0mw2c/ny5Vy8eLFY0LGxsVLiVXsAl6C5gNuxpt/DOlvRHf85LZUc7GSpc6IuofTnmX/+9kYFe15f32uWZKDEr9Xa36Z17NixgRytFazf75ctXibAHCYgyF4zl0UOayaIuI/ZTTsBx4kOIaxYVkpeh5U1Sec19t8mwGpZbLX2zwtqNBq5ePFigb7ElyaFHD6YfKrbC/KcCEe/3y8J1prIOAyWJocrENunLHDOhzEZ/nvYPUwcuF6WH4QBWDM1NZUkxdLdfvvteeyxx9JutzMzM5P19fVsbGwURTVssuCggLVA2FM6l8p1vGPFlTEQCfQZ7wncS1LgpQ0mikP/iK8QXpTCAkcRuo0s+0CnpqaysLBQDBzr4bSJDbHTPltbWwP7Li2k9kr2npBuJupQVLOdtRPgPTPWfMdMrOWVz2C8zPIaSWAMYc1ZS8vCyMhI5ubmsrW1lbW1tdIfilEgxTAQwNzD2jeUSjF5YdICq1ArztVgrS0+1ozFnpycHCgMGAZz6+uSTAd+1ifVMbEkyRF+jMMNN9yQc+fOpdls5ktf+lIuXbqUN7zhDel293cirK2t5eTJk8Wa018W24tr70SzJUexTEq4JtlpCENqisRr5MD13A9i2nrnSY0CgHUrKyslRz03N1eEi6J3hIoYvPbACDFQDwVzFY/zmIamRgkw2RgF1h9DYmNSx56steNT5qTmBfCSyBPrSGGCYa8NDNc1iQcjzX1MlrHuR0Ha5AUejWla3wJI5wxjrhXWMihX8JMMTgYV9GoeGW9JuRmThreEaSTAJzUBOQRsmp6ezpkzZ/L1r389TzzxRCGDtra2sry8XHYzUA2Cl4FwMBxFaOqTIkjPWJiTQW+4t7dX8mb0G0TBXHMfewjHb8kBHHY5pRGFPezu7m4pthgdHc3MzMwAdASWW+DtyR3ueKO6876HhS/8bdbURoTX+en3+8Uz21A4TuWaOIwaXjv+xUA4TPBn8Ha1B61jXMezGD0bYtcDH9ZeEFtrSOJWB9V0lO9dy7UNA/r9foEH7Xa7xH3OJQ0TEvC++4nCIKT010lse/3x8fHyLJJjx44V6MmjB3Z2dsopfGYdkwOCo0YTLCb3rhlVBNDlfPYqExMTA+facJ0kAx6knk8E1xaf6xOe2LI7r8lZrigYRequ6cUY8569p59wxthsROiT2cxGo1F29nB9Tn9IDpTe3s9ssdldKx7jtSNBVu1QPI9cj/tyPZNRjqcdS4J6OMzLZCHI4QVXCNn6DiNd7B1h9egMDO7zaUwKHcZys1Ok3vAK6WDGk8cFmB2kn3gbTkmnbW5uXpHfZBFnZ2czMjJSUgqjo6PloGDOr+X6tqjO4QHnahhrCIxRqxXcMZZDBRbYip0ceDDGB+vKffk8nqUOEUArCO309HSZJyMjowO8g2NJfgyFfW/Hm8wB64Xgeo4MG1EYOAMrU02+2BPaoNUKx2v15m4+b3hbw2f6R5zLuOBkiDNtADCmLzjmtNfx4nFOCjDMC+HJNZQ4qjleq/NjJHCZPMNlrGizuV8DurKyMnCygZWTI/GdW6SYnQVx7MdRIjs7O7l8+XKZXDzDxMRERkdHs7q6WphgFhTG0/EYbXd3t0BDlMRIAIGwMpsIajab5fsIPYLB/Dhnae/peJR55/fIyEguXrxY1m5mZqacZFez1ORCLQP0FcVhHEBkew0XMbimOTnYr+q1czzq9bcxc5xHf/CwhDYOjfw598cIh3u5GIE8pdGRvSeOysYQ+bb8Xq0AIXmeDzKqA2AnnZlge4FrUUqaLR+sFos0MzMzUIljC+3CgLW1teIV6yIIYkoLmWnz5OB0dcPzjY2NrK2tFUvIOPHUwN+LFy+W2JOY0ISOfww3UXb6aGPomJ45R6DoP9dDUfgs17e381wYCib7lU5bW1vl6Vujo6OZn5+/QjgRLg7JsmxYbvC89rBmKBkL5B9Q0F6T6yMPNuAYLOQBEsbQ1r+BldzPc2458XGa9B/Wmf648MPM68jIyMAjKeiL9wAbduNRD2tHnr5XB+oIR50rs5snAUty1t+n1TGolZljKBBMcm3EdgyU2k1iG2oXDb3841Ps6IPJI8ee/f5BUTwxKGOlOgdr3Ol0Mjs7m729vfL8FPrLdVF4PBFC53lx+sA1u6AP/jbcM8mSDOb2/IOQESoYEuIteMxeq9UqJ9A5rjRcZP4QNlfGME/0k+JwQh1kyM/2tOet4z57JK7JurOGsO4YUENGK5Nhqw0FhtlzyL2Yfz97lD45p4yh5zXmxWilXqtvOiHkTmAhDF9NgtBqsqJuVho2C6Nw0PFYWxbXVPrly5eL8tlb8R33315wZWWleFuTFniQycnJ8lh7Cyr9HR8fL09Z5rEBGxsbuXjx4sCWLoyVoT/XMgFkGEa/gci1UrAGJhbwooaVhs9JBr6DsVleXs7KykoRYJ8Xm2TgYUXE9dvb2yWOdymalZb1xDDQJ5TTj1BgTvBsHidrxrpZqYwC+AyGzn9bsZh3wgUqxVqtViH/XCRA/TAQ2GGVwy/LuHkY60DN4h/WnpdymoDAZfN/feLB84G0fN7fabUOHh3QaDTKjgaqMJL9Rd7c3Mz58+eztLRU2EziHi+coSETura2VggdcocjIyNZW1srR6xwvAgTyjWdk0z2SaXt7e2ymfjixYvZ2NjI3Nxcjh8/Xu5p618zzlZa+sL/CC9wH6jJOA2vaNzHNbCuYmLeu91uFhcXS/3w/Px8YUfpF98F9k1OTg6czkD/mON2e/C8HJCAYR/3d+EEzac1olg1C2+Iyb3rsMfxoGN2crjIFt93M+Lp9/cft4j8ORzC2NnjmhX32tizDjtCxe15KydwCIvA5CBkVoSjFBShrL2q46XJyckCiczoMUiOh8C70C9DO3sTrDtHLGL5p6amSryztraW6enpzM7OltjO5BJ94DWQAw8lmp2dTbvdzoULF7K+vl7IIwsbqMPWFGHnHB8jEMcrhpjARiMavufYKjlgjR2D9vv9UgHFvTFwKBTXdlgA3EcefN6Qwx366PyoT06wkCcHW9rYKwv7T//5vJUQCG1UUkNkKxn/12SZr881iVE9dp8DVHtMOwb+JoZGNl2x9U1TTgYB7KLV1oLO+FzWq13TlpxFpSABQ4Ag1AREXWzApNAPQ1ugGDnJ2dnZAfYVJOBjRpwa4f6GlEkK1Eao+/1+pqenMzIykmeeeSaXL18uyXzqeh1j25P6/p5v+oHAOj9piIyXquNN+mqD0mzuH27GEZmkTgwD7ZntYZgHBNWpDoTfrDifqT9rhYUxZY+tSSWU29f0/Pk+vGZZJF5nnX0NyBmvL/PNcZf1d50XrmNLjJKJLYomTDY1m81v3sNzbYmdw+t0OqWKhWB8cnLyqjGm2zDr4ap+L6w9Yqez//SvOs5k0hBSFrV+YhfpB1JDLNT8/PyAVcdzG+YZxnkMtr4jIyO5/vrrS4qHrWnAdW9It4JRDWXPybxDTnFiHobLlr9eH7yHYS/ju3TpUhYXF9NsNsvGYBTLHsj1qqyHY95G46CAAA+8tbU1EJ9x39q78brZUbP/NhLJAfLwNR2jG+mgdLXcJAfZB059cHVRHbZh+LgfRt9eMElRNrPKpPAc69OfOhwc0IHDVebK5riIBcQbEaS32/sH7BrGvZBmeFT/ZkFXV1fT6/UKWeNJd2yIAiJUc3NzpYhgY2Oj9NtCzv2Jm7CCeFqYu+QgQe4T75l8EMDExER54CyxF+8jNCAGhAIhsQIbhjm8YAze9cLCW9Fd2bK5uVl23zSbzUICoYDM8eLiYiYnJ4uiGVoCUTlImf5Q28yxJ9wD5UdAXWvL7iDWGiVoNPaLTTwWs9mOLz1HoB0TTDXK4zo2gMgShhm0laQYV+bScJZ1cqkoSsq8YajsSA5r16yc/X6/JOUZQHJw3qdP+GYRa7hqBeOaw/4+6jsoDXADsoA9pc6R1h6DxSB/SW0tC2jlRNBtfWuSxKQBi2mlcr5uZmameKGLFy/m8uXLmZqaKk/qQoDoux9LwALbCzr+Z57sMSDqnC9lTjqdTik4qM+qRWG2t7ezvLxcTlSAcEtyBUQj5sYzrKys5PLly3npS19aDE6SgbmywLogAAaZ9eZ1xoIxcggDRETogey8503oKA3Q3gQS88yp9UYHzDf3x8hgcIeVNnIf0EANec2I1+15KSeDdDzjhL2tXg09eP0wJfR7VwuS6QtVPb63vQlHLfIZJ5qBmBA14H9+6DfQypuqDXF4n2s7TuS+eAsvYqPRyPz8fJaXl0uBw/z8fKk8YaENyw3VMSYohQmR5GDLVnKQhyOuYr6Wl5dz6dKlgXX1cZMYPJ8t2+nsH3RG35IMHJC2vr6e9fX1wjX4aEzLihGDPY6NB6+jGBgll9HZkDK3NmA2avVWrdpjoUg+NADDzBE2oEKMhde4dkggKa6N4eZ79Oub4jmxZlzMOSznhpKDYBzB9QQMa4YlwxTTSssCshhMGvgf5eI6FF4nB0wgkJLzU+u4jsUhzqLY3HGQPShC56J0YBEKBpvsqqeRkZFcvny5MLqkXPge3sOxTnIQ12BYqD820YGRslLRz06nk/PnzxfkQZEAioZyA0sxXiafaiRhtpTTF+rnnqA0JqosS3gyQ0bH0jY6NoSOXzFaPucJmI/XxJiYTQZWr62tlXOZkD1kCMdEbrZmjIHSrBP3Nczt9/sDz3k5bKN58gKKEAwPDVsdE/G5w5jaOnbEC/l5G8Man2fxES68Sa/Xy/vf//6srKzkla98Zc6ePVsmjknkcOOJiYlMTU0VQbFg85t+UUxgJScFQ8nb7Oxsms1mqQF29U0ymJBmjFx3cXExKysr2dvby8mTJwfOucEoOqYGvnFN50Pxro6RzBR2u/tPpuYcWhd1JAcljHweYSeGRREQaK8pTDRpGT6PItYyw/fqH8NVw03Gxdhr5pjUB8YII0wlGejBWQXX7KLc/G9OgZQOa4BsoIxOixDWsAa8x70x0t6iOKx9Q5utfWF3GqEa1lAwJhOhxwPTDotNwfQolyn3TqeTpaWlPPzww1lYWMjU1FROnjxZyA8XvZtldHIaSw3EQ/ktwHgE0id4wWR/HyjGw57Sp8UxJmKXY8eOJUkp/bvuuusGyt0cq0DAtdvtbG5uDsRTXJOKKpcTMrcU8XPtycnJkiZh3pljlyzCOdjQ2aMR55FCWl1dHYjxhhnrWrkdwxMKWFZQSheXGNLv7e2VIgfkYmpqqqypySFDZyuVyUF7Qp5wxpoTehCvm59gzvCmNmoYWvr9gh87P6xZuRzX0GFXgACH3Ihltra2ijXzQ0SvhsERCCw0i0KsMTk5mXe961354Ac/mLW1tVy4cKHEZ3zGXsaxDM2QnBgBKLK8vJzjx48PLKrJJF7b2trM9vZOmY9ut5tGkj1ZegQJKD47O5tWq5WNjY2cP38+J0+eHIDxwGLuQcXUzs5ONjY2Bowbj6ZbW1vL8vJytra20mrt18sCXVutVjmDdmlpacDTMz/e+ufH5DmmMvvY7/eLspvRrUMGFMu5QZTO8b5ztsnB5nTH9Ht7e+VYELOzzhfzWfruNJFzmE6NMJ94WxSX8RI6gF74PgbJYzEBxtzb2x/Wrlk5HWPROee12HPppH9Ndfs3JAPtat7WrVaEAQVoNDI3N5fv/M7vzKc+9ak88sgjaTabOXv2bBEmV3x4wk2pszhUHuFJYKRRSsoJx1pJb3cx/Y9/PCObn82JP/xcel/ppdvrpf/cGHvE1Uk2v30ij0x9e7ZOn83mDTek8ZyHwmJvbGxkaWmpGBIgFTE//WYdQA4o8cTERJ566qksLi6WmHR7eztPPvlkNjc3c+zYsVI3zMZqDCTcAeuKogPVvS+V+2Pk6t39KJI5Bac1nMLgOza4jJGcOTXQGAJQBPG2i+Ht9XEIeHfGQCjkAgxvB7Oc20v6UGgzs1zT1WIgOq6JzBqhHNauSTkd/4CvrfFbW1uF1ECwmRB7WKyRqWU8Aq2ON+1JgadJClnCfkysYaezX7T+kpe8JJcuXcr58+eLIHqbEyQSv5OD/Bbj5D1gC2xdt9tNK8n40sOZeu9HMvbR30/jc5fSfOZcGocXfJR2/P7khnwyOxMT2ZmdzTOvfGW2rr8+j7785ekvLGTvOYvMPkhSVU5sm/7HiCAEGKGxsbEsLCxkbGws586dy8MPP1y8z+zsbEk5QWQxZh7zABGCAcD6m/W0IJPCYduc96A6t0i/nWZgjMiVrzsyMlL4AfYTd7vdTE1NlSNArSQutUP5eUwCpBOGAqV18QmpIwwUh8ABaRcWFgaMBJCac49BNXXBgbMBzebBM2YOa0cqJ8rBBekI1o2qG97z9wwRx8fHC75mYZzbS45OodhDAM9YKGIpdkrMzMzkxIkT2draKodVYYnxPnhv4lD6gNBRQGB2cWy0m9mnP5bmr7w7k+/7kzRXV9M4HJlctY1tbWVsayuzH/xg+knuGR/P43fdng+94860J16T7nMpCSshilLnUkktsYun2Wzm2LFjmZiYKNVExF/sLOE0Qc8L91pbW8vly5czOTlZNl2zxswF82gPsLi4WJRgampqoKrIFV++p2NYfttwYrzHx8czPj4+8OgD0lwwnxgCIyMUgGJ+0FKdISANBL/AtTDQzDmxv5lr7oWRcO631+uV1JFzupxBdVi7qnKihJ48ChGM2c2smVGD1WRQNCz0tcBYU+545JWVlUJ0ELOB5TmRnBi03+/nfe97X7rdbn7sx35soE7VeSkEAwGCLGHhNjdWMv2BP8rxd/9WJr78QBrLz+8J3EeOM8nY9nZeet8Xc/v9X8xX7vmTfPivvC2d69+cdmfw7CRX/rgqJ0mWl5ezvb2dqampzM/PZ29vL0tLS9ne3i5ehljV1TcoP0+6htQ6duxYFhYWiocz+8xvV1GRW2buyD27MMBeA8GmdtaG3lCQ9edzyIRrbTGkKA1ezKiA36ROGAfKA0TnGjTnnumnSdDkIOeb5AqlZb2sS57HYe3IzdYmalhMYhFbDqwFMRoQgBO/rWQO6mtqvW51Tg3F9OPMMRRzc3NlQRCG7e3tPPPMMyUnCjzxI+CbzWY5Iwgrx+6MkVYrj37tvWn/xj/Oy/+/D6R5lRjhm9EaSRr95O4vXcyNf++f5/7v+Gg+/5bvyu7Cq9PYGyuejjCDee/3989P2tzcLMal2+3m6aefzuLiYonJTp48mZ2dnaysrOTJJ58sUN2Pg2809k9yWFhYyPT0dNmnyvwgC8Bi4ByKiIcG2jpFghxA3gBlUYwkpbjfguv8p4koPByKjPFCcaxwKD3KhXLSb+YxGTzjlxANGYO1Rj6dZ7VhcFxuXsP9v5rsX1U5a+/SarXKQlBRwiTSARa2Vsy6I8Og7NU6SgwGhu/1euVRdcm+VZ2eni4xLIdAr6+v521ve1vOnDmTkydPZnx8vBR4w6pxLcct/X4/zfXVLP7hf5PX/lfvztjKVp7fDtVvvE1tJG/88Lm85s9+J5/9jo/kD9/50tx58S3pTr806R9UneAJifuJzZ9++ulcunSpIAwUsd/vZ3FxMZcvX06/v18lNT09nZmZmQE+oNls5tKlS2XPKwKP0DqeQimdE3YqxXEnRgXvaANvcqhGXMBnM641JMc7ujiA91lbP1AI2IkiIZdOGdEP8xom4pgH/nY4lxw8CKuuW/6GKoRqBYLydr1nPUlJiue5WrB7VHxZt83NzUJ7o5CGKGa/YBbPnDmTp59+Oo8//njOnj1bBJmYActvVDAyMpLJiYk8uv2JvPw/+gd540cf+z9cKes2vtPPG//kfN7wsfPZHftMls+ezVdff0fu/9Hvz/U7N2R3dzfPPPNM2foF7MOYmZwhf0vxP57SngM0gaJzPQQLggZYiiLjGZJBZWCuUU4rEFyAUyo1E23PlQw+7wWv66otjAd9sPKYLYU8RHGcb8egkBs3XKaP6ADjQJ4M/VFYHzpn43S1dk0HfAEdsGx1AE85k48SGaZ8V7MSnuS6uZqCk+4QOK7LBHH/fn8/53bvvfeWE9rPnz+fsbGxnDp1qkw0HgAafGx0NPOf+ETe9l/+nSw8tfwNKeZeO3n6+qT/3EVOn08mt17YtYC749vbOfPYY/n1n3osf7rRzZ2fvassPmV4MI4+T8kpAD578uTJkjQnrnT6hp0oCLbXmvV1KZzJmyRFuEFTeGSTQPwNpOU7u7u7AwUYCLeZXjwgaS/DzeTAQPB3bWQgxmqlrb0jBopjcwgReI+4u44fa0Ph4gfeuxrvciRb60kwhOVvdv07GDc0OcpDXk1huefy8nLZHpak7O6gasMW04Ntt9s5ceJE7rzzzgJl77///iQpdDiCQT8WPv3p3Ph3/k5GlpePmpr9/if7mpNGeseOZffGG9NNP/e/645cd+aN+dCbO9nt7y/gvR/dSv8L53P8i1/MqXPncvzpp9Pa2XneBmBjMnn/X0ye3HgoE//bZImPgbf2VCgOHgoFQSibzf0C9ZmZmWLJ69SWDa8ffssawDEYzhH7g7QQeCMsCz7X5HM0+t5oNIqRRR59ZArOovauTtXwvzcAANcxIsngYdy8z0OZnLYyVOf75N15lisyCsIwmgSxHdauqpyu9CDQRvObzWbJPRE8AwsO85weeHKwp60u3fNnNzc3yyPdiWkRAsgEngK2u7ubz372s2m1Wjl27FhOnjxZYqG5ublMTU3l9ttvz7PPPpvjx48P7CUcHR3JqdXP5ea/859lZHnlatNS2s54M199263Z+Dd+JreNvzqd2dlcnpvL9vZ25ibGs95Pvn374ASB9TvW87XG17J3992Zm53NnXt7Of6Zz2TzsU/m+vsfzPGLnWtS1M3J5Os3Jzf+xvWF3IJF5KAq1gOluXz5ckkTIGhYcJTHyXQMIZbfQoiCUd9cs5t1LMrr9bMxURwYXOK65MAb8rmlpaUsP2cw6Zs9myuB+L5TPaRE+AwybCLLcolRc+zZ7+8XQrAZ3vt/Scu5uMHoAsNUH/L9gvOcdLyGKwzM7p8buVSLz9JswYBTh8WmtsCkY7BwKNXS0lLW1tYK29hu7++nu/XWW9No7D8vkeTy/Px8Op1ObrnllmLVT506VZ4EPb7+x7njV/9+RpauTTF3T57MR/7+v57jr/uRLIydyPJzcezKyspzRMN+NQLz1+3uPwCJfOTUzEyWp6ezdsstObf4pnzk8n156Sc/nLe85/FMHGEcPvh9ydLsSL6jdTYLCwvFKpvc8DlEzCNCggU3TKXgwHlnb4nDytv7MD4Xxtee0cysY0s8pWGeDy+jwmdsbKzsvXWJIc+q2dvbf5YMr1uR8OgYKlCSSSp23RjWEpvDafgaIBJQiAviXWHGHLqu1mkr5vcFE0KHYWIH4MbdwxSNiSJexOMRB5ltI2cKaYCys6gMxMIDLGNxbr/99pw5c6Yo8zPPPFPyrQjIddddl2eeeSa9Xm8f0u19PMd+6e9l5INHK2a/0cji93xPLv7cz2X+llvS7Ixkq7e/bQ3L7mQ9ytFsNrO2tlbIqiTF6zR2mxlt3JFn3/ry/MkbdnPjBz6Qmz760UwsXxnz9pM8fHuyPb2XzfnNHM/xAUGHtUa4UUAMGF6NOZmamioEGfPjOA0hR3idvEfRXKCSZMBDoWhWdNYW5GS4B3z2ucd+/CINUtJ5bu84wZBTcgiZ6LpWw9takTFSpKfMKjtVh7NpNptlI4KNmXO11pXR0dFSGnlYe94HfLFohg+OQVgwQwRvP0KAvIMfAQDe4CmxVK5IQtDJ5S0vLxcBA9Ztbm6WEwY2NzeLcHKOy/Hjx0ux9Ozubs78e/9VRj58dcXsJ+ksLOTJX/zFLL31rWnMzqb/XMVOu90uG7h5jiXzxcJ1u92ysZpT/Xy0R6vVys7Wbhbn57P6kz+ZL77pTbnlwx/O9V/5SubOP5GJzf3i+V4zefc7UwR9YmKibIimcMK7VIxAut1uqUpxLIgC4XmTK/fkYpANd30PjxPl5be/6yoxezIMA2ck+ZmeVGtBKlFYYIPEdx1bYwBQQIgzH/RmZONcKGuIZ+ZUBeJPxux42jlO60ZNBhmRfkPle4c1Jpu6VqCiH9nnWBL21JYFOEAJHnAVb4plc9wDC8kO+16vl8uXL2dhYSHtdruci+NnfLDzBSs1Ojqa66+/PhfPP5m5f/J/z9iHH7jqWPtJtl/72jzxd/9uNs+eHRDSXq9X7jk3N3dFjJLsLxL5Rp53SYyMEGFJgeIro6N59gd+IBN/4S9kc/eB3PuJZ3P/S+/P7V/bzd5U8uovvSR37N2RMy85U7atYe0hLjBoJlucCqtTIBwcxuf84KiauKjTbJaJ2ptyLec1qaahcIV54Jgb9xcDiHAjH/QbxUIBEHofrMU8cA/vDkGB6nQRToAxepdLHVf6usgsaGRqaqqQdIyFeb9au6Y857D/TdZg3YBrCC1H+jsJ7fwT3+PIEJ4wDdGEYBsiosBY+9OnT2dxcbEI5+233z5QVoVFY2JoIyMjObP5YObe/YmrkjD9sWTrra/N6v/z19Oanc1Ur1cmGg+1vr6e8fHxcqykU1AjIyNZX1/PpUuXSnzL4+2TDMQqLD6MYLvdzvbOTprtV+fz37WXTut78rXrm/mrH2qk0W1ktDWa0WOjRRHsIVwhw71M+Dh0oDHHwyh/1q6utkHYkAmP3Wkt1sLQkL7hEX1GULd78AxLywCCTayHInBv3jdhw3yyaaLb7RZZQ4kJybgeMuI8LAeGMw9GByir8+zuNw+98lzXMXrdrnlXChfkf1fzs4C4eCp32AxdQ10WjsXj0QucOsDg19bWBryQk+lYvnZ7f+f9s88+mzNnzuTYsWMDNLe9BgK871W2c+a3/2maFw8/JqI3MpJnfuF7c/Ff/YXccOONmXiu5pSFarVahYzgoT8WOuKQc+fOZW9v/xDm06dPF+LKBAIxeI0sPIbsJd1W0ugmnW4n/dbBw5zYe4qC8WMPTvUOfWQeMXj2ePYizJ3PdjUqcEjDdfm7rhRyBsDxrc/jtWLxHluwuFZ96BpyZfIHeXHJn+UQ+aGPyEzNooJsmGfGzjg9Vy4y8Bw414mhqFMrz0s5rYzOUTmu4ObOG5G+gFVD+LA0VnYEw6etMVigDJNCIpgFbDQaWVhYGPDcFhKssQUOODWVL2fiTz596Nj7o6NZ/tt/O197y1uycX4naT+Z+fn5K0oaTY6YJOj3+2XDN+cczc/Pl61aLK4ZQBdSWNi8DgiAd8zwWZTaRJ6VBaF3dQseiM8C8yxstaBa+Riz85GGgsgHHpH+MB7WjfivTpNgrNrtdiF1bCQsW/yut7F5rp3esCKSIgLdQAZ1u91yMABpKOYdD4lcYQCQB5/LhKzbqx9GuNKu2XOCpRm8k73Qw8QPNdljltUwy82LyW8/Rs9J5fqpVCdOnMj29nY2NjYGrFuvd/A4B28G73a7mf76B5MHhyeA+0k2/+pfTfvf/Xcz++ij2bl4MZcuXcry8nLxFCyi96TSh7W1tRKPcwrBsWPHBk5RQAgxNii3c8meN3sZPoPQ2Ug4N1fXnZpkYb3m5ubKeTsIqg0cawWcxdPiSVutVmGGa+Ptqh7GUJNI5F7rk889LpSWPpDmwBByT3t8p4vqkk8bUdaPfuDBLfvMuVMudjbermYEg4JijE2CEVJ8wwd81QW/Dr6TDJxgx4+pYwuNYZFjQL9mz0pM57IvQyNeY7eAjz9xAA602Ldu/bT/xaNpHLJbp3PsWJ54xzvSeeqpctoB1R6Li4sDJ/4hMCj93t7BoU+tVivT09NZWFjI/Px8MWgWEo/DhA7Xwjqb/DByQJBROGJX5gX0YE9C+IHQcVwJ/fEh3U7I28vVwmaP6f6YieU1M6TJwflT9oT22DXXURcdeO4Mzb3nEuPp7zAPJn+4vyF5jVKKnCjv7hiezyGLRpfMs9fpsHakcnY6nXKCQF1c4PRGkoHqCQZmgoHv10o57G+8nffuYRUNYUw6ELuura3lxIkTSZK1tbUkybFjxwo8mtl9NJOf+sTQ8faTLP7QD2Xz9OmsXrpUPAUbZ7GSCC0WH4FIUizwzMxMpqeny+4chIqUhVNMCC5Kw3hpUPvOMyLELLItOhUr3AuoZoaUtaMcrQ5TuAcnFsKW+7iWOuThO47xWX/mx56Z/21QgN2Oeb2rw2MhLDLzzA4jPpNkQAHtBAx7QUL0yYbJHhqj02w2B9J47iuy7o386BAsuBHmsHbNytnr9TI9PT1gobrdbskZ0WkvkuNIBOFqpX31fXkSta2OCx3qvBRQmGQ/TCAVHyjL7D//cBpfXRt63+6JE9n5mZ/J+ORkms8xp6urq2k0GiU9lBwcOGxk4DE7vrNHSQYfLWevWwtMHctybVP8jl0QWsfr3I/5qZELAsh1USTnar3zCM/ksAKl8PEg9IMTFFAW5KHMt1hZ7pMcHIqFvGFAMCIYGT6PgbBxqSEmeU97ZL7juJ9403lQHA4Q3GWTrIeNFMaGPGkymPdkvC+YrXWcBtyiNtDvs2BeILwZr1sArhYE05jcjY2NUunhWM2UOrlWyA36yiQkB0XSzW43jQ998dCjRVZ/5EeyeeZMOnpIEF6TxeA1FB+hrRee8Vpg7ZUYp40NrzG/ycHGX3tmFALCzDEYUNPeD0hqssaxH57fqQc2tHNAGLANWE8FD8rP/RFilAEvCzKwF8Po0AevmdGEZcbjQjadhkkOCJ86NiUr4DOSmW8bTwwDDLqzDknKWbg2Uo7FuR+f4YQJ0Nfs7OxVi96TI5TT1Q0E+AgHLGPNCpowqifmWhWTQbXb7bInEaYMYaQ/PhEBIYF94zMIydbWVqbvuy/jH/3o0Hv2To5k5SfeWMgRvs8CQHC4OALraAvNWO0tk5Rr2FPW7KznFyF03AhkQpgxEPa69sjedjUwVkFoFB2YjgehUN2PLUQ4vQ/UMI/7I6z0nzUyScUYmAM8pEkfCzBzQp+RO8eLRm/cBxmAOTU6YVcN5X+OYY16WHc8vdMyJnbMXPv7jIkfwq0XHHNSWcOiUYrHBBFLASNZLDOSJm8cJx3VLAgoCYl/s3X2IiwKZYDtdjsnT57MhQsXkiSjIyNp/8mfpPlcMrpuO3e/JEszd2ZP5yLhkSETOEnAUMZxoo2UFzrJAKtXQziMimN17s/fxIqMHW9htOK+uDiBeUcQgWr2aPX8e64pMsermpyrCx78m/fpr8Mfhzf2fjZ0niMgpRlkEBafMQGEMcNo49EdEiCf3vnkmu8a9WCwHBpY9izjjUajoMtWq1XSizgUV9QNa1dVTrOttoJ0sNvtFoFjQpIMlOG5zpOJG9b8OkbAi4TVoh8IHhNKvS6LzHM7FxYW8tRTT+3vXpmbzPRHPnjoeNe+4/uyl4P9nfac9JFyMM5K4j0WEUNmCh7vwCMQkgwcguZUCiFCkqJMxNkIuxXLjCeKjEIOS11wXebPSmBGG4Wqj3PBOwL5rQi1F/Oaoshm9E3GmEDifrzvQoE6XuV37UmdQ3b+1EaDOZqeni7jcA43GYwdkWffxww98+UdLdzThhaHx/oe1o7clWIsb8+J1wAGOU5gwskxegGHeU1POAxjkoEtRHgO/5jAcJkXRdJMVqPRyMWLFzO5cy7Hn/7K0LF2x8aycs/r0++nBPCwajY2NZtoKIuQGJay59Hbg2p4xRi5nhlwQ0JDZd+7FiiEH+EzEcE8+homXpKUA70gdGzhESzWgDjLHh9vyf2skNyXfpvBpTlmq5Ue4+H7OSb3PGMsTfZYtiEMLbtOFZkIcn63ZqL5bDJ4XpAJM7PA5gau1o484MvFvAgai4v3NFTlpii18T/CY4Gum2tNLTx+n3uhPLaqCAoTmyTHjx/PU089lb37nsixS8OTm8/cOpHlW68vZAFHTNbPo6zHmlx5Wr2ZRmAT0NPblDwuz0utxFY2W2G8mVMbWPEkxai6GoZ4qK5mot8YOMbM3xySnKQYPr5rltaeizkwMYSX5m/6ZUiKF0d2nDt2CAAyoK2vrw+QW/ZOljmjLsf7XldfnzmGB7FS2pAynjrtZcPilA9ZkMPakSchIAB01Ft8fLSgA3J7SlsKBLBunjiEli1keE/fA3iL4eBeDBShYsHb7XZmZ2dz+sMfTg4LwO96XTI9n87K/qPyJicns7KyMsAe2hDVxIQXw0rE4hqe8x28COSBE/VYewQbb20v7XjeZ/s4TkN4au/uazhsYb15jdgouTINZgVHPrie58yygVIgS15Xy4fjPPpvOOr43IiA40LsUVkb1oK5Npx2CFVXA1lRWT8TfZ5zKy3ryOcpekCmjRCGtWs6GpMBIjwUJbTb7YHHkbtSxBbcR0DUi1A3lJ5nYBCXQWnXSsgCQEqQe+UzxBwnTpzIdK936A6U8d5EOjsHx262Wq0BppMFdexQQ/VacZIDATZxgwIiXE7SO/a0AKGcGABDYnKAKDKfca7S+TzGV8d8ZpGNEmyMUBj6xvURPL5vEqo2YJavZF9pYYSBpZY9GykTjSbbkE/QgpW5Do3gC7guTgg5xauTMrL3c9rMfTDUNRKwDPDZycnJrK6uHiKJB+3ILWPEWyY+ECB2qwN5UFo+fy24um64fihrSvEsJCYoLOz0q/ZczWYzI0tLmfvCF4aPs5ls/IXriyVH6YEiWFIH+rWXRNhZAG9Rq+PIRqMxUNY1DB4b3sOaA1+ZA9bEKGHYGjIHToc0m82Bp0ubcWUuGSdPCXf8xOdRBn/HY6IQAIHle44/va0QpWJ+YWd9hpS9Jc3Q3cZrmLGFKwGmej2YA69p/ThCGoajnGgho8Tf7ECxA2FOnUYb1o4khBxLMBncCAhhxpGJZGf+MMgyrHF9rB9wAOOAIEHlDyOXvLD0k2tMtloZWRteFZQ0szb1HQPerSahyCnaGLA4hooWDhMNNZOJojj9Ya+ZHGxJwtqyWR10wbxZGMy++hp4PCCat4mhXMSHeGSnwMbGxgYO8HZsyG97Nl+L8Tj2om8mcmzwHL+6n0YGIDSPz97NaRx7coguvl+HBSbpWBejBHtlk1J8H9nt9XplD2ktE4a5h7UjY05unhxYc8dF7MQgZvGjDJ6v12QBecajy8ZotjR1PpHXvEh4jPHx1STDy4I6U1PJc08Pw6rRlxqyMCcYCMeJvE+Mxmfr33XRgXfRJINHM6JEoAfPkwknx7ZWTHuh5MAzAnlRWF5z3Irlx+ia5MGroXiGtvABzIfTNlY4998kTqPRGDA+xL14PWC1CRuMVh0qeBMCymmvZlRTh2eGrs7ROg1D37knf+MlHZYwXkPfFxxzmiDAU5qZ43U6NTk5mbm5uStizKt5TJo7OTIyUh5Lx3uGCyjHsGS/C51tacf/6CNpbA0/TGn91luzfOrUQH4TSE0cxKQmg0qL4JLIZs+hWU8E3x62hnCM03CauTNpVBsChNnpAgu6DYHZWyp7kpQ1rNfJeUIbDO6JctSJffrtGJB1oG81vMX7cR36ZU/KOBi7DZY9qKEv8+tKprrmmV04zJ/DE/9ACDlGpwDCYYuRJOM3ymJ+jKSGtSOLEFh0LkiljmFSq9UqZ8OyOLRrUcy6WSmcjLZF539DAwsDifStra1sbm5m7tm9Q7eIZS7Z3dlJ9zm4QwqDlEGdm+v1egMxWHJQCI9BSw48J8JppakVk8VmTHglvmMUwdx7XRwHYhwRKLOL7hNGqI6bWfeaqIJIMQRF0WqvCmfAurjPvo8VsDY4Lpkz98GcGMUxFubQpFByYOBBU8zX7OzswPv0F4/K+FEwnwBog41XbLVaA8df1tyDIbDPAB7WjoS19lgbGxtZWdk/pY4YEOzsZw1aIT1B19rsOWo41O/3S+1rkisWgZgKb7W4uJidzc1c98wzh95v8S++PqPPVeGwiXp3d7c8KBfFaDabBV6hqAi3y7BQAFcK1VDIY+Qejjcd/xAD1d6rnlvD1zr1gtVH6Zgrk0o2gobz9pY2VKADewr6iYEwA2zkgKGnb642arVaZdsX97SS2cgxDtad+uf6eswHcSgkG9ccBlMxCvbGRnPMRavVGjjPCJTjeeX7oCrD9sPakbAW+OitYUlKZziakYVwLuxqePpamrF8vTi8ZvhiAdrb2yvHVbZ3dzP38Y8PvUd/ppHd629Ju90uZXLEqWZRa9KD/viUO8eleEtbRhNVjIXPE0c1m82y08fej88lB2SSyQvmxMpUx1sYOAuVwxQLFoJsQ+AYyZDdaIC/XWyBTNQwjvsZuvO3CxBMrNkY8DnH2iaQMBQ11HacbaNRhxNeEz5jA0Nz9Q8ssK/r68P422ge1o5Uztr68ABWBs9j9zhuwyfLPR+PWXtbWr2gwD57FgsQ1DYnFuzs7GRkczONzvDtObtzZ7Jz++tKwA67a8FJBj00E26W0BC/zskR8/B97oM3M0xn7J5DK1Ydf5ukq1MSO89te0OAiYFseDyX3K8mT2oIbnhmQ0SJm4+QNP8wLDywZ7QBttw5XjMpZ1IsOWDFuUadgqMPZoVBfqyXj3KxgbWyYpAtM04h4Zk9bqC0ldLGZFg7UjlZFHuW5KCc7fjx4wPPtuTIwW9Wc+e9aO4jr3c6+8XlFy5cyOLiYkZHR3P8+PGc/NKXMn7p0tDr95qtNFuTVwijIRDxax2/oJjAMyh0EwrJ4FORLRxOVdjAmJE2IvC1vTb0iesxF65/Zb24P9e28HrHB/f0/CJMyAPvu+gjuZJg4vveY4sg+0hJIKRzuE76o3B1DMe9GD9yM4zB9fgda4O2WGdYe0hQrsd4GKdTI5zEgeFh/7PjTGTMxS2HtSOV07S3PZiVdWRk/xEAwIZvtNmDJAcs5ic/+cnMzs7m+PHj5TgUBk653+rqai5dupReb//khrm5ucxOTR1aGfQH7xzLzVPtNHYP8lY1s2lFoj811LQXTQ42DWCdYX+TFEXo9Q42HuPxLZj2KKyFD4RybI1w1orrnLAV13lOBJynlNXb2dxsHOm/4zS+a0Rj7sIQmfmxN2H8JrzqbXN8vo49TdaQk8WzWqGHGU9eY+cQO6/YVYThqiusPB8OQ+grSg20pY9bW1uFNT6sHVm+V9dTmq53TPhCoOzV2rC49ZOf/GTe8IY35Lrrrku73S672ldXV/P4448XweVoTkq1vGh1e1X/LVneO/BKQA/DNTPGJmwM95gHb9RmEansQfi86wFFqgkZ5y+TgwfiGCobJuGt8EY0x3L2WAibSSMTXIzTRBZ9MBw1MYQSWeDMaBpemzBCdniYEo3QgvOkWFvLYJIBItLF+nhrs7j0hTVGvmtSyqQf13b+2tCfRl+p8UWxHfLY+NQosG7X/CAjJtSd5sawWsNyZd+sxv3+7M/+LDfddFNGRkZy7NixJPsTt729XY6uvO6664rl63Q6uRotNdIdyd7uwS4HxsrhYhwrwev+bVhlzwdcMQGE4nv+7CV9reQgN2rSw9VRkDkuY/S9iD/dVxs6Q1YbHEM9w3kjBUNk7mnvxN8YNeTC3tgElJlSrmfyBxTCRog6DPDfRhaMxZ7Scb8NaK/XK2V23NfzZmbbhpL7MG767/QRKMwkF/P3ggkhN4Jlqul5rU7qfjNa3WEm/4d/+IfzxBNPZG1tLVtbW+W4yUuXLqXRaGRmZqYwZxQxNJvN9HuHl0glKV7YZA+K5j2ljgUdgzkG7XQ6A0+YNns4jNnkt69lRhDBSnLFnkJDbggnjAN9MqR1mR6/raA1NLZiYxww2PXpffbC3uKFJzH68P/0jf7VbCrzxrEzNXGEgpsxdd8Zp5GeCaQa5eBsQAA1hOf7zuMmV0JsIy3iZD+ikTG84M3Wbiin4yEsQU3QfDMbk7e0tJRGo5GTJ0/mqaeeytTUVCYmJkruFUtFEQETMTo6mgu9CzlxyPWXGkuZnt2Pl9fX10su0PDWaRILFb9R3LrqJRl82A6EkONNoBjfQ5l8PbOzHE1JxRZHxVjxWRtDNcbgmNSCyed5nT46bmy1WuXITdbGMbHv650gtXfne4awGCGqlyzo3kFSe2BXqHmNDG35zWd3d3cLX2JSLsnAZuu6Vpi/naKysoEsWUuHRc55QxqBQg5rz/sRgDX5Y8v8QhVzmGsHoly4cCFPPvlkgWrnzp3bz122DzarNhr7x1biQff29sq5LZ1uJ38w+/7cc8i95z/7iWzuHFRqwDx7cZ1I5zUvCIjCCsd8IYg8pNUECItdEyAs/OTkZHmPxSZlRRjh/hjyJgdhiYvY/V7dut1u2apXx2A+ncKKYmjntA5MMSSKDZORRnKQbrEndAGCPTYe1saG+WCsZn8dZ9MXlN8MvFnUuiCda/CZkZGRMk+8Zm9cG1sMJ4bdT0V4wTGnG0KGUGDpbDm/kWYFBcZcvHgxjz/+eDY3N8sjF6ampnL27NmyjYkzfcbGxrK5uZmlpaUyQe32c+fDNA7H9TNL3TxzeSmb3YNSRVv2uvLFRIjjJgTWBAPf9/mmvOa4ycRPr9crxy7iHSkVNHTjuhYOM7LmAeyBzBwmB4w8iCHJgOcy81iP0+ufHEA79wuSxB7H64xRdG6wZkCZC8eTdTyPkebv5KA22Oc4oRyG99yHtWi321cQZiguc4GhdbztvvCeDSahgePYq7XnBWuZULxap9MpRzN+o80Bc6fTKZ7v5ptvLhBrdHQ0q6urWVtbGziecmJiIiMjI7n11ltz+fLlLC8vZ2VlZR/a9ns5ceFw5fzDH5rNmbFmumt7Rehr6JQMlsbxP+/7MybRuA6KiUdIDnKfzn9hZLjG7u5uFhcXy8lwGEcbBN/X64SAWwDt2ep4s2ZQPX4rqC2942GQlF+jb45lTczU3tdIwLGhn+NisqkukfN4uZYJNs83a2PPxhgdi2IYjBK5LkrXaDTKoxeAxTWcZv4dCtVzVbfnBWuxLMArYFxd7P5Cmq/hSXd81G63S8yDoHqiR0ZGcuONN2Z2djaPPPLIc3BsK+/8nw8/fvDVS9+RJzc7A9bO1teMmr0nE+x4tBZWPAkCYyjk7zihj2Jsb29naWmp5OtQZu6BgtqD0F/HqiaI8L4oeO0NkgMB4m8EHYjrWAkWFaWvldiMJfGe2WAbFebXUBY46+IKk0I1hOz1elfkaRkj8kGFD3Jm5XFc6bmmiIP714YUmQCym202akhS0mqtViubm5tXRZzPO+YEf9f09zfSbO35H4EgjkDoRkb2H7xTkzHJAaN2/PjxrK2tZXt7Ow81H0p34it53SH37ux1srS0VASv9nIYgWGEBn2CXLAXQviTAySAEACLatKCVAGxGdcGoqPEjrkMZ1kT9tPy+Xb7YIeP45w61+bcqqGXoTz9N+mD4Np719f2+vE/gsu8mdQhTsZA+NQG1tqwmr4wRr5n1hwDjNHie8iXn1DneNS8CvexEaev5HtZF1/Hss2aWKaGtecVc3LxeuMv1pj2fOPP2rWD1RFQJodHzZv9wzN0Ovt1vRzleOrUqVy4cCHrexvZbR7+mDWPw/DU0Mqeg5+ayOExFU6ZoHwonStGmKdWq1UevoRievsZn2ExjSa4jr0TuToesIMSMQ6TWfTPHsPzylozJoQMpWXefWiZGVfHecPYVsNZIwcXEng9HFqhJI3GwdYrjF9tPOtYnXmwUUpSHmiLMbNSO1NBX13uSKkentlyZTIL3WFOXjAh5IV0p7Ay3qv2zWyOywwVGBiPHFxaWsr999+f1dXVnDlzJtddd12OHz9eYMPc3FwmHtvKiYuH3wv4ZUE17KrjTwSJfC8T7yczI5CuBnFzKmJnZ2fgkRNJSjztVBWQsFZK+k/cg3JQyjg1NVUqaGpvbgGlX07FoMC1EDk2TFIEkntzPwszY7ESAoG5nufNXh45BJ6S6nIYAlRlux87qFgv1sJpITO+yBtrxUkeGCOeIsB2L7yvyS+clMMU5slGzsTdC1ZO5xC5iZXRMM5e5xtpxA27u7uFjeXgXyvN6upqfuu3fisf+chHMj4+njvvvDO33357brzxxoHHsk39+YXc8dXD70cci4IgSIzZeUoriKGePSufH5YnBT6xkD4tnwU1RDSc8mtWUAuU825JimWnj34oj1/3/46na1bR8TVrxX3dt1qpEWpDUT/zEkHf3Nws60zO0OM1jDXRRTWY89N4Uhs9+kH8nuQKI2HjgDNgTV2C6Ty/HzjlKjDDZ67NYzItI4fK5tUUhZsxKXgyjuIANnjBjirmParhhYgfzbzhrcbGxvKFL3wh9913X/EWJHr53Pr6etbW1rJ0/W6+ekdy1/CD3oti1eiAvzlPCMhjltPxDwc5WdgQWMeKWHQbNhQHw8T9TdHXfSM2qk9HN2RlThqNxsADce3JEHIzlYzJJJfjLBcYmOW0crq/hofJwWM6EHJD6rqKyfenkov54HMYI8eoe3t7BXWheCiqjRhG162G9TVRyPvc0wgT2TDEZo37/X6prIJU5ekCw9pVtQjL5cCVgRNXMBnT09Mv+FCvKzrVbmdiYiLz8/NZXV0dSAEg9EtLS1lYWMje3l6OHz+eG2+8MadPny5Hi9DH9dMjWTzezGGHe+2q1tWpGYQA5azzYTZOhjT009CVa9fJdXtEBM+K4OoR7zl0LhWFMoON0FFUkFy5P7ROHxh6m7l2vEkfuY69ds1um1zjNffbqMAnA3ituQ/eDSPE/DgH7Qocw3/GjVIy76yNDyLzHDpuZB7ITNijGhajlM4vm7wzYeXPH6oHh76TgwfnMmA/HsATjRC90Pra+jss5MTERDlLh9pXJnFiYiJnzpzJqVOn8oY3vCE33HBDGfze3l5mZmYyOzubW6dvye1rtycZ7jpn+l/JSPtg8drtdtk8nhwItR8CS0yIktEne0NbX5NDJpaSg/jNcRL3Ba7xGSwz9zLBw7XqLW7OXdrrWkgsYFZMfnw9iD/uj3HGqKBUXNfscE1gOZ70PSm84Pv2bFYsYnbWDMVzfhSjabbX96PvycHRLvTVJBqfrZEACm6vaiTE+bSuz6ZGwOs/rB3pOV0QDoxgAynCPIyCryfzhTQGiYJCkjQajdx8882lemhxcTHdbrfkOK1A05PTeeB7X5K3PTBcOWc/9GhG/so+7MOzcYre/Pz8gECT4rDHAlbyN4YkOSh6NpFkqJZkoDDB+bVk8FDvOha2R+a7oAXDZZNAEBmGo7bqyWBBA/CLtaYfTh/48/TLzxSpyTXDbis/fzNn5LOdijDMd2G9jYx36tSxIZ9xnrYu5jdMZh2JEZ0zttEwIeV1ddiHQcSrUkb4gpXTOwHszr2QeE1jbZMDbs9HafHIxLk17Dp9+nTuuuuufOUrX8nKykpRmrvvvjtzc3MlPmy325lY+O4kfzT0Pq2VjYxduJDcdFOJz2AcLWD0F6jr+LDZbBYSi75bkfya4RNzZ09o5UUAraAWaMdwGCPmybGVySmPz3EjfbT3c16W7/EaxsLwMjk4EYDXa1ILxTQhk2QAjTDvrKE9lpEb7/mHe5mrwMPWpBdGh+vgiFgrF1hQ0ljkpkIzExMTxUnRb7PQNTmEN3/BymlI4uJklJD/vcPfi3C1ZhJjGGPFINfW1rK+vj5wXQzAvffem7m5udx3331ZW1srnuHOO+/M1NRUsXi9u+9OZ3Y27SHPpxhdWcnp3/u9PP23/lZRBMeEwCKUxdbY1TwUzLOoTh04NjPC8PEh6+vrV3gTLLIhVXJg/XnNkBkhYD1sWLgnCIBr0W+8tmMwC5jTERhrs/SwoQgf46Mf9MHQlPW04aLPXM98h/thBtuEDM1Ggf99AgF1t0lKHtpyb2OI0UD+reBc25vRmUc8sWPQOow4rF2VvXGZV3KAyeu4BmFx0H/YTQ3pDmudTidra2u5fPlyLl68WCwt+J8Jb7VaeeUrX5m3ve1tmZ6eztraWp588sk89NBDRYn29vayd/ON6d05d+j9Trz//dl78MHiAThJgfGbYmchiMdhcZkv5gVW2zENc4ShYyHZRbO+vl4KFoBnXMvbmDi4jPcRtjo/icDUZJCVAyPItQyHk8HaYbzizMzMwFY0hNB7TYcRTGZTDeet2K6EYq2tAKS+6qM3TfY49vN9gMuNRmNgfXxOMkrIXNDf5OBRhHAD6IGLKFgrZIQy12538EkAVuDD2lWV0xbLi8jCMmGwgmbMaCijWTWzcfXnu93ufgpkaSkrKytFaDlJrl60fr+fl7zkJXnTm96UY8eOpdvt5vz583nmmWdKX/darSx+19sPPRFhdGUlYx/47zM+N5rp6ekBgfb4uN7Ozk42NjaKAOL9iCGcfuI7xLI2ak4RmaXk/xqO1kSS4She3hsCLAReS4wo36cQYnV1tRgIe02TQVzXMZjTUWbVfY3d3d0BhTCj7GqouorKzKshIVv1TAaZ0ON6PGgLrweUHB8fL0e6ttv7u5fYxAHnUHtAzx1rhNwDiw3lGQOfbTQODmW/mlLSjjzxncXf3t6+It4EwmEZ19bWrrDWbkwYi2rIwmJvb29nfX194OxWx14WFBM0119/fe6555488MAD2d3dzVNPPZVTp049R4t3cvG7fyTHfvPdGb18+cp+Jbn9g3+eP/+Z+3P9dd8zYP1hqIm/8RiMhxgUBbBwWaFMIgD52HTLk9T4PEqGMaoPB3NcyN/2DkYy9jz+27GjCSJ+LJg2oFZqvo+MYEjw2rzva/A/ho0+0V/LHV4U5pNxOqfq6yAbPPAJQsdootHYL4DA20LiUbaXZOCgcFCRn3LGdVB4X8Pn0hrOmzS1Zx+mJ7Srek4mBAXBjZtNhFG15dzb238cIAc24QX4LpaEEwuolKEqiPgxSaampvafrflcHtXWfHt7O5ubm8Vi3XLLLTl9+nR2d3dz+fLlPProowV2bp06lcvveMeh3nPu8l5e/3f/WdrPTbyLBbDsxA+GY4ZZBPmTk5PlNSy3PzM1NVUEI0l5+JPDASCe4yrPHcJpMslQD6G0wBs2sk7AYsdZwHjWnO8z5647xQCYzGFu+Az3dKke76FwzunaYJiErDeYE4Y4/nS+GmLJJ08gd8gbBhjk4hjZcbXrfalYM0ynmo0+GRl5Pvz0Pedch7UjD/iiEgWoY2KCBUS4GDBHZ6B4nkC8JhaY6zGJGxsbA14DCEK9Zq0gTASG4Oabb84jjzySCxcuZGxsLGfOnNm3hP1+Fn/8bTn9/v85jfNXJn4bSWY+87ksf/zjWXrDG9LrXbnrxgtlT+DYu7auGC28Ya2Ajp/9mlGL477kyh0Z3LdGGPZettq+br/fL57fa8v3zH4y/ygUCm3yzMSSBZQ+oASOKx2j4fU7nc6AwSJcQKFQAhBNTTCCOMySQgZxP+av0TiooDIq8fugHmSTH/Mvdj7IOh4aA+TCDMvWsHZknR1J3pohZGHsrvl87WUYbF3FYXjCIq2urqbfP3imBNaZ+xoWGabhUWZnZ3PLLbfk85//fJaWlrKxsZGZmZn9RxWevC3PvP0v5fp/9ttDz7Ft7u3l2P/wP2Tt2789vaoY3p6CReBve1Z7BxoWv04LYMmZD8euZmAR9ro/eDjDTiML/h/GGnINr08yeP6RYbkZy2GxohXf84TCJQf8gvfhMna+gxC7oGB9fX1AMVEglIN7DiO+JicnB85DQrZwGhgTV1/VxRmey06nk/X19YKCPGdeS+aI+JTP4YExZlcjR6/p4bmGsfZ09qA+fsGW0NUQ3rrjWAHhW19fL1aPwXEvLGQNq5k0hK7b7eb222/PE088kZWVlSwvL+fWW29Nv7+f6zr/rp/I3Gc+n5mHhlfDT3/mMznzy7+c83/rb2Wjf7Ab3rDccTZwyawuxopcollAe3zmF8GgjA0BBP46ZsTgOe3B+/ZYPuvHTDMxL9fjmsRUrBEC6jkHvhm+1hAfg01/nHqiL4bihozNZrP0e2JiItvb21lZWRmYP2SMuNGPuaihMnPMNkKnqRgr5BLyW8NrM8tmw+mzESDzwP91jbnjeirCXnD5nhk1Qyt7MDNyLJBjT0qxTFR48VkwGFkGxyJCwPikbR/SjKCjNOPj45mfn8/CwkLOnz+fZ599thAqy8vL2T5zJk/9p/9a7vjZv5/W6tYVY270+zn+27+dfq+XZ37u57Kl9A2ChNAmBzvb6R/KyYn0zAnfMQEA2UGODYuepGwqt2KykCaHHGuZIPHhyyhnTca4b9wjyUCKwBVJjjV5zQLn/tRzZQhnj2kDzDWTZH19veS3eaK4iS48lb0kjf44F+85NFwFPtNv56/r+JsYFm8LWrOT8JjprxGTkcRR0PbI/ZxMIJ3wk7hYjJ2dnYLZ+Y7TAUBdb0hmslBgBoUhYKJcEufqExrCkaSQLdPT02k2m3nwwQezuLiY7/3e7y0PWGq329m86fuy/B/fn2P/ye+msXnluBu9Xk78i3+RqU9/Ol/86389l1/60jQELxlnvcveZAkChEd0fG6D12w2MzMzc4VX5ultZmKZM0PjGmrzv+NBw1jDZYRnWExp9OKCCtZoe3t74MGxXgfWmPE5DLLygq6s8FtbW4XfMFOL0ab/LohwbtSPYuR7bMa2EYHprxXGZFS73R44v9hGln4wP4x5fX19wHtCPmF8TSxBxh3WjoS1LJatHO8BQ1w/yI2toIZ2njgzwI5d+LEwMFnuD38nKXmrqampjI6OZmlpKY899liWlpayvLxcyqtg4RZ/+N/NxMRiJv69D6cxZNdOo9fLxKOP5rX//r+f869+dS7/9A9k8zWvz8jobDbWN9JoNMoGaxNCxDE2XnigmpRx0n56enogTkehITYsOAidY1sbhNqTWXH9PRTf8LsOEQzX+I5hZHLwmAUn/e3NkAPGgRI7dElSmPpOZ//guPrp5eYZTB5ZASwboBLDdRM1u7u75cgbf89ryZgxSNyDuJlCg3a7PfDkd6fhyL0axqL4htF1u+Y8JwviIgKfxYpndSyFxTQEZlF8BIkFzlaaSULY6+oRXoOaJm0yPj6e2dnZ3HTTTTl58mQmJiYGTm1otVrpZz7nvu8/z8z6r+b4f/U7aV0aAnGTtPb2ct2nPpWzX/xUHnjzDXn6x380U7e9LaOZKf3EQnJgE9YQg4SF9okEsH7MB8JkT1nDRh/9UcYhQsiG03Gh189hCJ7aXtZriFBb2awkxGE1seOaZO5jKO37OGRinzAGFK9nxtupmmG5XPphz13PJddCjmq4znVNCGG48NiObdkg4rniM37KWB1SmC8Y1o6MOe3JwNTG1VhDWF2XMuEtDP34YULoIIOwIDn3hlA5voLRpcJlc3Mzk5OTmZ6ezste9rJsbm4WOIu1s+Xf3p3Kxe/8uTx9y525/R/8j5n+7FfSOMSQNTaTez/4ZO74k3+Ur7zx9zL12nfl3A98d3rNmTIX5DINF60AfI7f9qreNG0mnHEbSdiL1bDVhJKJn+TgVMPk4ChSEzt4faMf5MAKaiG2h0eg/cRoP7qD3/bsGAGu7cIUDJb7BDrzJmjPCYUsjNe7dBiziTyUD9m2stvhcEAbvAZkFPNA3+zV0Ydms1nOdqrDiau1qyonRQHD4BGMoPM+3AwrQSLfMY3jDrv4Oh6ytzWMc+wC48XJ7whEkhw7dixnzpwp/cTrMGHEHI9+/evZ3j6dC//hv5/bfvcDueW3/7c0d3YOfWTg+HYvr/zwE+n/2X+dM5//3Tzx4z+ccze8LXvPeb1+v1/gT51CsfBYMA2FnX4yK8ycu1YUGIyS85rn2oyj72nI6XSN92tyL3tn5p+0j70RENyMpr0JMkWrIbpruYGdjucMPVutVqkEwqMiixSrmFHlb88/84JR88n9nU6ncCSkACmU55xk+o4xgmWGZyFVZtTm+SKceEHKiTA44GcgGxsbAx6MgSUH8aShGx0zGeJktD1NHa9ZWCy8YHnDRGIDPC/ft0DTx09/+tP5tV/7tezs7OTtb397XvWmt+TyK789r/zlX874009fbWrS2EmmP/RU7v74r+bYKz+dB9/xI1m+++4yRyimjQ5jGBYT2ujYG5EGsBdlnAiPr8G6MeZa0U1quJrFMA3lRrGc+0NRzHiyhsngsZn2wCaL+BwygHyBEDiJgn7UxeMYAdaYMftRf8z9YbDUsoBxBMUxD3hLjxGjbuVkTvGmGBWMBkUhhsrMy9XaVZXz8uXLOXbs2FCrXscXCB5saqt1cC4pi8hC1KQF10EwuRcD8QkBfBcLZuhtq85izM/PD1DWjjNGR0czPz+fS5cuZWNjIw889FAunT2b1n/0H+Xev/f3MvLUU4d6UFpjMznziU9n9stfzcPf//15/F3vSvs5haKQ2p7DUIy5YJwwjSgLxtHKhnJ6DnnfHoy5djyKwQC+1cUGhtwmg3wfrxmfrbkFE18m9fA8nCqBUK+vrw+wxPbSjpWtWBjjutaZcTF+UNj4+PhA0t9xNbJrA0Zum7SYU1wYLZQOZ4SBg4Ry35KD3UzD4PiwdiQhhNB44rxoKJLzkFgas3K2ElZSU9jOqwGLYLScAHYi3aRAt9stEAOFppIDhQTidrvdvOxlL8sv/dIvZW9vL4uLi/nKV76SRx55JP1bb03jH/ydvOz3/rOM/dZTafQPr+KgTa6s5OW/8zs584Uv5As///PZPnWqxMBYb4wWnsdKxrjxEigZQmnvadLJgul40El3hAAlw9ha2J2Ps0DVfcFo8JmanDLMtpJ5vDY6NTRGvhg3a85vvD2Kg8J7vCib410UqNfrlSNQXNnF/0kGkAJyaMPH9ZE1x5g14WYjZVmteYZh7ZpiTjrphcEy1OSM2TEW3myaLW+9KyXZP+WuTujbMnM9oLW9QK/Xy8bGRmZnZ0syeX19PXNzc8WjMiF4tPn5+STJiRMncurUqXzqU5/KuXPn8vljx7L7M/8wt6z9Zk5+6ENpbQ5JiFat2evl1Je/nG/7L/6LfO3HfixPvfnNA8eamDCz0PT7B0yst33ZoFnYrQRcu477PefezWPj4M3QZQyCXb6OldvpLMf/JqCsKPayjh2RBTOYjM9y0e/3S51t7ZWNmKxQILf19fWBjAL3walg+ClE8PUdcnF/h0rEqVyfMaJ4KKGdE3Jpw3lYO7K21pPrxeJ154UQNn4jUIbCTJAn2lYTQTTV7LxbHc/wPbwB0JBJu3TpUlqtVoHAtJGRkWxubpYjLdvtdhYWFnLXXXflYx/7WJ5++unceOONefRv/+2sfs/35CW/8iuZeOSRI2Fuksw//XRe86u/mtGLF/Pwd39XdmZnrmAAmRvPIa97QVlg5sR5VQuw0xfN5kHOGYEDsjH2OgZinRxL+h72CKAA/41AOzfJvRBoUg4u5sDrIbAmn5Ap5qCO/9wvp064NmEAhsBzgHMwgYUcWB4J5Yg1IZwcl5u4RCYZF4bXzsZI7gV7ThTDLt1WyNbACmuPQAWQYxwUb3Nzc8Dy1fdjku3x6BfCgUeyVyeQd5rAp+mZPDLhMTIyktnZ2Zw4caIQMdvtdi58+7dn6Vd+Jcc+/OHMPPBARi9ezPSXv5TsbWX0kNRLa28v9/7Wb2Xky3+Yj3//S9M99W2ZmH9lWjNzabYP9kRipOxljDJqxTXs5O8arbBOzLPjOa5tKGlj4D5YURzKeL2tqDUhdZjxsQwkKQ88dphEisxw0Yx9q9UqoQtsM/dELhiH88wgM9bbR58yFvqGZ3UhPsbG88o8WvZrT86c7OzsDJyE8YJjTi+4rSGdwaL48dtecMdKSQa8AZPAdf3DgCmHYwDeYuXYBCFhIkwKATOcbLfFsmGgOOLkyZOFQFhbW8vU1FR22u2svf3t6b3tbemsraW7fiHHHvrT3P0bv5tjF4c/i6WR5J77Luae+y5mbeaTaYzM5Wvf/src/87XZvq6ezM7cZDqoe8IlR9q5PI9ezw8D4vtEkjGXMeWXiMEkDmzVzFSMSHI9+tCcMfCRkG+HmO0wrNOMJs8TAhlq6uWTDxxv2bzoLjdHpX1xRs6dWe+w0YGGaSih3n0vc1yu59GNHy2Dhsof63JrGHtSOW0FXDHasbRKRNeN5ythcrUuZXei2VG0vC5ZrysrDUBsr6+nvn5+Rw/frxAKk6D51BqJnRtba28Tv+NGsipjo2NZfbEjVn/7p/Kn776lXnLf/BPMv3oo1eFvDNrvSRLedUHPpKX/fHH8vArFtK48/uy+sY3Zv2lt8d7ExzT23shKPZu/owT28Ngq2OjGp3Yg9aG0kRQPefci98YDJrlhT6gJBsbG1lcXCxejZpo+jI1NTUQdqDMrpPFOPjoUmB7XTIKSjKRg+EhFDABRxxNbG4P6VQKhh194RpWUuaG2moz84e1I9naOleHJ+LCNbThxvZsjlPdUQTFOyacm7PC2dLjGbDmjlfoAx6SsrokmZmZydLSUj796U/nsccey+23355Xv/rVhRrnXpOTk1laWipCwTapnZ2dTE5OFiHudDppT70sn/+lX8qr/su/n5mHH0njiCciNpKM7nZzz2cvpf/Z30rvd383X3rjDVl6zfdk6zVvSX9uLrsibyzYyaBX85Ov7BnwFv6e4zJfq/4M5A4KwGecsyN+wwBYyK3s9Gt7e/uK9MnGxkaZYz+ykPuAGOwxUSBkBkjrfaUk/rf3trPR2kin3cloRtPdOMitE+JQckeenuuapLMM+wFNhqPoBkf5IKt8lj4zFuSKsO6wduSuFLyZPR3W1XDBC2MPicDUXs2dhZ302aXJIFNoSOz70JdhkGtiYiKXLl3K0tJS5ubmMjc3l8cffzznzp0rJyW87GUvGzAMKB40/fT09ABSsKFhIdfPnMln/v7/I6/8w3+Qhd/+WppL1/bM0kaS1u5u7v3II+l/9NGsnf2DPPuXXpcnX/7mrB6/uSy4YZzH59wvwkyf2AHDaxYMx+78b2PIZx17wmgiAza6NtJmgk3AoQTJIOly6tSpAbKHdbCH5rdZVpSn2WymM9rJ0txS5tZ2cumWr+Xhm7pZmt7Iky9/Mv30c+qxUzl136nc/Ic3Z2dzp/SFHUworbMEZpjpjxlz5r3b7ZaD27gu/bPHxdNafs1wD2tHek7HCvxmIYYRNK7RtCdzrEjnIG64h3G/mVrgiZPLpt/5PtCGa3Ci2sWLF3P58uWMjY1leXk54+Pjuemmm3LjjTeWYnW2pE1PT1/hYbg34/Mkt1r7p7Vt5lg++tZfypnXfC53/S/vy8IHv3pN+VFao9/P7DPPZOZXfi+3jH8g7/s3vyPte38yEzOz2XpOQQ3p+d9cwPj4+MDeWfffTCbzhhCacQSVsP5cm3mlDxZg5t4hgOeQNXR+empqauDxhA5vHDciEwMQstdNb3ov3ebXM773YP7KH6zk0tnzufMr/Vz/TC//wX+e/MN/ByFOVk+u5uTOyfRaB9sfgc3T09PlGTXIFfcxN9Hr9co2Oe8mskLz/Fg+D7sLp0D9uImnb2hXimMQJhxLDMzodA6eZsXAfPq5YVIyeCICC2+SyIrhcjcbAcdWLs3yxI6MjBRvuba2VjYwz8/Pl3Npd3Z2CqRCmWnE2EA5P44QAzPA8GU05469KRd++rU5dc9nc/t735vjjz6aVufw3e51a/SSkc2t/PB/+5GsnH4wX/m+78vjb3hDdqanB9IwPlTNCsfGZLOI9pB1bA48hDsg31czjmYpayV0iGGDznWI2/DKFIeMj49ncXExCwsLAzCaefU1SqXU+nrufvizuenC53Pr48u5/rkqy8Z9+7/7Sb7rj0fzP/x0M8cfP5uzXzibG//8xjSWD06RqOPQJCUWBDkhkyAXvuMCBWq7bcD6/X5BLK4r9nGjZqyNLup2JCFkxtMkgiGMd90b9liher1eOYQL2EMz08riGk5YWLDQ/ry9GsKL1T9x4kRuvPHGPP300xkbG8uxY8eysLBQ9t6xCCinEQHXAz4RU3BCgStBvCu+3W7nkVe/Ok++/OW54Utfyh1/8Ps59uzDaS4dfVYprZFk/vz5fPs/+2e54w//MH/8Ez+R5bvvzuhzJYGumME4JAcezMaDuWCu7BmHISMXKliQrbAmnOiHjcIwL8q6c82JiYl8/etfz/r6em666abSHxTEPEa/30+ntZJ3ffDDefUXHhg4NrKf5MnTo9m+az6f2bs1D83fnp/8f0+lsdFMf+e5nTETGUjN1Kyr5cZIDjmEFWdeaYzHBJHnxmhvY2OjFMbXBNuwdk2e03FOkismn5uQc7RnxVr5cCYXA9tyWKhqAsiC5yDdSu64yfDh5ptvzkte8pJyit/e3l6eeeaZsiDeZmVPCHxx2aDnwVDdeTeQQWNsLE99x3fkwutfm+s2vpCb3vNHWfjgQxlZX7+mYoYkafb7mX/22fzwr/xKnrjnnnzkr/9wZk++Ju2RkXKwNZbeuzoMY/3bHpPPGAmwrsMesYFRdnFJMriryN4VyOpqGR+oPDk5mWPHjuXSpUtZW1sbOBGC9e71euns7eVkZyl3fOF/yWu+sFzmrjfSyFe/41jec9sr8pWZhTQ359JoP3cc6cZzR+X0D47/bLfbmZ+fz87OzsDJ+qCFmoykJHBqairJYK2vWWSTbRgXj5XPEJcatlvR63ZNZwjRoXpTas0I8lkG57SIUy5YLVqtiHUy2P1x3tSPdbOVrskqBJPXqNflc5ye4OeHkL/ldSpDGK/7j+AyHyxkiU2a4zm38IZc+Jk3Zf77nskt731v5v/sIxlbWUv7GrijRpJ2p5Nbv/CFTP8nX8mln/7RPPud70rnObYWwTCBYyKPnJ0RBwLCWO1FnEs0AcL38CZ8nvszv/bssJwWegS/2+3m+PHjSZLV1dWS4zT0bDabufH8+fzkv/yXWVhZKYr5+PWj+dNve12++rJXZnN3IuNbW9lp7qTZGCyGqfOZOAUqxlZWVjI5OVk8n40WCgUzzjVcS2vik7HXyK7R2C+8p3g+STm9wcikbkdWCHFzK4nhkQfEgkAa4EEcR9aYHy/EJJjdrXOoCB6WidjWAmRDwncgJFZXV4v14md6erp4e8esTCzXTFKU1dafhUcR6HMNW/r9fjrdbpauuy5bv/AL2f63fiT9T/2vedP/9Pkce+yZa/akp57dycl/+M8z/9FH8uW//RPpjp7J3t7BziALhw0FVThGOzUEtVJ6/X0tvCdj9TyzLrC6KC79wqgCWzc29o97mZqaysrKSlZWVjI7OztgZG7onM+Pv/vdOfbcQ6j6SS7Oz+efv+0H8uypU2lsNdJuN8q6YGBRNj+fhH5gsBwHepvXxMTEwDhrA8h7w7IVXB+SCWTnvCcK691Ww9qRTxmrCQeTBHSq3nGPoNOZYeQOC41gGMqy8I4FaF64utLH1UYuufIOBX9ubW0t/X4/CwsLmZ6eHhg7J78tLCxkY2Nj6MZxxs2uHc9BnbTGU+P9JyfPJm/4hXz25es5/dE/yc2f+/3MfP7pNLY7R29T20nO/vmnc+Y3P5fH3vbdOXfuNQex88hqbvzcx9N48jmB6vfTbDTSeHszazedztO3vC7PrN+Wze0r0yH0vyY4eN15VBAOW+9cfG8WmXXiuiZDHA7hMZ22OPX443nHH/1u5qSYj9zWyq//2OvT65xNo3tQ2mnPDDeAzBmuQ1AhZygnOWOcDvl6V/Fg5AnL6gIQoz3GThjkNBKGHDh9WDvy+ZzeleIJpxNO3tZKZ2tpj+qAGYG35WEikgM2lkXkWgg/xgGM73Ny6365H2NjY2VHOw+uga3FOy8uLubUqVNJ9ve2wjAayqGUXjj+BsawKRqD0Ww2y26Jxshovvrd352nvu/1mfnKU7n13b+f05/9bEYEgYa1RpLG+7q57X0fzm358FU/myT5UnK68UBuvfHDufTqW/PIra/OE3e/JhvNk+n3B3PKrDHjI043Oce6wXRbKP0+aYterzdQU5qkwFgeZQCamZyczA3nzuX7/9k/y9zichnCs2ea+W9+7jszufrKjLRaA8/TRAE5L4oaVsaTpMB7lKXf7w+cooG8JAdPO6DfRoUuk+S7fNaEIjJLZRreknly2euwduRj52Gq6kkg3ksGN/kOi2tqBWVQxDoMBGXjmrZq3CM5eO6Hrb7ZUiw1SmFoznV4ZgWKWD8GbmJiIhMTE7lw4UJmZ2cH6HPGxEL7e84DAtN9LIkLAvwogWZzJp077s7n/p3bc+qhh/Ky3/iNTH/969cMd6+p9ZPm472cevzhnBx5OK98+Qfz8Du+M/df/4PZbY8MGBsUyp7OsRyQzeywx02Yg4ByXWQK+A/pMjY2lsXFxWxubuZVnU5+4F/8i8wuLh7IYrOZz9zz5kyufEdaOXASOAeQENkF8xy8z3ohQ44rIYYcfzu3y9w47UdIxw4oshGsKejCToXH0IMsX7DnhA01mWILanLAxJBZWrO29eKzqPw4uct1TC4woXVz3EszvKqZRmKw8fHxXLp0KVNTUzl27FjxaIxjdnY2q6uruXjxYnk4EQpGjtREmZ/oTLAPE2woiPc13CtVMa1WLr/61fn0L/9yjn3oQ7nlve/NzLkn09y99jTMtbTGXjL5+ZW84v7fz+lbHsyn/tW/nAs331jWlx/DWSx+HcoY8RjeG0nVRpfr4FWazWba483stj6RH/q1L2Vm+eA0xE6rlQ9953fm069/Y9LtppvugCG018c7441NUjEmICiIwMQW/eaadVqk5j+szLDC8CBsLeN1GzCMxwtOpbAo7iSL1e/3S34TJTQhwcK4bpUB8WMBMPmQDG5DMxNpb22lM0zmf1fK8BxF4Dn1nCTzjx8/XooUqBra2dnJk08+mXPnzpVjNm1drVQwkAgt0KkmwAxva3RhoqDTbmfprW/NE697Xc4sfyKnf/v+nPrygxkXY+nWayS//Zcb6Z68Ia956jXpdXo58fDDmX322ew2tjK7uJXmECPd6CWnH3kkb//v/0Eeetfb8oXXvDNbnYOHDdUwzeSe/66V0xCYcSPcGLdeb//EwtbmZra27s9bH/iTvOkT57KwIpjZbOb9b3pTPv3GN6Yj+cNhOOwxMvE2OI4cQUHoBzLiZ3GSw7YjYFN1jQytrPSJI187nc7A81TsUbmvCxiGtSOLEFwtYQuIwrne0tDTTBb42gtmL8c1+a49ta0vi+/X6BdGgiMoMBxAFhYNNMDfExMT2dnZycrKSo4fP168X6PRyDPPPJPPfOYzmZqayj333JNm86AQAWYSWGwFhVyAwTSsP2yOLVRA3kajke7ERB4bf0se+4U3Z2RlJbf+6Z/m2Of+KGceuziwl7TRT17RmsxT/8p/mMeWX7J/smCnk972dpZHzuWOTz2Zm/70z7PwyCOZeK7g3L0Zf3Yz9/7ae9P84VYeeOc7s/7cUac2qBg/4ivasJSL//b/BwrezezIUuY++VDu/eOPZOHc4xnf7pU+9ZMsT07mT17zmnzmjW/MXrc7QOqZRDSZZLnxJmefBkEYAuLCWHKkjL0rkNTkl1EQyo4hhnOgT3ZqwxDe1Vrjapj3b/yNv9G/8cYbB2pWsTQoLY//Y+IRrtrL1TDHrzHZXjwmxIpbxzwMlriOGI4+oaAmnJIUC9rr7R9rQh5zYWGhwNu9vb189rOfzfnz5/PSl740x44dK1Ybut2CgqWk4akhh5g/p50Ym2MZH/cCQcL3mJ/l9oXc/OHfzff/00+l5SKOkWTp1bflPT//VzI99fI0WoPnFTUbjbTPncvUgw/mrve/P6e/9rUrvHCv2cwX3/GOPPCjP5otjYk+0wfWwl6C+K0m+hDmZrOZ3tZWZtcfyNmvfDF3/MknM/mFlTQqEew1Gnno5pvznje8IU/Oz6c9MvhcToyrY8hOp5PFxcWBcku8v/dUYpCJS21YakeDc8HLI1sgMYwp84PCjo2NlWfGog/IuY/h6Xa7+drXvpYnnnhiqNU+clcKSsLAsPK2MLCU9ogmE4bBWeN6E05maWuowiQBDcHtJHQRHNIVCIrxPpbNsQWx5tNPP5319fWyz/P06dM5efJkWs8xg8Scvd5BUbNL9gzPsdQur7Oi8PkkA0ptIbQimGSY7hzL0ut/NvddPJ1Xvec9RUEbe8nCpx7JOx77L/Oxf/0vZP273pXp5uzBd/v99M6cycbx47n4ilfkzve+N3d94AMZFTPc7PXy8ve+N2k0ct873lHWgbVDOL3boiUjYH6BlENvdzdTi4s5++lP5/TnP5+zTz+Y9uXhBd9bc6P5/Ftfkd+/6Tuz3u2m1TjIx3I97udQiW1fjuPLmJoHpyuYe/CuET8lz3Fqo9EoZZ5cx+9Z9lFa58uR/+Rg/yjyjZM7rF2TcjqWQ/HooK2PGVS+XxcS+HULnyFxDQMNXQ1V2TdqD4uy1GwuCoJy4pnwsGNjYzl//nwef/zxPPDAA5mens7tt9+edrtdkuVMLvQ/3hMPnhycqt5o7CfXSYr7RAcTbTXUdSkdnzWzjcHZS/LFH/zBrI6s5k2/9+ECcRtJjl/czvf+2rvzqccfzBd/6idzc+NV6XQHa5q3pqbyxR/7sTx7zz2597d/O6e1WbzZ7ebl73lPdvu7+eS73pbJ7kJJVxm5sH614ez1e+ns7mbmmWdy3SOP5LrPfjanH3ww7e3toTt1+kmeumE0l2Zvzx/fe2+eOn0iPV2fZqYc5hMBZ54wzGb4kT+UGcKO8QBXHaLUuVmUGxmjX0ZDOJ69vb1C+rGGdmQgDRT7sHYkW4s1sMYbeiZX1toyAFIeTvzjXVlke0q/b0EASoLhPTF1gtm1r2bvkoMtZbb8xI6uu/3Sl76U9fX1TE5O5q677ir35pETxDIoTJ0uoBkK4m197IZjVbwnc4vRqRGAd/Ts9vv54rvekcfOfj0/9Y8fHYhBp9eTt/yLr+a2+345l972k3n2rW9Ob3y2KH6j0Ui/2cyzd92VC3/7F9N56B/nL//61zL+3EO6mr1eXvm+P8jsE1/Mub/49px/6b3pNE8M9BEjWsit3d0s7TyY45+/Ly//oy/kuscuZmxj41ACa3e8nS/fu5Cv33NHvvaK12V3fT5ra+vp7uwk6Q94TMf0w3LNhDT0yc/xYbw4Et6DOW+1WgOnJDpf22g0BiqeIP0MYe39YGtZM+7vvtF3hyvD2pG1tbBcpr9t2Q0z61pXyBU+b5jLAI35GYxpd9y/F8oDtheun+rkAgkUyXFTfe/Z2dnce++9aTQa+drXvlasrWNUn0RuA+PYs9Xa3xfKzpfk4BkfwCezvYzVJJPjazw98+0qmrHd41l+wy/kf8qv5F/7x48NkkRJbvraam585Nez+ugf5L53/NvZvP2edLsHJWadTiftyYWsvuHn8z+O/Gp+5h89nLHnjkQa6SR33PdkXvrgP836wvE88dbX5dnvfn1W5+8qsLzdTqbbl3Ly9/48p+67L5Nf/2LmFneHKuTqTLI5PZmPf/dUJtuvzMPf9rK0Jm9Kqz+VyZ2d7PSXivGiqMSGy4bFsBZFQdao7GGNCGkgamzcWX88JQ7AIYiJSdbI+uFiG+JcPmMCtCYz6w3pdbsqIfSLv/iL/euuu64IDBdCkHwkPuQQx3gYEthqWBgdY9JZBJCB+u9CKsgSAWM7nU55fNz09HRmZmYGiCq8J6et2RBAEJBU39jYyOXLlws86Xa7WV5eLsrdaDQGqokYE7lTGEFIKp+HxKZujAVzSP9GRH7wGZcHDkttNJvNrIxdzvjH/uv8VKWgtH6S7g1jOf+XXpcHXvWvZXlsOltb2wPGc2NyNcc++k/zE//dl9MeIjT9RtIdH83Fu16W3g9OJC9rpPX5vRz/9BfT/uhWGr3+FffsjDTy+G3H8tG3L2Tl+tdk+8RLM7V9LM2MZLQ9WkiSlZWVrK2tZXNzsyiRPTNQlXWzkcNrcq319fVMTU0NGDezrVZkZMCPoWf9TNIRnqHEeGHWiHwtBoVHOvI/KZbkoMJta2srX/3qV/Pss88OJYSuqpw/93M/12efnesIERSEbGJiorh+fjM42DIsUp0TNV2PkHogeBAgAEYCtg1hBfo2Go3Mzc0NPArCMJd+u3je5+L0+/3CqC0uLpbY5dlnn83MzEy5NhbblpUqI7w7KRzKzOgTQuMqFmIePKrP1KnLxZg7w992u52lkYuZ/uSv5Kd+9dFDj+zsJ9l4+ck88OM/nsdufUM6vcET+PqdTm5797vzmg+9LxObR9f5Dr3+dPLVu8fz+D23ZfnO783isdOZ6i8UA+SUwubmZpaXlwviYQ6YI1CbFarR2K8GQlFhjFnn5eXlIrPePO60C7LmeJGnAVA5lhzsfNra2hp4PD2yifyhhMg4elA7F+R5bW0t3W43Dz74YM6fP//82VqUxgXPTJonzBCA98Dmnog6OKd5sayoZnjNBNMHCzgEDN4bRfa9DYPssSkhS1IIBn5DBHAyvEkJrkPswev1gvd6vQHFZA4wOJ5n/kYhEbw6ncJ82AvP9Y5n481/M//g7D/Jv/JPvppbHrvS+zWSTH/xYl778D/N7Pc+kQd/+Iez+Vzfk6Sb5JF3vjP3v/F4fujX35vrHr9wTdvaeo3kobub+eTbb8mlV78hx0Zfk3ZjIY3tZHxjI73GwZO/MaQgAqccbLwsC8SVExMTA5+nYITneIJ+KIg3o8w1aqXp9XoDT0zHc5r/YJ6B2KwP9zBrbGObHDxiY+S5PbgoKGt5WLuq5/z5n//5/nXXXVc6a+KHG1BpQ5phGEw1tGV7lrG3hY6GQhoiM5F8DmjhiiBbS0gfvJsNBGPiXhAEFDHs7OxkbW2tHEvie5gZZoFJznsOaiMGk1cIFDG1NlYIpyGc54vxmAdgPO12O3utvdy//Dt52Z99ND/8nrUcPyhRHWj9JJfefFvu/7GfyNPzdwwYw3arlbU8m51zH8orPvblvP5j5zK13h+oMuon6TWTx24eyx9/7yvy0GvvzZndOzPVnC5sea/Xy/r6epaWlopSIQdGAZ1OJxsbG9nc3Ey73S7HrSA7GEs2ZGOU19bWCtpi3plLbx/jh9cxqqzh/Pz8AKfAwV91zOnyVRcy2GCw9mx8MJPPVjLk5sEHHzwU1h6ZSrEV4zV+o5hYKzpFqwUQOOnY1RNkC5dkYEsNv62kfh2q3wvuWNdlVPSpVnbHHPQNpUS5UT4n14lT6bM9NVZ0GCpwvo7SQh/jwaLTN0P8ZPDkB4cb/d1+zqx8d+7/ntvypTf/cd71mw/mtZ/eK0RPWZ8kJ/70kbzxC/8ov/3v/FDWb3tdJnb3OYNmq5Xj7ZvSuPVn88hLFnPfD30mN3z+odz6pUdy7+fX8pnXJs+ensnXbnh11iZvytjI2cw928hGbzNrvfWBdAOn07EfFl4CL0pqA5QC3GS8pGpccOA5Y06Nviid48AzIzBnAlycT8EBxB33N+RODtInJuaQzVpxLdPONBCH1gz/wPpczXP+tb/21/q33HJL6QSDpHPT09OlNhVyxJbDvxkUQo+SmSGzF6i9taGtKWmUIDmox2WXANc13IYUYIKIVfBSXHdjYyNra2vlaEyUiV3zwBqUGyFgcR1DekFqeE6CGw/p2k5DdoTOnt9PxbKV73a7hSSZPj6dr899Kb2v/Vb++n99MS855LGj50838js/fTZrt781N+28Lv2FhQFuAO+011vP9Gony9O9bDb6GdkYKSw5Y8Dg+hELKIFPHeh2u+Vg6Z2dneId2+12qfIC3dj7OP3Q6XTKlj8Ub0wwnSdKG7Y6XcLYbPCAxBgUFJD1tNPyJnuP3bUBZox9mPTm5maefPLJPPPMM8+fEPrZn/3Z/i233BJ/BrICAQTOopymla2Mpq1rhUFw7Z1shRx3mL5GILGeSUoKBaXn2tyXftOs7LaMsH4zMwcPIUr2j3Q0BMY6suhYTedUrVj1LgjgEFDafWPBbZ2BUW7MA2wm1h72enRsNJuj63l26rO581Mfy6s/dSnf9pn+FbFkr5F0242snjiTC3fckad/5I1ZOn1XOhncTD82NjYgZGYjNzc3CxPJyRPut+cjSWG0t7e3s76+XsKS2dnZ4nXgLkAIGFKMKOf5+KlfXnPkEuPJvcmD186i3W6XcdQKaedhg45xMmcAh+DTJzFA/H015bym53MiuFgSBIaOm2quXT6wjYnmPT5vgsjeE4E0sWPyyUrsxD73x7PVlDzW1Rt1sey1IuHlDIkZs5liQ9y6fMzGCviGlcZQwMiWmFHXsFDYs2OxYR+ZG+ZgZGQk09PT+6c99PqZ3JnO7XtvyfprXp//7p2fy/u//FDe/jtfzms/vXtQeNBPmnv9HD93LsfOncud938sl0/fmsff+cZceO23Z6NxKp3OQWGEPTZ94XS5TqeTubm5YuTYMMBZxcgU43AKanx8vGyaxkD5tAWUlWvyGtdHCZFXDDXKwnVQEMZR50RtUMyTYEztHU0+oehO5XGvGvVczTle1XP+1b/6V/s33HDDFWyrMT2elEoLs7XG00wStYcWPKjrWqiZVAuzoZ6hhsvLGDRKBf0NHHVc67E5bkGgeNYnxoo8qdGAC9UZB4toa+oUEfc2QQYCoc/eTeOQwWklH6PhWAp4xv9OTzSbzTQnm3lq4qvpP/mB/Ph/96Xc86XD0yb9kWT7dfP53E//63ls4rVptg7OsjWaMXEHPO33++U5r5ubm1lbWysn39EvEAMcBgaHlJZ3Avl4EIzkxsZGUZb19fUCYekHa0fIg8F0DEqYhuPg1AQMNGubHJSIsj4mA80C8z+ysbW1VdDN1tZWtra2cu7cuReWSqEhIJQmARttNU1Dowg+DpPYCsVjANDh9WkH/huFxBsioIY5LADNQjsMWrNYLkDnOrBsQCQWk3vipR0/E2NiyTFCJpmYHwSCubDn29vby9TU1ECfagKsZn+5B+OqU1uMhe/3er00d5u5Pfemc/yefPhvfClPfvgD+d7/9ctpda5U0sZeMvFny/n2z/96jv+11+czd/xY9p7baocyMrfc3ykeSDGeNTM5OZnd3d2srq6Ws5n8RG+MjLkDjD0Ig7I55g5DxFyjfMwdfalrmpETGxJgMD8u1+v3+yV9g9HFSOLRm82DogaMsQ2lQwJzMnW7qnICtwzx+PEAsICmnY3f8ZS8BpyxkjEoFtmezASQCSLHiPzmMyw0C8jEO3c17DuQASYJzPDWSubxu38YJPpfV/6w0BgyGnPo5Lf7y9x5YbmWx2MUYUhmxNHr9dJutjM/+Yo8+7bb84GbHs7df/AHOfPoo5nY3LxCSUc3t3LnP/zjNF+7mk/95b+czdnZAVlB6PBcEEX0G1SS7Hv548ePZ2FhIRcvXixb9/gc/SSWm5ycLOPe3d0t0BnjlaRAVIccZAjwxl4z9t06lKm3AhruMmeTk5PlnnhY+gsqg5dhfVBOUAPyCLwd1q6qnF5ox18+8AhYAsS1gqIczg3VVqnb7Q5ARV5PDmJeE0eeLE7wxrohfO4vijUs54pVtdexx8Ji4z0dkzA3NjgIIZ8B7vA5+u7PG4YaEmIc6BtxJnCR/hkt4MGAfybLhuWSIaMajUZ67XYu3H13nr355vTPnctdH/94bnrksznz9EranYON2Y0kL/3sZ3Pm61/Px37u53L5uY0BeB+jIpoF0HWn9O3s2bOlfC/JQIiE1+r3+wPlkqxhzbriUZELM77EhoedPwwZZ6+LjPO+99cSQ9YGnxJE5IH+wQVY1myY63ZNz+cEDthT1jEbneRmWBmU1gUD9pDcwykDPA+fxVqakLFHbLVapZayFgje5z5c17DZJVlmAnu9XolnON/W8YfznCZt7B2AXM7xutbWnthQlb4bqrl//O2ca71uGCriLBczGLEYpo/OzKQ/PZ37Tp3Kx9vfnbmnPpHX/8HH84r7VksBQiPJzOXLecuv/aN87d9+az7zku9LX4JX3x8oZ4bdhrDb7WZ6erps5CdMwFjPz88Xb0lcjhdCScxsEwsjs9Q5Y0BYbz9C0fyFDUydH3dYU68x2wgnJyeLTDvHjdxS5UQfDmtHlu/VqQ5bbgTKu/dZGHsuBlHDUgZtJURxwPHD0hxO1SDsxLTeV2gol2TA4pLqsFLwWSAZDJ/jRhbWXhDmlzQL8YuFkbkDjiEQTqV4zhFEGws/LxLBQZEduzgpzg/99RjteT0H3W434xMTae620rnuHflff/HuPPkvfzM/8C+XBhR0cmktr/h/vSej338+X/zL78jK9ulicIymHJciAxgqexCElngdZMT6A3Pd/9XV1aKk09PT5V6WSc8d8BuvajhqhIfRcybAMtnpdMo2Q2QWow2EdvhhA8t3ufcLUk5PHG6bHyfyrQQInhXRaQIviDtOp5ODukWsCwKTDD7lCeEy/OM79syMxWSBoa8tpZWDRXJfveOevuzt7WV9fT3r6+uZfS4Ow3A4dvIYaFZC+klfms3mwBExVqz6ezSzur3eQdmbCS36Z2LEqMWkU5K8pHt3Pv+j/2bS+I384O8tDcSizV4vd73/kzm1+UQ+8J0/m7XrrhuQBa+Hi85tdCAPXRu7srKS5eXlgTSHERrrA0RNBh/PYR6COXGuFDbYcj42Nla8r2N65ptwBuOCHvA5GH7HzMTAjKF+tIdloW7XVPhu1s/ETf2sQiakhi9mCukw16fZyxp++pq1xzakdkxL/0zk1OPq9Q62HZmaJ1B3Do1r878FzPNQJ9kNZxgDc2jY5Tws/UBYgaTMC0ZmYmJi4GRzj53+1V619uJWkrqvzie32+3c1HhFvvgTfz2N/Eq+/91LAzW2jW5y7I+fydu//E/yP/7ffigTC6/KSPvgCBCYV/ruviL0JoKArJubm5mfn8/8/HxhSV0BhHdiyxmK5djRSCs5SP84hp+cnCyfQ/aogcXAgHAw+N7/CeOMsd7e3i7ZB8e1sNaGwnVBiduRMacVtE5dWHkQCsdSdtt0EkvoRDSfN+wxO2ar7gmEIKiFk+vTbz5jCOHcoqF0TdRwbWAalhCD5fHVXotmDw6ScPLdCuax1oaFcWMsOKaDZiPC+tSGy/OAgUpSiksQOqdHGo39XPYN3bvy5X/1r6eRX81ffPfioIImOXn+fN7x//nN/OkPfVf2bvi+tEfnr+ibm+Nw+owiQNycOHEis7P75yCNjo4OPHAZYe90OuVIU4dBrEGn0ylxKmgCz7e+vp6dnZ3CuLI+nP5BnIhxQQn9CPpWq1UK9RkXnpm5TFK+R9aAQ+AOa0d6TseBZljtkuvXHRPWHs0w1MykWVQm1kwcwuaF5lpmIs2aoWA1HW4YbtiM5UYQ7D0wDIwXwYfAsLA7vqCZASSeRdkdS3quEDLPJf3D0EHZOxZFwC1MwC8jj9qAOJ72GmDIxsfHc7Z/Zx76qb+R2e3fzJs+8PgVJ+fd9VAntz384Tx2z8N530+/Nf3pV2Sse9BH998oamdnpzwagTzziRMnBvZjuoablARzQ7nf2tpaeZwga2JEApog1EhSSgcppklS0AnfNXID0fBd8y1spgeeEztbblhLii4Oa0d6TkNaC6E9FhNk2pv3vMim9hEOSA2sCe8b2nJ9qitQCDOCLD7UeU1I8Xm/5hjTBseQ0N6bvw2BDX1R6GFzA5vomJk+1PPkmKYmdAzHTFQZ4RheIyQIhllS5g4YX8N11tqx/9jYWM627szTP/4f51de/9/nTe/+dF71+e5AHDrSSV76hSfyb/ynv5lL192QJ17znVl9zUKeat6UbnsszWarGJVOZ38z+traWnl+DLtXIIhAGcTPY2Nj5YRE5gyIb5LMCoUi2/Ayp1NTUwNVasgHsNNPM8cRcD++s7e3V5hdjIvTRsgJyCQ58OiHtSM9p8kIkwlMillPw1VbZQt5nRC38hjeGu7y3Tp2qGMsCzsKYZjD/biW4VtthFhoPBD9AN5iOVl8Cz/NVjfJQF4W68r3vGnXXpIfj8eEmqEqc19XcNVw0sruefUYbOFNtgH1mmPjmb7r38oHbnldnvyD9+X1f/RwTl3UPZLMriazq0/m1od+K50/aGfttvk8evre9N82nqXpM3lq7yVpT+5kY2Mne3sHmxZgQTE+ZmgbjYMHACHY9N+QeGtrq6S/kCU8s+eW8Y2NjRWlQhaQVWLnmmgifnVJqQ0+sgB56HCF918wrEWokyvzZrxWB710GKG2YHvg9kAuAkB4WBTDaWBhXQ6HMjvB66M9anKE+3qHgosnDGcdB0MemQZ3aWOtKDXCYNwsFON2JZbhrJEGfR8WQ9JnQgPWwoJpZpTvAbG9xnX+2MgFr8912/12rm98W57+0Tvyyz/yR3nd738u3/X+J3P8Uu+K6qL2UicLn7mU1+bDyR8lvUYz3bvb6d/Sz6VLczl37lh672zkc2fvzOXNm9NonCmw0IjMhsT7IZ03xIhiSAk3kAuUGiPgax07dmwAvfjoTQwX5ZVWYMtPHQaB+Eil0cdmsznwQN26Hbll7MyZMwNxX6PRKFjcOwVMdfO52gpbWRw/JYNVJCyInzTNgjhmwvIzKU6VeOcE0K2G1iiVGTO8U23hsLjeUc+96Zc9Naw2c8G1USBDaD7jnySFBbaBMkll1GGYhhe20UQYCAHoB/Nvr+ySSc8luUHOjEoO8tfNZjM7/Z2srXw+d3zsQ3njhx/NwmL3eZ1B1G8m+WvJp37ibP7b33hLbr31tszOzhZoSyGBY35v2EbWCJOAnvSd/CIVVOwZ7Xa7JQ1y/PjxgdwzOVM8aKdzcD7Q7u5uNjY2Bowg6RnOOrYDGBkZKdVDeM37778/jz/++PMvfKczroZwRxAMxygWHmNrEyQmhmqIyft4Qlsoe7v6eYhYS/qA92Uh6TcT5njBVo/3rKAes71oHd+ZaKnJsDoJ753/Zn5RaiuujZIZWM+f18PC5KJtDuJ21Zbn3OtsoW40BosvvI8V5pOxTI7em8feflceeuOX89JPPJCXfvH+3PHVjWs6h6jRSzrvSd5/eSRf+MoDefTRx/Jt3/ZtxZuZLMRYJQf53NXV1SwtLeXZZ58tx83Mz89namoqs7OzmZmZKYUirDXzBAPPyf5GGBgws/X9fr8YCw7rgqzq9/ul/pb5MzoyREZvhrWrKmfN8hki1aRGHZswIMd/Zk3NuKJ8tVB7YrgPQsugbfHLIitGw3s6juQzhtjAGnt4+u/8JP0cduymoXpdWYIHc12nv8OuFsfYHj99MOLAcNRhguNSe3J7bNaSezEejJlzcK6lRi58DYS0rOVeNyPNu/PYm+/KF7/zu3LTZ7+el33hK5lfXMz84mLm1teTRj/NRvZPFKONJZf/fivv+W9bWVxczLPPPpuRkZG88Y1vHAgdSFPs7OwUQ8FWtGeffTYXLlwoytloNDI/P1/GxQ8GhWuBGEZHRzM9PT2wMcP5TeYZxr3dbmdqaqo4DuewLZ+O/dEfXj9U/w59J4NlbwgzFpKbW/gtzHgHCxrXSvZxeB2XAlOAcyiNaf9hQmjcj3CbPWabG7Gi4SpKbAQwTEHx1twTb0h/GRtex/EkQmAWmb9ruGqyzcwqBrCOwe2x677R8NJeU4/Xa8T81Ed1Op9be3Ebi263W+Dk6Ohophqnc+Hek/n6bffsx1uXLmVyfT0nTz6b224/l7wnaT2zl7vufCKtv9TN+xbHcu7cZnq9fW/z9a9/PTfeeGM5d4j5IVb2A3N7vV5WVlbKe2NjYzlz5kxuu+22ksP1weP9fj8rKytptfbrd5vNZjn5YmRkpKRF8Nj1LhLvNGJex8fHB56Ghow5/++9oVdr11S+x40MwegwqQsTGUAzkxk0BAHFpKP2aEAN4JsDdATB1PgwZpPvmHE0gWIG2vdiXHWi3ijCjDP34t6GvFYcK5AV0SQQY+t2uwNVLjY4hr61sjBH9I04zVAYg1IXqbuAxIbARtPPHzXkbzQa5VgPhwkYIZ661e31snPyZNbm5vLoxnw+9dDdad3VSu7qZXx8Lc0vJJ/73Fezvf3lEhdubGzk0qVLWVhYKGkhmvf3Eu8hV6dPn84999yTl7zkJUVGffg0p/FjXGH9MabNZjMbGxuFQHJ45PEhZ+Y+kF+vKUUVjn/NxTxv5fTi+CIIUM0G0hBUBmFvgODXKRV+22tZACGiuE4dw9l6W2ls2ewlDK092XyX3/ZUXMe5RTwgzQpowqnZPChhM4FFqz2T16Bmj4FNMKdWLnsX4Ga9b9GcAeOzx6UMDu9Xx0g2eDCaHsfU1FSZKx6zTgzm8k9CiZGRkWxv7x8Ud+zYTVlYOJfLly+Xa5Go52QEI5MkpcJnd3c3s7OzueWWW3L33XcXeEqelOocxj49PZ3jx4+n0Tg4yNypEEgjH15n74dCG4k5lYghYz2QeVhyfh/Wrkk56+JchN7Ww3FUMsh6WrhQdKAF33Pc6JjAE0JfgDEIAMKV5ArLBry1cuH5mVgrOPevUQMKY2UY5iVrpOCY0fDQHreG1VzDUNR9xlg4tYGAAb+88dnxppGL+QPmyE/fssGz90wOyBGIopmZmfIdYnMMVbPZzNzcXMbGxrK8vFyYzLqov9vt5sSJE3n1q1+dL37xi9na2sptt92WkydPDvQJBUVp19fX0+vtb4I+ffp0brjhhlKMgBI5XUKBQx2Do1DE2EZn/AZlgUAo8xs2vyAt5JXrsT6106vbkQ8yQgC5seEPJEJ9ckDtTfnf8Z5TElzTW3ZcgcN364IBE1EmPxBuhI737QEYmyfHSoZisij2XBgTlAQDxW4Ve2zPhWl1Fs1Q1kbC3q1OYBvmcm+fps48eHtTjS4cB9Efzx0C6+8Pq+YirWBizMbcxof/rRQ2usk+F3H69Om86lWvKtAWhSGeJfazQfe9HTbVY3AJnh0HzYaxDh0IwfB6dYUSa5qkFORjTMwPsDZGj8PakYSQFdQDMHvLaxYC8DuwCQhgyh7PZeFjYhy0cy97WxbCaRATR9ynhqx1jMZ79sY2Bs6R2sggTJ4bP/GbxXRxhgXTpXL0xXNgGJkMVmeBKtjHaAG0t3Xc53QL82kIDJzt9/vlJAIfs+Jr2kDUCMRe3SVv9ijkyJvNg5wsh3yxbhR72EggSxh14sXZ2dmiBLu7u2WrGYYZSF1Dd+QO0pHqIhtWK6phLwUS9bNXGo1GQSHIP7lUxsCYOIj8BSunBTUZ3MhLp0wueMFQICuyvZW9FpMH/GBBgWUIqCGgMX59f0NIC6c9MQpm2AhkYWx1cYQVytVNtoQYF8P5mkW1cuPJmRPgGAd2IxQYI67HnDh1RXxZHzhtxTScxgDYOzIHrG0tpP6ODQ7f95wiiPTHc8F6GpnU4VKNZCx3vV5voITPu1YwkP3+PlHk2lkMMeNjrSj7cyztkIj1ol8cW2KWnvdqmaj5Ccfeh7WrKqerS7wA/FBxwUI7ZmSharaVznvykoOYzJDFNLYXyILJb8ezXkT/74nlWp4ke1hfF6FOUphOGwjHVjXEtjcnpeM+OCa1YqNgtff3gkKEcC0/TrBeK+bfUMpooIZ6ZqvpO8jAuW8TNMM8DdfgmkDUjY2NgaqeiYmJTExMDGweoC8OIWpD1m63CwuKEWONGC/rbBbccWO32y3bzfDg9BNCrNvtli163ujuVBlz6FLMYcpKiFMj0uelnFas2nty0C+TX7OsKK/jIx87YsHk707n4Ah/rBqCZw9sKwrza6E0xLAVtbA5BrCQ1vDQ16uNBZDX84MgGYbSgFA2JF5Afrg33saHVNWbfLHaRjnus6GYYygEto45bfAMoYFohvgQOtSKYqARUHtHKyefXV1dLUYDKE3/LG/dbnegyLyWOYw9MgPC8L5b55n9PbgC0kQw1HWczzq5j1ZKG34jEebQFUIU2XuML0g5GYRTAg5i6aTL3txZOsgk1ZDWSuDPspAIWp2H9ERzLcMPx5QO0i24dT6vVlI3EykIoefC1t4wlH6a8DFUNMFkgoj7oQyOv8gp8hkrmI2eobwJG8ZDX707v0YQrAdxU30/C3lywOJasby2fIb9lt6pw/owb/VmBZQBRbWhNHRmrMTbvh55X7w/Rm9qamrAgDguR7F9OjzxL2FY7Uwslz6/2brDXB3WrjnP6cXnb7/uOIZBmtDx9WyJeB1371I7W6LkIE5zDOW6XX/Wwu/P1DDCZAPv4fkwKHyPsdlLeYym7i0kfA7jxm8+71jVOUczwZAztuYoFEQZn3eZIZ+z5/WewmRfgdg5gWGpUxHNZrMcBg3JQczlea1lxgQZXALX815GhBcPhnLXoVVt8DFStdFsNg9OmXA2wMiEeNLVZjRCFxtXy4xlDejLDhPvokGGMAb0fWpqqhwFelg7csuYj+eoPZ8rdpwETwa3f9VWm0n3gE3iWEkMjWHYrFD0xbGlvWF9b1suL7w/U38PRXUMipek7yyglcTj8nddAA3kcuyJQluJXLkCUcQ4sfYmhyykNnYmYFhD4DawnX57TRwGmIl1op3vbW9vD5wvxZg8bh5yPDIyUh54REhjogfFNqrx/fr9fvmeiUh7ZMe/Ri78XctxnRd2jO7Y1XKxs7NTOAI/TAmZYN55vY6fh7VrOgnBAsX/LJixtQXAE0szaZKkQDYLkO9hjF8TQxZAJs9Q2vCO/js/6vcM4/ieIbQ9VXKg1G6OSQ1jUS4LCHPHPYZ5dXuB+oleNSQGuntMZqKdukoyYMUZP17S1/XfFiTPCdeBhPFa1mzusLU9fvx4kpQaWXtvE44YasaB3NU5b3s7r2uvt7+bhjmpjSaxJoQkjoB7OSwzd+D15cG+rLHnz+vB/GHID2tHplJqj+TO0BGIAT7HIjGRdIROYYFY6Dru5Dq+D4YAwbN3ZOK4Nh4cb2NL5T54bBa2eg4QTpM2XAdFq+Nixs6c2dPT6utZuZkbM7YuGuB97gOx4YOS7R0R3omJieIVrYB1TE3DKNsL8n3LwDAI7eIUzx95zmTfCBF/rq+vDzC9eFugbr1mNmp+tJ5JR+7L/k6ef4rM8Bh6lBXG1nDeTLRlk5AECA2hWcs//bYi+jC1w9o15TkRBAuwldUKZM9oz2fiA/IjOThupPZWfBdh5X08RG0Q6BuTbDqbCXJ84jEZ3tFHw1Du4Xjac1HnRq3M9tRm+vAEQCsrKgRCu90uZA3rUG9FY4wIPGPiHgiCy/7MSDebzQEyj/HhVUx8sC71eGg1WeN4jt9Acntcy4xlpfbYRkrmBHxf+ooMoEBUFvFkAIcBhAoQSqRWiLtZx+3t7YFDv5xaAV5DnMEDFEXT38ypibPnrZy2DK5tdIzGBFrxbC2ZZFeJGJrVDKrdPlYZj2n8jmDx3dpbsJD1CQgmWRzf+n41sUI/EZB+vz9QzULfHKPibTASnhMbjhoaOnb3fHNd+mbW2tU2TjvUaILXgWteq7oIgfvVlU1cl/e4vplHH8BG/+oST9aKH2JXn36BAbZ3trcx9IQ1xYPS8JiU0nlnlZWGNUVJOWwM41AfkgZKajT2i/upsa0NN/JkXsFG9Wrtms+t9SJYwO01Gewwy2aLbiKFATm2Matm6FJ7ck+ecbwVin5wbQu5rXAyCEO5h+9tzwPc9Jjon+fARgplthHAuvOaYaHHym/GQCWLE9vMl2Mz+u0CBO5Vw1QEhvt47fmuy9WGxb8QS8DWOlSxbLAWeJLR0dFymDPvMYcgG/KDjB2DVJdf0hfXwXIKAq3uA8YJmXbMy3t2VhgFoy/mEPlAcY2mjJxcXVe3qyqn4Q2KxIJ6krlpjc3roNxCZmGkOV4w7LNA2+qwADXphDIxeX7dVhUldP6W65tCdzzm2Iox2ntbsHnfffNn7d1qtFF7YebdpFJd8cN8uqzMrC73dd4tGTyOhnWu409gIorKZ1mfmh1ljS0XVlSjIfrA3LiyqzYSPtyZa6GkXB9ICcRkUwDr5u96/Zgj10TzHaeNeHo33zG55PH7wO5+/8oTI64GaZMjlNM76O0dLTi2nnW9IBbZsMTXqIW6FkbglnN//s6wGM9CZ69rmt3KbetsBajHwbVqQsKe00JryG2YYyNDXMb/CI6JJXtLQ9FGozHgvVEoQ028sA2sa27xKMN27nv+XO1jQ2AExDWHGUAjDMbgmNJz6xwn967HwL2dN65jcW+S8JEiTuGxTsMQHnMFv8FrrAtzb5gL9KWwgz4b5jsEdNg0rB1ZhFAH/iyQlauOJ1AcFrUW1FpYLbQmJlhELJlTACgm17YyubFzoc7VWTm5Rw1T6Y+N0DDGlPEA+bhuXcLFQhGLmggitjNaGRane4Frq13Hkj4+pYauKCaxnZUHD5Gk7IHEUyPERhdAvjpW935NhyxuFm7Gb9lBwTwOhx18H2VEZpwNQOmRVfqbDNY019wA12Wd2BKI18Rbo6x2WswVZxl5rDZGL5gQMryBUGAS68Da6RB7ueTAAvN5zmZhMR071kLvulF7F3+PhaNxf5fP1Z7PAm/raWNSe1sUyYprQTFsIgayQDs9wBhsCBzbJSnW2dDY883asK+WRPjExMQVLDhjNlIZHx8vyfStra0BptzElfehMj+OZ1Fc3oegcvzm+arhsskTr4Ff93eJ6Wz4pqamBup+aRh2PDrXo79+DQ6AuefaKO/GxsZA9qHT6ZS553v2qg7d8NxGkczXYe3IM4S8WMAKrI6DW25UQ0ysFJPuYoaaeLFCWyBdnuaFqwP6GjbjkWrrZG/HZ4EstTdFwZh4Pp+kLI4hOTEdCm/0QP8NsfiuWV3PI8SPkQfeKDmAojYYzAuext+nsYmZ+/jZMMlB0X191pA9tpGKY8TaY9IP97vmCbgX5w0ZGXl9a3LP9+/1emWHyvr6evkOeVKMYrJvyPgssrS+vl7GiYGtYS7zxKn6EFSsBZ+1JyYeZy4Nj6/WjlROB/E1y8dvL6phli1dcqAUPt/TMZmhJwvgXJzZMecV65wf/QKWuD82JMlgoQX9H2YsksG9kfV3DGkMkfGQdW2lr2ko6bjLbDb9ATbinfr9fql8mZycHGABnXai1SEAa+owxd6cObbHsZLUa+CDsG3sUE4KyJ2D9dgsbyCRWg4gVmzM7JEce8PWTk5OFkSBIsNym2OwofAh1obYzlYYZWDM7UBssDHyxKfM3WHtyFQKC+ob2jLUSmQ3XXeQQRsWWVDrjlqZ7HlMDNQKZ3jKYg2Ld3mPH8dL9nTcw+OwQUHoeJ0xsfAWXHtHlNaxtRPjJpVqKERDMflu7cF9b+7jNeQaflKa42DG5pid/jhur+feMuN5c+hTk2HuG685tkZRkwwYY69l7dGZL54exjqxr9bf8xGs9JlQzs7A8bkLYugPBR8+h4iQxmSQx3hYu6Y8p2M7x4pYDMccjhUM2zyR9WvurCesjj0s1AjDMGE23kdRuI6T2L5PHdDXcdFhCKK+1t7e3kAi3dDZ84UAIKT2XBYw5sAQ216rNiAYDh/UxfesSDR7LIcfQDDDyXptEd46VQJs99h8T4cjDnGMfpi/+uFP9ZGonU6nxJ+sIdVAVCNx8HRtiJvNZoG2VPf45EEcCMpl5ISS2YvWENwlgMkBh5CkPFSpNrhuR7K13IhJcy6MzzCImnixAvlatuZcw7S0lcLCyWteTO6P0JtJtCDVuwSsUK6OsSJaCWB9k4NYzobKXtfemd916SDeEqtrYTXVnxxAydpjd7vdgUfpOeWBcSFZb09FPw3hUHqXq/E9G1YrV6t1sM/T61d7wDpONsoaprRWdITbW+vscczssxb8bSWibxgqv8a1WRNORajjaMN0kBFKzPtcC8Xk+kZIh4U4L0g5XW5msofYxnGi44Z6gRhoLYS8xu8aktma1t6P+kb3A4E2RGUya4MDCeH3nDpizHUsxhh93hGTbyKDz+HFMB4QCkkKYUEfGYMtsL0iguxicHtXCy5jNCdQx6Bct07B9Hq9AYNFXw35vC4uHmAeGVO9S4bPIuhU4jh142uxvsw1ntssKXPvNez3+2WvKmjC8oziuOjEYRtKZ6/oIg4K3i0r5gmcV0aXIPnsiIa1ay58xzL6fws5E1HHjXxn2Hs1qYD39aI4d1XHdEwGwmQj4XgXQTOcxOuZUPBrhuU2Jkwo73tuLMSm71FQ5xWtEHg5V82ABHiPuUCYPV98x5S94ToCynWZN3so978OPxiP58kFB/WmARsaowIjLxTIsDzJQP+YgyQFckIoJQcPqUXA69jf842RgaWlTxgLFNLK6i1wRnOMnUfZO+Rh/IyHo0MdelGx5MPjhrVr8pwWYgZnwaJTDo5ptftGoGplM2mAEBia0pdhcRNC7J0A9d5ELJoNRH09W3WUyZ9xv/03zYti2IaS2GBYIYBCZj4ZgxXefXUM5bgPQWLMjMcKxHqaE7AiObdMH/EGDisYJ78Ni11mhwGxgbNnA1VYyBkT3zUnYCfBnPLdra2topRGCawBhsXGGDRgeQJ+8nm4BG/U5rMYDMfkFCwYHfIzNjaW1dXV8tjBw9o15TkbjcbAgV618rD4vGccTefqeJTXvHAIAZbLMNkT65SKiQ+TVhYKQ3ELhftqMoOzYgzBDWWs8IyRsdmrmYhyQT4L6VpVhLuOsywIdfxXM+Y2BhRgEDcyf4ZnzIPz1k7fOM7HizAvzh+7z762iR+Xs3FtjEPNirJ+IyMjA6c7Qv54TT3vNeREOZ3aYQ74n90q9uKWXyMgZJMT+ixf7o/DK8ZrRnd7e7s4uMXFxUP175oqhBACFNKCa4Gw5TBuB+bwnr0Fk1vn8hDUWvitzP7tz+BBjelthVF8lMWGxjAGD+FN2yjZMMXEUNjTs2CG5yYj6lyXlQwP4LpRz5HZRDPUTt049qvhNekFjC73ofrL6+oUgg1E/T8hB2VvVgSjIHtfXuf4TWSmDqmsDDbgjjv91GjWk7kH2fj6FLHby09PT5d+MR4bVxQrSdkq5nXlkYDIsfO67Xa7PArCsjNU/w59J1fmDWvhR8AhLeo8JN+v2UfHdb6+4ZgV1B7cAlLDYUNAGw6ar2dFYCGBThgix6/0H69Hfy08NQtNPzAM3tTMe/V8OMbEQ9XoAmV0Xz0vPmDK5BJ99H7P2lvgXbifUyjMN323Z7TyJAePQ7Rxq9M59vi93sEZQrxPX3y4GbLGvfyMTqp3LAuTk5NlLChkzexj6CYmJq5ATMw1a4zHtMKZpbbcmiiiDyacrrZdLLkG5WTyDTGZfAucldeKyeJ40y6TZza2FlagiSFznSOsYx+u5bNhPVkWfBsRM5IWAAujlbRWTBaCRTTjaIVjrnydOmZnbHhb5tyGyder16qGVZ5Xo5KaCXds6fvUsboJF+c6WR+/7/5wHb+PUhjGM6f0iaMsXTtt9GWYa5ms5ZjTAvv9fnlaWb/fLycKAuVJTXlO6ZsNUnJQsEEJn2XaqGx8fHzAO29ubg4gicPaNec5LUx0gEl0rs/QpV5Md4TvmPRwKZthFIrK52pLZEW05zVh5TF4x4LjOCuIYxNe96IzRjOkTnnwfa4BVDTZg+JBBtnL02fm1/FlkgJF69jWHsjrZ29I6gIvUiMJGzx7b8+tr2mCyWtjZOQ4zkbJKSZqah1O+fuU1Jm9N9S3onsMbnhQyxh9IC3itagZfUIFF2lMTU1lb2+vKB3XsJHZ3NwcWDsjysPakakUOko9LO6fDtdkhgUMBWZQHqy9A59lsQydHIMxUMe+tResY1IaVrou5E4G96FyD+fhsLxebHt2KwafqaG3kQf/u+6XheTazp3aO1lZHOvYatugsAbJQTjinT3MC0JjDqD2moyJ6xoW1kQL62nPVm8+Zg36/YPHFpJ6YA1JbxB2WEmYS6df8HI8Od2Gl35MTEyUXSx7e3sDpJPZZubO5CQo0EfQOK60kbfc2oE5u1AbZLcjHwFoz2KMbbbKR9mzsI4FXf5nRboa22uYVk+wJ9rexP/T32EwlomrWT3HjHU8yPeHJZz9uhXQMNXen/vjde2VXTjh8fJ5K4U9FffjGsmBV6q9B/Psuapjw0ajUdIhhpSsCf1qNpsDSmV+oNsdfBIa/UOoLZhOZ9hwGX6DClwJ5WsSn/I/68RnOGnPGwvop+eVH+Au8gCJiVwBhbe3t684KtNy4U3xrVarbJm8WholOUI5XWvJAlDZ4hilHlwyyL4Og5dMrKGfJ5trGD6hZLZONRnCpFgB6//5vD9bExpWaMdYCKchrwmcmpV0//isYzyTKtzXkAehtOFhTvwZ1oFr1TldzzXQzpUsjAGBRkHoM4pehxomkOi/kZFjMIcbfuhSkrKzBkjo1Aqe1XN12PE0XIPxkykw+8vYmGsKBFyCxw+vmQz0NkAqfpzHNjpkPg33TSB+QzEnOSkUgnynFx9rZOG15+NaFg4W2Z6IwTifVMOH2sv5+7aCVnTHURZgYAyNa25vbxcjxPdsiY0CyIlayP15Cyow2J+zItcpA89TraBAORsOx8wuaGAMjv2MDEzAOX/pfhmq26Mb1RhN2PvV604fk4OtWX62aRHO9sGhzkkKK8s6cI/ak6LM7Arxk8uSg4IGx7v8Jp6EbAIhOoziWsy3jW6/v0842fB7HV3VdDXFTI5QTiAEB/Fy8+3t7RJA0xiYoZI9Gs1xaR2XMfEO+B00u6bW8NDBP4SS6fTa0jsWds2nd37UHtDGBUVBQbi3SR4+ZxbXRQU1QZJcuTve81OzqIydubeH9pEnvq69leeMH+JqG1QMYB0vW+k8NxgFr7MZfSsnxnBtbW0A4tnwbmxslBgVGRmmkKyj5cXeDQ/vkMiQ2UrHejWbzQKF6z2kNRIzOtza2ioeF8TZ6/WKUaizDoe1I4sQ8GKOJyEkfBI3i1DDNwtvDf9sbTxJjrvqGKbdbg/s2mcSYftsrTyJXjBbM4TRUAPFRbBN1GCczCKihPzPvV2CZmF0KgPrb1KqNmBYZ+af3/aeXnCUyx7YZIV35XQ6nYEN0ngUHwNSwzq+jzL7XjZsIyMjJRbz+kN4tVqtbG5uZmVlpfTLBscog+/wqARYWzOiNsgOVbrdbulrzTNYBgzZGQsIyOfe1qkl8w0Orbi/IbiJPkiwQ/Xv0Hcy/IGxFu6aRDF54kky22UlseD5u55cKypKY+jK5JFrwmKxEI4bEFxDaCtXTdjUxsLCXjcrU/1dvmMCxwwj46oN2TCragFwisXxjuepvnYtUEYpJqEML908P7UhqZl4xkDfuDf33drayuLiYkFiyJK9opUOVIThsGL5cx4X/cRI1F4YY7m1tVXmlXVkjVwu6pgYD+4wh/mrH3zc7XaLgvs+NdnmduSTrR3I2xs51vHnk1yhSHynJndojnH4vmOAWtBr2OyjLuqJZ5ItXBYWrmlPBoxzYwxMuOMUQ+1h8Z2FwwjEsB3qvya4uKdjuyQD7LgNjeeFsaC0nARHPM08UY9K+sDCY6VyzEaM6HsaHhp92OjyQ8jkkxy846TdbpedKBjZTqdTdnQ4t1obWp+wZ0/N0SCWWcfejoUtWy4PZB42NzcHECOe0PwLMkItLddl3E6fDWtHEkKGorXn8KIg3LV3NL1uy2LhMdkwLI60ZbQl83143RbTEMOvGdLWEMhetK5eYWLJfTlGcazJdS24VjD6QmG6F94K6GsZGiNUjM9Jc6MQhKSGb47F8UZ4AAu015rWarVKSVxteHnfCKvuP/CY91lL0A3KVZdS7u7uZm1tLRsbGwM1wIzd8kDYY9bWJJWNNV7Q6T8KBpiHra2t0mcbVTsaxuz1QDG73W55Crn1woeDDWvX/AhAOlR7BoTSFRGGLrWwGn74enzX2Jz3tre3y+JyP0MWe0I+Q7xggscQlolkshy/uEDadcQmHlB4W2ErAbGG59B9xiB4Tvjbh0HVBpC5pOFZa1bQyAT461wqrzMPfKZ+Jkg9x/TBDKgNl9FBPS/Mfa/XK14MT26IjmIRh25sbJQ4k/Fb8b22Xl/mB09t5JMcbIAw2sJY0W8TTC7zqwspmP+NjY0B44scs67eV1pvVavbkUUItgrAVTfDQzA9MIROUhaFxbPFZZIMlz1gLKgF216V/rAg3svHZ+in41UU0gJoS+u+mERhwfgfSGOqHaPQ6+0XFzAG4DCv93q9Em81m82Bz9FPlwfWVtYGwxDYyskcsTaQKgiz548xOT6vDXJywJoD1UAWjKfRaJTN3TYK9mbNZnMA2rEWeE0jEt8f5fA4LKcoo8ku70pBUWsmG+dSnwbIvb15gTVm3ZEBkz84ENbVRQmQefVDl+p2ZPkeg4UUMkx1/GbCwwQEpI0hJgLExHpfoyfXMZM9oBXVLKmtp72NldJxYZkEMar01zDSC2WhpK/2rNzbcIpdFXVcZpjM05C9qdgKxFy4jM9K6D6gkHgf+mxWuNPpDFT1WKA97mHIpOYNJiYmMjY2lp2dneLdDHWt6MzB6upqeQK24S6HW3N/PwOFNXBsbtmxA+n1egPj4+86FqUUjz5jLO3RmEMgMZ7TjwykP955ZDTGmtXZgKu1Iz2nGUYEqGbfPPnee8hiWDDpuDvsIN1Wh89h+ev41XEUVqyOPYfFW1ZSx0z1tjd7Wx9Z4THQJ+45MjJyBSPIPCYHhzT70GaKphk/i43X9PWsuCiIFYs+Ocbu9XqFjUwOHmHP+91ut9SX8lxKku+M1wJso2ylYEcHkG1tbW3gO+Qze71eVldXCynEPNFP5oSnhDUajayvrxdPy3tGdPQXY4lRG4YCjJI8H3YyjpsxAnzH0DfJwH2QOa5TK6dh9zBHcc3KCVOFcFANYw/K4CwwJnnstRAmOm6FsedzXNpoNAbOWjERYkiIlcUYGNZaES20XM8e2jGTPR0xi8mFeoyMy4YBw0JfEEYjBayy+8tnEXaUtS5E96LzGoXfVlLuyZxj5fkZGxvL1NRU6Q+vcT8biDpkMMpAcWAk6Xty8LiIra2tbG5uDng9X4tQAOja7+8XKjgVkWRgYzWKYKVy+aG9br+/v5GjPk3R6Q1zACbNTAImB5un+R7zbc7ERFCz2czKykrpzwuGtbu7u1ldXR3wlhsbGwM7B6wcCJXJCAZmC58MPpIPhffE20Ib+jJIYg+/VntJJslxci1YZqNNIPE9s3POXbF4jh/phwXFu2y8gDZuViALqA2J59n95/NYdRTEeWnmw4JqIoW4DIGl35xa53F43VgHb2yAuNnd3c3GxkaZbxTWaApv5Gdyer55D2RBP/gc40I5bSQsw4aXzMHa2toV8LZGJxh8zyMFKCANxmCn4DXCyHA+rpEC83FYu6pyXrx4Mevr60UgwdoIgckEL7jZMkNQe00scp0SqaEbAmGyhfugXCgU17CX5DVX0/h1FMKxFYvpHfhMPg//sTfCupt44D2IEWIn7ouw1B7U42aebECGKbsVjXwp80JDKD12EBC/va6cCuDYHcW0IHocRho8TRrhIyVheIxi+AG3vV6vyBg7Whwu2ShhIA2zWTfPJ4rnCif6inJBlnnOMFyMwWtx+fLlMn4/Xc5et3YUNm6GwIe1hr3ci+3F9mL71mmHlye82F5sL7b/U9uLyvlie7F9i7YXlfPF9mL7Fm0vKueL7cX2LdpeVM4X24vtW7S9qJwvthfbt2j7/wF/1tNn+3P8IQAAAABJRU5ErkJggg==", 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", 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" ] @@ -159,10 +159,56 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average IoU: 82.83\n", - "Average F1 Score: 90.30\n", - "Average Precision: 90.21\n", - "Average Recall: 90.89\n" + "00004.png\n", + "IoU: 88.14\n", + "F1 Score: 93.69\n", + "Precision: 97.29\n", + "Recall: 90.36\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00005.png\n", + "IoU: 90.04\n", + "F1 Score: 94.76\n", + "Precision: 96.10\n", + "Recall: 93.46\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average IoU: 83.41\n", + "Average F1 Score: 90.82\n", + "Average Precision: 90.09\n", + "Average Recall: 91.85\n" ] } ], @@ -174,7 +220,7 @@ "\n", "iou_list, f1_score_list, precision_list, recall_list = [],[],[],[]\n", "\n", - "file_name_list = os.listdir('/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240417/')\n", + "file_name_list = os.listdir('/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240418/')\n", "file_name_list = sorted(file_name_list)\n", "\n", "def evaluate_segmentation(label, output):\n", @@ -204,8 +250,8 @@ " return iou, f1_score, precision, recall\n", "\n", "\n", - "for file_name in file_name_list:\n", - " image_path_output = f'/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240417/{file_name}'\n", + "for file_name in file_name_list[0:6]:\n", + " image_path_output = f'/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240418/{file_name}'\n", " image_path_img = f'/workspaces/mmsegmentation-1/data/cag/images/test/{file_name}'\n", " image_path_label = f'/workspaces/mmsegmentation-1/data/cag/annotations/test/{file_name}'\n", "\n", @@ -220,39 +266,39 @@ " precision_list.append(precision)\n", " recall_list.append(recall)\n", " \n", - " if f1_score > 0.972 and f1_score < 0.975:\n", - " # Create a mask for pixels where label and output are both 1\n", - " overlap_mask = (label_img == 1) & (output_img == 1)\n", + " # if f1_score < 0.6:\n", + " # Create a mask for pixels where label and output are both 1\n", + " overlap_mask = (label_img == 1) & (output_img == 1)\n", "\n", - " # Create a mask for pixels where prediction is not possible (label is 1 but output is 0)\n", - " not_possible_mask = (label_img == 1) & (output_img == 0)\n", + " # Create a mask for pixels where prediction is not possible (label is 1 but output is 0)\n", + " not_possible_mask = (label_img == 1) & (output_img == 0)\n", "\n", - " # Create a mask for pixels where prediction is better (output is 1 but label is 0)\n", - " better_prediction_mask = (label_img == 0) & (output_img == 1)\n", + " # Create a mask for pixels where prediction is better (output is 1 but label is 0)\n", + " better_prediction_mask = (label_img == 0) & (output_img == 1)\n", "\n", - " # Create a copy of the original image\n", - " result_img = img.copy()\n", + " # Create a copy of the original image\n", + " result_img = img.copy()\n", "\n", - " # Define colors\n", - " green = (0, 255, 0) # Green for overlapping parts\n", - " red = (0, 0, 255) # Red for parts where prediction is not possible\n", - " yellow = (0, 255, 255) # Yellow for parts where prediction is better\n", + " # Define colors\n", + " green = (0, 255, 0) # Green for overlapping parts\n", + " red = (0, 0, 255) # Red for parts where prediction is not possible\n", + " yellow = (0, 255, 255) # Yellow for parts where prediction is better\n", "\n", - " # Draw masks on the result image\n", - " result_img[overlap_mask] = red\n", - " result_img[not_possible_mask] = yellow\n", - " result_img[better_prediction_mask] = green\n", + " # Draw masks on the result image\n", + " result_img[overlap_mask] = red\n", + " result_img[not_possible_mask] = yellow\n", + " result_img[better_prediction_mask] = green\n", "\n", - " # Display the result\n", - " print(file_name)\n", - " print(\"IoU: %.2f\" % (iou*100))\n", - " print(\"F1 Score: %.2f\" % (f1_score*100))\n", - " print(\"Precision: %.2f\" % (precision*100))\n", - " print(\"Recall: %.2f\" % (recall*100))\n", - " \n", - " plt.imshow(cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB))\n", - " plt.axis('off')\n", - " plt.show()\n", + " # Display the result\n", + " print(file_name)\n", + " print(\"IoU: %.2f\" % (iou*100))\n", + " print(\"F1 Score: %.2f\" % (f1_score*100))\n", + " print(\"Precision: %.2f\" % (precision*100))\n", + " print(\"Recall: %.2f\" % (recall*100))\n", + " \n", + " plt.imshow(cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB))\n", + " plt.axis('off')\n", + " plt.show()\n", " \n", "iou_average = sum(iou_list) / len(iou_list)\n", "f1_score_average = sum(f1_score_list) / len(f1_score_list)\n", diff --git a/nohup.out b/nohup.out index 1bfb25ccd6..8c53fd57f7 100644 --- a/nohup.out +++ b/nohup.out @@ -10652,3 +10652,2348 @@ Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/conver 04/17 23:50:37 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 04/17 23:50:37 - mmengine - INFO - Iter(train) [ 55000/160000] base_lr: 6.6246e-05 lr: 2.4493e-07 eta: 1 day, 5:09:57 time: 0.9945 data_time: 0.0045 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0073 decode.acc_seg: 99.7316 aux.loss_ce: 0.0083 aux.acc_seg: 98.9096 04/17 23:51:27 - mmengine - INFO - Iter(train) [ 55050/160000] base_lr: 6.6215e-05 lr: 2.4481e-07 eta: 1 day, 5:09:06 time: 0.9956 data_time: 0.0046 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0071 decode.acc_seg: 99.8264 aux.loss_ce: 0.0088 aux.acc_seg: 99.1026 +04/17 23:52:16 - mmengine - INFO - Iter(train) [ 55100/160000] base_lr: 6.6183e-05 lr: 2.4469e-07 eta: 1 day, 5:08:16 time: 0.9961 data_time: 0.0047 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0058 decode.acc_seg: 99.7845 aux.loss_ce: 0.0070 aux.acc_seg: 99.3715 +04/17 23:53:06 - mmengine - INFO - Iter(train) [ 55150/160000] base_lr: 6.6152e-05 lr: 2.4458e-07 eta: 1 day, 5:07:26 time: 0.9962 data_time: 0.0046 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0061 decode.acc_seg: 99.7978 aux.loss_ce: 0.0071 aux.acc_seg: 99.4160 +04/17 23:53:56 - mmengine - INFO - Iter(train) [ 55200/160000] base_lr: 6.6120e-05 lr: 2.4446e-07 eta: 1 day, 5:06:35 time: 0.9975 data_time: 0.0052 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6943 aux.loss_ce: 0.0079 aux.acc_seg: 99.1989 +04/17 23:54:46 - mmengine - INFO - Iter(train) [ 55250/160000] base_lr: 6.6089e-05 lr: 2.4434e-07 eta: 1 day, 5:05:45 time: 0.9950 data_time: 0.0046 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0060 decode.acc_seg: 99.7608 aux.loss_ce: 0.0073 aux.acc_seg: 99.4297 +04/17 23:55:36 - mmengine - INFO - Iter(train) [ 55300/160000] base_lr: 6.6057e-05 lr: 2.4423e-07 eta: 1 day, 5:04:55 time: 0.9966 data_time: 0.0049 memory: 8462 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7843 aux.loss_ce: 0.0069 aux.acc_seg: 99.4509 +04/17 23:56:26 - mmengine - INFO - Iter(train) [ 55350/160000] base_lr: 6.6026e-05 lr: 2.4411e-07 eta: 1 day, 5:04:04 time: 0.9961 data_time: 0.0047 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0062 decode.acc_seg: 99.7089 aux.loss_ce: 0.0080 aux.acc_seg: 99.0749 +04/17 23:57:15 - mmengine - INFO - Iter(train) [ 55400/160000] base_lr: 6.5994e-05 lr: 2.4399e-07 eta: 1 day, 5:03:14 time: 0.9969 data_time: 0.0048 memory: 8462 loss: 0.0116 decode.loss_ce: 0.0054 decode.acc_seg: 99.7566 aux.loss_ce: 0.0062 aux.acc_seg: 99.4513 +04/17 23:58:05 - mmengine - INFO - Iter(train) [ 55450/160000] base_lr: 6.5963e-05 lr: 2.4388e-07 eta: 1 day, 5:02:24 time: 0.9964 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0070 decode.acc_seg: 99.7166 aux.loss_ce: 0.0083 aux.acc_seg: 99.0023 +04/17 23:58:55 - mmengine - INFO - Iter(train) [ 55500/160000] base_lr: 6.5931e-05 lr: 2.4376e-07 eta: 1 day, 5:01:33 time: 0.9961 data_time: 0.0045 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7013 aux.loss_ce: 0.0074 aux.acc_seg: 99.1121 +04/17 23:59:45 - mmengine - INFO - Iter(train) [ 55550/160000] base_lr: 6.5899e-05 lr: 2.4364e-07 eta: 1 day, 5:00:43 time: 0.9968 data_time: 0.0048 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0063 decode.acc_seg: 99.6902 aux.loss_ce: 0.0083 aux.acc_seg: 98.8699 +04/18 00:00:35 - mmengine - INFO - Iter(train) [ 55600/160000] base_lr: 6.5868e-05 lr: 2.4353e-07 eta: 1 day, 4:59:53 time: 0.9970 data_time: 0.0051 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0067 decode.acc_seg: 99.6054 aux.loss_ce: 0.0081 aux.acc_seg: 98.8825 +04/18 00:01:25 - mmengine - 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mmengine - INFO - Iter(train) [ 56200/160000] base_lr: 6.5489e-05 lr: 2.4213e-07 eta: 1 day, 4:49:49 time: 0.9963 data_time: 0.0050 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0061 decode.acc_seg: 99.7324 aux.loss_ce: 0.0071 aux.acc_seg: 99.2636 +04/18 00:11:22 - mmengine - INFO - Iter(train) [ 56250/160000] base_lr: 6.5458e-05 lr: 2.4201e-07 eta: 1 day, 4:48:59 time: 0.9962 data_time: 0.0048 memory: 8462 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7345 aux.loss_ce: 0.0071 aux.acc_seg: 99.4146 +04/18 00:12:12 - mmengine - INFO - Iter(train) [ 56300/160000] base_lr: 6.5426e-05 lr: 2.4189e-07 eta: 1 day, 4:48:08 time: 0.9969 data_time: 0.0045 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0063 decode.acc_seg: 99.7549 aux.loss_ce: 0.0075 aux.acc_seg: 99.1474 +04/18 00:13:02 - mmengine - INFO - Iter(train) [ 56350/160000] base_lr: 6.5395e-05 lr: 2.4178e-07 eta: 1 day, 4:47:18 time: 0.9976 data_time: 0.0052 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6138 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memory: 8462 loss: 0.0133 decode.loss_ce: 0.0061 decode.acc_seg: 99.5913 aux.loss_ce: 0.0071 aux.acc_seg: 98.8617 +04/18 00:26:20 - mmengine - INFO - Iter(train) [ 57150/160000] base_lr: 6.4890e-05 lr: 2.3991e-07 eta: 1 day, 4:33:54 time: 0.9971 data_time: 0.0049 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0066 decode.acc_seg: 99.6857 aux.loss_ce: 0.0086 aux.acc_seg: 98.9967 +04/18 00:27:10 - mmengine - INFO - Iter(train) [ 57200/160000] base_lr: 6.4858e-05 lr: 2.3980e-07 eta: 1 day, 4:33:03 time: 0.9968 data_time: 0.0048 memory: 8462 loss: 0.0134 decode.loss_ce: 0.0062 decode.acc_seg: 99.7454 aux.loss_ce: 0.0072 aux.acc_seg: 99.1982 +04/18 00:27:59 - mmengine - INFO - Iter(train) [ 57250/160000] base_lr: 6.4827e-05 lr: 2.3968e-07 eta: 1 day, 4:32:13 time: 0.9970 data_time: 0.0048 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.8083 aux.loss_ce: 0.0071 aux.acc_seg: 99.4089 +04/18 00:28:49 - mmengine - INFO - Iter(train) [ 57300/160000] base_lr: 6.4795e-05 lr: 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aux.loss_ce: 0.0083 aux.acc_seg: 99.3893 +04/18 00:44:36 - mmengine - INFO - Iter(train) [ 58250/160000] base_lr: 6.4196e-05 lr: 2.3735e-07 eta: 1 day, 4:15:28 time: 0.9988 data_time: 0.0049 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.6920 aux.loss_ce: 0.0075 aux.acc_seg: 99.1848 +04/18 00:45:26 - mmengine - INFO - Iter(train) [ 58300/160000] base_lr: 6.4164e-05 lr: 2.3723e-07 eta: 1 day, 4:14:38 time: 0.9967 data_time: 0.0046 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.8030 aux.loss_ce: 0.0074 aux.acc_seg: 99.2216 +04/18 00:46:16 - mmengine - INFO - Iter(train) [ 58350/160000] base_lr: 6.4133e-05 lr: 2.3711e-07 eta: 1 day, 4:13:48 time: 0.9956 data_time: 0.0046 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0070 decode.acc_seg: 99.6845 aux.loss_ce: 0.0085 aux.acc_seg: 99.1455 +04/18 00:47:06 - mmengine - INFO - Iter(train) [ 58400/160000] base_lr: 6.4101e-05 lr: 2.3700e-07 eta: 1 day, 4:12:58 time: 0.9963 data_time: 0.0046 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0063 decode.acc_seg: 99.7093 aux.loss_ce: 0.0077 aux.acc_seg: 99.0812 +04/18 00:47:56 - mmengine - INFO - Iter(train) [ 58450/160000] base_lr: 6.4070e-05 lr: 2.3688e-07 eta: 1 day, 4:12:07 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.6807 aux.loss_ce: 0.0076 aux.acc_seg: 98.7923 +04/18 00:48:46 - mmengine - INFO - Iter(train) [ 58500/160000] base_lr: 6.4038e-05 lr: 2.3676e-07 eta: 1 day, 4:11:17 time: 0.9952 data_time: 0.0047 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0068 decode.acc_seg: 99.6386 aux.loss_ce: 0.0079 aux.acc_seg: 99.0294 +04/18 00:49:36 - mmengine - INFO - Iter(train) [ 58550/160000] base_lr: 6.4007e-05 lr: 2.3665e-07 eta: 1 day, 4:10:27 time: 0.9960 data_time: 0.0053 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0059 decode.acc_seg: 99.6319 aux.loss_ce: 0.0069 aux.acc_seg: 99.0234 +04/18 00:50:25 - mmengine - INFO - Iter(train) [ 58600/160000] base_lr: 6.3975e-05 lr: 2.3653e-07 eta: 1 day, 4:09:37 time: 0.9982 data_time: 0.0047 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7671 aux.loss_ce: 0.0074 aux.acc_seg: 99.1859 +04/18 00:51:15 - mmengine - INFO - Iter(train) [ 58650/160000] base_lr: 6.3944e-05 lr: 2.3641e-07 eta: 1 day, 4:08:46 time: 0.9959 data_time: 0.0049 memory: 8462 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7385 aux.loss_ce: 0.0072 aux.acc_seg: 98.9573 +04/18 00:52:05 - mmengine - INFO - Iter(train) [ 58700/160000] base_lr: 6.3912e-05 lr: 2.3630e-07 eta: 1 day, 4:07:56 time: 0.9980 data_time: 0.0051 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7580 aux.loss_ce: 0.0073 aux.acc_seg: 99.1995 +04/18 00:52:55 - mmengine - INFO - Iter(train) [ 58750/160000] base_lr: 6.3881e-05 lr: 2.3618e-07 eta: 1 day, 4:07:06 time: 0.9972 data_time: 0.0050 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.6897 aux.loss_ce: 0.0076 aux.acc_seg: 99.0902 +04/18 00:53:45 - mmengine - INFO - Iter(train) [ 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99.1943 +04/18 00:57:04 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 00:57:04 - mmengine - INFO - Iter(train) [ 59000/160000] base_lr: 6.3723e-05 lr: 2.3560e-07 eta: 1 day, 4:02:55 time: 0.9972 data_time: 0.0049 memory: 8462 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.8060 aux.loss_ce: 0.0066 aux.acc_seg: 99.4778 +04/18 00:57:54 - mmengine - INFO - Iter(train) [ 59050/160000] base_lr: 6.3691e-05 lr: 2.3548e-07 eta: 1 day, 4:02:05 time: 0.9980 data_time: 0.0049 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0076 decode.acc_seg: 99.6397 aux.loss_ce: 0.0081 aux.acc_seg: 98.9975 +04/18 00:58:44 - mmengine - INFO - Iter(train) [ 59100/160000] base_lr: 6.3660e-05 lr: 2.3536e-07 eta: 1 day, 4:01:14 time: 0.9956 data_time: 0.0049 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0064 decode.acc_seg: 99.6559 aux.loss_ce: 0.0077 aux.acc_seg: 99.0963 +04/18 00:59:34 - mmengine - INFO - Iter(train) [ 59150/160000] base_lr: 6.3628e-05 lr: 2.3525e-07 eta: 1 day, 4:00:24 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.6416 aux.loss_ce: 0.0075 aux.acc_seg: 99.0419 +04/18 01:00:24 - mmengine - INFO - Iter(train) [ 59200/160000] base_lr: 6.3597e-05 lr: 2.3513e-07 eta: 1 day, 3:59:34 time: 0.9968 data_time: 0.0047 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0058 decode.acc_seg: 99.7854 aux.loss_ce: 0.0070 aux.acc_seg: 99.3044 +04/18 01:01:13 - mmengine - INFO - Iter(train) [ 59250/160000] base_lr: 6.3565e-05 lr: 2.3501e-07 eta: 1 day, 3:58:44 time: 0.9961 data_time: 0.0047 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.6817 aux.loss_ce: 0.0077 aux.acc_seg: 99.2182 +04/18 01:02:03 - mmengine - INFO - Iter(train) [ 59300/160000] base_lr: 6.3534e-05 lr: 2.3490e-07 eta: 1 day, 3:57:53 time: 0.9978 data_time: 0.0051 memory: 8462 loss: 0.0131 decode.loss_ce: 0.0059 decode.acc_seg: 99.7379 aux.loss_ce: 0.0072 aux.acc_seg: 99.3759 +04/18 01:02:53 - mmengine - INFO - Iter(train) [ 59350/160000] base_lr: 6.3502e-05 lr: 2.3478e-07 eta: 1 day, 3:57:03 time: 0.9962 data_time: 0.0050 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0074 decode.acc_seg: 99.6422 aux.loss_ce: 0.0082 aux.acc_seg: 99.2203 +04/18 01:03:43 - mmengine - INFO - Iter(train) [ 59400/160000] base_lr: 6.3470e-05 lr: 2.3466e-07 eta: 1 day, 3:56:13 time: 0.9984 data_time: 0.0052 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0060 decode.acc_seg: 99.7980 aux.loss_ce: 0.0069 aux.acc_seg: 99.4974 +04/18 01:04:33 - mmengine - INFO - Iter(train) [ 59450/160000] base_lr: 6.3439e-05 lr: 2.3455e-07 eta: 1 day, 3:55:23 time: 0.9968 data_time: 0.0049 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0075 decode.acc_seg: 99.6365 aux.loss_ce: 0.0089 aux.acc_seg: 98.9510 +04/18 01:05:23 - mmengine - INFO - Iter(train) [ 59500/160000] base_lr: 6.3407e-05 lr: 2.3443e-07 eta: 1 day, 3:54:32 time: 0.9970 data_time: 0.0046 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7969 aux.loss_ce: 0.0070 aux.acc_seg: 99.3740 +04/18 01:06:13 - mmengine - INFO - Iter(train) [ 59550/160000] base_lr: 6.3376e-05 lr: 2.3431e-07 eta: 1 day, 3:53:42 time: 0.9966 data_time: 0.0047 memory: 8462 loss: 0.0134 decode.loss_ce: 0.0061 decode.acc_seg: 99.7887 aux.loss_ce: 0.0072 aux.acc_seg: 99.5459 +04/18 01:07:02 - mmengine - INFO - Iter(train) [ 59600/160000] base_lr: 6.3344e-05 lr: 2.3420e-07 eta: 1 day, 3:52:52 time: 0.9981 data_time: 0.0051 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.6906 aux.loss_ce: 0.0076 aux.acc_seg: 99.1089 +04/18 01:07:52 - mmengine - INFO - Iter(train) [ 59650/160000] base_lr: 6.3313e-05 lr: 2.3408e-07 eta: 1 day, 3:52:02 time: 0.9966 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0070 decode.acc_seg: 99.7128 aux.loss_ce: 0.0083 aux.acc_seg: 99.0110 +04/18 01:08:42 - mmengine - INFO - Iter(train) [ 59700/160000] base_lr: 6.3281e-05 lr: 2.3396e-07 eta: 1 day, 3:51:11 time: 0.9968 data_time: 0.0045 memory: 8462 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7534 aux.loss_ce: 0.0067 aux.acc_seg: 99.2552 +04/18 01:09:32 - mmengine - INFO - Iter(train) [ 59750/160000] base_lr: 6.3250e-05 lr: 2.3385e-07 eta: 1 day, 3:50:21 time: 0.9968 data_time: 0.0047 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0062 decode.acc_seg: 99.6611 aux.loss_ce: 0.0074 aux.acc_seg: 99.0505 +04/18 01:10:22 - mmengine - INFO - Iter(train) [ 59800/160000] base_lr: 6.3218e-05 lr: 2.3373e-07 eta: 1 day, 3:49:31 time: 0.9975 data_time: 0.0050 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.4905 aux.loss_ce: 0.0075 aux.acc_seg: 99.0028 +04/18 01:11:12 - mmengine - INFO - Iter(train) [ 59850/160000] base_lr: 6.3187e-05 lr: 2.3361e-07 eta: 1 day, 3:48:41 time: 0.9974 data_time: 0.0049 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0066 decode.acc_seg: 99.7030 aux.loss_ce: 0.0082 aux.acc_seg: 99.0513 +04/18 01:12:01 - mmengine - INFO - Iter(train) [ 59900/160000] base_lr: 6.3155e-05 lr: 2.3350e-07 eta: 1 day, 3:47:51 time: 0.9963 data_time: 0.0049 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0071 decode.acc_seg: 99.7084 aux.loss_ce: 0.0082 aux.acc_seg: 99.0517 +04/18 01:12:51 - mmengine - INFO - Iter(train) [ 59950/160000] base_lr: 6.3123e-05 lr: 2.3338e-07 eta: 1 day, 3:47:00 time: 0.9974 data_time: 0.0048 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0061 decode.acc_seg: 99.7242 aux.loss_ce: 0.0070 aux.acc_seg: 99.3149 +04/18 01:13:41 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 01:13:41 - mmengine - INFO - Iter(train) [ 60000/160000] base_lr: 6.3092e-05 lr: 2.3326e-07 eta: 1 day, 3:46:10 time: 0.9965 data_time: 0.0052 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0069 decode.acc_seg: 99.7009 aux.loss_ce: 0.0083 aux.acc_seg: 99.0499 +04/18 01:13:41 - mmengine - INFO - Saving checkpoint at 60000 iterations +04/18 01:13:51 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1160 data_time: 0.0016 memory: 4004 +04/18 01:13:57 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1156 data_time: 0.0015 memory: 4004 +04/18 01:14:03 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1155 data_time: 0.0015 memory: 4004 +04/18 01:14:08 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1156 data_time: 0.0015 memory: 4004 +04/18 01:14:09 - mmengine - INFO - per class results: +04/18 01:14:09 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.17 | 99.59 | 99.59 | 99.58 | 99.59 | +| contrast | 81.83 | 89.83 | 90.01 | 90.18 | 89.83 | ++------------+-------+-------+--------+-----------+--------+ +04/18 01:14:09 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2000 mIoU: 90.5000 mAcc: 94.7100 mFscore: 94.8000 mPrecision: 94.8800 mRecall: 94.7100 data_time: 0.0019 time: 0.1161 +04/18 01:14:58 - mmengine - INFO - Iter(train) [ 60050/160000] base_lr: 6.3060e-05 lr: 2.3315e-07 eta: 1 day, 3:45:20 time: 0.9959 data_time: 0.0046 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7782 aux.loss_ce: 0.0074 aux.acc_seg: 99.3649 +04/18 01:15:48 - mmengine - INFO - Iter(train) [ 60100/160000] base_lr: 6.3029e-05 lr: 2.3303e-07 eta: 1 day, 3:44:30 time: 0.9967 data_time: 0.0047 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0083 decode.acc_seg: 99.3910 aux.loss_ce: 0.0090 aux.acc_seg: 98.7228 +04/18 01:16:38 - mmengine - INFO - Iter(train) [ 60150/160000] base_lr: 6.2997e-05 lr: 2.3291e-07 eta: 1 day, 3:43:40 time: 0.9972 data_time: 0.0052 memory: 8462 loss: 0.0124 decode.loss_ce: 0.0055 decode.acc_seg: 99.7696 aux.loss_ce: 0.0069 aux.acc_seg: 99.1947 +04/18 01:17:28 - mmengine - INFO - Iter(train) [ 60200/160000] base_lr: 6.2966e-05 lr: 2.3280e-07 eta: 1 day, 3:42:49 time: 0.9970 data_time: 0.0050 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0062 decode.acc_seg: 99.7618 aux.loss_ce: 0.0085 aux.acc_seg: 99.2031 +04/18 01:18:18 - mmengine - INFO - Iter(train) [ 60250/160000] base_lr: 6.2934e-05 lr: 2.3268e-07 eta: 1 day, 3:41:59 time: 0.9972 data_time: 0.0050 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0065 decode.acc_seg: 99.6517 aux.loss_ce: 0.0079 aux.acc_seg: 99.0055 +04/18 01:19:08 - mmengine - INFO - Iter(train) [ 60300/160000] base_lr: 6.2903e-05 lr: 2.3256e-07 eta: 1 day, 3:41:09 time: 0.9966 data_time: 0.0046 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0066 decode.acc_seg: 99.7400 aux.loss_ce: 0.0082 aux.acc_seg: 99.1520 +04/18 01:19:57 - mmengine - INFO - Iter(train) [ 60350/160000] base_lr: 6.2871e-05 lr: 2.3245e-07 eta: 1 day, 3:40:19 time: 0.9976 data_time: 0.0044 memory: 8462 loss: 0.0122 decode.loss_ce: 0.0057 decode.acc_seg: 99.7942 aux.loss_ce: 0.0066 aux.acc_seg: 99.4230 +04/18 01:20:47 - mmengine - INFO - Iter(train) [ 60400/160000] base_lr: 6.2840e-05 lr: 2.3233e-07 eta: 1 day, 3:39:28 time: 0.9969 data_time: 0.0048 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0068 decode.acc_seg: 99.7206 aux.loss_ce: 0.0083 aux.acc_seg: 98.9014 +04/18 01:21:37 - mmengine - INFO - Iter(train) [ 60450/160000] base_lr: 6.2808e-05 lr: 2.3221e-07 eta: 1 day, 3:38:38 time: 0.9959 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0065 decode.acc_seg: 99.7553 aux.loss_ce: 0.0082 aux.acc_seg: 99.3738 +04/18 01:22:27 - mmengine - INFO - Iter(train) [ 60500/160000] base_lr: 6.2776e-05 lr: 2.3210e-07 eta: 1 day, 3:37:48 time: 0.9971 data_time: 0.0046 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0064 decode.acc_seg: 99.7364 aux.loss_ce: 0.0079 aux.acc_seg: 99.1091 +04/18 01:23:17 - mmengine - INFO - Iter(train) [ 60550/160000] base_lr: 6.2745e-05 lr: 2.3198e-07 eta: 1 day, 3:36:58 time: 0.9960 data_time: 0.0045 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0068 decode.acc_seg: 99.7866 aux.loss_ce: 0.0082 aux.acc_seg: 99.3393 +04/18 01:24:07 - mmengine - INFO - Iter(train) [ 60600/160000] base_lr: 6.2713e-05 lr: 2.3186e-07 eta: 1 day, 3:36:08 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0062 decode.acc_seg: 99.7169 aux.loss_ce: 0.0073 aux.acc_seg: 99.0591 +04/18 01:24:56 - mmengine - INFO - Iter(train) [ 60650/160000] base_lr: 6.2682e-05 lr: 2.3175e-07 eta: 1 day, 3:35:17 time: 0.9964 data_time: 0.0049 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7566 aux.loss_ce: 0.0073 aux.acc_seg: 99.3406 +04/18 01:25:46 - mmengine - INFO - Iter(train) [ 60700/160000] base_lr: 6.2650e-05 lr: 2.3163e-07 eta: 1 day, 3:34:27 time: 0.9955 data_time: 0.0045 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.7856 aux.loss_ce: 0.0075 aux.acc_seg: 99.3752 +04/18 01:26:36 - mmengine - INFO - Iter(train) [ 60750/160000] base_lr: 6.2619e-05 lr: 2.3151e-07 eta: 1 day, 3:33:37 time: 0.9950 data_time: 0.0046 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0067 decode.acc_seg: 99.7543 aux.loss_ce: 0.0079 aux.acc_seg: 99.1589 +04/18 01:27:26 - mmengine - INFO - Iter(train) [ 60800/160000] base_lr: 6.2587e-05 lr: 2.3140e-07 eta: 1 day, 3:32:47 time: 0.9964 data_time: 0.0048 memory: 8462 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.6580 aux.loss_ce: 0.0068 aux.acc_seg: 98.9519 +04/18 01:28:16 - mmengine - INFO - Iter(train) [ 60850/160000] base_lr: 6.2556e-05 lr: 2.3128e-07 eta: 1 day, 3:31:56 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0060 decode.acc_seg: 99.7095 aux.loss_ce: 0.0073 aux.acc_seg: 99.3185 +04/18 01:29:06 - mmengine - INFO - Iter(train) [ 60900/160000] base_lr: 6.2524e-05 lr: 2.3116e-07 eta: 1 day, 3:31:06 time: 0.9970 data_time: 0.0047 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0069 decode.acc_seg: 99.7679 aux.loss_ce: 0.0084 aux.acc_seg: 99.2031 +04/18 01:29:56 - mmengine - INFO - Iter(train) [ 60950/160000] base_lr: 6.2493e-05 lr: 2.3105e-07 eta: 1 day, 3:30:16 time: 0.9975 data_time: 0.0051 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0065 decode.acc_seg: 99.5949 aux.loss_ce: 0.0085 aux.acc_seg: 98.7904 +04/18 01:30:45 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 01:30:45 - mmengine - INFO - Iter(train) [ 61000/160000] base_lr: 6.2461e-05 lr: 2.3093e-07 eta: 1 day, 3:29:26 time: 0.9974 data_time: 0.0051 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0067 decode.acc_seg: 99.8030 aux.loss_ce: 0.0078 aux.acc_seg: 99.4591 +04/18 01:31:35 - mmengine - INFO - Iter(train) [ 61050/160000] base_lr: 6.2429e-05 lr: 2.3081e-07 eta: 1 day, 3:28:36 time: 0.9978 data_time: 0.0047 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.8186 aux.loss_ce: 0.0070 aux.acc_seg: 99.3097 +04/18 01:32:25 - mmengine - INFO - Iter(train) [ 61100/160000] base_lr: 6.2398e-05 lr: 2.3070e-07 eta: 1 day, 3:27:46 time: 0.9975 data_time: 0.0047 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0063 decode.acc_seg: 99.6538 aux.loss_ce: 0.0077 aux.acc_seg: 98.9876 +04/18 01:33:15 - mmengine - INFO - Iter(train) [ 61150/160000] base_lr: 6.2366e-05 lr: 2.3058e-07 eta: 1 day, 3:26:55 time: 0.9967 data_time: 0.0051 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.7581 aux.loss_ce: 0.0072 aux.acc_seg: 99.3671 +04/18 01:34:05 - mmengine - INFO - Iter(train) [ 61200/160000] base_lr: 6.2335e-05 lr: 2.3046e-07 eta: 1 day, 3:26:05 time: 0.9977 data_time: 0.0049 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0068 decode.acc_seg: 99.7314 aux.loss_ce: 0.0090 aux.acc_seg: 99.1180 +04/18 01:34:55 - mmengine - INFO - Iter(train) [ 61250/160000] base_lr: 6.2303e-05 lr: 2.3035e-07 eta: 1 day, 3:25:15 time: 0.9984 data_time: 0.0047 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0065 decode.acc_seg: 99.7868 aux.loss_ce: 0.0078 aux.acc_seg: 99.3105 +04/18 01:35:45 - mmengine - INFO - Iter(train) [ 61300/160000] base_lr: 6.2272e-05 lr: 2.3023e-07 eta: 1 day, 3:24:25 time: 0.9972 data_time: 0.0048 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0076 decode.acc_seg: 99.7469 aux.loss_ce: 0.0085 aux.acc_seg: 99.2950 +04/18 01:36:34 - mmengine - INFO - Iter(train) [ 61350/160000] base_lr: 6.2240e-05 lr: 2.3011e-07 eta: 1 day, 3:23:35 time: 0.9977 data_time: 0.0048 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0062 decode.acc_seg: 99.6969 aux.loss_ce: 0.0070 aux.acc_seg: 99.0492 +04/18 01:37:24 - mmengine - INFO - Iter(train) [ 61400/160000] base_lr: 6.2209e-05 lr: 2.3000e-07 eta: 1 day, 3:22:44 time: 0.9963 data_time: 0.0048 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.6357 aux.loss_ce: 0.0073 aux.acc_seg: 98.8478 +04/18 01:38:14 - mmengine - INFO - Iter(train) [ 61450/160000] base_lr: 6.2177e-05 lr: 2.2988e-07 eta: 1 day, 3:21:54 time: 0.9967 data_time: 0.0048 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0067 decode.acc_seg: 99.7492 aux.loss_ce: 0.0080 aux.acc_seg: 99.3401 +04/18 01:39:04 - mmengine - INFO - Iter(train) [ 61500/160000] base_lr: 6.2146e-05 lr: 2.2976e-07 eta: 1 day, 3:21:04 time: 0.9971 data_time: 0.0048 memory: 8462 loss: 0.0116 decode.loss_ce: 0.0053 decode.acc_seg: 99.7766 aux.loss_ce: 0.0063 aux.acc_seg: 99.2941 +04/18 01:39:54 - mmengine - INFO - Iter(train) [ 61550/160000] base_lr: 6.2114e-05 lr: 2.2965e-07 eta: 1 day, 3:20:14 time: 0.9985 data_time: 0.0051 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0066 decode.acc_seg: 99.6946 aux.loss_ce: 0.0079 aux.acc_seg: 99.2332 +04/18 01:40:44 - mmengine - INFO - Iter(train) [ 61600/160000] base_lr: 6.2082e-05 lr: 2.2953e-07 eta: 1 day, 3:19:24 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0071 decode.acc_seg: 99.8177 aux.loss_ce: 0.0085 aux.acc_seg: 99.5119 +04/18 01:41:34 - mmengine - INFO - Iter(train) [ 61650/160000] base_lr: 6.2051e-05 lr: 2.2941e-07 eta: 1 day, 3:18:34 time: 0.9976 data_time: 0.0047 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.7576 aux.loss_ce: 0.0076 aux.acc_seg: 99.3904 +04/18 01:42:24 - mmengine - INFO - Iter(train) [ 61700/160000] base_lr: 6.2019e-05 lr: 2.2930e-07 eta: 1 day, 3:17:43 time: 0.9971 data_time: 0.0048 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0060 decode.acc_seg: 99.7002 aux.loss_ce: 0.0075 aux.acc_seg: 99.2342 +04/18 01:43:13 - mmengine - INFO - Iter(train) [ 61750/160000] base_lr: 6.1988e-05 lr: 2.2918e-07 eta: 1 day, 3:16:53 time: 0.9980 data_time: 0.0053 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7612 aux.loss_ce: 0.0075 aux.acc_seg: 99.1423 +04/18 01:44:03 - mmengine - INFO - Iter(train) [ 61800/160000] base_lr: 6.1956e-05 lr: 2.2906e-07 eta: 1 day, 3:16:03 time: 0.9973 data_time: 0.0052 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.6469 aux.loss_ce: 0.0075 aux.acc_seg: 98.9435 +04/18 01:44:53 - mmengine - INFO - Iter(train) [ 61850/160000] base_lr: 6.1925e-05 lr: 2.2895e-07 eta: 1 day, 3:15:13 time: 0.9978 data_time: 0.0045 memory: 8462 loss: 0.0123 decode.loss_ce: 0.0058 decode.acc_seg: 99.7133 aux.loss_ce: 0.0065 aux.acc_seg: 99.1039 +04/18 01:45:43 - mmengine - INFO - Iter(train) [ 61900/160000] base_lr: 6.1893e-05 lr: 2.2883e-07 eta: 1 day, 3:14:23 time: 0.9968 data_time: 0.0050 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0069 decode.acc_seg: 99.7814 aux.loss_ce: 0.0079 aux.acc_seg: 99.4324 +04/18 01:46:33 - mmengine - INFO - Iter(train) [ 61950/160000] base_lr: 6.1862e-05 lr: 2.2872e-07 eta: 1 day, 3:13:33 time: 0.9974 data_time: 0.0047 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0065 decode.acc_seg: 99.6693 aux.loss_ce: 0.0077 aux.acc_seg: 99.1661 +04/18 01:47:23 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 01:47:23 - mmengine - INFO - Iter(train) [ 62000/160000] base_lr: 6.1830e-05 lr: 2.2860e-07 eta: 1 day, 3:12:42 time: 0.9955 data_time: 0.0049 memory: 8462 loss: 0.0130 decode.loss_ce: 0.0055 decode.acc_seg: 99.8205 aux.loss_ce: 0.0075 aux.acc_seg: 99.5090 +04/18 01:48:13 - mmengine - INFO - Iter(train) [ 62050/160000] base_lr: 6.1798e-05 lr: 2.2848e-07 eta: 1 day, 3:11:52 time: 0.9960 data_time: 0.0050 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0064 decode.acc_seg: 99.7391 aux.loss_ce: 0.0076 aux.acc_seg: 99.2418 +04/18 01:49:02 - mmengine - INFO - Iter(train) [ 62100/160000] base_lr: 6.1767e-05 lr: 2.2837e-07 eta: 1 day, 3:11:02 time: 0.9969 data_time: 0.0045 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0068 decode.acc_seg: 99.7988 aux.loss_ce: 0.0077 aux.acc_seg: 99.5373 +04/18 01:49:52 - mmengine - INFO - Iter(train) [ 62150/160000] base_lr: 6.1735e-05 lr: 2.2825e-07 eta: 1 day, 3:10:12 time: 0.9966 data_time: 0.0047 memory: 8462 loss: 0.0122 decode.loss_ce: 0.0056 decode.acc_seg: 99.6365 aux.loss_ce: 0.0066 aux.acc_seg: 99.0641 +04/18 01:50:42 - mmengine - INFO - Iter(train) [ 62200/160000] base_lr: 6.1704e-05 lr: 2.2813e-07 eta: 1 day, 3:09:22 time: 0.9986 data_time: 0.0054 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.7803 aux.loss_ce: 0.0077 aux.acc_seg: 99.3336 +04/18 01:51:32 - mmengine - INFO - Iter(train) [ 62250/160000] base_lr: 6.1672e-05 lr: 2.2802e-07 eta: 1 day, 3:08:31 time: 0.9963 data_time: 0.0048 memory: 8462 loss: 0.0119 decode.loss_ce: 0.0053 decode.acc_seg: 99.7755 aux.loss_ce: 0.0066 aux.acc_seg: 99.2903 +04/18 01:52:22 - mmengine - INFO - Iter(train) [ 62300/160000] base_lr: 6.1641e-05 lr: 2.2790e-07 eta: 1 day, 3:07:41 time: 0.9970 data_time: 0.0051 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0064 decode.acc_seg: 99.7145 aux.loss_ce: 0.0075 aux.acc_seg: 99.3723 +04/18 01:53:12 - mmengine - INFO - Iter(train) [ 62350/160000] base_lr: 6.1609e-05 lr: 2.2778e-07 eta: 1 day, 3:06:51 time: 0.9967 data_time: 0.0046 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7660 aux.loss_ce: 0.0073 aux.acc_seg: 99.2767 +04/18 01:54:02 - mmengine - INFO - Iter(train) [ 62400/160000] base_lr: 6.1578e-05 lr: 2.2767e-07 eta: 1 day, 3:06:01 time: 0.9980 data_time: 0.0047 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0061 decode.acc_seg: 99.7074 aux.loss_ce: 0.0072 aux.acc_seg: 99.1819 +04/18 01:54:52 - mmengine - INFO - Iter(train) [ 62450/160000] base_lr: 6.1546e-05 lr: 2.2755e-07 eta: 1 day, 3:05:11 time: 0.9972 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0065 decode.acc_seg: 99.6447 aux.loss_ce: 0.0082 aux.acc_seg: 98.9105 +04/18 01:55:41 - mmengine - INFO - Iter(train) [ 62500/160000] base_lr: 6.1515e-05 lr: 2.2743e-07 eta: 1 day, 3:04:21 time: 0.9970 data_time: 0.0047 memory: 8462 loss: 0.0123 decode.loss_ce: 0.0054 decode.acc_seg: 99.7746 aux.loss_ce: 0.0069 aux.acc_seg: 99.0730 +04/18 01:56:31 - mmengine - INFO - Iter(train) [ 62550/160000] base_lr: 6.1483e-05 lr: 2.2732e-07 eta: 1 day, 3:03:30 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0061 decode.acc_seg: 99.7644 aux.loss_ce: 0.0075 aux.acc_seg: 99.2481 +04/18 01:57:21 - mmengine - INFO - Iter(train) [ 62600/160000] base_lr: 6.1451e-05 lr: 2.2720e-07 eta: 1 day, 3:02:40 time: 0.9972 data_time: 0.0051 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0066 decode.acc_seg: 99.8302 aux.loss_ce: 0.0078 aux.acc_seg: 99.4967 +04/18 01:58:11 - mmengine - INFO - Iter(train) [ 62650/160000] base_lr: 6.1420e-05 lr: 2.2708e-07 eta: 1 day, 3:01:50 time: 0.9978 data_time: 0.0046 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.7139 aux.loss_ce: 0.0068 aux.acc_seg: 99.2033 +04/18 01:59:01 - mmengine - INFO - Iter(train) [ 62700/160000] base_lr: 6.1388e-05 lr: 2.2697e-07 eta: 1 day, 3:01:00 time: 0.9977 data_time: 0.0045 memory: 8462 loss: 0.0117 decode.loss_ce: 0.0053 decode.acc_seg: 99.8123 aux.loss_ce: 0.0064 aux.acc_seg: 99.2870 +04/18 01:59:51 - mmengine - INFO - Iter(train) [ 62750/160000] base_lr: 6.1357e-05 lr: 2.2685e-07 eta: 1 day, 3:00:10 time: 0.9971 data_time: 0.0048 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.8270 aux.loss_ce: 0.0077 aux.acc_seg: 99.4904 +04/18 02:00:41 - mmengine - INFO - Iter(train) [ 62800/160000] base_lr: 6.1325e-05 lr: 2.2673e-07 eta: 1 day, 2:59:20 time: 0.9975 data_time: 0.0046 memory: 8462 loss: 0.0134 decode.loss_ce: 0.0062 decode.acc_seg: 99.7332 aux.loss_ce: 0.0072 aux.acc_seg: 99.1310 +04/18 02:01:30 - mmengine - INFO - Iter(train) [ 62850/160000] base_lr: 6.1294e-05 lr: 2.2662e-07 eta: 1 day, 2:58:29 time: 0.9970 data_time: 0.0047 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0068 decode.acc_seg: 99.6639 aux.loss_ce: 0.0079 aux.acc_seg: 99.0673 +04/18 02:02:20 - mmengine - INFO - Iter(train) [ 62900/160000] base_lr: 6.1262e-05 lr: 2.2650e-07 eta: 1 day, 2:57:39 time: 0.9967 data_time: 0.0046 memory: 8462 loss: 0.0126 decode.loss_ce: 0.0059 decode.acc_seg: 99.7194 aux.loss_ce: 0.0068 aux.acc_seg: 99.1749 +04/18 02:03:10 - mmengine - INFO - Iter(train) [ 62950/160000] base_lr: 6.1231e-05 lr: 2.2638e-07 eta: 1 day, 2:56:49 time: 0.9971 data_time: 0.0049 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0061 decode.acc_seg: 99.6910 aux.loss_ce: 0.0071 aux.acc_seg: 99.0683 +04/18 02:04:00 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 02:04:00 - mmengine - INFO - Iter(train) [ 63000/160000] base_lr: 6.1199e-05 lr: 2.2627e-07 eta: 1 day, 2:55:59 time: 0.9970 data_time: 0.0051 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0059 decode.acc_seg: 99.7391 aux.loss_ce: 0.0073 aux.acc_seg: 99.4577 +04/18 02:04:50 - mmengine - INFO - Iter(train) [ 63050/160000] base_lr: 6.1168e-05 lr: 2.2615e-07 eta: 1 day, 2:55:09 time: 0.9969 data_time: 0.0047 memory: 8462 loss: 0.0121 decode.loss_ce: 0.0053 decode.acc_seg: 99.7906 aux.loss_ce: 0.0067 aux.acc_seg: 99.2176 +04/18 02:05:40 - mmengine - INFO - Iter(train) [ 63100/160000] base_lr: 6.1136e-05 lr: 2.2603e-07 eta: 1 day, 2:54:19 time: 0.9977 data_time: 0.0049 memory: 8462 loss: 0.0127 decode.loss_ce: 0.0059 decode.acc_seg: 99.7208 aux.loss_ce: 0.0068 aux.acc_seg: 99.0906 +04/18 02:06:30 - mmengine - INFO - Iter(train) [ 63150/160000] base_lr: 6.1104e-05 lr: 2.2592e-07 eta: 1 day, 2:53:29 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.7169 aux.loss_ce: 0.0070 aux.acc_seg: 99.2624 +04/18 02:07:20 - mmengine - INFO - Iter(train) [ 63200/160000] base_lr: 6.1073e-05 lr: 2.2580e-07 eta: 1 day, 2:52:38 time: 0.9967 data_time: 0.0047 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0068 decode.acc_seg: 99.7906 aux.loss_ce: 0.0080 aux.acc_seg: 99.3170 +04/18 02:08:09 - mmengine - INFO - Iter(train) [ 63250/160000] base_lr: 6.1041e-05 lr: 2.2568e-07 eta: 1 day, 2:51:48 time: 0.9979 data_time: 0.0050 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0057 decode.acc_seg: 99.7709 aux.loss_ce: 0.0072 aux.acc_seg: 99.2205 +04/18 02:08:59 - mmengine - INFO - Iter(train) [ 63300/160000] base_lr: 6.1010e-05 lr: 2.2557e-07 eta: 1 day, 2:50:58 time: 0.9969 data_time: 0.0047 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0057 decode.acc_seg: 99.6817 aux.loss_ce: 0.0075 aux.acc_seg: 99.1745 +04/18 02:09:49 - mmengine - INFO - Iter(train) [ 63350/160000] base_lr: 6.0978e-05 lr: 2.2545e-07 eta: 1 day, 2:50:08 time: 0.9983 data_time: 0.0056 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0069 decode.acc_seg: 99.7620 aux.loss_ce: 0.0089 aux.acc_seg: 99.2807 +04/18 02:10:39 - mmengine - INFO - Iter(train) [ 63400/160000] base_lr: 6.0947e-05 lr: 2.2533e-07 eta: 1 day, 2:49:18 time: 0.9960 data_time: 0.0049 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0068 decode.acc_seg: 99.7185 aux.loss_ce: 0.0083 aux.acc_seg: 99.2039 +04/18 02:11:29 - mmengine - INFO - Iter(train) [ 63450/160000] base_lr: 6.0915e-05 lr: 2.2522e-07 eta: 1 day, 2:48:28 time: 0.9980 data_time: 0.0053 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0064 decode.acc_seg: 99.8201 aux.loss_ce: 0.0077 aux.acc_seg: 99.3877 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174801 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174802 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174803 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174804 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174801 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174802 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174803 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174804 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 174770 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 174770 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 174770 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/18 02:20:10 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1925317996 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1925317996 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/18 02:20:11 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=101, + dilations=( + 1, + 1, + 1, + 1, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dropout_ratio=0.1, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + pretrained='open-mmlab://resnet101_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/R101_4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/18 02:20:13 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +04/18 02:20:14 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +04/18 02:20:14 - mmengine - INFO - load model from: open-mmlab://resnet101_v1c +04/18 02:20:14 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet101_v1c +04/18 02:20:14 - mmengine - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: fc.weight, fc.bias + +04/18 02:20:14 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/18 02:20:14 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/18 02:20:14 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/R101_4000. +04/18 02:20:46 - mmengine - INFO - Iter(train) [ 50/160000] lr: 9.9973e-03 eta: 1 day, 4:42:15 time: 0.5461 data_time: 0.0064 memory: 7635 loss: 0.1394 decode.loss_ce: 0.0903 decode.acc_seg: 96.5318 aux.loss_ce: 0.0491 aux.acc_seg: 96.5318 +04/18 02:21:14 - mmengine - INFO - Iter(train) [ 100/160000] lr: 9.9945e-03 eta: 1 day, 2:30:49 time: 0.5490 data_time: 0.0057 memory: 7635 loss: 0.0858 decode.loss_ce: 0.0528 decode.acc_seg: 97.9885 aux.loss_ce: 0.0330 aux.acc_seg: 97.1264 +04/18 02:21:41 - mmengine - INFO - Iter(train) [ 150/160000] lr: 9.9917e-03 eta: 1 day, 1:47:10 time: 0.5487 data_time: 0.0061 memory: 7635 loss: 0.0797 decode.loss_ce: 0.0480 decode.acc_seg: 98.1838 aux.loss_ce: 0.0317 aux.acc_seg: 96.6573 +Traceback (most recent call last): +Traceback (most recent call last): +Traceback (most recent call last): +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers + main()main()main()main() +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 592936 closing signal SIGINT + + + + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 592937 closing signal SIGINT + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 592938 closing signal SIGINT +runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 592939 closing signal SIGINT + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + model = self.train_loop.run() # type: ignoreoutputs = self.runner.model.train_step( + +model = self.train_loop.run() # type: ignoremodel = self.train_loop.run() # type: ignore File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 201, in update_params + self.run_iter(data_batch)self.run_iter(data_batch) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter +self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + self.step(**step_kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/scheduler/param_scheduler.py", line 115, in wrapper + outputs = self.runner.model.train_step(outputs = self.runner.model.train_step( + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + return wrapped(*args, **kwargs)outputs = self.runner.model.train_step( + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 253, in step + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss)optim_wrapper.update_params(parsed_loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 201, in update_params + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 201, in update_params + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 201, in update_params +self.optimizer.step(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper + self.step(**step_kwargs) +self.step(**step_kwargs) File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/scheduler/param_scheduler.py", line 115, in wrapper + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/scheduler/param_scheduler.py", line 115, in wrapper + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context +self.step(**step_kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/scheduler/param_scheduler.py", line 115, in wrapper +return wrapped(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 253, in step +return wrapped(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 253, in step + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/sgd.py", line 144, in step + return wrapped(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 253, in step + self.optimizer.step(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper + self.optimizer.step(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper + return func(*args, **kwargs) +self.optimizer.step(**kwargs) File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + + File "/opt/conda/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/sgd.py", line 144, in step + return func(*args, **kwargs)return func(*args, **kwargs) + + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + File "/opt/conda/lib/python3.8/site-packages/torch/optim/sgd.py", line 144, in step + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/sgd.py", line 144, in step + F.sgd(params_with_grad,F.sgd(params_with_grad,F.sgd(params_with_grad,F.sgd(params_with_grad, + + + + File "/opt/conda/lib/python3.8/site-packages/torch/optim/_functional.py", line 186, in sgd + File "/opt/conda/lib/python3.8/site-packages/torch/optim/_functional.py", line 194, in sgd + File "/opt/conda/lib/python3.8/site-packages/torch/optim/_functional.py", line 177, in sgd + File "/opt/conda/lib/python3.8/site-packages/torch/optim/_functional.py", line 194, in sgd + param.add_(d_p, alpha=alpha) buf.mul_(momentum).add_(d_p, alpha=1 - dampening)d_p = d_p.add(param, alpha=weight_decay) +param.add_(d_p, alpha=alpha) +KeyboardInterrupt + + +KeyboardInterruptKeyboardInterruptKeyboardInterrupt + + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 592901 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/18 02:22:14 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1770541458 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1770541458 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/18 02:22:14 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=101, + dilations=( + 1, + 1, + 1, + 1, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dropout_ratio=0.1, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + pretrained='open-mmlab://resnet101_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/R101_4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/18 02:22:16 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +04/18 02:22:17 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +04/18 02:22:18 - mmengine - INFO - load model from: open-mmlab://resnet101_v1c +04/18 02:22:18 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet101_v1c +04/18 02:22:18 - mmengine - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: fc.weight, fc.bias + +04/18 02:22:18 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/18 02:22:18 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/18 02:22:18 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/R101_4000. +04/18 02:22:50 - mmengine - INFO - Iter(train) [ 50/160000] lr: 9.9973e-03 eta: 1 day, 4:43:49 time: 0.5456 data_time: 0.0068 memory: 7635 loss: 0.1351 decode.loss_ce: 0.0875 decode.acc_seg: 97.6007 aux.loss_ce: 0.0476 aux.acc_seg: 97.6006 +04/18 02:23:17 - mmengine - INFO - Iter(train) [ 100/160000] lr: 9.9945e-03 eta: 1 day, 2:29:41 time: 0.5467 data_time: 0.0057 memory: 7635 loss: 0.0936 decode.loss_ce: 0.0562 decode.acc_seg: 97.6175 aux.loss_ce: 0.0374 aux.acc_seg: 96.2901 +04/18 02:23:45 - mmengine - INFO - Iter(train) [ 150/160000] lr: 9.9917e-03 eta: 1 day, 1:44:59 time: 0.5467 data_time: 0.0065 memory: 7635 loss: 0.0822 decode.loss_ce: 0.0503 decode.acc_seg: 97.9509 aux.loss_ce: 0.0319 aux.acc_seg: 97.1464 +04/18 02:24:12 - mmengine - INFO - Iter(train) [ 200/160000] lr: 9.9889e-03 eta: 1 day, 1:22:56 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0688 decode.loss_ce: 0.0416 decode.acc_seg: 98.1911 aux.loss_ce: 0.0272 aux.acc_seg: 96.7990 +04/18 02:24:40 - mmengine - INFO - Iter(train) [ 250/160000] lr: 9.9861e-03 eta: 1 day, 1:09:55 time: 0.5487 data_time: 0.0058 memory: 7635 loss: 0.0830 decode.loss_ce: 0.0518 decode.acc_seg: 98.1723 aux.loss_ce: 0.0313 aux.acc_seg: 96.9854 +04/18 02:25:07 - mmengine - INFO - Iter(train) [ 300/160000] lr: 9.9833e-03 eta: 1 day, 1:00:47 time: 0.5491 data_time: 0.0063 memory: 7635 loss: 0.0735 decode.loss_ce: 0.0464 decode.acc_seg: 98.2391 aux.loss_ce: 0.0270 aux.acc_seg: 97.6198 +04/18 02:25:34 - mmengine - INFO - Iter(train) [ 350/160000] lr: 9.9806e-03 eta: 1 day, 0:54:07 time: 0.5471 data_time: 0.0063 memory: 7635 loss: 0.0653 decode.loss_ce: 0.0413 decode.acc_seg: 98.3918 aux.loss_ce: 0.0241 aux.acc_seg: 97.8115 +04/18 02:26:02 - mmengine - INFO - Iter(train) [ 400/160000] lr: 9.9778e-03 eta: 1 day, 0:49:12 time: 0.5473 data_time: 0.0061 memory: 7635 loss: 0.0583 decode.loss_ce: 0.0371 decode.acc_seg: 98.5509 aux.loss_ce: 0.0212 aux.acc_seg: 98.1630 +04/18 02:26:29 - mmengine - INFO - Iter(train) [ 450/160000] lr: 9.9750e-03 eta: 1 day, 0:45:04 time: 0.5478 data_time: 0.0058 memory: 7635 loss: 0.0662 decode.loss_ce: 0.0430 decode.acc_seg: 98.4915 aux.loss_ce: 0.0232 aux.acc_seg: 98.0277 +04/18 02:26:57 - mmengine - INFO - Iter(train) [ 500/160000] lr: 9.9722e-03 eta: 1 day, 0:43:03 time: 0.5475 data_time: 0.0059 memory: 7635 loss: 0.0593 decode.loss_ce: 0.0378 decode.acc_seg: 98.2600 aux.loss_ce: 0.0215 aux.acc_seg: 97.6308 +04/18 02:27:24 - mmengine - INFO - Iter(train) [ 550/160000] lr: 9.9694e-03 eta: 1 day, 0:40:14 time: 0.5483 data_time: 0.0059 memory: 7635 loss: 0.0571 decode.loss_ce: 0.0364 decode.acc_seg: 97.7746 aux.loss_ce: 0.0207 aux.acc_seg: 96.9963 +04/18 02:27:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:27:52 - mmengine - INFO - Iter(train) [ 600/160000] lr: 9.9666e-03 eta: 1 day, 0:37:48 time: 0.5475 data_time: 0.0061 memory: 7635 loss: 0.0485 decode.loss_ce: 0.0307 decode.acc_seg: 99.0183 aux.loss_ce: 0.0178 aux.acc_seg: 98.3242 +04/18 02:28:19 - mmengine - INFO - Iter(train) [ 650/160000] lr: 9.9639e-03 eta: 1 day, 0:35:39 time: 0.5482 data_time: 0.0057 memory: 7635 loss: 0.0573 decode.loss_ce: 0.0367 decode.acc_seg: 99.0075 aux.loss_ce: 0.0206 aux.acc_seg: 98.4173 +04/18 02:28:46 - mmengine - INFO - Iter(train) [ 700/160000] lr: 9.9611e-03 eta: 1 day, 0:33:52 time: 0.5494 data_time: 0.0071 memory: 7635 loss: 0.0468 decode.loss_ce: 0.0293 decode.acc_seg: 98.9671 aux.loss_ce: 0.0175 aux.acc_seg: 98.4134 +04/18 02:29:14 - mmengine - INFO - Iter(train) [ 750/160000] lr: 9.9583e-03 eta: 1 day, 0:32:04 time: 0.5473 data_time: 0.0054 memory: 7635 loss: 0.0509 decode.loss_ce: 0.0322 decode.acc_seg: 98.5582 aux.loss_ce: 0.0187 aux.acc_seg: 97.6395 +04/18 02:29:41 - mmengine - INFO - Iter(train) [ 800/160000] lr: 9.9555e-03 eta: 1 day, 0:30:34 time: 0.5486 data_time: 0.0062 memory: 7635 loss: 0.0491 decode.loss_ce: 0.0310 decode.acc_seg: 99.2507 aux.loss_ce: 0.0181 aux.acc_seg: 98.8086 +04/18 02:30:09 - mmengine - INFO - Iter(train) [ 850/160000] lr: 9.9527e-03 eta: 1 day, 0:29:11 time: 0.5487 data_time: 0.0062 memory: 7635 loss: 0.0514 decode.loss_ce: 0.0329 decode.acc_seg: 98.4890 aux.loss_ce: 0.0185 aux.acc_seg: 97.9950 +04/18 02:30:36 - mmengine - INFO - Iter(train) [ 900/160000] lr: 9.9499e-03 eta: 1 day, 0:27:56 time: 0.5475 data_time: 0.0056 memory: 7635 loss: 0.0542 decode.loss_ce: 0.0350 decode.acc_seg: 98.8522 aux.loss_ce: 0.0192 aux.acc_seg: 98.3143 +04/18 02:31:03 - mmengine - INFO - Iter(train) [ 950/160000] lr: 9.9471e-03 eta: 1 day, 0:26:46 time: 0.5476 data_time: 0.0065 memory: 7635 loss: 0.0471 decode.loss_ce: 0.0298 decode.acc_seg: 98.2690 aux.loss_ce: 0.0173 aux.acc_seg: 97.9364 +04/18 02:31:31 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:31:31 - mmengine - INFO - Iter(train) [ 1000/160000] lr: 9.9444e-03 eta: 1 day, 0:25:43 time: 0.5505 data_time: 0.0067 memory: 7635 loss: 0.0458 decode.loss_ce: 0.0291 decode.acc_seg: 98.5917 aux.loss_ce: 0.0167 aux.acc_seg: 98.0520 +04/18 02:31:58 - mmengine - INFO - Iter(train) [ 1050/160000] lr: 9.9416e-03 eta: 1 day, 0:24:38 time: 0.5486 data_time: 0.0060 memory: 7635 loss: 0.0461 decode.loss_ce: 0.0289 decode.acc_seg: 99.0119 aux.loss_ce: 0.0172 aux.acc_seg: 98.5758 +04/18 02:32:26 - mmengine - INFO - Iter(train) [ 1100/160000] lr: 9.9388e-03 eta: 1 day, 0:23:44 time: 0.5486 data_time: 0.0055 memory: 7635 loss: 0.0462 decode.loss_ce: 0.0294 decode.acc_seg: 98.2671 aux.loss_ce: 0.0168 aux.acc_seg: 97.6591 +04/18 02:32:53 - mmengine - INFO - Iter(train) [ 1150/160000] lr: 9.9360e-03 eta: 1 day, 0:22:49 time: 0.5476 data_time: 0.0059 memory: 7635 loss: 0.0401 decode.loss_ce: 0.0249 decode.acc_seg: 98.8012 aux.loss_ce: 0.0152 aux.acc_seg: 98.5517 +04/18 02:33:21 - mmengine - INFO - Iter(train) [ 1200/160000] lr: 9.9332e-03 eta: 1 day, 0:21:56 time: 0.5482 data_time: 0.0059 memory: 7635 loss: 0.0461 decode.loss_ce: 0.0291 decode.acc_seg: 98.8809 aux.loss_ce: 0.0170 aux.acc_seg: 98.1678 +04/18 02:33:48 - mmengine - INFO - Iter(train) [ 1250/160000] lr: 9.9304e-03 eta: 1 day, 0:21:04 time: 0.5486 data_time: 0.0064 memory: 7635 loss: 0.0391 decode.loss_ce: 0.0240 decode.acc_seg: 98.9322 aux.loss_ce: 0.0151 aux.acc_seg: 98.2064 +04/18 02:34:16 - mmengine - INFO - Iter(train) [ 1300/160000] lr: 9.9276e-03 eta: 1 day, 0:20:12 time: 0.5475 data_time: 0.0066 memory: 7635 loss: 0.0476 decode.loss_ce: 0.0303 decode.acc_seg: 98.8741 aux.loss_ce: 0.0173 aux.acc_seg: 98.2177 +04/18 02:34:43 - mmengine - INFO - Iter(train) [ 1350/160000] lr: 9.9248e-03 eta: 1 day, 0:19:26 time: 0.5486 data_time: 0.0058 memory: 7635 loss: 0.0481 decode.loss_ce: 0.0312 decode.acc_seg: 98.3006 aux.loss_ce: 0.0170 aux.acc_seg: 97.7006 +04/18 02:35:10 - mmengine - INFO - Iter(train) [ 1400/160000] lr: 9.9221e-03 eta: 1 day, 0:18:39 time: 0.5489 data_time: 0.0063 memory: 7635 loss: 0.0418 decode.loss_ce: 0.0259 decode.acc_seg: 99.1931 aux.loss_ce: 0.0158 aux.acc_seg: 98.7233 +04/18 02:35:38 - mmengine - INFO - Iter(train) [ 1450/160000] lr: 9.9193e-03 eta: 1 day, 0:17:54 time: 0.5478 data_time: 0.0061 memory: 7635 loss: 0.0464 decode.loss_ce: 0.0294 decode.acc_seg: 98.9993 aux.loss_ce: 0.0170 aux.acc_seg: 98.6215 +04/18 02:36:05 - mmengine - INFO - Iter(train) [ 1500/160000] lr: 9.9165e-03 eta: 1 day, 0:17:09 time: 0.5479 data_time: 0.0060 memory: 7635 loss: 0.0441 decode.loss_ce: 0.0276 decode.acc_seg: 98.9414 aux.loss_ce: 0.0164 aux.acc_seg: 98.5452 +04/18 02:36:33 - mmengine - INFO - Iter(train) [ 1550/160000] lr: 9.9137e-03 eta: 1 day, 0:16:37 time: 0.5576 data_time: 0.0061 memory: 7635 loss: 0.0364 decode.loss_ce: 0.0221 decode.acc_seg: 99.5236 aux.loss_ce: 0.0143 aux.acc_seg: 99.0460 +04/18 02:37:00 - mmengine - INFO - Iter(train) [ 1600/160000] lr: 9.9109e-03 eta: 1 day, 0:16:04 time: 0.5485 data_time: 0.0061 memory: 7635 loss: 0.0416 decode.loss_ce: 0.0258 decode.acc_seg: 99.0429 aux.loss_ce: 0.0157 aux.acc_seg: 98.5963 +04/18 02:37:28 - mmengine - INFO - Iter(train) [ 1650/160000] lr: 9.9081e-03 eta: 1 day, 0:15:23 time: 0.5487 data_time: 0.0064 memory: 7635 loss: 0.0422 decode.loss_ce: 0.0264 decode.acc_seg: 99.1405 aux.loss_ce: 0.0158 aux.acc_seg: 98.7473 +04/18 02:37:55 - mmengine - INFO - Iter(train) [ 1700/160000] lr: 9.9053e-03 eta: 1 day, 0:14:43 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0418 decode.loss_ce: 0.0261 decode.acc_seg: 99.0495 aux.loss_ce: 0.0156 aux.acc_seg: 98.5112 +04/18 02:38:23 - mmengine - INFO - Iter(train) [ 1750/160000] lr: 9.9025e-03 eta: 1 day, 0:14:03 time: 0.5489 data_time: 0.0058 memory: 7635 loss: 0.0423 decode.loss_ce: 0.0264 decode.acc_seg: 98.7845 aux.loss_ce: 0.0159 aux.acc_seg: 98.3470 +04/18 02:38:50 - mmengine - INFO - Iter(train) [ 1800/160000] lr: 9.8998e-03 eta: 1 day, 0:13:24 time: 0.5480 data_time: 0.0059 memory: 7635 loss: 0.0401 decode.loss_ce: 0.0251 decode.acc_seg: 98.5924 aux.loss_ce: 0.0149 aux.acc_seg: 98.2556 +04/18 02:39:17 - mmengine - INFO - Iter(train) [ 1850/160000] lr: 9.8970e-03 eta: 1 day, 0:12:45 time: 0.5498 data_time: 0.0057 memory: 7635 loss: 0.0381 decode.loss_ce: 0.0236 decode.acc_seg: 99.0651 aux.loss_ce: 0.0144 aux.acc_seg: 98.5510 +04/18 02:39:45 - mmengine - INFO - Iter(train) [ 1900/160000] lr: 9.8942e-03 eta: 1 day, 0:12:09 time: 0.5500 data_time: 0.0064 memory: 7635 loss: 0.0368 decode.loss_ce: 0.0227 decode.acc_seg: 98.8979 aux.loss_ce: 0.0140 aux.acc_seg: 98.2182 +04/18 02:40:12 - mmengine - INFO - Iter(train) [ 1950/160000] lr: 9.8914e-03 eta: 1 day, 0:11:31 time: 0.5484 data_time: 0.0064 memory: 7635 loss: 0.0408 decode.loss_ce: 0.0254 decode.acc_seg: 98.6368 aux.loss_ce: 0.0154 aux.acc_seg: 98.1456 +04/18 02:40:40 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:40:40 - mmengine - INFO - Iter(train) [ 2000/160000] lr: 9.8886e-03 eta: 1 day, 0:10:54 time: 0.5498 data_time: 0.0057 memory: 7635 loss: 0.0379 decode.loss_ce: 0.0240 decode.acc_seg: 99.2053 aux.loss_ce: 0.0139 aux.acc_seg: 98.7584 +04/18 02:41:07 - mmengine - INFO - Iter(train) [ 2050/160000] lr: 9.8858e-03 eta: 1 day, 0:10:20 time: 0.5499 data_time: 0.0070 memory: 7635 loss: 0.0347 decode.loss_ce: 0.0211 decode.acc_seg: 99.1835 aux.loss_ce: 0.0135 aux.acc_seg: 98.8031 +04/18 02:41:35 - mmengine - INFO - Iter(train) [ 2100/160000] lr: 9.8830e-03 eta: 1 day, 0:09:45 time: 0.5491 data_time: 0.0069 memory: 7635 loss: 0.0420 decode.loss_ce: 0.0262 decode.acc_seg: 98.9922 aux.loss_ce: 0.0159 aux.acc_seg: 98.4855 +04/18 02:42:02 - mmengine - INFO - Iter(train) [ 2150/160000] lr: 9.8802e-03 eta: 1 day, 0:09:10 time: 0.5495 data_time: 0.0058 memory: 7635 loss: 0.0358 decode.loss_ce: 0.0222 decode.acc_seg: 99.1411 aux.loss_ce: 0.0136 aux.acc_seg: 98.6343 +04/18 02:42:30 - mmengine - INFO - Iter(train) [ 2200/160000] lr: 9.8775e-03 eta: 1 day, 0:08:32 time: 0.5481 data_time: 0.0061 memory: 7635 loss: 0.0408 decode.loss_ce: 0.0260 decode.acc_seg: 99.0074 aux.loss_ce: 0.0148 aux.acc_seg: 98.6520 +04/18 02:42:57 - mmengine - INFO - Iter(train) [ 2250/160000] lr: 9.8747e-03 eta: 1 day, 0:07:57 time: 0.5498 data_time: 0.0075 memory: 7635 loss: 0.0407 decode.loss_ce: 0.0253 decode.acc_seg: 98.9144 aux.loss_ce: 0.0154 aux.acc_seg: 98.1933 +04/18 02:43:24 - mmengine - INFO - Iter(train) [ 2300/160000] lr: 9.8719e-03 eta: 1 day, 0:07:21 time: 0.5472 data_time: 0.0059 memory: 7635 loss: 0.0358 decode.loss_ce: 0.0220 decode.acc_seg: 99.2257 aux.loss_ce: 0.0138 aux.acc_seg: 98.7956 +04/18 02:43:52 - mmengine - INFO - Iter(train) [ 2350/160000] lr: 9.8691e-03 eta: 1 day, 0:06:49 time: 0.5498 data_time: 0.0065 memory: 7635 loss: 0.0411 decode.loss_ce: 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99.2933 aux.loss_ce: 0.0136 aux.acc_seg: 98.6950 +04/18 02:46:09 - mmengine - INFO - Iter(train) [ 2600/160000] lr: 9.8551e-03 eta: 1 day, 0:04:06 time: 0.5507 data_time: 0.0060 memory: 7635 loss: 0.0354 decode.loss_ce: 0.0215 decode.acc_seg: 99.2549 aux.loss_ce: 0.0139 aux.acc_seg: 98.7345 +04/18 02:46:37 - mmengine - INFO - Iter(train) [ 2650/160000] lr: 9.8524e-03 eta: 1 day, 0:03:45 time: 0.5579 data_time: 0.0060 memory: 7635 loss: 0.0389 decode.loss_ce: 0.0242 decode.acc_seg: 99.0269 aux.loss_ce: 0.0147 aux.acc_seg: 98.4343 +04/18 02:47:04 - mmengine - INFO - Iter(train) [ 2700/160000] lr: 9.8496e-03 eta: 1 day, 0:03:14 time: 0.5500 data_time: 0.0064 memory: 7635 loss: 0.0323 decode.loss_ce: 0.0193 decode.acc_seg: 99.0790 aux.loss_ce: 0.0130 aux.acc_seg: 98.3951 +04/18 02:47:32 - mmengine - INFO - Iter(train) [ 2750/160000] lr: 9.8468e-03 eta: 1 day, 0:02:42 time: 0.5483 data_time: 0.0061 memory: 7635 loss: 0.0368 decode.loss_ce: 0.0225 decode.acc_seg: 99.2489 aux.loss_ce: 0.0142 aux.acc_seg: 98.7843 +04/18 02:47:59 - mmengine - INFO - Iter(train) [ 2800/160000] lr: 9.8440e-03 eta: 1 day, 0:02:10 time: 0.5489 data_time: 0.0057 memory: 7635 loss: 0.0415 decode.loss_ce: 0.0263 decode.acc_seg: 99.0236 aux.loss_ce: 0.0152 aux.acc_seg: 98.5974 +04/18 02:48:27 - mmengine - INFO - Iter(train) [ 2850/160000] lr: 9.8412e-03 eta: 1 day, 0:01:39 time: 0.5501 data_time: 0.0062 memory: 7635 loss: 0.0386 decode.loss_ce: 0.0239 decode.acc_seg: 99.1429 aux.loss_ce: 0.0147 aux.acc_seg: 98.5730 +04/18 02:48:54 - mmengine - INFO - Iter(train) [ 2900/160000] lr: 9.8384e-03 eta: 1 day, 0:01:06 time: 0.5494 data_time: 0.0059 memory: 7635 loss: 0.0350 decode.loss_ce: 0.0218 decode.acc_seg: 98.8841 aux.loss_ce: 0.0132 aux.acc_seg: 98.5057 +04/18 02:49:21 - mmengine - INFO - Iter(train) [ 2950/160000] lr: 9.8356e-03 eta: 1 day, 0:00:34 time: 0.5494 data_time: 0.0061 memory: 7635 loss: 0.0334 decode.loss_ce: 0.0203 decode.acc_seg: 99.3465 aux.loss_ce: 0.0130 aux.acc_seg: 98.6574 +04/18 02:49:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:49:49 - mmengine - INFO - Iter(train) [ 3000/160000] lr: 9.8328e-03 eta: 1 day, 0:00:02 time: 0.5490 data_time: 0.0057 memory: 7635 loss: 0.0348 decode.loss_ce: 0.0213 decode.acc_seg: 98.5440 aux.loss_ce: 0.0136 aux.acc_seg: 98.1440 +04/18 02:50:16 - mmengine - INFO - Iter(train) [ 3050/160000] lr: 9.8300e-03 eta: 23:59:30 time: 0.5473 data_time: 0.0061 memory: 7635 loss: 0.0345 decode.loss_ce: 0.0212 decode.acc_seg: 99.1501 aux.loss_ce: 0.0133 aux.acc_seg: 98.5846 +04/18 02:50:44 - mmengine - INFO - Iter(train) [ 3100/160000] lr: 9.8273e-03 eta: 23:58:59 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0375 decode.loss_ce: 0.0233 decode.acc_seg: 98.6369 aux.loss_ce: 0.0142 aux.acc_seg: 98.0972 +04/18 02:51:11 - mmengine - INFO - Iter(train) [ 3150/160000] lr: 9.8245e-03 eta: 23:58:28 time: 0.5484 data_time: 0.0059 memory: 7635 loss: 0.0327 decode.loss_ce: 0.0198 decode.acc_seg: 99.2376 aux.loss_ce: 0.0129 aux.acc_seg: 98.8457 +04/18 02:51:39 - mmengine - INFO - Iter(train) [ 3200/160000] lr: 9.8217e-03 eta: 23:57:55 time: 0.5483 data_time: 0.0059 memory: 7635 loss: 0.0337 decode.loss_ce: 0.0206 decode.acc_seg: 99.3318 aux.loss_ce: 0.0131 aux.acc_seg: 98.8579 +04/18 02:52:06 - mmengine - INFO - Iter(train) [ 3250/160000] lr: 9.8189e-03 eta: 23:57:24 time: 0.5481 data_time: 0.0056 memory: 7635 loss: 0.0344 decode.loss_ce: 0.0214 decode.acc_seg: 99.1192 aux.loss_ce: 0.0130 aux.acc_seg: 98.8572 +04/18 02:52:33 - mmengine - INFO - Iter(train) [ 3300/160000] lr: 9.8161e-03 eta: 23:56:51 time: 0.5476 data_time: 0.0056 memory: 7635 loss: 0.0364 decode.loss_ce: 0.0222 decode.acc_seg: 99.0077 aux.loss_ce: 0.0142 aux.acc_seg: 98.1403 +04/18 02:53:01 - mmengine - INFO - Iter(train) [ 3350/160000] lr: 9.8133e-03 eta: 23:56:17 time: 0.5477 data_time: 0.0063 memory: 7635 loss: 0.0344 decode.loss_ce: 0.0212 decode.acc_seg: 98.9128 aux.loss_ce: 0.0132 aux.acc_seg: 98.4770 +04/18 02:53:28 - mmengine - INFO - Iter(train) [ 3400/160000] lr: 9.8105e-03 eta: 23:55:46 time: 0.5483 data_time: 0.0056 memory: 7635 loss: 0.0381 decode.loss_ce: 0.0239 decode.acc_seg: 98.9027 aux.loss_ce: 0.0143 aux.acc_seg: 98.3586 +04/18 02:53:56 - mmengine - INFO - Iter(train) [ 3450/160000] lr: 9.8077e-03 eta: 23:55:15 time: 0.5497 data_time: 0.0059 memory: 7635 loss: 0.0315 decode.loss_ce: 0.0190 decode.acc_seg: 99.1760 aux.loss_ce: 0.0126 aux.acc_seg: 98.7520 +04/18 02:54:23 - mmengine - INFO - Iter(train) [ 3500/160000] lr: 9.8049e-03 eta: 23:54:45 time: 0.5499 data_time: 0.0064 memory: 7635 loss: 0.0307 decode.loss_ce: 0.0185 decode.acc_seg: 99.1366 aux.loss_ce: 0.0122 aux.acc_seg: 98.8712 +04/18 02:54:51 - mmengine - INFO - Iter(train) [ 3550/160000] lr: 9.8021e-03 eta: 23:54:15 time: 0.5485 data_time: 0.0060 memory: 7635 loss: 0.0334 decode.loss_ce: 0.0206 decode.acc_seg: 98.8063 aux.loss_ce: 0.0128 aux.acc_seg: 98.5059 +04/18 02:55:18 - mmengine - INFO - Iter(train) [ 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0.5487 data_time: 0.0059 memory: 7635 loss: 0.0302 decode.loss_ce: 0.0181 decode.acc_seg: 99.4102 aux.loss_ce: 0.0121 aux.acc_seg: 98.9926 +04/18 02:57:35 - mmengine - INFO - Iter(train) [ 3850/160000] lr: 9.7854e-03 eta: 23:51:27 time: 0.5496 data_time: 0.0064 memory: 7635 loss: 0.0317 decode.loss_ce: 0.0197 decode.acc_seg: 99.2857 aux.loss_ce: 0.0120 aux.acc_seg: 98.7676 +04/18 02:58:03 - mmengine - INFO - Iter(train) [ 3900/160000] lr: 9.7826e-03 eta: 23:50:56 time: 0.5485 data_time: 0.0058 memory: 7635 loss: 0.0323 decode.loss_ce: 0.0196 decode.acc_seg: 98.9332 aux.loss_ce: 0.0127 aux.acc_seg: 98.2819 +04/18 02:58:30 - mmengine - INFO - Iter(train) [ 3950/160000] lr: 9.7798e-03 eta: 23:50:26 time: 0.5494 data_time: 0.0066 memory: 7635 loss: 0.0311 decode.loss_ce: 0.0189 decode.acc_seg: 99.3972 aux.loss_ce: 0.0122 aux.acc_seg: 98.9279 +04/18 02:58:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:58:58 - mmengine - INFO - Iter(train) [ 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0.5488 data_time: 0.0059 memory: 7635 loss: 0.0281 decode.loss_ce: 0.0168 decode.acc_seg: 99.5010 aux.loss_ce: 0.0114 aux.acc_seg: 99.1861 +04/18 03:01:15 - mmengine - INFO - Iter(train) [ 4250/160000] lr: 9.7631e-03 eta: 23:47:31 time: 0.5507 data_time: 0.0066 memory: 7635 loss: 0.0352 decode.loss_ce: 0.0211 decode.acc_seg: 99.2168 aux.loss_ce: 0.0141 aux.acc_seg: 98.4035 +04/18 03:01:42 - mmengine - INFO - Iter(train) [ 4300/160000] lr: 9.7603e-03 eta: 23:47:01 time: 0.5481 data_time: 0.0060 memory: 7635 loss: 0.0297 decode.loss_ce: 0.0178 decode.acc_seg: 99.3208 aux.loss_ce: 0.0119 aux.acc_seg: 98.8874 +04/18 03:02:10 - mmengine - INFO - Iter(train) [ 4350/160000] lr: 9.7575e-03 eta: 23:46:31 time: 0.5472 data_time: 0.0059 memory: 7635 loss: 0.0298 decode.loss_ce: 0.0177 decode.acc_seg: 99.3001 aux.loss_ce: 0.0120 aux.acc_seg: 98.7158 +04/18 03:02:37 - mmengine - INFO - Iter(train) [ 4400/160000] lr: 9.7547e-03 eta: 23:46:02 time: 0.5486 data_time: 0.0063 memory: 7635 loss: 0.0314 decode.loss_ce: 0.0191 decode.acc_seg: 99.0993 aux.loss_ce: 0.0123 aux.acc_seg: 98.6669 +04/18 03:03:05 - mmengine - INFO - Iter(train) [ 4450/160000] lr: 9.7519e-03 eta: 23:45:33 time: 0.5493 data_time: 0.0058 memory: 7635 loss: 0.0312 decode.loss_ce: 0.0191 decode.acc_seg: 99.4136 aux.loss_ce: 0.0121 aux.acc_seg: 98.9943 +04/18 03:03:32 - mmengine - INFO - Iter(train) [ 4500/160000] lr: 9.7491e-03 eta: 23:45:04 time: 0.5473 data_time: 0.0061 memory: 7635 loss: 0.0307 decode.loss_ce: 0.0186 decode.acc_seg: 98.9962 aux.loss_ce: 0.0121 aux.acc_seg: 98.5571 +04/18 03:04:00 - mmengine - INFO - Iter(train) [ 4550/160000] lr: 9.7463e-03 eta: 23:44:33 time: 0.5480 data_time: 0.0068 memory: 7635 loss: 0.0311 decode.loss_ce: 0.0189 decode.acc_seg: 99.1083 aux.loss_ce: 0.0122 aux.acc_seg: 98.6803 +04/18 03:04:27 - mmengine - INFO - Iter(train) [ 4600/160000] lr: 9.7435e-03 eta: 23:44:04 time: 0.5486 data_time: 0.0063 memory: 7635 loss: 0.0330 decode.loss_ce: 0.0208 decode.acc_seg: 99.1116 aux.loss_ce: 0.0123 aux.acc_seg: 98.6354 +04/18 03:04:55 - mmengine - INFO - Iter(train) [ 4650/160000] lr: 9.7407e-03 eta: 23:43:34 time: 0.5480 data_time: 0.0057 memory: 7635 loss: 0.0337 decode.loss_ce: 0.0208 decode.acc_seg: 99.4368 aux.loss_ce: 0.0128 aux.acc_seg: 99.0678 +04/18 03:05:22 - mmengine - INFO - Iter(train) [ 4700/160000] lr: 9.7379e-03 eta: 23:43:04 time: 0.5483 data_time: 0.0067 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0175 decode.acc_seg: 99.5893 aux.loss_ce: 0.0119 aux.acc_seg: 99.0988 +04/18 03:05:49 - mmengine - INFO - Iter(train) [ 4750/160000] lr: 9.7351e-03 eta: 23:42:35 time: 0.5488 data_time: 0.0060 memory: 7635 loss: 0.0282 decode.loss_ce: 0.0171 decode.acc_seg: 99.1003 aux.loss_ce: 0.0111 aux.acc_seg: 98.5092 +04/18 03:06:17 - mmengine - INFO - Iter(train) [ 4800/160000] lr: 9.7323e-03 eta: 23:42:12 time: 0.5575 data_time: 0.0061 memory: 7635 loss: 0.0288 decode.loss_ce: 0.0173 decode.acc_seg: 99.3198 aux.loss_ce: 0.0115 aux.acc_seg: 98.9315 +04/18 03:06:44 - mmengine - INFO - Iter(train) [ 4850/160000] lr: 9.7296e-03 eta: 23:41:43 time: 0.5492 data_time: 0.0058 memory: 7635 loss: 0.0287 decode.loss_ce: 0.0169 decode.acc_seg: 99.5131 aux.loss_ce: 0.0118 aux.acc_seg: 98.9789 +04/18 03:07:12 - mmengine - INFO - Iter(train) [ 4900/160000] lr: 9.7268e-03 eta: 23:41:13 time: 0.5480 data_time: 0.0066 memory: 7635 loss: 0.0279 decode.loss_ce: 0.0165 decode.acc_seg: 99.4431 aux.loss_ce: 0.0114 aux.acc_seg: 98.9892 +04/18 03:07:39 - mmengine - INFO - Iter(train) [ 4950/160000] lr: 9.7240e-03 eta: 23:40:44 time: 0.5482 data_time: 0.0063 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0177 decode.acc_seg: 99.3356 aux.loss_ce: 0.0117 aux.acc_seg: 98.8717 +04/18 03:08:07 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:08:07 - mmengine - INFO - Iter(train) [ 5000/160000] lr: 9.7212e-03 eta: 23:40:14 time: 0.5493 data_time: 0.0061 memory: 7635 loss: 0.0278 decode.loss_ce: 0.0166 decode.acc_seg: 99.2800 aux.loss_ce: 0.0113 aux.acc_seg: 98.8735 +04/18 03:08:34 - mmengine - INFO - Iter(train) [ 5050/160000] lr: 9.7184e-03 eta: 23:39:44 time: 0.5496 data_time: 0.0063 memory: 7635 loss: 0.0299 decode.loss_ce: 0.0178 decode.acc_seg: 99.3108 aux.loss_ce: 0.0121 aux.acc_seg: 98.7395 +04/18 03:09:02 - mmengine - INFO - Iter(train) [ 5100/160000] lr: 9.7156e-03 eta: 23:39:15 time: 0.5485 data_time: 0.0059 memory: 7635 loss: 0.0287 decode.loss_ce: 0.0170 decode.acc_seg: 99.3157 aux.loss_ce: 0.0117 aux.acc_seg: 98.9011 +04/18 03:09:29 - mmengine - INFO - Iter(train) [ 5150/160000] lr: 9.7128e-03 eta: 23:38:46 time: 0.5492 data_time: 0.0058 memory: 7635 loss: 0.0331 decode.loss_ce: 0.0203 decode.acc_seg: 98.6803 aux.loss_ce: 0.0128 aux.acc_seg: 98.2692 +04/18 03:09:56 - mmengine - INFO - Iter(train) [ 5200/160000] lr: 9.7100e-03 eta: 23:38:18 time: 0.5486 data_time: 0.0059 memory: 7635 loss: 0.0281 decode.loss_ce: 0.0166 decode.acc_seg: 99.4009 aux.loss_ce: 0.0115 aux.acc_seg: 98.8865 +04/18 03:10:24 - mmengine - INFO - Iter(train) [ 5250/160000] lr: 9.7072e-03 eta: 23:37:48 time: 0.5481 data_time: 0.0061 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0179 decode.acc_seg: 99.3403 aux.loss_ce: 0.0116 aux.acc_seg: 98.8310 +04/18 03:10:51 - mmengine - INFO - Iter(train) [ 5300/160000] lr: 9.7044e-03 eta: 23:37:19 time: 0.5487 data_time: 0.0071 memory: 7635 loss: 0.0315 decode.loss_ce: 0.0190 decode.acc_seg: 99.2280 aux.loss_ce: 0.0125 aux.acc_seg: 98.6730 +04/18 03:11:19 - mmengine - INFO - Iter(train) [ 5350/160000] lr: 9.7016e-03 eta: 23:36:50 time: 0.5479 data_time: 0.0060 memory: 7635 loss: 0.0302 decode.loss_ce: 0.0182 decode.acc_seg: 99.4350 aux.loss_ce: 0.0120 aux.acc_seg: 98.9041 +04/18 03:11:46 - mmengine - INFO - Iter(train) [ 5400/160000] lr: 9.6988e-03 eta: 23:36:20 time: 0.5480 data_time: 0.0066 memory: 7635 loss: 0.0296 decode.loss_ce: 0.0179 decode.acc_seg: 99.2143 aux.loss_ce: 0.0117 aux.acc_seg: 98.7977 +04/18 03:12:14 - mmengine - INFO - Iter(train) [ 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0.5488 data_time: 0.0062 memory: 7635 loss: 0.0275 decode.loss_ce: 0.0164 decode.acc_seg: 99.4640 aux.loss_ce: 0.0112 aux.acc_seg: 99.0702 +04/18 03:14:31 - mmengine - INFO - Iter(train) [ 5700/160000] lr: 9.6821e-03 eta: 23:33:24 time: 0.5480 data_time: 0.0063 memory: 7635 loss: 0.0277 decode.loss_ce: 0.0167 decode.acc_seg: 99.5169 aux.loss_ce: 0.0110 aux.acc_seg: 99.0870 +04/18 03:14:58 - mmengine - INFO - Iter(train) [ 5750/160000] lr: 9.6793e-03 eta: 23:32:55 time: 0.5487 data_time: 0.0065 memory: 7635 loss: 0.0268 decode.loss_ce: 0.0159 decode.acc_seg: 99.3650 aux.loss_ce: 0.0109 aux.acc_seg: 98.9162 +04/18 03:15:26 - mmengine - INFO - Iter(train) [ 5800/160000] lr: 9.6765e-03 eta: 23:32:26 time: 0.5484 data_time: 0.0058 memory: 7635 loss: 0.0273 decode.loss_ce: 0.0165 decode.acc_seg: 99.4540 aux.loss_ce: 0.0109 aux.acc_seg: 99.1590 +04/18 03:15:53 - mmengine - INFO - Iter(train) [ 5850/160000] lr: 9.6737e-03 eta: 23:31:57 time: 0.5490 data_time: 0.0069 memory: 7635 loss: 0.0270 decode.loss_ce: 0.0156 decode.acc_seg: 99.2741 aux.loss_ce: 0.0114 aux.acc_seg: 98.6178 +04/18 03:16:21 - mmengine - INFO - Iter(train) [ 5900/160000] lr: 9.6709e-03 eta: 23:31:34 time: 0.5472 data_time: 0.0066 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0144 decode.acc_seg: 99.6537 aux.loss_ce: 0.0102 aux.acc_seg: 99.2810 +04/18 03:16:48 - mmengine - INFO - Iter(train) [ 5950/160000] lr: 9.6681e-03 eta: 23:31:03 time: 0.5462 data_time: 0.0064 memory: 7635 loss: 0.0311 decode.loss_ce: 0.0186 decode.acc_seg: 99.5471 aux.loss_ce: 0.0125 aux.acc_seg: 99.0972 +04/18 03:17:15 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:17:15 - mmengine - INFO - Iter(train) [ 6000/160000] lr: 9.6653e-03 eta: 23:30:34 time: 0.5482 data_time: 0.0069 memory: 7635 loss: 0.0296 decode.loss_ce: 0.0179 decode.acc_seg: 99.0438 aux.loss_ce: 0.0117 aux.acc_seg: 98.4300 +04/18 03:17:43 - mmengine - INFO - Iter(train) [ 6050/160000] lr: 9.6625e-03 eta: 23:30:05 time: 0.5487 data_time: 0.0067 memory: 7635 loss: 0.0271 decode.loss_ce: 0.0160 decode.acc_seg: 99.4235 aux.loss_ce: 0.0111 aux.acc_seg: 98.7734 +04/18 03:18:10 - mmengine - INFO - Iter(train) [ 6100/160000] lr: 9.6597e-03 eta: 23:29:36 time: 0.5486 data_time: 0.0073 memory: 7635 loss: 0.0248 decode.loss_ce: 0.0142 decode.acc_seg: 99.3794 aux.loss_ce: 0.0106 aux.acc_seg: 98.8412 +04/18 03:18:38 - mmengine - INFO - Iter(train) [ 6150/160000] lr: 9.6569e-03 eta: 23:29:07 time: 0.5479 data_time: 0.0065 memory: 7635 loss: 0.0274 decode.loss_ce: 0.0160 decode.acc_seg: 99.5241 aux.loss_ce: 0.0115 aux.acc_seg: 98.9821 +04/18 03:19:05 - mmengine - INFO - Iter(train) [ 6200/160000] lr: 9.6541e-03 eta: 23:28:39 time: 0.5495 data_time: 0.0062 memory: 7635 loss: 0.0243 decode.loss_ce: 0.0142 decode.acc_seg: 99.4569 aux.loss_ce: 0.0101 aux.acc_seg: 99.0397 +04/18 03:19:32 - mmengine - INFO - Iter(train) [ 6250/160000] lr: 9.6513e-03 eta: 23:28:10 time: 0.5487 data_time: 0.0070 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0156 decode.acc_seg: 99.1268 aux.loss_ce: 0.0105 aux.acc_seg: 98.6498 +04/18 03:20:00 - mmengine - INFO - Iter(train) [ 6300/160000] lr: 9.6485e-03 eta: 23:27:41 time: 0.5485 data_time: 0.0056 memory: 7635 loss: 0.0288 decode.loss_ce: 0.0171 decode.acc_seg: 99.1766 aux.loss_ce: 0.0117 aux.acc_seg: 98.7195 +04/18 03:20:27 - mmengine - INFO - Iter(train) [ 6350/160000] lr: 9.6457e-03 eta: 23:27:11 time: 0.5473 data_time: 0.0059 memory: 7635 loss: 0.0277 decode.loss_ce: 0.0166 decode.acc_seg: 99.3714 aux.loss_ce: 0.0111 aux.acc_seg: 99.0213 +04/18 03:20:55 - mmengine - INFO - Iter(train) [ 6400/160000] lr: 9.6429e-03 eta: 23:26:42 time: 0.5484 data_time: 0.0065 memory: 7635 loss: 0.0238 decode.loss_ce: 0.0140 decode.acc_seg: 99.4395 aux.loss_ce: 0.0098 aux.acc_seg: 98.9776 +04/18 03:21:22 - mmengine - INFO - Iter(train) [ 6450/160000] lr: 9.6401e-03 eta: 23:26:13 time: 0.5465 data_time: 0.0062 memory: 7635 loss: 0.0280 decode.loss_ce: 0.0163 decode.acc_seg: 99.2767 aux.loss_ce: 0.0118 aux.acc_seg: 98.8447 +04/18 03:21:49 - mmengine - INFO - Iter(train) [ 6500/160000] lr: 9.6373e-03 eta: 23:25:43 time: 0.5475 data_time: 0.0065 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0153 decode.acc_seg: 98.8082 aux.loss_ce: 0.0106 aux.acc_seg: 98.3924 +04/18 03:22:17 - mmengine - INFO - Iter(train) [ 6550/160000] lr: 9.6345e-03 eta: 23:25:14 time: 0.5489 data_time: 0.0065 memory: 7635 loss: 0.0227 decode.loss_ce: 0.0132 decode.acc_seg: 99.4503 aux.loss_ce: 0.0095 aux.acc_seg: 99.0750 +04/18 03:22:44 - mmengine - INFO - Iter(train) [ 6600/160000] lr: 9.6317e-03 eta: 23:24:44 time: 0.5475 data_time: 0.0064 memory: 7635 loss: 0.0270 decode.loss_ce: 0.0161 decode.acc_seg: 98.9626 aux.loss_ce: 0.0109 aux.acc_seg: 98.5077 +04/18 03:23:12 - mmengine - INFO - Iter(train) [ 6650/160000] lr: 9.6290e-03 eta: 23:24:15 time: 0.5468 data_time: 0.0059 memory: 7635 loss: 0.0272 decode.loss_ce: 0.0159 decode.acc_seg: 99.0431 aux.loss_ce: 0.0113 aux.acc_seg: 98.3189 +04/18 03:23:39 - mmengine - INFO - Iter(train) [ 6700/160000] lr: 9.6262e-03 eta: 23:23:46 time: 0.5487 data_time: 0.0060 memory: 7635 loss: 0.0306 decode.loss_ce: 0.0186 decode.acc_seg: 99.2869 aux.loss_ce: 0.0120 aux.acc_seg: 98.6516 +04/18 03:24:06 - mmengine - INFO - Iter(train) [ 6750/160000] lr: 9.6234e-03 eta: 23:23:17 time: 0.5487 data_time: 0.0064 memory: 7635 loss: 0.0287 decode.loss_ce: 0.0174 decode.acc_seg: 99.5493 aux.loss_ce: 0.0113 aux.acc_seg: 99.0694 +04/18 03:24:34 - mmengine - INFO - Iter(train) [ 6800/160000] lr: 9.6206e-03 eta: 23:22:49 time: 0.5477 data_time: 0.0062 memory: 7635 loss: 0.0279 decode.loss_ce: 0.0163 decode.acc_seg: 99.2764 aux.loss_ce: 0.0116 aux.acc_seg: 98.6606 +04/18 03:25:01 - mmengine - INFO - Iter(train) [ 6850/160000] lr: 9.6178e-03 eta: 23:22:20 time: 0.5486 data_time: 0.0058 memory: 7635 loss: 0.0270 decode.loss_ce: 0.0162 decode.acc_seg: 99.4166 aux.loss_ce: 0.0108 aux.acc_seg: 98.8656 +04/18 03:25:29 - mmengine - INFO - Iter(train) [ 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+04/18 03:27:19 - mmengine - INFO - Iter(train) [ 7100/160000] lr: 9.6038e-03 eta: 23:20:00 time: 0.5488 data_time: 0.0067 memory: 7635 loss: 0.0264 decode.loss_ce: 0.0153 decode.acc_seg: 99.6102 aux.loss_ce: 0.0111 aux.acc_seg: 99.2232 +04/18 03:27:46 - mmengine - INFO - Iter(train) [ 7150/160000] lr: 9.6010e-03 eta: 23:19:31 time: 0.5486 data_time: 0.0063 memory: 7635 loss: 0.0264 decode.loss_ce: 0.0157 decode.acc_seg: 99.2992 aux.loss_ce: 0.0107 aux.acc_seg: 98.8308 +04/18 03:28:13 - mmengine - INFO - Iter(train) [ 7200/160000] lr: 9.5982e-03 eta: 23:19:02 time: 0.5487 data_time: 0.0066 memory: 7635 loss: 0.0250 decode.loss_ce: 0.0145 decode.acc_seg: 99.5803 aux.loss_ce: 0.0104 aux.acc_seg: 99.1144 +04/18 03:28:41 - mmengine - INFO - Iter(train) [ 7250/160000] lr: 9.5954e-03 eta: 23:18:33 time: 0.5475 data_time: 0.0060 memory: 7635 loss: 0.0232 decode.loss_ce: 0.0135 decode.acc_seg: 99.2552 aux.loss_ce: 0.0097 aux.acc_seg: 98.7329 +04/18 03:29:08 - mmengine - INFO - Iter(train) [ 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0.5492 data_time: 0.0056 memory: 7635 loss: 0.0243 decode.loss_ce: 0.0143 decode.acc_seg: 99.3121 aux.loss_ce: 0.0100 aux.acc_seg: 98.9280 +04/18 03:31:25 - mmengine - INFO - Iter(train) [ 7550/160000] lr: 9.5786e-03 eta: 23:15:40 time: 0.5472 data_time: 0.0057 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0126 decode.acc_seg: 99.5523 aux.loss_ce: 0.0093 aux.acc_seg: 99.1000 +04/18 03:31:53 - mmengine - INFO - Iter(train) [ 7600/160000] lr: 9.5758e-03 eta: 23:15:12 time: 0.5488 data_time: 0.0066 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0144 decode.acc_seg: 99.3397 aux.loss_ce: 0.0102 aux.acc_seg: 98.9182 +04/18 03:32:20 - mmengine - INFO - Iter(train) [ 7650/160000] lr: 9.5730e-03 eta: 23:14:43 time: 0.5485 data_time: 0.0061 memory: 7635 loss: 0.0265 decode.loss_ce: 0.0158 decode.acc_seg: 99.3401 aux.loss_ce: 0.0107 aux.acc_seg: 98.8952 +04/18 03:32:47 - mmengine - INFO - Iter(train) [ 7700/160000] lr: 9.5702e-03 eta: 23:14:16 time: 0.5497 data_time: 0.0062 memory: 7635 loss: 0.0237 decode.loss_ce: 0.0136 decode.acc_seg: 99.4659 aux.loss_ce: 0.0101 aux.acc_seg: 98.9071 +04/18 03:33:15 - mmengine - INFO - Iter(train) [ 7750/160000] lr: 9.5674e-03 eta: 23:13:47 time: 0.5481 data_time: 0.0063 memory: 7635 loss: 0.0251 decode.loss_ce: 0.0149 decode.acc_seg: 99.3489 aux.loss_ce: 0.0101 aux.acc_seg: 98.9504 +04/18 03:33:42 - mmengine - INFO - Iter(train) [ 7800/160000] lr: 9.5646e-03 eta: 23:13:18 time: 0.5488 data_time: 0.0061 memory: 7635 loss: 0.0258 decode.loss_ce: 0.0152 decode.acc_seg: 99.0079 aux.loss_ce: 0.0105 aux.acc_seg: 98.6009 +04/18 03:34:10 - mmengine - INFO - Iter(train) [ 7850/160000] lr: 9.5618e-03 eta: 23:12:49 time: 0.5474 data_time: 0.0061 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0120 decode.acc_seg: 99.4060 aux.loss_ce: 0.0092 aux.acc_seg: 98.8746 +04/18 03:34:37 - mmengine - INFO - Iter(train) [ 7900/160000] lr: 9.5590e-03 eta: 23:12:20 time: 0.5460 data_time: 0.0062 memory: 7635 loss: 0.0242 decode.loss_ce: 0.0140 decode.acc_seg: 99.5603 aux.loss_ce: 0.0102 aux.acc_seg: 99.1700 +04/18 03:35:04 - mmengine - INFO - Iter(train) [ 7950/160000] lr: 9.5562e-03 eta: 23:11:52 time: 0.5482 data_time: 0.0066 memory: 7635 loss: 0.0298 decode.loss_ce: 0.0182 decode.acc_seg: 99.1676 aux.loss_ce: 0.0115 aux.acc_seg: 98.7913 +04/18 03:35:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:35:32 - mmengine - INFO - Iter(train) [ 8000/160000] lr: 9.5534e-03 eta: 23:11:25 time: 0.5589 data_time: 0.0057 memory: 7635 loss: 0.0228 decode.loss_ce: 0.0131 decode.acc_seg: 99.5768 aux.loss_ce: 0.0096 aux.acc_seg: 99.0025 +04/18 03:35:59 - mmengine - INFO - Iter(train) [ 8050/160000] lr: 9.5506e-03 eta: 23:10:59 time: 0.5485 data_time: 0.0062 memory: 7635 loss: 0.0239 decode.loss_ce: 0.0138 decode.acc_seg: 99.4815 aux.loss_ce: 0.0101 aux.acc_seg: 99.1381 +04/18 03:36:27 - mmengine - INFO - Iter(train) [ 8100/160000] lr: 9.5478e-03 eta: 23:10:30 time: 0.5485 data_time: 0.0061 memory: 7635 loss: 0.0244 decode.loss_ce: 0.0143 decode.acc_seg: 99.3567 aux.loss_ce: 0.0101 aux.acc_seg: 98.9232 +04/18 03:36:54 - mmengine - INFO - Iter(train) [ 8150/160000] lr: 9.5450e-03 eta: 23:10:03 time: 0.5492 data_time: 0.0059 memory: 7635 loss: 0.0249 decode.loss_ce: 0.0142 decode.acc_seg: 99.4530 aux.loss_ce: 0.0107 aux.acc_seg: 98.9367 +04/18 03:37:22 - mmengine - INFO - Iter(train) [ 8200/160000] lr: 9.5422e-03 eta: 23:09:35 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0140 decode.acc_seg: 99.4133 aux.loss_ce: 0.0106 aux.acc_seg: 98.7640 +04/18 03:37:49 - mmengine - INFO - Iter(train) [ 8250/160000] lr: 9.5394e-03 eta: 23:09:06 time: 0.5481 data_time: 0.0059 memory: 7635 loss: 0.0251 decode.loss_ce: 0.0143 decode.acc_seg: 99.2445 aux.loss_ce: 0.0109 aux.acc_seg: 98.7236 +04/18 03:38:17 - mmengine - INFO - Iter(train) [ 8300/160000] lr: 9.5366e-03 eta: 23:08:37 time: 0.5480 data_time: 0.0059 memory: 7635 loss: 0.0239 decode.loss_ce: 0.0136 decode.acc_seg: 99.4275 aux.loss_ce: 0.0103 aux.acc_seg: 98.7861 +04/18 03:38:44 - mmengine - INFO - Iter(train) [ 8350/160000] lr: 9.5338e-03 eta: 23:08:08 time: 0.5482 data_time: 0.0070 memory: 7635 loss: 0.0264 decode.loss_ce: 0.0158 decode.acc_seg: 99.3280 aux.loss_ce: 0.0106 aux.acc_seg: 99.0131 +04/18 03:39:11 - mmengine - INFO - Iter(train) [ 8400/160000] lr: 9.5310e-03 eta: 23:07:40 time: 0.5491 data_time: 0.0057 memory: 7635 loss: 0.0277 decode.loss_ce: 0.0164 decode.acc_seg: 99.3407 aux.loss_ce: 0.0113 aux.acc_seg: 98.7556 +04/18 03:39:39 - mmengine - INFO - Iter(train) [ 8450/160000] lr: 9.5282e-03 eta: 23:07:11 time: 0.5479 data_time: 0.0067 memory: 7635 loss: 0.0247 decode.loss_ce: 0.0143 decode.acc_seg: 99.2147 aux.loss_ce: 0.0104 aux.acc_seg: 98.7143 +04/18 03:40:06 - mmengine - INFO - Iter(train) [ 8500/160000] lr: 9.5254e-03 eta: 23:06:43 time: 0.5479 data_time: 0.0066 memory: 7635 loss: 0.0243 decode.loss_ce: 0.0140 decode.acc_seg: 99.5707 aux.loss_ce: 0.0103 aux.acc_seg: 99.1184 +04/18 03:40:34 - mmengine - INFO - Iter(train) [ 8550/160000] lr: 9.5226e-03 eta: 23:06:14 time: 0.5482 data_time: 0.0063 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0145 decode.acc_seg: 99.4205 aux.loss_ce: 0.0101 aux.acc_seg: 99.0105 +04/18 03:41:01 - mmengine - INFO - Iter(train) [ 8600/160000] lr: 9.5198e-03 eta: 23:05:46 time: 0.5482 data_time: 0.0064 memory: 7635 loss: 0.0248 decode.loss_ce: 0.0146 decode.acc_seg: 99.4130 aux.loss_ce: 0.0102 aux.acc_seg: 98.9951 +04/18 03:41:28 - mmengine - INFO - Iter(train) [ 8650/160000] lr: 9.5170e-03 eta: 23:05:17 time: 0.5483 data_time: 0.0064 memory: 7635 loss: 0.0276 decode.loss_ce: 0.0165 decode.acc_seg: 99.3017 aux.loss_ce: 0.0111 aux.acc_seg: 98.7202 +04/18 03:41:56 - mmengine - INFO - Iter(train) [ 8700/160000] lr: 9.5142e-03 eta: 23:04:49 time: 0.5479 data_time: 0.0062 memory: 7635 loss: 0.0253 decode.loss_ce: 0.0152 decode.acc_seg: 99.3296 aux.loss_ce: 0.0101 aux.acc_seg: 98.8963 +04/18 03:42:23 - mmengine - INFO - Iter(train) [ 8750/160000] lr: 9.5114e-03 eta: 23:04:21 time: 0.5482 data_time: 0.0061 memory: 7635 loss: 0.0240 decode.loss_ce: 0.0138 decode.acc_seg: 98.9489 aux.loss_ce: 0.0102 aux.acc_seg: 98.0512 +04/18 03:42:51 - mmengine - INFO - Iter(train) [ 8800/160000] lr: 9.5086e-03 eta: 23:03:52 time: 0.5478 data_time: 0.0057 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0129 decode.acc_seg: 99.4946 aux.loss_ce: 0.0096 aux.acc_seg: 99.1075 +04/18 03:43:18 - mmengine - INFO - Iter(train) [ 8850/160000] lr: 9.5058e-03 eta: 23:03:23 time: 0.5477 data_time: 0.0064 memory: 7635 loss: 0.0240 decode.loss_ce: 0.0139 decode.acc_seg: 99.2327 aux.loss_ce: 0.0100 aux.acc_seg: 98.6191 +04/18 03:43:45 - mmengine - INFO - Iter(train) [ 8900/160000] lr: 9.5030e-03 eta: 23:02:55 time: 0.5486 data_time: 0.0062 memory: 7635 loss: 0.0227 decode.loss_ce: 0.0128 decode.acc_seg: 99.4642 aux.loss_ce: 0.0099 aux.acc_seg: 99.0090 +04/18 03:44:13 - mmengine - INFO - Iter(train) [ 8950/160000] lr: 9.5002e-03 eta: 23:02:27 time: 0.5496 data_time: 0.0064 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0126 decode.acc_seg: 99.5750 aux.loss_ce: 0.0095 aux.acc_seg: 99.3172 +04/18 03:44:40 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:44:40 - mmengine - INFO - Iter(train) [ 9000/160000] lr: 9.4974e-03 eta: 23:02:00 time: 0.5492 data_time: 0.0061 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0130 decode.acc_seg: 99.4808 aux.loss_ce: 0.0094 aux.acc_seg: 98.8997 +04/18 03:45:08 - mmengine - INFO - Iter(train) [ 9050/160000] lr: 9.4946e-03 eta: 23:01:32 time: 0.5477 data_time: 0.0061 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0129 decode.acc_seg: 99.4164 aux.loss_ce: 0.0093 aux.acc_seg: 98.9769 +04/18 03:45:35 - mmengine - INFO - Iter(train) [ 9100/160000] lr: 9.4918e-03 eta: 23:01:07 time: 0.5567 data_time: 0.0059 memory: 7635 loss: 0.0238 decode.loss_ce: 0.0135 decode.acc_seg: 99.5447 aux.loss_ce: 0.0102 aux.acc_seg: 99.0881 +04/18 03:46:03 - mmengine - INFO - Iter(train) [ 9150/160000] lr: 9.4890e-03 eta: 23:00:38 time: 0.5485 data_time: 0.0065 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0141 decode.acc_seg: 99.6255 aux.loss_ce: 0.0104 aux.acc_seg: 99.1031 +04/18 03:46:30 - mmengine - INFO - Iter(train) [ 9200/160000] lr: 9.4862e-03 eta: 23:00:10 time: 0.5483 data_time: 0.0057 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0131 decode.acc_seg: 99.3305 aux.loss_ce: 0.0099 aux.acc_seg: 98.7550 +04/18 03:46:57 - mmengine - INFO - Iter(train) [ 9250/160000] lr: 9.4834e-03 eta: 22:59:42 time: 0.5492 data_time: 0.0058 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0125 decode.acc_seg: 99.6259 aux.loss_ce: 0.0097 aux.acc_seg: 99.1253 +04/18 03:47:25 - mmengine - INFO - Iter(train) [ 9300/160000] lr: 9.4806e-03 eta: 22:59:14 time: 0.5475 data_time: 0.0067 memory: 7635 loss: 0.0248 decode.loss_ce: 0.0144 decode.acc_seg: 99.5245 aux.loss_ce: 0.0104 aux.acc_seg: 98.9099 +04/18 03:47:52 - mmengine - INFO - Iter(train) [ 9350/160000] lr: 9.4778e-03 eta: 22:58:46 time: 0.5485 data_time: 0.0058 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0121 decode.acc_seg: 99.5957 aux.loss_ce: 0.0094 aux.acc_seg: 99.1341 +04/18 03:48:20 - mmengine - INFO - Iter(train) [ 9400/160000] lr: 9.4750e-03 eta: 22:58:18 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0239 decode.loss_ce: 0.0140 decode.acc_seg: 99.3546 aux.loss_ce: 0.0099 aux.acc_seg: 98.8052 +04/18 03:48:47 - mmengine - INFO - Iter(train) [ 9450/160000] lr: 9.4722e-03 eta: 22:57:50 time: 0.5483 data_time: 0.0065 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0153 decode.acc_seg: 99.1859 aux.loss_ce: 0.0108 aux.acc_seg: 98.5625 +04/18 03:49:15 - mmengine - INFO - Iter(train) [ 9500/160000] lr: 9.4694e-03 eta: 22:57:22 time: 0.5482 data_time: 0.0062 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0127 decode.acc_seg: 99.4252 aux.loss_ce: 0.0092 aux.acc_seg: 98.7907 +04/18 03:49:42 - mmengine - INFO - Iter(train) [ 9550/160000] lr: 9.4666e-03 eta: 22:56:54 time: 0.5474 data_time: 0.0068 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0130 decode.acc_seg: 99.5365 aux.loss_ce: 0.0100 aux.acc_seg: 98.8636 +04/18 03:50:09 - mmengine - INFO - Iter(train) [ 9600/160000] lr: 9.4638e-03 eta: 22:56:26 time: 0.5483 data_time: 0.0063 memory: 7635 loss: 0.0244 decode.loss_ce: 0.0140 decode.acc_seg: 99.4267 aux.loss_ce: 0.0104 aux.acc_seg: 98.9110 +04/18 03:50:37 - mmengine - INFO - Iter(train) [ 9650/160000] lr: 9.4610e-03 eta: 22:55:58 time: 0.5474 data_time: 0.0058 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0118 decode.acc_seg: 99.5340 aux.loss_ce: 0.0089 aux.acc_seg: 99.1682 +04/18 03:51:04 - mmengine - INFO - Iter(train) [ 9700/160000] lr: 9.4582e-03 eta: 22:55:31 time: 0.5489 data_time: 0.0068 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0123 decode.acc_seg: 99.4224 aux.loss_ce: 0.0093 aux.acc_seg: 98.6237 +04/18 03:51:32 - mmengine - INFO - Iter(train) [ 9750/160000] lr: 9.4554e-03 eta: 22:55:03 time: 0.5488 data_time: 0.0058 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0120 decode.acc_seg: 99.6173 aux.loss_ce: 0.0091 aux.acc_seg: 99.2518 +04/18 03:51:59 - mmengine - INFO - Iter(train) [ 9800/160000] lr: 9.4526e-03 eta: 22:54:34 time: 0.5483 data_time: 0.0066 memory: 7635 loss: 0.0229 decode.loss_ce: 0.0133 decode.acc_seg: 99.6406 aux.loss_ce: 0.0096 aux.acc_seg: 99.3035 +04/18 03:52:27 - mmengine - INFO - Iter(train) [ 9850/160000] lr: 9.4498e-03 eta: 22:54:06 time: 0.5480 data_time: 0.0064 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0125 decode.acc_seg: 99.4288 aux.loss_ce: 0.0094 aux.acc_seg: 98.8976 +04/18 03:52:54 - mmengine - INFO - Iter(train) [ 9900/160000] lr: 9.4470e-03 eta: 22:53:38 time: 0.5480 data_time: 0.0068 memory: 7635 loss: 0.0232 decode.loss_ce: 0.0132 decode.acc_seg: 99.6505 aux.loss_ce: 0.0099 aux.acc_seg: 99.2436 +04/18 03:53:21 - mmengine - INFO - Iter(train) [ 9950/160000] lr: 9.4442e-03 eta: 22:53:10 time: 0.5498 data_time: 0.0057 memory: 7635 loss: 0.0249 decode.loss_ce: 0.0142 decode.acc_seg: 99.4144 aux.loss_ce: 0.0106 aux.acc_seg: 98.5002 +04/18 03:53:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:53:49 - mmengine - INFO - Iter(train) [ 10000/160000] lr: 9.4414e-03 eta: 22:52:42 time: 0.5481 data_time: 0.0058 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0132 decode.acc_seg: 99.3968 aux.loss_ce: 0.0098 aux.acc_seg: 99.0562 +04/18 03:53:49 - mmengine - INFO - Saving checkpoint at 10000 iterations +04/18 03:53:54 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:10 time: 0.0475 data_time: 0.0015 memory: 5542 +04/18 03:53:56 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:05 time: 0.0473 data_time: 0.0014 memory: 1657 +04/18 03:53:59 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0462 data_time: 0.0015 memory: 1657 +04/18 03:54:01 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0450 data_time: 0.0012 memory: 1657 +04/18 03:54:01 - mmengine - INFO - per class results: +04/18 03:54:01 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 98.95 | 99.35 | 99.47 | 99.6 | 99.35 | +| contrast | 78.15 | 90.4 | 87.74 | 85.23 | 90.4 | ++------------+-------+-------+--------+-----------+--------+ +04/18 03:54:01 - mmengine - INFO - Iter(val) [200/200] aAcc: 98.9900 mIoU: 88.5500 mAcc: 94.8700 mFscore: 93.6100 mPrecision: 92.4200 mRecall: 94.8700 data_time: 0.0023 time: 0.0518 +04/18 03:54:29 - mmengine - INFO - Iter(train) [ 10050/160000] lr: 9.4386e-03 eta: 22:52:17 time: 0.5487 data_time: 0.0069 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0122 decode.acc_seg: 99.3901 aux.loss_ce: 0.0089 aux.acc_seg: 98.9715 +04/18 03:54:56 - mmengine - INFO - Iter(train) [ 10100/160000] lr: 9.4358e-03 eta: 22:51:49 time: 0.5489 data_time: 0.0062 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.5948 aux.loss_ce: 0.0090 aux.acc_seg: 99.1281 +04/18 03:55:24 - mmengine - INFO - Iter(train) [ 10150/160000] lr: 9.4330e-03 eta: 22:51:24 time: 0.5693 data_time: 0.0059 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0120 decode.acc_seg: 99.5793 aux.loss_ce: 0.0094 aux.acc_seg: 99.0387 +04/18 03:55:51 - mmengine - INFO - Iter(train) [ 10200/160000] lr: 9.4302e-03 eta: 22:50:55 time: 0.5478 data_time: 0.0067 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0122 decode.acc_seg: 99.4923 aux.loss_ce: 0.0092 aux.acc_seg: 98.9777 +04/18 03:56:18 - mmengine - INFO - Iter(train) [ 10250/160000] lr: 9.4274e-03 eta: 22:50:27 time: 0.5491 data_time: 0.0055 memory: 7635 loss: 0.0257 decode.loss_ce: 0.0153 decode.acc_seg: 98.8754 aux.loss_ce: 0.0104 aux.acc_seg: 98.3413 +04/18 03:56:46 - mmengine - INFO - Iter(train) [ 10300/160000] lr: 9.4246e-03 eta: 22:50:00 time: 0.5485 data_time: 0.0058 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0124 decode.acc_seg: 99.2995 aux.loss_ce: 0.0091 aux.acc_seg: 98.8486 +04/18 03:57:13 - mmengine - INFO - Iter(train) [ 10350/160000] lr: 9.4218e-03 eta: 22:49:31 time: 0.5486 data_time: 0.0059 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0121 decode.acc_seg: 99.4930 aux.loss_ce: 0.0089 aux.acc_seg: 98.9901 +04/18 03:57:41 - mmengine - INFO - Iter(train) [ 10400/160000] lr: 9.4190e-03 eta: 22:49:03 time: 0.5489 data_time: 0.0062 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0127 decode.acc_seg: 99.5553 aux.loss_ce: 0.0098 aux.acc_seg: 99.2027 +04/18 03:58:08 - mmengine - INFO - Iter(train) [ 10450/160000] lr: 9.4162e-03 eta: 22:48:35 time: 0.5489 data_time: 0.0060 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0133 decode.acc_seg: 99.4981 aux.loss_ce: 0.0098 aux.acc_seg: 99.0229 +04/18 03:58:36 - mmengine - INFO - Iter(train) [ 10500/160000] lr: 9.4134e-03 eta: 22:48:09 time: 0.5479 data_time: 0.0055 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0122 decode.acc_seg: 99.5740 aux.loss_ce: 0.0094 aux.acc_seg: 99.2808 +04/18 03:59:03 - mmengine - INFO - Iter(train) [ 10550/160000] lr: 9.4106e-03 eta: 22:47:41 time: 0.5484 data_time: 0.0060 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0124 decode.acc_seg: 99.5886 aux.loss_ce: 0.0091 aux.acc_seg: 99.0820 +04/18 03:59:30 - mmengine - INFO - Iter(train) [ 10600/160000] lr: 9.4078e-03 eta: 22:47:12 time: 0.5491 data_time: 0.0067 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0129 decode.acc_seg: 99.5251 aux.loss_ce: 0.0094 aux.acc_seg: 99.0472 +04/18 03:59:58 - mmengine - INFO - Iter(train) [ 10650/160000] lr: 9.4050e-03 eta: 22:46:45 time: 0.5491 data_time: 0.0063 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0110 decode.acc_seg: 99.5851 aux.loss_ce: 0.0085 aux.acc_seg: 99.1895 +04/18 04:00:25 - mmengine - INFO - Iter(train) [ 10700/160000] lr: 9.4022e-03 eta: 22:46:17 time: 0.5487 data_time: 0.0068 memory: 7635 loss: 0.0242 decode.loss_ce: 0.0137 decode.acc_seg: 99.5319 aux.loss_ce: 0.0105 aux.acc_seg: 98.8897 +04/18 04:00:53 - mmengine - INFO - Iter(train) [ 10750/160000] lr: 9.3993e-03 eta: 22:45:49 time: 0.5480 data_time: 0.0059 memory: 7635 loss: 0.0221 decode.loss_ce: 0.0127 decode.acc_seg: 99.5176 aux.loss_ce: 0.0094 aux.acc_seg: 99.0525 +04/18 04:01:20 - mmengine - INFO - Iter(train) [ 10800/160000] lr: 9.3965e-03 eta: 22:45:21 time: 0.5488 data_time: 0.0066 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0124 decode.acc_seg: 99.5129 aux.loss_ce: 0.0096 aux.acc_seg: 98.9785 +04/18 04:01:48 - mmengine - INFO - Iter(train) [ 10850/160000] lr: 9.3937e-03 eta: 22:44:54 time: 0.5498 data_time: 0.0065 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0131 decode.acc_seg: 99.6201 aux.loss_ce: 0.0095 aux.acc_seg: 99.2350 +04/18 04:02:15 - mmengine - INFO - Iter(train) [ 10900/160000] lr: 9.3909e-03 eta: 22:44:26 time: 0.5482 data_time: 0.0068 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0119 decode.acc_seg: 99.4088 aux.loss_ce: 0.0092 aux.acc_seg: 99.0273 +04/18 04:02:42 - mmengine - INFO - Iter(train) [ 10950/160000] lr: 9.3881e-03 eta: 22:43:58 time: 0.5486 data_time: 0.0064 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0112 decode.acc_seg: 99.6139 aux.loss_ce: 0.0089 aux.acc_seg: 99.1864 +04/18 04:03:10 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:03:10 - mmengine - INFO - Iter(train) [ 11000/160000] lr: 9.3853e-03 eta: 22:43:30 time: 0.5479 data_time: 0.0064 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0134 decode.acc_seg: 99.5029 aux.loss_ce: 0.0101 aux.acc_seg: 98.9841 +04/18 04:03:37 - mmengine - INFO - Iter(train) [ 11050/160000] lr: 9.3825e-03 eta: 22:43:03 time: 0.5497 data_time: 0.0067 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0134 decode.acc_seg: 99.4128 aux.loss_ce: 0.0101 aux.acc_seg: 99.0168 +04/18 04:04:05 - mmengine - INFO - Iter(train) [ 11100/160000] lr: 9.3797e-03 eta: 22:42:35 time: 0.5491 data_time: 0.0060 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0129 decode.acc_seg: 99.6453 aux.loss_ce: 0.0096 aux.acc_seg: 99.2537 +04/18 04:04:32 - mmengine - INFO - Iter(train) [ 11150/160000] lr: 9.3769e-03 eta: 22:42:07 time: 0.5487 data_time: 0.0060 memory: 7635 loss: 0.0259 decode.loss_ce: 0.0149 decode.acc_seg: 99.3362 aux.loss_ce: 0.0110 aux.acc_seg: 98.9176 +04/18 04:05:00 - mmengine - INFO - Iter(train) [ 11200/160000] lr: 9.3741e-03 eta: 22:41:40 time: 0.5480 data_time: 0.0065 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0128 decode.acc_seg: 99.4145 aux.loss_ce: 0.0098 aux.acc_seg: 98.8956 +04/18 04:05:27 - mmengine - INFO - Iter(train) [ 11250/160000] lr: 9.3713e-03 eta: 22:41:14 time: 0.5502 data_time: 0.0061 memory: 7635 loss: 0.0227 decode.loss_ce: 0.0132 decode.acc_seg: 99.4885 aux.loss_ce: 0.0095 aux.acc_seg: 99.0182 +04/18 04:05:55 - mmengine - INFO - Iter(train) [ 11300/160000] lr: 9.3685e-03 eta: 22:40:47 time: 0.5487 data_time: 0.0064 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0111 decode.acc_seg: 99.3999 aux.loss_ce: 0.0089 aux.acc_seg: 98.7281 +04/18 04:06:22 - mmengine - INFO - Iter(train) [ 11350/160000] lr: 9.3657e-03 eta: 22:40:19 time: 0.5476 data_time: 0.0072 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.4430 aux.loss_ce: 0.0089 aux.acc_seg: 98.7743 +04/18 04:06:49 - mmengine - INFO - Iter(train) [ 11400/160000] lr: 9.3629e-03 eta: 22:39:51 time: 0.5485 data_time: 0.0065 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0120 decode.acc_seg: 99.4951 aux.loss_ce: 0.0091 aux.acc_seg: 99.0124 +04/18 04:07:17 - mmengine - INFO - Iter(train) [ 11450/160000] lr: 9.3601e-03 eta: 22:39:23 time: 0.5487 data_time: 0.0058 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0110 decode.acc_seg: 99.5588 aux.loss_ce: 0.0088 aux.acc_seg: 99.0472 +04/18 04:07:44 - mmengine - INFO - Iter(train) [ 11500/160000] lr: 9.3573e-03 eta: 22:38:55 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0119 decode.acc_seg: 99.4657 aux.loss_ce: 0.0093 aux.acc_seg: 98.9974 +04/18 04:08:12 - mmengine - INFO - Iter(train) [ 11550/160000] lr: 9.3545e-03 eta: 22:38:27 time: 0.5477 data_time: 0.0062 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0121 decode.acc_seg: 99.6387 aux.loss_ce: 0.0088 aux.acc_seg: 99.1696 +04/18 04:08:39 - mmengine - INFO - Iter(train) [ 11600/160000] lr: 9.3517e-03 eta: 22:38:00 time: 0.5493 data_time: 0.0060 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0113 decode.acc_seg: 99.5864 aux.loss_ce: 0.0087 aux.acc_seg: 99.1900 +04/18 04:09:07 - mmengine - INFO - Iter(train) [ 11650/160000] lr: 9.3489e-03 eta: 22:37:32 time: 0.5483 data_time: 0.0061 memory: 7635 loss: 0.0250 decode.loss_ce: 0.0142 decode.acc_seg: 99.3481 aux.loss_ce: 0.0108 aux.acc_seg: 98.6688 +04/18 04:09:34 - mmengine - INFO - Iter(train) [ 11700/160000] lr: 9.3461e-03 eta: 22:37:04 time: 0.5491 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0107 decode.acc_seg: 99.5366 aux.loss_ce: 0.0085 aux.acc_seg: 99.1977 +04/18 04:10:01 - mmengine - INFO - Iter(train) [ 11750/160000] lr: 9.3433e-03 eta: 22:36:36 time: 0.5486 data_time: 0.0058 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0115 decode.acc_seg: 99.5639 aux.loss_ce: 0.0085 aux.acc_seg: 99.1105 +04/18 04:10:29 - mmengine 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upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:12:18 - mmengine - INFO - Iter(train) [ 12000/160000] lr: 9.3292e-03 eta: 22:34:16 time: 0.5470 data_time: 0.0058 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0122 decode.acc_seg: 99.4463 aux.loss_ce: 0.0089 aux.acc_seg: 98.8301 +04/18 04:12:46 - mmengine - INFO - Iter(train) [ 12050/160000] lr: 9.3264e-03 eta: 22:33:48 time: 0.5477 data_time: 0.0060 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0122 decode.acc_seg: 99.6193 aux.loss_ce: 0.0094 aux.acc_seg: 99.1158 +04/18 04:13:13 - mmengine - INFO - Iter(train) [ 12100/160000] lr: 9.3236e-03 eta: 22:33:20 time: 0.5478 data_time: 0.0063 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0131 decode.acc_seg: 99.3755 aux.loss_ce: 0.0094 aux.acc_seg: 98.8880 +04/18 04:13:41 - mmengine - INFO - Iter(train) [ 12150/160000] lr: 9.3208e-03 eta: 22:32:52 time: 0.5485 data_time: 0.0058 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0120 decode.acc_seg: 99.4139 aux.loss_ce: 0.0094 aux.acc_seg: 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Iter(train) [ 12400/160000] lr: 9.3068e-03 eta: 22:30:37 time: 0.5491 data_time: 0.0061 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.5073 aux.loss_ce: 0.0086 aux.acc_seg: 98.8900 +04/18 04:16:26 - mmengine - INFO - Iter(train) [ 12450/160000] lr: 9.3040e-03 eta: 22:30:09 time: 0.5496 data_time: 0.0067 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0127 decode.acc_seg: 99.5250 aux.loss_ce: 0.0096 aux.acc_seg: 98.9526 +04/18 04:16:53 - mmengine - INFO - Iter(train) [ 12500/160000] lr: 9.3012e-03 eta: 22:29:42 time: 0.5496 data_time: 0.0061 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0120 decode.acc_seg: 99.2399 aux.loss_ce: 0.0095 aux.acc_seg: 98.6911 +04/18 04:17:20 - mmengine - INFO - Iter(train) [ 12550/160000] lr: 9.2984e-03 eta: 22:29:14 time: 0.5482 data_time: 0.0061 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0126 decode.acc_seg: 99.5748 aux.loss_ce: 0.0093 aux.acc_seg: 99.1762 +04/18 04:17:48 - mmengine - INFO - Iter(train) [ 12600/160000] lr: 9.2955e-03 eta: 22:28:46 time: 0.5485 data_time: 0.0060 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0115 decode.acc_seg: 99.2990 aux.loss_ce: 0.0089 aux.acc_seg: 98.8092 +04/18 04:18:15 - mmengine - INFO - Iter(train) [ 12650/160000] lr: 9.2927e-03 eta: 22:28:19 time: 0.5488 data_time: 0.0057 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0107 decode.acc_seg: 99.5843 aux.loss_ce: 0.0084 aux.acc_seg: 99.1313 +04/18 04:18:43 - mmengine - INFO - Iter(train) [ 12700/160000] lr: 9.2899e-03 eta: 22:27:53 time: 0.5496 data_time: 0.0061 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0107 decode.acc_seg: 99.6871 aux.loss_ce: 0.0087 aux.acc_seg: 99.2489 +04/18 04:19:10 - mmengine - INFO - Iter(train) [ 12750/160000] lr: 9.2871e-03 eta: 22:27:25 time: 0.5492 data_time: 0.0060 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0118 decode.acc_seg: 99.5813 aux.loss_ce: 0.0091 aux.acc_seg: 99.1307 +04/18 04:19:38 - mmengine - INFO - Iter(train) [ 12800/160000] lr: 9.2843e-03 eta: 22:26:57 time: 0.5488 data_time: 0.0054 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0111 decode.acc_seg: 99.3687 aux.loss_ce: 0.0090 aux.acc_seg: 98.7031 +04/18 04:20:05 - mmengine - INFO - Iter(train) [ 12850/160000] lr: 9.2815e-03 eta: 22:26:30 time: 0.5492 data_time: 0.0058 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0116 decode.acc_seg: 99.6304 aux.loss_ce: 0.0089 aux.acc_seg: 99.1039 +04/18 04:20:33 - mmengine - INFO - Iter(train) [ 12900/160000] lr: 9.2787e-03 eta: 22:26:02 time: 0.5487 data_time: 0.0063 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0121 decode.acc_seg: 99.5192 aux.loss_ce: 0.0094 aux.acc_seg: 99.0376 +04/18 04:21:00 - mmengine - INFO - Iter(train) [ 12950/160000] lr: 9.2759e-03 eta: 22:25:34 time: 0.5485 data_time: 0.0060 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0113 decode.acc_seg: 99.5872 aux.loss_ce: 0.0090 aux.acc_seg: 99.1456 +04/18 04:21:27 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:21:27 - mmengine - INFO - Iter(train) [ 13000/160000] lr: 9.2731e-03 eta: 22:25:07 time: 0.5489 data_time: 0.0058 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.6518 aux.loss_ce: 0.0085 aux.acc_seg: 99.3849 +04/18 04:21:55 - mmengine - INFO - Iter(train) [ 13050/160000] lr: 9.2703e-03 eta: 22:24:39 time: 0.5494 data_time: 0.0070 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0116 decode.acc_seg: 99.5750 aux.loss_ce: 0.0086 aux.acc_seg: 99.0207 +04/18 04:22:22 - mmengine - INFO - Iter(train) [ 13100/160000] lr: 9.2675e-03 eta: 22:24:11 time: 0.5494 data_time: 0.0060 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0106 decode.acc_seg: 99.4333 aux.loss_ce: 0.0081 aux.acc_seg: 99.0598 +04/18 04:22:50 - mmengine - INFO - Iter(train) [ 13150/160000] lr: 9.2647e-03 eta: 22:23:44 time: 0.5481 data_time: 0.0058 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0111 decode.acc_seg: 99.7065 aux.loss_ce: 0.0089 aux.acc_seg: 99.2218 +04/18 04:23:17 - mmengine - INFO - Iter(train) [ 13200/160000] lr: 9.2618e-03 eta: 22:23:16 time: 0.5474 data_time: 0.0059 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0115 decode.acc_seg: 99.3992 aux.loss_ce: 0.0089 aux.acc_seg: 98.8688 +04/18 04:23:45 - mmengine - INFO - Iter(train) [ 13250/160000] lr: 9.2590e-03 eta: 22:22:49 time: 0.5493 data_time: 0.0065 memory: 7635 loss: 0.0231 decode.loss_ce: 0.0133 decode.acc_seg: 99.5006 aux.loss_ce: 0.0099 aux.acc_seg: 98.9125 +04/18 04:24:12 - mmengine - INFO - Iter(train) [ 13300/160000] lr: 9.2562e-03 eta: 22:22:21 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0117 decode.acc_seg: 99.5731 aux.loss_ce: 0.0089 aux.acc_seg: 99.1207 +04/18 04:24:40 - mmengine - INFO - Iter(train) [ 13350/160000] lr: 9.2534e-03 eta: 22:21:54 time: 0.5500 data_time: 0.0068 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.5807 aux.loss_ce: 0.0085 aux.acc_seg: 99.0040 +04/18 04:25:07 - mmengine - INFO - Iter(train) [ 13400/160000] lr: 9.2506e-03 eta: 22:21:28 time: 0.5474 data_time: 0.0060 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0107 decode.acc_seg: 99.5396 aux.loss_ce: 0.0087 aux.acc_seg: 98.9564 +04/18 04:25:35 - mmengine - INFO - Iter(train) [ 13450/160000] lr: 9.2478e-03 eta: 22:21:01 time: 0.5490 data_time: 0.0060 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.6347 aux.loss_ce: 0.0089 aux.acc_seg: 99.2900 +04/18 04:26:02 - mmengine - INFO - Iter(train) [ 13500/160000] lr: 9.2450e-03 eta: 22:20:33 time: 0.5492 data_time: 0.0061 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0120 decode.acc_seg: 99.7210 aux.loss_ce: 0.0093 aux.acc_seg: 99.3200 +04/18 04:26:30 - mmengine - INFO - Iter(train) [ 13550/160000] lr: 9.2422e-03 eta: 22:20:06 time: 0.5489 data_time: 0.0063 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0118 decode.acc_seg: 99.6255 aux.loss_ce: 0.0093 aux.acc_seg: 99.3737 +04/18 04:26:57 - mmengine - INFO - Iter(train) [ 13600/160000] lr: 9.2394e-03 eta: 22:19:39 time: 0.5493 data_time: 0.0055 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0114 decode.acc_seg: 99.3721 aux.loss_ce: 0.0093 aux.acc_seg: 98.8181 +04/18 04:27:25 - mmengine - INFO - Iter(train) [ 13650/160000] lr: 9.2366e-03 eta: 22:19:11 time: 0.5491 data_time: 0.0064 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0116 decode.acc_seg: 99.5900 aux.loss_ce: 0.0088 aux.acc_seg: 99.2604 +04/18 04:27:52 - mmengine - INFO - Iter(train) [ 13700/160000] lr: 9.2338e-03 eta: 22:18:44 time: 0.5496 data_time: 0.0060 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0117 decode.acc_seg: 99.6017 aux.loss_ce: 0.0092 aux.acc_seg: 99.2126 +04/18 04:28:20 - mmengine - INFO - Iter(train) [ 13750/160000] lr: 9.2309e-03 eta: 22:18:18 time: 0.5484 data_time: 0.0058 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0120 decode.acc_seg: 99.3927 aux.loss_ce: 0.0092 aux.acc_seg: 98.9446 +04/18 04:28:47 - mmengine - INFO - Iter(train) [ 13800/160000] lr: 9.2281e-03 eta: 22:17:50 time: 0.5484 data_time: 0.0060 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0118 decode.acc_seg: 99.4184 aux.loss_ce: 0.0091 aux.acc_seg: 98.8906 +04/18 04:29:14 - mmengine 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aux.acc_seg: 99.0235 +04/18 04:31:04 - mmengine - INFO - Iter(train) [ 14050/160000] lr: 9.2141e-03 eta: 22:15:33 time: 0.5495 data_time: 0.0069 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0120 decode.acc_seg: 99.5310 aux.loss_ce: 0.0090 aux.acc_seg: 99.1996 +04/18 04:31:32 - mmengine - INFO - Iter(train) [ 14100/160000] lr: 9.2113e-03 eta: 22:15:05 time: 0.5483 data_time: 0.0058 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0109 decode.acc_seg: 99.5172 aux.loss_ce: 0.0090 aux.acc_seg: 99.0437 +04/18 04:31:59 - mmengine - INFO - Iter(train) [ 14150/160000] lr: 9.2085e-03 eta: 22:14:38 time: 0.5491 data_time: 0.0062 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0122 decode.acc_seg: 99.6517 aux.loss_ce: 0.0096 aux.acc_seg: 99.2456 +04/18 04:32:27 - mmengine - INFO - Iter(train) [ 14200/160000] lr: 9.2057e-03 eta: 22:14:10 time: 0.5494 data_time: 0.0060 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0123 decode.acc_seg: 99.5095 aux.loss_ce: 0.0097 aux.acc_seg: 99.1067 +04/18 04:32:54 - mmengine 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9.1916e-03 eta: 22:11:56 time: 0.5595 data_time: 0.0067 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0112 decode.acc_seg: 99.6026 aux.loss_ce: 0.0089 aux.acc_seg: 99.0494 +04/18 04:35:12 - mmengine - INFO - Iter(train) [ 14500/160000] lr: 9.1888e-03 eta: 22:11:29 time: 0.5491 data_time: 0.0066 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0117 decode.acc_seg: 99.3559 aux.loss_ce: 0.0089 aux.acc_seg: 98.6353 +04/18 04:35:39 - mmengine - INFO - Iter(train) [ 14550/160000] lr: 9.1860e-03 eta: 22:11:02 time: 0.5500 data_time: 0.0059 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0115 decode.acc_seg: 99.5761 aux.loss_ce: 0.0089 aux.acc_seg: 99.1471 +04/18 04:36:07 - mmengine - INFO - Iter(train) [ 14600/160000] lr: 9.1832e-03 eta: 22:10:34 time: 0.5500 data_time: 0.0060 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.6475 aux.loss_ce: 0.0082 aux.acc_seg: 99.0929 +04/18 04:36:34 - mmengine - INFO - Iter(train) [ 14650/160000] lr: 9.1804e-03 eta: 22:10:07 time: 0.5498 data_time: 0.0065 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0113 decode.acc_seg: 99.6323 aux.loss_ce: 0.0092 aux.acc_seg: 99.2687 +04/18 04:37:02 - mmengine - INFO - Iter(train) [ 14700/160000] lr: 9.1776e-03 eta: 22:09:40 time: 0.5487 data_time: 0.0062 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0104 decode.acc_seg: 99.6775 aux.loss_ce: 0.0083 aux.acc_seg: 99.3504 +04/18 04:37:29 - mmengine - INFO - Iter(train) [ 14750/160000] lr: 9.1747e-03 eta: 22:09:13 time: 0.5499 data_time: 0.0065 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.5389 aux.loss_ce: 0.0087 aux.acc_seg: 98.9362 +04/18 04:37:57 - mmengine - INFO - Iter(train) [ 14800/160000] lr: 9.1719e-03 eta: 22:08:46 time: 0.5510 data_time: 0.0068 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0116 decode.acc_seg: 99.5256 aux.loss_ce: 0.0090 aux.acc_seg: 99.0493 +04/18 04:38:24 - mmengine - INFO - Iter(train) [ 14850/160000] lr: 9.1691e-03 eta: 22:08:20 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0115 decode.acc_seg: 99.7200 aux.loss_ce: 0.0090 aux.acc_seg: 99.3581 +04/18 04:38:52 - mmengine - INFO - Iter(train) [ 14900/160000] lr: 9.1663e-03 eta: 22:07:52 time: 0.5493 data_time: 0.0065 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0107 decode.acc_seg: 99.6327 aux.loss_ce: 0.0082 aux.acc_seg: 99.1156 +04/18 04:39:19 - mmengine - INFO - Iter(train) [ 14950/160000] lr: 9.1635e-03 eta: 22:07:25 time: 0.5489 data_time: 0.0061 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0106 decode.acc_seg: 99.5702 aux.loss_ce: 0.0090 aux.acc_seg: 98.9697 +04/18 04:39:47 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:39:47 - mmengine - INFO - Iter(train) [ 15000/160000] lr: 9.1607e-03 eta: 22:06:58 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0106 decode.acc_seg: 99.6482 aux.loss_ce: 0.0084 aux.acc_seg: 99.2591 +04/18 04:40:14 - mmengine - INFO - Iter(train) [ 15050/160000] lr: 9.1579e-03 eta: 22:06:30 time: 0.5496 data_time: 0.0066 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0111 decode.acc_seg: 99.5513 aux.loss_ce: 0.0087 aux.acc_seg: 99.0436 +04/18 04:40:41 - mmengine - INFO - Iter(train) [ 15100/160000] lr: 9.1551e-03 eta: 22:06:03 time: 0.5490 data_time: 0.0060 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0122 decode.acc_seg: 99.5356 aux.loss_ce: 0.0095 aux.acc_seg: 99.0978 +04/18 04:41:09 - mmengine - INFO - Iter(train) [ 15150/160000] lr: 9.1522e-03 eta: 22:05:36 time: 0.5500 data_time: 0.0062 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0115 decode.acc_seg: 99.5110 aux.loss_ce: 0.0092 aux.acc_seg: 98.9477 +04/18 04:41:36 - mmengine - INFO - Iter(train) [ 15200/160000] lr: 9.1494e-03 eta: 22:05:09 time: 0.5495 data_time: 0.0066 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0112 decode.acc_seg: 99.5566 aux.loss_ce: 0.0088 aux.acc_seg: 99.2162 +04/18 04:42:04 - mmengine - INFO - Iter(train) [ 15250/160000] lr: 9.1466e-03 eta: 22:04:41 time: 0.5492 data_time: 0.0065 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0110 decode.acc_seg: 99.4129 aux.loss_ce: 0.0091 aux.acc_seg: 98.9281 +04/18 04:42:31 - mmengine - INFO - Iter(train) [ 15300/160000] lr: 9.1438e-03 eta: 22:04:14 time: 0.5493 data_time: 0.0063 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0107 decode.acc_seg: 99.5400 aux.loss_ce: 0.0089 aux.acc_seg: 98.9194 +04/18 04:42:59 - mmengine - INFO - Iter(train) [ 15350/160000] lr: 9.1410e-03 eta: 22:03:47 time: 0.5485 data_time: 0.0063 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0107 decode.acc_seg: 99.3506 aux.loss_ce: 0.0088 aux.acc_seg: 98.7174 +04/18 04:43:26 - mmengine - INFO - Iter(train) [ 15400/160000] lr: 9.1382e-03 eta: 22:03:19 time: 0.5493 data_time: 0.0060 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0114 decode.acc_seg: 99.4677 aux.loss_ce: 0.0089 aux.acc_seg: 98.9253 +04/18 04:43:54 - mmengine - INFO - Iter(train) [ 15450/160000] lr: 9.1354e-03 eta: 22:02:52 time: 0.5500 data_time: 0.0068 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0117 decode.acc_seg: 99.5312 aux.loss_ce: 0.0093 aux.acc_seg: 99.0074 +04/18 04:44:21 - mmengine - INFO - Iter(train) [ 15500/160000] lr: 9.1326e-03 eta: 22:02:25 time: 0.5504 data_time: 0.0069 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0116 decode.acc_seg: 99.4061 aux.loss_ce: 0.0095 aux.acc_seg: 98.9144 +04/18 04:44:49 - mmengine - INFO - Iter(train) [ 15550/160000] lr: 9.1297e-03 eta: 22:02:00 time: 0.5478 data_time: 0.0062 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0109 decode.acc_seg: 99.6532 aux.loss_ce: 0.0086 aux.acc_seg: 99.2592 +04/18 04:45:16 - mmengine - INFO - Iter(train) [ 15600/160000] lr: 9.1269e-03 eta: 22:01:33 time: 0.5504 data_time: 0.0062 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0106 decode.acc_seg: 99.5452 aux.loss_ce: 0.0092 aux.acc_seg: 98.8504 +04/18 04:45:44 - mmengine - INFO - Iter(train) [ 15650/160000] lr: 9.1241e-03 eta: 22:01:05 time: 0.5498 data_time: 0.0061 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0116 decode.acc_seg: 99.4576 aux.loss_ce: 0.0093 aux.acc_seg: 98.7809 +04/18 04:46:11 - mmengine - INFO - Iter(train) [ 15700/160000] lr: 9.1213e-03 eta: 22:00:38 time: 0.5502 data_time: 0.0057 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0141 decode.acc_seg: 99.4235 aux.loss_ce: 0.0105 aux.acc_seg: 98.8319 +04/18 04:46:39 - mmengine - INFO - Iter(train) [ 15750/160000] lr: 9.1185e-03 eta: 22:00:11 time: 0.5491 data_time: 0.0069 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0113 decode.acc_seg: 99.6474 aux.loss_ce: 0.0093 aux.acc_seg: 99.1494 +04/18 04:47:06 - mmengine - INFO - Iter(train) [ 15800/160000] lr: 9.1157e-03 eta: 21:59:44 time: 0.5513 data_time: 0.0058 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.6280 aux.loss_ce: 0.0086 aux.acc_seg: 99.1859 +04/18 04:47:34 - mmengine - INFO - Iter(train) [ 15850/160000] lr: 9.1129e-03 eta: 21:59:17 time: 0.5495 data_time: 0.0060 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0105 decode.acc_seg: 99.6848 aux.loss_ce: 0.0088 aux.acc_seg: 99.1300 +04/18 04:48:02 - mmengine - INFO - Iter(train) [ 15900/160000] lr: 9.1101e-03 eta: 21:58:51 time: 0.5592 data_time: 0.0056 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0116 decode.acc_seg: 99.5984 aux.loss_ce: 0.0092 aux.acc_seg: 99.1928 +04/18 04:48:29 - mmengine - INFO - Iter(train) [ 15950/160000] lr: 9.1072e-03 eta: 21:58:24 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0117 decode.acc_seg: 99.6102 aux.loss_ce: 0.0090 aux.acc_seg: 98.9958 +04/18 04:48:57 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:48:57 - mmengine - INFO - Iter(train) [ 16000/160000] lr: 9.1044e-03 eta: 21:57:57 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0098 decode.acc_seg: 99.6333 aux.loss_ce: 0.0085 aux.acc_seg: 99.1935 +04/18 04:49:24 - mmengine - INFO - Iter(train) [ 16050/160000] lr: 9.1016e-03 eta: 21:57:30 time: 0.5509 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0092 decode.acc_seg: 99.6289 aux.loss_ce: 0.0075 aux.acc_seg: 99.4611 +04/18 04:49:52 - mmengine - INFO - Iter(train) [ 16100/160000] lr: 9.0988e-03 eta: 21:57:03 time: 0.5509 data_time: 0.0058 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0095 decode.acc_seg: 99.5643 aux.loss_ce: 0.0079 aux.acc_seg: 99.2123 +04/18 04:50:19 - mmengine - INFO - Iter(train) [ 16150/160000] lr: 9.0960e-03 eta: 21:56:37 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0106 decode.acc_seg: 99.3545 aux.loss_ce: 0.0086 aux.acc_seg: 98.8721 +04/18 04:50:47 - mmengine - INFO - Iter(train) [ 16200/160000] lr: 9.0932e-03 eta: 21:56:10 time: 0.5496 data_time: 0.0062 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0108 decode.acc_seg: 99.6251 aux.loss_ce: 0.0088 aux.acc_seg: 99.1516 +04/18 04:51:14 - mmengine - INFO - Iter(train) [ 16250/160000] lr: 9.0904e-03 eta: 21:55:43 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.5217 aux.loss_ce: 0.0087 aux.acc_seg: 99.0500 +04/18 04:51:42 - mmengine - INFO - Iter(train) [ 16300/160000] lr: 9.0875e-03 eta: 21:55:16 time: 0.5493 data_time: 0.0063 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0108 decode.acc_seg: 99.4192 aux.loss_ce: 0.0084 aux.acc_seg: 99.1014 +04/18 04:52:09 - mmengine - INFO - Iter(train) [ 16350/160000] lr: 9.0847e-03 eta: 21:54:49 time: 0.5497 data_time: 0.0057 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0105 decode.acc_seg: 99.6097 aux.loss_ce: 0.0089 aux.acc_seg: 99.0688 +04/18 04:52:37 - mmengine - INFO - Iter(train) [ 16400/160000] lr: 9.0819e-03 eta: 21:54:22 time: 0.5507 data_time: 0.0061 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0126 decode.acc_seg: 99.4523 aux.loss_ce: 0.0091 aux.acc_seg: 99.0592 +04/18 04:53:04 - mmengine - INFO - Iter(train) [ 16450/160000] lr: 9.0791e-03 eta: 21:53:55 time: 0.5515 data_time: 0.0068 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0097 decode.acc_seg: 99.6587 aux.loss_ce: 0.0084 aux.acc_seg: 99.2566 +04/18 04:53:32 - mmengine - INFO - Iter(train) [ 16500/160000] lr: 9.0763e-03 eta: 21:53:27 time: 0.5503 data_time: 0.0057 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0093 decode.acc_seg: 99.5905 aux.loss_ce: 0.0077 aux.acc_seg: 99.2222 +04/18 04:53:59 - mmengine - INFO - Iter(train) [ 16550/160000] lr: 9.0735e-03 eta: 21:53:01 time: 0.5504 data_time: 0.0068 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.6407 aux.loss_ce: 0.0085 aux.acc_seg: 99.3345 +04/18 04:54:27 - mmengine - INFO - Iter(train) [ 16600/160000] lr: 9.0706e-03 eta: 21:52:36 time: 0.5699 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0100 decode.acc_seg: 99.7334 aux.loss_ce: 0.0082 aux.acc_seg: 99.4706 +04/18 04:54:55 - mmengine - INFO - Iter(train) [ 16650/160000] lr: 9.0678e-03 eta: 21:52:08 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0112 decode.acc_seg: 99.5617 aux.loss_ce: 0.0088 aux.acc_seg: 99.1335 +04/18 04:55:22 - mmengine - INFO - Iter(train) [ 16700/160000] lr: 9.0650e-03 eta: 21:51:41 time: 0.5510 data_time: 0.0058 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.6317 aux.loss_ce: 0.0084 aux.acc_seg: 99.2899 +04/18 04:55:50 - mmengine - INFO - Iter(train) [ 16750/160000] lr: 9.0622e-03 eta: 21:51:14 time: 0.5502 data_time: 0.0069 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0111 decode.acc_seg: 99.6498 aux.loss_ce: 0.0084 aux.acc_seg: 99.2376 +04/18 04:56:17 - mmengine - INFO - Iter(train) [ 16800/160000] lr: 9.0594e-03 eta: 21:50:48 time: 0.5512 data_time: 0.0062 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0117 decode.acc_seg: 99.6216 aux.loss_ce: 0.0091 aux.acc_seg: 99.2575 +04/18 04:56:45 - mmengine - INFO - Iter(train) [ 16850/160000] lr: 9.0566e-03 eta: 21:50:20 time: 0.5495 data_time: 0.0060 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0107 decode.acc_seg: 99.6372 aux.loss_ce: 0.0089 aux.acc_seg: 99.2227 +04/18 04:57:12 - mmengine - INFO - Iter(train) [ 16900/160000] lr: 9.0538e-03 eta: 21:49:53 time: 0.5494 data_time: 0.0058 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0101 decode.acc_seg: 99.3711 aux.loss_ce: 0.0080 aux.acc_seg: 98.9654 +04/18 04:57:40 - mmengine - INFO - Iter(train) [ 16950/160000] lr: 9.0509e-03 eta: 21:49:27 time: 0.5595 data_time: 0.0060 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0112 decode.acc_seg: 99.5635 aux.loss_ce: 0.0090 aux.acc_seg: 98.9095 +04/18 04:58:07 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:58:07 - mmengine - INFO - Iter(train) [ 17000/160000] lr: 9.0481e-03 eta: 21:49:00 time: 0.5507 data_time: 0.0058 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0129 decode.acc_seg: 99.0251 aux.loss_ce: 0.0094 aux.acc_seg: 98.6746 +04/18 04:58:35 - mmengine - INFO - Iter(train) [ 17050/160000] lr: 9.0453e-03 eta: 21:48:33 time: 0.5500 data_time: 0.0062 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.5245 aux.loss_ce: 0.0084 aux.acc_seg: 99.0348 +04/18 04:59:02 - mmengine - INFO - Iter(train) [ 17100/160000] lr: 9.0425e-03 eta: 21:48:06 time: 0.5504 data_time: 0.0054 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0116 decode.acc_seg: 99.5734 aux.loss_ce: 0.0091 aux.acc_seg: 99.1453 +04/18 04:59:30 - mmengine - INFO - Iter(train) [ 17150/160000] lr: 9.0397e-03 eta: 21:47:40 time: 0.5507 data_time: 0.0059 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0115 decode.acc_seg: 99.5340 aux.loss_ce: 0.0089 aux.acc_seg: 99.0964 +04/18 04:59:57 - mmengine - INFO - Iter(train) [ 17200/160000] lr: 9.0369e-03 eta: 21:47:13 time: 0.5511 data_time: 0.0070 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5158 aux.loss_ce: 0.0086 aux.acc_seg: 99.1407 +04/18 05:00:25 - mmengine - INFO - Iter(train) [ 17250/160000] lr: 9.0340e-03 eta: 21:46:46 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0104 decode.acc_seg: 99.5226 aux.loss_ce: 0.0081 aux.acc_seg: 99.0396 +04/18 05:00:52 - mmengine - INFO - Iter(train) [ 17300/160000] lr: 9.0312e-03 eta: 21:46:19 time: 0.5496 data_time: 0.0058 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0104 decode.acc_seg: 99.5439 aux.loss_ce: 0.0092 aux.acc_seg: 99.0997 +04/18 05:01:20 - mmengine - INFO - Iter(train) [ 17350/160000] lr: 9.0284e-03 eta: 21:45:52 time: 0.5498 data_time: 0.0068 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0107 decode.acc_seg: 99.7268 aux.loss_ce: 0.0089 aux.acc_seg: 99.3399 +04/18 05:01:48 - mmengine - INFO - Iter(train) [ 17400/160000] lr: 9.0256e-03 eta: 21:45:25 time: 0.5511 data_time: 0.0071 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0105 decode.acc_seg: 99.5956 aux.loss_ce: 0.0089 aux.acc_seg: 99.0271 +04/18 05:02:15 - mmengine - INFO - Iter(train) [ 17450/160000] lr: 9.0228e-03 eta: 21:44:58 time: 0.5498 data_time: 0.0058 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0103 decode.acc_seg: 99.6334 aux.loss_ce: 0.0080 aux.acc_seg: 99.0758 +04/18 05:02:43 - mmengine - INFO - Iter(train) [ 17500/160000] lr: 9.0200e-03 eta: 21:44:31 time: 0.5496 data_time: 0.0059 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0116 decode.acc_seg: 99.4657 aux.loss_ce: 0.0094 aux.acc_seg: 99.0963 +04/18 05:03:10 - mmengine - INFO - Iter(train) [ 17550/160000] lr: 9.0171e-03 eta: 21:44:04 time: 0.5508 data_time: 0.0062 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0100 decode.acc_seg: 99.6763 aux.loss_ce: 0.0085 aux.acc_seg: 99.2926 +04/18 05:03:38 - mmengine - INFO - Iter(train) [ 17600/160000] lr: 9.0143e-03 eta: 21:43:37 time: 0.5513 data_time: 0.0066 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0101 decode.acc_seg: 99.6868 aux.loss_ce: 0.0084 aux.acc_seg: 99.3676 +04/18 05:04:05 - mmengine - INFO - Iter(train) [ 17650/160000] lr: 9.0115e-03 eta: 21:43:10 time: 0.5506 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0099 decode.acc_seg: 99.6650 aux.loss_ce: 0.0083 aux.acc_seg: 99.2867 +04/18 05:04:33 - mmengine - INFO - Iter(train) [ 17700/160000] lr: 9.0087e-03 eta: 21:42:45 time: 0.5503 data_time: 0.0065 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.4845 aux.loss_ce: 0.0086 aux.acc_seg: 98.9722 +04/18 05:05:00 - mmengine - INFO - Iter(train) [ 17750/160000] lr: 9.0059e-03 eta: 21:42:18 time: 0.5516 data_time: 0.0066 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.7449 aux.loss_ce: 0.0084 aux.acc_seg: 99.3885 +04/18 05:05:28 - mmengine - INFO - Iter(train) [ 17800/160000] lr: 9.0031e-03 eta: 21:41:51 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0113 decode.acc_seg: 99.6144 aux.loss_ce: 0.0085 aux.acc_seg: 99.2468 +04/18 05:05:55 - mmengine - INFO - Iter(train) [ 17850/160000] lr: 9.0002e-03 eta: 21:41:24 time: 0.5495 data_time: 0.0061 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0111 decode.acc_seg: 99.5973 aux.loss_ce: 0.0092 aux.acc_seg: 98.9994 +04/18 05:06:23 - mmengine - INFO - Iter(train) [ 17900/160000] lr: 8.9974e-03 eta: 21:40:57 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0103 decode.acc_seg: 99.5664 aux.loss_ce: 0.0085 aux.acc_seg: 99.0761 +04/18 05:06:51 - mmengine - INFO - Iter(train) [ 17950/160000] lr: 8.9946e-03 eta: 21:40:30 time: 0.5504 data_time: 0.0056 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5418 aux.loss_ce: 0.0086 aux.acc_seg: 98.7377 +04/18 05:07:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:07:18 - mmengine - INFO - Iter(train) [ 18000/160000] lr: 8.9918e-03 eta: 21:40:03 time: 0.5504 data_time: 0.0069 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.4904 aux.loss_ce: 0.0085 aux.acc_seg: 98.9013 +04/18 05:07:46 - mmengine - INFO - Iter(train) [ 18050/160000] lr: 8.9890e-03 eta: 21:39:37 time: 0.5611 data_time: 0.0070 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.6426 aux.loss_ce: 0.0080 aux.acc_seg: 99.0942 +04/18 05:08:13 - mmengine - INFO - Iter(train) [ 18100/160000] lr: 8.9862e-03 eta: 21:39:10 time: 0.5509 data_time: 0.0060 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0110 decode.acc_seg: 99.6600 aux.loss_ce: 0.0088 aux.acc_seg: 99.1778 +04/18 05:08:41 - mmengine - INFO - Iter(train) [ 18150/160000] lr: 8.9833e-03 eta: 21:38:43 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5716 aux.loss_ce: 0.0085 aux.acc_seg: 99.0636 +04/18 05:09:08 - mmengine - INFO - Iter(train) [ 18200/160000] lr: 8.9805e-03 eta: 21:38:16 time: 0.5493 data_time: 0.0061 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0098 decode.acc_seg: 99.6521 aux.loss_ce: 0.0087 aux.acc_seg: 99.1826 +04/18 05:09:36 - mmengine - INFO - Iter(train) [ 18250/160000] lr: 8.9777e-03 eta: 21:37:49 time: 0.5515 data_time: 0.0057 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.6871 aux.loss_ce: 0.0086 aux.acc_seg: 99.2124 +04/18 05:10:03 - mmengine - INFO - Iter(train) [ 18300/160000] lr: 8.9749e-03 eta: 21:37:22 time: 0.5509 data_time: 0.0063 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.6368 aux.loss_ce: 0.0083 aux.acc_seg: 99.2934 +04/18 05:10:31 - mmengine - INFO - Iter(train) [ 18350/160000] lr: 8.9721e-03 eta: 21:36:55 time: 0.5511 data_time: 0.0058 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0109 decode.acc_seg: 99.6661 aux.loss_ce: 0.0091 aux.acc_seg: 99.1489 +04/18 05:10:58 - mmengine - INFO - Iter(train) [ 18400/160000] lr: 8.9692e-03 eta: 21:36:28 time: 0.5509 data_time: 0.0062 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.6330 aux.loss_ce: 0.0082 aux.acc_seg: 99.2747 +04/18 05:11:26 - mmengine - INFO - Iter(train) [ 18450/160000] lr: 8.9664e-03 eta: 21:36:01 time: 0.5518 data_time: 0.0064 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.6861 aux.loss_ce: 0.0084 aux.acc_seg: 99.2772 +04/18 05:11:54 - mmengine - INFO - Iter(train) [ 18500/160000] lr: 8.9636e-03 eta: 21:35:35 time: 0.5521 data_time: 0.0065 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0103 decode.acc_seg: 99.5523 aux.loss_ce: 0.0089 aux.acc_seg: 98.8003 +04/18 05:12:21 - mmengine - INFO - Iter(train) [ 18550/160000] lr: 8.9608e-03 eta: 21:35:08 time: 0.5509 data_time: 0.0065 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.6337 aux.loss_ce: 0.0088 aux.acc_seg: 99.2847 +04/18 05:12:49 - mmengine - INFO - Iter(train) [ 18600/160000] lr: 8.9580e-03 eta: 21:34:41 time: 0.5507 data_time: 0.0067 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.6078 aux.loss_ce: 0.0083 aux.acc_seg: 99.1495 +04/18 05:13:16 - mmengine - INFO - Iter(train) [ 18650/160000] lr: 8.9551e-03 eta: 21:34:14 time: 0.5496 data_time: 0.0058 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0102 decode.acc_seg: 99.6562 aux.loss_ce: 0.0084 aux.acc_seg: 99.2815 +04/18 05:13:44 - mmengine - INFO - Iter(train) [ 18700/160000] lr: 8.9523e-03 eta: 21:33:46 time: 0.5494 data_time: 0.0060 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0100 decode.acc_seg: 99.6137 aux.loss_ce: 0.0088 aux.acc_seg: 99.2285 +04/18 05:14:11 - mmengine - INFO - Iter(train) [ 18750/160000] lr: 8.9495e-03 eta: 21:33:21 time: 0.5596 data_time: 0.0066 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.4499 aux.loss_ce: 0.0090 aux.acc_seg: 98.6691 +04/18 05:14:39 - mmengine - INFO - Iter(train) [ 18800/160000] lr: 8.9467e-03 eta: 21:32:54 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.5760 aux.loss_ce: 0.0088 aux.acc_seg: 99.1145 +04/18 05:15:07 - mmengine - INFO - Iter(train) [ 18850/160000] lr: 8.9439e-03 eta: 21:32:28 time: 0.5528 data_time: 0.0071 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.6401 aux.loss_ce: 0.0088 aux.acc_seg: 99.1317 +04/18 05:15:34 - mmengine - INFO - Iter(train) [ 18900/160000] lr: 8.9411e-03 eta: 21:32:01 time: 0.5518 data_time: 0.0059 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0102 decode.acc_seg: 99.5771 aux.loss_ce: 0.0085 aux.acc_seg: 98.9853 +04/18 05:16:02 - mmengine - INFO - Iter(train) [ 18950/160000] lr: 8.9382e-03 eta: 21:31:34 time: 0.5523 data_time: 0.0060 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.5743 aux.loss_ce: 0.0086 aux.acc_seg: 99.2046 +04/18 05:16:29 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:16:29 - mmengine - INFO - Iter(train) [ 19000/160000] lr: 8.9354e-03 eta: 21:31:07 time: 0.5514 data_time: 0.0054 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0105 decode.acc_seg: 99.5222 aux.loss_ce: 0.0089 aux.acc_seg: 99.1184 +04/18 05:16:57 - mmengine - INFO - Iter(train) [ 19050/160000] lr: 8.9326e-03 eta: 21:30:40 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0096 decode.acc_seg: 99.6870 aux.loss_ce: 0.0078 aux.acc_seg: 99.2786 +04/18 05:17:24 - mmengine - INFO - Iter(train) [ 19100/160000] lr: 8.9298e-03 eta: 21:30:13 time: 0.5521 data_time: 0.0072 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0112 decode.acc_seg: 99.5139 aux.loss_ce: 0.0092 aux.acc_seg: 98.9241 +04/18 05:17:52 - mmengine - INFO - Iter(train) [ 19150/160000] lr: 8.9270e-03 eta: 21:29:47 time: 0.5511 data_time: 0.0065 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0102 decode.acc_seg: 99.5030 aux.loss_ce: 0.0086 aux.acc_seg: 98.9733 +04/18 05:18:20 - mmengine - INFO - Iter(train) [ 19200/160000] lr: 8.9241e-03 eta: 21:29:21 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.6538 aux.loss_ce: 0.0087 aux.acc_seg: 99.2344 +04/18 05:18:47 - mmengine - INFO - Iter(train) [ 19250/160000] lr: 8.9213e-03 eta: 21:28:54 time: 0.5512 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.4306 aux.loss_ce: 0.0087 aux.acc_seg: 98.9574 +04/18 05:19:15 - mmengine - INFO - Iter(train) [ 19300/160000] lr: 8.9185e-03 eta: 21:28:27 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0109 decode.acc_seg: 99.5718 aux.loss_ce: 0.0093 aux.acc_seg: 98.9916 +04/18 05:19:42 - mmengine - INFO - Iter(train) [ 19350/160000] lr: 8.9157e-03 eta: 21:28:01 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0106 decode.acc_seg: 99.3697 aux.loss_ce: 0.0088 aux.acc_seg: 98.7476 +04/18 05:20:10 - mmengine - INFO - Iter(train) [ 19400/160000] lr: 8.9129e-03 eta: 21:27:34 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0106 decode.acc_seg: 99.7349 aux.loss_ce: 0.0084 aux.acc_seg: 99.3744 +04/18 05:20:37 - mmengine - INFO - Iter(train) [ 19450/160000] lr: 8.9100e-03 eta: 21:27:07 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.6332 aux.loss_ce: 0.0085 aux.acc_seg: 99.2365 +04/18 05:21:05 - mmengine - INFO - Iter(train) [ 19500/160000] lr: 8.9072e-03 eta: 21:26:40 time: 0.5513 data_time: 0.0065 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0102 decode.acc_seg: 99.6510 aux.loss_ce: 0.0085 aux.acc_seg: 99.2619 +04/18 05:21:33 - mmengine - INFO - Iter(train) [ 19550/160000] lr: 8.9044e-03 eta: 21:26:13 time: 0.5508 data_time: 0.0071 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0100 decode.acc_seg: 99.7253 aux.loss_ce: 0.0087 aux.acc_seg: 99.3011 +04/18 05:22:00 - mmengine - INFO - Iter(train) [ 19600/160000] lr: 8.9016e-03 eta: 21:25:47 time: 0.5517 data_time: 0.0061 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0107 decode.acc_seg: 99.5919 aux.loss_ce: 0.0083 aux.acc_seg: 99.2030 +04/18 05:22:28 - mmengine - INFO - Iter(train) [ 19650/160000] lr: 8.8987e-03 eta: 21:25:20 time: 0.5516 data_time: 0.0066 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.6017 aux.loss_ce: 0.0079 aux.acc_seg: 99.0779 +04/18 05:22:55 - mmengine - INFO - Iter(train) [ 19700/160000] lr: 8.8959e-03 eta: 21:24:53 time: 0.5512 data_time: 0.0063 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.6183 aux.loss_ce: 0.0083 aux.acc_seg: 99.2425 +04/18 05:23:23 - mmengine - INFO - Iter(train) [ 19750/160000] lr: 8.8931e-03 eta: 21:24:26 time: 0.5523 data_time: 0.0059 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0105 decode.acc_seg: 99.6702 aux.loss_ce: 0.0084 aux.acc_seg: 99.3751 +04/18 05:23:50 - mmengine - INFO - Iter(train) [ 19800/160000] lr: 8.8903e-03 eta: 21:24:00 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0119 decode.acc_seg: 99.6434 aux.loss_ce: 0.0091 aux.acc_seg: 99.2486 +04/18 05:24:18 - mmengine - INFO - Iter(train) [ 19850/160000] lr: 8.8875e-03 eta: 21:23:34 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0103 decode.acc_seg: 99.5049 aux.loss_ce: 0.0083 aux.acc_seg: 99.0289 +04/18 05:24:46 - mmengine - INFO - Iter(train) [ 19900/160000] lr: 8.8846e-03 eta: 21:23:07 time: 0.5499 data_time: 0.0060 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.6104 aux.loss_ce: 0.0085 aux.acc_seg: 99.0854 +04/18 05:25:13 - mmengine - INFO - Iter(train) [ 19950/160000] lr: 8.8818e-03 eta: 21:22:41 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0104 decode.acc_seg: 99.5382 aux.loss_ce: 0.0088 aux.acc_seg: 98.8459 +04/18 05:25:41 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:25:41 - mmengine - INFO - Iter(train) [ 20000/160000] lr: 8.8790e-03 eta: 21:22:14 time: 0.5526 data_time: 0.0070 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.4759 aux.loss_ce: 0.0081 aux.acc_seg: 98.9945 +04/18 05:25:41 - mmengine - INFO - Saving checkpoint at 20000 iterations +04/18 05:25:45 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0464 data_time: 0.0013 memory: 1657 +04/18 05:25:47 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0463 data_time: 0.0013 memory: 1657 +04/18 05:25:50 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0468 data_time: 0.0015 memory: 1657 +04/18 05:25:52 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0458 data_time: 0.0014 memory: 1657 +04/18 05:25:52 - mmengine - INFO - per class results: +04/18 05:25:52 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.05 | 99.56 | 99.52 | 99.49 | 99.56 | +| contrast | 79.18 | 87.62 | 88.38 | 89.15 | 87.62 | ++------------+-------+-------+--------+-----------+--------+ +04/18 05:25:52 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0800 mIoU: 89.1100 mAcc: 93.5900 mFscore: 93.9500 mPrecision: 94.3200 mRecall: 93.5900 data_time: 0.0014 time: 0.0465 +04/18 05:26:20 - mmengine - INFO - Iter(train) [ 20050/160000] lr: 8.8762e-03 eta: 21:21:48 time: 0.5514 data_time: 0.0057 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0120 decode.acc_seg: 99.6273 aux.loss_ce: 0.0090 aux.acc_seg: 99.1736 +04/18 05:26:47 - mmengine - INFO - Iter(train) [ 20100/160000] lr: 8.8734e-03 eta: 21:21:21 time: 0.5521 data_time: 0.0061 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.5410 aux.loss_ce: 0.0083 aux.acc_seg: 99.0590 +04/18 05:27:15 - mmengine - INFO - Iter(train) [ 20150/160000] lr: 8.8705e-03 eta: 21:20:54 time: 0.5500 data_time: 0.0060 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.5425 aux.loss_ce: 0.0086 aux.acc_seg: 98.8522 +04/18 05:27:42 - mmengine - INFO - Iter(train) [ 20200/160000] lr: 8.8677e-03 eta: 21:20:27 time: 0.5511 data_time: 0.0059 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.7218 aux.loss_ce: 0.0082 aux.acc_seg: 99.2821 +04/18 05:28:10 - mmengine - INFO - Iter(train) [ 20250/160000] lr: 8.8649e-03 eta: 21:20:00 time: 0.5521 data_time: 0.0061 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0102 decode.acc_seg: 99.5185 aux.loss_ce: 0.0085 aux.acc_seg: 99.1319 +04/18 05:28:38 - mmengine - INFO - Iter(train) [ 20300/160000] lr: 8.8621e-03 eta: 21:19:34 time: 0.5510 data_time: 0.0057 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.6010 aux.loss_ce: 0.0085 aux.acc_seg: 98.9961 +04/18 05:29:05 - mmengine - INFO - Iter(train) [ 20350/160000] lr: 8.8592e-03 eta: 21:19:07 time: 0.5508 data_time: 0.0064 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.6302 aux.loss_ce: 0.0087 aux.acc_seg: 99.1408 +04/18 05:29:33 - mmengine - INFO - Iter(train) [ 20400/160000] lr: 8.8564e-03 eta: 21:18:40 time: 0.5528 data_time: 0.0057 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0106 decode.acc_seg: 99.6640 aux.loss_ce: 0.0089 aux.acc_seg: 99.3505 +04/18 05:30:00 - mmengine - INFO - Iter(train) [ 20450/160000] lr: 8.8536e-03 eta: 21:18:14 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.5840 aux.loss_ce: 0.0082 aux.acc_seg: 99.1034 +04/18 05:30:28 - mmengine - INFO - Iter(train) [ 20500/160000] lr: 8.8508e-03 eta: 21:17:48 time: 0.5521 data_time: 0.0067 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0098 decode.acc_seg: 99.6780 aux.loss_ce: 0.0083 aux.acc_seg: 99.3616 +04/18 05:30:56 - mmengine - INFO - Iter(train) [ 20550/160000] lr: 8.8480e-03 eta: 21:17:21 time: 0.5506 data_time: 0.0061 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0117 decode.acc_seg: 99.4849 aux.loss_ce: 0.0092 aux.acc_seg: 98.8701 +04/18 05:31:23 - mmengine - INFO - Iter(train) [ 20600/160000] lr: 8.8451e-03 eta: 21:16:54 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0103 decode.acc_seg: 99.5954 aux.loss_ce: 0.0089 aux.acc_seg: 99.0963 +04/18 05:31:51 - mmengine - INFO - Iter(train) [ 20650/160000] lr: 8.8423e-03 eta: 21:16:27 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.7395 aux.loss_ce: 0.0083 aux.acc_seg: 99.3160 +04/18 05:32:18 - mmengine - INFO - Iter(train) [ 20700/160000] lr: 8.8395e-03 eta: 21:16:01 time: 0.5512 data_time: 0.0057 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0122 decode.acc_seg: 99.4198 aux.loss_ce: 0.0093 aux.acc_seg: 98.7784 +04/18 05:32:46 - mmengine - INFO - Iter(train) [ 20750/160000] lr: 8.8367e-03 eta: 21:15:34 time: 0.5497 data_time: 0.0060 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0101 decode.acc_seg: 99.6177 aux.loss_ce: 0.0085 aux.acc_seg: 99.2023 +04/18 05:33:14 - mmengine - INFO - Iter(train) [ 20800/160000] lr: 8.8338e-03 eta: 21:15:07 time: 0.5523 data_time: 0.0072 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0106 decode.acc_seg: 99.5582 aux.loss_ce: 0.0089 aux.acc_seg: 99.0932 +04/18 05:33:41 - mmengine - INFO - Iter(train) [ 20850/160000] lr: 8.8310e-03 eta: 21:14:41 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0095 decode.acc_seg: 99.5618 aux.loss_ce: 0.0078 aux.acc_seg: 99.1218 +04/18 05:34:09 - mmengine - INFO - Iter(train) [ 20900/160000] lr: 8.8282e-03 eta: 21:14:15 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0105 decode.acc_seg: 99.6472 aux.loss_ce: 0.0087 aux.acc_seg: 99.1203 +04/18 05:34:36 - mmengine - INFO - Iter(train) [ 20950/160000] lr: 8.8254e-03 eta: 21:13:48 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0110 decode.acc_seg: 99.6499 aux.loss_ce: 0.0090 aux.acc_seg: 99.1760 +04/18 05:35:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:35:04 - mmengine - INFO - Iter(train) [ 21000/160000] lr: 8.8225e-03 eta: 21:13:21 time: 0.5513 data_time: 0.0058 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0097 decode.acc_seg: 99.5724 aux.loss_ce: 0.0079 aux.acc_seg: 99.1061 +04/18 05:35:32 - mmengine - INFO - Iter(train) [ 21050/160000] lr: 8.8197e-03 eta: 21:12:54 time: 0.5525 data_time: 0.0056 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.5926 aux.loss_ce: 0.0084 aux.acc_seg: 99.2459 +04/18 05:35:59 - mmengine - INFO - Iter(train) [ 21100/160000] lr: 8.8169e-03 eta: 21:12:28 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.5698 aux.loss_ce: 0.0083 aux.acc_seg: 99.0493 +04/18 05:36:27 - mmengine - INFO - Iter(train) [ 21150/160000] lr: 8.8141e-03 eta: 21:12:01 time: 0.5513 data_time: 0.0059 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.5793 aux.loss_ce: 0.0082 aux.acc_seg: 99.1617 +04/18 05:36:55 - mmengine - INFO - Iter(train) [ 21200/160000] lr: 8.8112e-03 eta: 21:11:34 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0090 decode.acc_seg: 99.5918 aux.loss_ce: 0.0078 aux.acc_seg: 99.0768 +04/18 05:37:22 - mmengine - INFO - Iter(train) [ 21250/160000] lr: 8.8084e-03 eta: 21:11:08 time: 0.5513 data_time: 0.0067 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0095 decode.acc_seg: 99.5050 aux.loss_ce: 0.0086 aux.acc_seg: 98.8276 +04/18 05:37:50 - mmengine - INFO - Iter(train) [ 21300/160000] lr: 8.8056e-03 eta: 21:10:41 time: 0.5502 data_time: 0.0057 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.6953 aux.loss_ce: 0.0081 aux.acc_seg: 99.3996 +04/18 05:38:17 - mmengine - INFO - Iter(train) [ 21350/160000] lr: 8.8028e-03 eta: 21:10:14 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.4920 aux.loss_ce: 0.0080 aux.acc_seg: 98.9112 +04/18 05:38:45 - mmengine - INFO - Iter(train) [ 21400/160000] lr: 8.7999e-03 eta: 21:09:47 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0110 decode.acc_seg: 99.5637 aux.loss_ce: 0.0084 aux.acc_seg: 99.2282 +04/18 05:39:13 - mmengine - INFO - Iter(train) [ 21450/160000] lr: 8.7971e-03 eta: 21:09:21 time: 0.5530 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0095 decode.acc_seg: 99.6919 aux.loss_ce: 0.0078 aux.acc_seg: 99.2809 +04/18 05:39:40 - mmengine - INFO - Iter(train) [ 21500/160000] lr: 8.7943e-03 eta: 21:08:54 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0224 decode.loss_ce: 0.0130 decode.acc_seg: 99.5067 aux.loss_ce: 0.0094 aux.acc_seg: 99.1303 +04/18 05:40:08 - mmengine - INFO - Iter(train) [ 21550/160000] lr: 8.7915e-03 eta: 21:08:28 time: 0.5595 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.6182 aux.loss_ce: 0.0080 aux.acc_seg: 99.1472 +04/18 05:40:35 - mmengine - INFO - Iter(train) [ 21600/160000] lr: 8.7886e-03 eta: 21:08:01 time: 0.5515 data_time: 0.0058 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.6891 aux.loss_ce: 0.0086 aux.acc_seg: 99.2013 +04/18 05:41:03 - mmengine - INFO - Iter(train) [ 21650/160000] lr: 8.7858e-03 eta: 21:07:34 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0100 decode.acc_seg: 99.5457 aux.loss_ce: 0.0083 aux.acc_seg: 98.9417 +04/18 05:41:31 - mmengine - INFO - Iter(train) [ 21700/160000] lr: 8.7830e-03 eta: 21:07:08 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.7236 aux.loss_ce: 0.0082 aux.acc_seg: 99.4304 +04/18 05:41:58 - mmengine - INFO - Iter(train) [ 21750/160000] lr: 8.7802e-03 eta: 21:06:42 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.7230 aux.loss_ce: 0.0090 aux.acc_seg: 99.4003 +04/18 05:42:26 - mmengine - INFO - Iter(train) [ 21800/160000] lr: 8.7773e-03 eta: 21:06:15 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.6181 aux.loss_ce: 0.0086 aux.acc_seg: 99.2075 +04/18 05:42:54 - mmengine - INFO - Iter(train) [ 21850/160000] lr: 8.7745e-03 eta: 21:05:49 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.5230 aux.loss_ce: 0.0082 aux.acc_seg: 98.9681 +04/18 05:43:22 - mmengine - INFO - Iter(train) [ 21900/160000] lr: 8.7717e-03 eta: 21:05:23 time: 0.5621 data_time: 0.0071 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.5763 aux.loss_ce: 0.0084 aux.acc_seg: 99.0456 +04/18 05:43:49 - mmengine - INFO - Iter(train) [ 21950/160000] lr: 8.7689e-03 eta: 21:04:57 time: 0.5519 data_time: 0.0071 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0089 decode.acc_seg: 99.6490 aux.loss_ce: 0.0077 aux.acc_seg: 99.1749 +04/18 05:44:17 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:44:17 - mmengine - INFO - Iter(train) [ 22000/160000] lr: 8.7660e-03 eta: 21:04:30 time: 0.5518 data_time: 0.0057 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.6772 aux.loss_ce: 0.0085 aux.acc_seg: 99.2457 +04/18 05:44:45 - mmengine - INFO - Iter(train) [ 22050/160000] lr: 8.7632e-03 eta: 21:04:04 time: 0.5551 data_time: 0.0065 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.6129 aux.loss_ce: 0.0085 aux.acc_seg: 99.1654 +04/18 05:45:12 - mmengine - INFO - Iter(train) [ 22100/160000] lr: 8.7604e-03 eta: 21:03:37 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0099 decode.acc_seg: 99.5454 aux.loss_ce: 0.0083 aux.acc_seg: 99.1231 +04/18 05:45:40 - mmengine - INFO - Iter(train) [ 22150/160000] lr: 8.7576e-03 eta: 21:03:11 time: 0.5527 data_time: 0.0056 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0098 decode.acc_seg: 99.6480 aux.loss_ce: 0.0087 aux.acc_seg: 99.1308 +04/18 05:46:07 - mmengine - INFO - Iter(train) [ 22200/160000] lr: 8.7547e-03 eta: 21:02:45 time: 0.5541 data_time: 0.0061 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5435 aux.loss_ce: 0.0085 aux.acc_seg: 98.9551 +04/18 05:46:35 - mmengine - INFO - Iter(train) [ 22250/160000] lr: 8.7519e-03 eta: 21:02:18 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0098 decode.acc_seg: 99.6762 aux.loss_ce: 0.0080 aux.acc_seg: 99.3376 +04/18 05:47:03 - mmengine - INFO - Iter(train) [ 22300/160000] lr: 8.7491e-03 eta: 21:01:52 time: 0.5527 data_time: 0.0060 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0101 decode.acc_seg: 99.4088 aux.loss_ce: 0.0087 aux.acc_seg: 98.6343 +04/18 05:47:30 - mmengine - INFO - Iter(train) [ 22350/160000] lr: 8.7463e-03 eta: 21:01:25 time: 0.5531 data_time: 0.0059 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.5303 aux.loss_ce: 0.0086 aux.acc_seg: 98.9007 +04/18 05:47:58 - mmengine - INFO - Iter(train) [ 22400/160000] lr: 8.7434e-03 eta: 21:00:59 time: 0.5521 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.7247 aux.loss_ce: 0.0082 aux.acc_seg: 99.3110 +04/18 05:48:26 - mmengine - INFO - Iter(train) [ 22450/160000] lr: 8.7406e-03 eta: 21:00:32 time: 0.5526 data_time: 0.0067 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0103 decode.acc_seg: 99.5754 aux.loss_ce: 0.0089 aux.acc_seg: 98.9209 +04/18 05:48:53 - mmengine - INFO - Iter(train) [ 22500/160000] lr: 8.7378e-03 eta: 21:00:06 time: 0.5520 data_time: 0.0054 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.6193 aux.loss_ce: 0.0084 aux.acc_seg: 99.2662 +04/18 05:49:21 - mmengine - INFO - Iter(train) [ 22550/160000] lr: 8.7350e-03 eta: 20:59:39 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0100 decode.acc_seg: 99.6494 aux.loss_ce: 0.0085 aux.acc_seg: 99.2477 +04/18 05:49:49 - mmengine - INFO - Iter(train) [ 22600/160000] lr: 8.7321e-03 eta: 20:59:13 time: 0.5533 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.4904 aux.loss_ce: 0.0080 aux.acc_seg: 98.8156 +04/18 05:50:16 - mmengine - INFO - Iter(train) [ 22650/160000] lr: 8.7293e-03 eta: 20:58:47 time: 0.5545 data_time: 0.0056 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0102 decode.acc_seg: 99.6898 aux.loss_ce: 0.0089 aux.acc_seg: 99.2451 +04/18 05:50:44 - mmengine - INFO - Iter(train) [ 22700/160000] lr: 8.7265e-03 eta: 20:58:20 time: 0.5535 data_time: 0.0063 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0098 decode.acc_seg: 99.6656 aux.loss_ce: 0.0085 aux.acc_seg: 99.1815 +04/18 05:51:12 - mmengine - INFO - Iter(train) [ 22750/160000] lr: 8.7236e-03 eta: 20:57:53 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0102 decode.acc_seg: 99.6284 aux.loss_ce: 0.0084 aux.acc_seg: 99.1419 +04/18 05:51:39 - mmengine - INFO - Iter(train) [ 22800/160000] lr: 8.7208e-03 eta: 20:57:27 time: 0.5536 data_time: 0.0070 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0122 decode.acc_seg: 99.6552 aux.loss_ce: 0.0096 aux.acc_seg: 99.1392 +04/18 05:52:07 - mmengine - INFO - Iter(train) [ 22850/160000] lr: 8.7180e-03 eta: 20:57:00 time: 0.5536 data_time: 0.0061 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0107 decode.acc_seg: 99.5021 aux.loss_ce: 0.0085 aux.acc_seg: 99.2125 +04/18 05:52:35 - mmengine - INFO - Iter(train) [ 22900/160000] lr: 8.7152e-03 eta: 20:56:33 time: 0.5529 data_time: 0.0061 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.6344 aux.loss_ce: 0.0081 aux.acc_seg: 99.2249 +04/18 05:53:02 - mmengine - INFO - Iter(train) [ 22950/160000] lr: 8.7123e-03 eta: 20:56:07 time: 0.5539 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.5120 aux.loss_ce: 0.0080 aux.acc_seg: 99.1488 +04/18 05:53:30 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:53:30 - mmengine - INFO - Iter(train) [ 23000/160000] lr: 8.7095e-03 eta: 20:55:41 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.5515 aux.loss_ce: 0.0081 aux.acc_seg: 99.0876 +04/18 05:53:58 - mmengine - INFO - Iter(train) [ 23050/160000] lr: 8.7067e-03 eta: 20:55:15 time: 0.5530 data_time: 0.0063 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0098 decode.acc_seg: 99.6166 aux.loss_ce: 0.0083 aux.acc_seg: 99.1988 +04/18 05:54:25 - mmengine - INFO - Iter(train) [ 23100/160000] lr: 8.7038e-03 eta: 20:54:48 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.7205 aux.loss_ce: 0.0082 aux.acc_seg: 99.4087 +04/18 05:54:53 - mmengine - INFO - Iter(train) [ 23150/160000] lr: 8.7010e-03 eta: 20:54:22 time: 0.5532 data_time: 0.0058 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.7180 aux.loss_ce: 0.0082 aux.acc_seg: 99.4081 +04/18 05:55:21 - mmengine - INFO - Iter(train) [ 23200/160000] lr: 8.6982e-03 eta: 20:53:55 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.6326 aux.loss_ce: 0.0084 aux.acc_seg: 99.2283 +04/18 05:55:48 - mmengine 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0.0066 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0088 decode.acc_seg: 99.6012 aux.loss_ce: 0.0077 aux.acc_seg: 99.2838 +04/18 05:59:58 - mmengine - INFO - Iter(train) [ 23700/160000] lr: 8.6699e-03 eta: 20:49:30 time: 0.5640 data_time: 0.0056 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0102 decode.acc_seg: 99.4806 aux.loss_ce: 0.0090 aux.acc_seg: 99.0386 +04/18 06:00:25 - mmengine - INFO - Iter(train) [ 23750/160000] lr: 8.6671e-03 eta: 20:49:04 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6102 aux.loss_ce: 0.0080 aux.acc_seg: 99.1569 +04/18 06:00:53 - mmengine - INFO - Iter(train) [ 23800/160000] lr: 8.6642e-03 eta: 20:48:37 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0103 decode.acc_seg: 99.6158 aux.loss_ce: 0.0086 aux.acc_seg: 99.3257 +04/18 06:01:21 - mmengine - INFO - Iter(train) [ 23850/160000] lr: 8.6614e-03 eta: 20:48:10 time: 0.5531 data_time: 0.0060 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.5465 aux.loss_ce: 0.0086 aux.acc_seg: 99.0709 +04/18 06:01:48 - mmengine - INFO - Iter(train) [ 23900/160000] lr: 8.6586e-03 eta: 20:47:44 time: 0.5531 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.5972 aux.loss_ce: 0.0088 aux.acc_seg: 99.0875 +04/18 06:02:16 - mmengine - INFO - Iter(train) [ 23950/160000] lr: 8.6558e-03 eta: 20:47:17 time: 0.5530 data_time: 0.0058 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0101 decode.acc_seg: 99.3958 aux.loss_ce: 0.0082 aux.acc_seg: 98.8238 +04/18 06:02:44 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:02:44 - mmengine - INFO - Iter(train) [ 24000/160000] lr: 8.6529e-03 eta: 20:46:50 time: 0.5530 data_time: 0.0057 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.6724 aux.loss_ce: 0.0083 aux.acc_seg: 98.9887 +04/18 06:03:11 - mmengine - INFO - Iter(train) [ 24050/160000] lr: 8.6501e-03 eta: 20:46:24 time: 0.5532 data_time: 0.0067 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0130 decode.acc_seg: 99.6555 aux.loss_ce: 0.0093 aux.acc_seg: 99.1264 +04/18 06:03:39 - mmengine - INFO - Iter(train) [ 24100/160000] lr: 8.6473e-03 eta: 20:45:58 time: 0.5545 data_time: 0.0057 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0105 decode.acc_seg: 99.4915 aux.loss_ce: 0.0085 aux.acc_seg: 98.9536 +04/18 06:04:07 - mmengine - INFO - Iter(train) [ 24150/160000] lr: 8.6444e-03 eta: 20:45:32 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0092 decode.acc_seg: 99.6381 aux.loss_ce: 0.0078 aux.acc_seg: 99.0611 +04/18 06:04:34 - mmengine - INFO - Iter(train) [ 24200/160000] lr: 8.6416e-03 eta: 20:45:05 time: 0.5539 data_time: 0.0060 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0119 decode.acc_seg: 99.5026 aux.loss_ce: 0.0090 aux.acc_seg: 98.9678 +04/18 06:05:02 - mmengine - INFO - Iter(train) [ 24250/160000] lr: 8.6388e-03 eta: 20:44:38 time: 0.5525 data_time: 0.0059 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.5783 aux.loss_ce: 0.0078 aux.acc_seg: 99.0633 +04/18 06:05:30 - mmengine - INFO - Iter(train) [ 24300/160000] lr: 8.6359e-03 eta: 20:44:11 time: 0.5530 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0104 decode.acc_seg: 99.5662 aux.loss_ce: 0.0089 aux.acc_seg: 99.2458 +04/18 06:05:57 - mmengine - INFO - Iter(train) [ 24350/160000] lr: 8.6331e-03 eta: 20:43:45 time: 0.5536 data_time: 0.0068 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0108 decode.acc_seg: 99.5570 aux.loss_ce: 0.0090 aux.acc_seg: 99.0466 +04/18 06:06:25 - mmengine - INFO - Iter(train) [ 24400/160000] lr: 8.6303e-03 eta: 20:43:18 time: 0.5525 data_time: 0.0070 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0083 decode.acc_seg: 99.6946 aux.loss_ce: 0.0073 aux.acc_seg: 99.3561 +04/18 06:06:53 - mmengine - INFO - Iter(train) [ 24450/160000] lr: 8.6275e-03 eta: 20:42:51 time: 0.5517 data_time: 0.0069 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0085 decode.acc_seg: 99.6692 aux.loss_ce: 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mmengine - INFO - Iter(train) [ 24700/160000] lr: 8.6133e-03 eta: 20:40:38 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.6115 aux.loss_ce: 0.0082 aux.acc_seg: 99.1424 +04/18 06:09:38 - mmengine - INFO - Iter(train) [ 24750/160000] lr: 8.6105e-03 eta: 20:40:11 time: 0.5536 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.5449 aux.loss_ce: 0.0078 aux.acc_seg: 99.0828 +04/18 06:10:06 - mmengine - INFO - Iter(train) [ 24800/160000] lr: 8.6076e-03 eta: 20:39:45 time: 0.5536 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0091 decode.acc_seg: 99.7059 aux.loss_ce: 0.0076 aux.acc_seg: 99.2213 +04/18 06:10:34 - mmengine - INFO - Iter(train) [ 24850/160000] lr: 8.6048e-03 eta: 20:39:18 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6322 aux.loss_ce: 0.0079 aux.acc_seg: 99.3381 +04/18 06:11:01 - mmengine - INFO - Iter(train) [ 24900/160000] lr: 8.6020e-03 eta: 20:38:51 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6635 aux.loss_ce: 0.0084 aux.acc_seg: 98.9992 +04/18 06:11:29 - mmengine - INFO - Iter(train) [ 24950/160000] lr: 8.5991e-03 eta: 20:38:24 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.5434 aux.loss_ce: 0.0083 aux.acc_seg: 98.9474 +04/18 06:11:57 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:11:57 - mmengine - INFO - Iter(train) [ 25000/160000] lr: 8.5963e-03 eta: 20:37:57 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.5576 aux.loss_ce: 0.0081 aux.acc_seg: 99.0066 +04/18 06:12:24 - mmengine - INFO - Iter(train) [ 25050/160000] lr: 8.5935e-03 eta: 20:37:30 time: 0.5531 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.6518 aux.loss_ce: 0.0082 aux.acc_seg: 99.0782 +04/18 06:12:52 - mmengine - INFO - Iter(train) [ 25100/160000] lr: 8.5906e-03 eta: 20:37:04 time: 0.5525 data_time: 0.0060 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.6090 aux.loss_ce: 0.0083 aux.acc_seg: 99.0969 +04/18 06:13:20 - mmengine - INFO - Iter(train) [ 25150/160000] lr: 8.5878e-03 eta: 20:36:38 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0234 decode.loss_ce: 0.0139 decode.acc_seg: 99.2514 aux.loss_ce: 0.0096 aux.acc_seg: 98.7681 +04/18 06:13:48 - mmengine - INFO - Iter(train) [ 25200/160000] lr: 8.5850e-03 eta: 20:36:11 time: 0.5537 data_time: 0.0062 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.5507 aux.loss_ce: 0.0087 aux.acc_seg: 99.0808 +04/18 06:14:15 - mmengine - INFO - Iter(train) [ 25250/160000] lr: 8.5821e-03 eta: 20:35:45 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0100 decode.acc_seg: 99.5605 aux.loss_ce: 0.0089 aux.acc_seg: 98.8799 +04/18 06:14:43 - mmengine - INFO - Iter(train) [ 25300/160000] lr: 8.5793e-03 eta: 20:35:18 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0119 decode.acc_seg: 99.6689 aux.loss_ce: 0.0096 aux.acc_seg: 99.2425 +04/18 06:15:11 - mmengine - INFO - Iter(train) [ 25350/160000] lr: 8.5765e-03 eta: 20:34:52 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0091 decode.acc_seg: 99.7281 aux.loss_ce: 0.0076 aux.acc_seg: 99.4073 +04/18 06:15:38 - mmengine - INFO - Iter(train) [ 25400/160000] lr: 8.5736e-03 eta: 20:34:25 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.6294 aux.loss_ce: 0.0087 aux.acc_seg: 99.0431 +04/18 06:16:06 - mmengine - INFO - Iter(train) [ 25450/160000] lr: 8.5708e-03 eta: 20:33:58 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0102 decode.acc_seg: 99.6443 aux.loss_ce: 0.0087 aux.acc_seg: 99.1878 +04/18 06:16:34 - mmengine - INFO - Iter(train) [ 25500/160000] lr: 8.5680e-03 eta: 20:33:32 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0103 decode.acc_seg: 99.6306 aux.loss_ce: 0.0089 aux.acc_seg: 99.1586 +04/18 06:17:01 - mmengine - INFO - Iter(train) [ 25550/160000] lr: 8.5651e-03 eta: 20:33:05 time: 0.5539 data_time: 0.0062 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.6760 aux.loss_ce: 0.0085 aux.acc_seg: 99.2848 +04/18 06:17:29 - mmengine - INFO - Iter(train) [ 25600/160000] lr: 8.5623e-03 eta: 20:32:38 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0098 decode.acc_seg: 99.6841 aux.loss_ce: 0.0084 aux.acc_seg: 99.1635 +04/18 06:17:57 - mmengine - INFO - Iter(train) [ 25650/160000] lr: 8.5595e-03 eta: 20:32:12 time: 0.5549 data_time: 0.0061 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0107 decode.acc_seg: 99.5690 aux.loss_ce: 0.0086 aux.acc_seg: 98.9656 +04/18 06:18:24 - mmengine - INFO - Iter(train) [ 25700/160000] lr: 8.5566e-03 eta: 20:31:45 time: 0.5554 data_time: 0.0069 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0107 decode.acc_seg: 99.6641 aux.loss_ce: 0.0086 aux.acc_seg: 99.2069 +04/18 06:18:52 - mmengine - INFO - Iter(train) [ 25750/160000] lr: 8.5538e-03 eta: 20:31:19 time: 0.5546 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.5224 aux.loss_ce: 0.0083 aux.acc_seg: 98.9250 +04/18 06:19:20 - mmengine - INFO - Iter(train) [ 25800/160000] lr: 8.5510e-03 eta: 20:30:52 time: 0.5558 data_time: 0.0058 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.5760 aux.loss_ce: 0.0084 aux.acc_seg: 98.9920 +04/18 06:19:48 - mmengine - INFO - Iter(train) [ 25850/160000] lr: 8.5481e-03 eta: 20:30:26 time: 0.5557 data_time: 0.0065 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0100 decode.acc_seg: 99.6197 aux.loss_ce: 0.0085 aux.acc_seg: 99.2267 +04/18 06:20:15 - mmengine - INFO - Iter(train) [ 25900/160000] lr: 8.5453e-03 eta: 20:30:00 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.7183 aux.loss_ce: 0.0083 aux.acc_seg: 99.2774 +04/18 06:20:43 - mmengine - INFO - Iter(train) [ 25950/160000] lr: 8.5425e-03 eta: 20:29:33 time: 0.5543 data_time: 0.0056 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0098 decode.acc_seg: 99.6219 aux.loss_ce: 0.0090 aux.acc_seg: 99.0978 +04/18 06:21:11 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:21:11 - mmengine - INFO - Iter(train) [ 26000/160000] lr: 8.5396e-03 eta: 20:29:07 time: 0.5536 data_time: 0.0067 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0102 decode.acc_seg: 99.6169 aux.loss_ce: 0.0086 aux.acc_seg: 99.1028 +04/18 06:21:39 - mmengine - INFO - Iter(train) [ 26050/160000] lr: 8.5368e-03 eta: 20:28:40 time: 0.5528 data_time: 0.0073 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0100 decode.acc_seg: 99.6387 aux.loss_ce: 0.0083 aux.acc_seg: 99.1585 +04/18 06:22:06 - mmengine - INFO - Iter(train) [ 26100/160000] lr: 8.5340e-03 eta: 20:28:13 time: 0.5531 data_time: 0.0060 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.6833 aux.loss_ce: 0.0084 aux.acc_seg: 99.2471 +04/18 06:22:34 - mmengine - INFO - Iter(train) [ 26150/160000] lr: 8.5311e-03 eta: 20:27:47 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0096 decode.acc_seg: 99.6755 aux.loss_ce: 0.0092 aux.acc_seg: 98.9544 +04/18 06:23:02 - mmengine - INFO - Iter(train) [ 26200/160000] lr: 8.5283e-03 eta: 20:27:20 time: 0.5529 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6167 aux.loss_ce: 0.0081 aux.acc_seg: 99.0394 +04/18 06:23:29 - mmengine - INFO - Iter(train) [ 26250/160000] lr: 8.5255e-03 eta: 20:26:54 time: 0.5622 data_time: 0.0070 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.5710 aux.loss_ce: 0.0085 aux.acc_seg: 98.9657 +04/18 06:23:57 - mmengine - INFO - Iter(train) [ 26300/160000] lr: 8.5226e-03 eta: 20:26:28 time: 0.5558 data_time: 0.0070 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0097 decode.acc_seg: 99.5872 aux.loss_ce: 0.0086 aux.acc_seg: 99.1512 +04/18 06:24:25 - mmengine - INFO - Iter(train) [ 26350/160000] lr: 8.5198e-03 eta: 20:26:01 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0091 decode.acc_seg: 99.5209 aux.loss_ce: 0.0078 aux.acc_seg: 99.1055 +04/18 06:24:53 - mmengine - INFO - Iter(train) [ 26400/160000] lr: 8.5170e-03 eta: 20:25:35 time: 0.5538 data_time: 0.0059 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0089 decode.acc_seg: 99.6354 aux.loss_ce: 0.0077 aux.acc_seg: 99.2619 +04/18 06:25:20 - mmengine - INFO - Iter(train) [ 26450/160000] lr: 8.5141e-03 eta: 20:25:08 time: 0.5545 data_time: 0.0063 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0082 decode.acc_seg: 99.6066 aux.loss_ce: 0.0073 aux.acc_seg: 99.0895 +04/18 06:25:48 - mmengine - INFO - Iter(train) [ 26500/160000] lr: 8.5113e-03 eta: 20:24:42 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6694 aux.loss_ce: 0.0080 aux.acc_seg: 99.1294 +04/18 06:26:16 - mmengine - INFO - Iter(train) [ 26550/160000] lr: 8.5085e-03 eta: 20:24:15 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0089 decode.acc_seg: 99.6081 aux.loss_ce: 0.0078 aux.acc_seg: 99.1234 +04/18 06:26:44 - mmengine - INFO - Iter(train) [ 26600/160000] lr: 8.5056e-03 eta: 20:23:49 time: 0.5548 data_time: 0.0067 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.5845 aux.loss_ce: 0.0081 aux.acc_seg: 99.1042 +04/18 06:27:11 - mmengine - INFO - Iter(train) [ 26650/160000] lr: 8.5028e-03 eta: 20:23:23 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.5620 aux.loss_ce: 0.0082 aux.acc_seg: 99.1332 +04/18 06:27:39 - mmengine - INFO - Iter(train) [ 26700/160000] lr: 8.5000e-03 eta: 20:22:56 time: 0.5545 data_time: 0.0066 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0105 decode.acc_seg: 99.4620 aux.loss_ce: 0.0089 aux.acc_seg: 98.9699 +04/18 06:28:07 - mmengine - INFO - Iter(train) 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time: 0.5555 data_time: 0.0068 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0095 decode.acc_seg: 99.6460 aux.loss_ce: 0.0086 aux.acc_seg: 99.1088 +04/18 06:30:26 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:30:26 - mmengine - INFO - Iter(train) [ 27000/160000] lr: 8.4829e-03 eta: 20:20:18 time: 0.5542 data_time: 0.0074 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.6244 aux.loss_ce: 0.0086 aux.acc_seg: 99.0749 +04/18 06:30:53 - mmengine - INFO - Iter(train) [ 27050/160000] lr: 8.4801e-03 eta: 20:19:51 time: 0.5542 data_time: 0.0070 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6664 aux.loss_ce: 0.0082 aux.acc_seg: 99.1160 +04/18 06:31:21 - mmengine - INFO - Iter(train) [ 27100/160000] lr: 8.4773e-03 eta: 20:19:25 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0099 decode.acc_seg: 99.4372 aux.loss_ce: 0.0082 aux.acc_seg: 98.8480 +04/18 06:31:49 - mmengine - INFO - Iter(train) [ 27150/160000] lr: 8.4744e-03 eta: 20:18:58 time: 0.5541 data_time: 0.0056 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.7565 aux.loss_ce: 0.0076 aux.acc_seg: 99.4385 +04/18 06:32:17 - mmengine - INFO - Iter(train) [ 27200/160000] lr: 8.4716e-03 eta: 20:18:32 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.5640 aux.loss_ce: 0.0081 aux.acc_seg: 98.9797 +04/18 06:32:44 - mmengine - INFO - Iter(train) [ 27250/160000] lr: 8.4688e-03 eta: 20:18:05 time: 0.5553 data_time: 0.0062 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0101 decode.acc_seg: 99.5849 aux.loss_ce: 0.0082 aux.acc_seg: 99.2076 +04/18 06:33:12 - mmengine - INFO - Iter(train) [ 27300/160000] lr: 8.4659e-03 eta: 20:17:39 time: 0.5558 data_time: 0.0065 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.4665 aux.loss_ce: 0.0082 aux.acc_seg: 99.2051 +04/18 06:33:40 - mmengine - INFO - Iter(train) [ 27350/160000] lr: 8.4631e-03 eta: 20:17:13 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.5988 aux.loss_ce: 0.0082 aux.acc_seg: 98.9725 +04/18 06:34:08 - mmengine - INFO - Iter(train) [ 27400/160000] lr: 8.4602e-03 eta: 20:16:47 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0094 decode.acc_seg: 99.6114 aux.loss_ce: 0.0078 aux.acc_seg: 99.2443 +04/18 06:34:35 - mmengine - INFO - Iter(train) [ 27450/160000] lr: 8.4574e-03 eta: 20:16:20 time: 0.5562 data_time: 0.0062 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0091 decode.acc_seg: 99.7229 aux.loss_ce: 0.0081 aux.acc_seg: 99.2241 +04/18 06:35:03 - mmengine - INFO - Iter(train) [ 27500/160000] lr: 8.4546e-03 eta: 20:15:54 time: 0.5559 data_time: 0.0057 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.5677 aux.loss_ce: 0.0083 aux.acc_seg: 99.1057 +04/18 06:35:31 - mmengine - INFO - Iter(train) [ 27550/160000] lr: 8.4517e-03 eta: 20:15:27 time: 0.5555 data_time: 0.0067 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.7557 aux.loss_ce: 0.0083 aux.acc_seg: 99.3876 +04/18 06:35:59 - mmengine - INFO - Iter(train) [ 27600/160000] lr: 8.4489e-03 eta: 20:15:01 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0094 decode.acc_seg: 99.7061 aux.loss_ce: 0.0083 aux.acc_seg: 99.2312 +04/18 06:36:26 - mmengine - INFO - Iter(train) [ 27650/160000] lr: 8.4461e-03 eta: 20:14:34 time: 0.5547 data_time: 0.0068 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0101 decode.acc_seg: 99.4464 aux.loss_ce: 0.0090 aux.acc_seg: 98.5444 +04/18 06:36:54 - mmengine - INFO - Iter(train) [ 27700/160000] lr: 8.4432e-03 eta: 20:14:08 time: 0.5551 data_time: 0.0062 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.6277 aux.loss_ce: 0.0081 aux.acc_seg: 99.2190 +04/18 06:37:22 - mmengine - INFO - Iter(train) [ 27750/160000] lr: 8.4404e-03 eta: 20:13:41 time: 0.5538 data_time: 0.0061 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.5312 aux.loss_ce: 0.0086 aux.acc_seg: 98.8728 +04/18 06:37:50 - mmengine - INFO - Iter(train) [ 27800/160000] lr: 8.4375e-03 eta: 20:13:14 time: 0.5547 data_time: 0.0061 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0098 decode.acc_seg: 99.6878 aux.loss_ce: 0.0086 aux.acc_seg: 99.2142 +04/18 06:38:17 - mmengine - INFO - Iter(train) [ 27850/160000] lr: 8.4347e-03 eta: 20:12:48 time: 0.5538 data_time: 0.0063 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0124 decode.acc_seg: 99.5290 aux.loss_ce: 0.0093 aux.acc_seg: 99.2845 +04/18 06:38:45 - mmengine - INFO - Iter(train) [ 27900/160000] lr: 8.4319e-03 eta: 20:12:21 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0087 decode.acc_seg: 99.6517 aux.loss_ce: 0.0077 aux.acc_seg: 99.2413 +04/18 06:39:13 - mmengine - INFO - Iter(train) [ 27950/160000] lr: 8.4290e-03 eta: 20:11:54 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.5343 aux.loss_ce: 0.0086 aux.acc_seg: 99.1739 +04/18 06:39:40 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:39:40 - mmengine - INFO - Iter(train) [ 28000/160000] lr: 8.4262e-03 eta: 20:11:27 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.5786 aux.loss_ce: 0.0079 aux.acc_seg: 99.1202 +04/18 06:40:08 - mmengine - INFO - Iter(train) [ 28050/160000] lr: 8.4233e-03 eta: 20:11:01 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.7616 aux.loss_ce: 0.0082 aux.acc_seg: 99.3387 +04/18 06:40:36 - mmengine - INFO - Iter(train) [ 28100/160000] lr: 8.4205e-03 eta: 20:10:34 time: 0.5538 data_time: 0.0060 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0098 decode.acc_seg: 99.4782 aux.loss_ce: 0.0085 aux.acc_seg: 99.0201 +04/18 06:41:03 - mmengine - INFO - Iter(train) [ 28150/160000] lr: 8.4177e-03 eta: 20:10:07 time: 0.5535 data_time: 0.0063 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0097 decode.acc_seg: 99.6655 aux.loss_ce: 0.0083 aux.acc_seg: 99.2508 +04/18 06:41:31 - mmengine - INFO - Iter(train) [ 28200/160000] lr: 8.4148e-03 eta: 20:09:40 time: 0.5526 data_time: 0.0061 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.4870 aux.loss_ce: 0.0088 aux.acc_seg: 99.1462 +04/18 06:41:59 - mmengine - INFO - Iter(train) [ 28250/160000] lr: 8.4120e-03 eta: 20:09:13 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0094 decode.acc_seg: 99.5909 aux.loss_ce: 0.0082 aux.acc_seg: 99.0626 +04/18 06:42:27 - mmengine - INFO - Iter(train) [ 28300/160000] lr: 8.4092e-03 eta: 20:08:47 time: 0.5556 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0093 decode.acc_seg: 99.6944 aux.loss_ce: 0.0080 aux.acc_seg: 99.3399 +04/18 06:42:54 - mmengine - INFO - Iter(train) [ 28350/160000] lr: 8.4063e-03 eta: 20:08:20 time: 0.5536 data_time: 0.0059 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0093 decode.acc_seg: 99.7280 aux.loss_ce: 0.0083 aux.acc_seg: 99.4580 +04/18 06:43:22 - mmengine - INFO - Iter(train) [ 28400/160000] lr: 8.4035e-03 eta: 20:07:54 time: 0.5551 data_time: 0.0069 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0089 decode.acc_seg: 99.6412 aux.loss_ce: 0.0078 aux.acc_seg: 99.1178 +04/18 06:43:50 - mmengine - INFO - Iter(train) [ 28450/160000] lr: 8.4006e-03 eta: 20:07:28 time: 0.5532 data_time: 0.0059 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.5481 aux.loss_ce: 0.0085 aux.acc_seg: 99.0436 +04/18 06:44:18 - mmengine - INFO - Iter(train) [ 28500/160000] lr: 8.3978e-03 eta: 20:07:02 time: 0.5532 data_time: 0.0066 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0099 decode.acc_seg: 99.5734 aux.loss_ce: 0.0088 aux.acc_seg: 99.0263 +04/18 06:44:46 - mmengine - INFO - Iter(train) [ 28550/160000] lr: 8.3950e-03 eta: 20:06:35 time: 0.5546 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0091 decode.acc_seg: 99.6369 aux.loss_ce: 0.0081 aux.acc_seg: 99.3268 +04/18 06:45:13 - mmengine - INFO - Iter(train) [ 28600/160000] lr: 8.3921e-03 eta: 20:06:08 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0095 decode.acc_seg: 99.5734 aux.loss_ce: 0.0081 aux.acc_seg: 98.9872 +04/18 06:45:41 - mmengine - INFO - Iter(train) [ 28650/160000] lr: 8.3893e-03 eta: 20:05:42 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.7258 aux.loss_ce: 0.0082 aux.acc_seg: 99.3052 +04/18 06:46:09 - mmengine - INFO - Iter(train) [ 28700/160000] lr: 8.3864e-03 eta: 20:05:15 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0080 decode.acc_seg: 99.5783 aux.loss_ce: 0.0076 aux.acc_seg: 99.2734 +04/18 06:46:36 - mmengine - INFO - Iter(train) [ 28750/160000] lr: 8.3836e-03 eta: 20:04:48 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0103 decode.acc_seg: 99.5633 aux.loss_ce: 0.0090 aux.acc_seg: 99.0564 +04/18 06:47:04 - mmengine - INFO - Iter(train) [ 28800/160000] lr: 8.3808e-03 eta: 20:04:21 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0109 decode.acc_seg: 99.5342 aux.loss_ce: 0.0088 aux.acc_seg: 99.1306 +04/18 06:47:32 - mmengine - INFO - Iter(train) [ 28850/160000] lr: 8.3779e-03 eta: 20:03:54 time: 0.5549 data_time: 0.0061 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.6271 aux.loss_ce: 0.0084 aux.acc_seg: 99.1467 +04/18 06:48:00 - mmengine - INFO - Iter(train) [ 28900/160000] lr: 8.3751e-03 eta: 20:03:27 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0097 decode.acc_seg: 99.5158 aux.loss_ce: 0.0085 aux.acc_seg: 98.9241 +04/18 06:48:27 - mmengine - INFO - Iter(train) [ 28950/160000] lr: 8.3722e-03 eta: 20:03:01 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0080 decode.acc_seg: 99.6894 aux.loss_ce: 0.0070 aux.acc_seg: 99.1712 +04/18 06:48:55 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:48:55 - mmengine - INFO - Iter(train) [ 29000/160000] lr: 8.3694e-03 eta: 20:02:34 time: 0.5523 data_time: 0.0068 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.5784 aux.loss_ce: 0.0083 aux.acc_seg: 99.1201 +04/18 06:49:23 - mmengine - INFO - Iter(train) [ 29050/160000] lr: 8.3666e-03 eta: 20:02:07 time: 0.5536 data_time: 0.0062 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.6448 aux.loss_ce: 0.0083 aux.acc_seg: 99.1606 +04/18 06:49:50 - mmengine - INFO - Iter(train) [ 29100/160000] lr: 8.3637e-03 eta: 20:01:40 time: 0.5520 data_time: 0.0066 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.5769 aux.loss_ce: 0.0080 aux.acc_seg: 99.1119 +04/18 06:50:18 - mmengine - INFO - Iter(train) [ 29150/160000] lr: 8.3609e-03 eta: 20:01:13 time: 0.5529 data_time: 0.0066 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6526 aux.loss_ce: 0.0078 aux.acc_seg: 99.1117 +04/18 06:50:46 - mmengine - INFO - Iter(train) [ 29200/160000] lr: 8.3580e-03 eta: 20:00:46 time: 0.5541 data_time: 0.0058 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.6968 aux.loss_ce: 0.0073 aux.acc_seg: 99.3466 +04/18 06:51:13 - mmengine - INFO - Iter(train) [ 29250/160000] lr: 8.3552e-03 eta: 20:00:20 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0095 decode.acc_seg: 99.6399 aux.loss_ce: 0.0080 aux.acc_seg: 99.0940 +04/18 06:51:41 - mmengine - INFO - Iter(train) [ 29300/160000] lr: 8.3524e-03 eta: 19:59:53 time: 0.5537 data_time: 0.0070 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6080 aux.loss_ce: 0.0080 aux.acc_seg: 98.9253 +04/18 06:52:09 - mmengine - INFO - Iter(train) [ 29350/160000] lr: 8.3495e-03 eta: 19:59:26 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0095 decode.acc_seg: 99.6967 aux.loss_ce: 0.0081 aux.acc_seg: 99.2575 +04/18 06:52:36 - mmengine - INFO - Iter(train) [ 29400/160000] lr: 8.3467e-03 eta: 19:58:58 time: 0.5512 data_time: 0.0062 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.6092 aux.loss_ce: 0.0085 aux.acc_seg: 99.0195 +04/18 06:53:04 - mmengine - INFO - Iter(train) [ 29450/160000] lr: 8.3438e-03 eta: 19:58:32 time: 0.5548 data_time: 0.0058 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0088 decode.acc_seg: 99.6880 aux.loss_ce: 0.0078 aux.acc_seg: 99.3057 +04/18 06:53:32 - mmengine - INFO - Iter(train) [ 29500/160000] lr: 8.3410e-03 eta: 19:58:05 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.7135 aux.loss_ce: 0.0081 aux.acc_seg: 99.4377 +04/18 06:54:00 - mmengine - INFO - Iter(train) [ 29550/160000] lr: 8.3381e-03 eta: 19:57:38 time: 0.5548 data_time: 0.0067 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.5240 aux.loss_ce: 0.0082 aux.acc_seg: 99.0030 +04/18 06:54:27 - mmengine - INFO - Iter(train) [ 29600/160000] lr: 8.3353e-03 eta: 19:57:11 time: 0.5544 data_time: 0.0068 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0097 decode.acc_seg: 99.7782 aux.loss_ce: 0.0088 aux.acc_seg: 99.4354 +04/18 06:54:55 - mmengine - INFO - Iter(train) [ 29650/160000] lr: 8.3325e-03 eta: 19:56:45 time: 0.5536 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.7258 aux.loss_ce: 0.0082 aux.acc_seg: 99.3110 +04/18 06:55:23 - mmengine - INFO - Iter(train) [ 29700/160000] lr: 8.3296e-03 eta: 19:56:18 time: 0.5535 data_time: 0.0059 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.3491 aux.loss_ce: 0.0084 aux.acc_seg: 98.8539 +04/18 06:55:50 - mmengine - INFO - Iter(train) [ 29750/160000] lr: 8.3268e-03 eta: 19:55:51 time: 0.5531 data_time: 0.0061 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0089 decode.acc_seg: 99.5722 aux.loss_ce: 0.0078 aux.acc_seg: 99.1220 +04/18 06:56:18 - mmengine - INFO - Iter(train) [ 29800/160000] lr: 8.3239e-03 eta: 19:55:24 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0103 decode.acc_seg: 99.6004 aux.loss_ce: 0.0080 aux.acc_seg: 99.2584 +04/18 06:56:46 - mmengine - INFO - Iter(train) [ 29850/160000] lr: 8.3211e-03 eta: 19:54:57 time: 0.5534 data_time: 0.0060 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0108 decode.acc_seg: 99.6187 aux.loss_ce: 0.0089 aux.acc_seg: 99.1012 +04/18 06:57:13 - mmengine - INFO - Iter(train) [ 29900/160000] lr: 8.3182e-03 eta: 19:54:30 time: 0.5540 data_time: 0.0059 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6822 aux.loss_ce: 0.0084 aux.acc_seg: 99.1999 +04/18 06:57:41 - mmengine - INFO - Iter(train) [ 29950/160000] lr: 8.3154e-03 eta: 19:54:03 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0084 decode.acc_seg: 99.6488 aux.loss_ce: 0.0076 aux.acc_seg: 99.1222 +04/18 06:58:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:58:09 - mmengine - INFO - Iter(train) [ 30000/160000] lr: 8.3126e-03 eta: 19:53:37 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0098 decode.acc_seg: 99.6754 aux.loss_ce: 0.0083 aux.acc_seg: 99.3052 +04/18 06:58:09 - mmengine - INFO - Saving checkpoint at 30000 iterations +04/18 06:58:13 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:06 time: 0.0462 data_time: 0.0014 memory: 1657 +04/18 06:58:15 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0475 data_time: 0.0014 memory: 1657 +04/18 06:58:18 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0463 data_time: 0.0014 memory: 1657 +04/18 06:58:20 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0457 data_time: 0.0013 memory: 1657 +04/18 06:58:20 - mmengine - INFO - per class results: +04/18 06:58:20 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.47 | 99.53 | 99.58 | 99.47 | +| contrast | 79.8 | 89.93 | 88.77 | 87.63 | 89.93 | ++------------+-------+-------+--------+-----------+--------+ +04/18 06:58:20 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0900 mIoU: 89.4300 mAcc: 94.7000 mFscore: 94.1500 mPrecision: 93.6000 mRecall: 94.7000 data_time: 0.0015 time: 0.0464 +04/18 06:58:48 - mmengine - INFO - Iter(train) [ 30050/160000] lr: 8.3097e-03 eta: 19:53:11 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.6932 aux.loss_ce: 0.0085 aux.acc_seg: 99.2504 +04/18 06:59:16 - mmengine - INFO - Iter(train) [ 30100/160000] lr: 8.3069e-03 eta: 19:52:44 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.6229 aux.loss_ce: 0.0082 aux.acc_seg: 99.0599 +04/18 06:59:43 - mmengine - INFO - Iter(train) [ 30150/160000] lr: 8.3040e-03 eta: 19:52:17 time: 0.5537 data_time: 0.0066 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0097 decode.acc_seg: 99.7074 aux.loss_ce: 0.0081 aux.acc_seg: 99.2696 +04/18 07:00:11 - mmengine - INFO - Iter(train) [ 30200/160000] lr: 8.3012e-03 eta: 19:51:50 time: 0.5526 data_time: 0.0061 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0101 decode.acc_seg: 99.6636 aux.loss_ce: 0.0083 aux.acc_seg: 99.2550 +04/18 07:00:39 - mmengine - INFO - Iter(train) [ 30250/160000] lr: 8.2983e-03 eta: 19:51:24 time: 0.5532 data_time: 0.0063 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0098 decode.acc_seg: 99.6634 aux.loss_ce: 0.0083 aux.acc_seg: 99.2562 +04/18 07:01:06 - mmengine - INFO - Iter(train) [ 30300/160000] lr: 8.2955e-03 eta: 19:50:57 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.6403 aux.loss_ce: 0.0079 aux.acc_seg: 99.2498 +04/18 07:01:34 - mmengine - INFO - Iter(train) [ 30350/160000] lr: 8.2927e-03 eta: 19:50:30 time: 0.5537 data_time: 0.0066 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0094 decode.acc_seg: 99.6887 aux.loss_ce: 0.0086 aux.acc_seg: 99.3160 +04/18 07:02:02 - mmengine - INFO - Iter(train) [ 30400/160000] lr: 8.2898e-03 eta: 19:50:03 time: 0.5544 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.6344 aux.loss_ce: 0.0077 aux.acc_seg: 99.1701 +04/18 07:02:29 - mmengine - INFO - Iter(train) [ 30450/160000] lr: 8.2870e-03 eta: 19:49:36 time: 0.5528 data_time: 0.0057 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.6485 aux.loss_ce: 0.0084 aux.acc_seg: 99.3006 +04/18 07:02:57 - mmengine - INFO - Iter(train) [ 30500/160000] lr: 8.2841e-03 eta: 19:49:10 time: 0.5638 data_time: 0.0068 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.6327 aux.loss_ce: 0.0081 aux.acc_seg: 99.2662 +04/18 07:03:25 - mmengine - INFO - Iter(train) [ 30550/160000] lr: 8.2813e-03 eta: 19:48:43 time: 0.5538 data_time: 0.0063 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0099 decode.acc_seg: 99.6390 aux.loss_ce: 0.0081 aux.acc_seg: 99.1124 +04/18 07:03:53 - mmengine - INFO - Iter(train) [ 30600/160000] lr: 8.2784e-03 eta: 19:48:16 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0093 decode.acc_seg: 99.6712 aux.loss_ce: 0.0087 aux.acc_seg: 99.2329 +04/18 07:04:20 - mmengine - INFO - Iter(train) [ 30650/160000] lr: 8.2756e-03 eta: 19:47:49 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0106 decode.acc_seg: 99.4262 aux.loss_ce: 0.0093 aux.acc_seg: 99.0763 +04/18 07:04:48 - mmengine - INFO - Iter(train) [ 30700/160000] lr: 8.2728e-03 eta: 19:47:22 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0103 decode.acc_seg: 99.4432 aux.loss_ce: 0.0094 aux.acc_seg: 98.5764 +04/18 07:05:16 - mmengine - INFO - Iter(train) [ 30750/160000] lr: 8.2699e-03 eta: 19:46:55 time: 0.5530 data_time: 0.0077 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0096 decode.acc_seg: 99.5905 aux.loss_ce: 0.0081 aux.acc_seg: 99.1282 +04/18 07:05:43 - mmengine - INFO - Iter(train) [ 30800/160000] lr: 8.2671e-03 eta: 19:46:28 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6326 aux.loss_ce: 0.0080 aux.acc_seg: 99.1488 +04/18 07:06:11 - mmengine - INFO - Iter(train) [ 30850/160000] lr: 8.2642e-03 eta: 19:46:01 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0082 decode.acc_seg: 99.5945 aux.loss_ce: 0.0074 aux.acc_seg: 99.1685 +04/18 07:06:39 - mmengine - INFO - Iter(train) [ 30900/160000] lr: 8.2614e-03 eta: 19:45:34 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0095 decode.acc_seg: 99.7173 aux.loss_ce: 0.0081 aux.acc_seg: 99.3325 +04/18 07:07:07 - mmengine - INFO - Iter(train) [ 30950/160000] lr: 8.2585e-03 eta: 19:45:07 time: 0.5541 data_time: 0.0059 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.6530 aux.loss_ce: 0.0080 aux.acc_seg: 99.1598 +04/18 07:07:34 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:07:34 - mmengine - INFO - Iter(train) [ 31000/160000] lr: 8.2557e-03 eta: 19:44:40 time: 0.5538 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.6911 aux.loss_ce: 0.0084 aux.acc_seg: 99.1821 +04/18 07:08:02 - mmengine - INFO - Iter(train) [ 31050/160000] lr: 8.2528e-03 eta: 19:44:14 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0102 decode.acc_seg: 99.5356 aux.loss_ce: 0.0087 aux.acc_seg: 99.1998 +04/18 07:08:30 - mmengine - INFO - Iter(train) [ 31100/160000] lr: 8.2500e-03 eta: 19:43:47 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0089 decode.acc_seg: 99.5303 aux.loss_ce: 0.0076 aux.acc_seg: 99.0437 +04/18 07:08:58 - mmengine - INFO - Iter(train) [ 31150/160000] lr: 8.2471e-03 eta: 19:43:20 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.7702 aux.loss_ce: 0.0083 aux.acc_seg: 99.3576 +04/18 07:09:25 - mmengine - INFO - Iter(train) [ 31200/160000] lr: 8.2443e-03 eta: 19:42:53 time: 0.5550 data_time: 0.0062 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0094 decode.acc_seg: 99.6806 aux.loss_ce: 0.0087 aux.acc_seg: 99.3296 +04/18 07:09:53 - mmengine - INFO - Iter(train) [ 31250/160000] lr: 8.2415e-03 eta: 19:42:26 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.5939 aux.loss_ce: 0.0082 aux.acc_seg: 99.2475 +04/18 07:10:21 - mmengine - INFO - Iter(train) [ 31300/160000] lr: 8.2386e-03 eta: 19:42:00 time: 0.5541 data_time: 0.0060 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0090 decode.acc_seg: 99.6605 aux.loss_ce: 0.0077 aux.acc_seg: 99.1757 +04/18 07:10:48 - mmengine - INFO - Iter(train) [ 31350/160000] lr: 8.2358e-03 eta: 19:41:33 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6571 aux.loss_ce: 0.0082 aux.acc_seg: 99.0974 +04/18 07:11:16 - mmengine - INFO - Iter(train) [ 31400/160000] lr: 8.2329e-03 eta: 19:41:06 time: 0.5538 data_time: 0.0069 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6479 aux.loss_ce: 0.0081 aux.acc_seg: 98.9635 +04/18 07:11:44 - mmengine - INFO - Iter(train) [ 31450/160000] lr: 8.2301e-03 eta: 19:40:39 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6761 aux.loss_ce: 0.0079 aux.acc_seg: 99.3599 +04/18 07:12:12 - mmengine - INFO - Iter(train) [ 31500/160000] lr: 8.2272e-03 eta: 19:40:12 time: 0.5548 data_time: 0.0062 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.7297 aux.loss_ce: 0.0081 aux.acc_seg: 99.3115 +04/18 07:12:39 - mmengine - INFO - Iter(train) [ 31550/160000] lr: 8.2244e-03 eta: 19:39:45 time: 0.5535 data_time: 0.0068 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.6945 aux.loss_ce: 0.0082 aux.acc_seg: 99.3441 +04/18 07:13:07 - mmengine - INFO - Iter(train) [ 31600/160000] lr: 8.2215e-03 eta: 19:39:19 time: 0.5717 data_time: 0.0058 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.5731 aux.loss_ce: 0.0080 aux.acc_seg: 99.0817 +04/18 07:13:35 - mmengine - INFO - Iter(train) [ 31650/160000] lr: 8.2187e-03 eta: 19:38:52 time: 0.5542 data_time: 0.0068 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0095 decode.acc_seg: 99.6840 aux.loss_ce: 0.0085 aux.acc_seg: 99.1050 +04/18 07:14:03 - mmengine - INFO - Iter(train) [ 31700/160000] lr: 8.2158e-03 eta: 19:38:25 time: 0.5544 data_time: 0.0059 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6806 aux.loss_ce: 0.0077 aux.acc_seg: 99.2702 +04/18 07:14:30 - mmengine - INFO - Iter(train) [ 31750/160000] lr: 8.2130e-03 eta: 19:37:58 time: 0.5534 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.6800 aux.loss_ce: 0.0078 aux.acc_seg: 99.0911 +04/18 07:14:58 - mmengine - INFO - Iter(train) [ 31800/160000] lr: 8.2101e-03 eta: 19:37:31 time: 0.5537 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.6931 aux.loss_ce: 0.0073 aux.acc_seg: 99.2317 +04/18 07:15:26 - mmengine - INFO - Iter(train) [ 31850/160000] lr: 8.2073e-03 eta: 19:37:04 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0108 decode.acc_seg: 99.6500 aux.loss_ce: 0.0087 aux.acc_seg: 99.3303 +04/18 07:15:53 - mmengine - INFO - Iter(train) [ 31900/160000] lr: 8.2045e-03 eta: 19:36:37 time: 0.5531 data_time: 0.0062 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.6323 aux.loss_ce: 0.0084 aux.acc_seg: 99.2227 +04/18 07:16:21 - mmengine - INFO - Iter(train) [ 31950/160000] lr: 8.2016e-03 eta: 19:36:10 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0088 decode.acc_seg: 99.6806 aux.loss_ce: 0.0079 aux.acc_seg: 99.1612 +04/18 07:16:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:16:49 - mmengine - INFO - Iter(train) [ 32000/160000] lr: 8.1988e-03 eta: 19:35:43 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.6737 aux.loss_ce: 0.0075 aux.acc_seg: 99.1329 +04/18 07:17:16 - mmengine - INFO - Iter(train) [ 32050/160000] lr: 8.1959e-03 eta: 19:35:16 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6179 aux.loss_ce: 0.0078 aux.acc_seg: 99.2297 +04/18 07:17:44 - mmengine - INFO - Iter(train) [ 32100/160000] lr: 8.1931e-03 eta: 19:34:49 time: 0.5544 data_time: 0.0057 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0085 decode.acc_seg: 99.6239 aux.loss_ce: 0.0071 aux.acc_seg: 99.1700 +04/18 07:18:12 - mmengine - INFO - Iter(train) [ 32150/160000] lr: 8.1902e-03 eta: 19:34:22 time: 0.5551 data_time: 0.0058 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0103 decode.acc_seg: 99.5864 aux.loss_ce: 0.0083 aux.acc_seg: 99.2222 +04/18 07:18:40 - mmengine - INFO - Iter(train) [ 32200/160000] lr: 8.1874e-03 eta: 19:33:55 time: 0.5546 data_time: 0.0065 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0108 decode.acc_seg: 99.4581 aux.loss_ce: 0.0091 aux.acc_seg: 98.7620 +04/18 07:19:07 - mmengine - INFO - Iter(train) [ 32250/160000] lr: 8.1845e-03 eta: 19:33:28 time: 0.5551 data_time: 0.0069 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0093 decode.acc_seg: 99.5605 aux.loss_ce: 0.0079 aux.acc_seg: 99.0670 +04/18 07:19:35 - mmengine - INFO - Iter(train) [ 32300/160000] lr: 8.1817e-03 eta: 19:33:02 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0103 decode.acc_seg: 99.6199 aux.loss_ce: 0.0088 aux.acc_seg: 99.1172 +04/18 07:20:03 - mmengine - INFO - Iter(train) [ 32350/160000] lr: 8.1788e-03 eta: 19:32:35 time: 0.5535 data_time: 0.0066 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6283 aux.loss_ce: 0.0080 aux.acc_seg: 99.1041 +04/18 07:20:31 - mmengine - INFO - Iter(train) [ 32400/160000] lr: 8.1760e-03 eta: 19:32:08 time: 0.5545 data_time: 0.0071 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0117 decode.acc_seg: 99.4681 aux.loss_ce: 0.0096 aux.acc_seg: 98.9940 +04/18 07:20:58 - mmengine - INFO - Iter(train) [ 32450/160000] lr: 8.1731e-03 eta: 19:31:41 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.6467 aux.loss_ce: 0.0077 aux.acc_seg: 99.0788 +04/18 07:21:26 - mmengine - INFO - Iter(train) [ 32500/160000] lr: 8.1703e-03 eta: 19:31:14 time: 0.5550 data_time: 0.0063 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0120 decode.acc_seg: 99.3916 aux.loss_ce: 0.0087 aux.acc_seg: 99.2522 +04/18 07:21:54 - mmengine - INFO - Iter(train) [ 32550/160000] lr: 8.1674e-03 eta: 19:30:47 time: 0.5538 data_time: 0.0069 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.6855 aux.loss_ce: 0.0083 aux.acc_seg: 99.2477 +04/18 07:22:21 - mmengine - INFO - Iter(train) [ 32600/160000] lr: 8.1646e-03 eta: 19:30:20 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0085 decode.acc_seg: 99.6750 aux.loss_ce: 0.0075 aux.acc_seg: 99.2866 +04/18 07:22:49 - mmengine - INFO - Iter(train) [ 32650/160000] lr: 8.1617e-03 eta: 19:29:53 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0100 decode.acc_seg: 99.4937 aux.loss_ce: 0.0086 aux.acc_seg: 98.9815 +04/18 07:23:17 - mmengine - INFO - Iter(train) [ 32700/160000] lr: 8.1589e-03 eta: 19:29:27 time: 0.5529 data_time: 0.0061 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.6044 aux.loss_ce: 0.0082 aux.acc_seg: 99.1128 +04/18 07:23:45 - mmengine - INFO - Iter(train) [ 32750/160000] lr: 8.1560e-03 eta: 19:29:00 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6833 aux.loss_ce: 0.0080 aux.acc_seg: 99.1595 +04/18 07:24:13 - mmengine - INFO - Iter(train) [ 32800/160000] lr: 8.1532e-03 eta: 19:28:33 time: 0.5539 data_time: 0.0059 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0085 decode.acc_seg: 99.6199 aux.loss_ce: 0.0076 aux.acc_seg: 99.2431 +04/18 07:24:40 - mmengine - INFO - Iter(train) [ 32850/160000] lr: 8.1503e-03 eta: 19:28:06 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.6441 aux.loss_ce: 0.0082 aux.acc_seg: 99.1406 +04/18 07:25:08 - mmengine - INFO - Iter(train) [ 32900/160000] lr: 8.1475e-03 eta: 19:27:39 time: 0.5537 data_time: 0.0060 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0099 decode.acc_seg: 99.4864 aux.loss_ce: 0.0086 aux.acc_seg: 99.0148 +04/18 07:25:36 - mmengine - INFO - Iter(train) [ 32950/160000] lr: 8.1446e-03 eta: 19:27:12 time: 0.5534 data_time: 0.0058 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0082 decode.acc_seg: 99.5735 aux.loss_ce: 0.0071 aux.acc_seg: 99.2791 +04/18 07:26:03 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:26:03 - mmengine - INFO - Iter(train) [ 33000/160000] lr: 8.1418e-03 eta: 19:26:45 time: 0.5538 data_time: 0.0066 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0082 decode.acc_seg: 99.6367 aux.loss_ce: 0.0076 aux.acc_seg: 99.0615 +04/18 07:26:31 - mmengine - INFO - Iter(train) [ 33050/160000] lr: 8.1389e-03 eta: 19:26:17 time: 0.5534 data_time: 0.0056 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0083 decode.acc_seg: 99.4673 aux.loss_ce: 0.0074 aux.acc_seg: 99.0739 +04/18 07:26:59 - mmengine - INFO - Iter(train) [ 33100/160000] lr: 8.1361e-03 eta: 19:25:50 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0089 decode.acc_seg: 99.6405 aux.loss_ce: 0.0085 aux.acc_seg: 99.1338 +04/18 07:27:26 - mmengine - INFO - Iter(train) [ 33150/160000] lr: 8.1332e-03 eta: 19:25:23 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.5017 aux.loss_ce: 0.0087 aux.acc_seg: 99.0799 +04/18 07:27:54 - mmengine - INFO - Iter(train) [ 33200/160000] lr: 8.1304e-03 eta: 19:24:56 time: 0.5534 data_time: 0.0071 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.5747 aux.loss_ce: 0.0081 aux.acc_seg: 99.1100 +04/18 07:28:22 - mmengine - INFO - Iter(train) [ 33250/160000] lr: 8.1275e-03 eta: 19:24:29 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0085 decode.acc_seg: 99.7563 aux.loss_ce: 0.0076 aux.acc_seg: 99.3136 +04/18 07:28:49 - mmengine - INFO - Iter(train) [ 33300/160000] lr: 8.1247e-03 eta: 19:24:02 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0083 decode.acc_seg: 99.7503 aux.loss_ce: 0.0079 aux.acc_seg: 99.3759 +04/18 07:29:17 - mmengine - INFO - Iter(train) [ 33350/160000] lr: 8.1218e-03 eta: 19:23:35 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6707 aux.loss_ce: 0.0079 aux.acc_seg: 99.0377 +04/18 07:29:45 - mmengine - INFO - Iter(train) [ 33400/160000] lr: 8.1190e-03 eta: 19:23:08 time: 0.5534 data_time: 0.0069 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.5971 aux.loss_ce: 0.0075 aux.acc_seg: 98.8136 +04/18 07:30:12 - mmengine - INFO - Iter(train) [ 33450/160000] lr: 8.1161e-03 eta: 19:22:41 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6467 aux.loss_ce: 0.0084 aux.acc_seg: 99.2325 +04/18 07:30:40 - mmengine - INFO - Iter(train) [ 33500/160000] lr: 8.1133e-03 eta: 19:22:14 time: 0.5527 data_time: 0.0063 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0085 decode.acc_seg: 99.6486 aux.loss_ce: 0.0077 aux.acc_seg: 99.1936 +04/18 07:31:08 - mmengine - INFO - Iter(train) [ 33550/160000] lr: 8.1104e-03 eta: 19:21:46 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0087 decode.acc_seg: 99.6886 aux.loss_ce: 0.0075 aux.acc_seg: 99.3878 +04/18 07:31:35 - mmengine - INFO - Iter(train) [ 33600/160000] lr: 8.1076e-03 eta: 19:21:19 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.6814 aux.loss_ce: 0.0073 aux.acc_seg: 99.2887 +04/18 07:32:03 - mmengine - INFO - Iter(train) [ 33650/160000] lr: 8.1047e-03 eta: 19:20:52 time: 0.5537 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6370 aux.loss_ce: 0.0081 aux.acc_seg: 99.2607 +04/18 07:32:31 - mmengine - INFO - Iter(train) [ 33700/160000] lr: 8.1019e-03 eta: 19:20:25 time: 0.5624 data_time: 0.0066 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.6526 aux.loss_ce: 0.0081 aux.acc_seg: 99.2915 +04/18 07:32:59 - mmengine - INFO - Iter(train) [ 33750/160000] lr: 8.0990e-03 eta: 19:19:58 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0102 decode.acc_seg: 99.6324 aux.loss_ce: 0.0085 aux.acc_seg: 99.3615 +04/18 07:33:26 - mmengine - INFO - Iter(train) [ 33800/160000] lr: 8.0962e-03 eta: 19:19:31 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0093 decode.acc_seg: 99.6343 aux.loss_ce: 0.0080 aux.acc_seg: 99.2024 +04/18 07:33:54 - mmengine - INFO - Iter(train) [ 33850/160000] lr: 8.0933e-03 eta: 19:19:04 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0102 decode.acc_seg: 99.7551 aux.loss_ce: 0.0084 aux.acc_seg: 99.4288 +04/18 07:34:22 - mmengine - INFO - Iter(train) [ 33900/160000] lr: 8.0905e-03 eta: 19:18:37 time: 0.5521 data_time: 0.0061 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.5685 aux.loss_ce: 0.0079 aux.acc_seg: 99.0632 +04/18 07:34:49 - mmengine - INFO - Iter(train) [ 33950/160000] lr: 8.0876e-03 eta: 19:18:09 time: 0.5523 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.6301 aux.loss_ce: 0.0083 aux.acc_seg: 99.0987 +04/18 07:35:17 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:35:17 - mmengine - INFO - Iter(train) [ 34000/160000] lr: 8.0848e-03 eta: 19:17:42 time: 0.5528 data_time: 0.0066 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0098 decode.acc_seg: 99.6192 aux.loss_ce: 0.0085 aux.acc_seg: 98.8934 +04/18 07:35:45 - mmengine - INFO - Iter(train) [ 34050/160000] lr: 8.0819e-03 eta: 19:17:15 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0127 decode.acc_seg: 99.1635 aux.loss_ce: 0.0090 aux.acc_seg: 98.8476 +04/18 07:36:12 - mmengine - INFO - Iter(train) [ 34100/160000] lr: 8.0791e-03 eta: 19:16:47 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0124 decode.acc_seg: 99.4928 aux.loss_ce: 0.0092 aux.acc_seg: 99.1305 +04/18 07:36:40 - mmengine - INFO - Iter(train) [ 34150/160000] lr: 8.0762e-03 eta: 19:16:20 time: 0.5552 data_time: 0.0062 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.6648 aux.loss_ce: 0.0082 aux.acc_seg: 99.2626 +04/18 07:37:08 - mmengine - INFO - Iter(train) [ 34200/160000] lr: 8.0734e-03 eta: 19:15:53 time: 0.5536 data_time: 0.0062 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.6975 aux.loss_ce: 0.0083 aux.acc_seg: 99.3648 +04/18 07:37:35 - mmengine - INFO - Iter(train) [ 34250/160000] lr: 8.0705e-03 eta: 19:15:26 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.6301 aux.loss_ce: 0.0086 aux.acc_seg: 99.1169 +04/18 07:38:03 - mmengine - INFO - Iter(train) [ 34300/160000] lr: 8.0677e-03 eta: 19:14:59 time: 0.5546 data_time: 0.0063 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0108 decode.acc_seg: 99.7034 aux.loss_ce: 0.0093 aux.acc_seg: 99.1738 +04/18 07:38:31 - mmengine - INFO - Iter(train) [ 34350/160000] lr: 8.0648e-03 eta: 19:14:33 time: 0.5553 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.6557 aux.loss_ce: 0.0080 aux.acc_seg: 99.2526 +04/18 07:38:59 - mmengine - INFO - Iter(train) [ 34400/160000] lr: 8.0620e-03 eta: 19:14:06 time: 0.5548 data_time: 0.0067 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0100 decode.acc_seg: 99.4610 aux.loss_ce: 0.0086 aux.acc_seg: 98.9637 +04/18 07:39:26 - mmengine - INFO - Iter(train) [ 34450/160000] lr: 8.0591e-03 eta: 19:13:39 time: 0.5554 data_time: 0.0057 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0098 decode.acc_seg: 99.6735 aux.loss_ce: 0.0088 aux.acc_seg: 99.1503 +04/18 07:39:54 - mmengine - INFO - Iter(train) [ 34500/160000] lr: 8.0563e-03 eta: 19:13:12 time: 0.5548 data_time: 0.0063 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0098 decode.acc_seg: 99.5838 aux.loss_ce: 0.0085 aux.acc_seg: 99.2102 +04/18 07:40:22 - mmengine - INFO - Iter(train) [ 34550/160000] lr: 8.0534e-03 eta: 19:12:45 time: 0.5552 data_time: 0.0060 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.5566 aux.loss_ce: 0.0075 aux.acc_seg: 99.0582 +04/18 07:40:50 - mmengine - INFO - Iter(train) [ 34600/160000] lr: 8.0506e-03 eta: 19:12:19 time: 0.5556 data_time: 0.0070 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6641 aux.loss_ce: 0.0078 aux.acc_seg: 99.1263 +04/18 07:41:18 - mmengine - INFO - Iter(train) [ 34650/160000] lr: 8.0477e-03 eta: 19:11:52 time: 0.5545 data_time: 0.0063 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0100 decode.acc_seg: 99.5957 aux.loss_ce: 0.0086 aux.acc_seg: 99.1197 +04/18 07:41:45 - mmengine - INFO - Iter(train) [ 34700/160000] lr: 8.0448e-03 eta: 19:11:25 time: 0.5540 data_time: 0.0060 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.5585 aux.loss_ce: 0.0078 aux.acc_seg: 99.0187 +04/18 07:42:13 - mmengine - INFO - Iter(train) [ 34750/160000] lr: 8.0420e-03 eta: 19:10:58 time: 0.5547 data_time: 0.0058 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.6587 aux.loss_ce: 0.0081 aux.acc_seg: 99.2609 +04/18 07:42:41 - mmengine - INFO - Iter(train) [ 34800/160000] lr: 8.0391e-03 eta: 19:10:32 time: 0.5561 data_time: 0.0071 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0086 decode.acc_seg: 99.6828 aux.loss_ce: 0.0076 aux.acc_seg: 99.2761 +04/18 07:43:09 - mmengine - INFO - Iter(train) [ 34850/160000] lr: 8.0363e-03 eta: 19:10:05 time: 0.5544 data_time: 0.0061 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0100 decode.acc_seg: 99.6472 aux.loss_ce: 0.0093 aux.acc_seg: 99.2065 +04/18 07:43:37 - mmengine - INFO - Iter(train) [ 34900/160000] lr: 8.0334e-03 eta: 19:09:38 time: 0.5544 data_time: 0.0059 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.7450 aux.loss_ce: 0.0079 aux.acc_seg: 99.3112 +04/18 07:44:04 - mmengine - INFO - Iter(train) [ 34950/160000] lr: 8.0306e-03 eta: 19:09:11 time: 0.5557 data_time: 0.0063 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.6366 aux.loss_ce: 0.0074 aux.acc_seg: 99.2044 +04/18 07:44:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:44:32 - mmengine - INFO - Iter(train) [ 35000/160000] lr: 8.0277e-03 eta: 19:08:45 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6536 aux.loss_ce: 0.0081 aux.acc_seg: 98.9813 +04/18 07:45:00 - mmengine - INFO - Iter(train) [ 35050/160000] lr: 8.0249e-03 eta: 19:08:18 time: 0.5559 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.5843 aux.loss_ce: 0.0075 aux.acc_seg: 99.2393 +04/18 07:45:28 - mmengine - INFO - Iter(train) [ 35100/160000] lr: 8.0220e-03 eta: 19:07:51 time: 0.5569 data_time: 0.0060 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6353 aux.loss_ce: 0.0078 aux.acc_seg: 99.0710 +04/18 07:45:56 - mmengine - INFO - Iter(train) [ 35150/160000] lr: 8.0192e-03 eta: 19:07:24 time: 0.5550 data_time: 0.0060 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0102 decode.acc_seg: 99.5936 aux.loss_ce: 0.0088 aux.acc_seg: 98.9582 +04/18 07:46:23 - mmengine - INFO - Iter(train) [ 35200/160000] lr: 8.0163e-03 eta: 19:06:57 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.5790 aux.loss_ce: 0.0083 aux.acc_seg: 99.0090 +04/18 07:46:51 - mmengine - INFO - Iter(train) [ 35250/160000] lr: 8.0135e-03 eta: 19:06:30 time: 0.5546 data_time: 0.0064 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0109 decode.acc_seg: 99.6765 aux.loss_ce: 0.0088 aux.acc_seg: 99.2990 +04/18 07:47:19 - mmengine - INFO - Iter(train) [ 35300/160000] lr: 8.0106e-03 eta: 19:06:04 time: 0.5561 data_time: 0.0062 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6948 aux.loss_ce: 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mmengine - INFO - Iter(train) [ 35550/160000] lr: 7.9963e-03 eta: 19:03:49 time: 0.5559 data_time: 0.0073 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0096 decode.acc_seg: 99.7468 aux.loss_ce: 0.0082 aux.acc_seg: 99.4647 +04/18 07:50:06 - mmengine - INFO - Iter(train) [ 35600/160000] lr: 7.9935e-03 eta: 19:03:22 time: 0.5548 data_time: 0.0062 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0093 decode.acc_seg: 99.5835 aux.loss_ce: 0.0089 aux.acc_seg: 99.1254 +04/18 07:50:33 - mmengine - INFO - Iter(train) [ 35650/160000] lr: 7.9906e-03 eta: 19:02:55 time: 0.5532 data_time: 0.0058 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6332 aux.loss_ce: 0.0079 aux.acc_seg: 99.2542 +04/18 07:51:01 - mmengine - INFO - Iter(train) [ 35700/160000] lr: 7.9878e-03 eta: 19:02:29 time: 0.5561 data_time: 0.0058 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0093 decode.acc_seg: 99.5657 aux.loss_ce: 0.0077 aux.acc_seg: 99.2030 +04/18 07:51:29 - mmengine - INFO - Iter(train) [ 35750/160000] lr: 7.9849e-03 eta: 19:02:02 time: 0.5547 data_time: 0.0067 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0099 decode.acc_seg: 99.7052 aux.loss_ce: 0.0087 aux.acc_seg: 99.2721 +04/18 07:51:57 - mmengine - INFO - Iter(train) [ 35800/160000] lr: 7.9820e-03 eta: 19:01:35 time: 0.5581 data_time: 0.0071 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.5608 aux.loss_ce: 0.0080 aux.acc_seg: 99.0271 +04/18 07:52:25 - mmengine - INFO - Iter(train) [ 35850/160000] lr: 7.9792e-03 eta: 19:01:08 time: 0.5644 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0095 decode.acc_seg: 99.5785 aux.loss_ce: 0.0086 aux.acc_seg: 98.9521 +04/18 07:52:52 - mmengine - INFO - Iter(train) [ 35900/160000] lr: 7.9763e-03 eta: 19:00:42 time: 0.5556 data_time: 0.0064 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.5304 aux.loss_ce: 0.0083 aux.acc_seg: 99.1608 +04/18 07:53:20 - mmengine - INFO - Iter(train) [ 35950/160000] lr: 7.9735e-03 eta: 19:00:15 time: 0.5558 data_time: 0.0069 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0095 decode.acc_seg: 99.6070 aux.loss_ce: 0.0082 aux.acc_seg: 99.2854 +04/18 07:53:48 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:53:48 - mmengine - INFO - Iter(train) [ 36000/160000] lr: 7.9706e-03 eta: 18:59:49 time: 0.5550 data_time: 0.0065 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0098 decode.acc_seg: 99.7172 aux.loss_ce: 0.0082 aux.acc_seg: 99.4210 +04/18 07:54:16 - mmengine - INFO - Iter(train) [ 36050/160000] lr: 7.9678e-03 eta: 18:59:22 time: 0.5554 data_time: 0.0066 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.4981 aux.loss_ce: 0.0089 aux.acc_seg: 98.9285 +04/18 07:54:44 - mmengine - INFO - Iter(train) [ 36100/160000] lr: 7.9649e-03 eta: 18:58:55 time: 0.5553 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0088 decode.acc_seg: 99.6424 aux.loss_ce: 0.0079 aux.acc_seg: 99.1971 +04/18 07:55:12 - mmengine - INFO - Iter(train) [ 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0.5554 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6775 aux.loss_ce: 0.0079 aux.acc_seg: 99.3327 +04/18 07:57:31 - mmengine - INFO - Iter(train) [ 36400/160000] lr: 7.9478e-03 eta: 18:56:14 time: 0.5567 data_time: 0.0060 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0089 decode.acc_seg: 99.5826 aux.loss_ce: 0.0078 aux.acc_seg: 99.0872 +04/18 07:57:58 - mmengine - INFO - Iter(train) [ 36450/160000] lr: 7.9449e-03 eta: 18:55:47 time: 0.5553 data_time: 0.0065 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6811 aux.loss_ce: 0.0082 aux.acc_seg: 99.2513 +04/18 07:58:26 - mmengine - INFO - Iter(train) [ 36500/160000] lr: 7.9421e-03 eta: 18:55:20 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.7375 aux.loss_ce: 0.0079 aux.acc_seg: 99.3763 +04/18 07:58:54 - mmengine - INFO - Iter(train) [ 36550/160000] lr: 7.9392e-03 eta: 18:54:53 time: 0.5528 data_time: 0.0059 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0084 decode.acc_seg: 99.7747 aux.loss_ce: 0.0076 aux.acc_seg: 99.4019 +04/18 07:59:22 - mmengine - INFO - Iter(train) [ 36600/160000] lr: 7.9363e-03 eta: 18:54:26 time: 0.5534 data_time: 0.0068 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6769 aux.loss_ce: 0.0080 aux.acc_seg: 99.1664 +04/18 07:59:49 - mmengine - INFO - Iter(train) [ 36650/160000] lr: 7.9335e-03 eta: 18:53:59 time: 0.5552 data_time: 0.0063 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.5338 aux.loss_ce: 0.0084 aux.acc_seg: 99.1665 +04/18 08:00:17 - mmengine - INFO - Iter(train) [ 36700/160000] lr: 7.9306e-03 eta: 18:53:32 time: 0.5555 data_time: 0.0063 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0109 decode.acc_seg: 99.6617 aux.loss_ce: 0.0095 aux.acc_seg: 99.2718 +04/18 08:00:45 - mmengine - INFO - Iter(train) [ 36750/160000] lr: 7.9278e-03 eta: 18:53:05 time: 0.5557 data_time: 0.0060 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.6224 aux.loss_ce: 0.0086 aux.acc_seg: 99.1976 +04/18 08:01:13 - mmengine - INFO - Iter(train) [ 36800/160000] lr: 7.9249e-03 eta: 18:52:38 time: 0.5542 data_time: 0.0060 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.7107 aux.loss_ce: 0.0086 aux.acc_seg: 99.1987 +04/18 08:01:40 - mmengine - INFO - Iter(train) [ 36850/160000] lr: 7.9221e-03 eta: 18:52:11 time: 0.5554 data_time: 0.0064 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0084 decode.acc_seg: 99.7684 aux.loss_ce: 0.0073 aux.acc_seg: 99.3653 +04/18 08:02:08 - mmengine - INFO - Iter(train) [ 36900/160000] lr: 7.9192e-03 eta: 18:51:44 time: 0.5554 data_time: 0.0061 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0097 decode.acc_seg: 99.6262 aux.loss_ce: 0.0084 aux.acc_seg: 99.1335 +04/18 08:02:36 - mmengine - INFO - Iter(train) [ 36950/160000] lr: 7.9163e-03 eta: 18:51:17 time: 0.5564 data_time: 0.0059 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6028 aux.loss_ce: 0.0081 aux.acc_seg: 99.0002 +04/18 08:03:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:03:04 - mmengine - INFO - Iter(train) [ 37000/160000] lr: 7.9135e-03 eta: 18:50:51 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0089 decode.acc_seg: 99.7021 aux.loss_ce: 0.0077 aux.acc_seg: 99.1801 +04/18 08:03:32 - mmengine - INFO - Iter(train) [ 37050/160000] lr: 7.9106e-03 eta: 18:50:24 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0106 decode.acc_seg: 99.6867 aux.loss_ce: 0.0082 aux.acc_seg: 99.2112 +04/18 08:03:59 - mmengine - INFO - Iter(train) [ 37100/160000] lr: 7.9078e-03 eta: 18:49:57 time: 0.5555 data_time: 0.0062 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.6346 aux.loss_ce: 0.0084 aux.acc_seg: 99.0377 +04/18 08:04:27 - mmengine - INFO - Iter(train) [ 37150/160000] lr: 7.9049e-03 eta: 18:49:30 time: 0.5554 data_time: 0.0061 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.5802 aux.loss_ce: 0.0084 aux.acc_seg: 99.2209 +04/18 08:04:55 - mmengine - INFO - Iter(train) [ 37200/160000] lr: 7.9020e-03 eta: 18:49:03 time: 0.5567 data_time: 0.0067 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0093 decode.acc_seg: 99.6939 aux.loss_ce: 0.0080 aux.acc_seg: 99.2533 +04/18 08:05:23 - mmengine - INFO - Iter(train) [ 37250/160000] lr: 7.8992e-03 eta: 18:48:36 time: 0.5557 data_time: 0.0063 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.6069 aux.loss_ce: 0.0086 aux.acc_seg: 98.9867 +04/18 08:05:50 - mmengine - INFO - Iter(train) [ 37300/160000] lr: 7.8963e-03 eta: 18:48:09 time: 0.5555 data_time: 0.0070 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.4745 aux.loss_ce: 0.0082 aux.acc_seg: 99.0009 +04/18 08:06:18 - mmengine - INFO - Iter(train) [ 37350/160000] lr: 7.8935e-03 eta: 18:47:42 time: 0.5573 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.7446 aux.loss_ce: 0.0077 aux.acc_seg: 99.3535 +04/18 08:06:46 - mmengine - INFO - Iter(train) [ 37400/160000] lr: 7.8906e-03 eta: 18:47:15 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0084 decode.acc_seg: 99.6960 aux.loss_ce: 0.0073 aux.acc_seg: 99.2872 +04/18 08:07:14 - mmengine - INFO - Iter(train) [ 37450/160000] lr: 7.8877e-03 eta: 18:46:48 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6699 aux.loss_ce: 0.0084 aux.acc_seg: 99.2398 +04/18 08:07:41 - mmengine - INFO - Iter(train) [ 37500/160000] lr: 7.8849e-03 eta: 18:46:20 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.6231 aux.loss_ce: 0.0084 aux.acc_seg: 99.0577 +04/18 08:08:09 - mmengine - INFO - Iter(train) [ 37550/160000] lr: 7.8820e-03 eta: 18:45:53 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.7350 aux.loss_ce: 0.0084 aux.acc_seg: 99.3443 +04/18 08:08:37 - mmengine - INFO - Iter(train) [ 37600/160000] lr: 7.8792e-03 eta: 18:45:26 time: 0.5532 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0079 decode.acc_seg: 99.6883 aux.loss_ce: 0.0071 aux.acc_seg: 99.3621 +04/18 08:09:05 - mmengine - INFO - Iter(train) [ 37650/160000] lr: 7.8763e-03 eta: 18:44:59 time: 0.5553 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.6845 aux.loss_ce: 0.0074 aux.acc_seg: 98.9697 +04/18 08:09:32 - mmengine - INFO - Iter(train) [ 37700/160000] lr: 7.8734e-03 eta: 18:44:32 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0112 decode.acc_seg: 99.4838 aux.loss_ce: 0.0088 aux.acc_seg: 99.1152 +04/18 08:10:00 - mmengine - INFO - Iter(train) [ 37750/160000] lr: 7.8706e-03 eta: 18:44:05 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0108 decode.acc_seg: 99.6160 aux.loss_ce: 0.0098 aux.acc_seg: 99.0363 +04/18 08:10:28 - mmengine - INFO - Iter(train) [ 37800/160000] lr: 7.8677e-03 eta: 18:43:38 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0105 decode.acc_seg: 99.2393 aux.loss_ce: 0.0085 aux.acc_seg: 98.8298 +04/18 08:10:56 - mmengine - INFO - Iter(train) [ 37850/160000] lr: 7.8649e-03 eta: 18:43:11 time: 0.5554 data_time: 0.0060 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0090 decode.acc_seg: 99.6859 aux.loss_ce: 0.0080 aux.acc_seg: 99.2764 +04/18 08:11:23 - mmengine - INFO - Iter(train) [ 37900/160000] lr: 7.8620e-03 eta: 18:42:44 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0108 decode.acc_seg: 99.5868 aux.loss_ce: 0.0087 aux.acc_seg: 99.1085 +04/18 08:11:51 - mmengine - INFO - Iter(train) [ 37950/160000] lr: 7.8591e-03 eta: 18:42:16 time: 0.5546 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0101 decode.acc_seg: 99.6949 aux.loss_ce: 0.0086 aux.acc_seg: 99.2368 +04/18 08:12:19 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:12:19 - mmengine - INFO - Iter(train) [ 38000/160000] lr: 7.8563e-03 eta: 18:41:49 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6777 aux.loss_ce: 0.0079 aux.acc_seg: 99.1671 +04/18 08:12:47 - mmengine - INFO - Iter(train) [ 38050/160000] lr: 7.8534e-03 eta: 18:41:23 time: 0.5631 data_time: 0.0062 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0073 decode.acc_seg: 99.5980 aux.loss_ce: 0.0070 aux.acc_seg: 99.1495 +04/18 08:13:14 - mmengine - INFO - Iter(train) [ 38100/160000] lr: 7.8506e-03 eta: 18:40:56 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.6484 aux.loss_ce: 0.0083 aux.acc_seg: 98.9731 +04/18 08:13:42 - mmengine - INFO - Iter(train) [ 38150/160000] lr: 7.8477e-03 eta: 18:40:28 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6856 aux.loss_ce: 0.0077 aux.acc_seg: 99.0777 +04/18 08:14:10 - mmengine - INFO - Iter(train) [ 38200/160000] lr: 7.8448e-03 eta: 18:40:01 time: 0.5559 data_time: 0.0057 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6652 aux.loss_ce: 0.0077 aux.acc_seg: 99.2684 +04/18 08:14:38 - mmengine - INFO - Iter(train) [ 38250/160000] lr: 7.8420e-03 eta: 18:39:34 time: 0.5537 data_time: 0.0071 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0107 decode.acc_seg: 99.4609 aux.loss_ce: 0.0092 aux.acc_seg: 98.8550 +04/18 08:15:05 - mmengine - INFO - Iter(train) [ 38300/160000] lr: 7.8391e-03 eta: 18:39:07 time: 0.5519 data_time: 0.0060 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.5614 aux.loss_ce: 0.0081 aux.acc_seg: 99.1699 +04/18 08:15:33 - mmengine - INFO - Iter(train) [ 38350/160000] lr: 7.8363e-03 eta: 18:38:40 time: 0.5548 data_time: 0.0061 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.5927 aux.loss_ce: 0.0080 aux.acc_seg: 99.0862 +04/18 08:16:01 - mmengine - INFO - Iter(train) [ 38400/160000] lr: 7.8334e-03 eta: 18:38:13 time: 0.5544 data_time: 0.0064 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.7965 aux.loss_ce: 0.0082 aux.acc_seg: 99.5151 +04/18 08:16:28 - mmengine - INFO - Iter(train) [ 38450/160000] lr: 7.8305e-03 eta: 18:37:45 time: 0.5540 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0083 decode.acc_seg: 99.6346 aux.loss_ce: 0.0073 aux.acc_seg: 99.1762 +04/18 08:16:56 - mmengine - INFO - Iter(train) [ 38500/160000] lr: 7.8277e-03 eta: 18:37:18 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0096 decode.acc_seg: 99.5115 aux.loss_ce: 0.0090 aux.acc_seg: 98.9328 +04/18 08:17:24 - mmengine - INFO - Iter(train) [ 38550/160000] lr: 7.8248e-03 eta: 18:36:51 time: 0.5544 data_time: 0.0064 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.6286 aux.loss_ce: 0.0083 aux.acc_seg: 99.1783 +04/18 08:17:52 - mmengine - INFO - Iter(train) [ 38600/160000] lr: 7.8219e-03 eta: 18:36:24 time: 0.5539 data_time: 0.0059 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6795 aux.loss_ce: 0.0081 aux.acc_seg: 99.2602 +04/18 08:18:19 - mmengine - INFO - Iter(train) [ 38650/160000] lr: 7.8191e-03 eta: 18:35:57 time: 0.5525 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.5373 aux.loss_ce: 0.0087 aux.acc_seg: 98.7154 +04/18 08:18:47 - mmengine - INFO - Iter(train) [ 38700/160000] lr: 7.8162e-03 eta: 18:35:29 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0084 decode.acc_seg: 99.6734 aux.loss_ce: 0.0082 aux.acc_seg: 99.0753 +04/18 08:19:15 - mmengine - INFO - Iter(train) [ 38750/160000] lr: 7.8134e-03 eta: 18:35:02 time: 0.5524 data_time: 0.0062 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6527 aux.loss_ce: 0.0077 aux.acc_seg: 99.2320 +04/18 08:19:42 - mmengine - INFO - Iter(train) [ 38800/160000] lr: 7.8105e-03 eta: 18:34:35 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.5894 aux.loss_ce: 0.0083 aux.acc_seg: 99.0193 +04/18 08:20:10 - mmengine - INFO - Iter(train) [ 38850/160000] lr: 7.8076e-03 eta: 18:34:07 time: 0.5535 data_time: 0.0067 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0089 decode.acc_seg: 99.6009 aux.loss_ce: 0.0087 aux.acc_seg: 98.7836 +04/18 08:20:38 - mmengine - INFO - Iter(train) [ 38900/160000] lr: 7.8048e-03 eta: 18:33:40 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.6873 aux.loss_ce: 0.0080 aux.acc_seg: 99.2844 +04/18 08:21:05 - mmengine - INFO - Iter(train) [ 38950/160000] lr: 7.8019e-03 eta: 18:33:13 time: 0.5547 data_time: 0.0062 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0102 decode.acc_seg: 99.6688 aux.loss_ce: 0.0087 aux.acc_seg: 99.1081 +04/18 08:21:33 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:21:33 - mmengine - INFO - Iter(train) [ 39000/160000] lr: 7.7990e-03 eta: 18:32:45 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0090 decode.acc_seg: 99.6576 aux.loss_ce: 0.0080 aux.acc_seg: 99.2667 +04/18 08:22:01 - mmengine - INFO - Iter(train) [ 39050/160000] lr: 7.7962e-03 eta: 18:32:18 time: 0.5548 data_time: 0.0071 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.6274 aux.loss_ce: 0.0081 aux.acc_seg: 99.0817 +04/18 08:22:29 - mmengine - INFO - Iter(train) [ 39100/160000] lr: 7.7933e-03 eta: 18:31:51 time: 0.5544 data_time: 0.0059 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0099 decode.acc_seg: 99.5752 aux.loss_ce: 0.0081 aux.acc_seg: 99.3050 +04/18 08:22:56 - mmengine - INFO - Iter(train) [ 39150/160000] lr: 7.7904e-03 eta: 18:31:24 time: 0.5536 data_time: 0.0059 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.5658 aux.loss_ce: 0.0083 aux.acc_seg: 99.1587 +04/18 08:23:24 - mmengine - INFO - Iter(train) [ 39200/160000] lr: 7.7876e-03 eta: 18:30:57 time: 0.5538 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.5652 aux.loss_ce: 0.0077 aux.acc_seg: 98.9863 +04/18 08:23:52 - mmengine - INFO - Iter(train) [ 39250/160000] lr: 7.7847e-03 eta: 18:30:30 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0098 decode.acc_seg: 99.7126 aux.loss_ce: 0.0081 aux.acc_seg: 99.4448 +04/18 08:24:19 - mmengine - INFO - Iter(train) [ 39300/160000] lr: 7.7819e-03 eta: 18:30:02 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0116 decode.acc_seg: 99.6580 aux.loss_ce: 0.0094 aux.acc_seg: 99.0866 From 3bba778481b6456c39d2d388d47939354fd4919e Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Fri, 19 Apr 2024 08:22:05 +0000 Subject: [PATCH 18/24] 2024.04.19 --- ...se_upernet_8xb2-amp-160k_ade20k-512x512.py | 4 +- nohup.out | 30299 ++++++++++++++++ 2 files changed, 30301 insertions(+), 2 deletions(-) diff --git a/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py b/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py index b146635db2..7045dfc87e 100644 --- a/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py +++ b/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py @@ -1,12 +1,12 @@ _base_ = [ - '../_base_/models/upernet_mae.py', '../_base_/datasets/ade20k.py', + '../_base_/models/upernet_mae.py', '../_base_/datasets/cag.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' ] crop_size = (512, 512) data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, - pretrained='./pretrain/mae_pretrain_vit_base_mmcls.pth', + pretrained='/workspaces/mmsegmentation-1/configs/mae/mae_pretrain_vit_base.pth', backbone=dict( type='MAE', img_size=(512, 512), diff --git a/nohup.out b/nohup.out index 8c53fd57f7..7c03785cbd 100644 --- a/nohup.out +++ b/nohup.out @@ -12997,3 +12997,30302 @@ unexpected key in source state_dict: fc.weight, fc.bias 04/18 08:23:24 - mmengine - INFO - Iter(train) [ 39200/160000] lr: 7.7876e-03 eta: 18:30:57 time: 0.5538 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.5652 aux.loss_ce: 0.0077 aux.acc_seg: 98.9863 04/18 08:23:52 - mmengine - INFO - Iter(train) [ 39250/160000] lr: 7.7847e-03 eta: 18:30:30 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0098 decode.acc_seg: 99.7126 aux.loss_ce: 0.0081 aux.acc_seg: 99.4448 04/18 08:24:19 - mmengine - INFO - Iter(train) [ 39300/160000] lr: 7.7819e-03 eta: 18:30:02 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0116 decode.acc_seg: 99.6580 aux.loss_ce: 0.0094 aux.acc_seg: 99.0866 +04/18 08:24:47 - mmengine - INFO - Iter(train) [ 39350/160000] lr: 7.7790e-03 eta: 18:29:35 time: 0.5529 data_time: 0.0069 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.7506 aux.loss_ce: 0.0077 aux.acc_seg: 99.2886 +04/18 08:25:15 - mmengine - INFO - Iter(train) [ 39400/160000] lr: 7.7761e-03 eta: 18:29:08 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.6969 aux.loss_ce: 0.0085 aux.acc_seg: 99.2848 +04/18 08:25:42 - mmengine - INFO - Iter(train) [ 39450/160000] lr: 7.7733e-03 eta: 18:28:40 time: 0.5536 data_time: 0.0070 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.5288 aux.loss_ce: 0.0082 aux.acc_seg: 99.0371 +04/18 08:26:10 - mmengine - INFO - Iter(train) [ 39500/160000] lr: 7.7704e-03 eta: 18:28:13 time: 0.5525 data_time: 0.0067 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0109 decode.acc_seg: 99.4141 aux.loss_ce: 0.0093 aux.acc_seg: 98.8440 +04/18 08:26:38 - mmengine - INFO - Iter(train) [ 39550/160000] lr: 7.7675e-03 eta: 18:27:45 time: 0.5533 data_time: 0.0057 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0092 decode.acc_seg: 99.7376 aux.loss_ce: 0.0085 aux.acc_seg: 99.2864 +04/18 08:27:05 - mmengine - INFO - Iter(train) [ 39600/160000] lr: 7.7647e-03 eta: 18:27:18 time: 0.5536 data_time: 0.0071 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0105 decode.acc_seg: 99.5536 aux.loss_ce: 0.0091 aux.acc_seg: 99.0755 +04/18 08:27:33 - mmengine - INFO - Iter(train) [ 39650/160000] lr: 7.7618e-03 eta: 18:26:51 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6278 aux.loss_ce: 0.0077 aux.acc_seg: 99.2082 +04/18 08:28:01 - mmengine - INFO - Iter(train) [ 39700/160000] lr: 7.7589e-03 eta: 18:26:23 time: 0.5530 data_time: 0.0059 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.4593 aux.loss_ce: 0.0086 aux.acc_seg: 98.7346 +04/18 08:28:28 - mmengine - INFO - Iter(train) [ 39750/160000] lr: 7.7561e-03 eta: 18:25:56 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.6967 aux.loss_ce: 0.0086 aux.acc_seg: 99.1289 +04/18 08:28:56 - mmengine - INFO - Iter(train) [ 39800/160000] lr: 7.7532e-03 eta: 18:25:29 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6865 aux.loss_ce: 0.0081 aux.acc_seg: 99.2378 +04/18 08:29:24 - mmengine - INFO - Iter(train) [ 39850/160000] lr: 7.7503e-03 eta: 18:25:01 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.6639 aux.loss_ce: 0.0083 aux.acc_seg: 99.2950 +04/18 08:29:51 - mmengine - INFO - Iter(train) [ 39900/160000] lr: 7.7475e-03 eta: 18:24:34 time: 0.5523 data_time: 0.0071 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.5162 aux.loss_ce: 0.0084 aux.acc_seg: 98.6649 +04/18 08:30:19 - mmengine - INFO - Iter(train) [ 39950/160000] lr: 7.7446e-03 eta: 18:24:06 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0080 decode.acc_seg: 99.7220 aux.loss_ce: 0.0071 aux.acc_seg: 99.3871 +04/18 08:30:47 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:30:47 - mmengine - INFO - Iter(train) [ 40000/160000] lr: 7.7417e-03 eta: 18:23:38 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6464 aux.loss_ce: 0.0077 aux.acc_seg: 99.1118 +04/18 08:30:47 - mmengine - INFO - Saving checkpoint at 40000 iterations +04/18 08:30:51 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:06 time: 0.0462 data_time: 0.0014 memory: 1657 +04/18 08:30:53 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0463 data_time: 0.0015 memory: 1657 +04/18 08:30:55 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0464 data_time: 0.0014 memory: 1657 +04/18 08:30:57 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0456 data_time: 0.0014 memory: 1657 +04/18 08:30:58 - mmengine - INFO - per class results: +04/18 08:30:58 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.47 | 99.53 | 99.58 | 99.47 | +| contrast | 79.93 | 90.03 | 88.85 | 87.69 | 90.03 | ++------------+-------+-------+--------+-----------+--------+ +04/18 08:30:58 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1000 mIoU: 89.5000 mAcc: 94.7500 mFscore: 94.1900 mPrecision: 93.6400 mRecall: 94.7500 data_time: 0.0015 time: 0.0463 +04/18 08:31:25 - mmengine - INFO - Iter(train) [ 40050/160000] lr: 7.7389e-03 eta: 18:23:11 time: 0.5502 data_time: 0.0066 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6489 aux.loss_ce: 0.0078 aux.acc_seg: 99.2826 +04/18 08:31:53 - mmengine - INFO - Iter(train) [ 40100/160000] lr: 7.7360e-03 eta: 18:22:43 time: 0.5498 data_time: 0.0061 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.6363 aux.loss_ce: 0.0073 aux.acc_seg: 99.0268 +04/18 08:32:20 - mmengine - INFO - Iter(train) [ 40150/160000] lr: 7.7332e-03 eta: 18:22:16 time: 0.5515 data_time: 0.0062 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0084 decode.acc_seg: 99.6901 aux.loss_ce: 0.0076 aux.acc_seg: 99.2650 +04/18 08:32:48 - mmengine - INFO - Iter(train) [ 40200/160000] lr: 7.7303e-03 eta: 18:21:48 time: 0.5500 data_time: 0.0062 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0116 decode.acc_seg: 99.6566 aux.loss_ce: 0.0098 aux.acc_seg: 99.1296 +04/18 08:33:15 - mmengine - INFO - Iter(train) [ 40250/160000] lr: 7.7274e-03 eta: 18:21:20 time: 0.5496 data_time: 0.0062 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.5221 aux.loss_ce: 0.0085 aux.acc_seg: 99.0771 +04/18 08:33:43 - mmengine - INFO - Iter(train) [ 40300/160000] lr: 7.7246e-03 eta: 18:20:53 time: 0.5524 data_time: 0.0067 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.6153 aux.loss_ce: 0.0079 aux.acc_seg: 99.1534 +04/18 08:34:10 - mmengine - INFO - Iter(train) [ 40350/160000] lr: 7.7217e-03 eta: 18:20:25 time: 0.5520 data_time: 0.0069 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0086 decode.acc_seg: 99.7101 aux.loss_ce: 0.0074 aux.acc_seg: 99.4150 +04/18 08:34:38 - mmengine - INFO - Iter(train) [ 40400/160000] lr: 7.7188e-03 eta: 18:19:57 time: 0.5489 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6420 aux.loss_ce: 0.0079 aux.acc_seg: 99.0813 +04/18 08:35:06 - mmengine - INFO - Iter(train) [ 40450/160000] lr: 7.7160e-03 eta: 18:19:29 time: 0.5513 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.6397 aux.loss_ce: 0.0074 aux.acc_seg: 99.1655 +04/18 08:35:33 - mmengine - INFO - Iter(train) [ 40500/160000] lr: 7.7131e-03 eta: 18:19:02 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0090 decode.acc_seg: 99.6161 aux.loss_ce: 0.0078 aux.acc_seg: 99.1043 +04/18 08:36:01 - mmengine - INFO - Iter(train) [ 40550/160000] lr: 7.7102e-03 eta: 18:18:34 time: 0.5513 data_time: 0.0067 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.5161 aux.loss_ce: 0.0080 aux.acc_seg: 98.9918 +04/18 08:36:28 - mmengine - INFO - Iter(train) [ 40600/160000] lr: 7.7074e-03 eta: 18:18:06 time: 0.5497 data_time: 0.0069 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.6079 aux.loss_ce: 0.0080 aux.acc_seg: 99.0249 +04/18 08:36:56 - mmengine - INFO - Iter(train) [ 40650/160000] lr: 7.7045e-03 eta: 18:17:39 time: 0.5511 data_time: 0.0061 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.5480 aux.loss_ce: 0.0082 aux.acc_seg: 99.1893 +04/18 08:37:23 - mmengine - INFO - Iter(train) [ 40700/160000] lr: 7.7016e-03 eta: 18:17:11 time: 0.5499 data_time: 0.0060 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0106 decode.acc_seg: 99.5810 aux.loss_ce: 0.0095 aux.acc_seg: 99.2526 +04/18 08:37:51 - mmengine - INFO - Iter(train) [ 40750/160000] lr: 7.6988e-03 eta: 18:16:43 time: 0.5503 data_time: 0.0067 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6409 aux.loss_ce: 0.0081 aux.acc_seg: 99.0313 +04/18 08:38:18 - mmengine - INFO - Iter(train) [ 40800/160000] lr: 7.6959e-03 eta: 18:16:15 time: 0.5496 data_time: 0.0067 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0108 decode.acc_seg: 99.6670 aux.loss_ce: 0.0088 aux.acc_seg: 99.2678 +04/18 08:38:46 - mmengine - INFO - Iter(train) [ 40850/160000] lr: 7.6930e-03 eta: 18:15:48 time: 0.5506 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0087 decode.acc_seg: 99.5914 aux.loss_ce: 0.0077 aux.acc_seg: 99.2438 +04/18 08:39:13 - mmengine - INFO - Iter(train) [ 40900/160000] lr: 7.6901e-03 eta: 18:15:20 time: 0.5510 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0087 decode.acc_seg: 99.6388 aux.loss_ce: 0.0077 aux.acc_seg: 99.3010 +04/18 08:39:41 - mmengine - INFO - Iter(train) [ 40950/160000] lr: 7.6873e-03 eta: 18:14:52 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.7011 aux.loss_ce: 0.0078 aux.acc_seg: 99.4434 +04/18 08:40:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:40:09 - mmengine - INFO - Iter(train) [ 41000/160000] lr: 7.6844e-03 eta: 18:14:25 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0093 decode.acc_seg: 99.6187 aux.loss_ce: 0.0085 aux.acc_seg: 98.9475 +04/18 08:40:36 - mmengine - INFO - Iter(train) [ 41050/160000] lr: 7.6815e-03 eta: 18:13:57 time: 0.5507 data_time: 0.0066 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6560 aux.loss_ce: 0.0081 aux.acc_seg: 99.3476 +04/18 08:41:04 - mmengine - INFO - Iter(train) [ 41100/160000] lr: 7.6787e-03 eta: 18:13:29 time: 0.5513 data_time: 0.0060 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6568 aux.loss_ce: 0.0077 aux.acc_seg: 99.0934 +04/18 08:41:31 - mmengine - INFO - Iter(train) [ 41150/160000] lr: 7.6758e-03 eta: 18:13:01 time: 0.5521 data_time: 0.0067 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0087 decode.acc_seg: 99.7006 aux.loss_ce: 0.0076 aux.acc_seg: 99.2484 +04/18 08:41:59 - mmengine - INFO - Iter(train) [ 41200/160000] lr: 7.6729e-03 eta: 18:12:34 time: 0.5503 data_time: 0.0064 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0097 decode.acc_seg: 99.5850 aux.loss_ce: 0.0087 aux.acc_seg: 99.0829 +04/18 08:42:26 - mmengine - INFO - Iter(train) [ 41250/160000] lr: 7.6701e-03 eta: 18:12:06 time: 0.5498 data_time: 0.0062 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.6686 aux.loss_ce: 0.0086 aux.acc_seg: 98.9568 +04/18 08:42:54 - mmengine - INFO - Iter(train) [ 41300/160000] lr: 7.6672e-03 eta: 18:11:39 time: 0.5518 data_time: 0.0070 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6409 aux.loss_ce: 0.0080 aux.acc_seg: 99.1268 +04/18 08:43:22 - mmengine - INFO - Iter(train) [ 41350/160000] lr: 7.6643e-03 eta: 18:11:11 time: 0.5498 data_time: 0.0061 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0081 decode.acc_seg: 99.6580 aux.loss_ce: 0.0075 aux.acc_seg: 99.0657 +04/18 08:43:49 - mmengine - INFO - Iter(train) [ 41400/160000] lr: 7.6615e-03 eta: 18:10:43 time: 0.5502 data_time: 0.0059 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0086 decode.acc_seg: 99.6943 aux.loss_ce: 0.0076 aux.acc_seg: 99.2686 +04/18 08:44:17 - mmengine - INFO - Iter(train) [ 41450/160000] lr: 7.6586e-03 eta: 18:10:15 time: 0.5504 data_time: 0.0062 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.5800 aux.loss_ce: 0.0084 aux.acc_seg: 99.1014 +04/18 08:44:44 - mmengine - INFO - Iter(train) [ 41500/160000] lr: 7.6557e-03 eta: 18:09:48 time: 0.5502 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0081 decode.acc_seg: 99.6276 aux.loss_ce: 0.0077 aux.acc_seg: 99.1901 +04/18 08:45:12 - mmengine - INFO - Iter(train) [ 41550/160000] lr: 7.6529e-03 eta: 18:09:20 time: 0.5602 data_time: 0.0078 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0080 decode.acc_seg: 99.6048 aux.loss_ce: 0.0076 aux.acc_seg: 99.1132 +04/18 08:45:39 - mmengine - INFO - Iter(train) [ 41600/160000] lr: 7.6500e-03 eta: 18:08:53 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.6599 aux.loss_ce: 0.0085 aux.acc_seg: 99.1570 +04/18 08:46:07 - mmengine - INFO - Iter(train) [ 41650/160000] lr: 7.6471e-03 eta: 18:08:25 time: 0.5505 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0083 decode.acc_seg: 99.6052 aux.loss_ce: 0.0079 aux.acc_seg: 99.1905 +04/18 08:46:35 - mmengine - INFO - Iter(train) [ 41700/160000] lr: 7.6442e-03 eta: 18:07:57 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.6144 aux.loss_ce: 0.0083 aux.acc_seg: 99.2826 +04/18 08:47:02 - mmengine - INFO - Iter(train) [ 41750/160000] lr: 7.6414e-03 eta: 18:07:29 time: 0.5497 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6094 aux.loss_ce: 0.0080 aux.acc_seg: 99.2477 +04/18 08:47:30 - mmengine - INFO - Iter(train) [ 41800/160000] lr: 7.6385e-03 eta: 18:07:02 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0111 decode.acc_seg: 99.6124 aux.loss_ce: 0.0092 aux.acc_seg: 99.1284 +04/18 08:47:57 - mmengine - INFO - Iter(train) [ 41850/160000] lr: 7.6356e-03 eta: 18:06:34 time: 0.5502 data_time: 0.0059 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.5851 aux.loss_ce: 0.0079 aux.acc_seg: 98.9687 +04/18 08:48:25 - mmengine - INFO - Iter(train) [ 41900/160000] lr: 7.6328e-03 eta: 18:06:07 time: 0.5513 data_time: 0.0059 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0113 decode.acc_seg: 99.1785 aux.loss_ce: 0.0088 aux.acc_seg: 98.7809 +04/18 08:48:52 - mmengine - INFO - Iter(train) [ 41950/160000] lr: 7.6299e-03 eta: 18:05:39 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.7153 aux.loss_ce: 0.0086 aux.acc_seg: 99.2450 +04/18 08:49:20 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:49:20 - mmengine - INFO - Iter(train) [ 42000/160000] lr: 7.6270e-03 eta: 18:05:11 time: 0.5518 data_time: 0.0068 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.7354 aux.loss_ce: 0.0086 aux.acc_seg: 99.2950 +04/18 08:49:48 - mmengine - INFO - Iter(train) [ 42050/160000] lr: 7.6242e-03 eta: 18:04:44 time: 0.5514 data_time: 0.0069 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0093 decode.acc_seg: 99.6522 aux.loss_ce: 0.0082 aux.acc_seg: 99.2332 +04/18 08:50:15 - mmengine - INFO - Iter(train) [ 42100/160000] lr: 7.6213e-03 eta: 18:04:16 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.6117 aux.loss_ce: 0.0082 aux.acc_seg: 98.8979 +04/18 08:50:43 - mmengine - INFO - Iter(train) [ 42150/160000] lr: 7.6184e-03 eta: 18:03:48 time: 0.5510 data_time: 0.0067 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.4720 aux.loss_ce: 0.0081 aux.acc_seg: 99.0637 +04/18 08:51:10 - mmengine - INFO - Iter(train) [ 42200/160000] lr: 7.6155e-03 eta: 18:03:21 time: 0.5513 data_time: 0.0068 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.6223 aux.loss_ce: 0.0085 aux.acc_seg: 99.1876 +04/18 08:51:38 - mmengine - INFO - Iter(train) [ 42250/160000] lr: 7.6127e-03 eta: 18:02:53 time: 0.5508 data_time: 0.0059 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0098 decode.acc_seg: 99.6596 aux.loss_ce: 0.0088 aux.acc_seg: 99.2562 +04/18 08:52:05 - mmengine - INFO - Iter(train) [ 42300/160000] lr: 7.6098e-03 eta: 18:02:25 time: 0.5529 data_time: 0.0075 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.6082 aux.loss_ce: 0.0087 aux.acc_seg: 99.1274 +04/18 08:52:33 - mmengine - INFO - Iter(train) [ 42350/160000] lr: 7.6069e-03 eta: 18:01:58 time: 0.5608 data_time: 0.0065 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.6665 aux.loss_ce: 0.0077 aux.acc_seg: 99.0905 +04/18 08:53:01 - mmengine - INFO - Iter(train) [ 42400/160000] lr: 7.6041e-03 eta: 18:01:31 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.7198 aux.loss_ce: 0.0081 aux.acc_seg: 99.2658 +04/18 08:53:28 - mmengine - INFO - Iter(train) [ 42450/160000] lr: 7.6012e-03 eta: 18:01:03 time: 0.5516 data_time: 0.0059 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.7198 aux.loss_ce: 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mmengine - INFO - Iter(train) [ 42700/160000] lr: 7.5868e-03 eta: 17:58:46 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0100 decode.acc_seg: 99.7200 aux.loss_ce: 0.0082 aux.acc_seg: 99.3009 +04/18 08:56:14 - mmengine - INFO - Iter(train) [ 42750/160000] lr: 7.5840e-03 eta: 17:58:18 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6605 aux.loss_ce: 0.0084 aux.acc_seg: 99.1475 +04/18 08:56:42 - mmengine - INFO - Iter(train) [ 42800/160000] lr: 7.5811e-03 eta: 17:57:50 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0073 decode.acc_seg: 99.7220 aux.loss_ce: 0.0070 aux.acc_seg: 99.3004 +04/18 08:57:09 - mmengine - INFO - Iter(train) [ 42850/160000] lr: 7.5782e-03 eta: 17:57:23 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.7429 aux.loss_ce: 0.0077 aux.acc_seg: 99.2773 +04/18 08:57:37 - mmengine - INFO - Iter(train) [ 42900/160000] lr: 7.5753e-03 eta: 17:56:55 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0122 decode.acc_seg: 99.5868 aux.loss_ce: 0.0097 aux.acc_seg: 99.0719 +04/18 08:58:04 - mmengine - INFO - Iter(train) [ 42950/160000] lr: 7.5725e-03 eta: 17:56:27 time: 0.5522 data_time: 0.0069 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6851 aux.loss_ce: 0.0079 aux.acc_seg: 99.1350 +04/18 08:58:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:58:32 - mmengine - INFO - Iter(train) [ 43000/160000] lr: 7.5696e-03 eta: 17:56:00 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.5961 aux.loss_ce: 0.0088 aux.acc_seg: 99.0003 +04/18 08:59:00 - mmengine - INFO - Iter(train) [ 43050/160000] lr: 7.5667e-03 eta: 17:55:32 time: 0.5530 data_time: 0.0069 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.4495 aux.loss_ce: 0.0085 aux.acc_seg: 98.6427 +04/18 08:59:27 - mmengine - INFO - Iter(train) [ 43100/160000] lr: 7.5638e-03 eta: 17:55:05 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0097 decode.acc_seg: 99.7053 aux.loss_ce: 0.0085 aux.acc_seg: 99.2823 +04/18 08:59:55 - mmengine - INFO - Iter(train) [ 43150/160000] lr: 7.5610e-03 eta: 17:54:37 time: 0.5523 data_time: 0.0061 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0102 decode.acc_seg: 99.6934 aux.loss_ce: 0.0092 aux.acc_seg: 99.2806 +04/18 09:00:22 - mmengine - INFO - Iter(train) [ 43200/160000] lr: 7.5581e-03 eta: 17:54:09 time: 0.5502 data_time: 0.0060 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0086 decode.acc_seg: 99.6169 aux.loss_ce: 0.0084 aux.acc_seg: 99.1678 +04/18 09:00:50 - mmengine - INFO - Iter(train) [ 43250/160000] lr: 7.5552e-03 eta: 17:53:42 time: 0.5511 data_time: 0.0070 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.8030 aux.loss_ce: 0.0081 aux.acc_seg: 99.2701 +04/18 09:01:18 - mmengine - INFO - Iter(train) [ 43300/160000] lr: 7.5524e-03 eta: 17:53:14 time: 0.5518 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.7141 aux.loss_ce: 0.0076 aux.acc_seg: 99.3835 +04/18 09:01:45 - mmengine - INFO - Iter(train) [ 43350/160000] lr: 7.5495e-03 eta: 17:52:47 time: 0.5523 data_time: 0.0068 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0087 decode.acc_seg: 99.6002 aux.loss_ce: 0.0079 aux.acc_seg: 99.1570 +04/18 09:02:13 - mmengine - INFO - Iter(train) [ 43400/160000] lr: 7.5466e-03 eta: 17:52:19 time: 0.5535 data_time: 0.0069 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0096 decode.acc_seg: 99.7343 aux.loss_ce: 0.0085 aux.acc_seg: 99.3286 +04/18 09:02:41 - mmengine - INFO - Iter(train) [ 43450/160000] lr: 7.5437e-03 eta: 17:51:52 time: 0.5505 data_time: 0.0058 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.6410 aux.loss_ce: 0.0082 aux.acc_seg: 99.2811 +04/18 09:03:08 - mmengine - INFO - Iter(train) [ 43500/160000] lr: 7.5409e-03 eta: 17:51:24 time: 0.5511 data_time: 0.0071 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6983 aux.loss_ce: 0.0078 aux.acc_seg: 99.2352 +04/18 09:03:36 - mmengine - INFO - Iter(train) [ 43550/160000] lr: 7.5380e-03 eta: 17:50:57 time: 0.5500 data_time: 0.0059 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0091 decode.acc_seg: 99.5979 aux.loss_ce: 0.0083 aux.acc_seg: 99.1764 +04/18 09:04:03 - mmengine - INFO - Iter(train) [ 43600/160000] lr: 7.5351e-03 eta: 17:50:29 time: 0.5506 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.7025 aux.loss_ce: 0.0082 aux.acc_seg: 99.3903 +04/18 09:04:31 - mmengine - INFO - Iter(train) [ 43650/160000] lr: 7.5322e-03 eta: 17:50:02 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5419 aux.loss_ce: 0.0085 aux.acc_seg: 99.0887 +04/18 09:04:59 - mmengine - INFO - Iter(train) [ 43700/160000] lr: 7.5294e-03 eta: 17:49:34 time: 0.5517 data_time: 0.0058 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0092 decode.acc_seg: 99.7018 aux.loss_ce: 0.0086 aux.acc_seg: 99.1895 +04/18 09:05:26 - mmengine - INFO - Iter(train) [ 43750/160000] lr: 7.5265e-03 eta: 17:49:07 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.6678 aux.loss_ce: 0.0084 aux.acc_seg: 99.2646 +04/18 09:05:54 - mmengine - INFO - Iter(train) [ 43800/160000] lr: 7.5236e-03 eta: 17:48:39 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.6360 aux.loss_ce: 0.0082 aux.acc_seg: 98.9284 +04/18 09:06:21 - mmengine - INFO - Iter(train) [ 43850/160000] lr: 7.5207e-03 eta: 17:48:11 time: 0.5518 data_time: 0.0061 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0087 decode.acc_seg: 99.7040 aux.loss_ce: 0.0078 aux.acc_seg: 99.3532 +04/18 09:06:49 - mmengine - INFO - Iter(train) [ 43900/160000] lr: 7.5179e-03 eta: 17:47:44 time: 0.5518 data_time: 0.0067 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6299 aux.loss_ce: 0.0081 aux.acc_seg: 99.2554 +04/18 09:07:17 - mmengine - INFO - Iter(train) [ 43950/160000] lr: 7.5150e-03 eta: 17:47:16 time: 0.5502 data_time: 0.0063 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0093 decode.acc_seg: 99.5738 aux.loss_ce: 0.0082 aux.acc_seg: 99.0312 +04/18 09:07:44 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:07:44 - mmengine - INFO - Iter(train) [ 44000/160000] lr: 7.5121e-03 eta: 17:46:49 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.5694 aux.loss_ce: 0.0082 aux.acc_seg: 99.0113 +04/18 09:08:12 - mmengine - INFO - Iter(train) [ 44050/160000] lr: 7.5092e-03 eta: 17:46:21 time: 0.5525 data_time: 0.0067 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.7020 aux.loss_ce: 0.0083 aux.acc_seg: 99.2927 +04/18 09:08:39 - mmengine - INFO - Iter(train) [ 44100/160000] lr: 7.5064e-03 eta: 17:45:54 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.7297 aux.loss_ce: 0.0081 aux.acc_seg: 99.3670 +04/18 09:09:07 - mmengine - INFO - Iter(train) [ 44150/160000] lr: 7.5035e-03 eta: 17:45:26 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0095 decode.acc_seg: 99.6385 aux.loss_ce: 0.0090 aux.acc_seg: 99.0378 +04/18 09:09:35 - mmengine - INFO - Iter(train) [ 44200/160000] lr: 7.5006e-03 eta: 17:44:59 time: 0.5520 data_time: 0.0074 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.6935 aux.loss_ce: 0.0085 aux.acc_seg: 99.0993 +04/18 09:10:02 - mmengine - INFO - Iter(train) [ 44250/160000] lr: 7.4977e-03 eta: 17:44:31 time: 0.5524 data_time: 0.0062 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0103 decode.acc_seg: 99.6043 aux.loss_ce: 0.0093 aux.acc_seg: 98.8139 +04/18 09:10:30 - mmengine - INFO - Iter(train) [ 44300/160000] lr: 7.4949e-03 eta: 17:44:03 time: 0.5511 data_time: 0.0058 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0107 decode.acc_seg: 99.6945 aux.loss_ce: 0.0090 aux.acc_seg: 99.2631 +04/18 09:10:57 - mmengine - INFO - Iter(train) [ 44350/160000] lr: 7.4920e-03 eta: 17:43:36 time: 0.5506 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6607 aux.loss_ce: 0.0079 aux.acc_seg: 99.0323 +04/18 09:11:25 - mmengine - INFO - Iter(train) [ 44400/160000] lr: 7.4891e-03 eta: 17:43:08 time: 0.5532 data_time: 0.0067 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.6331 aux.loss_ce: 0.0085 aux.acc_seg: 99.1103 +04/18 09:11:53 - mmengine - INFO - Iter(train) [ 44450/160000] lr: 7.4862e-03 eta: 17:42:41 time: 0.5526 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.5473 aux.loss_ce: 0.0082 aux.acc_seg: 99.2693 +04/18 09:12:20 - mmengine - INFO - Iter(train) [ 44500/160000] lr: 7.4833e-03 eta: 17:42:14 time: 0.5713 data_time: 0.0069 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.7069 aux.loss_ce: 0.0089 aux.acc_seg: 99.3418 +04/18 09:12:48 - mmengine - INFO - Iter(train) [ 44550/160000] lr: 7.4805e-03 eta: 17:41:46 time: 0.5519 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.6710 aux.loss_ce: 0.0083 aux.acc_seg: 99.2764 +04/18 09:13:16 - mmengine - INFO - Iter(train) [ 44600/160000] lr: 7.4776e-03 eta: 17:41:19 time: 0.5518 data_time: 0.0060 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0100 decode.acc_seg: 99.6036 aux.loss_ce: 0.0087 aux.acc_seg: 99.1800 +04/18 09:13:43 - mmengine - INFO - Iter(train) [ 44650/160000] lr: 7.4747e-03 eta: 17:40:51 time: 0.5531 data_time: 0.0062 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0108 decode.acc_seg: 99.5868 aux.loss_ce: 0.0093 aux.acc_seg: 99.1148 +04/18 09:14:11 - mmengine - INFO - Iter(train) [ 44700/160000] lr: 7.4718e-03 eta: 17:40:23 time: 0.5532 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.7260 aux.loss_ce: 0.0076 aux.acc_seg: 99.3047 +04/18 09:14:39 - mmengine - INFO - Iter(train) [ 44750/160000] lr: 7.4690e-03 eta: 17:39:56 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0094 decode.acc_seg: 99.6604 aux.loss_ce: 0.0094 aux.acc_seg: 99.2559 +04/18 09:15:06 - mmengine - INFO - Iter(train) [ 44800/160000] lr: 7.4661e-03 eta: 17:39:29 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.7090 aux.loss_ce: 0.0075 aux.acc_seg: 99.2836 +04/18 09:15:34 - mmengine - INFO - Iter(train) [ 44850/160000] lr: 7.4632e-03 eta: 17:39:01 time: 0.5527 data_time: 0.0063 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0093 decode.acc_seg: 99.7059 aux.loss_ce: 0.0083 aux.acc_seg: 99.1607 +04/18 09:16:01 - mmengine - INFO - Iter(train) [ 44900/160000] lr: 7.4603e-03 eta: 17:38:33 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0088 decode.acc_seg: 99.6642 aux.loss_ce: 0.0080 aux.acc_seg: 99.3046 +04/18 09:16:29 - mmengine - INFO - Iter(train) [ 44950/160000] lr: 7.4575e-03 eta: 17:38:06 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0095 decode.acc_seg: 99.5405 aux.loss_ce: 0.0080 aux.acc_seg: 99.0601 +04/18 09:16:57 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:16:57 - mmengine - INFO - Iter(train) [ 45000/160000] lr: 7.4546e-03 eta: 17:37:38 time: 0.5516 data_time: 0.0058 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.6920 aux.loss_ce: 0.0082 aux.acc_seg: 99.1603 +04/18 09:17:24 - mmengine - INFO - Iter(train) [ 45050/160000] lr: 7.4517e-03 eta: 17:37:11 time: 0.5528 data_time: 0.0059 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.7298 aux.loss_ce: 0.0084 aux.acc_seg: 99.3738 +04/18 09:17:52 - mmengine - INFO - Iter(train) [ 45100/160000] lr: 7.4488e-03 eta: 17:36:43 time: 0.5535 data_time: 0.0062 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0104 decode.acc_seg: 99.5881 aux.loss_ce: 0.0088 aux.acc_seg: 99.1511 +04/18 09:18:20 - mmengine - INFO - Iter(train) [ 45150/160000] lr: 7.4459e-03 eta: 17:36:16 time: 0.5501 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6174 aux.loss_ce: 0.0080 aux.acc_seg: 99.2241 +04/18 09:18:47 - mmengine - INFO - Iter(train) [ 45200/160000] lr: 7.4431e-03 eta: 17:35:48 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0106 decode.acc_seg: 99.2593 aux.loss_ce: 0.0086 aux.acc_seg: 98.8551 +04/18 09:19:15 - mmengine - INFO - Iter(train) [ 45250/160000] lr: 7.4402e-03 eta: 17:35:21 time: 0.5512 data_time: 0.0066 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6578 aux.loss_ce: 0.0082 aux.acc_seg: 99.1809 +04/18 09:19:42 - mmengine - INFO - Iter(train) [ 45300/160000] lr: 7.4373e-03 eta: 17:34:53 time: 0.5518 data_time: 0.0065 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6301 aux.loss_ce: 0.0082 aux.acc_seg: 99.1590 +04/18 09:20:10 - mmengine - INFO - Iter(train) [ 45350/160000] lr: 7.4344e-03 eta: 17:34:26 time: 0.5525 data_time: 0.0058 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0096 decode.acc_seg: 99.2689 aux.loss_ce: 0.0088 aux.acc_seg: 98.5504 +04/18 09:20:38 - mmengine - INFO - Iter(train) [ 45400/160000] lr: 7.4316e-03 eta: 17:33:58 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0092 decode.acc_seg: 99.6595 aux.loss_ce: 0.0085 aux.acc_seg: 99.1810 +04/18 09:21:05 - mmengine - INFO - Iter(train) [ 45450/160000] lr: 7.4287e-03 eta: 17:33:31 time: 0.5519 data_time: 0.0073 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0084 decode.acc_seg: 99.5793 aux.loss_ce: 0.0075 aux.acc_seg: 99.0155 +04/18 09:21:33 - mmengine - INFO - Iter(train) [ 45500/160000] lr: 7.4258e-03 eta: 17:33:03 time: 0.5536 data_time: 0.0067 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.6611 aux.loss_ce: 0.0080 aux.acc_seg: 99.2641 +04/18 09:22:00 - mmengine - INFO - Iter(train) [ 45550/160000] lr: 7.4229e-03 eta: 17:32:36 time: 0.5537 data_time: 0.0072 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0082 decode.acc_seg: 99.6822 aux.loss_ce: 0.0075 aux.acc_seg: 99.3224 +04/18 09:22:28 - mmengine - INFO - Iter(train) [ 45600/160000] lr: 7.4200e-03 eta: 17:32:08 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.6726 aux.loss_ce: 0.0076 aux.acc_seg: 99.3276 +04/18 09:22:56 - mmengine - INFO - Iter(train) [ 45650/160000] lr: 7.4172e-03 eta: 17:31:41 time: 0.5516 data_time: 0.0068 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.6734 aux.loss_ce: 0.0073 aux.acc_seg: 99.1992 +04/18 09:23:23 - mmengine - INFO - Iter(train) [ 45700/160000] lr: 7.4143e-03 eta: 17:31:13 time: 0.5521 data_time: 0.0059 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.6832 aux.loss_ce: 0.0076 aux.acc_seg: 99.1064 +04/18 09:23:51 - mmengine - INFO - Iter(train) [ 45750/160000] lr: 7.4114e-03 eta: 17:30:46 time: 0.5528 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.6417 aux.loss_ce: 0.0082 aux.acc_seg: 98.9037 +04/18 09:24:19 - mmengine - INFO - Iter(train) [ 45800/160000] lr: 7.4085e-03 eta: 17:30:18 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.7640 aux.loss_ce: 0.0077 aux.acc_seg: 99.3542 +04/18 09:24:46 - mmengine - INFO - Iter(train) [ 45850/160000] lr: 7.4056e-03 eta: 17:29:51 time: 0.5615 data_time: 0.0066 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0091 decode.acc_seg: 99.5231 aux.loss_ce: 0.0086 aux.acc_seg: 99.2666 +04/18 09:25:14 - mmengine - INFO - Iter(train) [ 45900/160000] lr: 7.4028e-03 eta: 17:29:23 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.6744 aux.loss_ce: 0.0079 aux.acc_seg: 99.1490 +04/18 09:25:42 - mmengine - INFO - Iter(train) [ 45950/160000] lr: 7.3999e-03 eta: 17:28:56 time: 0.5509 data_time: 0.0065 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.7808 aux.loss_ce: 0.0070 aux.acc_seg: 99.5138 +04/18 09:26:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:26:09 - mmengine - INFO - Iter(train) [ 46000/160000] lr: 7.3970e-03 eta: 17:28:28 time: 0.5508 data_time: 0.0061 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7713 aux.loss_ce: 0.0076 aux.acc_seg: 99.3100 +04/18 09:26:37 - mmengine - INFO - Iter(train) [ 46050/160000] lr: 7.3941e-03 eta: 17:28:01 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0086 decode.acc_seg: 99.6272 aux.loss_ce: 0.0085 aux.acc_seg: 99.0365 +04/18 09:27:05 - mmengine - INFO - Iter(train) [ 46100/160000] lr: 7.3912e-03 eta: 17:27:33 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0081 decode.acc_seg: 99.7292 aux.loss_ce: 0.0076 aux.acc_seg: 99.2253 +04/18 09:27:32 - mmengine - INFO - Iter(train) [ 46150/160000] lr: 7.3884e-03 eta: 17:27:06 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0082 decode.acc_seg: 99.7085 aux.loss_ce: 0.0071 aux.acc_seg: 99.2450 +04/18 09:28:00 - mmengine - INFO - Iter(train) [ 46200/160000] lr: 7.3855e-03 eta: 17:26:38 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.7254 aux.loss_ce: 0.0084 aux.acc_seg: 99.3106 +04/18 09:28:27 - mmengine - INFO - Iter(train) [ 46250/160000] lr: 7.3826e-03 eta: 17:26:11 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.6780 aux.loss_ce: 0.0080 aux.acc_seg: 98.9218 +04/18 09:28:55 - mmengine - INFO - Iter(train) [ 46300/160000] lr: 7.3797e-03 eta: 17:25:43 time: 0.5525 data_time: 0.0059 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0095 decode.acc_seg: 99.6510 aux.loss_ce: 0.0083 aux.acc_seg: 99.0337 +04/18 09:29:23 - mmengine - INFO - Iter(train) [ 46350/160000] lr: 7.3768e-03 eta: 17:25:16 time: 0.5534 data_time: 0.0070 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.5838 aux.loss_ce: 0.0086 aux.acc_seg: 99.1583 +04/18 09:29:50 - mmengine - INFO - Iter(train) [ 46400/160000] lr: 7.3739e-03 eta: 17:24:48 time: 0.5523 data_time: 0.0058 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6799 aux.loss_ce: 0.0078 aux.acc_seg: 99.2291 +04/18 09:30:18 - mmengine - INFO - Iter(train) [ 46450/160000] lr: 7.3711e-03 eta: 17:24:21 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0082 decode.acc_seg: 99.6817 aux.loss_ce: 0.0080 aux.acc_seg: 99.2268 +04/18 09:30:46 - mmengine - INFO - Iter(train) [ 46500/160000] lr: 7.3682e-03 eta: 17:23:53 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.3092 aux.loss_ce: 0.0085 aux.acc_seg: 98.7722 +04/18 09:31:13 - mmengine - INFO - Iter(train) [ 46550/160000] lr: 7.3653e-03 eta: 17:23:26 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6584 aux.loss_ce: 0.0081 aux.acc_seg: 99.2076 +04/18 09:31:41 - mmengine - INFO - Iter(train) [ 46600/160000] lr: 7.3624e-03 eta: 17:22:58 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.6082 aux.loss_ce: 0.0083 aux.acc_seg: 98.9571 +04/18 09:32:08 - mmengine - INFO - Iter(train) [ 46650/160000] lr: 7.3595e-03 eta: 17:22:31 time: 0.5520 data_time: 0.0071 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.7278 aux.loss_ce: 0.0086 aux.acc_seg: 99.2349 +04/18 09:32:36 - mmengine - INFO - Iter(train) [ 46700/160000] lr: 7.3567e-03 eta: 17:22:03 time: 0.5520 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6774 aux.loss_ce: 0.0080 aux.acc_seg: 99.1701 +04/18 09:33:04 - mmengine - INFO - Iter(train) [ 46750/160000] lr: 7.3538e-03 eta: 17:21:36 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.6366 aux.loss_ce: 0.0080 aux.acc_seg: 99.0657 +04/18 09:33:31 - mmengine - INFO - Iter(train) [ 46800/160000] lr: 7.3509e-03 eta: 17:21:08 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.6181 aux.loss_ce: 0.0075 aux.acc_seg: 99.1819 +04/18 09:33:59 - mmengine - INFO - Iter(train) [ 46850/160000] lr: 7.3480e-03 eta: 17:20:41 time: 0.5534 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.6229 aux.loss_ce: 0.0079 aux.acc_seg: 98.9841 +04/18 09:34:27 - mmengine - INFO - Iter(train) [ 46900/160000] lr: 7.3451e-03 eta: 17:20:13 time: 0.5522 data_time: 0.0069 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0074 decode.acc_seg: 99.7402 aux.loss_ce: 0.0069 aux.acc_seg: 99.2221 +04/18 09:34:54 - mmengine - INFO - Iter(train) [ 46950/160000] lr: 7.3422e-03 eta: 17:19:46 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0103 decode.acc_seg: 99.5117 aux.loss_ce: 0.0085 aux.acc_seg: 98.9362 +04/18 09:35:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:35:22 - mmengine - INFO - Iter(train) [ 47000/160000] lr: 7.3394e-03 eta: 17:19:18 time: 0.5526 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.6680 aux.loss_ce: 0.0085 aux.acc_seg: 99.1042 +04/18 09:35:50 - mmengine - INFO - Iter(train) [ 47050/160000] lr: 7.3365e-03 eta: 17:18:51 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0115 decode.acc_seg: 99.3422 aux.loss_ce: 0.0090 aux.acc_seg: 98.6979 +04/18 09:36:17 - mmengine - INFO - Iter(train) [ 47100/160000] lr: 7.3336e-03 eta: 17:18:23 time: 0.5527 data_time: 0.0071 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0103 decode.acc_seg: 99.6514 aux.loss_ce: 0.0087 aux.acc_seg: 99.1013 +04/18 09:36:45 - mmengine - INFO - Iter(train) [ 47150/160000] lr: 7.3307e-03 eta: 17:17:56 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.7099 aux.loss_ce: 0.0078 aux.acc_seg: 99.3170 +04/18 09:37:12 - mmengine - INFO - Iter(train) [ 47200/160000] lr: 7.3278e-03 eta: 17:17:28 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.6957 aux.loss_ce: 0.0086 aux.acc_seg: 99.2879 +04/18 09:37:40 - mmengine - INFO - Iter(train) [ 47250/160000] lr: 7.3249e-03 eta: 17:17:00 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0095 decode.acc_seg: 99.5607 aux.loss_ce: 0.0082 aux.acc_seg: 99.0628 +04/18 09:38:08 - mmengine - INFO - Iter(train) [ 47300/160000] lr: 7.3221e-03 eta: 17:16:33 time: 0.5520 data_time: 0.0059 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0091 decode.acc_seg: 99.6764 aux.loss_ce: 0.0082 aux.acc_seg: 99.3887 +04/18 09:38:35 - mmengine - INFO - Iter(train) [ 47350/160000] lr: 7.3192e-03 eta: 17:16:05 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6683 aux.loss_ce: 0.0081 aux.acc_seg: 99.2015 +04/18 09:39:03 - mmengine - INFO - Iter(train) [ 47400/160000] lr: 7.3163e-03 eta: 17:15:38 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.6044 aux.loss_ce: 0.0075 aux.acc_seg: 99.1012 +04/18 09:39:30 - mmengine - INFO - Iter(train) [ 47450/160000] lr: 7.3134e-03 eta: 17:15:10 time: 0.5514 data_time: 0.0067 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6548 aux.loss_ce: 0.0084 aux.acc_seg: 99.2237 +04/18 09:39:58 - mmengine - INFO - Iter(train) [ 47500/160000] lr: 7.3105e-03 eta: 17:14:42 time: 0.5513 data_time: 0.0066 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.6684 aux.loss_ce: 0.0085 aux.acc_seg: 99.2130 +04/18 09:40:26 - mmengine - INFO - Iter(train) [ 47550/160000] lr: 7.3076e-03 eta: 17:14:15 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0126 decode.acc_seg: 99.5839 aux.loss_ce: 0.0100 aux.acc_seg: 98.9649 +04/18 09:40:53 - mmengine - INFO - Iter(train) [ 47600/160000] lr: 7.3048e-03 eta: 17:13:47 time: 0.5520 data_time: 0.0068 memory: 7635 loss: 0.0236 decode.loss_ce: 0.0134 decode.acc_seg: 99.2194 aux.loss_ce: 0.0102 aux.acc_seg: 98.6823 +04/18 09:41:21 - mmengine - INFO - Iter(train) [ 47650/160000] lr: 7.3019e-03 eta: 17:13:20 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0108 decode.acc_seg: 99.6519 aux.loss_ce: 0.0089 aux.acc_seg: 99.2672 +04/18 09:41:48 - mmengine - INFO - Iter(train) [ 47700/160000] lr: 7.2990e-03 eta: 17:12:52 time: 0.5513 data_time: 0.0060 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.4692 aux.loss_ce: 0.0086 aux.acc_seg: 99.1116 +04/18 09:42:16 - mmengine - INFO - Iter(train) [ 47750/160000] lr: 7.2961e-03 eta: 17:12:25 time: 0.5505 data_time: 0.0066 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6764 aux.loss_ce: 0.0079 aux.acc_seg: 99.1522 +04/18 09:42:44 - mmengine - INFO - Iter(train) [ 47800/160000] lr: 7.2932e-03 eta: 17:11:57 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6787 aux.loss_ce: 0.0080 aux.acc_seg: 99.2792 +04/18 09:43:11 - mmengine - INFO - Iter(train) [ 47850/160000] lr: 7.2903e-03 eta: 17:11:30 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.7006 aux.loss_ce: 0.0079 aux.acc_seg: 99.2769 +04/18 09:43:39 - mmengine - INFO - Iter(train) [ 47900/160000] lr: 7.2874e-03 eta: 17:11:02 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.6428 aux.loss_ce: 0.0083 aux.acc_seg: 98.9472 +04/18 09:44:07 - mmengine - INFO - Iter(train) [ 47950/160000] lr: 7.2846e-03 eta: 17:10:35 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6430 aux.loss_ce: 0.0082 aux.acc_seg: 99.1258 +04/18 09:44:34 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:44:34 - mmengine - INFO - Iter(train) [ 48000/160000] lr: 7.2817e-03 eta: 17:10:07 time: 0.5605 data_time: 0.0063 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.5721 aux.loss_ce: 0.0083 aux.acc_seg: 99.0805 +04/18 09:45:02 - mmengine - INFO - Iter(train) [ 48050/160000] lr: 7.2788e-03 eta: 17:09:40 time: 0.5507 data_time: 0.0067 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.7004 aux.loss_ce: 0.0074 aux.acc_seg: 99.3549 +04/18 09:45:29 - mmengine - INFO - Iter(train) [ 48100/160000] lr: 7.2759e-03 eta: 17:09:12 time: 0.5531 data_time: 0.0066 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0096 decode.acc_seg: 99.6485 aux.loss_ce: 0.0086 aux.acc_seg: 99.1604 +04/18 09:45:57 - mmengine - INFO - Iter(train) [ 48150/160000] lr: 7.2730e-03 eta: 17:08:44 time: 0.5517 data_time: 0.0062 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.6278 aux.loss_ce: 0.0080 aux.acc_seg: 99.2423 +04/18 09:46:25 - mmengine - INFO - Iter(train) [ 48200/160000] lr: 7.2701e-03 eta: 17:08:17 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0088 decode.acc_seg: 99.5866 aux.loss_ce: 0.0079 aux.acc_seg: 99.2974 +04/18 09:46:52 - mmengine - INFO - Iter(train) [ 48250/160000] lr: 7.2672e-03 eta: 17:07:49 time: 0.5526 data_time: 0.0067 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.5598 aux.loss_ce: 0.0084 aux.acc_seg: 98.9615 +04/18 09:47:20 - mmengine - INFO - Iter(train) [ 48300/160000] lr: 7.2644e-03 eta: 17:07:22 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0123 decode.acc_seg: 99.5892 aux.loss_ce: 0.0096 aux.acc_seg: 98.7660 +04/18 09:47:48 - mmengine - INFO - Iter(train) [ 48350/160000] lr: 7.2615e-03 eta: 17:06:54 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0085 decode.acc_seg: 99.6997 aux.loss_ce: 0.0076 aux.acc_seg: 99.2211 +04/18 09:48:15 - mmengine - INFO - Iter(train) [ 48400/160000] lr: 7.2586e-03 eta: 17:06:27 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0094 decode.acc_seg: 99.6227 aux.loss_ce: 0.0078 aux.acc_seg: 99.1485 +04/18 09:48:43 - mmengine - INFO - Iter(train) [ 48450/160000] lr: 7.2557e-03 eta: 17:05:59 time: 0.5516 data_time: 0.0062 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0088 decode.acc_seg: 99.6257 aux.loss_ce: 0.0076 aux.acc_seg: 99.0685 +04/18 09:49:10 - mmengine - INFO - Iter(train) [ 48500/160000] lr: 7.2528e-03 eta: 17:05:31 time: 0.5507 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0093 decode.acc_seg: 99.7208 aux.loss_ce: 0.0079 aux.acc_seg: 99.2226 +04/18 09:49:38 - mmengine - INFO - Iter(train) [ 48550/160000] lr: 7.2499e-03 eta: 17:05:04 time: 0.5540 data_time: 0.0076 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0088 decode.acc_seg: 99.3439 aux.loss_ce: 0.0077 aux.acc_seg: 98.9624 +04/18 09:50:06 - mmengine - INFO - Iter(train) [ 48600/160000] lr: 7.2470e-03 eta: 17:04:36 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.7175 aux.loss_ce: 0.0078 aux.acc_seg: 99.3053 +04/18 09:50:33 - mmengine - INFO - Iter(train) 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time: 0.5526 data_time: 0.0064 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.6964 aux.loss_ce: 0.0085 aux.acc_seg: 99.1631 +04/18 09:52:51 - mmengine - INFO - Iter(train) [ 48900/160000] lr: 7.2297e-03 eta: 17:01:51 time: 0.5520 data_time: 0.0063 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6101 aux.loss_ce: 0.0082 aux.acc_seg: 99.1882 +04/18 09:53:19 - mmengine - INFO - Iter(train) [ 48950/160000] lr: 7.2268e-03 eta: 17:01:24 time: 0.5513 data_time: 0.0065 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.7277 aux.loss_ce: 0.0077 aux.acc_seg: 99.3268 +04/18 09:53:47 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:53:47 - mmengine - INFO - Iter(train) [ 49000/160000] lr: 7.2239e-03 eta: 17:00:56 time: 0.5522 data_time: 0.0072 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.6124 aux.loss_ce: 0.0084 aux.acc_seg: 99.0455 +04/18 09:54:14 - mmengine - INFO - Iter(train) [ 49050/160000] lr: 7.2211e-03 eta: 17:00:29 time: 0.5514 data_time: 0.0068 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.6682 aux.loss_ce: 0.0085 aux.acc_seg: 99.1941 +04/18 09:54:42 - mmengine - INFO - Iter(train) [ 49100/160000] lr: 7.2182e-03 eta: 17:00:01 time: 0.5519 data_time: 0.0071 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6275 aux.loss_ce: 0.0077 aux.acc_seg: 99.1561 +04/18 09:55:10 - mmengine - INFO - Iter(train) [ 49150/160000] lr: 7.2153e-03 eta: 16:59:34 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.5423 aux.loss_ce: 0.0080 aux.acc_seg: 99.0732 +04/18 09:55:37 - mmengine - INFO - Iter(train) [ 49200/160000] lr: 7.2124e-03 eta: 16:59:06 time: 0.5534 data_time: 0.0067 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.7293 aux.loss_ce: 0.0073 aux.acc_seg: 99.3897 +04/18 09:56:05 - mmengine - INFO - Iter(train) [ 49250/160000] lr: 7.2095e-03 eta: 16:58:39 time: 0.5522 data_time: 0.0060 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.5699 aux.loss_ce: 0.0076 aux.acc_seg: 99.1997 +04/18 09:56:32 - mmengine - INFO - Iter(train) [ 49300/160000] lr: 7.2066e-03 eta: 16:58:11 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.5998 aux.loss_ce: 0.0077 aux.acc_seg: 99.1120 +04/18 09:57:00 - mmengine - INFO - Iter(train) [ 49350/160000] lr: 7.2037e-03 eta: 16:57:43 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.6954 aux.loss_ce: 0.0076 aux.acc_seg: 99.2864 +04/18 09:57:27 - mmengine - INFO - Iter(train) [ 49400/160000] lr: 7.2008e-03 eta: 16:57:16 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6588 aux.loss_ce: 0.0076 aux.acc_seg: 99.0170 +04/18 09:57:55 - mmengine - INFO - Iter(train) [ 49450/160000] lr: 7.1979e-03 eta: 16:56:48 time: 0.5498 data_time: 0.0057 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7878 aux.loss_ce: 0.0080 aux.acc_seg: 99.3705 +04/18 09:58:23 - mmengine - INFO - Iter(train) [ 49500/160000] lr: 7.1951e-03 eta: 16:56:20 time: 0.5513 data_time: 0.0066 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0091 decode.acc_seg: 99.6921 aux.loss_ce: 0.0077 aux.acc_seg: 99.1593 +04/18 09:58:50 - mmengine - INFO - Iter(train) [ 49550/160000] lr: 7.1922e-03 eta: 16:55:53 time: 0.5501 data_time: 0.0060 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.6333 aux.loss_ce: 0.0084 aux.acc_seg: 99.1590 +04/18 09:59:18 - mmengine - INFO - Iter(train) [ 49600/160000] lr: 7.1893e-03 eta: 16:55:25 time: 0.5501 data_time: 0.0060 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.5110 aux.loss_ce: 0.0079 aux.acc_seg: 98.7459 +04/18 09:59:45 - mmengine - INFO - Iter(train) [ 49650/160000] lr: 7.1864e-03 eta: 16:54:58 time: 0.5508 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.6520 aux.loss_ce: 0.0082 aux.acc_seg: 99.2351 +04/18 10:00:13 - mmengine - INFO - Iter(train) [ 49700/160000] lr: 7.1835e-03 eta: 16:54:30 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6666 aux.loss_ce: 0.0073 aux.acc_seg: 99.3447 +04/18 10:00:41 - mmengine - INFO - Iter(train) [ 49750/160000] lr: 7.1806e-03 eta: 16:54:02 time: 0.5512 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6932 aux.loss_ce: 0.0072 aux.acc_seg: 99.3723 +04/18 10:01:08 - mmengine - INFO - Iter(train) [ 49800/160000] lr: 7.1777e-03 eta: 16:53:35 time: 0.5522 data_time: 0.0077 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.7171 aux.loss_ce: 0.0083 aux.acc_seg: 99.3166 +04/18 10:01:36 - mmengine - INFO - Iter(train) [ 49850/160000] lr: 7.1748e-03 eta: 16:53:07 time: 0.5522 data_time: 0.0061 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.7813 aux.loss_ce: 0.0082 aux.acc_seg: 99.4865 +04/18 10:02:04 - mmengine - INFO - Iter(train) [ 49900/160000] lr: 7.1719e-03 eta: 16:52:40 time: 0.5525 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7205 aux.loss_ce: 0.0077 aux.acc_seg: 99.1055 +04/18 10:02:31 - mmengine - INFO - Iter(train) [ 49950/160000] lr: 7.1690e-03 eta: 16:52:12 time: 0.5514 data_time: 0.0073 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.7235 aux.loss_ce: 0.0074 aux.acc_seg: 99.3393 +04/18 10:02:59 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:02:59 - mmengine - INFO - Iter(train) [ 50000/160000] lr: 7.1662e-03 eta: 16:51:45 time: 0.5518 data_time: 0.0062 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0086 decode.acc_seg: 99.6929 aux.loss_ce: 0.0083 aux.acc_seg: 99.2558 +04/18 10:02:59 - mmengine - INFO - Saving checkpoint at 50000 iterations +04/18 10:03:03 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0466 data_time: 0.0015 memory: 1657 +04/18 10:03:05 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0468 data_time: 0.0013 memory: 1657 +04/18 10:03:07 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0470 data_time: 0.0016 memory: 1657 +04/18 10:03:10 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0460 data_time: 0.0013 memory: 1657 +04/18 10:03:10 - mmengine - INFO - per class results: +04/18 10:03:10 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.46 | 99.53 | 99.6 | 99.46 | +| contrast | 79.91 | 90.32 | 88.83 | 87.39 | 90.32 | ++------------+-------+-------+--------+-----------+--------+ +04/18 10:03:10 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0900 mIoU: 89.4800 mAcc: 94.8900 mFscore: 94.1800 mPrecision: 93.4900 mRecall: 94.8900 data_time: 0.0015 time: 0.0467 +04/18 10:03:37 - mmengine - INFO - Iter(train) [ 50050/160000] lr: 7.1633e-03 eta: 16:51:17 time: 0.5494 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.6303 aux.loss_ce: 0.0078 aux.acc_seg: 99.1614 +04/18 10:04:05 - mmengine - INFO - Iter(train) [ 50100/160000] lr: 7.1604e-03 eta: 16:50:50 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.7437 aux.loss_ce: 0.0078 aux.acc_seg: 99.2997 +04/18 10:04:33 - mmengine - INFO - Iter(train) [ 50150/160000] lr: 7.1575e-03 eta: 16:50:22 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.7126 aux.loss_ce: 0.0075 aux.acc_seg: 99.1648 +04/18 10:05:00 - mmengine - INFO - Iter(train) [ 50200/160000] lr: 7.1546e-03 eta: 16:49:55 time: 0.5508 data_time: 0.0068 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0087 decode.acc_seg: 99.7084 aux.loss_ce: 0.0076 aux.acc_seg: 99.4479 +04/18 10:05:28 - mmengine - INFO - Iter(train) [ 50250/160000] lr: 7.1517e-03 eta: 16:49:27 time: 0.5500 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6487 aux.loss_ce: 0.0082 aux.acc_seg: 99.1045 +04/18 10:05:55 - mmengine - INFO - Iter(train) [ 50300/160000] lr: 7.1488e-03 eta: 16:48:59 time: 0.5500 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6172 aux.loss_ce: 0.0081 aux.acc_seg: 98.9217 +04/18 10:06:23 - mmengine - INFO - Iter(train) [ 50350/160000] lr: 7.1459e-03 eta: 16:48:32 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0084 decode.acc_seg: 99.4938 aux.loss_ce: 0.0076 aux.acc_seg: 99.0082 +04/18 10:06:51 - mmengine - INFO - Iter(train) [ 50400/160000] lr: 7.1430e-03 eta: 16:48:04 time: 0.5517 data_time: 0.0069 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0091 decode.acc_seg: 99.6501 aux.loss_ce: 0.0088 aux.acc_seg: 99.2349 +04/18 10:07:18 - mmengine - INFO - Iter(train) [ 50450/160000] lr: 7.1401e-03 eta: 16:47:37 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.5913 aux.loss_ce: 0.0083 aux.acc_seg: 99.2395 +04/18 10:07:46 - mmengine - INFO - Iter(train) [ 50500/160000] lr: 7.1372e-03 eta: 16:47:09 time: 0.5531 data_time: 0.0066 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0091 decode.acc_seg: 99.7045 aux.loss_ce: 0.0085 aux.acc_seg: 99.1563 +04/18 10:08:14 - mmengine - INFO - Iter(train) [ 50550/160000] lr: 7.1343e-03 eta: 16:46:42 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0103 decode.acc_seg: 99.6216 aux.loss_ce: 0.0083 aux.acc_seg: 99.1870 +04/18 10:08:41 - mmengine - INFO - Iter(train) [ 50600/160000] lr: 7.1315e-03 eta: 16:46:14 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6038 aux.loss_ce: 0.0079 aux.acc_seg: 99.2425 +04/18 10:09:09 - mmengine - INFO - Iter(train) [ 50650/160000] lr: 7.1286e-03 eta: 16:45:46 time: 0.5527 data_time: 0.0073 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0091 decode.acc_seg: 99.6921 aux.loss_ce: 0.0085 aux.acc_seg: 99.2167 +04/18 10:09:36 - mmengine - INFO - Iter(train) [ 50700/160000] lr: 7.1257e-03 eta: 16:45:19 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.6984 aux.loss_ce: 0.0082 aux.acc_seg: 99.3204 +04/18 10:10:04 - mmengine - INFO - Iter(train) [ 50750/160000] lr: 7.1228e-03 eta: 16:44:51 time: 0.5508 data_time: 0.0060 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7176 aux.loss_ce: 0.0072 aux.acc_seg: 99.1421 +04/18 10:10:31 - mmengine - INFO - Iter(train) [ 50800/160000] lr: 7.1199e-03 eta: 16:44:23 time: 0.5517 data_time: 0.0061 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.6450 aux.loss_ce: 0.0081 aux.acc_seg: 99.1481 +04/18 10:10:59 - mmengine - INFO - Iter(train) [ 50850/160000] lr: 7.1170e-03 eta: 16:43:56 time: 0.5520 data_time: 0.0070 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6152 aux.loss_ce: 0.0084 aux.acc_seg: 99.1262 +04/18 10:11:27 - mmengine - INFO - Iter(train) [ 50900/160000] lr: 7.1141e-03 eta: 16:43:28 time: 0.5511 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.7204 aux.loss_ce: 0.0077 aux.acc_seg: 99.4347 +04/18 10:11:54 - mmengine - INFO - Iter(train) [ 50950/160000] lr: 7.1112e-03 eta: 16:43:01 time: 0.5504 data_time: 0.0060 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.7308 aux.loss_ce: 0.0077 aux.acc_seg: 99.2590 +04/18 10:12:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:12:22 - mmengine - INFO - Iter(train) [ 51000/160000] lr: 7.1083e-03 eta: 16:42:33 time: 0.5508 data_time: 0.0061 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.7273 aux.loss_ce: 0.0080 aux.acc_seg: 99.3155 +04/18 10:12:49 - mmengine - INFO - Iter(train) [ 51050/160000] lr: 7.1054e-03 eta: 16:42:06 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6755 aux.loss_ce: 0.0080 aux.acc_seg: 99.0463 +04/18 10:13:17 - mmengine - INFO - Iter(train) [ 51100/160000] lr: 7.1025e-03 eta: 16:41:38 time: 0.5522 data_time: 0.0068 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6352 aux.loss_ce: 0.0081 aux.acc_seg: 99.0726 +04/18 10:13:45 - mmengine - INFO - Iter(train) [ 51150/160000] lr: 7.0996e-03 eta: 16:41:10 time: 0.5523 data_time: 0.0060 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.6531 aux.loss_ce: 0.0072 aux.acc_seg: 99.2215 +04/18 10:14:12 - mmengine - INFO - Iter(train) [ 51200/160000] lr: 7.0967e-03 eta: 16:40:43 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.5941 aux.loss_ce: 0.0082 aux.acc_seg: 98.9841 +04/18 10:14:40 - mmengine - INFO - Iter(train) [ 51250/160000] lr: 7.0938e-03 eta: 16:40:15 time: 0.5519 data_time: 0.0072 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0083 decode.acc_seg: 99.7270 aux.loss_ce: 0.0075 aux.acc_seg: 99.2428 +04/18 10:15:07 - mmengine - INFO - Iter(train) [ 51300/160000] lr: 7.0910e-03 eta: 16:39:48 time: 0.5517 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.4575 aux.loss_ce: 0.0082 aux.acc_seg: 99.0960 +04/18 10:15:35 - mmengine - INFO - Iter(train) [ 51350/160000] lr: 7.0881e-03 eta: 16:39:20 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.7530 aux.loss_ce: 0.0074 aux.acc_seg: 99.3126 +04/18 10:16:03 - mmengine - INFO - Iter(train) [ 51400/160000] lr: 7.0852e-03 eta: 16:38:52 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.7535 aux.loss_ce: 0.0078 aux.acc_seg: 99.3334 +04/18 10:16:30 - mmengine - INFO - Iter(train) [ 51450/160000] lr: 7.0823e-03 eta: 16:38:25 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.6828 aux.loss_ce: 0.0083 aux.acc_seg: 99.2489 +04/18 10:16:58 - mmengine - INFO - Iter(train) [ 51500/160000] lr: 7.0794e-03 eta: 16:37:57 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.6384 aux.loss_ce: 0.0081 aux.acc_seg: 99.1660 +04/18 10:17:25 - mmengine - INFO - Iter(train) [ 51550/160000] lr: 7.0765e-03 eta: 16:37:30 time: 0.5522 data_time: 0.0066 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.7622 aux.loss_ce: 0.0075 aux.acc_seg: 99.3407 +04/18 10:17:53 - mmengine - INFO - Iter(train) [ 51600/160000] lr: 7.0736e-03 eta: 16:37:02 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.7211 aux.loss_ce: 0.0079 aux.acc_seg: 99.2453 +04/18 10:18:21 - mmengine - INFO - Iter(train) [ 51650/160000] lr: 7.0707e-03 eta: 16:36:34 time: 0.5527 data_time: 0.0068 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.4807 aux.loss_ce: 0.0081 aux.acc_seg: 99.0744 +04/18 10:18:48 - mmengine - INFO - Iter(train) [ 51700/160000] lr: 7.0678e-03 eta: 16:36:07 time: 0.5525 data_time: 0.0075 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.7381 aux.loss_ce: 0.0081 aux.acc_seg: 99.2805 +04/18 10:19:16 - mmengine - INFO - Iter(train) [ 51750/160000] lr: 7.0649e-03 eta: 16:35:39 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.6535 aux.loss_ce: 0.0082 aux.acc_seg: 99.1571 +04/18 10:19:43 - mmengine - INFO - Iter(train) [ 51800/160000] lr: 7.0620e-03 eta: 16:35:12 time: 0.5509 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.7637 aux.loss_ce: 0.0073 aux.acc_seg: 99.2836 +04/18 10:20:11 - mmengine - INFO - Iter(train) [ 51850/160000] lr: 7.0591e-03 eta: 16:34:44 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0098 decode.acc_seg: 99.6417 aux.loss_ce: 0.0085 aux.acc_seg: 99.1693 +04/18 10:20:39 - mmengine - INFO - Iter(train) [ 51900/160000] lr: 7.0562e-03 eta: 16:34:16 time: 0.5524 data_time: 0.0062 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0091 decode.acc_seg: 99.6486 aux.loss_ce: 0.0082 aux.acc_seg: 99.2789 +04/18 10:21:06 - mmengine - INFO - Iter(train) [ 51950/160000] lr: 7.0533e-03 eta: 16:33:49 time: 0.5602 data_time: 0.0062 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.6376 aux.loss_ce: 0.0082 aux.acc_seg: 99.2391 +04/18 10:21:34 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:21:34 - mmengine - INFO - Iter(train) [ 52000/160000] lr: 7.0504e-03 eta: 16:33:21 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0085 decode.acc_seg: 99.6520 aux.loss_ce: 0.0078 aux.acc_seg: 99.3771 +04/18 10:22:02 - mmengine - INFO - Iter(train) [ 52050/160000] lr: 7.0475e-03 eta: 16:32:54 time: 0.5515 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6364 aux.loss_ce: 0.0075 aux.acc_seg: 99.0461 +04/18 10:22:29 - mmengine - INFO - Iter(train) [ 52100/160000] lr: 7.0446e-03 eta: 16:32:26 time: 0.5516 data_time: 0.0070 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.6502 aux.loss_ce: 0.0087 aux.acc_seg: 99.2046 +04/18 10:22:57 - mmengine - INFO - Iter(train) [ 52150/160000] lr: 7.0417e-03 eta: 16:31:59 time: 0.5526 data_time: 0.0069 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6889 aux.loss_ce: 0.0079 aux.acc_seg: 99.3339 +04/18 10:23:24 - mmengine - INFO - Iter(train) [ 52200/160000] lr: 7.0388e-03 eta: 16:31:31 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0075 decode.acc_seg: 99.6093 aux.loss_ce: 0.0070 aux.acc_seg: 99.0957 +04/18 10:23:52 - mmengine - INFO - Iter(train) [ 52250/160000] lr: 7.0359e-03 eta: 16:31:03 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0092 decode.acc_seg: 99.6466 aux.loss_ce: 0.0088 aux.acc_seg: 98.8050 +04/18 10:24:19 - mmengine - INFO - Iter(train) [ 52300/160000] lr: 7.0330e-03 eta: 16:30:36 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0086 decode.acc_seg: 99.6104 aux.loss_ce: 0.0081 aux.acc_seg: 99.2305 +04/18 10:24:47 - mmengine - INFO - Iter(train) [ 52350/160000] lr: 7.0301e-03 eta: 16:30:08 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.7419 aux.loss_ce: 0.0085 aux.acc_seg: 99.3154 +04/18 10:25:15 - mmengine - INFO - Iter(train) [ 52400/160000] lr: 7.0272e-03 eta: 16:29:41 time: 0.5508 data_time: 0.0066 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0088 decode.acc_seg: 99.7513 aux.loss_ce: 0.0086 aux.acc_seg: 99.3443 +04/18 10:25:42 - mmengine - INFO - Iter(train) [ 52450/160000] lr: 7.0244e-03 eta: 16:29:13 time: 0.5508 data_time: 0.0060 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6203 aux.loss_ce: 0.0074 aux.acc_seg: 99.1570 +04/18 10:26:10 - mmengine - INFO - Iter(train) [ 52500/160000] lr: 7.0215e-03 eta: 16:28:45 time: 0.5515 data_time: 0.0059 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.7168 aux.loss_ce: 0.0074 aux.acc_seg: 99.2850 +04/18 10:26:37 - mmengine - INFO - Iter(train) [ 52550/160000] lr: 7.0186e-03 eta: 16:28:18 time: 0.5516 data_time: 0.0064 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6927 aux.loss_ce: 0.0074 aux.acc_seg: 99.2032 +04/18 10:27:05 - mmengine - INFO - Iter(train) [ 52600/160000] lr: 7.0157e-03 eta: 16:27:50 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6718 aux.loss_ce: 0.0081 aux.acc_seg: 99.2196 +04/18 10:27:33 - mmengine - INFO - Iter(train) [ 52650/160000] lr: 7.0128e-03 eta: 16:27:23 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0076 decode.acc_seg: 99.7131 aux.loss_ce: 0.0072 aux.acc_seg: 99.2583 +04/18 10:28:00 - mmengine - INFO - Iter(train) [ 52700/160000] lr: 7.0099e-03 eta: 16:26:55 time: 0.5527 data_time: 0.0070 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0102 decode.acc_seg: 99.6510 aux.loss_ce: 0.0081 aux.acc_seg: 99.2446 +04/18 10:28:28 - mmengine - INFO - Iter(train) [ 52750/160000] lr: 7.0070e-03 eta: 16:26:27 time: 0.5522 data_time: 0.0068 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0114 decode.acc_seg: 99.5508 aux.loss_ce: 0.0095 aux.acc_seg: 99.0664 +04/18 10:28:55 - mmengine - INFO - Iter(train) [ 52800/160000] lr: 7.0041e-03 eta: 16:26:00 time: 0.5517 data_time: 0.0071 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0091 decode.acc_seg: 99.5591 aux.loss_ce: 0.0077 aux.acc_seg: 98.9380 +04/18 10:29:23 - mmengine - INFO - Iter(train) [ 52850/160000] lr: 7.0012e-03 eta: 16:25:32 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.6599 aux.loss_ce: 0.0083 aux.acc_seg: 99.0653 +04/18 10:29:51 - mmengine - INFO - Iter(train) [ 52900/160000] lr: 6.9983e-03 eta: 16:25:05 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0097 decode.acc_seg: 99.7003 aux.loss_ce: 0.0088 aux.acc_seg: 99.2614 +04/18 10:30:18 - mmengine - INFO - Iter(train) [ 52950/160000] lr: 6.9954e-03 eta: 16:24:37 time: 0.5528 data_time: 0.0063 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.5527 aux.loss_ce: 0.0083 aux.acc_seg: 99.1763 +04/18 10:30:46 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:30:46 - mmengine - INFO - Iter(train) [ 53000/160000] lr: 6.9925e-03 eta: 16:24:09 time: 0.5511 data_time: 0.0055 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0100 decode.acc_seg: 99.7108 aux.loss_ce: 0.0090 aux.acc_seg: 99.2771 +04/18 10:31:14 - mmengine - INFO - Iter(train) [ 53050/160000] lr: 6.9896e-03 eta: 16:23:42 time: 0.5529 data_time: 0.0059 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0101 decode.acc_seg: 99.6829 aux.loss_ce: 0.0085 aux.acc_seg: 99.2652 +04/18 10:31:41 - mmengine - INFO - Iter(train) [ 53100/160000] lr: 6.9867e-03 eta: 16:23:15 time: 0.5501 data_time: 0.0058 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0098 decode.acc_seg: 99.5369 aux.loss_ce: 0.0088 aux.acc_seg: 98.8965 +04/18 10:32:09 - mmengine - INFO - Iter(train) [ 53150/160000] lr: 6.9838e-03 eta: 16:22:47 time: 0.5526 data_time: 0.0064 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.6242 aux.loss_ce: 0.0081 aux.acc_seg: 99.2612 +04/18 10:32:36 - mmengine - INFO - Iter(train) [ 53200/160000] lr: 6.9809e-03 eta: 16:22:19 time: 0.5519 data_time: 0.0068 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0103 decode.acc_seg: 99.5462 aux.loss_ce: 0.0086 aux.acc_seg: 98.9160 +04/18 10:33:04 - mmengine - INFO - Iter(train) [ 53250/160000] lr: 6.9780e-03 eta: 16:21:52 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0099 decode.acc_seg: 99.6872 aux.loss_ce: 0.0086 aux.acc_seg: 99.2883 +04/18 10:33:32 - mmengine - INFO - Iter(train) [ 53300/160000] lr: 6.9751e-03 eta: 16:21:24 time: 0.5513 data_time: 0.0061 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.6056 aux.loss_ce: 0.0082 aux.acc_seg: 99.2646 +04/18 10:33:59 - mmengine - INFO - Iter(train) [ 53350/160000] lr: 6.9722e-03 eta: 16:20:57 time: 0.5496 data_time: 0.0068 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.7437 aux.loss_ce: 0.0078 aux.acc_seg: 99.2231 +04/18 10:34:27 - mmengine - INFO - Iter(train) [ 53400/160000] lr: 6.9693e-03 eta: 16:20:29 time: 0.5525 data_time: 0.0071 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0100 decode.acc_seg: 99.6415 aux.loss_ce: 0.0082 aux.acc_seg: 99.1047 +04/18 10:34:54 - mmengine - INFO - Iter(train) [ 53450/160000] lr: 6.9664e-03 eta: 16:20:01 time: 0.5512 data_time: 0.0071 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.6616 aux.loss_ce: 0.0076 aux.acc_seg: 99.1767 +04/18 10:35:22 - mmengine - INFO - Iter(train) [ 53500/160000] lr: 6.9635e-03 eta: 16:19:34 time: 0.5510 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6323 aux.loss_ce: 0.0079 aux.acc_seg: 99.0246 +04/18 10:35:50 - mmengine - INFO - Iter(train) [ 53550/160000] lr: 6.9606e-03 eta: 16:19:06 time: 0.5522 data_time: 0.0071 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.6636 aux.loss_ce: 0.0084 aux.acc_seg: 99.0689 +04/18 10:36:17 - mmengine - INFO - Iter(train) [ 53600/160000] lr: 6.9577e-03 eta: 16:18:39 time: 0.5532 data_time: 0.0071 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6490 aux.loss_ce: 0.0080 aux.acc_seg: 99.1546 +04/18 10:36:45 - mmengine - INFO - Iter(train) [ 53650/160000] lr: 6.9548e-03 eta: 16:18:11 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.7125 aux.loss_ce: 0.0083 aux.acc_seg: 99.2896 +04/18 10:37:12 - mmengine - INFO - Iter(train) [ 53700/160000] lr: 6.9519e-03 eta: 16:17:44 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.7014 aux.loss_ce: 0.0083 aux.acc_seg: 99.2769 +04/18 10:37:40 - mmengine - INFO - Iter(train) [ 53750/160000] lr: 6.9490e-03 eta: 16:17:16 time: 0.5508 data_time: 0.0071 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.6697 aux.loss_ce: 0.0080 aux.acc_seg: 99.1458 +04/18 10:38:08 - mmengine - INFO - Iter(train) [ 53800/160000] lr: 6.9461e-03 eta: 16:16:48 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.7752 aux.loss_ce: 0.0074 aux.acc_seg: 99.4241 +04/18 10:38:35 - mmengine - INFO - Iter(train) [ 53850/160000] lr: 6.9432e-03 eta: 16:16:21 time: 0.5525 data_time: 0.0059 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.6603 aux.loss_ce: 0.0076 aux.acc_seg: 99.3434 +04/18 10:39:03 - mmengine - INFO - Iter(train) [ 53900/160000] lr: 6.9403e-03 eta: 16:15:53 time: 0.5512 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.6707 aux.loss_ce: 0.0079 aux.acc_seg: 99.0522 +04/18 10:39:30 - mmengine - INFO - Iter(train) [ 53950/160000] lr: 6.9374e-03 eta: 16:15:26 time: 0.5522 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.6368 aux.loss_ce: 0.0078 aux.acc_seg: 99.3043 +04/18 10:39:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:39:58 - mmengine - INFO - Iter(train) [ 54000/160000] lr: 6.9345e-03 eta: 16:14:58 time: 0.5502 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.7044 aux.loss_ce: 0.0079 aux.acc_seg: 99.3671 +04/18 10:40:26 - mmengine - INFO - Iter(train) [ 54050/160000] lr: 6.9316e-03 eta: 16:14:30 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.6016 aux.loss_ce: 0.0081 aux.acc_seg: 99.1320 +04/18 10:40:53 - mmengine - INFO - Iter(train) [ 54100/160000] lr: 6.9287e-03 eta: 16:14:03 time: 0.5505 data_time: 0.0068 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0091 decode.acc_seg: 99.6835 aux.loss_ce: 0.0078 aux.acc_seg: 99.2524 +04/18 10:41:21 - mmengine - INFO - Iter(train) [ 54150/160000] lr: 6.9258e-03 eta: 16:13:35 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.6569 aux.loss_ce: 0.0078 aux.acc_seg: 99.2126 +04/18 10:41:49 - mmengine - INFO - Iter(train) [ 54200/160000] lr: 6.9229e-03 eta: 16:13:08 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.7202 aux.loss_ce: 0.0074 aux.acc_seg: 99.4156 +04/18 10:42:16 - mmengine - INFO - Iter(train) [ 54250/160000] lr: 6.9200e-03 eta: 16:12:40 time: 0.5512 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0084 decode.acc_seg: 99.5613 aux.loss_ce: 0.0075 aux.acc_seg: 99.0467 +04/18 10:42:44 - mmengine - INFO - Iter(train) [ 54300/160000] lr: 6.9171e-03 eta: 16:12:13 time: 0.5514 data_time: 0.0062 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7068 aux.loss_ce: 0.0077 aux.acc_seg: 99.3682 +04/18 10:43:11 - mmengine - INFO - Iter(train) [ 54350/160000] lr: 6.9142e-03 eta: 16:11:45 time: 0.5512 data_time: 0.0071 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0084 decode.acc_seg: 99.6916 aux.loss_ce: 0.0076 aux.acc_seg: 99.2680 +04/18 10:43:39 - mmengine - INFO - Iter(train) [ 54400/160000] lr: 6.9113e-03 eta: 16:11:17 time: 0.5515 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.6791 aux.loss_ce: 0.0084 aux.acc_seg: 99.2279 +04/18 10:44:07 - mmengine - INFO - Iter(train) [ 54450/160000] lr: 6.9084e-03 eta: 16:10:50 time: 0.5521 data_time: 0.0067 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.6384 aux.loss_ce: 0.0084 aux.acc_seg: 99.0820 +04/18 10:44:34 - mmengine - INFO - Iter(train) [ 54500/160000] lr: 6.9055e-03 eta: 16:10:22 time: 0.5512 data_time: 0.0068 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.5789 aux.loss_ce: 0.0085 aux.acc_seg: 99.2597 +04/18 10:45:02 - mmengine - INFO - Iter(train) [ 54550/160000] lr: 6.9025e-03 eta: 16:09:55 time: 0.5517 data_time: 0.0070 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.6681 aux.loss_ce: 0.0079 aux.acc_seg: 99.1036 +04/18 10:45:29 - mmengine - INFO - Iter(train) [ 54600/160000] lr: 6.8996e-03 eta: 16:09:27 time: 0.5519 data_time: 0.0058 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7135 aux.loss_ce: 0.0080 aux.acc_seg: 99.3480 +04/18 10:45:57 - mmengine - INFO - Iter(train) [ 54650/160000] lr: 6.8967e-03 eta: 16:08:59 time: 0.5526 data_time: 0.0073 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0085 decode.acc_seg: 99.6518 aux.loss_ce: 0.0078 aux.acc_seg: 99.1810 +04/18 10:46:25 - mmengine - INFO - Iter(train) [ 54700/160000] lr: 6.8938e-03 eta: 16:08:32 time: 0.5516 data_time: 0.0067 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0093 decode.acc_seg: 99.6024 aux.loss_ce: 0.0091 aux.acc_seg: 98.9346 +04/18 10:46:52 - mmengine - INFO - Iter(train) [ 54750/160000] lr: 6.8909e-03 eta: 16:08:04 time: 0.5523 data_time: 0.0064 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7321 aux.loss_ce: 0.0079 aux.acc_seg: 99.2522 +04/18 10:47:20 - mmengine - INFO - Iter(train) [ 54800/160000] lr: 6.8880e-03 eta: 16:07:37 time: 0.5510 data_time: 0.0070 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.6336 aux.loss_ce: 0.0080 aux.acc_seg: 99.1231 +04/18 10:47:47 - mmengine - INFO - Iter(train) [ 54850/160000] lr: 6.8851e-03 eta: 16:07:09 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6531 aux.loss_ce: 0.0082 aux.acc_seg: 99.1193 +04/18 10:48:15 - mmengine - INFO - Iter(train) [ 54900/160000] lr: 6.8822e-03 eta: 16:06:42 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0119 decode.acc_seg: 99.6003 aux.loss_ce: 0.0087 aux.acc_seg: 99.1993 +04/18 10:48:43 - mmengine - INFO - Iter(train) [ 54950/160000] lr: 6.8793e-03 eta: 16:06:14 time: 0.5521 data_time: 0.0066 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.6529 aux.loss_ce: 0.0087 aux.acc_seg: 99.1139 +04/18 10:49:10 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:49:10 - mmengine - INFO - Iter(train) [ 55000/160000] lr: 6.8764e-03 eta: 16:05:46 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0100 decode.acc_seg: 99.3012 aux.loss_ce: 0.0084 aux.acc_seg: 98.9002 +04/18 10:49:38 - mmengine - INFO - Iter(train) [ 55050/160000] lr: 6.8735e-03 eta: 16:05:19 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0095 decode.acc_seg: 99.6247 aux.loss_ce: 0.0085 aux.acc_seg: 99.1119 +04/18 10:50:05 - mmengine - INFO - Iter(train) [ 55100/160000] lr: 6.8706e-03 eta: 16:04:51 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0094 decode.acc_seg: 99.6411 aux.loss_ce: 0.0089 aux.acc_seg: 99.0154 +04/18 10:50:33 - mmengine - INFO - Iter(train) [ 55150/160000] lr: 6.8677e-03 eta: 16:04:24 time: 0.5511 data_time: 0.0063 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6661 aux.loss_ce: 0.0077 aux.acc_seg: 99.2380 +04/18 10:51:01 - mmengine - INFO - Iter(train) [ 55200/160000] lr: 6.8648e-03 eta: 16:03:56 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6775 aux.loss_ce: 0.0080 aux.acc_seg: 99.2329 +04/18 10:51:28 - mmengine - INFO - Iter(train) [ 55250/160000] lr: 6.8619e-03 eta: 16:03:29 time: 0.5520 data_time: 0.0069 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.5274 aux.loss_ce: 0.0079 aux.acc_seg: 98.9709 +04/18 10:51:56 - mmengine - INFO - Iter(train) [ 55300/160000] lr: 6.8590e-03 eta: 16:03:01 time: 0.5508 data_time: 0.0067 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.6504 aux.loss_ce: 0.0082 aux.acc_seg: 99.0874 +04/18 10:52:24 - mmengine - INFO - Iter(train) [ 55350/160000] lr: 6.8561e-03 eta: 16:02:34 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6454 aux.loss_ce: 0.0077 aux.acc_seg: 99.1481 +04/18 10:52:51 - mmengine - INFO - Iter(train) [ 55400/160000] lr: 6.8532e-03 eta: 16:02:06 time: 0.5520 data_time: 0.0076 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0104 decode.acc_seg: 99.7279 aux.loss_ce: 0.0088 aux.acc_seg: 99.1906 +04/18 10:53:19 - mmengine - INFO - Iter(train) [ 55450/160000] lr: 6.8503e-03 eta: 16:01:38 time: 0.5512 data_time: 0.0067 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0121 decode.acc_seg: 99.5101 aux.loss_ce: 0.0096 aux.acc_seg: 98.7386 +04/18 10:53:46 - mmengine - INFO - Iter(train) [ 55500/160000] lr: 6.8474e-03 eta: 16:01:11 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6796 aux.loss_ce: 0.0077 aux.acc_seg: 99.2816 +04/18 10:54:14 - mmengine - INFO - Iter(train) [ 55550/160000] lr: 6.8445e-03 eta: 16:00:43 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.6401 aux.loss_ce: 0.0089 aux.acc_seg: 99.1049 +04/18 10:54:42 - mmengine - INFO - Iter(train) [ 55600/160000] lr: 6.8416e-03 eta: 16:00:16 time: 0.5524 data_time: 0.0070 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.4155 aux.loss_ce: 0.0081 aux.acc_seg: 99.0135 +04/18 10:55:09 - mmengine - INFO - Iter(train) [ 55650/160000] lr: 6.8386e-03 eta: 15:59:48 time: 0.5518 data_time: 0.0062 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6309 aux.loss_ce: 0.0079 aux.acc_seg: 99.1927 +04/18 10:55:37 - mmengine - INFO - Iter(train) [ 55700/160000] lr: 6.8357e-03 eta: 15:59:20 time: 0.5519 data_time: 0.0062 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7025 aux.loss_ce: 0.0079 aux.acc_seg: 99.2126 +04/18 10:56:04 - mmengine - INFO - Iter(train) [ 55750/160000] lr: 6.8328e-03 eta: 15:58:53 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.5903 aux.loss_ce: 0.0079 aux.acc_seg: 99.2051 +04/18 10:56:32 - mmengine - INFO - Iter(train) [ 55800/160000] lr: 6.8299e-03 eta: 15:58:25 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.7313 aux.loss_ce: 0.0076 aux.acc_seg: 99.3532 +04/18 10:57:00 - mmengine - INFO - Iter(train) [ 55850/160000] lr: 6.8270e-03 eta: 15:57:58 time: 0.5504 data_time: 0.0061 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0079 decode.acc_seg: 99.6006 aux.loss_ce: 0.0074 aux.acc_seg: 99.1119 +04/18 10:57:27 - mmengine - INFO - Iter(train) [ 55900/160000] lr: 6.8241e-03 eta: 15:57:30 time: 0.5519 data_time: 0.0066 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6524 aux.loss_ce: 0.0075 aux.acc_seg: 99.1038 +04/18 10:57:55 - mmengine - INFO - Iter(train) [ 55950/160000] lr: 6.8212e-03 eta: 15:57:03 time: 0.5494 data_time: 0.0055 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6177 aux.loss_ce: 0.0081 aux.acc_seg: 99.1803 +04/18 10:58:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:58:22 - mmengine - INFO - Iter(train) [ 56000/160000] lr: 6.8183e-03 eta: 15:56:35 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6518 aux.loss_ce: 0.0076 aux.acc_seg: 99.2232 +04/18 10:58:50 - mmengine - INFO - Iter(train) [ 56050/160000] lr: 6.8154e-03 eta: 15:56:07 time: 0.5505 data_time: 0.0064 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0084 decode.acc_seg: 99.6863 aux.loss_ce: 0.0075 aux.acc_seg: 99.2343 +04/18 10:59:18 - mmengine - INFO - Iter(train) [ 56100/160000] lr: 6.8125e-03 eta: 15:55:40 time: 0.5526 data_time: 0.0062 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.7315 aux.loss_ce: 0.0081 aux.acc_seg: 99.4270 +04/18 10:59:45 - mmengine - INFO - Iter(train) [ 56150/160000] lr: 6.8096e-03 eta: 15:55:12 time: 0.5525 data_time: 0.0072 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.7353 aux.loss_ce: 0.0077 aux.acc_seg: 99.2964 +04/18 11:00:13 - mmengine - INFO - Iter(train) [ 56200/160000] lr: 6.8067e-03 eta: 15:54:44 time: 0.5529 data_time: 0.0066 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.7451 aux.loss_ce: 0.0085 aux.acc_seg: 99.1852 +04/18 11:00:40 - mmengine - INFO - Iter(train) [ 56250/160000] lr: 6.8038e-03 eta: 15:54:17 time: 0.5504 data_time: 0.0064 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.5820 aux.loss_ce: 0.0084 aux.acc_seg: 99.1267 +04/18 11:01:08 - mmengine - INFO - Iter(train) [ 56300/160000] lr: 6.8009e-03 eta: 15:53:50 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.5940 aux.loss_ce: 0.0077 aux.acc_seg: 99.0654 +04/18 11:01:36 - mmengine - INFO - Iter(train) [ 56350/160000] lr: 6.7980e-03 eta: 15:53:22 time: 0.5511 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.6688 aux.loss_ce: 0.0083 aux.acc_seg: 99.1933 +04/18 11:02:03 - mmengine - INFO - Iter(train) [ 56400/160000] lr: 6.7950e-03 eta: 15:52:54 time: 0.5526 data_time: 0.0062 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.5991 aux.loss_ce: 0.0084 aux.acc_seg: 99.0885 +04/18 11:02:31 - mmengine - INFO - Iter(train) [ 56450/160000] lr: 6.7921e-03 eta: 15:52:27 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6034 aux.loss_ce: 0.0082 aux.acc_seg: 99.0778 +04/18 11:02:58 - mmengine - INFO - Iter(train) [ 56500/160000] lr: 6.7892e-03 eta: 15:51:59 time: 0.5517 data_time: 0.0071 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.5968 aux.loss_ce: 0.0084 aux.acc_seg: 99.0685 +04/18 11:03:26 - mmengine - INFO - Iter(train) [ 56550/160000] lr: 6.7863e-03 eta: 15:51:31 time: 0.5508 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0087 decode.acc_seg: 99.6239 aux.loss_ce: 0.0085 aux.acc_seg: 99.1067 +04/18 11:03:54 - mmengine - INFO - Iter(train) [ 56600/160000] lr: 6.7834e-03 eta: 15:51:04 time: 0.5506 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7949 aux.loss_ce: 0.0077 aux.acc_seg: 99.3800 +04/18 11:04:21 - mmengine - INFO - Iter(train) [ 56650/160000] lr: 6.7805e-03 eta: 15:50:36 time: 0.5518 data_time: 0.0073 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.6842 aux.loss_ce: 0.0079 aux.acc_seg: 98.9669 +04/18 11:04:49 - mmengine - INFO - Iter(train) [ 56700/160000] lr: 6.7776e-03 eta: 15:50:08 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7064 aux.loss_ce: 0.0075 aux.acc_seg: 99.3327 +04/18 11:05:16 - mmengine - INFO - Iter(train) [ 56750/160000] lr: 6.7747e-03 eta: 15:49:41 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0088 decode.acc_seg: 99.5516 aux.loss_ce: 0.0080 aux.acc_seg: 99.0128 +04/18 11:05:44 - mmengine - INFO - Iter(train) [ 56800/160000] lr: 6.7718e-03 eta: 15:49:13 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0084 decode.acc_seg: 99.6427 aux.loss_ce: 0.0076 aux.acc_seg: 98.9308 +04/18 11:06:11 - mmengine - INFO - Iter(train) [ 56850/160000] lr: 6.7689e-03 eta: 15:48:46 time: 0.5513 data_time: 0.0060 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7216 aux.loss_ce: 0.0076 aux.acc_seg: 99.3689 +04/18 11:06:39 - mmengine - INFO - Iter(train) [ 56900/160000] lr: 6.7660e-03 eta: 15:48:18 time: 0.5510 data_time: 0.0059 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0091 decode.acc_seg: 99.6036 aux.loss_ce: 0.0088 aux.acc_seg: 98.8121 +04/18 11:07:07 - mmengine - INFO - Iter(train) [ 56950/160000] lr: 6.7630e-03 eta: 15:47:50 time: 0.5505 data_time: 0.0067 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6259 aux.loss_ce: 0.0082 aux.acc_seg: 99.0293 +04/18 11:07:34 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:07:34 - mmengine - INFO - Iter(train) [ 57000/160000] lr: 6.7601e-03 eta: 15:47:23 time: 0.5509 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.6693 aux.loss_ce: 0.0081 aux.acc_seg: 99.0869 +04/18 11:08:02 - mmengine - INFO - Iter(train) [ 57050/160000] lr: 6.7572e-03 eta: 15:46:55 time: 0.5509 data_time: 0.0060 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6544 aux.loss_ce: 0.0081 aux.acc_seg: 99.0747 +04/18 11:08:29 - mmengine - INFO - Iter(train) [ 57100/160000] lr: 6.7543e-03 eta: 15:46:27 time: 0.5516 data_time: 0.0067 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0102 decode.acc_seg: 99.7098 aux.loss_ce: 0.0091 aux.acc_seg: 99.2833 +04/18 11:08:57 - mmengine - INFO - Iter(train) [ 57150/160000] lr: 6.7514e-03 eta: 15:46:00 time: 0.5514 data_time: 0.0069 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.5404 aux.loss_ce: 0.0084 aux.acc_seg: 99.0448 +04/18 11:09:25 - mmengine - INFO - Iter(train) [ 57200/160000] lr: 6.7485e-03 eta: 15:45:32 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.7238 aux.loss_ce: 0.0083 aux.acc_seg: 99.3373 +04/18 11:09:52 - mmengine - INFO - Iter(train) [ 57250/160000] lr: 6.7456e-03 eta: 15:45:05 time: 0.5520 data_time: 0.0066 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6117 aux.loss_ce: 0.0077 aux.acc_seg: 99.0710 +04/18 11:10:20 - mmengine - INFO - Iter(train) [ 57300/160000] lr: 6.7427e-03 eta: 15:44:37 time: 0.5508 data_time: 0.0064 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.5892 aux.loss_ce: 0.0080 aux.acc_seg: 99.1467 +04/18 11:10:47 - mmengine - INFO - Iter(train) [ 57350/160000] lr: 6.7398e-03 eta: 15:44:09 time: 0.5499 data_time: 0.0062 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.6417 aux.loss_ce: 0.0086 aux.acc_seg: 99.1454 +04/18 11:11:15 - mmengine - INFO - Iter(train) [ 57400/160000] lr: 6.7369e-03 eta: 15:43:42 time: 0.5508 data_time: 0.0066 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0086 decode.acc_seg: 99.8440 aux.loss_ce: 0.0083 aux.acc_seg: 99.5678 +04/18 11:11:43 - mmengine - INFO - Iter(train) [ 57450/160000] lr: 6.7339e-03 eta: 15:43:14 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.7033 aux.loss_ce: 0.0080 aux.acc_seg: 99.3217 +04/18 11:12:10 - mmengine - INFO - Iter(train) [ 57500/160000] lr: 6.7310e-03 eta: 15:42:47 time: 0.5516 data_time: 0.0073 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.6173 aux.loss_ce: 0.0085 aux.acc_seg: 99.0421 +04/18 11:12:38 - mmengine - INFO - Iter(train) [ 57550/160000] lr: 6.7281e-03 eta: 15:42:19 time: 0.5508 data_time: 0.0067 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7353 aux.loss_ce: 0.0077 aux.acc_seg: 99.3336 +04/18 11:13:05 - mmengine - INFO - Iter(train) [ 57600/160000] lr: 6.7252e-03 eta: 15:41:51 time: 0.5518 data_time: 0.0068 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0093 decode.acc_seg: 99.4840 aux.loss_ce: 0.0082 aux.acc_seg: 99.0304 +04/18 11:13:33 - mmengine - INFO - Iter(train) [ 57650/160000] lr: 6.7223e-03 eta: 15:41:24 time: 0.5531 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0087 decode.acc_seg: 99.6527 aux.loss_ce: 0.0078 aux.acc_seg: 99.0683 +04/18 11:14:00 - mmengine - INFO - Iter(train) [ 57700/160000] lr: 6.7194e-03 eta: 15:40:56 time: 0.5517 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6759 aux.loss_ce: 0.0078 aux.acc_seg: 99.2591 +04/18 11:14:28 - mmengine - INFO - Iter(train) [ 57750/160000] lr: 6.7165e-03 eta: 15:40:28 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.7263 aux.loss_ce: 0.0084 aux.acc_seg: 99.3308 +04/18 11:14:56 - mmengine - INFO - Iter(train) [ 57800/160000] lr: 6.7136e-03 eta: 15:40:01 time: 0.5523 data_time: 0.0061 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.7467 aux.loss_ce: 0.0074 aux.acc_seg: 99.4081 +04/18 11:15:23 - mmengine - INFO - Iter(train) [ 57850/160000] lr: 6.7107e-03 eta: 15:39:33 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.6671 aux.loss_ce: 0.0079 aux.acc_seg: 99.3294 +04/18 11:15:51 - mmengine - INFO - Iter(train) [ 57900/160000] lr: 6.7077e-03 eta: 15:39:05 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0082 decode.acc_seg: 99.6827 aux.loss_ce: 0.0085 aux.acc_seg: 99.1842 +04/18 11:16:18 - mmengine - INFO - Iter(train) [ 57950/160000] lr: 6.7048e-03 eta: 15:38:38 time: 0.5503 data_time: 0.0060 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.6693 aux.loss_ce: 0.0072 aux.acc_seg: 99.3571 +04/18 11:16:46 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:16:46 - mmengine - INFO - Iter(train) [ 58000/160000] lr: 6.7019e-03 eta: 15:38:10 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7209 aux.loss_ce: 0.0077 aux.acc_seg: 99.3996 +04/18 11:17:13 - mmengine - INFO - Iter(train) [ 58050/160000] lr: 6.6990e-03 eta: 15:37:42 time: 0.5505 data_time: 0.0068 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.7552 aux.loss_ce: 0.0080 aux.acc_seg: 99.3280 +04/18 11:17:41 - mmengine - INFO - Iter(train) [ 58100/160000] lr: 6.6961e-03 eta: 15:37:15 time: 0.5514 data_time: 0.0067 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6637 aux.loss_ce: 0.0080 aux.acc_seg: 99.1197 +04/18 11:18:08 - mmengine - INFO - Iter(train) [ 58150/160000] lr: 6.6932e-03 eta: 15:36:47 time: 0.5507 data_time: 0.0069 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0107 decode.acc_seg: 99.4823 aux.loss_ce: 0.0088 aux.acc_seg: 98.7581 +04/18 11:18:36 - mmengine - INFO - Iter(train) [ 58200/160000] lr: 6.6903e-03 eta: 15:36:19 time: 0.5510 data_time: 0.0069 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0093 decode.acc_seg: 99.5644 aux.loss_ce: 0.0087 aux.acc_seg: 99.1653 +04/18 11:19:04 - mmengine - INFO - Iter(train) [ 58250/160000] lr: 6.6873e-03 eta: 15:35:52 time: 0.5503 data_time: 0.0065 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.5499 aux.loss_ce: 0.0084 aux.acc_seg: 99.2001 +04/18 11:19:31 - mmengine - INFO - Iter(train) [ 58300/160000] lr: 6.6844e-03 eta: 15:35:24 time: 0.5503 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0081 decode.acc_seg: 99.6187 aux.loss_ce: 0.0076 aux.acc_seg: 99.1308 +04/18 11:19:59 - mmengine - INFO - Iter(train) [ 58350/160000] lr: 6.6815e-03 eta: 15:34:56 time: 0.5507 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0085 decode.acc_seg: 99.6239 aux.loss_ce: 0.0082 aux.acc_seg: 99.1610 +04/18 11:20:26 - mmengine - INFO - Iter(train) [ 58400/160000] lr: 6.6786e-03 eta: 15:34:29 time: 0.5600 data_time: 0.0066 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0072 decode.acc_seg: 99.6520 aux.loss_ce: 0.0067 aux.acc_seg: 99.0705 +04/18 11:20:54 - mmengine - INFO - Iter(train) [ 58450/160000] lr: 6.6757e-03 eta: 15:34:01 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6987 aux.loss_ce: 0.0075 aux.acc_seg: 99.2962 +04/18 11:21:22 - mmengine - INFO - Iter(train) [ 58500/160000] lr: 6.6728e-03 eta: 15:33:34 time: 0.5530 data_time: 0.0070 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7561 aux.loss_ce: 0.0079 aux.acc_seg: 99.3450 +04/18 11:21:49 - mmengine - INFO - Iter(train) [ 58550/160000] lr: 6.6699e-03 eta: 15:33:06 time: 0.5514 data_time: 0.0062 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6677 aux.loss_ce: 0.0077 aux.acc_seg: 99.0487 +04/18 11:22:17 - mmengine - INFO - Iter(train) [ 58600/160000] lr: 6.6670e-03 eta: 15:32:39 time: 0.5516 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6812 aux.loss_ce: 0.0077 aux.acc_seg: 99.1712 +04/18 11:22:44 - mmengine - INFO - Iter(train) [ 58650/160000] lr: 6.6640e-03 eta: 15:32:11 time: 0.5511 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.5533 aux.loss_ce: 0.0079 aux.acc_seg: 98.9416 +04/18 11:23:12 - mmengine - INFO - Iter(train) [ 58700/160000] lr: 6.6611e-03 eta: 15:31:43 time: 0.5511 data_time: 0.0062 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.7086 aux.loss_ce: 0.0072 aux.acc_seg: 99.3958 +04/18 11:23:40 - mmengine - INFO - Iter(train) [ 58750/160000] lr: 6.6582e-03 eta: 15:31:16 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.5885 aux.loss_ce: 0.0086 aux.acc_seg: 98.8952 +04/18 11:24:07 - mmengine - INFO - Iter(train) [ 58800/160000] lr: 6.6553e-03 eta: 15:30:48 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6488 aux.loss_ce: 0.0078 aux.acc_seg: 98.9652 +04/18 11:24:35 - mmengine - INFO - Iter(train) [ 58850/160000] lr: 6.6524e-03 eta: 15:30:20 time: 0.5511 data_time: 0.0060 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.5907 aux.loss_ce: 0.0076 aux.acc_seg: 98.9631 +04/18 11:25:02 - mmengine - INFO - Iter(train) [ 58900/160000] lr: 6.6495e-03 eta: 15:29:53 time: 0.5522 data_time: 0.0067 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6446 aux.loss_ce: 0.0081 aux.acc_seg: 99.4057 +04/18 11:25:30 - mmengine - INFO - Iter(train) [ 58950/160000] lr: 6.6465e-03 eta: 15:29:25 time: 0.5521 data_time: 0.0066 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6372 aux.loss_ce: 0.0079 aux.acc_seg: 99.0109 +04/18 11:25:57 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:25:57 - mmengine - INFO - Iter(train) [ 59000/160000] lr: 6.6436e-03 eta: 15:28:58 time: 0.5523 data_time: 0.0066 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.5790 aux.loss_ce: 0.0079 aux.acc_seg: 99.1814 +04/18 11:26:25 - mmengine - INFO - Iter(train) [ 59050/160000] lr: 6.6407e-03 eta: 15:28:30 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6516 aux.loss_ce: 0.0078 aux.acc_seg: 99.1557 +04/18 11:26:53 - mmengine - INFO - Iter(train) [ 59100/160000] lr: 6.6378e-03 eta: 15:28:02 time: 0.5521 data_time: 0.0071 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.6309 aux.loss_ce: 0.0079 aux.acc_seg: 99.1999 +04/18 11:27:20 - mmengine - INFO - Iter(train) [ 59150/160000] lr: 6.6349e-03 eta: 15:27:35 time: 0.5497 data_time: 0.0068 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.6934 aux.loss_ce: 0.0073 aux.acc_seg: 99.2998 +04/18 11:27:48 - mmengine - INFO - Iter(train) [ 59200/160000] lr: 6.6320e-03 eta: 15:27:07 time: 0.5516 data_time: 0.0062 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.7056 aux.loss_ce: 0.0074 aux.acc_seg: 99.2040 +04/18 11:28:15 - mmengine - INFO - Iter(train) [ 59250/160000] lr: 6.6291e-03 eta: 15:26:39 time: 0.5504 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.5614 aux.loss_ce: 0.0074 aux.acc_seg: 98.7409 +04/18 11:28:43 - mmengine - INFO - Iter(train) [ 59300/160000] lr: 6.6261e-03 eta: 15:26:12 time: 0.5523 data_time: 0.0071 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6745 aux.loss_ce: 0.0078 aux.acc_seg: 99.0722 +04/18 11:29:10 - mmengine - INFO - Iter(train) [ 59350/160000] lr: 6.6232e-03 eta: 15:25:44 time: 0.5518 data_time: 0.0069 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7550 aux.loss_ce: 0.0073 aux.acc_seg: 99.2683 +04/18 11:29:38 - mmengine - INFO - Iter(train) [ 59400/160000] lr: 6.6203e-03 eta: 15:25:17 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7341 aux.loss_ce: 0.0074 aux.acc_seg: 99.1190 +04/18 11:30:06 - mmengine - INFO - Iter(train) [ 59450/160000] lr: 6.6174e-03 eta: 15:24:49 time: 0.5511 data_time: 0.0068 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6515 aux.loss_ce: 0.0078 aux.acc_seg: 99.1786 +04/18 11:30:33 - mmengine - INFO - Iter(train) [ 59500/160000] lr: 6.6145e-03 eta: 15:24:21 time: 0.5499 data_time: 0.0061 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.7619 aux.loss_ce: 0.0075 aux.acc_seg: 99.3440 +04/18 11:31:01 - mmengine - INFO - Iter(train) [ 59550/160000] lr: 6.6116e-03 eta: 15:23:54 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0089 decode.acc_seg: 99.5975 aux.loss_ce: 0.0085 aux.acc_seg: 98.8824 +04/18 11:31:28 - mmengine - INFO - Iter(train) [ 59600/160000] lr: 6.6086e-03 eta: 15:23:26 time: 0.5508 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6656 aux.loss_ce: 0.0081 aux.acc_seg: 99.1330 +04/18 11:31:56 - mmengine - INFO - Iter(train) [ 59650/160000] lr: 6.6057e-03 eta: 15:22:58 time: 0.5508 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.6439 aux.loss_ce: 0.0081 aux.acc_seg: 99.0078 +04/18 11:32:24 - mmengine - INFO - Iter(train) [ 59700/160000] lr: 6.6028e-03 eta: 15:22:31 time: 0.5511 data_time: 0.0068 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0085 decode.acc_seg: 99.6826 aux.loss_ce: 0.0083 aux.acc_seg: 99.2955 +04/18 11:32:51 - mmengine - INFO - Iter(train) [ 59750/160000] lr: 6.5999e-03 eta: 15:22:03 time: 0.5512 data_time: 0.0072 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0091 decode.acc_seg: 99.6603 aux.loss_ce: 0.0080 aux.acc_seg: 99.3613 +04/18 11:33:19 - mmengine - INFO - Iter(train) [ 59800/160000] lr: 6.5970e-03 eta: 15:21:35 time: 0.5516 data_time: 0.0069 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.7003 aux.loss_ce: 0.0079 aux.acc_seg: 99.1389 +04/18 11:33:46 - mmengine - INFO - Iter(train) [ 59850/160000] lr: 6.5940e-03 eta: 15:21:08 time: 0.5512 data_time: 0.0073 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6959 aux.loss_ce: 0.0081 aux.acc_seg: 99.3964 +04/18 11:34:14 - mmengine - INFO - Iter(train) [ 59900/160000] lr: 6.5911e-03 eta: 15:20:40 time: 0.5511 data_time: 0.0063 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.4831 aux.loss_ce: 0.0087 aux.acc_seg: 98.9328 +04/18 11:34:41 - mmengine - INFO - Iter(train) [ 59950/160000] lr: 6.5882e-03 eta: 15:20:13 time: 0.5516 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.7497 aux.loss_ce: 0.0073 aux.acc_seg: 99.2151 +04/18 11:35:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:35:09 - mmengine - INFO - Iter(train) [ 60000/160000] lr: 6.5853e-03 eta: 15:19:45 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6785 aux.loss_ce: 0.0081 aux.acc_seg: 99.2693 +04/18 11:35:09 - mmengine - INFO - Saving checkpoint at 60000 iterations +04/18 11:35:13 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0462 data_time: 0.0013 memory: 1657 +04/18 11:35:15 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0462 data_time: 0.0014 memory: 1657 +04/18 11:35:18 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0462 data_time: 0.0013 memory: 1657 +04/18 11:35:20 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0454 data_time: 0.0013 memory: 1657 +04/18 11:35:20 - mmengine - INFO - per class results: +04/18 11:35:20 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.58 | 99.53 | 99.48 | 99.58 | +| contrast | 79.36 | 87.46 | 88.49 | 89.55 | 87.46 | ++------------+-------+-------+--------+-----------+--------+ +04/18 11:35:20 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0900 mIoU: 89.2100 mAcc: 93.5200 mFscore: 94.0100 mPrecision: 94.5200 mRecall: 93.5200 data_time: 0.0015 time: 0.0465 +04/18 11:35:48 - mmengine - INFO - Iter(train) [ 60050/160000] lr: 6.5824e-03 eta: 15:19:18 time: 0.5519 data_time: 0.0062 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.7064 aux.loss_ce: 0.0078 aux.acc_seg: 99.3352 +04/18 11:36:15 - mmengine - INFO - Iter(train) [ 60100/160000] lr: 6.5795e-03 eta: 15:18:50 time: 0.5499 data_time: 0.0063 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.4396 aux.loss_ce: 0.0081 aux.acc_seg: 98.9150 +04/18 11:36:43 - mmengine - INFO - Iter(train) [ 60150/160000] lr: 6.5765e-03 eta: 15:18:22 time: 0.5512 data_time: 0.0060 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0109 decode.acc_seg: 99.6948 aux.loss_ce: 0.0086 aux.acc_seg: 99.2352 +04/18 11:37:10 - mmengine - INFO - Iter(train) [ 60200/160000] lr: 6.5736e-03 eta: 15:17:54 time: 0.5514 data_time: 0.0066 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.6398 aux.loss_ce: 0.0081 aux.acc_seg: 99.0908 +04/18 11:37:38 - mmengine - INFO - Iter(train) [ 60250/160000] lr: 6.5707e-03 eta: 15:17:27 time: 0.5507 data_time: 0.0063 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.7389 aux.loss_ce: 0.0083 aux.acc_seg: 99.3309 +04/18 11:38:05 - mmengine - INFO - Iter(train) [ 60300/160000] lr: 6.5678e-03 eta: 15:16:59 time: 0.5512 data_time: 0.0060 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0088 decode.acc_seg: 99.5622 aux.loss_ce: 0.0078 aux.acc_seg: 99.2293 +04/18 11:38:33 - mmengine - INFO - Iter(train) [ 60350/160000] lr: 6.5649e-03 eta: 15:16:32 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.7232 aux.loss_ce: 0.0070 aux.acc_seg: 99.2214 +04/18 11:39:01 - mmengine - INFO - Iter(train) [ 60400/160000] lr: 6.5619e-03 eta: 15:16:04 time: 0.5524 data_time: 0.0070 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0078 decode.acc_seg: 99.7540 aux.loss_ce: 0.0081 aux.acc_seg: 99.2719 +04/18 11:39:28 - mmengine - INFO - Iter(train) [ 60450/160000] lr: 6.5590e-03 eta: 15:15:36 time: 0.5500 data_time: 0.0063 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.5952 aux.loss_ce: 0.0077 aux.acc_seg: 99.0477 +04/18 11:39:56 - mmengine - INFO - Iter(train) [ 60500/160000] lr: 6.5561e-03 eta: 15:15:09 time: 0.5512 data_time: 0.0061 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0083 decode.acc_seg: 99.7226 aux.loss_ce: 0.0082 aux.acc_seg: 99.1763 +04/18 11:40:23 - mmengine - INFO - Iter(train) [ 60550/160000] lr: 6.5532e-03 eta: 15:14:41 time: 0.5591 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7407 aux.loss_ce: 0.0075 aux.acc_seg: 99.0932 +04/18 11:40:51 - mmengine - INFO - Iter(train) [ 60600/160000] lr: 6.5503e-03 eta: 15:14:14 time: 0.5582 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7237 aux.loss_ce: 0.0074 aux.acc_seg: 99.2720 +04/18 11:41:19 - mmengine - INFO - Iter(train) [ 60650/160000] lr: 6.5473e-03 eta: 15:13:46 time: 0.5513 data_time: 0.0058 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.7244 aux.loss_ce: 0.0080 aux.acc_seg: 99.3115 +04/18 11:41:46 - mmengine - INFO - Iter(train) [ 60700/160000] lr: 6.5444e-03 eta: 15:13:18 time: 0.5509 data_time: 0.0062 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.6064 aux.loss_ce: 0.0075 aux.acc_seg: 99.2377 +04/18 11:42:14 - mmengine - INFO - Iter(train) [ 60750/160000] lr: 6.5415e-03 eta: 15:12:51 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6486 aux.loss_ce: 0.0072 aux.acc_seg: 99.1142 +04/18 11:42:41 - mmengine - INFO - Iter(train) [ 60800/160000] lr: 6.5386e-03 eta: 15:12:23 time: 0.5524 data_time: 0.0068 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6813 aux.loss_ce: 0.0077 aux.acc_seg: 99.2957 +04/18 11:43:09 - mmengine - INFO - Iter(train) [ 60850/160000] lr: 6.5357e-03 eta: 15:11:55 time: 0.5508 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.7236 aux.loss_ce: 0.0072 aux.acc_seg: 99.3913 +04/18 11:43:36 - mmengine - INFO - Iter(train) [ 60900/160000] lr: 6.5327e-03 eta: 15:11:28 time: 0.5526 data_time: 0.0075 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.6771 aux.loss_ce: 0.0076 aux.acc_seg: 99.3098 +04/18 11:44:04 - mmengine - INFO - Iter(train) [ 60950/160000] lr: 6.5298e-03 eta: 15:11:00 time: 0.5517 data_time: 0.0059 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6767 aux.loss_ce: 0.0075 aux.acc_seg: 99.2650 +04/18 11:44:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:44:32 - mmengine - INFO - Iter(train) [ 61000/160000] lr: 6.5269e-03 eta: 15:10:32 time: 0.5503 data_time: 0.0064 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.6779 aux.loss_ce: 0.0075 aux.acc_seg: 99.0499 +04/18 11:44:59 - mmengine - INFO - Iter(train) [ 61050/160000] lr: 6.5240e-03 eta: 15:10:05 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7244 aux.loss_ce: 0.0076 aux.acc_seg: 99.3732 +04/18 11:45:27 - mmengine - INFO - Iter(train) [ 61100/160000] lr: 6.5211e-03 eta: 15:09:37 time: 0.5503 data_time: 0.0061 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0072 decode.acc_seg: 99.7668 aux.loss_ce: 0.0067 aux.acc_seg: 99.5061 +04/18 11:45:54 - mmengine - INFO - Iter(train) [ 61150/160000] lr: 6.5181e-03 eta: 15:09:10 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.7355 aux.loss_ce: 0.0079 aux.acc_seg: 99.3558 +04/18 11:46:22 - mmengine - INFO - Iter(train) [ 61200/160000] lr: 6.5152e-03 eta: 15:08:42 time: 0.5528 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.7243 aux.loss_ce: 0.0079 aux.acc_seg: 99.3180 +04/18 11:46:49 - mmengine - INFO - Iter(train) [ 61250/160000] lr: 6.5123e-03 eta: 15:08:14 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6181 aux.loss_ce: 0.0081 aux.acc_seg: 98.9738 +04/18 11:47:17 - mmengine - INFO - Iter(train) [ 61300/160000] lr: 6.5094e-03 eta: 15:07:47 time: 0.5513 data_time: 0.0072 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6649 aux.loss_ce: 0.0074 aux.acc_seg: 99.2748 +04/18 11:47:45 - mmengine - INFO - Iter(train) [ 61350/160000] lr: 6.5064e-03 eta: 15:07:19 time: 0.5510 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0071 decode.acc_seg: 99.6904 aux.loss_ce: 0.0070 aux.acc_seg: 99.2872 +04/18 11:48:12 - mmengine - INFO - Iter(train) [ 61400/160000] lr: 6.5035e-03 eta: 15:06:51 time: 0.5499 data_time: 0.0060 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.7398 aux.loss_ce: 0.0079 aux.acc_seg: 99.1993 +04/18 11:48:40 - mmengine - INFO - Iter(train) [ 61450/160000] lr: 6.5006e-03 eta: 15:06:24 time: 0.5509 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6499 aux.loss_ce: 0.0073 aux.acc_seg: 99.1858 +04/18 11:49:07 - mmengine - INFO - Iter(train) [ 61500/160000] lr: 6.4977e-03 eta: 15:05:56 time: 0.5512 data_time: 0.0066 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.6922 aux.loss_ce: 0.0080 aux.acc_seg: 99.2148 +04/18 11:49:35 - mmengine - INFO - Iter(train) [ 61550/160000] lr: 6.4948e-03 eta: 15:05:29 time: 0.5604 data_time: 0.0062 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6339 aux.loss_ce: 0.0078 aux.acc_seg: 99.1366 +04/18 11:50:02 - mmengine - INFO - Iter(train) [ 61600/160000] lr: 6.4918e-03 eta: 15:05:01 time: 0.5510 data_time: 0.0071 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.7356 aux.loss_ce: 0.0076 aux.acc_seg: 99.3462 +04/18 11:50:30 - mmengine - INFO - Iter(train) [ 61650/160000] lr: 6.4889e-03 eta: 15:04:34 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7243 aux.loss_ce: 0.0072 aux.acc_seg: 99.2740 +04/18 11:50:58 - mmengine - INFO - Iter(train) [ 61700/160000] lr: 6.4860e-03 eta: 15:04:06 time: 0.5504 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.7153 aux.loss_ce: 0.0074 aux.acc_seg: 99.3555 +04/18 11:51:25 - mmengine - INFO - Iter(train) [ 61750/160000] lr: 6.4831e-03 eta: 15:03:38 time: 0.5530 data_time: 0.0072 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7630 aux.loss_ce: 0.0078 aux.acc_seg: 99.3668 +04/18 11:51:53 - mmengine - INFO - Iter(train) [ 61800/160000] lr: 6.4801e-03 eta: 15:03:11 time: 0.5507 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.6725 aux.loss_ce: 0.0074 aux.acc_seg: 99.3766 +04/18 11:52:20 - mmengine - INFO - Iter(train) [ 61850/160000] lr: 6.4772e-03 eta: 15:02:43 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6899 aux.loss_ce: 0.0077 aux.acc_seg: 99.1867 +04/18 11:52:48 - mmengine - INFO - Iter(train) [ 61900/160000] lr: 6.4743e-03 eta: 15:02:15 time: 0.5504 data_time: 0.0065 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7381 aux.loss_ce: 0.0077 aux.acc_seg: 99.1766 +04/18 11:53:16 - mmengine - INFO - Iter(train) [ 61950/160000] lr: 6.4714e-03 eta: 15:01:48 time: 0.5504 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7315 aux.loss_ce: 0.0076 aux.acc_seg: 99.3332 +04/18 11:53:43 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:53:43 - mmengine - INFO - Iter(train) [ 62000/160000] lr: 6.4684e-03 eta: 15:01:20 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7339 aux.loss_ce: 0.0075 aux.acc_seg: 99.2385 +04/18 11:54:11 - mmengine - INFO - Iter(train) [ 62050/160000] lr: 6.4655e-03 eta: 15:00:52 time: 0.5513 data_time: 0.0068 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.7033 aux.loss_ce: 0.0076 aux.acc_seg: 98.9886 +04/18 11:54:38 - mmengine - INFO - Iter(train) [ 62100/160000] lr: 6.4626e-03 eta: 15:00:25 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6926 aux.loss_ce: 0.0080 aux.acc_seg: 99.2916 +04/18 11:55:06 - mmengine - INFO - Iter(train) [ 62150/160000] lr: 6.4597e-03 eta: 14:59:57 time: 0.5506 data_time: 0.0072 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7195 aux.loss_ce: 0.0069 aux.acc_seg: 99.2236 +04/18 11:55:33 - mmengine - INFO - Iter(train) [ 62200/160000] lr: 6.4567e-03 eta: 14:59:30 time: 0.5500 data_time: 0.0061 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.5876 aux.loss_ce: 0.0076 aux.acc_seg: 99.0355 +04/18 11:56:01 - mmengine - INFO - Iter(train) [ 62250/160000] lr: 6.4538e-03 eta: 14:59:02 time: 0.5513 data_time: 0.0061 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0088 decode.acc_seg: 99.6231 aux.loss_ce: 0.0077 aux.acc_seg: 99.2679 +04/18 11:56:29 - mmengine - INFO - Iter(train) [ 62300/160000] lr: 6.4509e-03 eta: 14:58:34 time: 0.5528 data_time: 0.0058 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6235 aux.loss_ce: 0.0084 aux.acc_seg: 99.0924 +04/18 11:56:56 - mmengine - INFO - Iter(train) [ 62350/160000] lr: 6.4480e-03 eta: 14:58:07 time: 0.5515 data_time: 0.0059 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.6250 aux.loss_ce: 0.0080 aux.acc_seg: 99.3446 +04/18 11:57:24 - mmengine - INFO - Iter(train) [ 62400/160000] lr: 6.4450e-03 eta: 14:57:39 time: 0.5521 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7289 aux.loss_ce: 0.0074 aux.acc_seg: 99.4224 +04/18 11:57:51 - mmengine - INFO - Iter(train) [ 62450/160000] lr: 6.4421e-03 eta: 14:57:11 time: 0.5504 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.7499 aux.loss_ce: 0.0076 aux.acc_seg: 99.4639 +04/18 11:58:19 - mmengine - INFO - Iter(train) [ 62500/160000] lr: 6.4392e-03 eta: 14:56:44 time: 0.5516 data_time: 0.0067 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0085 decode.acc_seg: 99.6426 aux.loss_ce: 0.0079 aux.acc_seg: 99.1708 +04/18 11:58:46 - mmengine - INFO - Iter(train) [ 62550/160000] lr: 6.4363e-03 eta: 14:56:16 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0105 decode.acc_seg: 99.4515 aux.loss_ce: 0.0091 aux.acc_seg: 98.9024 +04/18 11:59:14 - mmengine - INFO - Iter(train) [ 62600/160000] lr: 6.4333e-03 eta: 14:55:48 time: 0.5528 data_time: 0.0059 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0091 decode.acc_seg: 99.5965 aux.loss_ce: 0.0083 aux.acc_seg: 99.0153 +04/18 11:59:42 - mmengine - INFO - Iter(train) [ 62650/160000] lr: 6.4304e-03 eta: 14:55:21 time: 0.5504 data_time: 0.0068 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.6124 aux.loss_ce: 0.0084 aux.acc_seg: 99.0512 +04/18 12:00:09 - mmengine - INFO - Iter(train) [ 62700/160000] lr: 6.4275e-03 eta: 14:54:53 time: 0.5606 data_time: 0.0062 memory: 7635 loss: 0.0266 decode.loss_ce: 0.0156 decode.acc_seg: 99.2701 aux.loss_ce: 0.0110 aux.acc_seg: 98.7589 +04/18 12:00:37 - mmengine - INFO - Iter(train) [ 62750/160000] lr: 6.4246e-03 eta: 14:54:26 time: 0.5517 data_time: 0.0062 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0124 decode.acc_seg: 99.6608 aux.loss_ce: 0.0095 aux.acc_seg: 99.0810 +04/18 12:01:05 - mmengine - INFO - Iter(train) [ 62800/160000] lr: 6.4216e-03 eta: 14:53:58 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.4710 aux.loss_ce: 0.0087 aux.acc_seg: 98.8008 +04/18 12:01:32 - mmengine - INFO - Iter(train) [ 62850/160000] lr: 6.4187e-03 eta: 14:53:31 time: 0.5511 data_time: 0.0070 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0111 decode.acc_seg: 99.6174 aux.loss_ce: 0.0090 aux.acc_seg: 99.0654 +04/18 12:02:00 - mmengine - INFO - Iter(train) [ 62900/160000] lr: 6.4158e-03 eta: 14:53:03 time: 0.5524 data_time: 0.0069 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0085 decode.acc_seg: 99.6057 aux.loss_ce: 0.0076 aux.acc_seg: 99.3256 +04/18 12:02:27 - mmengine - INFO - Iter(train) [ 62950/160000] lr: 6.4129e-03 eta: 14:52:35 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0080 decode.acc_seg: 99.6496 aux.loss_ce: 0.0073 aux.acc_seg: 99.2011 +04/18 12:02:55 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:02:55 - mmengine - INFO - Iter(train) [ 63000/160000] lr: 6.4099e-03 eta: 14:52:08 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.3012 aux.loss_ce: 0.0087 aux.acc_seg: 98.7721 +04/18 12:03:22 - mmengine - INFO - Iter(train) [ 63050/160000] lr: 6.4070e-03 eta: 14:51:40 time: 0.5515 data_time: 0.0058 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0083 decode.acc_seg: 99.7809 aux.loss_ce: 0.0079 aux.acc_seg: 99.3269 +04/18 12:03:50 - mmengine - INFO - Iter(train) [ 63100/160000] lr: 6.4041e-03 eta: 14:51:13 time: 0.5504 data_time: 0.0068 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0105 decode.acc_seg: 99.6111 aux.loss_ce: 0.0090 aux.acc_seg: 99.1572 +04/18 12:04:18 - mmengine - INFO - Iter(train) [ 63150/160000] lr: 6.4011e-03 eta: 14:50:45 time: 0.5515 data_time: 0.0062 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.7617 aux.loss_ce: 0.0079 aux.acc_seg: 99.3432 +04/18 12:04:45 - mmengine - INFO - Iter(train) [ 63200/160000] lr: 6.3982e-03 eta: 14:50:17 time: 0.5522 data_time: 0.0072 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.6676 aux.loss_ce: 0.0078 aux.acc_seg: 99.3055 +04/18 12:05:13 - mmengine - INFO - Iter(train) [ 63250/160000] lr: 6.3953e-03 eta: 14:49:50 time: 0.5516 data_time: 0.0068 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.5938 aux.loss_ce: 0.0084 aux.acc_seg: 99.3444 +04/18 12:05:40 - mmengine - INFO - Iter(train) [ 63300/160000] lr: 6.3924e-03 eta: 14:49:22 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0133 decode.acc_seg: 99.6986 aux.loss_ce: 0.0101 aux.acc_seg: 99.2738 +04/18 12:06:08 - mmengine - INFO - Iter(train) [ 63350/160000] lr: 6.3894e-03 eta: 14:48:55 time: 0.5510 data_time: 0.0069 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.7083 aux.loss_ce: 0.0086 aux.acc_seg: 99.1220 +04/18 12:06:36 - mmengine - INFO - Iter(train) [ 63400/160000] lr: 6.3865e-03 eta: 14:48:27 time: 0.5513 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.7265 aux.loss_ce: 0.0080 aux.acc_seg: 99.3199 +04/18 12:07:03 - mmengine - INFO - Iter(train) [ 63450/160000] lr: 6.3836e-03 eta: 14:47:59 time: 0.5518 data_time: 0.0062 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0085 decode.acc_seg: 99.7110 aux.loss_ce: 0.0073 aux.acc_seg: 99.2965 +04/18 12:07:31 - mmengine - INFO - Iter(train) [ 63500/160000] lr: 6.3806e-03 eta: 14:47:32 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6314 aux.loss_ce: 0.0081 aux.acc_seg: 99.1665 +04/18 12:07:58 - mmengine - INFO - Iter(train) [ 63550/160000] lr: 6.3777e-03 eta: 14:47:04 time: 0.5510 data_time: 0.0071 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.6786 aux.loss_ce: 0.0080 aux.acc_seg: 99.1355 +04/18 12:08:26 - mmengine - INFO - Iter(train) [ 63600/160000] lr: 6.3748e-03 eta: 14:46:36 time: 0.5505 data_time: 0.0071 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6197 aux.loss_ce: 0.0082 aux.acc_seg: 99.1724 +04/18 12:08:53 - mmengine - INFO - Iter(train) [ 63650/160000] lr: 6.3719e-03 eta: 14:46:09 time: 0.5515 data_time: 0.0068 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0091 decode.acc_seg: 99.6713 aux.loss_ce: 0.0087 aux.acc_seg: 98.9584 +04/18 12:09:21 - mmengine - INFO - Iter(train) [ 63700/160000] lr: 6.3689e-03 eta: 14:45:41 time: 0.5517 data_time: 0.0060 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0119 decode.acc_seg: 99.4674 aux.loss_ce: 0.0098 aux.acc_seg: 98.8377 +04/18 12:09:49 - mmengine - INFO - Iter(train) [ 63750/160000] lr: 6.3660e-03 eta: 14:45:14 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.5516 aux.loss_ce: 0.0085 aux.acc_seg: 99.1558 +04/18 12:10:16 - mmengine - INFO - Iter(train) [ 63800/160000] lr: 6.3631e-03 eta: 14:44:46 time: 0.5507 data_time: 0.0062 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.6948 aux.loss_ce: 0.0082 aux.acc_seg: 99.1970 +04/18 12:10:44 - mmengine - INFO - Iter(train) [ 63850/160000] lr: 6.3601e-03 eta: 14:44:19 time: 0.5519 data_time: 0.0061 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.7017 aux.loss_ce: 0.0080 aux.acc_seg: 99.1553 +04/18 12:11:12 - mmengine - INFO - Iter(train) [ 63900/160000] lr: 6.3572e-03 eta: 14:43:51 time: 0.5513 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.7107 aux.loss_ce: 0.0073 aux.acc_seg: 99.2823 +04/18 12:11:39 - mmengine - INFO - Iter(train) [ 63950/160000] lr: 6.3543e-03 eta: 14:43:23 time: 0.5515 data_time: 0.0067 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0087 decode.acc_seg: 99.6617 aux.loss_ce: 0.0082 aux.acc_seg: 99.1934 +04/18 12:12:07 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:12:07 - mmengine - INFO - Iter(train) [ 64000/160000] lr: 6.3513e-03 eta: 14:42:56 time: 0.5514 data_time: 0.0070 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0083 decode.acc_seg: 99.6565 aux.loss_ce: 0.0076 aux.acc_seg: 99.2448 +04/18 12:12:34 - mmengine - INFO - Iter(train) [ 64050/160000] lr: 6.3484e-03 eta: 14:42:28 time: 0.5506 data_time: 0.0063 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.6697 aux.loss_ce: 0.0071 aux.acc_seg: 99.0233 +04/18 12:13:02 - mmengine - INFO - Iter(train) [ 64100/160000] lr: 6.3455e-03 eta: 14:42:01 time: 0.5509 data_time: 0.0069 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.6822 aux.loss_ce: 0.0085 aux.acc_seg: 99.2085 +04/18 12:13:29 - mmengine - INFO - Iter(train) [ 64150/160000] lr: 6.3426e-03 eta: 14:41:33 time: 0.5498 data_time: 0.0063 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.5502 aux.loss_ce: 0.0081 aux.acc_seg: 98.8747 +04/18 12:13:57 - mmengine - INFO - Iter(train) [ 64200/160000] lr: 6.3396e-03 eta: 14:41:05 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6749 aux.loss_ce: 0.0078 aux.acc_seg: 99.2393 +04/18 12:14:25 - mmengine - INFO - Iter(train) [ 64250/160000] lr: 6.3367e-03 eta: 14:40:38 time: 0.5511 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0082 decode.acc_seg: 99.6074 aux.loss_ce: 0.0074 aux.acc_seg: 99.1533 +04/18 12:14:52 - mmengine - INFO - Iter(train) [ 64300/160000] lr: 6.3338e-03 eta: 14:40:10 time: 0.5516 data_time: 0.0059 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0085 decode.acc_seg: 99.5456 aux.loss_ce: 0.0084 aux.acc_seg: 98.9036 +04/18 12:15:20 - mmengine - INFO - Iter(train) [ 64350/160000] lr: 6.3308e-03 eta: 14:39:42 time: 0.5512 data_time: 0.0066 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0098 decode.acc_seg: 99.5765 aux.loss_ce: 0.0088 aux.acc_seg: 99.2641 +04/18 12:15:47 - mmengine - INFO - Iter(train) [ 64400/160000] lr: 6.3279e-03 eta: 14:39:15 time: 0.5505 data_time: 0.0061 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.6955 aux.loss_ce: 0.0080 aux.acc_seg: 99.3121 +04/18 12:16:15 - mmengine - INFO - Iter(train) [ 64450/160000] lr: 6.3250e-03 eta: 14:38:47 time: 0.5507 data_time: 0.0063 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.6198 aux.loss_ce: 0.0082 aux.acc_seg: 99.0871 +04/18 12:16:42 - mmengine - INFO - Iter(train) [ 64500/160000] lr: 6.3220e-03 eta: 14:38:19 time: 0.5518 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.5845 aux.loss_ce: 0.0082 aux.acc_seg: 99.1028 +04/18 12:17:10 - mmengine - INFO - Iter(train) [ 64550/160000] lr: 6.3191e-03 eta: 14:37:52 time: 0.5518 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.6962 aux.loss_ce: 0.0076 aux.acc_seg: 99.2999 +04/18 12:17:38 - mmengine - INFO - Iter(train) [ 64600/160000] lr: 6.3162e-03 eta: 14:37:24 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6766 aux.loss_ce: 0.0077 aux.acc_seg: 99.3655 +04/18 12:18:05 - mmengine - INFO - Iter(train) [ 64650/160000] lr: 6.3132e-03 eta: 14:36:57 time: 0.5502 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.5746 aux.loss_ce: 0.0085 aux.acc_seg: 99.2217 +04/18 12:18:33 - mmengine - INFO - Iter(train) [ 64700/160000] lr: 6.3103e-03 eta: 14:36:29 time: 0.5505 data_time: 0.0072 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.6564 aux.loss_ce: 0.0079 aux.acc_seg: 99.1640 +04/18 12:19:00 - mmengine - INFO - Iter(train) [ 64750/160000] lr: 6.3074e-03 eta: 14:36:01 time: 0.5514 data_time: 0.0070 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0112 decode.acc_seg: 99.6309 aux.loss_ce: 0.0088 aux.acc_seg: 99.1497 +04/18 12:19:28 - mmengine - INFO - Iter(train) [ 64800/160000] lr: 6.3044e-03 eta: 14:35:34 time: 0.5519 data_time: 0.0070 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0095 decode.acc_seg: 99.6595 aux.loss_ce: 0.0085 aux.acc_seg: 99.1340 +04/18 12:19:56 - mmengine - INFO - Iter(train) [ 64850/160000] lr: 6.3015e-03 eta: 14:35:06 time: 0.5502 data_time: 0.0066 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.6979 aux.loss_ce: 0.0080 aux.acc_seg: 99.3130 +04/18 12:20:23 - mmengine - INFO - Iter(train) [ 64900/160000] lr: 6.2986e-03 eta: 14:34:39 time: 0.5522 data_time: 0.0070 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6875 aux.loss_ce: 0.0076 aux.acc_seg: 99.2385 +04/18 12:20:51 - mmengine - INFO - Iter(train) [ 64950/160000] lr: 6.2956e-03 eta: 14:34:11 time: 0.5515 data_time: 0.0067 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7477 aux.loss_ce: 0.0079 aux.acc_seg: 99.1247 +04/18 12:21:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:21:18 - mmengine - INFO - Iter(train) [ 65000/160000] lr: 6.2927e-03 eta: 14:33:44 time: 0.5523 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7310 aux.loss_ce: 0.0074 aux.acc_seg: 99.1592 +04/18 12:21:46 - mmengine - INFO - Iter(train) [ 65050/160000] lr: 6.2898e-03 eta: 14:33:16 time: 0.5518 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.7298 aux.loss_ce: 0.0075 aux.acc_seg: 99.3653 +04/18 12:22:14 - mmengine - INFO - Iter(train) [ 65100/160000] lr: 6.2868e-03 eta: 14:32:48 time: 0.5513 data_time: 0.0059 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7375 aux.loss_ce: 0.0079 aux.acc_seg: 99.2416 +04/18 12:22:41 - mmengine - INFO - Iter(train) [ 65150/160000] lr: 6.2839e-03 eta: 14:32:21 time: 0.5517 data_time: 0.0061 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0100 decode.acc_seg: 99.6408 aux.loss_ce: 0.0090 aux.acc_seg: 99.1256 +04/18 12:23:09 - mmengine - INFO - Iter(train) [ 65200/160000] lr: 6.2810e-03 eta: 14:31:53 time: 0.5528 data_time: 0.0072 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0099 decode.acc_seg: 99.6358 aux.loss_ce: 0.0087 aux.acc_seg: 99.0165 +04/18 12:23:36 - mmengine - INFO - Iter(train) [ 65250/160000] lr: 6.2780e-03 eta: 14:31:25 time: 0.5512 data_time: 0.0059 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.6083 aux.loss_ce: 0.0078 aux.acc_seg: 99.0692 +04/18 12:24:04 - mmengine - INFO - Iter(train) [ 65300/160000] lr: 6.2751e-03 eta: 14:30:58 time: 0.5505 data_time: 0.0066 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.7094 aux.loss_ce: 0.0080 aux.acc_seg: 99.0721 +04/18 12:24:31 - mmengine - INFO - Iter(train) [ 65350/160000] lr: 6.2722e-03 eta: 14:30:30 time: 0.5510 data_time: 0.0068 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6728 aux.loss_ce: 0.0081 aux.acc_seg: 99.2450 +04/18 12:24:59 - mmengine - INFO - Iter(train) [ 65400/160000] lr: 6.2692e-03 eta: 14:30:02 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0086 decode.acc_seg: 99.6277 aux.loss_ce: 0.0084 aux.acc_seg: 98.9611 +04/18 12:25:27 - mmengine - INFO - Iter(train) [ 65450/160000] lr: 6.2663e-03 eta: 14:29:35 time: 0.5513 data_time: 0.0062 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6113 aux.loss_ce: 0.0079 aux.acc_seg: 99.1420 +04/18 12:25:54 - mmengine - INFO - Iter(train) [ 65500/160000] lr: 6.2634e-03 eta: 14:29:07 time: 0.5505 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0077 decode.acc_seg: 99.6889 aux.loss_ce: 0.0072 aux.acc_seg: 99.1988 +04/18 12:26:22 - mmengine - INFO - Iter(train) [ 65550/160000] lr: 6.2604e-03 eta: 14:28:40 time: 0.5523 data_time: 0.0074 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0092 decode.acc_seg: 99.7055 aux.loss_ce: 0.0087 aux.acc_seg: 99.0686 +04/18 12:26:49 - mmengine - INFO - Iter(train) [ 65600/160000] lr: 6.2575e-03 eta: 14:28:12 time: 0.5511 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6802 aux.loss_ce: 0.0076 aux.acc_seg: 99.2186 +04/18 12:27:17 - mmengine - INFO - Iter(train) [ 65650/160000] lr: 6.2546e-03 eta: 14:27:44 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0075 decode.acc_seg: 99.6564 aux.loss_ce: 0.0070 aux.acc_seg: 99.3765 +04/18 12:27:44 - mmengine - INFO - Iter(train) [ 65700/160000] lr: 6.2516e-03 eta: 14:27:17 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0115 decode.acc_seg: 99.6966 aux.loss_ce: 0.0087 aux.acc_seg: 99.4294 +04/18 12:28:12 - mmengine - INFO - Iter(train) [ 65750/160000] lr: 6.2487e-03 eta: 14:26:49 time: 0.5521 data_time: 0.0067 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.5690 aux.loss_ce: 0.0076 aux.acc_seg: 99.0780 +04/18 12:28:40 - mmengine - INFO - Iter(train) [ 65800/160000] lr: 6.2458e-03 eta: 14:26:21 time: 0.5508 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.5194 aux.loss_ce: 0.0080 aux.acc_seg: 98.9784 +04/18 12:29:07 - mmengine - INFO - Iter(train) [ 65850/160000] lr: 6.2428e-03 eta: 14:25:54 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0080 decode.acc_seg: 99.7318 aux.loss_ce: 0.0073 aux.acc_seg: 99.1998 +04/18 12:29:35 - mmengine - INFO - Iter(train) [ 65900/160000] lr: 6.2399e-03 eta: 14:25:26 time: 0.5510 data_time: 0.0058 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0091 decode.acc_seg: 99.7058 aux.loss_ce: 0.0079 aux.acc_seg: 99.3279 +04/18 12:30:03 - mmengine - INFO - Iter(train) [ 65950/160000] lr: 6.2369e-03 eta: 14:24:59 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0092 decode.acc_seg: 99.6469 aux.loss_ce: 0.0085 aux.acc_seg: 98.9905 +04/18 12:30:30 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:30:30 - mmengine - INFO - Iter(train) [ 66000/160000] lr: 6.2340e-03 eta: 14:24:32 time: 0.5514 data_time: 0.0080 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.7491 aux.loss_ce: 0.0081 aux.acc_seg: 99.3939 +04/18 12:30:58 - mmengine - INFO - Iter(train) [ 66050/160000] lr: 6.2311e-03 eta: 14:24:04 time: 0.5508 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7186 aux.loss_ce: 0.0075 aux.acc_seg: 99.1418 +04/18 12:31:25 - mmengine - INFO - Iter(train) [ 66100/160000] lr: 6.2281e-03 eta: 14:23:36 time: 0.5509 data_time: 0.0061 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0079 decode.acc_seg: 99.6423 aux.loss_ce: 0.0074 aux.acc_seg: 99.1426 +04/18 12:31:53 - mmengine - INFO - Iter(train) [ 66150/160000] lr: 6.2252e-03 eta: 14:23:09 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.7011 aux.loss_ce: 0.0078 aux.acc_seg: 99.2793 +04/18 12:32:21 - mmengine - INFO - Iter(train) [ 66200/160000] lr: 6.2223e-03 eta: 14:22:41 time: 0.5513 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.7555 aux.loss_ce: 0.0079 aux.acc_seg: 99.2439 +04/18 12:32:48 - mmengine - INFO - Iter(train) [ 66250/160000] lr: 6.2193e-03 eta: 14:22:13 time: 0.5502 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0069 decode.acc_seg: 99.6030 aux.loss_ce: 0.0068 aux.acc_seg: 99.2440 +04/18 12:33:16 - mmengine - INFO - Iter(train) [ 66300/160000] lr: 6.2164e-03 eta: 14:21:46 time: 0.5508 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7398 aux.loss_ce: 0.0076 aux.acc_seg: 99.4539 +04/18 12:33:43 - mmengine - INFO - Iter(train) [ 66350/160000] lr: 6.2135e-03 eta: 14:21:18 time: 0.5521 data_time: 0.0068 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.6774 aux.loss_ce: 0.0072 aux.acc_seg: 99.1939 +04/18 12:34:11 - mmengine - INFO - Iter(train) [ 66400/160000] lr: 6.2105e-03 eta: 14:20:50 time: 0.5514 data_time: 0.0061 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.6902 aux.loss_ce: 0.0073 aux.acc_seg: 99.3798 +04/18 12:34:38 - mmengine - INFO - Iter(train) [ 66450/160000] lr: 6.2076e-03 eta: 14:20:23 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.7005 aux.loss_ce: 0.0078 aux.acc_seg: 99.1853 +04/18 12:35:06 - mmengine - INFO - Iter(train) [ 66500/160000] lr: 6.2046e-03 eta: 14:19:55 time: 0.5516 data_time: 0.0062 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.8232 aux.loss_ce: 0.0075 aux.acc_seg: 99.3772 +04/18 12:35:33 - mmengine - INFO - Iter(train) [ 66550/160000] lr: 6.2017e-03 eta: 14:19:28 time: 0.5500 data_time: 0.0082 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6459 aux.loss_ce: 0.0077 aux.acc_seg: 99.1036 +04/18 12:36:01 - mmengine - INFO - Iter(train) [ 66600/160000] lr: 6.1988e-03 eta: 14:19:00 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6597 aux.loss_ce: 0.0074 aux.acc_seg: 99.0806 +04/18 12:36:29 - mmengine - INFO - Iter(train) [ 66650/160000] lr: 6.1958e-03 eta: 14:18:32 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6656 aux.loss_ce: 0.0074 aux.acc_seg: 99.1944 +04/18 12:36:56 - mmengine - INFO - Iter(train) [ 66700/160000] lr: 6.1929e-03 eta: 14:18:05 time: 0.5526 data_time: 0.0072 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.6866 aux.loss_ce: 0.0073 aux.acc_seg: 99.2916 +04/18 12:37:24 - mmengine - INFO - Iter(train) [ 66750/160000] lr: 6.1899e-03 eta: 14:17:37 time: 0.5517 data_time: 0.0062 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.7363 aux.loss_ce: 0.0079 aux.acc_seg: 99.0746 +04/18 12:37:51 - mmengine - INFO - Iter(train) [ 66800/160000] lr: 6.1870e-03 eta: 14:17:09 time: 0.5501 data_time: 0.0062 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.5464 aux.loss_ce: 0.0076 aux.acc_seg: 98.8960 +04/18 12:38:19 - mmengine - INFO - Iter(train) [ 66850/160000] lr: 6.1841e-03 eta: 14:16:42 time: 0.5516 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6633 aux.loss_ce: 0.0078 aux.acc_seg: 99.1499 +04/18 12:38:46 - mmengine - INFO - Iter(train) [ 66900/160000] lr: 6.1811e-03 eta: 14:16:14 time: 0.5518 data_time: 0.0062 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6593 aux.loss_ce: 0.0074 aux.acc_seg: 99.3145 +04/18 12:39:14 - mmengine - INFO - Iter(train) [ 66950/160000] lr: 6.1782e-03 eta: 14:15:47 time: 0.5515 data_time: 0.0066 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.6510 aux.loss_ce: 0.0080 aux.acc_seg: 99.1521 +04/18 12:39:42 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:39:42 - mmengine - INFO - Iter(train) [ 67000/160000] lr: 6.1753e-03 eta: 14:15:19 time: 0.5611 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6480 aux.loss_ce: 0.0075 aux.acc_seg: 99.1254 +04/18 12:40:09 - mmengine - INFO - Iter(train) [ 67050/160000] lr: 6.1723e-03 eta: 14:14:51 time: 0.5510 data_time: 0.0059 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6650 aux.loss_ce: 0.0078 aux.acc_seg: 99.1677 +04/18 12:40:37 - mmengine - INFO - Iter(train) [ 67100/160000] lr: 6.1694e-03 eta: 14:14:24 time: 0.5521 data_time: 0.0060 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.6387 aux.loss_ce: 0.0073 aux.acc_seg: 99.0540 +04/18 12:41:05 - mmengine - INFO - Iter(train) [ 67150/160000] lr: 6.1664e-03 eta: 14:13:56 time: 0.5517 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7343 aux.loss_ce: 0.0073 aux.acc_seg: 99.3545 +04/18 12:41:32 - mmengine - INFO - Iter(train) [ 67200/160000] lr: 6.1635e-03 eta: 14:13:29 time: 0.5520 data_time: 0.0076 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.6905 aux.loss_ce: 0.0075 aux.acc_seg: 99.4012 +04/18 12:42:00 - mmengine - INFO - Iter(train) [ 67250/160000] lr: 6.1606e-03 eta: 14:13:01 time: 0.5515 data_time: 0.0071 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.7496 aux.loss_ce: 0.0073 aux.acc_seg: 99.2722 +04/18 12:42:27 - mmengine - INFO - Iter(train) [ 67300/160000] lr: 6.1576e-03 eta: 14:12:33 time: 0.5520 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7684 aux.loss_ce: 0.0069 aux.acc_seg: 99.2039 +04/18 12:42:55 - mmengine - INFO - Iter(train) [ 67350/160000] lr: 6.1547e-03 eta: 14:12:06 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7545 aux.loss_ce: 0.0080 aux.acc_seg: 99.4624 +04/18 12:43:22 - mmengine - INFO - Iter(train) [ 67400/160000] lr: 6.1517e-03 eta: 14:11:38 time: 0.5511 data_time: 0.0068 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0090 decode.acc_seg: 99.6899 aux.loss_ce: 0.0077 aux.acc_seg: 99.2165 +04/18 12:43:50 - mmengine - INFO - Iter(train) [ 67450/160000] lr: 6.1488e-03 eta: 14:11:10 time: 0.5508 data_time: 0.0064 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0099 decode.acc_seg: 99.5389 aux.loss_ce: 0.0087 aux.acc_seg: 98.9109 +04/18 12:44:17 - mmengine - INFO - Iter(train) [ 67500/160000] lr: 6.1458e-03 eta: 14:10:43 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0107 decode.acc_seg: 99.4827 aux.loss_ce: 0.0089 aux.acc_seg: 99.0738 +04/18 12:44:45 - mmengine - INFO - Iter(train) [ 67550/160000] lr: 6.1429e-03 eta: 14:10:15 time: 0.5509 data_time: 0.0062 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0087 decode.acc_seg: 99.7127 aux.loss_ce: 0.0083 aux.acc_seg: 99.0096 +04/18 12:45:13 - mmengine - INFO - Iter(train) [ 67600/160000] lr: 6.1400e-03 eta: 14:09:47 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.5878 aux.loss_ce: 0.0087 aux.acc_seg: 99.0252 +04/18 12:45:40 - mmengine - INFO - Iter(train) [ 67650/160000] lr: 6.1370e-03 eta: 14:09:20 time: 0.5486 data_time: 0.0066 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0095 decode.acc_seg: 99.6152 aux.loss_ce: 0.0086 aux.acc_seg: 99.1368 +04/18 12:46:08 - mmengine - INFO - Iter(train) [ 67700/160000] lr: 6.1341e-03 eta: 14:08:52 time: 0.5500 data_time: 0.0062 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0111 decode.acc_seg: 99.6368 aux.loss_ce: 0.0093 aux.acc_seg: 99.0959 +04/18 12:46:35 - mmengine - INFO - Iter(train) [ 67750/160000] lr: 6.1311e-03 eta: 14:08:24 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6267 aux.loss_ce: 0.0080 aux.acc_seg: 99.2095 +04/18 12:47:03 - mmengine - INFO - Iter(train) [ 67800/160000] lr: 6.1282e-03 eta: 14:07:57 time: 0.5505 data_time: 0.0064 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0085 decode.acc_seg: 99.7315 aux.loss_ce: 0.0077 aux.acc_seg: 99.3533 +04/18 12:47:30 - mmengine - INFO - Iter(train) [ 67850/160000] lr: 6.1253e-03 eta: 14:07:29 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.6808 aux.loss_ce: 0.0076 aux.acc_seg: 99.2458 +04/18 12:47:58 - mmengine 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aux.acc_seg: 99.1469 +04/18 12:49:48 - mmengine - INFO - Iter(train) [ 68100/160000] lr: 6.1105e-03 eta: 14:05:11 time: 0.5508 data_time: 0.0060 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.6007 aux.loss_ce: 0.0081 aux.acc_seg: 98.8534 +04/18 12:50:16 - mmengine - INFO - Iter(train) [ 68150/160000] lr: 6.1076e-03 eta: 14:04:44 time: 0.5515 data_time: 0.0067 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.7796 aux.loss_ce: 0.0079 aux.acc_seg: 99.3534 +04/18 12:50:44 - mmengine - INFO - Iter(train) [ 68200/160000] lr: 6.1047e-03 eta: 14:04:16 time: 0.5523 data_time: 0.0074 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.7065 aux.loss_ce: 0.0078 aux.acc_seg: 99.1721 +04/18 12:51:11 - mmengine - INFO - Iter(train) [ 68250/160000] lr: 6.1017e-03 eta: 14:03:49 time: 0.5510 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.4453 aux.loss_ce: 0.0081 aux.acc_seg: 99.0548 +04/18 12:51:39 - mmengine 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6.0870e-03 eta: 14:01:30 time: 0.5518 data_time: 0.0061 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.6494 aux.loss_ce: 0.0074 aux.acc_seg: 99.0561 +04/18 12:53:57 - mmengine - INFO - Iter(train) [ 68550/160000] lr: 6.0840e-03 eta: 14:01:03 time: 0.5506 data_time: 0.0069 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.7048 aux.loss_ce: 0.0077 aux.acc_seg: 99.2647 +04/18 12:54:24 - mmengine - INFO - Iter(train) [ 68600/160000] lr: 6.0811e-03 eta: 14:00:35 time: 0.5503 data_time: 0.0062 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6004 aux.loss_ce: 0.0073 aux.acc_seg: 99.1581 +04/18 12:54:52 - mmengine - INFO - Iter(train) [ 68650/160000] lr: 6.0782e-03 eta: 14:00:07 time: 0.5499 data_time: 0.0058 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.6217 aux.loss_ce: 0.0080 aux.acc_seg: 99.0794 +04/18 12:55:19 - mmengine - INFO - Iter(train) [ 68700/160000] lr: 6.0752e-03 eta: 13:59:40 time: 0.5519 data_time: 0.0070 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7984 aux.loss_ce: 0.0076 aux.acc_seg: 99.4544 +04/18 12:55:47 - mmengine - INFO - Iter(train) [ 68750/160000] lr: 6.0723e-03 eta: 13:59:12 time: 0.5500 data_time: 0.0058 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6758 aux.loss_ce: 0.0077 aux.acc_seg: 99.0948 +04/18 12:56:14 - mmengine - INFO - Iter(train) [ 68800/160000] lr: 6.0693e-03 eta: 13:58:44 time: 0.5506 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6945 aux.loss_ce: 0.0074 aux.acc_seg: 99.2737 +04/18 12:56:42 - mmengine - INFO - Iter(train) [ 68850/160000] lr: 6.0664e-03 eta: 13:58:17 time: 0.5505 data_time: 0.0061 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.7359 aux.loss_ce: 0.0078 aux.acc_seg: 99.2151 +04/18 12:57:09 - mmengine - INFO - Iter(train) [ 68900/160000] lr: 6.0634e-03 eta: 13:57:49 time: 0.5498 data_time: 0.0059 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7167 aux.loss_ce: 0.0072 aux.acc_seg: 99.2746 +04/18 12:57:37 - mmengine - INFO - Iter(train) [ 68950/160000] lr: 6.0605e-03 eta: 13:57:21 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6468 aux.loss_ce: 0.0074 aux.acc_seg: 99.1611 +04/18 12:58:05 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:58:05 - mmengine - INFO - Iter(train) [ 69000/160000] lr: 6.0575e-03 eta: 13:56:54 time: 0.5516 data_time: 0.0062 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6240 aux.loss_ce: 0.0073 aux.acc_seg: 99.1300 +04/18 12:58:32 - mmengine - INFO - Iter(train) [ 69050/160000] lr: 6.0546e-03 eta: 13:56:26 time: 0.5504 data_time: 0.0061 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6925 aux.loss_ce: 0.0071 aux.acc_seg: 99.2249 +04/18 12:59:00 - mmengine - INFO - Iter(train) [ 69100/160000] lr: 6.0516e-03 eta: 13:55:59 time: 0.5507 data_time: 0.0063 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.6224 aux.loss_ce: 0.0081 aux.acc_seg: 99.2496 +04/18 12:59:27 - mmengine - INFO - Iter(train) [ 69150/160000] lr: 6.0487e-03 eta: 13:55:31 time: 0.5623 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.5975 aux.loss_ce: 0.0076 aux.acc_seg: 99.0157 +04/18 12:59:55 - mmengine - INFO - Iter(train) [ 69200/160000] lr: 6.0458e-03 eta: 13:55:04 time: 0.5602 data_time: 0.0068 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.7068 aux.loss_ce: 0.0080 aux.acc_seg: 99.0401 +04/18 13:00:23 - mmengine - INFO - Iter(train) [ 69250/160000] lr: 6.0428e-03 eta: 13:54:36 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.7402 aux.loss_ce: 0.0078 aux.acc_seg: 99.2205 +04/18 13:00:50 - mmengine - INFO - Iter(train) [ 69300/160000] lr: 6.0399e-03 eta: 13:54:09 time: 0.5508 data_time: 0.0065 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.7506 aux.loss_ce: 0.0077 aux.acc_seg: 99.2965 +04/18 13:01:18 - mmengine - INFO - Iter(train) [ 69350/160000] lr: 6.0369e-03 eta: 13:53:41 time: 0.5513 data_time: 0.0069 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0079 decode.acc_seg: 99.7013 aux.loss_ce: 0.0074 aux.acc_seg: 99.3340 +04/18 13:01:45 - mmengine - INFO - Iter(train) [ 69400/160000] lr: 6.0340e-03 eta: 13:53:13 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6690 aux.loss_ce: 0.0073 aux.acc_seg: 99.2468 +04/18 13:02:13 - mmengine - INFO - Iter(train) [ 69450/160000] lr: 6.0310e-03 eta: 13:52:46 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0105 decode.acc_seg: 99.6007 aux.loss_ce: 0.0089 aux.acc_seg: 99.1673 +04/18 13:02:41 - mmengine - INFO - Iter(train) [ 69500/160000] lr: 6.0281e-03 eta: 13:52:18 time: 0.5520 data_time: 0.0061 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0078 decode.acc_seg: 99.7236 aux.loss_ce: 0.0073 aux.acc_seg: 99.3158 +04/18 13:03:08 - mmengine - INFO - Iter(train) [ 69550/160000] lr: 6.0251e-03 eta: 13:51:50 time: 0.5511 data_time: 0.0061 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.6690 aux.loss_ce: 0.0074 aux.acc_seg: 99.1807 +04/18 13:03:36 - mmengine - INFO - Iter(train) [ 69600/160000] lr: 6.0222e-03 eta: 13:51:23 time: 0.5529 data_time: 0.0069 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0096 decode.acc_seg: 99.6646 aux.loss_ce: 0.0086 aux.acc_seg: 99.1276 +04/18 13:04:03 - mmengine - INFO - Iter(train) [ 69650/160000] lr: 6.0192e-03 eta: 13:50:55 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6944 aux.loss_ce: 0.0079 aux.acc_seg: 99.4189 +04/18 13:04:31 - mmengine - INFO - Iter(train) [ 69700/160000] lr: 6.0163e-03 eta: 13:50:28 time: 0.5500 data_time: 0.0070 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0093 decode.acc_seg: 99.6805 aux.loss_ce: 0.0084 aux.acc_seg: 99.1603 +04/18 13:04:58 - mmengine - INFO - Iter(train) [ 69750/160000] lr: 6.0133e-03 eta: 13:50:00 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0111 decode.acc_seg: 99.6716 aux.loss_ce: 0.0092 aux.acc_seg: 99.1631 +04/18 13:05:26 - mmengine - INFO - Iter(train) [ 69800/160000] lr: 6.0104e-03 eta: 13:49:32 time: 0.5505 data_time: 0.0066 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.5812 aux.loss_ce: 0.0087 aux.acc_seg: 99.0966 +04/18 13:05:54 - mmengine - INFO - Iter(train) [ 69850/160000] lr: 6.0074e-03 eta: 13:49:05 time: 0.5523 data_time: 0.0062 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.7193 aux.loss_ce: 0.0083 aux.acc_seg: 99.1334 +04/18 13:06:21 - mmengine - INFO - Iter(train) [ 69900/160000] lr: 6.0045e-03 eta: 13:48:37 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0082 decode.acc_seg: 99.6904 aux.loss_ce: 0.0080 aux.acc_seg: 99.2169 +04/18 13:06:49 - mmengine - INFO - Iter(train) [ 69950/160000] lr: 6.0015e-03 eta: 13:48:09 time: 0.5511 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.6339 aux.loss_ce: 0.0076 aux.acc_seg: 99.2690 +04/18 13:07:16 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:07:16 - mmengine - INFO - Iter(train) [ 70000/160000] lr: 5.9986e-03 eta: 13:47:42 time: 0.5509 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7218 aux.loss_ce: 0.0071 aux.acc_seg: 99.2356 +04/18 13:07:16 - mmengine - INFO - Saving checkpoint at 70000 iterations +04/18 13:07:20 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0468 data_time: 0.0015 memory: 1657 +04/18 13:07:23 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0466 data_time: 0.0014 memory: 1657 +04/18 13:07:25 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0466 data_time: 0.0013 memory: 1657 +04/18 13:07:27 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0458 data_time: 0.0012 memory: 1657 +04/18 13:07:28 - mmengine - INFO - per class results: +04/18 13:07:28 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.44 | 99.53 | 99.62 | 99.44 | +| contrast | 80.05 | 90.89 | 88.92 | 87.04 | 90.89 | ++------------+-------+-------+--------+-----------+--------+ +04/18 13:07:28 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1000 mIoU: 89.5600 mAcc: 95.1600 mFscore: 94.2200 mPrecision: 93.3300 mRecall: 95.1600 data_time: 0.0015 time: 0.0467 +04/18 13:07:55 - mmengine - INFO - Iter(train) [ 70050/160000] lr: 5.9956e-03 eta: 13:47:14 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.7017 aux.loss_ce: 0.0077 aux.acc_seg: 99.0495 +04/18 13:08:23 - mmengine - INFO - Iter(train) [ 70100/160000] lr: 5.9927e-03 eta: 13:46:47 time: 0.5523 data_time: 0.0077 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.7711 aux.loss_ce: 0.0075 aux.acc_seg: 99.3266 +04/18 13:08:50 - mmengine - INFO - Iter(train) [ 70150/160000] lr: 5.9897e-03 eta: 13:46:19 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6188 aux.loss_ce: 0.0082 aux.acc_seg: 98.9286 +04/18 13:09:18 - mmengine - INFO - Iter(train) [ 70200/160000] lr: 5.9868e-03 eta: 13:45:52 time: 0.5494 data_time: 0.0061 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6087 aux.loss_ce: 0.0078 aux.acc_seg: 99.0937 +04/18 13:09:45 - mmengine - INFO - Iter(train) [ 70250/160000] lr: 5.9838e-03 eta: 13:45:24 time: 0.5502 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0085 decode.acc_seg: 99.7354 aux.loss_ce: 0.0076 aux.acc_seg: 99.2040 +04/18 13:10:13 - mmengine - INFO - Iter(train) [ 70300/160000] lr: 5.9809e-03 eta: 13:44:57 time: 0.5513 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6964 aux.loss_ce: 0.0078 aux.acc_seg: 99.2430 +04/18 13:10:41 - mmengine - INFO - Iter(train) [ 70350/160000] lr: 5.9779e-03 eta: 13:44:29 time: 0.5516 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6499 aux.loss_ce: 0.0075 aux.acc_seg: 99.1297 +04/18 13:11:08 - mmengine - INFO - Iter(train) [ 70400/160000] lr: 5.9750e-03 eta: 13:44:01 time: 0.5504 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.6212 aux.loss_ce: 0.0075 aux.acc_seg: 99.3167 +04/18 13:11:36 - mmengine - INFO - Iter(train) [ 70450/160000] lr: 5.9720e-03 eta: 13:43:34 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.6600 aux.loss_ce: 0.0080 aux.acc_seg: 99.1551 +04/18 13:12:04 - mmengine - INFO - Iter(train) [ 70500/160000] lr: 5.9691e-03 eta: 13:43:06 time: 0.5520 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.7507 aux.loss_ce: 0.0075 aux.acc_seg: 99.5346 +04/18 13:12:31 - mmengine - INFO - Iter(train) [ 70550/160000] lr: 5.9661e-03 eta: 13:42:39 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.7468 aux.loss_ce: 0.0080 aux.acc_seg: 99.0608 +04/18 13:12:59 - mmengine - INFO - Iter(train) [ 70600/160000] lr: 5.9632e-03 eta: 13:42:11 time: 0.5515 data_time: 0.0069 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6384 aux.loss_ce: 0.0078 aux.acc_seg: 98.9050 +04/18 13:13:26 - mmengine - INFO - Iter(train) [ 70650/160000] lr: 5.9602e-03 eta: 13:41:44 time: 0.5515 data_time: 0.0068 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.7053 aux.loss_ce: 0.0082 aux.acc_seg: 99.2291 +04/18 13:13:54 - mmengine - INFO - Iter(train) [ 70700/160000] lr: 5.9573e-03 eta: 13:41:16 time: 0.5520 data_time: 0.0071 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7040 aux.loss_ce: 0.0077 aux.acc_seg: 99.1316 +04/18 13:14:22 - mmengine - INFO - Iter(train) [ 70750/160000] lr: 5.9543e-03 eta: 13:40:48 time: 0.5527 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.5953 aux.loss_ce: 0.0078 aux.acc_seg: 98.9807 +04/18 13:14:49 - mmengine - INFO - Iter(train) [ 70800/160000] lr: 5.9514e-03 eta: 13:40:21 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6740 aux.loss_ce: 0.0073 aux.acc_seg: 99.1641 +04/18 13:15:17 - mmengine - INFO - Iter(train) [ 70850/160000] lr: 5.9484e-03 eta: 13:39:53 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0081 decode.acc_seg: 99.6813 aux.loss_ce: 0.0082 aux.acc_seg: 99.0903 +04/18 13:15:44 - mmengine - INFO - Iter(train) [ 70900/160000] lr: 5.9455e-03 eta: 13:39:26 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.5258 aux.loss_ce: 0.0084 aux.acc_seg: 98.8321 +04/18 13:16:12 - mmengine - INFO - Iter(train) [ 70950/160000] lr: 5.9425e-03 eta: 13:38:58 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.6739 aux.loss_ce: 0.0082 aux.acc_seg: 98.9123 +04/18 13:16:40 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:16:40 - mmengine - INFO - Iter(train) [ 71000/160000] lr: 5.9396e-03 eta: 13:38:30 time: 0.5512 data_time: 0.0060 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.5271 aux.loss_ce: 0.0075 aux.acc_seg: 99.0655 +04/18 13:17:07 - mmengine - INFO - Iter(train) [ 71050/160000] lr: 5.9366e-03 eta: 13:38:03 time: 0.5532 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7773 aux.loss_ce: 0.0075 aux.acc_seg: 99.3194 +04/18 13:17:35 - mmengine - INFO - Iter(train) [ 71100/160000] lr: 5.9337e-03 eta: 13:37:35 time: 0.5505 data_time: 0.0062 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.6755 aux.loss_ce: 0.0078 aux.acc_seg: 99.1154 +04/18 13:18:02 - mmengine - INFO - Iter(train) [ 71150/160000] lr: 5.9307e-03 eta: 13:37:08 time: 0.5518 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.6461 aux.loss_ce: 0.0072 aux.acc_seg: 99.0748 +04/18 13:18:30 - mmengine - INFO - Iter(train) [ 71200/160000] lr: 5.9278e-03 eta: 13:36:40 time: 0.5517 data_time: 0.0068 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7775 aux.loss_ce: 0.0081 aux.acc_seg: 99.2649 +04/18 13:18:58 - mmengine - INFO - Iter(train) [ 71250/160000] lr: 5.9248e-03 eta: 13:36:12 time: 0.5516 data_time: 0.0071 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.5831 aux.loss_ce: 0.0072 aux.acc_seg: 98.8583 +04/18 13:19:25 - mmengine - INFO - Iter(train) [ 71300/160000] lr: 5.9218e-03 eta: 13:35:45 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6793 aux.loss_ce: 0.0074 aux.acc_seg: 99.3607 +04/18 13:19:53 - mmengine - INFO - Iter(train) [ 71350/160000] lr: 5.9189e-03 eta: 13:35:18 time: 0.5516 data_time: 0.0062 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.7224 aux.loss_ce: 0.0078 aux.acc_seg: 99.3318 +04/18 13:20:21 - mmengine - INFO - Iter(train) [ 71400/160000] lr: 5.9159e-03 eta: 13:34:50 time: 0.5523 data_time: 0.0062 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6738 aux.loss_ce: 0.0081 aux.acc_seg: 99.1942 +04/18 13:20:48 - mmengine - INFO - Iter(train) [ 71450/160000] lr: 5.9130e-03 eta: 13:34:22 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.6629 aux.loss_ce: 0.0080 aux.acc_seg: 99.0391 +04/18 13:21:16 - mmengine - INFO - Iter(train) [ 71500/160000] lr: 5.9100e-03 eta: 13:33:55 time: 0.5531 data_time: 0.0060 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7650 aux.loss_ce: 0.0074 aux.acc_seg: 99.3747 +04/18 13:21:44 - mmengine - INFO - Iter(train) [ 71550/160000] lr: 5.9071e-03 eta: 13:33:27 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7768 aux.loss_ce: 0.0076 aux.acc_seg: 99.4062 +04/18 13:22:11 - mmengine - INFO - Iter(train) [ 71600/160000] lr: 5.9041e-03 eta: 13:33:00 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7050 aux.loss_ce: 0.0072 aux.acc_seg: 99.2457 +04/18 13:22:39 - mmengine - INFO - Iter(train) [ 71650/160000] lr: 5.9012e-03 eta: 13:32:32 time: 0.5524 data_time: 0.0075 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0074 decode.acc_seg: 99.7500 aux.loss_ce: 0.0080 aux.acc_seg: 99.1837 +04/18 13:23:06 - mmengine - INFO - Iter(train) [ 71700/160000] lr: 5.8982e-03 eta: 13:32:05 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0083 decode.acc_seg: 99.7140 aux.loss_ce: 0.0079 aux.acc_seg: 99.2368 +04/18 13:23:34 - mmengine - INFO - Iter(train) [ 71750/160000] lr: 5.8953e-03 eta: 13:31:37 time: 0.5511 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.6075 aux.loss_ce: 0.0073 aux.acc_seg: 99.2095 +04/18 13:24:02 - mmengine - INFO - Iter(train) [ 71800/160000] lr: 5.8923e-03 eta: 13:31:10 time: 0.5530 data_time: 0.0067 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0082 decode.acc_seg: 99.6995 aux.loss_ce: 0.0074 aux.acc_seg: 99.3881 +04/18 13:24:29 - mmengine - INFO - Iter(train) [ 71850/160000] lr: 5.8893e-03 eta: 13:30:42 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.7274 aux.loss_ce: 0.0074 aux.acc_seg: 99.4112 +04/18 13:24:57 - mmengine - INFO - Iter(train) [ 71900/160000] lr: 5.8864e-03 eta: 13:30:14 time: 0.5520 data_time: 0.0061 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.5666 aux.loss_ce: 0.0079 aux.acc_seg: 98.9028 +04/18 13:25:25 - mmengine - INFO - Iter(train) [ 71950/160000] lr: 5.8834e-03 eta: 13:29:47 time: 0.5530 data_time: 0.0067 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.7374 aux.loss_ce: 0.0077 aux.acc_seg: 99.2367 +04/18 13:25:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:25:52 - mmengine - INFO - Iter(train) [ 72000/160000] lr: 5.8805e-03 eta: 13:29:19 time: 0.5529 data_time: 0.0064 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0085 decode.acc_seg: 99.7155 aux.loss_ce: 0.0083 aux.acc_seg: 99.0468 +04/18 13:26:20 - mmengine - INFO - Iter(train) [ 72050/160000] lr: 5.8775e-03 eta: 13:28:52 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.6279 aux.loss_ce: 0.0080 aux.acc_seg: 99.0722 +04/18 13:26:47 - mmengine - INFO - Iter(train) [ 72100/160000] lr: 5.8746e-03 eta: 13:28:24 time: 0.5514 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.6089 aux.loss_ce: 0.0074 aux.acc_seg: 99.1879 +04/18 13:27:15 - mmengine - INFO - Iter(train) [ 72150/160000] lr: 5.8716e-03 eta: 13:27:57 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.7412 aux.loss_ce: 0.0080 aux.acc_seg: 99.2281 +04/18 13:27:43 - mmengine - INFO - Iter(train) [ 72200/160000] lr: 5.8687e-03 eta: 13:27:29 time: 0.5529 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.5743 aux.loss_ce: 0.0078 aux.acc_seg: 99.1511 +04/18 13:28:10 - mmengine - INFO - Iter(train) [ 72250/160000] lr: 5.8657e-03 eta: 13:27:02 time: 0.5520 data_time: 0.0064 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6676 aux.loss_ce: 0.0072 aux.acc_seg: 99.2157 +04/18 13:28:38 - mmengine - INFO - Iter(train) [ 72300/160000] lr: 5.8627e-03 eta: 13:26:34 time: 0.5533 data_time: 0.0060 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0079 decode.acc_seg: 99.6785 aux.loss_ce: 0.0072 aux.acc_seg: 99.3123 +04/18 13:29:06 - mmengine - INFO - Iter(train) [ 72350/160000] lr: 5.8598e-03 eta: 13:26:06 time: 0.5529 data_time: 0.0070 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6458 aux.loss_ce: 0.0081 aux.acc_seg: 99.1272 +04/18 13:29:33 - mmengine - INFO - Iter(train) [ 72400/160000] lr: 5.8568e-03 eta: 13:25:39 time: 0.5621 data_time: 0.0071 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0102 decode.acc_seg: 99.5074 aux.loss_ce: 0.0084 aux.acc_seg: 99.0326 +04/18 13:30:01 - mmengine - INFO - Iter(train) [ 72450/160000] lr: 5.8539e-03 eta: 13:25:12 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.6523 aux.loss_ce: 0.0077 aux.acc_seg: 99.0721 +04/18 13:30:29 - mmengine - INFO - Iter(train) [ 72500/160000] lr: 5.8509e-03 eta: 13:24:44 time: 0.5510 data_time: 0.0060 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6679 aux.loss_ce: 0.0078 aux.acc_seg: 99.3586 +04/18 13:30:56 - mmengine - INFO - Iter(train) [ 72550/160000] lr: 5.8480e-03 eta: 13:24:16 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7038 aux.loss_ce: 0.0071 aux.acc_seg: 99.1717 +04/18 13:31:24 - mmengine - INFO - Iter(train) [ 72600/160000] lr: 5.8450e-03 eta: 13:23:49 time: 0.5533 data_time: 0.0059 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.6857 aux.loss_ce: 0.0074 aux.acc_seg: 98.9161 +04/18 13:31:52 - mmengine - INFO - Iter(train) [ 72650/160000] lr: 5.8420e-03 eta: 13:23:21 time: 0.5540 data_time: 0.0071 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.7041 aux.loss_ce: 0.0080 aux.acc_seg: 99.2405 +04/18 13:32:19 - mmengine - INFO - Iter(train) [ 72700/160000] lr: 5.8391e-03 eta: 13:22:54 time: 0.5524 data_time: 0.0066 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.6450 aux.loss_ce: 0.0079 aux.acc_seg: 98.9261 +04/18 13:32:47 - mmengine - INFO - Iter(train) [ 72750/160000] lr: 5.8361e-03 eta: 13:22:26 time: 0.5525 data_time: 0.0069 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0118 decode.acc_seg: 99.3946 aux.loss_ce: 0.0090 aux.acc_seg: 99.1690 +04/18 13:33:14 - mmengine - INFO - Iter(train) [ 72800/160000] lr: 5.8332e-03 eta: 13:21:59 time: 0.5519 data_time: 0.0058 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0103 decode.acc_seg: 99.4988 aux.loss_ce: 0.0088 aux.acc_seg: 98.8207 +04/18 13:33:42 - mmengine - INFO - Iter(train) [ 72850/160000] lr: 5.8302e-03 eta: 13:21:31 time: 0.5530 data_time: 0.0068 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0111 decode.acc_seg: 99.6811 aux.loss_ce: 0.0090 aux.acc_seg: 99.2533 +04/18 13:34:10 - mmengine - INFO - Iter(train) [ 72900/160000] lr: 5.8272e-03 eta: 13:21:04 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0108 decode.acc_seg: 99.4739 aux.loss_ce: 0.0086 aux.acc_seg: 99.0827 +04/18 13:34:37 - mmengine - INFO - Iter(train) [ 72950/160000] lr: 5.8243e-03 eta: 13:20:36 time: 0.5532 data_time: 0.0072 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.6920 aux.loss_ce: 0.0086 aux.acc_seg: 99.1225 +04/18 13:35:05 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:35:05 - mmengine - INFO - Iter(train) [ 73000/160000] lr: 5.8213e-03 eta: 13:20:08 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.6268 aux.loss_ce: 0.0081 aux.acc_seg: 99.2789 +04/18 13:35:33 - mmengine - INFO - Iter(train) [ 73050/160000] lr: 5.8184e-03 eta: 13:19:41 time: 0.5532 data_time: 0.0064 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.6858 aux.loss_ce: 0.0081 aux.acc_seg: 99.2652 +04/18 13:36:00 - mmengine - INFO - Iter(train) [ 73100/160000] lr: 5.8154e-03 eta: 13:19:13 time: 0.5517 data_time: 0.0071 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0081 decode.acc_seg: 99.6793 aux.loss_ce: 0.0078 aux.acc_seg: 99.1027 +04/18 13:36:28 - mmengine - INFO - Iter(train) [ 73150/160000] lr: 5.8125e-03 eta: 13:18:46 time: 0.5520 data_time: 0.0069 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.5923 aux.loss_ce: 0.0077 aux.acc_seg: 99.0482 +04/18 13:36:55 - mmengine - INFO - Iter(train) [ 73200/160000] lr: 5.8095e-03 eta: 13:18:18 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.6848 aux.loss_ce: 0.0082 aux.acc_seg: 99.1988 +04/18 13:37:23 - mmengine - INFO - Iter(train) [ 73250/160000] lr: 5.8065e-03 eta: 13:17:51 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.7367 aux.loss_ce: 0.0079 aux.acc_seg: 99.3934 +04/18 13:37:51 - mmengine - INFO - Iter(train) [ 73300/160000] lr: 5.8036e-03 eta: 13:17:23 time: 0.5531 data_time: 0.0061 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.7201 aux.loss_ce: 0.0078 aux.acc_seg: 99.3034 +04/18 13:38:18 - mmengine - INFO - Iter(train) [ 73350/160000] lr: 5.8006e-03 eta: 13:16:56 time: 0.5530 data_time: 0.0072 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.7479 aux.loss_ce: 0.0075 aux.acc_seg: 99.3849 +04/18 13:38:46 - mmengine - INFO - Iter(train) [ 73400/160000] lr: 5.7976e-03 eta: 13:16:28 time: 0.5514 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0100 decode.acc_seg: 99.3410 aux.loss_ce: 0.0089 aux.acc_seg: 98.5963 +04/18 13:39:14 - mmengine - INFO - Iter(train) [ 73450/160000] lr: 5.7947e-03 eta: 13:16:01 time: 0.5611 data_time: 0.0071 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.7078 aux.loss_ce: 0.0079 aux.acc_seg: 99.0885 +04/18 13:39:41 - mmengine - INFO - Iter(train) [ 73500/160000] lr: 5.7917e-03 eta: 13:15:33 time: 0.5535 data_time: 0.0065 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6415 aux.loss_ce: 0.0082 aux.acc_seg: 99.0867 +04/18 13:40:09 - mmengine - INFO - Iter(train) [ 73550/160000] lr: 5.7888e-03 eta: 13:15:05 time: 0.5513 data_time: 0.0058 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.6291 aux.loss_ce: 0.0073 aux.acc_seg: 99.2481 +04/18 13:40:37 - mmengine - INFO - Iter(train) [ 73600/160000] lr: 5.7858e-03 eta: 13:14:38 time: 0.5520 data_time: 0.0071 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0083 decode.acc_seg: 99.6764 aux.loss_ce: 0.0082 aux.acc_seg: 99.1766 +04/18 13:41:04 - mmengine - INFO - Iter(train) [ 73650/160000] lr: 5.7828e-03 eta: 13:14:10 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.6997 aux.loss_ce: 0.0077 aux.acc_seg: 99.1902 +04/18 13:41:32 - mmengine - INFO - Iter(train) [ 73700/160000] lr: 5.7799e-03 eta: 13:13:43 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.5741 aux.loss_ce: 0.0083 aux.acc_seg: 99.1199 +04/18 13:42:00 - mmengine - INFO - Iter(train) [ 73750/160000] lr: 5.7769e-03 eta: 13:13:15 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.6207 aux.loss_ce: 0.0078 aux.acc_seg: 98.9251 +04/18 13:42:27 - mmengine - INFO - Iter(train) [ 73800/160000] lr: 5.7740e-03 eta: 13:12:48 time: 0.5527 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7057 aux.loss_ce: 0.0073 aux.acc_seg: 99.1655 +04/18 13:42:55 - mmengine - INFO - Iter(train) [ 73850/160000] lr: 5.7710e-03 eta: 13:12:20 time: 0.5541 data_time: 0.0060 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.6880 aux.loss_ce: 0.0073 aux.acc_seg: 99.2225 +04/18 13:43:22 - mmengine - INFO - Iter(train) [ 73900/160000] lr: 5.7680e-03 eta: 13:11:53 time: 0.5520 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0091 decode.acc_seg: 99.7266 aux.loss_ce: 0.0077 aux.acc_seg: 99.3498 +04/18 13:43:50 - mmengine - INFO - Iter(train) [ 73950/160000] lr: 5.7651e-03 eta: 13:11:25 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6967 aux.loss_ce: 0.0075 aux.acc_seg: 99.1593 +04/18 13:44:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:44:18 - mmengine - INFO - Iter(train) [ 74000/160000] lr: 5.7621e-03 eta: 13:10:58 time: 0.5507 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.7504 aux.loss_ce: 0.0075 aux.acc_seg: 99.2293 +04/18 13:44:45 - mmengine - INFO - Iter(train) [ 74050/160000] lr: 5.7591e-03 eta: 13:10:30 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7253 aux.loss_ce: 0.0074 aux.acc_seg: 99.3150 +04/18 13:45:13 - mmengine - INFO - Iter(train) [ 74100/160000] lr: 5.7562e-03 eta: 13:10:02 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7438 aux.loss_ce: 0.0073 aux.acc_seg: 99.2490 +04/18 13:45:41 - mmengine - INFO - Iter(train) [ 74150/160000] lr: 5.7532e-03 eta: 13:09:35 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6561 aux.loss_ce: 0.0072 aux.acc_seg: 99.1373 +04/18 13:46:08 - mmengine - INFO - Iter(train) [ 74200/160000] lr: 5.7503e-03 eta: 13:09:07 time: 0.5527 data_time: 0.0068 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.6842 aux.loss_ce: 0.0073 aux.acc_seg: 99.4061 +04/18 13:46:36 - mmengine - INFO - Iter(train) [ 74250/160000] lr: 5.7473e-03 eta: 13:08:40 time: 0.5513 data_time: 0.0061 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7683 aux.loss_ce: 0.0074 aux.acc_seg: 99.3338 +04/18 13:47:04 - mmengine - INFO - Iter(train) [ 74300/160000] lr: 5.7443e-03 eta: 13:08:12 time: 0.5521 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7014 aux.loss_ce: 0.0073 aux.acc_seg: 99.2202 +04/18 13:47:31 - mmengine - INFO - Iter(train) [ 74350/160000] lr: 5.7414e-03 eta: 13:07:45 time: 0.5522 data_time: 0.0071 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7216 aux.loss_ce: 0.0076 aux.acc_seg: 99.2443 +04/18 13:47:59 - mmengine - INFO - Iter(train) [ 74400/160000] lr: 5.7384e-03 eta: 13:07:17 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7744 aux.loss_ce: 0.0072 aux.acc_seg: 99.3647 +04/18 13:48:27 - mmengine - INFO - Iter(train) [ 74450/160000] lr: 5.7354e-03 eta: 13:06:50 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6039 aux.loss_ce: 0.0078 aux.acc_seg: 99.1303 +04/18 13:48:54 - mmengine - INFO - Iter(train) [ 74500/160000] lr: 5.7325e-03 eta: 13:06:22 time: 0.5527 data_time: 0.0060 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6873 aux.loss_ce: 0.0075 aux.acc_seg: 99.2661 +04/18 13:49:22 - mmengine - INFO - Iter(train) [ 74550/160000] lr: 5.7295e-03 eta: 13:05:55 time: 0.5601 data_time: 0.0067 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7502 aux.loss_ce: 0.0072 aux.acc_seg: 99.4673 +04/18 13:49:50 - mmengine - INFO - Iter(train) [ 74600/160000] lr: 5.7265e-03 eta: 13:05:27 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6539 aux.loss_ce: 0.0074 aux.acc_seg: 99.1780 +04/18 13:50:17 - mmengine - INFO - Iter(train) [ 74650/160000] lr: 5.7236e-03 eta: 13:05:00 time: 0.5522 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6793 aux.loss_ce: 0.0076 aux.acc_seg: 99.0724 +04/18 13:50:45 - mmengine - INFO - Iter(train) [ 74700/160000] lr: 5.7206e-03 eta: 13:04:32 time: 0.5520 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.7586 aux.loss_ce: 0.0072 aux.acc_seg: 99.3429 +04/18 13:51:13 - mmengine - INFO - Iter(train) [ 74750/160000] lr: 5.7176e-03 eta: 13:04:05 time: 0.5539 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7895 aux.loss_ce: 0.0070 aux.acc_seg: 99.3579 +04/18 13:51:40 - mmengine - INFO - Iter(train) [ 74800/160000] lr: 5.7147e-03 eta: 13:03:37 time: 0.5555 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0072 decode.acc_seg: 99.6549 aux.loss_ce: 0.0069 aux.acc_seg: 99.0606 +04/18 13:52:08 - mmengine 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aux.acc_seg: 99.3453 +04/18 13:53:59 - mmengine - INFO - Iter(train) [ 75050/160000] lr: 5.6999e-03 eta: 13:01:20 time: 0.5542 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.6854 aux.loss_ce: 0.0077 aux.acc_seg: 99.3029 +04/18 13:54:26 - mmengine - INFO - Iter(train) [ 75100/160000] lr: 5.6969e-03 eta: 13:00:52 time: 0.5522 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.6496 aux.loss_ce: 0.0079 aux.acc_seg: 99.2293 +04/18 13:54:54 - mmengine - INFO - Iter(train) [ 75150/160000] lr: 5.6939e-03 eta: 13:00:24 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0086 decode.acc_seg: 99.7335 aux.loss_ce: 0.0076 aux.acc_seg: 99.2230 +04/18 13:55:22 - mmengine - INFO - Iter(train) [ 75200/160000] lr: 5.6910e-03 eta: 12:59:57 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.6819 aux.loss_ce: 0.0083 aux.acc_seg: 99.0951 +04/18 13:55:49 - mmengine 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5.6761e-03 eta: 12:57:39 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7062 aux.loss_ce: 0.0076 aux.acc_seg: 99.2773 +04/18 13:58:07 - mmengine - INFO - Iter(train) [ 75500/160000] lr: 5.6731e-03 eta: 12:57:12 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6880 aux.loss_ce: 0.0079 aux.acc_seg: 99.1724 +04/18 13:58:35 - mmengine - INFO - Iter(train) [ 75550/160000] lr: 5.6702e-03 eta: 12:56:44 time: 0.5509 data_time: 0.0061 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.5934 aux.loss_ce: 0.0082 aux.acc_seg: 99.0724 +04/18 13:59:03 - mmengine - INFO - Iter(train) [ 75600/160000] lr: 5.6672e-03 eta: 12:56:17 time: 0.5612 data_time: 0.0064 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0098 decode.acc_seg: 99.6265 aux.loss_ce: 0.0088 aux.acc_seg: 99.2264 +04/18 13:59:31 - mmengine - INFO - Iter(train) [ 75650/160000] lr: 5.6642e-03 eta: 12:55:49 time: 0.5617 data_time: 0.0067 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0086 decode.acc_seg: 99.6624 aux.loss_ce: 0.0076 aux.acc_seg: 99.1029 +04/18 13:59:58 - mmengine - INFO - Iter(train) [ 75700/160000] lr: 5.6613e-03 eta: 12:55:22 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6754 aux.loss_ce: 0.0077 aux.acc_seg: 99.2651 +04/18 14:00:26 - mmengine - INFO - Iter(train) [ 75750/160000] lr: 5.6583e-03 eta: 12:54:54 time: 0.5508 data_time: 0.0063 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0076 decode.acc_seg: 99.6983 aux.loss_ce: 0.0072 aux.acc_seg: 99.2643 +04/18 14:00:54 - mmengine - INFO - Iter(train) [ 75800/160000] lr: 5.6553e-03 eta: 12:54:27 time: 0.5529 data_time: 0.0073 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6488 aux.loss_ce: 0.0078 aux.acc_seg: 99.1465 +04/18 14:01:21 - mmengine - INFO - Iter(train) [ 75850/160000] lr: 5.6524e-03 eta: 12:53:59 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0082 decode.acc_seg: 99.5838 aux.loss_ce: 0.0080 aux.acc_seg: 99.0612 +04/18 14:01:49 - mmengine - INFO - Iter(train) [ 75900/160000] lr: 5.6494e-03 eta: 12:53:32 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7929 aux.loss_ce: 0.0072 aux.acc_seg: 99.5135 +04/18 14:02:17 - mmengine - INFO - Iter(train) [ 75950/160000] lr: 5.6464e-03 eta: 12:53:04 time: 0.5544 data_time: 0.0070 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.6447 aux.loss_ce: 0.0080 aux.acc_seg: 99.0114 +04/18 14:02:44 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:02:44 - mmengine - INFO - Iter(train) [ 76000/160000] lr: 5.6435e-03 eta: 12:52:37 time: 0.5505 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6880 aux.loss_ce: 0.0079 aux.acc_seg: 99.0818 +04/18 14:03:12 - mmengine - INFO - Iter(train) [ 76050/160000] lr: 5.6405e-03 eta: 12:52:09 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0078 decode.acc_seg: 99.7631 aux.loss_ce: 0.0082 aux.acc_seg: 99.4489 +04/18 14:03:39 - mmengine - INFO - Iter(train) [ 76100/160000] lr: 5.6375e-03 eta: 12:51:41 time: 0.5519 data_time: 0.0066 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6859 aux.loss_ce: 0.0072 aux.acc_seg: 99.2662 +04/18 14:04:07 - mmengine - INFO - Iter(train) [ 76150/160000] lr: 5.6346e-03 eta: 12:51:14 time: 0.5537 data_time: 0.0062 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7334 aux.loss_ce: 0.0077 aux.acc_seg: 99.4172 +04/18 14:04:35 - mmengine - INFO - Iter(train) [ 76200/160000] lr: 5.6316e-03 eta: 12:50:46 time: 0.5527 data_time: 0.0066 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7077 aux.loss_ce: 0.0075 aux.acc_seg: 99.2603 +04/18 14:05:02 - mmengine - INFO - Iter(train) [ 76250/160000] lr: 5.6286e-03 eta: 12:50:19 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6560 aux.loss_ce: 0.0073 aux.acc_seg: 99.2674 +04/18 14:05:30 - mmengine - INFO - Iter(train) [ 76300/160000] lr: 5.6256e-03 eta: 12:49:51 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6660 aux.loss_ce: 0.0073 aux.acc_seg: 98.9780 +04/18 14:05:58 - mmengine - INFO - Iter(train) [ 76350/160000] lr: 5.6227e-03 eta: 12:49:24 time: 0.5534 data_time: 0.0067 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0103 decode.acc_seg: 99.7115 aux.loss_ce: 0.0088 aux.acc_seg: 99.1650 +04/18 14:06:25 - mmengine - INFO - Iter(train) [ 76400/160000] lr: 5.6197e-03 eta: 12:48:56 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0084 decode.acc_seg: 99.6535 aux.loss_ce: 0.0085 aux.acc_seg: 99.1377 +04/18 14:06:53 - mmengine - INFO - Iter(train) [ 76450/160000] lr: 5.6167e-03 eta: 12:48:29 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.6810 aux.loss_ce: 0.0085 aux.acc_seg: 99.1442 +04/18 14:07:21 - mmengine - INFO - Iter(train) [ 76500/160000] lr: 5.6138e-03 eta: 12:48:01 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.6929 aux.loss_ce: 0.0078 aux.acc_seg: 99.1059 +04/18 14:07:48 - mmengine - INFO - Iter(train) [ 76550/160000] lr: 5.6108e-03 eta: 12:47:34 time: 0.5533 data_time: 0.0074 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.6104 aux.loss_ce: 0.0074 aux.acc_seg: 99.3080 +04/18 14:08:16 - mmengine - INFO - Iter(train) [ 76600/160000] lr: 5.6078e-03 eta: 12:47:06 time: 0.5530 data_time: 0.0072 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6520 aux.loss_ce: 0.0079 aux.acc_seg: 99.2299 +04/18 14:08:44 - mmengine - INFO - Iter(train) [ 76650/160000] lr: 5.6048e-03 eta: 12:46:39 time: 0.5530 data_time: 0.0071 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7184 aux.loss_ce: 0.0078 aux.acc_seg: 99.2104 +04/18 14:09:11 - mmengine - INFO - Iter(train) [ 76700/160000] lr: 5.6019e-03 eta: 12:46:11 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7515 aux.loss_ce: 0.0073 aux.acc_seg: 99.2829 +04/18 14:09:39 - mmengine - INFO - Iter(train) [ 76750/160000] lr: 5.5989e-03 eta: 12:45:44 time: 0.5530 data_time: 0.0067 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7278 aux.loss_ce: 0.0082 aux.acc_seg: 99.1805 +04/18 14:10:07 - mmengine - INFO - Iter(train) [ 76800/160000] lr: 5.5959e-03 eta: 12:45:16 time: 0.5519 data_time: 0.0059 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0086 decode.acc_seg: 99.6566 aux.loss_ce: 0.0082 aux.acc_seg: 99.1994 +04/18 14:10:34 - mmengine - INFO - Iter(train) [ 76850/160000] lr: 5.5930e-03 eta: 12:44:49 time: 0.5539 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.7783 aux.loss_ce: 0.0076 aux.acc_seg: 99.3851 +04/18 14:11:02 - mmengine - INFO - Iter(train) [ 76900/160000] lr: 5.5900e-03 eta: 12:44:21 time: 0.5537 data_time: 0.0069 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.5828 aux.loss_ce: 0.0083 aux.acc_seg: 99.1943 +04/18 14:11:30 - mmengine - INFO - Iter(train) [ 76950/160000] lr: 5.5870e-03 eta: 12:43:54 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.7047 aux.loss_ce: 0.0079 aux.acc_seg: 99.2355 +04/18 14:11:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:11:58 - mmengine - INFO - Iter(train) [ 77000/160000] lr: 5.5840e-03 eta: 12:43:26 time: 0.5531 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0094 decode.acc_seg: 99.5891 aux.loss_ce: 0.0079 aux.acc_seg: 99.2018 +04/18 14:12:25 - mmengine - INFO - Iter(train) [ 77050/160000] lr: 5.5811e-03 eta: 12:42:59 time: 0.5541 data_time: 0.0073 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0097 decode.acc_seg: 99.5165 aux.loss_ce: 0.0083 aux.acc_seg: 98.8960 +04/18 14:12:53 - mmengine - INFO - Iter(train) [ 77100/160000] lr: 5.5781e-03 eta: 12:42:31 time: 0.5531 data_time: 0.0061 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.7272 aux.loss_ce: 0.0082 aux.acc_seg: 99.2239 +04/18 14:13:21 - mmengine - INFO - Iter(train) [ 77150/160000] lr: 5.5751e-03 eta: 12:42:04 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.7736 aux.loss_ce: 0.0074 aux.acc_seg: 99.4412 +04/18 14:13:48 - mmengine - INFO - Iter(train) [ 77200/160000] lr: 5.5721e-03 eta: 12:41:36 time: 0.5531 data_time: 0.0071 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0096 decode.acc_seg: 99.7185 aux.loss_ce: 0.0081 aux.acc_seg: 99.3146 +04/18 14:14:16 - mmengine - INFO - Iter(train) [ 77250/160000] lr: 5.5692e-03 eta: 12:41:09 time: 0.5540 data_time: 0.0064 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6916 aux.loss_ce: 0.0074 aux.acc_seg: 98.9935 +04/18 14:14:44 - mmengine - INFO - Iter(train) [ 77300/160000] lr: 5.5662e-03 eta: 12:40:41 time: 0.5532 data_time: 0.0073 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0077 decode.acc_seg: 99.6530 aux.loss_ce: 0.0072 aux.acc_seg: 99.3362 +04/18 14:15:11 - mmengine - INFO - Iter(train) [ 77350/160000] lr: 5.5632e-03 eta: 12:40:14 time: 0.5533 data_time: 0.0073 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.7839 aux.loss_ce: 0.0075 aux.acc_seg: 99.4560 +04/18 14:15:39 - mmengine - INFO - Iter(train) [ 77400/160000] lr: 5.5602e-03 eta: 12:39:46 time: 0.5534 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.5231 aux.loss_ce: 0.0074 aux.acc_seg: 99.0528 +04/18 14:16:07 - mmengine - INFO - Iter(train) [ 77450/160000] lr: 5.5573e-03 eta: 12:39:18 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6844 aux.loss_ce: 0.0076 aux.acc_seg: 99.3047 +04/18 14:16:34 - mmengine - INFO - Iter(train) [ 77500/160000] lr: 5.5543e-03 eta: 12:38:51 time: 0.5543 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.8195 aux.loss_ce: 0.0074 aux.acc_seg: 99.4947 +04/18 14:17:02 - mmengine - INFO - Iter(train) [ 77550/160000] lr: 5.5513e-03 eta: 12:38:23 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.6561 aux.loss_ce: 0.0077 aux.acc_seg: 99.2005 +04/18 14:17:30 - mmengine - INFO - Iter(train) [ 77600/160000] lr: 5.5483e-03 eta: 12:37:56 time: 0.5542 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.6532 aux.loss_ce: 0.0081 aux.acc_seg: 99.0906 +04/18 14:17:57 - mmengine - INFO - Iter(train) [ 77650/160000] lr: 5.5454e-03 eta: 12:37:28 time: 0.5527 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.6670 aux.loss_ce: 0.0079 aux.acc_seg: 99.1526 +04/18 14:18:25 - mmengine - INFO - Iter(train) [ 77700/160000] lr: 5.5424e-03 eta: 12:37:01 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.6713 aux.loss_ce: 0.0078 aux.acc_seg: 99.3465 +04/18 14:18:53 - mmengine - INFO - Iter(train) [ 77750/160000] lr: 5.5394e-03 eta: 12:36:34 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6013 aux.loss_ce: 0.0079 aux.acc_seg: 99.0311 +04/18 14:19:21 - mmengine - INFO - Iter(train) [ 77800/160000] lr: 5.5364e-03 eta: 12:36:06 time: 0.5526 data_time: 0.0057 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7191 aux.loss_ce: 0.0076 aux.acc_seg: 99.1973 +04/18 14:19:48 - mmengine - INFO - Iter(train) [ 77850/160000] lr: 5.5335e-03 eta: 12:35:39 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6448 aux.loss_ce: 0.0081 aux.acc_seg: 99.0735 +04/18 14:20:16 - mmengine - INFO - Iter(train) [ 77900/160000] lr: 5.5305e-03 eta: 12:35:11 time: 0.5545 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.7859 aux.loss_ce: 0.0075 aux.acc_seg: 99.3713 +04/18 14:20:44 - mmengine - INFO - Iter(train) [ 77950/160000] lr: 5.5275e-03 eta: 12:34:44 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.7121 aux.loss_ce: 0.0080 aux.acc_seg: 99.0719 +04/18 14:21:11 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:21:11 - mmengine - INFO - Iter(train) [ 78000/160000] lr: 5.5245e-03 eta: 12:34:16 time: 0.5513 data_time: 0.0062 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6497 aux.loss_ce: 0.0077 aux.acc_seg: 99.0651 +04/18 14:21:39 - mmengine - INFO - Iter(train) [ 78050/160000] lr: 5.5216e-03 eta: 12:33:49 time: 0.5553 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6351 aux.loss_ce: 0.0076 aux.acc_seg: 99.0310 +04/18 14:22:07 - mmengine - INFO - Iter(train) [ 78100/160000] lr: 5.5186e-03 eta: 12:33:21 time: 0.5536 data_time: 0.0069 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.7007 aux.loss_ce: 0.0081 aux.acc_seg: 99.2872 +04/18 14:22:34 - mmengine - INFO - Iter(train) [ 78150/160000] lr: 5.5156e-03 eta: 12:32:53 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6365 aux.loss_ce: 0.0076 aux.acc_seg: 99.1067 +04/18 14:23:02 - mmengine - INFO - Iter(train) [ 78200/160000] lr: 5.5126e-03 eta: 12:32:26 time: 0.5523 data_time: 0.0067 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0080 decode.acc_seg: 99.7033 aux.loss_ce: 0.0085 aux.acc_seg: 99.1899 +04/18 14:23:30 - mmengine - INFO - Iter(train) [ 78250/160000] lr: 5.5096e-03 eta: 12:31:58 time: 0.5516 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7140 aux.loss_ce: 0.0072 aux.acc_seg: 99.2377 +04/18 14:23:57 - mmengine - INFO - Iter(train) [ 78300/160000] lr: 5.5067e-03 eta: 12:31:31 time: 0.5529 data_time: 0.0060 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6404 aux.loss_ce: 0.0074 aux.acc_seg: 99.1546 +04/18 14:24:25 - mmengine - INFO - Iter(train) [ 78350/160000] lr: 5.5037e-03 eta: 12:31:03 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7976 aux.loss_ce: 0.0071 aux.acc_seg: 99.3376 +04/18 14:24:53 - mmengine - INFO - Iter(train) [ 78400/160000] lr: 5.5007e-03 eta: 12:30:36 time: 0.5523 data_time: 0.0074 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.7155 aux.loss_ce: 0.0077 aux.acc_seg: 99.3938 +04/18 14:25:20 - mmengine - INFO - Iter(train) [ 78450/160000] lr: 5.4977e-03 eta: 12:30:08 time: 0.5541 data_time: 0.0061 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6658 aux.loss_ce: 0.0080 aux.acc_seg: 99.1904 +04/18 14:25:48 - mmengine - INFO - Iter(train) [ 78500/160000] lr: 5.4948e-03 eta: 12:29:41 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0082 decode.acc_seg: 99.7759 aux.loss_ce: 0.0082 aux.acc_seg: 99.3614 +04/18 14:26:16 - mmengine - INFO - Iter(train) [ 78550/160000] lr: 5.4918e-03 eta: 12:29:13 time: 0.5521 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6882 aux.loss_ce: 0.0074 aux.acc_seg: 99.4184 +04/18 14:26:43 - mmengine - INFO - Iter(train) [ 78600/160000] lr: 5.4888e-03 eta: 12:28:46 time: 0.5546 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7941 aux.loss_ce: 0.0077 aux.acc_seg: 99.3733 +04/18 14:27:11 - mmengine - INFO - Iter(train) [ 78650/160000] lr: 5.4858e-03 eta: 12:28:18 time: 0.5536 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.6781 aux.loss_ce: 0.0075 aux.acc_seg: 99.1870 +04/18 14:27:39 - mmengine - INFO - Iter(train) [ 78700/160000] lr: 5.4828e-03 eta: 12:27:51 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0073 decode.acc_seg: 99.7382 aux.loss_ce: 0.0078 aux.acc_seg: 99.1807 +04/18 14:28:06 - mmengine - INFO - Iter(train) [ 78750/160000] lr: 5.4799e-03 eta: 12:27:23 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7094 aux.loss_ce: 0.0078 aux.acc_seg: 99.2322 +04/18 14:28:34 - mmengine - INFO - Iter(train) [ 78800/160000] lr: 5.4769e-03 eta: 12:26:56 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6613 aux.loss_ce: 0.0074 aux.acc_seg: 99.0807 +04/18 14:29:02 - mmengine - INFO - Iter(train) [ 78850/160000] lr: 5.4739e-03 eta: 12:26:28 time: 0.5516 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.7021 aux.loss_ce: 0.0078 aux.acc_seg: 99.1442 +04/18 14:29:30 - mmengine - INFO - Iter(train) [ 78900/160000] lr: 5.4709e-03 eta: 12:26:01 time: 0.5537 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.6992 aux.loss_ce: 0.0075 aux.acc_seg: 99.3137 +04/18 14:29:57 - mmengine - INFO - Iter(train) [ 78950/160000] lr: 5.4679e-03 eta: 12:25:33 time: 0.5531 data_time: 0.0074 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.6041 aux.loss_ce: 0.0077 aux.acc_seg: 99.0901 +04/18 14:30:25 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:30:25 - mmengine - INFO - Iter(train) [ 79000/160000] lr: 5.4650e-03 eta: 12:25:06 time: 0.5531 data_time: 0.0072 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0074 decode.acc_seg: 99.7106 aux.loss_ce: 0.0071 aux.acc_seg: 99.2305 +04/18 14:30:53 - mmengine - INFO - Iter(train) [ 79050/160000] lr: 5.4620e-03 eta: 12:24:38 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7114 aux.loss_ce: 0.0074 aux.acc_seg: 99.4676 +04/18 14:31:21 - mmengine - INFO - Iter(train) [ 79100/160000] lr: 5.4590e-03 eta: 12:24:11 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6846 aux.loss_ce: 0.0078 aux.acc_seg: 99.1245 +04/18 14:31:48 - mmengine - INFO - Iter(train) [ 79150/160000] lr: 5.4560e-03 eta: 12:23:43 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6811 aux.loss_ce: 0.0079 aux.acc_seg: 99.1734 +04/18 14:32:16 - mmengine - INFO - Iter(train) [ 79200/160000] lr: 5.4530e-03 eta: 12:23:16 time: 0.5547 data_time: 0.0075 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6526 aux.loss_ce: 0.0074 aux.acc_seg: 99.3362 +04/18 14:32:44 - mmengine - INFO - Iter(train) [ 79250/160000] lr: 5.4501e-03 eta: 12:22:48 time: 0.5550 data_time: 0.0072 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7242 aux.loss_ce: 0.0074 aux.acc_seg: 99.3617 +04/18 14:33:11 - mmengine - INFO - Iter(train) [ 79300/160000] lr: 5.4471e-03 eta: 12:22:21 time: 0.5529 data_time: 0.0063 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7284 aux.loss_ce: 0.0073 aux.acc_seg: 99.1774 +04/18 14:33:39 - mmengine - INFO - Iter(train) [ 79350/160000] lr: 5.4441e-03 eta: 12:21:53 time: 0.5535 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.7021 aux.loss_ce: 0.0072 aux.acc_seg: 99.0726 +04/18 14:34:07 - mmengine - INFO - Iter(train) [ 79400/160000] lr: 5.4411e-03 eta: 12:21:26 time: 0.5528 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0073 decode.acc_seg: 99.7086 aux.loss_ce: 0.0081 aux.acc_seg: 99.2033 +04/18 14:34:34 - mmengine - INFO - Iter(train) [ 79450/160000] lr: 5.4381e-03 eta: 12:20:58 time: 0.5532 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6589 aux.loss_ce: 0.0071 aux.acc_seg: 99.1291 +04/18 14:35:02 - mmengine - INFO - Iter(train) [ 79500/160000] lr: 5.4351e-03 eta: 12:20:31 time: 0.5532 data_time: 0.0066 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6635 aux.loss_ce: 0.0079 aux.acc_seg: 99.1943 +04/18 14:35:30 - mmengine - INFO - Iter(train) [ 79550/160000] lr: 5.4322e-03 eta: 12:20:03 time: 0.5551 data_time: 0.0075 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.7507 aux.loss_ce: 0.0078 aux.acc_seg: 99.2860 +04/18 14:35:57 - mmengine - INFO - Iter(train) [ 79600/160000] lr: 5.4292e-03 eta: 12:19:36 time: 0.5517 data_time: 0.0069 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.7174 aux.loss_ce: 0.0080 aux.acc_seg: 99.2744 +04/18 14:36:25 - mmengine - INFO - Iter(train) [ 79650/160000] lr: 5.4262e-03 eta: 12:19:08 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6916 aux.loss_ce: 0.0082 aux.acc_seg: 99.2051 +04/18 14:36:53 - mmengine - INFO - Iter(train) [ 79700/160000] lr: 5.4232e-03 eta: 12:18:41 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0115 decode.acc_seg: 99.3102 aux.loss_ce: 0.0094 aux.acc_seg: 98.8945 +04/18 14:37:20 - mmengine - INFO - Iter(train) [ 79750/160000] lr: 5.4202e-03 eta: 12:18:13 time: 0.5535 data_time: 0.0061 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.5147 aux.loss_ce: 0.0083 aux.acc_seg: 99.0007 +04/18 14:37:48 - mmengine - INFO - Iter(train) [ 79800/160000] lr: 5.4172e-03 eta: 12:17:46 time: 0.5529 data_time: 0.0071 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.5624 aux.loss_ce: 0.0085 aux.acc_seg: 99.2125 +04/18 14:38:16 - mmengine - INFO - Iter(train) [ 79850/160000] lr: 5.4143e-03 eta: 12:17:18 time: 0.5536 data_time: 0.0066 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6892 aux.loss_ce: 0.0078 aux.acc_seg: 99.3053 +04/18 14:38:44 - mmengine - INFO - Iter(train) [ 79900/160000] lr: 5.4113e-03 eta: 12:16:51 time: 0.5626 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7086 aux.loss_ce: 0.0075 aux.acc_seg: 99.1970 +04/18 14:39:11 - mmengine - INFO - Iter(train) [ 79950/160000] lr: 5.4083e-03 eta: 12:16:23 time: 0.5522 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.5859 aux.loss_ce: 0.0082 aux.acc_seg: 99.0210 +04/18 14:39:39 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:39:39 - mmengine - INFO - Iter(train) [ 80000/160000] lr: 5.4053e-03 eta: 12:15:56 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7456 aux.loss_ce: 0.0077 aux.acc_seg: 99.3921 +04/18 14:39:39 - mmengine - INFO - Saving checkpoint at 80000 iterations +04/18 14:39:43 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0461 data_time: 0.0014 memory: 1657 +04/18 14:39:45 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0463 data_time: 0.0015 memory: 1657 +04/18 14:39:48 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0466 data_time: 0.0013 memory: 1657 +04/18 14:39:50 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0457 data_time: 0.0015 memory: 1657 +04/18 14:39:50 - mmengine - INFO - per class results: +04/18 14:39:50 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.09 | 99.53 | 99.54 | 99.56 | 99.53 | +| contrast | 80.33 | 89.42 | 89.09 | 88.77 | 89.42 | ++------------+-------+-------+--------+-----------+--------+ +04/18 14:39:50 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1300 mIoU: 89.7100 mAcc: 94.4700 mFscore: 94.3200 mPrecision: 94.1700 mRecall: 94.4700 data_time: 0.0016 time: 0.0467 +04/18 14:40:18 - mmengine - INFO - Iter(train) [ 80050/160000] lr: 5.4023e-03 eta: 12:15:28 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.7221 aux.loss_ce: 0.0074 aux.acc_seg: 99.2517 +04/18 14:40:46 - mmengine - INFO - Iter(train) [ 80100/160000] lr: 5.3993e-03 eta: 12:15:01 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.7221 aux.loss_ce: 0.0081 aux.acc_seg: 99.3535 +04/18 14:41:13 - mmengine - INFO - Iter(train) [ 80150/160000] lr: 5.3964e-03 eta: 12:14:33 time: 0.5540 data_time: 0.0070 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0077 decode.acc_seg: 99.6485 aux.loss_ce: 0.0084 aux.acc_seg: 99.0163 +04/18 14:41:41 - mmengine - INFO - Iter(train) [ 80200/160000] lr: 5.3934e-03 eta: 12:14:06 time: 0.5531 data_time: 0.0058 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6730 aux.loss_ce: 0.0077 aux.acc_seg: 99.1230 +04/18 14:42:09 - mmengine - INFO - Iter(train) [ 80250/160000] lr: 5.3904e-03 eta: 12:13:38 time: 0.5530 data_time: 0.0062 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0078 decode.acc_seg: 99.7345 aux.loss_ce: 0.0071 aux.acc_seg: 99.4181 +04/18 14:42:36 - mmengine - INFO - Iter(train) [ 80300/160000] lr: 5.3874e-03 eta: 12:13:11 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.6847 aux.loss_ce: 0.0075 aux.acc_seg: 99.0840 +04/18 14:43:04 - mmengine - INFO - Iter(train) [ 80350/160000] lr: 5.3844e-03 eta: 12:12:43 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.7005 aux.loss_ce: 0.0080 aux.acc_seg: 99.3281 +04/18 14:43:32 - mmengine - INFO - Iter(train) [ 80400/160000] lr: 5.3814e-03 eta: 12:12:16 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.7069 aux.loss_ce: 0.0082 aux.acc_seg: 99.2474 +04/18 14:43:59 - mmengine - INFO - Iter(train) [ 80450/160000] lr: 5.3784e-03 eta: 12:11:48 time: 0.5531 data_time: 0.0070 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.6648 aux.loss_ce: 0.0075 aux.acc_seg: 99.0881 +04/18 14:44:27 - mmengine - INFO - Iter(train) [ 80500/160000] lr: 5.3755e-03 eta: 12:11:21 time: 0.5526 data_time: 0.0060 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6853 aux.loss_ce: 0.0077 aux.acc_seg: 99.1436 +04/18 14:44:55 - mmengine - INFO - Iter(train) [ 80550/160000] lr: 5.3725e-03 eta: 12:10:53 time: 0.5547 data_time: 0.0072 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0070 decode.acc_seg: 99.7228 aux.loss_ce: 0.0068 aux.acc_seg: 99.3650 +04/18 14:45:23 - mmengine - INFO - Iter(train) [ 80600/160000] lr: 5.3695e-03 eta: 12:10:26 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7419 aux.loss_ce: 0.0070 aux.acc_seg: 99.3039 +04/18 14:45:50 - mmengine - INFO - Iter(train) [ 80650/160000] lr: 5.3665e-03 eta: 12:09:58 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.6632 aux.loss_ce: 0.0075 aux.acc_seg: 99.2009 +04/18 14:46:18 - mmengine - INFO - Iter(train) [ 80700/160000] lr: 5.3635e-03 eta: 12:09:31 time: 0.5541 data_time: 0.0078 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.6882 aux.loss_ce: 0.0075 aux.acc_seg: 99.1914 +04/18 14:46:46 - mmengine - INFO - Iter(train) [ 80750/160000] lr: 5.3605e-03 eta: 12:09:03 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6524 aux.loss_ce: 0.0075 aux.acc_seg: 98.9561 +04/18 14:47:13 - mmengine - INFO - Iter(train) [ 80800/160000] lr: 5.3575e-03 eta: 12:08:36 time: 0.5532 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.7296 aux.loss_ce: 0.0071 aux.acc_seg: 99.1956 +04/18 14:47:41 - mmengine - INFO - Iter(train) [ 80850/160000] lr: 5.3545e-03 eta: 12:08:08 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.6763 aux.loss_ce: 0.0070 aux.acc_seg: 99.1087 +04/18 14:48:09 - mmengine - INFO - Iter(train) [ 80900/160000] lr: 5.3516e-03 eta: 12:07:41 time: 0.5536 data_time: 0.0073 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0080 decode.acc_seg: 99.7311 aux.loss_ce: 0.0085 aux.acc_seg: 99.1544 +04/18 14:48:36 - mmengine - INFO - Iter(train) [ 80950/160000] lr: 5.3486e-03 eta: 12:07:13 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0074 decode.acc_seg: 99.7464 aux.loss_ce: 0.0071 aux.acc_seg: 99.3919 +04/18 14:49:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:49:04 - mmengine - INFO - Iter(train) [ 81000/160000] lr: 5.3456e-03 eta: 12:06:46 time: 0.5528 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.5862 aux.loss_ce: 0.0077 aux.acc_seg: 99.0725 +04/18 14:49:32 - mmengine - INFO - Iter(train) [ 81050/160000] lr: 5.3426e-03 eta: 12:06:18 time: 0.5526 data_time: 0.0072 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7510 aux.loss_ce: 0.0070 aux.acc_seg: 99.4220 +04/18 14:50:00 - mmengine - INFO - Iter(train) [ 81100/160000] lr: 5.3396e-03 eta: 12:05:51 time: 0.5517 data_time: 0.0062 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6530 aux.loss_ce: 0.0078 aux.acc_seg: 99.1486 +04/18 14:50:27 - mmengine - INFO - Iter(train) [ 81150/160000] lr: 5.3366e-03 eta: 12:05:23 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0073 decode.acc_seg: 99.7410 aux.loss_ce: 0.0071 aux.acc_seg: 99.3360 +04/18 14:50:55 - mmengine - INFO - Iter(train) [ 81200/160000] lr: 5.3336e-03 eta: 12:04:56 time: 0.5545 data_time: 0.0079 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6817 aux.loss_ce: 0.0073 aux.acc_seg: 99.2269 +04/18 14:51:23 - mmengine - INFO - Iter(train) [ 81250/160000] lr: 5.3306e-03 eta: 12:04:28 time: 0.5537 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7697 aux.loss_ce: 0.0077 aux.acc_seg: 99.3053 +04/18 14:51:50 - mmengine - INFO - Iter(train) [ 81300/160000] lr: 5.3277e-03 eta: 12:04:01 time: 0.5535 data_time: 0.0071 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6440 aux.loss_ce: 0.0073 aux.acc_seg: 99.1206 +04/18 14:52:18 - mmengine - INFO - Iter(train) [ 81350/160000] lr: 5.3247e-03 eta: 12:03:33 time: 0.5528 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.7443 aux.loss_ce: 0.0074 aux.acc_seg: 99.0421 +04/18 14:52:46 - mmengine - INFO - Iter(train) [ 81400/160000] lr: 5.3217e-03 eta: 12:03:06 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7526 aux.loss_ce: 0.0074 aux.acc_seg: 99.3560 +04/18 14:53:13 - mmengine - INFO - Iter(train) [ 81450/160000] lr: 5.3187e-03 eta: 12:02:38 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.6834 aux.loss_ce: 0.0076 aux.acc_seg: 99.1532 +04/18 14:53:41 - mmengine - INFO - Iter(train) [ 81500/160000] lr: 5.3157e-03 eta: 12:02:10 time: 0.5549 data_time: 0.0073 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.7202 aux.loss_ce: 0.0072 aux.acc_seg: 99.2405 +04/18 14:54:09 - mmengine - INFO - Iter(train) [ 81550/160000] lr: 5.3127e-03 eta: 12:01:43 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0085 decode.acc_seg: 99.7066 aux.loss_ce: 0.0078 aux.acc_seg: 99.3187 +04/18 14:54:36 - mmengine - INFO - Iter(train) [ 81600/160000] lr: 5.3097e-03 eta: 12:01:15 time: 0.5529 data_time: 0.0069 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.6075 aux.loss_ce: 0.0081 aux.acc_seg: 99.0890 +04/18 14:55:04 - mmengine - INFO - Iter(train) [ 81650/160000] lr: 5.3067e-03 eta: 12:00:48 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.7453 aux.loss_ce: 0.0079 aux.acc_seg: 99.2027 +04/18 14:55:32 - mmengine - INFO - Iter(train) [ 81700/160000] lr: 5.3037e-03 eta: 12:00:20 time: 0.5525 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.7040 aux.loss_ce: 0.0075 aux.acc_seg: 99.0768 +04/18 14:55:59 - mmengine - INFO - Iter(train) [ 81750/160000] lr: 5.3007e-03 eta: 11:59:53 time: 0.5535 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.6970 aux.loss_ce: 0.0073 aux.acc_seg: 99.2434 +04/18 14:56:27 - mmengine - INFO - Iter(train) [ 81800/160000] lr: 5.2978e-03 eta: 11:59:25 time: 0.5534 data_time: 0.0068 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6740 aux.loss_ce: 0.0078 aux.acc_seg: 99.1517 +04/18 14:56:55 - mmengine - INFO - Iter(train) [ 81850/160000] lr: 5.2948e-03 eta: 11:58:58 time: 0.5533 data_time: 0.0060 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0069 decode.acc_seg: 99.7755 aux.loss_ce: 0.0067 aux.acc_seg: 99.4009 +04/18 14:57:23 - mmengine - INFO - Iter(train) [ 81900/160000] lr: 5.2918e-03 eta: 11:58:30 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.6841 aux.loss_ce: 0.0072 aux.acc_seg: 99.2474 +04/18 14:57:50 - mmengine - INFO - Iter(train) [ 81950/160000] lr: 5.2888e-03 eta: 11:58:03 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0065 decode.acc_seg: 99.8104 aux.loss_ce: 0.0065 aux.acc_seg: 99.4801 +04/18 14:58:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:58:18 - mmengine - INFO - Iter(train) [ 82000/160000] lr: 5.2858e-03 eta: 11:57:35 time: 0.5540 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.7541 aux.loss_ce: 0.0071 aux.acc_seg: 99.4308 +04/18 14:58:46 - mmengine - INFO - Iter(train) [ 82050/160000] lr: 5.2828e-03 eta: 11:57:08 time: 0.5534 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0078 decode.acc_seg: 99.6637 aux.loss_ce: 0.0082 aux.acc_seg: 99.0720 +04/18 14:59:14 - mmengine - INFO - Iter(train) [ 82100/160000] lr: 5.2798e-03 eta: 11:56:41 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6006 aux.loss_ce: 0.0078 aux.acc_seg: 99.0711 +04/18 14:59:41 - mmengine - INFO - Iter(train) [ 82150/160000] lr: 5.2768e-03 eta: 11:56:13 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.7355 aux.loss_ce: 0.0077 aux.acc_seg: 99.1730 +04/18 15:00:09 - mmengine - INFO - Iter(train) [ 82200/160000] lr: 5.2738e-03 eta: 11:55:45 time: 0.5544 data_time: 0.0062 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6276 aux.loss_ce: 0.0077 aux.acc_seg: 99.0049 +04/18 15:00:37 - mmengine - INFO - Iter(train) [ 82250/160000] lr: 5.2708e-03 eta: 11:55:18 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0086 decode.acc_seg: 99.6283 aux.loss_ce: 0.0075 aux.acc_seg: 99.3985 +04/18 15:01:04 - mmengine - INFO - Iter(train) [ 82300/160000] lr: 5.2678e-03 eta: 11:54:50 time: 0.5550 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6887 aux.loss_ce: 0.0075 aux.acc_seg: 99.0437 +04/18 15:01:32 - mmengine - INFO - Iter(train) [ 82350/160000] lr: 5.2648e-03 eta: 11:54:23 time: 0.5541 data_time: 0.0070 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.6777 aux.loss_ce: 0.0074 aux.acc_seg: 99.2339 +04/18 15:02:00 - mmengine - INFO - Iter(train) [ 82400/160000] lr: 5.2618e-03 eta: 11:53:55 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6851 aux.loss_ce: 0.0076 aux.acc_seg: 99.2001 +04/18 15:02:27 - mmengine - INFO - Iter(train) [ 82450/160000] lr: 5.2589e-03 eta: 11:53:28 time: 0.5537 data_time: 0.0069 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.7082 aux.loss_ce: 0.0083 aux.acc_seg: 99.3728 +04/18 15:02:55 - mmengine - INFO - Iter(train) [ 82500/160000] lr: 5.2559e-03 eta: 11:53:00 time: 0.5536 data_time: 0.0067 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0147 decode.acc_seg: 99.1263 aux.loss_ce: 0.0099 aux.acc_seg: 98.6214 +04/18 15:03:23 - mmengine - INFO - Iter(train) [ 82550/160000] lr: 5.2529e-03 eta: 11:52:33 time: 0.5535 data_time: 0.0069 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0099 decode.acc_seg: 99.6870 aux.loss_ce: 0.0089 aux.acc_seg: 99.1781 +04/18 15:03:50 - mmengine - INFO - Iter(train) [ 82600/160000] lr: 5.2499e-03 eta: 11:52:05 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6472 aux.loss_ce: 0.0081 aux.acc_seg: 99.2063 +04/18 15:04:18 - mmengine - INFO - Iter(train) [ 82650/160000] lr: 5.2469e-03 eta: 11:51:38 time: 0.5618 data_time: 0.0068 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0091 decode.acc_seg: 99.6465 aux.loss_ce: 0.0079 aux.acc_seg: 99.1975 +04/18 15:04:46 - mmengine - INFO - Iter(train) [ 82700/160000] lr: 5.2439e-03 eta: 11:51:10 time: 0.5523 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.7294 aux.loss_ce: 0.0074 aux.acc_seg: 99.3320 +04/18 15:05:13 - mmengine - INFO - Iter(train) [ 82750/160000] lr: 5.2409e-03 eta: 11:50:43 time: 0.5535 data_time: 0.0073 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6490 aux.loss_ce: 0.0080 aux.acc_seg: 99.2598 +04/18 15:05:41 - mmengine - INFO - Iter(train) [ 82800/160000] lr: 5.2379e-03 eta: 11:50:15 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0094 decode.acc_seg: 99.6695 aux.loss_ce: 0.0082 aux.acc_seg: 99.2054 +04/18 15:06:09 - mmengine - INFO - Iter(train) [ 82850/160000] lr: 5.2349e-03 eta: 11:49:47 time: 0.5548 data_time: 0.0071 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6936 aux.loss_ce: 0.0077 aux.acc_seg: 99.1436 +04/18 15:06:36 - mmengine - INFO - Iter(train) [ 82900/160000] lr: 5.2319e-03 eta: 11:49:20 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6571 aux.loss_ce: 0.0077 aux.acc_seg: 99.1663 +04/18 15:07:04 - mmengine - INFO - Iter(train) [ 82950/160000] lr: 5.2289e-03 eta: 11:48:52 time: 0.5527 data_time: 0.0065 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6861 aux.loss_ce: 0.0074 aux.acc_seg: 99.4237 +04/18 15:07:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:07:32 - mmengine - INFO - Iter(train) [ 83000/160000] lr: 5.2259e-03 eta: 11:48:25 time: 0.5541 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6954 aux.loss_ce: 0.0074 aux.acc_seg: 99.1837 +04/18 15:07:59 - mmengine - INFO - Iter(train) [ 83050/160000] lr: 5.2229e-03 eta: 11:47:57 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6863 aux.loss_ce: 0.0078 aux.acc_seg: 99.1245 +04/18 15:08:27 - mmengine - INFO - Iter(train) [ 83100/160000] lr: 5.2199e-03 eta: 11:47:30 time: 0.5610 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7141 aux.loss_ce: 0.0070 aux.acc_seg: 99.1758 +04/18 15:08:55 - mmengine - INFO - Iter(train) [ 83150/160000] lr: 5.2169e-03 eta: 11:47:02 time: 0.5549 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7163 aux.loss_ce: 0.0073 aux.acc_seg: 99.2763 +04/18 15:09:23 - mmengine - INFO - Iter(train) [ 83200/160000] lr: 5.2139e-03 eta: 11:46:35 time: 0.5541 data_time: 0.0064 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6291 aux.loss_ce: 0.0076 aux.acc_seg: 98.9625 +04/18 15:09:50 - mmengine - INFO - Iter(train) [ 83250/160000] lr: 5.2109e-03 eta: 11:46:07 time: 0.5529 data_time: 0.0070 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7611 aux.loss_ce: 0.0070 aux.acc_seg: 99.2933 +04/18 15:10:18 - mmengine - INFO - Iter(train) [ 83300/160000] lr: 5.2079e-03 eta: 11:45:40 time: 0.5558 data_time: 0.0061 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7195 aux.loss_ce: 0.0073 aux.acc_seg: 99.1021 +04/18 15:10:46 - mmengine - INFO - Iter(train) [ 83350/160000] lr: 5.2049e-03 eta: 11:45:12 time: 0.5535 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.6616 aux.loss_ce: 0.0075 aux.acc_seg: 99.2410 +04/18 15:11:14 - mmengine - INFO - Iter(train) [ 83400/160000] lr: 5.2019e-03 eta: 11:44:45 time: 0.5545 data_time: 0.0073 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.7234 aux.loss_ce: 0.0079 aux.acc_seg: 99.1467 +04/18 15:11:41 - mmengine - INFO - Iter(train) [ 83450/160000] lr: 5.1989e-03 eta: 11:44:17 time: 0.5533 data_time: 0.0070 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7294 aux.loss_ce: 0.0072 aux.acc_seg: 99.3606 +04/18 15:12:09 - mmengine - INFO - Iter(train) [ 83500/160000] lr: 5.1959e-03 eta: 11:43:50 time: 0.5541 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6798 aux.loss_ce: 0.0077 aux.acc_seg: 99.1134 +04/18 15:12:37 - mmengine - INFO - Iter(train) [ 83550/160000] lr: 5.1929e-03 eta: 11:43:22 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0076 decode.acc_seg: 99.7350 aux.loss_ce: 0.0082 aux.acc_seg: 99.1143 +04/18 15:13:04 - mmengine - INFO - Iter(train) [ 83600/160000] lr: 5.1900e-03 eta: 11:42:55 time: 0.5539 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.6842 aux.loss_ce: 0.0076 aux.acc_seg: 99.1166 +04/18 15:13:32 - mmengine - INFO - Iter(train) [ 83650/160000] lr: 5.1870e-03 eta: 11:42:27 time: 0.5526 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.6420 aux.loss_ce: 0.0077 aux.acc_seg: 99.1487 +04/18 15:14:00 - mmengine - INFO - Iter(train) [ 83700/160000] lr: 5.1840e-03 eta: 11:42:00 time: 0.5528 data_time: 0.0066 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.6781 aux.loss_ce: 0.0071 aux.acc_seg: 99.2856 +04/18 15:14:27 - mmengine - INFO - Iter(train) [ 83750/160000] lr: 5.1810e-03 eta: 11:41:32 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0087 decode.acc_seg: 99.6272 aux.loss_ce: 0.0083 aux.acc_seg: 99.1007 +04/18 15:14:55 - mmengine - INFO - Iter(train) [ 83800/160000] lr: 5.1780e-03 eta: 11:41:05 time: 0.5538 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6845 aux.loss_ce: 0.0074 aux.acc_seg: 99.2111 +04/18 15:15:23 - mmengine 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aux.acc_seg: 99.2795 +04/18 15:17:14 - mmengine - INFO - Iter(train) [ 84050/160000] lr: 5.1630e-03 eta: 11:38:47 time: 0.5528 data_time: 0.0063 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7567 aux.loss_ce: 0.0076 aux.acc_seg: 99.4097 +04/18 15:17:41 - mmengine - INFO - Iter(train) [ 84100/160000] lr: 5.1600e-03 eta: 11:38:19 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7196 aux.loss_ce: 0.0077 aux.acc_seg: 99.2937 +04/18 15:18:09 - mmengine - INFO - Iter(train) [ 84150/160000] lr: 5.1570e-03 eta: 11:37:52 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6591 aux.loss_ce: 0.0076 aux.acc_seg: 99.2619 +04/18 15:18:37 - mmengine - INFO - Iter(train) [ 84200/160000] lr: 5.1540e-03 eta: 11:37:25 time: 0.5541 data_time: 0.0062 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0075 decode.acc_seg: 99.7203 aux.loss_ce: 0.0069 aux.acc_seg: 99.3240 +04/18 15:19:05 - mmengine 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5.1390e-03 eta: 11:35:07 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7334 aux.loss_ce: 0.0073 aux.acc_seg: 99.2209 +04/18 15:21:23 - mmengine - INFO - Iter(train) [ 84500/160000] lr: 5.1360e-03 eta: 11:34:39 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.7145 aux.loss_ce: 0.0071 aux.acc_seg: 99.2967 +04/18 15:21:51 - mmengine - INFO - Iter(train) [ 84550/160000] lr: 5.1330e-03 eta: 11:34:12 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.6774 aux.loss_ce: 0.0075 aux.acc_seg: 98.9341 +04/18 15:22:18 - mmengine - INFO - Iter(train) [ 84600/160000] lr: 5.1300e-03 eta: 11:33:44 time: 0.5535 data_time: 0.0069 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.6275 aux.loss_ce: 0.0082 aux.acc_seg: 99.0343 +04/18 15:22:46 - mmengine - INFO - Iter(train) [ 84650/160000] lr: 5.1270e-03 eta: 11:33:17 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.6758 aux.loss_ce: 0.0077 aux.acc_seg: 98.8980 +04/18 15:23:14 - mmengine - INFO - Iter(train) [ 84700/160000] lr: 5.1239e-03 eta: 11:32:49 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0101 decode.acc_seg: 99.6681 aux.loss_ce: 0.0084 aux.acc_seg: 99.2866 +04/18 15:23:42 - mmengine - INFO - Iter(train) [ 84750/160000] lr: 5.1209e-03 eta: 11:32:22 time: 0.5524 data_time: 0.0066 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6438 aux.loss_ce: 0.0078 aux.acc_seg: 99.1773 +04/18 15:24:09 - mmengine - INFO - Iter(train) [ 84800/160000] lr: 5.1179e-03 eta: 11:31:54 time: 0.5625 data_time: 0.0059 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7628 aux.loss_ce: 0.0077 aux.acc_seg: 99.3494 +04/18 15:24:37 - mmengine - INFO - Iter(train) [ 84850/160000] lr: 5.1149e-03 eta: 11:31:27 time: 0.5527 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0070 decode.acc_seg: 99.7654 aux.loss_ce: 0.0069 aux.acc_seg: 99.4826 +04/18 15:25:05 - mmengine - INFO - Iter(train) [ 84900/160000] lr: 5.1119e-03 eta: 11:30:59 time: 0.5560 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.6209 aux.loss_ce: 0.0080 aux.acc_seg: 98.9641 +04/18 15:25:32 - mmengine - INFO - Iter(train) [ 84950/160000] lr: 5.1089e-03 eta: 11:30:32 time: 0.5538 data_time: 0.0070 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7670 aux.loss_ce: 0.0071 aux.acc_seg: 99.3470 +04/18 15:26:00 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:26:00 - mmengine - INFO - Iter(train) [ 85000/160000] lr: 5.1059e-03 eta: 11:30:04 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.6502 aux.loss_ce: 0.0082 aux.acc_seg: 98.8712 +04/18 15:26:28 - mmengine - INFO - Iter(train) [ 85050/160000] lr: 5.1029e-03 eta: 11:29:37 time: 0.5550 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0077 decode.acc_seg: 99.5624 aux.loss_ce: 0.0072 aux.acc_seg: 99.3143 +04/18 15:26:56 - mmengine - INFO - Iter(train) [ 85100/160000] lr: 5.0999e-03 eta: 11:29:09 time: 0.5558 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.5568 aux.loss_ce: 0.0082 aux.acc_seg: 99.1658 +04/18 15:27:23 - mmengine - INFO - Iter(train) [ 85150/160000] lr: 5.0969e-03 eta: 11:28:42 time: 0.5537 data_time: 0.0072 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7728 aux.loss_ce: 0.0075 aux.acc_seg: 99.3503 +04/18 15:27:51 - mmengine - INFO - Iter(train) [ 85200/160000] lr: 5.0939e-03 eta: 11:28:14 time: 0.5544 data_time: 0.0072 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6938 aux.loss_ce: 0.0077 aux.acc_seg: 99.1798 +04/18 15:28:19 - mmengine - INFO - Iter(train) [ 85250/160000] lr: 5.0909e-03 eta: 11:27:47 time: 0.5535 data_time: 0.0063 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.6203 aux.loss_ce: 0.0081 aux.acc_seg: 99.1729 +04/18 15:28:47 - mmengine - INFO - Iter(train) [ 85300/160000] lr: 5.0879e-03 eta: 11:27:19 time: 0.5635 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7382 aux.loss_ce: 0.0076 aux.acc_seg: 99.4714 +04/18 15:29:14 - mmengine - INFO - Iter(train) [ 85350/160000] lr: 5.0849e-03 eta: 11:26:52 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6572 aux.loss_ce: 0.0077 aux.acc_seg: 99.1914 +04/18 15:29:42 - mmengine - INFO - Iter(train) [ 85400/160000] lr: 5.0819e-03 eta: 11:26:24 time: 0.5531 data_time: 0.0066 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.7693 aux.loss_ce: 0.0071 aux.acc_seg: 99.3942 +04/18 15:30:10 - mmengine - INFO - Iter(train) [ 85450/160000] lr: 5.0789e-03 eta: 11:25:57 time: 0.5522 data_time: 0.0068 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.6550 aux.loss_ce: 0.0071 aux.acc_seg: 99.2377 +04/18 15:30:37 - mmengine - INFO - Iter(train) [ 85500/160000] lr: 5.0759e-03 eta: 11:25:29 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7290 aux.loss_ce: 0.0076 aux.acc_seg: 99.3373 +04/18 15:31:05 - mmengine - INFO - Iter(train) [ 85550/160000] lr: 5.0729e-03 eta: 11:25:02 time: 0.5521 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6910 aux.loss_ce: 0.0077 aux.acc_seg: 99.3348 +04/18 15:31:33 - mmengine - INFO - Iter(train) [ 85600/160000] lr: 5.0699e-03 eta: 11:24:34 time: 0.5540 data_time: 0.0072 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.7341 aux.loss_ce: 0.0076 aux.acc_seg: 99.1769 +04/18 15:32:00 - mmengine - INFO - Iter(train) [ 85650/160000] lr: 5.0669e-03 eta: 11:24:07 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.5660 aux.loss_ce: 0.0080 aux.acc_seg: 99.0636 +04/18 15:32:28 - mmengine - INFO - Iter(train) [ 85700/160000] lr: 5.0639e-03 eta: 11:23:39 time: 0.5530 data_time: 0.0063 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7385 aux.loss_ce: 0.0073 aux.acc_seg: 99.3439 +04/18 15:32:56 - mmengine - INFO - Iter(train) [ 85750/160000] lr: 5.0609e-03 eta: 11:23:12 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.8116 aux.loss_ce: 0.0075 aux.acc_seg: 99.3443 +04/18 15:33:23 - mmengine - INFO - Iter(train) [ 85800/160000] lr: 5.0578e-03 eta: 11:22:44 time: 0.5526 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6855 aux.loss_ce: 0.0074 aux.acc_seg: 99.2805 +04/18 15:33:51 - mmengine - INFO - Iter(train) [ 85850/160000] lr: 5.0548e-03 eta: 11:22:16 time: 0.5528 data_time: 0.0073 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.7166 aux.loss_ce: 0.0078 aux.acc_seg: 99.2358 +04/18 15:34:19 - mmengine - INFO - Iter(train) [ 85900/160000] lr: 5.0518e-03 eta: 11:21:49 time: 0.5523 data_time: 0.0068 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.6441 aux.loss_ce: 0.0072 aux.acc_seg: 99.1346 +04/18 15:34:47 - mmengine - INFO - Iter(train) [ 85950/160000] lr: 5.0488e-03 eta: 11:21:21 time: 0.5537 data_time: 0.0073 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.6896 aux.loss_ce: 0.0077 aux.acc_seg: 99.3023 +04/18 15:35:14 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:35:14 - mmengine - INFO - Iter(train) [ 86000/160000] lr: 5.0458e-03 eta: 11:20:54 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7148 aux.loss_ce: 0.0075 aux.acc_seg: 99.0259 +04/18 15:35:42 - mmengine - INFO - Iter(train) [ 86050/160000] lr: 5.0428e-03 eta: 11:20:26 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7795 aux.loss_ce: 0.0073 aux.acc_seg: 99.4060 +04/18 15:36:10 - mmengine - INFO - Iter(train) [ 86100/160000] lr: 5.0398e-03 eta: 11:19:59 time: 0.5544 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.5991 aux.loss_ce: 0.0077 aux.acc_seg: 99.0725 +04/18 15:36:37 - mmengine - INFO - Iter(train) [ 86150/160000] lr: 5.0368e-03 eta: 11:19:31 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.8203 aux.loss_ce: 0.0070 aux.acc_seg: 99.3801 +04/18 15:37:05 - mmengine - INFO - Iter(train) [ 86200/160000] lr: 5.0338e-03 eta: 11:19:04 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7426 aux.loss_ce: 0.0073 aux.acc_seg: 99.3389 +04/18 15:37:33 - mmengine - INFO - Iter(train) [ 86250/160000] lr: 5.0308e-03 eta: 11:18:36 time: 0.5532 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.7746 aux.loss_ce: 0.0073 aux.acc_seg: 99.3625 +04/18 15:38:00 - mmengine - INFO - Iter(train) [ 86300/160000] lr: 5.0278e-03 eta: 11:18:09 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6654 aux.loss_ce: 0.0072 aux.acc_seg: 99.1960 +04/18 15:38:28 - mmengine - INFO - Iter(train) [ 86350/160000] lr: 5.0248e-03 eta: 11:17:41 time: 0.5529 data_time: 0.0068 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7266 aux.loss_ce: 0.0068 aux.acc_seg: 99.2655 +04/18 15:38:56 - mmengine - INFO - Iter(train) [ 86400/160000] lr: 5.0218e-03 eta: 11:17:14 time: 0.5542 data_time: 0.0075 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.6494 aux.loss_ce: 0.0077 aux.acc_seg: 98.9733 +04/18 15:39:24 - mmengine - INFO - Iter(train) [ 86450/160000] lr: 5.0187e-03 eta: 11:16:46 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7817 aux.loss_ce: 0.0074 aux.acc_seg: 99.4470 +04/18 15:39:52 - mmengine - INFO - Iter(train) [ 86500/160000] lr: 5.0157e-03 eta: 11:16:19 time: 0.5537 data_time: 0.0068 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0068 decode.acc_seg: 99.7622 aux.loss_ce: 0.0067 aux.acc_seg: 99.3397 +04/18 15:40:19 - mmengine - INFO - Iter(train) [ 86550/160000] lr: 5.0127e-03 eta: 11:15:51 time: 0.5546 data_time: 0.0074 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7285 aux.loss_ce: 0.0070 aux.acc_seg: 99.3051 +04/18 15:40:47 - mmengine - INFO - Iter(train) [ 86600/160000] lr: 5.0097e-03 eta: 11:15:24 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7585 aux.loss_ce: 0.0075 aux.acc_seg: 99.2744 +04/18 15:41:15 - mmengine - INFO - Iter(train) [ 86650/160000] lr: 5.0067e-03 eta: 11:14:56 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6712 aux.loss_ce: 0.0073 aux.acc_seg: 99.2598 +04/18 15:41:42 - mmengine - INFO - Iter(train) [ 86700/160000] lr: 5.0037e-03 eta: 11:14:29 time: 0.5550 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.7520 aux.loss_ce: 0.0075 aux.acc_seg: 99.2879 +04/18 15:42:10 - mmengine - INFO - Iter(train) [ 86750/160000] lr: 5.0007e-03 eta: 11:14:01 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7194 aux.loss_ce: 0.0071 aux.acc_seg: 99.3279 +04/18 15:42:38 - mmengine - INFO - Iter(train) [ 86800/160000] lr: 4.9977e-03 eta: 11:13:34 time: 0.5544 data_time: 0.0064 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.6115 aux.loss_ce: 0.0075 aux.acc_seg: 98.7851 +04/18 15:43:06 - mmengine - INFO - Iter(train) [ 86850/160000] lr: 4.9947e-03 eta: 11:13:06 time: 0.5544 data_time: 0.0061 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7292 aux.loss_ce: 0.0067 aux.acc_seg: 99.2359 +04/18 15:43:33 - mmengine - INFO - Iter(train) [ 86900/160000] lr: 4.9916e-03 eta: 11:12:39 time: 0.5528 data_time: 0.0062 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.6617 aux.loss_ce: 0.0074 aux.acc_seg: 99.1792 +04/18 15:44:01 - mmengine - INFO - Iter(train) [ 86950/160000] lr: 4.9886e-03 eta: 11:12:11 time: 0.5533 data_time: 0.0059 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7668 aux.loss_ce: 0.0077 aux.acc_seg: 99.3586 +04/18 15:44:29 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:44:29 - mmengine - INFO - Iter(train) [ 87000/160000] lr: 4.9856e-03 eta: 11:11:44 time: 0.5541 data_time: 0.0071 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7103 aux.loss_ce: 0.0068 aux.acc_seg: 99.3274 +04/18 15:44:56 - mmengine - INFO - Iter(train) [ 87050/160000] lr: 4.9826e-03 eta: 11:11:16 time: 0.5550 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.6572 aux.loss_ce: 0.0075 aux.acc_seg: 98.9265 +04/18 15:45:24 - mmengine - INFO - Iter(train) [ 87100/160000] lr: 4.9796e-03 eta: 11:10:49 time: 0.5545 data_time: 0.0067 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0080 decode.acc_seg: 99.6841 aux.loss_ce: 0.0076 aux.acc_seg: 99.2471 +04/18 15:45:52 - mmengine - INFO - Iter(train) [ 87150/160000] lr: 4.9766e-03 eta: 11:10:21 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0074 decode.acc_seg: 99.7620 aux.loss_ce: 0.0071 aux.acc_seg: 99.3556 +04/18 15:46:19 - mmengine - INFO - Iter(train) [ 87200/160000] lr: 4.9736e-03 eta: 11:09:53 time: 0.5535 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6668 aux.loss_ce: 0.0076 aux.acc_seg: 99.0356 +04/18 15:46:47 - mmengine - INFO - Iter(train) [ 87250/160000] lr: 4.9706e-03 eta: 11:09:26 time: 0.5544 data_time: 0.0066 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7335 aux.loss_ce: 0.0072 aux.acc_seg: 99.2883 +04/18 15:47:15 - mmengine - INFO - Iter(train) [ 87300/160000] lr: 4.9676e-03 eta: 11:08:58 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0124 decode.acc_seg: 99.3621 aux.loss_ce: 0.0095 aux.acc_seg: 99.0041 +04/18 15:47:43 - mmengine - INFO - Iter(train) [ 87350/160000] lr: 4.9645e-03 eta: 11:08:31 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0231 decode.loss_ce: 0.0136 decode.acc_seg: 99.5957 aux.loss_ce: 0.0095 aux.acc_seg: 99.2460 +04/18 15:48:10 - mmengine - INFO - Iter(train) [ 87400/160000] lr: 4.9615e-03 eta: 11:08:03 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.7156 aux.loss_ce: 0.0086 aux.acc_seg: 99.2757 +04/18 15:48:38 - mmengine - INFO - Iter(train) [ 87450/160000] lr: 4.9585e-03 eta: 11:07:36 time: 0.5541 data_time: 0.0070 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.8028 aux.loss_ce: 0.0080 aux.acc_seg: 99.5077 +04/18 15:49:06 - mmengine - INFO - Iter(train) [ 87500/160000] lr: 4.9555e-03 eta: 11:07:08 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6773 aux.loss_ce: 0.0080 aux.acc_seg: 99.0063 +04/18 15:49:34 - mmengine - INFO - Iter(train) [ 87550/160000] lr: 4.9525e-03 eta: 11:06:41 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.8137 aux.loss_ce: 0.0074 aux.acc_seg: 99.5594 +04/18 15:50:01 - mmengine - INFO - Iter(train) [ 87600/160000] lr: 4.9495e-03 eta: 11:06:13 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7032 aux.loss_ce: 0.0075 aux.acc_seg: 99.2753 +04/18 15:50:29 - mmengine - INFO - Iter(train) [ 87650/160000] lr: 4.9465e-03 eta: 11:05:46 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.7018 aux.loss_ce: 0.0078 aux.acc_seg: 99.2401 +04/18 15:50:57 - mmengine - INFO - Iter(train) [ 87700/160000] lr: 4.9434e-03 eta: 11:05:18 time: 0.5522 data_time: 0.0062 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.7213 aux.loss_ce: 0.0070 aux.acc_seg: 99.3948 +04/18 15:51:24 - mmengine - INFO - Iter(train) [ 87750/160000] lr: 4.9404e-03 eta: 11:04:51 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0083 decode.acc_seg: 99.6844 aux.loss_ce: 0.0074 aux.acc_seg: 99.4108 +04/18 15:51:52 - mmengine - INFO - Iter(train) [ 87800/160000] lr: 4.9374e-03 eta: 11:04:23 time: 0.5544 data_time: 0.0077 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0070 decode.acc_seg: 99.7691 aux.loss_ce: 0.0076 aux.acc_seg: 99.2150 +04/18 15:52:20 - mmengine - INFO - Iter(train) [ 87850/160000] lr: 4.9344e-03 eta: 11:03:56 time: 0.5553 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7274 aux.loss_ce: 0.0071 aux.acc_seg: 99.1962 +04/18 15:52:48 - mmengine - INFO - Iter(train) [ 87900/160000] lr: 4.9314e-03 eta: 11:03:28 time: 0.5538 data_time: 0.0070 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6800 aux.loss_ce: 0.0080 aux.acc_seg: 99.1891 +04/18 15:53:15 - mmengine - INFO - Iter(train) [ 87950/160000] lr: 4.9284e-03 eta: 11:03:01 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.7053 aux.loss_ce: 0.0070 aux.acc_seg: 99.3465 +04/18 15:53:43 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:53:43 - mmengine - INFO - Iter(train) [ 88000/160000] lr: 4.9254e-03 eta: 11:02:33 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7036 aux.loss_ce: 0.0072 aux.acc_seg: 99.0846 +04/18 15:54:11 - mmengine - INFO - Iter(train) [ 88050/160000] lr: 4.9223e-03 eta: 11:02:06 time: 0.5529 data_time: 0.0066 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.4520 aux.loss_ce: 0.0082 aux.acc_seg: 99.1279 +04/18 15:54:38 - mmengine - INFO - Iter(train) [ 88100/160000] lr: 4.9193e-03 eta: 11:01:38 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.6961 aux.loss_ce: 0.0071 aux.acc_seg: 99.1393 +04/18 15:55:06 - mmengine - INFO - Iter(train) [ 88150/160000] lr: 4.9163e-03 eta: 11:01:11 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7077 aux.loss_ce: 0.0074 aux.acc_seg: 99.4089 +04/18 15:55:34 - mmengine - INFO - Iter(train) [ 88200/160000] lr: 4.9133e-03 eta: 11:00:43 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7110 aux.loss_ce: 0.0077 aux.acc_seg: 99.1214 +04/18 15:56:02 - mmengine - INFO - Iter(train) [ 88250/160000] lr: 4.9103e-03 eta: 11:00:16 time: 0.5534 data_time: 0.0060 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6861 aux.loss_ce: 0.0077 aux.acc_seg: 99.0960 +04/18 15:56:29 - mmengine - INFO - Iter(train) [ 88300/160000] lr: 4.9073e-03 eta: 10:59:48 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0071 decode.acc_seg: 99.7285 aux.loss_ce: 0.0070 aux.acc_seg: 99.1835 +04/18 15:56:57 - mmengine - INFO - Iter(train) [ 88350/160000] lr: 4.9042e-03 eta: 10:59:20 time: 0.5532 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.7697 aux.loss_ce: 0.0070 aux.acc_seg: 99.2548 +04/18 15:57:25 - mmengine - INFO - Iter(train) [ 88400/160000] lr: 4.9012e-03 eta: 10:58:53 time: 0.5537 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.7449 aux.loss_ce: 0.0078 aux.acc_seg: 99.2819 +04/18 15:57:52 - mmengine - INFO - Iter(train) [ 88450/160000] lr: 4.8982e-03 eta: 10:58:25 time: 0.5517 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7245 aux.loss_ce: 0.0074 aux.acc_seg: 99.2621 +04/18 15:58:20 - mmengine - INFO - Iter(train) [ 88500/160000] lr: 4.8952e-03 eta: 10:57:58 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7017 aux.loss_ce: 0.0074 aux.acc_seg: 99.0706 +04/18 15:58:48 - mmengine - INFO - Iter(train) [ 88550/160000] lr: 4.8922e-03 eta: 10:57:31 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.5434 aux.loss_ce: 0.0077 aux.acc_seg: 99.1254 +04/18 15:59:16 - mmengine - INFO - Iter(train) [ 88600/160000] lr: 4.8891e-03 eta: 10:57:03 time: 0.5532 data_time: 0.0063 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7069 aux.loss_ce: 0.0082 aux.acc_seg: 98.9732 +04/18 15:59:44 - mmengine - INFO - Iter(train) [ 88650/160000] lr: 4.8861e-03 eta: 10:56:35 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7394 aux.loss_ce: 0.0076 aux.acc_seg: 99.3766 +04/18 16:00:11 - mmengine - INFO - Iter(train) [ 88700/160000] lr: 4.8831e-03 eta: 10:56:08 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6796 aux.loss_ce: 0.0073 aux.acc_seg: 99.2290 +04/18 16:00:39 - mmengine - INFO - Iter(train) [ 88750/160000] lr: 4.8801e-03 eta: 10:55:40 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7298 aux.loss_ce: 0.0069 aux.acc_seg: 99.1305 +04/18 16:01:07 - mmengine - INFO - Iter(train) [ 88800/160000] lr: 4.8771e-03 eta: 10:55:13 time: 0.5526 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.5977 aux.loss_ce: 0.0077 aux.acc_seg: 99.0127 +04/18 16:01:34 - mmengine - INFO - Iter(train) [ 88850/160000] lr: 4.8741e-03 eta: 10:54:45 time: 0.5544 data_time: 0.0066 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0093 decode.acc_seg: 99.5275 aux.loss_ce: 0.0084 aux.acc_seg: 99.0695 +04/18 16:02:02 - mmengine - INFO - Iter(train) [ 88900/160000] lr: 4.8710e-03 eta: 10:54:18 time: 0.5525 data_time: 0.0060 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6779 aux.loss_ce: 0.0078 aux.acc_seg: 99.1474 +04/18 16:02:30 - mmengine - INFO - Iter(train) [ 88950/160000] lr: 4.8680e-03 eta: 10:53:50 time: 0.5552 data_time: 0.0072 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6627 aux.loss_ce: 0.0080 aux.acc_seg: 99.1783 +04/18 16:02:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:02:58 - mmengine - INFO - Iter(train) [ 89000/160000] lr: 4.8650e-03 eta: 10:53:23 time: 0.5524 data_time: 0.0067 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7494 aux.loss_ce: 0.0069 aux.acc_seg: 99.2952 +04/18 16:03:25 - mmengine - INFO - Iter(train) [ 89050/160000] lr: 4.8620e-03 eta: 10:52:55 time: 0.5539 data_time: 0.0073 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6391 aux.loss_ce: 0.0079 aux.acc_seg: 99.3408 +04/18 16:03:53 - mmengine - INFO - Iter(train) [ 89100/160000] lr: 4.8590e-03 eta: 10:52:28 time: 0.5538 data_time: 0.0061 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.6519 aux.loss_ce: 0.0085 aux.acc_seg: 99.1333 +04/18 16:04:21 - mmengine - INFO - Iter(train) [ 89150/160000] lr: 4.8559e-03 eta: 10:52:00 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.6053 aux.loss_ce: 0.0078 aux.acc_seg: 99.1386 +04/18 16:04:48 - mmengine - INFO - Iter(train) [ 89200/160000] lr: 4.8529e-03 eta: 10:51:33 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.7516 aux.loss_ce: 0.0079 aux.acc_seg: 99.3876 +04/18 16:05:16 - mmengine - INFO - Iter(train) [ 89250/160000] lr: 4.8499e-03 eta: 10:51:05 time: 0.5555 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6994 aux.loss_ce: 0.0074 aux.acc_seg: 99.2689 +04/18 16:05:44 - mmengine - INFO - Iter(train) [ 89300/160000] lr: 4.8469e-03 eta: 10:50:38 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.6260 aux.loss_ce: 0.0074 aux.acc_seg: 99.0972 +04/18 16:06:12 - mmengine - INFO - Iter(train) [ 89350/160000] lr: 4.8438e-03 eta: 10:50:10 time: 0.5546 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6920 aux.loss_ce: 0.0075 aux.acc_seg: 99.0008 +04/18 16:06:39 - mmengine - INFO - Iter(train) [ 89400/160000] lr: 4.8408e-03 eta: 10:49:43 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.6706 aux.loss_ce: 0.0069 aux.acc_seg: 99.0152 +04/18 16:07:07 - mmengine - INFO - Iter(train) [ 89450/160000] lr: 4.8378e-03 eta: 10:49:15 time: 0.5539 data_time: 0.0074 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.7231 aux.loss_ce: 0.0075 aux.acc_seg: 99.2122 +04/18 16:07:35 - mmengine - INFO - Iter(train) [ 89500/160000] lr: 4.8348e-03 eta: 10:48:47 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0069 decode.acc_seg: 99.7463 aux.loss_ce: 0.0068 aux.acc_seg: 99.3187 +04/18 16:08:03 - mmengine - INFO - Iter(train) [ 89550/160000] lr: 4.8318e-03 eta: 10:48:20 time: 0.5644 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7202 aux.loss_ce: 0.0073 aux.acc_seg: 99.1396 +04/18 16:08:30 - mmengine - INFO - Iter(train) [ 89600/160000] lr: 4.8287e-03 eta: 10:47:53 time: 0.5543 data_time: 0.0071 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7922 aux.loss_ce: 0.0070 aux.acc_seg: 99.4041 +04/18 16:08:58 - mmengine - INFO - Iter(train) [ 89650/160000] lr: 4.8257e-03 eta: 10:47:25 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7718 aux.loss_ce: 0.0072 aux.acc_seg: 99.2868 +04/18 16:09:26 - mmengine - INFO - Iter(train) [ 89700/160000] lr: 4.8227e-03 eta: 10:46:58 time: 0.5551 data_time: 0.0080 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.6888 aux.loss_ce: 0.0069 aux.acc_seg: 99.3211 +04/18 16:09:54 - mmengine - INFO - Iter(train) [ 89750/160000] lr: 4.8197e-03 eta: 10:46:30 time: 0.5533 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.5870 aux.loss_ce: 0.0075 aux.acc_seg: 98.8684 +04/18 16:10:21 - mmengine - INFO - Iter(train) [ 89800/160000] lr: 4.8166e-03 eta: 10:46:02 time: 0.5538 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0081 decode.acc_seg: 99.8167 aux.loss_ce: 0.0072 aux.acc_seg: 99.5503 +04/18 16:10:49 - mmengine - INFO - Iter(train) [ 89850/160000] lr: 4.8136e-03 eta: 10:45:35 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0085 decode.acc_seg: 99.7402 aux.loss_ce: 0.0085 aux.acc_seg: 99.2199 +04/18 16:11:17 - mmengine - INFO - Iter(train) [ 89900/160000] lr: 4.8106e-03 eta: 10:45:07 time: 0.5541 data_time: 0.0062 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0090 decode.acc_seg: 99.6343 aux.loss_ce: 0.0075 aux.acc_seg: 99.0417 +04/18 16:11:44 - mmengine - INFO - Iter(train) [ 89950/160000] lr: 4.8076e-03 eta: 10:44:40 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.6977 aux.loss_ce: 0.0081 aux.acc_seg: 99.2770 +04/18 16:12:12 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:12:12 - mmengine - INFO - Iter(train) [ 90000/160000] lr: 4.8045e-03 eta: 10:44:12 time: 0.5534 data_time: 0.0065 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6110 aux.loss_ce: 0.0079 aux.acc_seg: 98.7445 +04/18 16:12:12 - mmengine - INFO - Saving checkpoint at 90000 iterations +04/18 16:12:16 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0469 data_time: 0.0016 memory: 1657 +04/18 16:12:18 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0467 data_time: 0.0013 memory: 1657 +04/18 16:12:21 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0468 data_time: 0.0014 memory: 1657 +04/18 16:12:23 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0451 data_time: 0.0012 memory: 1657 +04/18 16:12:23 - mmengine - INFO - per class results: +04/18 16:12:23 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 98.99 | 99.43 | 99.49 | 99.55 | 99.43 | +| contrast | 78.56 | 89.25 | 87.99 | 86.76 | 89.25 | ++------------+-------+-------+--------+-----------+--------+ +04/18 16:12:23 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0300 mIoU: 88.7700 mAcc: 94.3400 mFscore: 93.7400 mPrecision: 93.1600 mRecall: 94.3400 data_time: 0.0016 time: 0.0468 +04/18 16:12:51 - mmengine - INFO - Iter(train) [ 90050/160000] lr: 4.8015e-03 eta: 10:43:45 time: 0.5541 data_time: 0.0073 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0088 decode.acc_seg: 99.7517 aux.loss_ce: 0.0079 aux.acc_seg: 99.4351 +04/18 16:13:19 - mmengine - INFO - Iter(train) [ 90100/160000] lr: 4.7985e-03 eta: 10:43:17 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.7671 aux.loss_ce: 0.0075 aux.acc_seg: 99.3597 +04/18 16:13:46 - mmengine - INFO - Iter(train) [ 90150/160000] lr: 4.7955e-03 eta: 10:42:50 time: 0.5537 data_time: 0.0068 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6665 aux.loss_ce: 0.0081 aux.acc_seg: 99.1957 +04/18 16:14:14 - mmengine - INFO - Iter(train) [ 90200/160000] lr: 4.7924e-03 eta: 10:42:22 time: 0.5526 data_time: 0.0057 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.6493 aux.loss_ce: 0.0070 aux.acc_seg: 99.1049 +04/18 16:14:42 - mmengine - INFO - Iter(train) [ 90250/160000] lr: 4.7894e-03 eta: 10:41:55 time: 0.5531 data_time: 0.0066 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0081 decode.acc_seg: 99.6822 aux.loss_ce: 0.0083 aux.acc_seg: 99.0961 +04/18 16:15:10 - mmengine - INFO - Iter(train) [ 90300/160000] lr: 4.7864e-03 eta: 10:41:27 time: 0.5532 data_time: 0.0068 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0098 decode.acc_seg: 99.6121 aux.loss_ce: 0.0084 aux.acc_seg: 99.2744 +04/18 16:15:37 - mmengine - INFO - Iter(train) [ 90350/160000] lr: 4.7834e-03 eta: 10:41:00 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0068 decode.acc_seg: 99.6592 aux.loss_ce: 0.0067 aux.acc_seg: 99.2595 +04/18 16:16:05 - mmengine - INFO - Iter(train) [ 90400/160000] lr: 4.7803e-03 eta: 10:40:32 time: 0.5538 data_time: 0.0071 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7413 aux.loss_ce: 0.0075 aux.acc_seg: 99.2717 +04/18 16:16:33 - mmengine - INFO - Iter(train) [ 90450/160000] lr: 4.7773e-03 eta: 10:40:04 time: 0.5532 data_time: 0.0066 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7602 aux.loss_ce: 0.0075 aux.acc_seg: 99.4248 +04/18 16:17:00 - mmengine - INFO - Iter(train) [ 90500/160000] lr: 4.7743e-03 eta: 10:39:37 time: 0.5643 data_time: 0.0076 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0075 decode.acc_seg: 99.6515 aux.loss_ce: 0.0080 aux.acc_seg: 99.1366 +04/18 16:17:28 - mmengine - INFO - Iter(train) [ 90550/160000] lr: 4.7713e-03 eta: 10:39:09 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.6815 aux.loss_ce: 0.0075 aux.acc_seg: 99.3804 +04/18 16:17:56 - mmengine - INFO - Iter(train) [ 90600/160000] lr: 4.7682e-03 eta: 10:38:42 time: 0.5634 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0077 decode.acc_seg: 99.7453 aux.loss_ce: 0.0082 aux.acc_seg: 99.2341 +04/18 16:18:24 - mmengine - INFO - Iter(train) [ 90650/160000] lr: 4.7652e-03 eta: 10:38:15 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7275 aux.loss_ce: 0.0076 aux.acc_seg: 99.4253 +04/18 16:18:52 - mmengine - INFO - Iter(train) [ 90700/160000] lr: 4.7622e-03 eta: 10:37:47 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6181 aux.loss_ce: 0.0077 aux.acc_seg: 99.0476 +04/18 16:19:19 - mmengine - INFO - Iter(train) [ 90750/160000] lr: 4.7592e-03 eta: 10:37:20 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.5783 aux.loss_ce: 0.0081 aux.acc_seg: 99.0585 +04/18 16:19:47 - mmengine - INFO - Iter(train) [ 90800/160000] lr: 4.7561e-03 eta: 10:36:52 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0101 decode.acc_seg: 99.7410 aux.loss_ce: 0.0084 aux.acc_seg: 99.3400 +04/18 16:20:15 - mmengine - INFO - Iter(train) [ 90850/160000] lr: 4.7531e-03 eta: 10:36:24 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6765 aux.loss_ce: 0.0080 aux.acc_seg: 99.1732 +04/18 16:20:42 - mmengine - INFO - Iter(train) [ 90900/160000] lr: 4.7501e-03 eta: 10:35:57 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.6199 aux.loss_ce: 0.0074 aux.acc_seg: 99.3221 +04/18 16:21:10 - mmengine - INFO - Iter(train) [ 90950/160000] lr: 4.7470e-03 eta: 10:35:29 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.5703 aux.loss_ce: 0.0079 aux.acc_seg: 99.0297 +04/18 16:21:38 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:21:38 - mmengine - INFO - Iter(train) [ 91000/160000] lr: 4.7440e-03 eta: 10:35:02 time: 0.5558 data_time: 0.0069 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.5575 aux.loss_ce: 0.0081 aux.acc_seg: 99.2346 +04/18 16:22:05 - mmengine - INFO - Iter(train) [ 91050/160000] lr: 4.7410e-03 eta: 10:34:34 time: 0.5534 data_time: 0.0071 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6915 aux.loss_ce: 0.0084 aux.acc_seg: 99.2305 +04/18 16:22:33 - mmengine - INFO - Iter(train) [ 91100/160000] lr: 4.7380e-03 eta: 10:34:07 time: 0.5547 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6708 aux.loss_ce: 0.0072 aux.acc_seg: 99.2778 +04/18 16:23:01 - mmengine - INFO - Iter(train) [ 91150/160000] lr: 4.7349e-03 eta: 10:33:39 time: 0.5542 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7328 aux.loss_ce: 0.0074 aux.acc_seg: 99.3443 +04/18 16:23:29 - mmengine - INFO - Iter(train) [ 91200/160000] lr: 4.7319e-03 eta: 10:33:12 time: 0.5562 data_time: 0.0079 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0068 decode.acc_seg: 99.7856 aux.loss_ce: 0.0068 aux.acc_seg: 99.3442 +04/18 16:23:56 - mmengine - INFO - Iter(train) [ 91250/160000] lr: 4.7289e-03 eta: 10:32:44 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7177 aux.loss_ce: 0.0073 aux.acc_seg: 99.3159 +04/18 16:24:24 - mmengine - INFO - Iter(train) [ 91300/160000] lr: 4.7258e-03 eta: 10:32:17 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0083 decode.acc_seg: 99.7583 aux.loss_ce: 0.0074 aux.acc_seg: 99.3390 +04/18 16:24:52 - mmengine - INFO - Iter(train) [ 91350/160000] lr: 4.7228e-03 eta: 10:31:49 time: 0.5560 data_time: 0.0063 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0088 decode.acc_seg: 99.7421 aux.loss_ce: 0.0084 aux.acc_seg: 99.2332 +04/18 16:25:19 - mmengine - INFO - Iter(train) [ 91400/160000] lr: 4.7198e-03 eta: 10:31:21 time: 0.5546 data_time: 0.0067 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7305 aux.loss_ce: 0.0076 aux.acc_seg: 99.3952 +04/18 16:25:47 - mmengine - INFO - Iter(train) [ 91450/160000] lr: 4.7168e-03 eta: 10:30:54 time: 0.5536 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.7017 aux.loss_ce: 0.0080 aux.acc_seg: 99.1828 +04/18 16:26:15 - mmengine - INFO - Iter(train) [ 91500/160000] lr: 4.7137e-03 eta: 10:30:26 time: 0.5532 data_time: 0.0070 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7032 aux.loss_ce: 0.0073 aux.acc_seg: 99.1995 +04/18 16:26:43 - mmengine - INFO - Iter(train) [ 91550/160000] lr: 4.7107e-03 eta: 10:29:59 time: 0.5549 data_time: 0.0060 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.7380 aux.loss_ce: 0.0075 aux.acc_seg: 99.1542 +04/18 16:27:10 - mmengine - INFO - Iter(train) [ 91600/160000] lr: 4.7077e-03 eta: 10:29:31 time: 0.5535 data_time: 0.0067 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6866 aux.loss_ce: 0.0074 aux.acc_seg: 99.2903 +04/18 16:27:38 - mmengine - INFO - Iter(train) [ 91650/160000] lr: 4.7046e-03 eta: 10:29:04 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0065 decode.acc_seg: 99.7046 aux.loss_ce: 0.0067 aux.acc_seg: 99.1618 +04/18 16:28:06 - mmengine - INFO - Iter(train) [ 91700/160000] lr: 4.7016e-03 eta: 10:28:36 time: 0.5545 data_time: 0.0062 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7621 aux.loss_ce: 0.0069 aux.acc_seg: 99.4017 +04/18 16:28:34 - mmengine - INFO - Iter(train) [ 91750/160000] lr: 4.6986e-03 eta: 10:28:09 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.6921 aux.loss_ce: 0.0076 aux.acc_seg: 99.2706 +04/18 16:29:02 - mmengine - INFO - Iter(train) [ 91800/160000] lr: 4.6955e-03 eta: 10:27:41 time: 0.5538 data_time: 0.0069 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7231 aux.loss_ce: 0.0073 aux.acc_seg: 99.1256 +04/18 16:29:29 - mmengine - INFO - Iter(train) [ 91850/160000] lr: 4.6925e-03 eta: 10:27:14 time: 0.5539 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7126 aux.loss_ce: 0.0073 aux.acc_seg: 99.1622 +04/18 16:29:57 - mmengine - INFO - Iter(train) [ 91900/160000] lr: 4.6895e-03 eta: 10:26:46 time: 0.5536 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0068 decode.acc_seg: 99.7128 aux.loss_ce: 0.0068 aux.acc_seg: 99.2221 +04/18 16:30:25 - mmengine - INFO - Iter(train) [ 91950/160000] lr: 4.6864e-03 eta: 10:26:19 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0081 decode.acc_seg: 99.5994 aux.loss_ce: 0.0077 aux.acc_seg: 99.2487 +04/18 16:30:53 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:30:53 - mmengine - INFO - Iter(train) [ 92000/160000] lr: 4.6834e-03 eta: 10:25:51 time: 0.5554 data_time: 0.0074 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.6864 aux.loss_ce: 0.0069 aux.acc_seg: 99.2065 +04/18 16:31:20 - mmengine - INFO - Iter(train) [ 92050/160000] lr: 4.6804e-03 eta: 10:25:24 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7125 aux.loss_ce: 0.0069 aux.acc_seg: 99.1871 +04/18 16:31:48 - mmengine - INFO - Iter(train) [ 92100/160000] lr: 4.6773e-03 eta: 10:24:56 time: 0.5545 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6840 aux.loss_ce: 0.0077 aux.acc_seg: 99.2039 +04/18 16:32:16 - mmengine - INFO - Iter(train) [ 92150/160000] lr: 4.6743e-03 eta: 10:24:29 time: 0.5534 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7873 aux.loss_ce: 0.0072 aux.acc_seg: 99.4144 +04/18 16:32:43 - mmengine - INFO - Iter(train) [ 92200/160000] lr: 4.6713e-03 eta: 10:24:01 time: 0.5551 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7953 aux.loss_ce: 0.0069 aux.acc_seg: 99.4600 +04/18 16:33:11 - mmengine - INFO - Iter(train) [ 92250/160000] lr: 4.6682e-03 eta: 10:23:34 time: 0.5552 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.6154 aux.loss_ce: 0.0075 aux.acc_seg: 99.2498 +04/18 16:33:39 - mmengine - INFO - Iter(train) [ 92300/160000] lr: 4.6652e-03 eta: 10:23:06 time: 0.5536 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7125 aux.loss_ce: 0.0073 aux.acc_seg: 99.1919 +04/18 16:34:07 - mmengine - INFO - Iter(train) [ 92350/160000] lr: 4.6622e-03 eta: 10:22:39 time: 0.5560 data_time: 0.0071 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7135 aux.loss_ce: 0.0074 aux.acc_seg: 99.3181 +04/18 16:34:34 - mmengine - INFO - Iter(train) [ 92400/160000] lr: 4.6591e-03 eta: 10:22:11 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.5176 aux.loss_ce: 0.0074 aux.acc_seg: 99.0997 +04/18 16:35:02 - mmengine - INFO - Iter(train) [ 92450/160000] lr: 4.6561e-03 eta: 10:21:44 time: 0.5544 data_time: 0.0068 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6918 aux.loss_ce: 0.0076 aux.acc_seg: 99.3507 +04/18 16:35:30 - mmengine - INFO - Iter(train) [ 92500/160000] lr: 4.6531e-03 eta: 10:21:16 time: 0.5534 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7289 aux.loss_ce: 0.0069 aux.acc_seg: 99.3187 +04/18 16:35:58 - mmengine - INFO - Iter(train) [ 92550/160000] lr: 4.6500e-03 eta: 10:20:48 time: 0.5542 data_time: 0.0070 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.5818 aux.loss_ce: 0.0071 aux.acc_seg: 98.9644 +04/18 16:36:25 - mmengine - INFO - Iter(train) [ 92600/160000] lr: 4.6470e-03 eta: 10:20:21 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7667 aux.loss_ce: 0.0068 aux.acc_seg: 99.2359 +04/18 16:36:53 - mmengine - INFO - Iter(train) [ 92650/160000] lr: 4.6439e-03 eta: 10:19:53 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.7192 aux.loss_ce: 0.0080 aux.acc_seg: 99.1530 +04/18 16:37:21 - mmengine - INFO - Iter(train) [ 92700/160000] lr: 4.6409e-03 eta: 10:19:26 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6608 aux.loss_ce: 0.0073 aux.acc_seg: 99.2210 +04/18 16:37:48 - mmengine - INFO - Iter(train) [ 92750/160000] lr: 4.6379e-03 eta: 10:18:58 time: 0.5545 data_time: 0.0065 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6756 aux.loss_ce: 0.0074 aux.acc_seg: 99.1357 +04/18 16:38:16 - mmengine - INFO - Iter(train) [ 92800/160000] lr: 4.6348e-03 eta: 10:18:31 time: 0.5544 data_time: 0.0071 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6621 aux.loss_ce: 0.0077 aux.acc_seg: 99.0524 +04/18 16:38:44 - mmengine - INFO - Iter(train) [ 92850/160000] lr: 4.6318e-03 eta: 10:18:03 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.7317 aux.loss_ce: 0.0078 aux.acc_seg: 99.3055 +04/18 16:39:12 - mmengine - INFO - Iter(train) [ 92900/160000] lr: 4.6288e-03 eta: 10:17:36 time: 0.5529 data_time: 0.0072 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.7147 aux.loss_ce: 0.0083 aux.acc_seg: 99.0110 +04/18 16:39:40 - mmengine - INFO - Iter(train) [ 92950/160000] lr: 4.6257e-03 eta: 10:17:08 time: 0.5548 data_time: 0.0063 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6904 aux.loss_ce: 0.0073 aux.acc_seg: 99.1941 +04/18 16:40:07 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:40:07 - mmengine - INFO - Iter(train) [ 93000/160000] lr: 4.6227e-03 eta: 10:16:41 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.7231 aux.loss_ce: 0.0076 aux.acc_seg: 99.0689 +04/18 16:40:35 - mmengine - INFO - Iter(train) [ 93050/160000] lr: 4.6197e-03 eta: 10:16:13 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7027 aux.loss_ce: 0.0075 aux.acc_seg: 99.0228 +04/18 16:41:03 - mmengine - INFO - Iter(train) [ 93100/160000] lr: 4.6166e-03 eta: 10:15:46 time: 0.5525 data_time: 0.0069 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.6995 aux.loss_ce: 0.0076 aux.acc_seg: 99.1519 +04/18 16:41:30 - mmengine - INFO - Iter(train) [ 93150/160000] lr: 4.6136e-03 eta: 10:15:18 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.7832 aux.loss_ce: 0.0076 aux.acc_seg: 99.2928 +04/18 16:41:58 - mmengine - INFO - Iter(train) [ 93200/160000] lr: 4.6105e-03 eta: 10:14:51 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.6873 aux.loss_ce: 0.0068 aux.acc_seg: 99.1235 +04/18 16:42:26 - mmengine 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4.5953e-03 eta: 10:12:33 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.7462 aux.loss_ce: 0.0075 aux.acc_seg: 99.3246 +04/18 16:44:45 - mmengine - INFO - Iter(train) [ 93500/160000] lr: 4.5923e-03 eta: 10:12:05 time: 0.5547 data_time: 0.0064 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7552 aux.loss_ce: 0.0070 aux.acc_seg: 99.3195 +04/18 16:45:12 - mmengine - INFO - Iter(train) [ 93550/160000] lr: 4.5893e-03 eta: 10:11:38 time: 0.5537 data_time: 0.0072 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7588 aux.loss_ce: 0.0072 aux.acc_seg: 99.3825 +04/18 16:45:40 - mmengine - INFO - Iter(train) [ 93600/160000] lr: 4.5862e-03 eta: 10:11:10 time: 0.5536 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.6302 aux.loss_ce: 0.0073 aux.acc_seg: 99.0357 +04/18 16:46:08 - mmengine - INFO - Iter(train) [ 93650/160000] lr: 4.5832e-03 eta: 10:10:43 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.7753 aux.loss_ce: 0.0079 aux.acc_seg: 99.3404 +04/18 16:46:35 - mmengine - INFO - Iter(train) [ 93700/160000] lr: 4.5801e-03 eta: 10:10:15 time: 0.5544 data_time: 0.0061 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0066 decode.acc_seg: 99.7736 aux.loss_ce: 0.0067 aux.acc_seg: 99.4164 +04/18 16:47:03 - mmengine - INFO - Iter(train) [ 93750/160000] lr: 4.5771e-03 eta: 10:09:48 time: 0.5539 data_time: 0.0081 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0073 decode.acc_seg: 99.7229 aux.loss_ce: 0.0078 aux.acc_seg: 99.1734 +04/18 16:47:31 - mmengine - INFO - Iter(train) [ 93800/160000] lr: 4.5741e-03 eta: 10:09:20 time: 0.5542 data_time: 0.0068 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7807 aux.loss_ce: 0.0072 aux.acc_seg: 99.2726 +04/18 16:47:59 - mmengine - INFO - Iter(train) [ 93850/160000] lr: 4.5710e-03 eta: 10:08:53 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7389 aux.loss_ce: 0.0071 aux.acc_seg: 99.1090 +04/18 16:48:27 - mmengine - INFO - Iter(train) [ 93900/160000] lr: 4.5680e-03 eta: 10:08:25 time: 0.5543 data_time: 0.0078 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6845 aux.loss_ce: 0.0073 aux.acc_seg: 99.1134 +04/18 16:48:54 - mmengine - INFO - Iter(train) [ 93950/160000] lr: 4.5649e-03 eta: 10:07:58 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6857 aux.loss_ce: 0.0073 aux.acc_seg: 99.0434 +04/18 16:49:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:49:22 - mmengine - INFO - Iter(train) [ 94000/160000] lr: 4.5619e-03 eta: 10:07:30 time: 0.5557 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7552 aux.loss_ce: 0.0068 aux.acc_seg: 99.4005 +04/18 16:49:50 - mmengine - INFO - Iter(train) [ 94050/160000] lr: 4.5589e-03 eta: 10:07:03 time: 0.5542 data_time: 0.0072 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0070 decode.acc_seg: 99.6277 aux.loss_ce: 0.0080 aux.acc_seg: 98.4986 +04/18 16:50:18 - mmengine - INFO - Iter(train) [ 94100/160000] lr: 4.5558e-03 eta: 10:06:35 time: 0.5549 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7393 aux.loss_ce: 0.0069 aux.acc_seg: 99.3925 +04/18 16:50:45 - mmengine - INFO - Iter(train) [ 94150/160000] lr: 4.5528e-03 eta: 10:06:08 time: 0.5538 data_time: 0.0062 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6656 aux.loss_ce: 0.0074 aux.acc_seg: 99.0781 +04/18 16:51:13 - mmengine - INFO - Iter(train) [ 94200/160000] lr: 4.5497e-03 eta: 10:05:40 time: 0.5537 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0068 decode.acc_seg: 99.6878 aux.loss_ce: 0.0068 aux.acc_seg: 99.2552 +04/18 16:51:41 - mmengine - INFO - Iter(train) [ 94250/160000] lr: 4.5467e-03 eta: 10:05:12 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6560 aux.loss_ce: 0.0072 aux.acc_seg: 99.1100 +04/18 16:52:08 - mmengine - INFO - Iter(train) [ 94300/160000] lr: 4.5436e-03 eta: 10:04:45 time: 0.5532 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7941 aux.loss_ce: 0.0076 aux.acc_seg: 99.3337 +04/18 16:52:36 - mmengine - INFO - Iter(train) [ 94350/160000] lr: 4.5406e-03 eta: 10:04:17 time: 0.5547 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.6806 aux.loss_ce: 0.0072 aux.acc_seg: 99.1579 +04/18 16:53:04 - mmengine - INFO - Iter(train) [ 94400/160000] lr: 4.5375e-03 eta: 10:03:50 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.6963 aux.loss_ce: 0.0072 aux.acc_seg: 99.0783 +04/18 16:53:32 - mmengine - INFO - Iter(train) [ 94450/160000] lr: 4.5345e-03 eta: 10:03:22 time: 0.5536 data_time: 0.0067 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7437 aux.loss_ce: 0.0072 aux.acc_seg: 99.2533 +04/18 16:53:59 - mmengine - INFO - Iter(train) [ 94500/160000] lr: 4.5315e-03 eta: 10:02:55 time: 0.5544 data_time: 0.0071 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.8111 aux.loss_ce: 0.0070 aux.acc_seg: 99.3376 +04/18 16:54:27 - mmengine - INFO - Iter(train) [ 94550/160000] lr: 4.5284e-03 eta: 10:02:27 time: 0.5529 data_time: 0.0068 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0066 decode.acc_seg: 99.7833 aux.loss_ce: 0.0067 aux.acc_seg: 99.3541 +04/18 16:54:55 - mmengine - INFO - Iter(train) [ 94600/160000] lr: 4.5254e-03 eta: 10:02:00 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7384 aux.loss_ce: 0.0076 aux.acc_seg: 99.1609 +04/18 16:55:23 - mmengine - INFO - Iter(train) [ 94650/160000] lr: 4.5223e-03 eta: 10:01:32 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7441 aux.loss_ce: 0.0076 aux.acc_seg: 99.2318 +04/18 16:55:50 - mmengine - INFO - Iter(train) [ 94700/160000] lr: 4.5193e-03 eta: 10:01:05 time: 0.5552 data_time: 0.0062 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.6926 aux.loss_ce: 0.0073 aux.acc_seg: 99.1946 +04/18 16:56:18 - mmengine - INFO - Iter(train) [ 94750/160000] lr: 4.5162e-03 eta: 10:00:37 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.6918 aux.loss_ce: 0.0069 aux.acc_seg: 99.3544 +04/18 16:56:46 - mmengine - INFO - Iter(train) [ 94800/160000] lr: 4.5132e-03 eta: 10:00:09 time: 0.5624 data_time: 0.0072 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.6819 aux.loss_ce: 0.0072 aux.acc_seg: 99.2666 +04/18 16:57:13 - mmengine - INFO - Iter(train) [ 94850/160000] lr: 4.5101e-03 eta: 9:59:42 time: 0.5553 data_time: 0.0059 memory: 7635 loss: 0.0124 decode.loss_ce: 0.0059 decode.acc_seg: 99.7990 aux.loss_ce: 0.0065 aux.acc_seg: 99.3868 +04/18 16:57:41 - mmengine - INFO - Iter(train) [ 94900/160000] lr: 4.5071e-03 eta: 9:59:14 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7571 aux.loss_ce: 0.0074 aux.acc_seg: 99.2595 +04/18 16:58:09 - mmengine - INFO - Iter(train) [ 94950/160000] lr: 4.5040e-03 eta: 9:58:47 time: 0.5638 data_time: 0.0060 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6574 aux.loss_ce: 0.0082 aux.acc_seg: 99.1121 +04/18 16:58:37 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:58:37 - mmengine - INFO - Iter(train) [ 95000/160000] lr: 4.5010e-03 eta: 9:58:19 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6932 aux.loss_ce: 0.0072 aux.acc_seg: 99.2917 +04/18 16:59:05 - mmengine - INFO - Iter(train) [ 95050/160000] lr: 4.4980e-03 eta: 9:57:52 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7259 aux.loss_ce: 0.0072 aux.acc_seg: 99.1473 +04/18 16:59:32 - mmengine - INFO - Iter(train) [ 95100/160000] lr: 4.4949e-03 eta: 9:57:24 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7579 aux.loss_ce: 0.0068 aux.acc_seg: 99.3444 +04/18 17:00:00 - mmengine - INFO - Iter(train) [ 95150/160000] lr: 4.4919e-03 eta: 9:56:57 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.6657 aux.loss_ce: 0.0081 aux.acc_seg: 99.2908 +04/18 17:00:28 - mmengine - INFO - Iter(train) [ 95200/160000] lr: 4.4888e-03 eta: 9:56:29 time: 0.5539 data_time: 0.0072 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.5678 aux.loss_ce: 0.0079 aux.acc_seg: 99.0845 +04/18 17:00:55 - mmengine - INFO - Iter(train) [ 95250/160000] lr: 4.4858e-03 eta: 9:56:02 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0103 decode.acc_seg: 99.4879 aux.loss_ce: 0.0080 aux.acc_seg: 99.0593 +04/18 17:01:23 - mmengine - INFO - Iter(train) [ 95300/160000] lr: 4.4827e-03 eta: 9:55:34 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0108 decode.acc_seg: 99.6470 aux.loss_ce: 0.0091 aux.acc_seg: 99.1091 +04/18 17:01:51 - mmengine - INFO - Iter(train) [ 95350/160000] lr: 4.4797e-03 eta: 9:55:07 time: 0.5540 data_time: 0.0064 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0100 decode.acc_seg: 99.6401 aux.loss_ce: 0.0079 aux.acc_seg: 99.2100 +04/18 17:02:19 - mmengine - INFO - Iter(train) [ 95400/160000] lr: 4.4766e-03 eta: 9:54:39 time: 0.5550 data_time: 0.0072 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0091 decode.acc_seg: 99.6162 aux.loss_ce: 0.0080 aux.acc_seg: 99.3341 +04/18 17:02:46 - mmengine - INFO - Iter(train) [ 95450/160000] lr: 4.4736e-03 eta: 9:54:11 time: 0.5549 data_time: 0.0068 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6016 aux.loss_ce: 0.0079 aux.acc_seg: 99.1653 +04/18 17:03:14 - mmengine - INFO - Iter(train) [ 95500/160000] lr: 4.4705e-03 eta: 9:53:44 time: 0.5543 data_time: 0.0068 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.7846 aux.loss_ce: 0.0072 aux.acc_seg: 99.3245 +04/18 17:03:42 - mmengine - INFO - Iter(train) [ 95550/160000] lr: 4.4675e-03 eta: 9:53:16 time: 0.5537 data_time: 0.0065 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0105 decode.acc_seg: 99.6505 aux.loss_ce: 0.0090 aux.acc_seg: 99.2621 +04/18 17:04:10 - mmengine - INFO - Iter(train) [ 95600/160000] lr: 4.4644e-03 eta: 9:52:49 time: 0.5546 data_time: 0.0077 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0100 decode.acc_seg: 99.6714 aux.loss_ce: 0.0092 aux.acc_seg: 99.0884 +04/18 17:04:37 - mmengine - INFO - Iter(train) [ 95650/160000] lr: 4.4614e-03 eta: 9:52:21 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.6899 aux.loss_ce: 0.0075 aux.acc_seg: 99.3049 +04/18 17:05:05 - mmengine - INFO - Iter(train) [ 95700/160000] lr: 4.4583e-03 eta: 9:51:54 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.6909 aux.loss_ce: 0.0077 aux.acc_seg: 99.1796 +04/18 17:05:33 - mmengine - INFO - Iter(train) [ 95750/160000] lr: 4.4553e-03 eta: 9:51:26 time: 0.5537 data_time: 0.0065 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.7422 aux.loss_ce: 0.0081 aux.acc_seg: 99.1709 +04/18 17:06:00 - mmengine - INFO - Iter(train) [ 95800/160000] lr: 4.4522e-03 eta: 9:50:59 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0086 decode.acc_seg: 99.4585 aux.loss_ce: 0.0083 aux.acc_seg: 98.8168 +04/18 17:06:28 - mmengine - INFO - Iter(train) [ 95850/160000] lr: 4.4492e-03 eta: 9:50:31 time: 0.5546 data_time: 0.0073 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.6819 aux.loss_ce: 0.0079 aux.acc_seg: 99.1382 +04/18 17:06:56 - mmengine - INFO - Iter(train) [ 95900/160000] lr: 4.4461e-03 eta: 9:50:04 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.7220 aux.loss_ce: 0.0079 aux.acc_seg: 99.2452 +04/18 17:07:24 - mmengine - INFO - Iter(train) [ 95950/160000] lr: 4.4431e-03 eta: 9:49:36 time: 0.5551 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7738 aux.loss_ce: 0.0073 aux.acc_seg: 99.3678 +04/18 17:07:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:07:52 - mmengine - INFO - Iter(train) [ 96000/160000] lr: 4.4400e-03 eta: 9:49:09 time: 0.5632 data_time: 0.0064 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7273 aux.loss_ce: 0.0073 aux.acc_seg: 99.2279 +04/18 17:08:19 - mmengine - INFO - Iter(train) [ 96050/160000] lr: 4.4370e-03 eta: 9:48:41 time: 0.5545 data_time: 0.0074 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6448 aux.loss_ce: 0.0073 aux.acc_seg: 99.2266 +04/18 17:08:47 - mmengine - INFO - Iter(train) [ 96100/160000] lr: 4.4339e-03 eta: 9:48:13 time: 0.5546 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0082 decode.acc_seg: 99.6461 aux.loss_ce: 0.0075 aux.acc_seg: 99.1035 +04/18 17:09:15 - mmengine - INFO - Iter(train) [ 96150/160000] lr: 4.4309e-03 eta: 9:47:46 time: 0.5545 data_time: 0.0076 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7221 aux.loss_ce: 0.0074 aux.acc_seg: 99.1570 +04/18 17:09:42 - mmengine - INFO - Iter(train) [ 96200/160000] lr: 4.4278e-03 eta: 9:47:18 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7612 aux.loss_ce: 0.0074 aux.acc_seg: 99.3772 +04/18 17:10:10 - mmengine - INFO - Iter(train) [ 96250/160000] lr: 4.4248e-03 eta: 9:46:51 time: 0.5543 data_time: 0.0060 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0078 decode.acc_seg: 99.6583 aux.loss_ce: 0.0081 aux.acc_seg: 99.0301 +04/18 17:10:38 - mmengine - INFO - Iter(train) [ 96300/160000] lr: 4.4217e-03 eta: 9:46:23 time: 0.5542 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.7119 aux.loss_ce: 0.0080 aux.acc_seg: 99.1693 +04/18 17:11:06 - mmengine - INFO - Iter(train) [ 96350/160000] lr: 4.4187e-03 eta: 9:45:56 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.6655 aux.loss_ce: 0.0073 aux.acc_seg: 99.1374 +04/18 17:11:33 - mmengine - INFO - Iter(train) [ 96400/160000] lr: 4.4156e-03 eta: 9:45:28 time: 0.5550 data_time: 0.0072 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.7654 aux.loss_ce: 0.0065 aux.acc_seg: 99.3459 +04/18 17:12:01 - mmengine - INFO - Iter(train) [ 96450/160000] lr: 4.4125e-03 eta: 9:45:01 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6296 aux.loss_ce: 0.0077 aux.acc_seg: 98.8319 +04/18 17:12:29 - mmengine - INFO - Iter(train) [ 96500/160000] lr: 4.4095e-03 eta: 9:44:33 time: 0.5550 data_time: 0.0081 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7513 aux.loss_ce: 0.0069 aux.acc_seg: 99.2723 +04/18 17:12:57 - mmengine - INFO - Iter(train) [ 96550/160000] lr: 4.4064e-03 eta: 9:44:06 time: 0.5556 data_time: 0.0075 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7525 aux.loss_ce: 0.0077 aux.acc_seg: 99.3343 +04/18 17:13:24 - mmengine - INFO - Iter(train) [ 96600/160000] lr: 4.4034e-03 eta: 9:43:38 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7320 aux.loss_ce: 0.0073 aux.acc_seg: 99.3683 +04/18 17:13:52 - mmengine - INFO - Iter(train) [ 96650/160000] lr: 4.4003e-03 eta: 9:43:10 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7233 aux.loss_ce: 0.0073 aux.acc_seg: 99.2402 +04/18 17:14:20 - mmengine - INFO - Iter(train) [ 96700/160000] lr: 4.3973e-03 eta: 9:42:43 time: 0.5549 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7185 aux.loss_ce: 0.0069 aux.acc_seg: 99.1770 +04/18 17:14:48 - mmengine - INFO - Iter(train) [ 96750/160000] lr: 4.3942e-03 eta: 9:42:15 time: 0.5568 data_time: 0.0073 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.7624 aux.loss_ce: 0.0077 aux.acc_seg: 99.2085 +04/18 17:15:15 - mmengine - INFO - Iter(train) [ 96800/160000] lr: 4.3912e-03 eta: 9:41:48 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.5921 aux.loss_ce: 0.0077 aux.acc_seg: 98.8240 +04/18 17:15:43 - mmengine - INFO - Iter(train) [ 96850/160000] lr: 4.3881e-03 eta: 9:41:20 time: 0.5558 data_time: 0.0083 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7164 aux.loss_ce: 0.0069 aux.acc_seg: 99.2104 +04/18 17:16:11 - mmengine - INFO - Iter(train) [ 96900/160000] lr: 4.3851e-03 eta: 9:40:53 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0086 decode.acc_seg: 99.6477 aux.loss_ce: 0.0087 aux.acc_seg: 99.0242 +04/18 17:16:39 - mmengine - INFO - Iter(train) [ 96950/160000] lr: 4.3820e-03 eta: 9:40:25 time: 0.5621 data_time: 0.0067 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.6933 aux.loss_ce: 0.0077 aux.acc_seg: 99.1102 +04/18 17:17:06 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:17:06 - mmengine - INFO - Iter(train) [ 97000/160000] lr: 4.3789e-03 eta: 9:39:58 time: 0.5567 data_time: 0.0077 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.5578 aux.loss_ce: 0.0079 aux.acc_seg: 99.0413 +04/18 17:17:34 - mmengine - INFO - Iter(train) [ 97050/160000] lr: 4.3759e-03 eta: 9:39:30 time: 0.5549 data_time: 0.0069 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0093 decode.acc_seg: 99.7400 aux.loss_ce: 0.0086 aux.acc_seg: 99.1406 +04/18 17:18:02 - mmengine - INFO - Iter(train) [ 97100/160000] lr: 4.3728e-03 eta: 9:39:03 time: 0.5626 data_time: 0.0069 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.6466 aux.loss_ce: 0.0074 aux.acc_seg: 99.1657 +04/18 17:18:30 - mmengine - INFO - Iter(train) [ 97150/160000] lr: 4.3698e-03 eta: 9:38:35 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7083 aux.loss_ce: 0.0070 aux.acc_seg: 99.2443 +04/18 17:18:58 - mmengine - INFO - Iter(train) [ 97200/160000] lr: 4.3667e-03 eta: 9:38:08 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7644 aux.loss_ce: 0.0073 aux.acc_seg: 99.2749 +04/18 17:19:25 - mmengine - INFO - Iter(train) [ 97250/160000] lr: 4.3637e-03 eta: 9:37:40 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7007 aux.loss_ce: 0.0067 aux.acc_seg: 99.1323 +04/18 17:19:53 - mmengine - INFO - Iter(train) [ 97300/160000] lr: 4.3606e-03 eta: 9:37:13 time: 0.5536 data_time: 0.0065 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7317 aux.loss_ce: 0.0074 aux.acc_seg: 99.1363 +04/18 17:20:21 - mmengine - INFO - Iter(train) [ 97350/160000] lr: 4.3575e-03 eta: 9:36:45 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.6843 aux.loss_ce: 0.0068 aux.acc_seg: 99.1193 +04/18 17:20:48 - mmengine - INFO - Iter(train) [ 97400/160000] lr: 4.3545e-03 eta: 9:36:17 time: 0.5542 data_time: 0.0070 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.6987 aux.loss_ce: 0.0078 aux.acc_seg: 99.2604 +04/18 17:21:16 - mmengine - INFO - Iter(train) [ 97450/160000] lr: 4.3514e-03 eta: 9:35:50 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6772 aux.loss_ce: 0.0074 aux.acc_seg: 99.1599 +04/18 17:21:44 - mmengine - INFO - Iter(train) [ 97500/160000] lr: 4.3484e-03 eta: 9:35:22 time: 0.5556 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7450 aux.loss_ce: 0.0070 aux.acc_seg: 99.1950 +04/18 17:22:12 - mmengine - INFO - Iter(train) [ 97550/160000] lr: 4.3453e-03 eta: 9:34:55 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.6667 aux.loss_ce: 0.0069 aux.acc_seg: 99.1509 +04/18 17:22:39 - mmengine - INFO - Iter(train) [ 97600/160000] lr: 4.3422e-03 eta: 9:34:27 time: 0.5554 data_time: 0.0073 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.6469 aux.loss_ce: 0.0076 aux.acc_seg: 99.0324 +04/18 17:23:07 - mmengine - INFO - Iter(train) [ 97650/160000] lr: 4.3392e-03 eta: 9:34:00 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7666 aux.loss_ce: 0.0075 aux.acc_seg: 99.3659 +04/18 17:23:35 - mmengine - INFO - Iter(train) [ 97700/160000] lr: 4.3361e-03 eta: 9:33:32 time: 0.5561 data_time: 0.0063 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6807 aux.loss_ce: 0.0075 aux.acc_seg: 99.2209 +04/18 17:24:03 - mmengine - INFO - Iter(train) [ 97750/160000] lr: 4.3331e-03 eta: 9:33:05 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7061 aux.loss_ce: 0.0076 aux.acc_seg: 99.2447 +04/18 17:24:30 - mmengine - INFO - Iter(train) [ 97800/160000] lr: 4.3300e-03 eta: 9:32:37 time: 0.5560 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.6998 aux.loss_ce: 0.0071 aux.acc_seg: 99.1749 +04/18 17:24:58 - mmengine - INFO - Iter(train) [ 97850/160000] lr: 4.3269e-03 eta: 9:32:10 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6622 aux.loss_ce: 0.0074 aux.acc_seg: 99.0404 +04/18 17:25:26 - mmengine - INFO - Iter(train) [ 97900/160000] lr: 4.3239e-03 eta: 9:31:42 time: 0.5540 data_time: 0.0070 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7380 aux.loss_ce: 0.0072 aux.acc_seg: 99.3701 +04/18 17:25:54 - mmengine - INFO - Iter(train) [ 97950/160000] lr: 4.3208e-03 eta: 9:31:14 time: 0.5563 data_time: 0.0064 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7115 aux.loss_ce: 0.0071 aux.acc_seg: 99.0584 +04/18 17:26:21 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:26:21 - mmengine - INFO - Iter(train) [ 98000/160000] lr: 4.3178e-03 eta: 9:30:47 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.7242 aux.loss_ce: 0.0078 aux.acc_seg: 99.2871 +04/18 17:26:49 - mmengine - INFO - Iter(train) [ 98050/160000] lr: 4.3147e-03 eta: 9:30:19 time: 0.5557 data_time: 0.0071 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7675 aux.loss_ce: 0.0071 aux.acc_seg: 99.3314 +04/18 17:27:17 - mmengine - INFO - Iter(train) [ 98100/160000] lr: 4.3116e-03 eta: 9:29:52 time: 0.5544 data_time: 0.0080 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7635 aux.loss_ce: 0.0071 aux.acc_seg: 99.3004 +04/18 17:27:45 - mmengine - INFO - Iter(train) [ 98150/160000] lr: 4.3086e-03 eta: 9:29:24 time: 0.5616 data_time: 0.0064 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7717 aux.loss_ce: 0.0071 aux.acc_seg: 99.2148 +04/18 17:28:13 - mmengine - INFO - Iter(train) [ 98200/160000] lr: 4.3055e-03 eta: 9:28:57 time: 0.5636 data_time: 0.0074 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7410 aux.loss_ce: 0.0070 aux.acc_seg: 99.2487 +04/18 17:28:40 - mmengine - INFO - Iter(train) [ 98250/160000] lr: 4.3025e-03 eta: 9:28:29 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6316 aux.loss_ce: 0.0073 aux.acc_seg: 99.0919 +04/18 17:29:08 - mmengine - INFO - Iter(train) [ 98300/160000] lr: 4.2994e-03 eta: 9:28:02 time: 0.5546 data_time: 0.0067 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6526 aux.loss_ce: 0.0075 aux.acc_seg: 99.1125 +04/18 17:29:36 - mmengine - INFO - Iter(train) [ 98350/160000] lr: 4.2963e-03 eta: 9:27:34 time: 0.5553 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.6505 aux.loss_ce: 0.0071 aux.acc_seg: 99.1550 +04/18 17:30:04 - mmengine - INFO - Iter(train) [ 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0.5544 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6967 aux.loss_ce: 0.0077 aux.acc_seg: 99.3668 +04/18 17:32:22 - mmengine - INFO - Iter(train) [ 98650/160000] lr: 4.2779e-03 eta: 9:24:49 time: 0.5551 data_time: 0.0068 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.5887 aux.loss_ce: 0.0076 aux.acc_seg: 99.0882 +04/18 17:32:50 - mmengine - INFO - Iter(train) [ 98700/160000] lr: 4.2749e-03 eta: 9:24:21 time: 0.5542 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.7547 aux.loss_ce: 0.0074 aux.acc_seg: 99.2813 +04/18 17:33:18 - mmengine - INFO - Iter(train) [ 98750/160000] lr: 4.2718e-03 eta: 9:23:54 time: 0.5552 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7483 aux.loss_ce: 0.0069 aux.acc_seg: 99.3092 +04/18 17:33:46 - mmengine - INFO - Iter(train) [ 98800/160000] lr: 4.2688e-03 eta: 9:23:26 time: 0.5534 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.7491 aux.loss_ce: 0.0078 aux.acc_seg: 99.3162 +04/18 17:34:13 - mmengine - INFO - Iter(train) [ 98850/160000] lr: 4.2657e-03 eta: 9:22:59 time: 0.5554 data_time: 0.0060 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.6961 aux.loss_ce: 0.0080 aux.acc_seg: 99.2959 +04/18 17:34:41 - mmengine - INFO - Iter(train) [ 98900/160000] lr: 4.2626e-03 eta: 9:22:31 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0117 decode.acc_seg: 99.6341 aux.loss_ce: 0.0098 aux.acc_seg: 99.1272 +04/18 17:35:09 - mmengine - INFO - Iter(train) [ 98950/160000] lr: 4.2596e-03 eta: 9:22:04 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.6702 aux.loss_ce: 0.0086 aux.acc_seg: 99.2450 +04/18 17:35:36 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:35:36 - mmengine - INFO - Iter(train) [ 99000/160000] lr: 4.2565e-03 eta: 9:21:36 time: 0.5561 data_time: 0.0065 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0100 decode.acc_seg: 99.7582 aux.loss_ce: 0.0089 aux.acc_seg: 99.3401 +04/18 17:36:04 - mmengine - INFO - Iter(train) [ 99050/160000] lr: 4.2534e-03 eta: 9:21:09 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.6223 aux.loss_ce: 0.0079 aux.acc_seg: 98.9206 +04/18 17:36:32 - mmengine - INFO - Iter(train) [ 99100/160000] lr: 4.2504e-03 eta: 9:20:41 time: 0.5555 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.5854 aux.loss_ce: 0.0076 aux.acc_seg: 99.0214 +04/18 17:37:00 - mmengine - INFO - Iter(train) [ 99150/160000] lr: 4.2473e-03 eta: 9:20:13 time: 0.5547 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6884 aux.loss_ce: 0.0075 aux.acc_seg: 99.1795 +04/18 17:37:28 - mmengine - INFO - Iter(train) [ 99200/160000] lr: 4.2442e-03 eta: 9:19:46 time: 0.5554 data_time: 0.0060 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.6729 aux.loss_ce: 0.0070 aux.acc_seg: 98.8970 +04/18 17:37:55 - mmengine - INFO - Iter(train) [ 99250/160000] lr: 4.2412e-03 eta: 9:19:18 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7354 aux.loss_ce: 0.0072 aux.acc_seg: 99.3119 +04/18 17:38:23 - mmengine - INFO - Iter(train) [ 99300/160000] lr: 4.2381e-03 eta: 9:18:51 time: 0.5542 data_time: 0.0064 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6769 aux.loss_ce: 0.0081 aux.acc_seg: 99.1219 +04/18 17:38:51 - mmengine - INFO - Iter(train) [ 99350/160000] lr: 4.2350e-03 eta: 9:18:23 time: 0.5549 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7375 aux.loss_ce: 0.0077 aux.acc_seg: 99.3154 +04/18 17:39:19 - mmengine - INFO - Iter(train) [ 99400/160000] lr: 4.2319e-03 eta: 9:17:56 time: 0.5561 data_time: 0.0076 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.5961 aux.loss_ce: 0.0082 aux.acc_seg: 99.1252 +04/18 17:39:46 - mmengine - INFO - Iter(train) [ 99450/160000] lr: 4.2289e-03 eta: 9:17:28 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6661 aux.loss_ce: 0.0077 aux.acc_seg: 99.2162 +04/18 17:40:14 - mmengine - INFO - Iter(train) [ 99500/160000] lr: 4.2258e-03 eta: 9:17:01 time: 0.5546 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.5756 aux.loss_ce: 0.0077 aux.acc_seg: 99.0845 +04/18 17:40:42 - mmengine - INFO - Iter(train) [ 99550/160000] lr: 4.2227e-03 eta: 9:16:33 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.7802 aux.loss_ce: 0.0073 aux.acc_seg: 99.4399 +04/18 17:41:10 - mmengine - INFO - Iter(train) [ 99600/160000] lr: 4.2197e-03 eta: 9:16:06 time: 0.5573 data_time: 0.0072 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0075 decode.acc_seg: 99.6706 aux.loss_ce: 0.0079 aux.acc_seg: 99.1669 +04/18 17:41:37 - mmengine - INFO - Iter(train) [ 99650/160000] lr: 4.2166e-03 eta: 9:15:38 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7578 aux.loss_ce: 0.0074 aux.acc_seg: 99.3618 +04/18 17:42:05 - mmengine - INFO - Iter(train) [ 99700/160000] lr: 4.2135e-03 eta: 9:15:11 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7287 aux.loss_ce: 0.0073 aux.acc_seg: 99.1139 +04/18 17:42:33 - mmengine - INFO - Iter(train) [ 99750/160000] lr: 4.2105e-03 eta: 9:14:43 time: 0.5559 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6978 aux.loss_ce: 0.0074 aux.acc_seg: 99.2369 +04/18 17:43:01 - mmengine - INFO - Iter(train) [ 99800/160000] lr: 4.2074e-03 eta: 9:14:15 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.7423 aux.loss_ce: 0.0078 aux.acc_seg: 99.2477 +04/18 17:43:28 - mmengine - INFO - Iter(train) [ 99850/160000] lr: 4.2043e-03 eta: 9:13:48 time: 0.5541 data_time: 0.0062 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7376 aux.loss_ce: 0.0070 aux.acc_seg: 99.2662 +04/18 17:43:56 - mmengine - INFO - Iter(train) [ 99900/160000] lr: 4.2013e-03 eta: 9:13:20 time: 0.5547 data_time: 0.0075 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6868 aux.loss_ce: 0.0078 aux.acc_seg: 99.4017 +04/18 17:44:24 - mmengine - INFO - Iter(train) [ 99950/160000] lr: 4.1982e-03 eta: 9:12:53 time: 0.5559 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7042 aux.loss_ce: 0.0074 aux.acc_seg: 99.1630 +04/18 17:44:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:44:52 - mmengine - INFO - Iter(train) [100000/160000] lr: 4.1951e-03 eta: 9:12:25 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7372 aux.loss_ce: 0.0077 aux.acc_seg: 99.2908 +04/18 17:44:52 - mmengine - INFO - Saving checkpoint at 100000 iterations +04/18 17:44:56 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0462 data_time: 0.0015 memory: 1657 +04/18 17:44:58 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0465 data_time: 0.0015 memory: 1657 +04/18 17:45:00 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0472 data_time: 0.0015 memory: 1657 +04/18 17:45:03 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0458 data_time: 0.0014 memory: 1657 +04/18 17:45:03 - mmengine - INFO - per class results: +04/18 17:45:03 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.07 | 99.44 | 99.53 | 99.62 | 99.44 | +| contrast | 80.19 | 90.93 | 89.01 | 87.16 | 90.93 | ++------------+-------+-------+--------+-----------+--------+ +04/18 17:45:03 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1000 mIoU: 89.6300 mAcc: 95.1900 mFscore: 94.2700 mPrecision: 93.3900 mRecall: 95.1900 data_time: 0.0015 time: 0.0466 +04/18 17:45:31 - mmengine - INFO - Iter(train) [100050/160000] lr: 4.1920e-03 eta: 9:11:58 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.7095 aux.loss_ce: 0.0080 aux.acc_seg: 99.1308 +04/18 17:45:58 - mmengine - INFO - Iter(train) [100100/160000] lr: 4.1890e-03 eta: 9:11:30 time: 0.5565 data_time: 0.0071 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.6163 aux.loss_ce: 0.0075 aux.acc_seg: 99.1165 +04/18 17:46:26 - mmengine - INFO - Iter(train) [100150/160000] lr: 4.1859e-03 eta: 9:11:03 time: 0.5543 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.6577 aux.loss_ce: 0.0077 aux.acc_seg: 99.1933 +04/18 17:46:54 - mmengine - INFO - Iter(train) [100200/160000] lr: 4.1828e-03 eta: 9:10:35 time: 0.5547 data_time: 0.0067 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7790 aux.loss_ce: 0.0073 aux.acc_seg: 99.3054 +04/18 17:47:22 - mmengine - INFO - Iter(train) [100250/160000] lr: 4.1798e-03 eta: 9:10:08 time: 0.5552 data_time: 0.0063 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7389 aux.loss_ce: 0.0079 aux.acc_seg: 99.2993 +04/18 17:47:49 - mmengine - INFO - Iter(train) [100300/160000] lr: 4.1767e-03 eta: 9:09:40 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7589 aux.loss_ce: 0.0070 aux.acc_seg: 99.3131 +04/18 17:48:17 - mmengine - INFO - Iter(train) [100350/160000] lr: 4.1736e-03 eta: 9:09:13 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.7858 aux.loss_ce: 0.0077 aux.acc_seg: 99.3250 +04/18 17:48:45 - mmengine - INFO - Iter(train) [100400/160000] lr: 4.1705e-03 eta: 9:08:45 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7776 aux.loss_ce: 0.0074 aux.acc_seg: 99.4458 +04/18 17:49:13 - mmengine - INFO - Iter(train) [100450/160000] lr: 4.1675e-03 eta: 9:08:17 time: 0.5536 data_time: 0.0068 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.6532 aux.loss_ce: 0.0079 aux.acc_seg: 99.0548 +04/18 17:49:41 - mmengine - INFO - Iter(train) [100500/160000] lr: 4.1644e-03 eta: 9:07:50 time: 0.5540 data_time: 0.0069 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.7375 aux.loss_ce: 0.0079 aux.acc_seg: 99.4618 +04/18 17:50:08 - mmengine - INFO - Iter(train) [100550/160000] lr: 4.1613e-03 eta: 9:07:22 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.7165 aux.loss_ce: 0.0078 aux.acc_seg: 99.1873 +04/18 17:50:36 - mmengine - INFO - Iter(train) [100600/160000] lr: 4.1582e-03 eta: 9:06:55 time: 0.5562 data_time: 0.0060 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.6803 aux.loss_ce: 0.0081 aux.acc_seg: 99.2851 +04/18 17:51:04 - mmengine - INFO - Iter(train) [100650/160000] lr: 4.1552e-03 eta: 9:06:27 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6935 aux.loss_ce: 0.0077 aux.acc_seg: 99.1413 +04/18 17:51:31 - mmengine - INFO - Iter(train) [100700/160000] lr: 4.1521e-03 eta: 9:06:00 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0091 decode.acc_seg: 99.5483 aux.loss_ce: 0.0086 aux.acc_seg: 98.9243 +04/18 17:51:59 - mmengine - INFO - Iter(train) [100750/160000] lr: 4.1490e-03 eta: 9:05:32 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0084 decode.acc_seg: 99.6213 aux.loss_ce: 0.0083 aux.acc_seg: 99.0323 +04/18 17:52:27 - mmengine - INFO - Iter(train) [100800/160000] lr: 4.1459e-03 eta: 9:05:05 time: 0.5547 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6117 aux.loss_ce: 0.0078 aux.acc_seg: 99.1339 +04/18 17:52:55 - mmengine - INFO - Iter(train) [100850/160000] lr: 4.1429e-03 eta: 9:04:37 time: 0.5561 data_time: 0.0072 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.6839 aux.loss_ce: 0.0078 aux.acc_seg: 99.2059 +04/18 17:53:23 - mmengine - INFO - Iter(train) [100900/160000] lr: 4.1398e-03 eta: 9:04:09 time: 0.5554 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6908 aux.loss_ce: 0.0073 aux.acc_seg: 99.3784 +04/18 17:53:50 - mmengine - INFO - Iter(train) [100950/160000] lr: 4.1367e-03 eta: 9:03:42 time: 0.5545 data_time: 0.0067 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.8081 aux.loss_ce: 0.0076 aux.acc_seg: 99.3566 +04/18 17:54:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:54:18 - mmengine - INFO - Iter(train) [101000/160000] lr: 4.1336e-03 eta: 9:03:14 time: 0.5547 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6223 aux.loss_ce: 0.0076 aux.acc_seg: 99.1516 +04/18 17:54:46 - mmengine - INFO - Iter(train) [101050/160000] lr: 4.1306e-03 eta: 9:02:47 time: 0.5568 data_time: 0.0066 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.6987 aux.loss_ce: 0.0083 aux.acc_seg: 99.3050 +04/18 17:55:14 - mmengine - INFO - Iter(train) [101100/160000] lr: 4.1275e-03 eta: 9:02:19 time: 0.5555 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7017 aux.loss_ce: 0.0076 aux.acc_seg: 99.1898 +04/18 17:55:41 - mmengine - INFO - Iter(train) [101150/160000] lr: 4.1244e-03 eta: 9:01:52 time: 0.5542 data_time: 0.0080 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.6719 aux.loss_ce: 0.0076 aux.acc_seg: 99.1834 +04/18 17:56:09 - mmengine - INFO - Iter(train) [101200/160000] lr: 4.1213e-03 eta: 9:01:24 time: 0.5546 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.6968 aux.loss_ce: 0.0071 aux.acc_seg: 99.3033 +04/18 17:56:37 - mmengine - INFO - Iter(train) [101250/160000] lr: 4.1182e-03 eta: 9:00:57 time: 0.5557 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0077 decode.acc_seg: 99.6788 aux.loss_ce: 0.0072 aux.acc_seg: 99.1690 +04/18 17:57:05 - mmengine - INFO - Iter(train) [101300/160000] lr: 4.1152e-03 eta: 9:00:29 time: 0.5537 data_time: 0.0073 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.7045 aux.loss_ce: 0.0077 aux.acc_seg: 99.1835 +04/18 17:57:32 - mmengine - INFO - Iter(train) [101350/160000] lr: 4.1121e-03 eta: 9:00:02 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7789 aux.loss_ce: 0.0076 aux.acc_seg: 99.2284 +04/18 17:58:00 - mmengine - INFO - Iter(train) [101400/160000] lr: 4.1090e-03 eta: 8:59:34 time: 0.5628 data_time: 0.0062 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.4895 aux.loss_ce: 0.0077 aux.acc_seg: 99.0331 +04/18 17:58:28 - mmengine - INFO - Iter(train) [101450/160000] lr: 4.1059e-03 eta: 8:59:06 time: 0.5548 data_time: 0.0067 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.6900 aux.loss_ce: 0.0082 aux.acc_seg: 99.1838 +04/18 17:58:56 - mmengine - INFO - Iter(train) [101500/160000] lr: 4.1029e-03 eta: 8:58:39 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7442 aux.loss_ce: 0.0072 aux.acc_seg: 99.3762 +04/18 17:59:24 - mmengine - INFO - Iter(train) [101550/160000] lr: 4.0998e-03 eta: 8:58:11 time: 0.5647 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7145 aux.loss_ce: 0.0075 aux.acc_seg: 99.0725 +04/18 17:59:51 - mmengine - INFO - Iter(train) [101600/160000] lr: 4.0967e-03 eta: 8:57:44 time: 0.5545 data_time: 0.0075 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.7160 aux.loss_ce: 0.0073 aux.acc_seg: 99.2603 +04/18 18:00:19 - mmengine - INFO - Iter(train) [101650/160000] lr: 4.0936e-03 eta: 8:57:16 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.7094 aux.loss_ce: 0.0080 aux.acc_seg: 99.0880 +04/18 18:00:47 - mmengine - INFO - Iter(train) [101700/160000] lr: 4.0905e-03 eta: 8:56:49 time: 0.5558 data_time: 0.0068 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7360 aux.loss_ce: 0.0076 aux.acc_seg: 99.4184 +04/18 18:01:15 - mmengine - INFO - Iter(train) [101750/160000] lr: 4.0875e-03 eta: 8:56:21 time: 0.5538 data_time: 0.0063 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6214 aux.loss_ce: 0.0076 aux.acc_seg: 99.1718 +04/18 18:01:42 - mmengine - INFO - Iter(train) [101800/160000] lr: 4.0844e-03 eta: 8:55:54 time: 0.5538 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6907 aux.loss_ce: 0.0074 aux.acc_seg: 99.0748 +04/18 18:02:10 - mmengine - INFO - Iter(train) [101850/160000] lr: 4.0813e-03 eta: 8:55:26 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.7403 aux.loss_ce: 0.0078 aux.acc_seg: 99.4285 +04/18 18:02:38 - mmengine - INFO - Iter(train) [101900/160000] lr: 4.0782e-03 eta: 8:54:58 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7054 aux.loss_ce: 0.0075 aux.acc_seg: 99.3196 +04/18 18:03:06 - mmengine - INFO - Iter(train) [101950/160000] lr: 4.0751e-03 eta: 8:54:31 time: 0.5563 data_time: 0.0059 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.7217 aux.loss_ce: 0.0074 aux.acc_seg: 99.2308 +04/18 18:03:33 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:03:33 - mmengine - INFO - Iter(train) [102000/160000] lr: 4.0721e-03 eta: 8:54:03 time: 0.5546 data_time: 0.0056 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6972 aux.loss_ce: 0.0078 aux.acc_seg: 99.2855 +04/18 18:04:01 - mmengine - INFO - Iter(train) [102050/160000] lr: 4.0690e-03 eta: 8:53:36 time: 0.5556 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7612 aux.loss_ce: 0.0075 aux.acc_seg: 99.3612 +04/18 18:04:29 - mmengine - INFO - Iter(train) [102100/160000] lr: 4.0659e-03 eta: 8:53:08 time: 0.5556 data_time: 0.0069 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7481 aux.loss_ce: 0.0072 aux.acc_seg: 99.3841 +04/18 18:04:57 - mmengine - INFO - Iter(train) [102150/160000] lr: 4.0628e-03 eta: 8:52:41 time: 0.5547 data_time: 0.0071 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7428 aux.loss_ce: 0.0074 aux.acc_seg: 99.4309 +04/18 18:05:25 - mmengine - INFO - Iter(train) [102200/160000] lr: 4.0597e-03 eta: 8:52:13 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.7394 aux.loss_ce: 0.0079 aux.acc_seg: 99.1361 +04/18 18:05:52 - mmengine - INFO - Iter(train) [102250/160000] lr: 4.0566e-03 eta: 8:51:46 time: 0.5551 data_time: 0.0073 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7008 aux.loss_ce: 0.0079 aux.acc_seg: 99.1044 +04/18 18:06:20 - mmengine - INFO - Iter(train) [102300/160000] lr: 4.0536e-03 eta: 8:51:18 time: 0.5558 data_time: 0.0067 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7474 aux.loss_ce: 0.0070 aux.acc_seg: 99.4012 +04/18 18:06:48 - mmengine - INFO - Iter(train) [102350/160000] lr: 4.0505e-03 eta: 8:50:51 time: 0.5554 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0071 decode.acc_seg: 99.6811 aux.loss_ce: 0.0070 aux.acc_seg: 99.3768 +04/18 18:07:16 - mmengine - INFO - Iter(train) [102400/160000] lr: 4.0474e-03 eta: 8:50:23 time: 0.5557 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0075 decode.acc_seg: 99.5892 aux.loss_ce: 0.0078 aux.acc_seg: 99.0476 +04/18 18:07:43 - mmengine - INFO - Iter(train) [102450/160000] lr: 4.0443e-03 eta: 8:49:56 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7593 aux.loss_ce: 0.0068 aux.acc_seg: 99.3846 +04/18 18:08:11 - mmengine - INFO - Iter(train) [102500/160000] lr: 4.0412e-03 eta: 8:49:28 time: 0.5534 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7579 aux.loss_ce: 0.0069 aux.acc_seg: 99.1947 +04/18 18:08:39 - mmengine - INFO - Iter(train) [102550/160000] lr: 4.0381e-03 eta: 8:49:01 time: 0.5557 data_time: 0.0073 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.7660 aux.loss_ce: 0.0077 aux.acc_seg: 99.3835 +04/18 18:09:07 - mmengine - INFO - Iter(train) [102600/160000] lr: 4.0351e-03 eta: 8:48:33 time: 0.5553 data_time: 0.0072 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7365 aux.loss_ce: 0.0069 aux.acc_seg: 99.1929 +04/18 18:09:35 - mmengine - INFO - Iter(train) [102650/160000] lr: 4.0320e-03 eta: 8:48:05 time: 0.5547 data_time: 0.0077 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.7366 aux.loss_ce: 0.0069 aux.acc_seg: 99.3207 +04/18 18:10:02 - mmengine - INFO - Iter(train) [102700/160000] lr: 4.0289e-03 eta: 8:47:38 time: 0.5551 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7978 aux.loss_ce: 0.0071 aux.acc_seg: 99.5609 +04/18 18:10:30 - mmengine - INFO - Iter(train) [102750/160000] lr: 4.0258e-03 eta: 8:47:10 time: 0.5563 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6626 aux.loss_ce: 0.0074 aux.acc_seg: 99.0096 +04/18 18:10:58 - mmengine - INFO - Iter(train) [102800/160000] lr: 4.0227e-03 eta: 8:46:43 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7425 aux.loss_ce: 0.0072 aux.acc_seg: 99.2924 +04/18 18:11:26 - mmengine - INFO - Iter(train) [102850/160000] lr: 4.0196e-03 eta: 8:46:15 time: 0.5558 data_time: 0.0063 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6971 aux.loss_ce: 0.0072 aux.acc_seg: 99.1423 +04/18 18:11:54 - mmengine - INFO - Iter(train) [102900/160000] lr: 4.0165e-03 eta: 8:45:48 time: 0.5554 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7680 aux.loss_ce: 0.0075 aux.acc_seg: 99.3282 +04/18 18:12:21 - mmengine - INFO - Iter(train) [102950/160000] lr: 4.0134e-03 eta: 8:45:20 time: 0.5552 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7619 aux.loss_ce: 0.0073 aux.acc_seg: 99.3407 +04/18 18:12:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:12:49 - mmengine - INFO - Iter(train) [103000/160000] lr: 4.0104e-03 eta: 8:44:53 time: 0.5548 data_time: 0.0072 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.7692 aux.loss_ce: 0.0080 aux.acc_seg: 99.3644 +04/18 18:13:17 - mmengine - INFO - Iter(train) [103050/160000] lr: 4.0073e-03 eta: 8:44:25 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.7481 aux.loss_ce: 0.0071 aux.acc_seg: 99.2605 +04/18 18:13:44 - mmengine - INFO - Iter(train) [103100/160000] lr: 4.0042e-03 eta: 8:43:57 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7490 aux.loss_ce: 0.0071 aux.acc_seg: 99.2566 +04/18 18:14:12 - mmengine - INFO - Iter(train) [103150/160000] lr: 4.0011e-03 eta: 8:43:30 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.7028 aux.loss_ce: 0.0080 aux.acc_seg: 99.1325 +04/18 18:14:40 - mmengine - INFO - Iter(train) [103200/160000] lr: 3.9980e-03 eta: 8:43:02 time: 0.5540 data_time: 0.0063 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.7586 aux.loss_ce: 0.0076 aux.acc_seg: 99.3856 +04/18 18:15:08 - mmengine - INFO - Iter(train) [103250/160000] lr: 3.9949e-03 eta: 8:42:35 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0065 decode.acc_seg: 99.8170 aux.loss_ce: 0.0068 aux.acc_seg: 99.4820 +04/18 18:15:35 - mmengine - INFO - Iter(train) [103300/160000] lr: 3.9918e-03 eta: 8:42:07 time: 0.5553 data_time: 0.0075 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.6673 aux.loss_ce: 0.0083 aux.acc_seg: 99.0726 +04/18 18:16:03 - mmengine - INFO - Iter(train) [103350/160000] lr: 3.9887e-03 eta: 8:41:40 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0070 decode.acc_seg: 99.7534 aux.loss_ce: 0.0077 aux.acc_seg: 99.4044 +04/18 18:16:31 - mmengine - INFO - Iter(train) [103400/160000] lr: 3.9857e-03 eta: 8:41:12 time: 0.5556 data_time: 0.0077 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0074 decode.acc_seg: 99.6698 aux.loss_ce: 0.0081 aux.acc_seg: 98.9725 +04/18 18:16:59 - mmengine - INFO - Iter(train) [103450/160000] lr: 3.9826e-03 eta: 8:40:44 time: 0.5536 data_time: 0.0062 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7816 aux.loss_ce: 0.0073 aux.acc_seg: 99.2882 +04/18 18:17:26 - mmengine - INFO - Iter(train) [103500/160000] lr: 3.9795e-03 eta: 8:40:17 time: 0.5540 data_time: 0.0071 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.4746 aux.loss_ce: 0.0076 aux.acc_seg: 99.2682 +04/18 18:17:54 - mmengine - INFO - Iter(train) [103550/160000] lr: 3.9764e-03 eta: 8:39:49 time: 0.5635 data_time: 0.0063 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6692 aux.loss_ce: 0.0082 aux.acc_seg: 99.2133 +04/18 18:18:22 - mmengine - INFO - Iter(train) [103600/160000] lr: 3.9733e-03 eta: 8:39:22 time: 0.5538 data_time: 0.0069 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.7363 aux.loss_ce: 0.0079 aux.acc_seg: 99.2662 +04/18 18:18:50 - mmengine - INFO - Iter(train) [103650/160000] lr: 3.9702e-03 eta: 8:38:54 time: 0.5544 data_time: 0.0062 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.6899 aux.loss_ce: 0.0070 aux.acc_seg: 99.1610 +04/18 18:19:18 - mmengine - INFO - Iter(train) [103700/160000] lr: 3.9671e-03 eta: 8:38:27 time: 0.5572 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7503 aux.loss_ce: 0.0074 aux.acc_seg: 99.4403 +04/18 18:19:46 - mmengine - INFO - Iter(train) [103750/160000] lr: 3.9640e-03 eta: 8:37:59 time: 0.5565 data_time: 0.0071 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.6954 aux.loss_ce: 0.0079 aux.acc_seg: 99.1589 +04/18 18:20:13 - mmengine - INFO - Iter(train) [103800/160000] lr: 3.9609e-03 eta: 8:37:32 time: 0.5537 data_time: 0.0066 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6759 aux.loss_ce: 0.0080 aux.acc_seg: 99.1508 +04/18 18:20:41 - mmengine - INFO - Iter(train) [103850/160000] lr: 3.9578e-03 eta: 8:37:04 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0092 decode.acc_seg: 99.7488 aux.loss_ce: 0.0078 aux.acc_seg: 99.3685 +04/18 18:21:09 - mmengine - INFO - Iter(train) [103900/160000] lr: 3.9548e-03 eta: 8:36:37 time: 0.5563 data_time: 0.0065 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.8124 aux.loss_ce: 0.0073 aux.acc_seg: 99.4577 +04/18 18:21:37 - mmengine - INFO - Iter(train) [103950/160000] lr: 3.9517e-03 eta: 8:36:09 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.6668 aux.loss_ce: 0.0074 aux.acc_seg: 98.9861 +04/18 18:22:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:22:04 - mmengine - INFO - Iter(train) [104000/160000] lr: 3.9486e-03 eta: 8:35:41 time: 0.5547 data_time: 0.0072 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7065 aux.loss_ce: 0.0077 aux.acc_seg: 99.3050 +04/18 18:22:32 - mmengine - INFO - Iter(train) [104050/160000] lr: 3.9455e-03 eta: 8:35:14 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6196 aux.loss_ce: 0.0073 aux.acc_seg: 99.0684 +04/18 18:23:00 - mmengine - INFO - Iter(train) [104100/160000] lr: 3.9424e-03 eta: 8:34:46 time: 0.5548 data_time: 0.0074 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.6983 aux.loss_ce: 0.0072 aux.acc_seg: 99.3368 +04/18 18:23:28 - mmengine - INFO - Iter(train) [104150/160000] lr: 3.9393e-03 eta: 8:34:19 time: 0.5542 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.8059 aux.loss_ce: 0.0074 aux.acc_seg: 99.3739 +04/18 18:23:55 - mmengine - INFO - Iter(train) [104200/160000] lr: 3.9362e-03 eta: 8:33:51 time: 0.5543 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7657 aux.loss_ce: 0.0069 aux.acc_seg: 99.5224 +04/18 18:24:23 - mmengine - INFO - Iter(train) [104250/160000] lr: 3.9331e-03 eta: 8:33:24 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.8130 aux.loss_ce: 0.0071 aux.acc_seg: 99.3957 +04/18 18:24:51 - mmengine - INFO - Iter(train) [104300/160000] lr: 3.9300e-03 eta: 8:32:56 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7690 aux.loss_ce: 0.0074 aux.acc_seg: 99.3905 +04/18 18:25:19 - mmengine - INFO - Iter(train) [104350/160000] lr: 3.9269e-03 eta: 8:32:28 time: 0.5543 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7915 aux.loss_ce: 0.0072 aux.acc_seg: 99.4453 +04/18 18:25:46 - mmengine - INFO - Iter(train) [104400/160000] lr: 3.9238e-03 eta: 8:32:01 time: 0.5546 data_time: 0.0070 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.6736 aux.loss_ce: 0.0076 aux.acc_seg: 99.2351 +04/18 18:26:14 - mmengine - INFO - Iter(train) [104450/160000] lr: 3.9207e-03 eta: 8:31:33 time: 0.5548 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7692 aux.loss_ce: 0.0071 aux.acc_seg: 99.3866 +04/18 18:26:42 - mmengine - INFO - Iter(train) [104500/160000] lr: 3.9176e-03 eta: 8:31:06 time: 0.5535 data_time: 0.0067 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.6496 aux.loss_ce: 0.0079 aux.acc_seg: 99.0063 +04/18 18:27:10 - mmengine - INFO - Iter(train) [104550/160000] lr: 3.9145e-03 eta: 8:30:38 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7830 aux.loss_ce: 0.0068 aux.acc_seg: 99.2777 +04/18 18:27:37 - mmengine - INFO - Iter(train) [104600/160000] lr: 3.9114e-03 eta: 8:30:11 time: 0.5547 data_time: 0.0067 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.5623 aux.loss_ce: 0.0078 aux.acc_seg: 98.9859 +04/18 18:28:05 - mmengine - INFO - Iter(train) [104650/160000] lr: 3.9083e-03 eta: 8:29:43 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6039 aux.loss_ce: 0.0076 aux.acc_seg: 99.2819 +04/18 18:28:33 - mmengine - INFO - Iter(train) [104700/160000] lr: 3.9052e-03 eta: 8:29:16 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7609 aux.loss_ce: 0.0075 aux.acc_seg: 99.2799 +04/18 18:29:01 - mmengine - INFO - Iter(train) [104750/160000] lr: 3.9021e-03 eta: 8:28:48 time: 0.5547 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.7188 aux.loss_ce: 0.0079 aux.acc_seg: 99.0936 +04/18 18:29:29 - mmengine - INFO - Iter(train) [104800/160000] lr: 3.8990e-03 eta: 8:28:21 time: 0.5535 data_time: 0.0069 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7534 aux.loss_ce: 0.0069 aux.acc_seg: 99.4607 +04/18 18:29:56 - mmengine - INFO - Iter(train) [104850/160000] lr: 3.8960e-03 eta: 8:27:53 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.7664 aux.loss_ce: 0.0078 aux.acc_seg: 99.3903 +04/18 18:30:24 - mmengine - INFO - Iter(train) [104900/160000] lr: 3.8929e-03 eta: 8:27:25 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.6962 aux.loss_ce: 0.0082 aux.acc_seg: 99.1224 +04/18 18:30:52 - mmengine - INFO - Iter(train) [104950/160000] lr: 3.8898e-03 eta: 8:26:58 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6739 aux.loss_ce: 0.0075 aux.acc_seg: 99.1560 +04/18 18:31:20 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:31:20 - mmengine - INFO - Iter(train) [105000/160000] lr: 3.8867e-03 eta: 8:26:30 time: 0.5538 data_time: 0.0060 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.6804 aux.loss_ce: 0.0081 aux.acc_seg: 99.2293 +04/18 18:31:47 - mmengine - INFO - Iter(train) [105050/160000] lr: 3.8836e-03 eta: 8:26:03 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7019 aux.loss_ce: 0.0076 aux.acc_seg: 99.3044 +04/18 18:32:15 - mmengine - INFO - Iter(train) [105100/160000] lr: 3.8805e-03 eta: 8:25:35 time: 0.5554 data_time: 0.0066 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7594 aux.loss_ce: 0.0068 aux.acc_seg: 99.5090 +04/18 18:32:43 - mmengine - INFO - Iter(train) [105150/160000] lr: 3.8774e-03 eta: 8:25:08 time: 0.5548 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7445 aux.loss_ce: 0.0070 aux.acc_seg: 99.3206 +04/18 18:33:11 - mmengine - INFO - Iter(train) [105200/160000] lr: 3.8743e-03 eta: 8:24:40 time: 0.5550 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7156 aux.loss_ce: 0.0074 aux.acc_seg: 99.3709 +04/18 18:33:38 - mmengine - INFO - Iter(train) [105250/160000] lr: 3.8712e-03 eta: 8:24:12 time: 0.5539 data_time: 0.0062 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0066 decode.acc_seg: 99.7749 aux.loss_ce: 0.0067 aux.acc_seg: 99.3821 +04/18 18:34:06 - mmengine - INFO - Iter(train) [105300/160000] lr: 3.8681e-03 eta: 8:23:45 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7286 aux.loss_ce: 0.0076 aux.acc_seg: 99.3585 +04/18 18:34:34 - mmengine - INFO - Iter(train) [105350/160000] lr: 3.8650e-03 eta: 8:23:17 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7470 aux.loss_ce: 0.0073 aux.acc_seg: 99.2317 +04/18 18:35:02 - mmengine - INFO - Iter(train) [105400/160000] lr: 3.8619e-03 eta: 8:22:50 time: 0.5548 data_time: 0.0070 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0091 decode.acc_seg: 99.6312 aux.loss_ce: 0.0079 aux.acc_seg: 99.1449 +04/18 18:35:29 - mmengine - INFO - Iter(train) [105450/160000] lr: 3.8588e-03 eta: 8:22:22 time: 0.5547 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.5889 aux.loss_ce: 0.0084 aux.acc_seg: 99.1507 +04/18 18:35:57 - mmengine - INFO - Iter(train) [105500/160000] lr: 3.8557e-03 eta: 8:21:55 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.5057 aux.loss_ce: 0.0075 aux.acc_seg: 99.1350 +04/18 18:36:25 - mmengine - INFO - Iter(train) [105550/160000] lr: 3.8526e-03 eta: 8:21:27 time: 0.5560 data_time: 0.0069 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.6590 aux.loss_ce: 0.0080 aux.acc_seg: 98.8886 +04/18 18:36:53 - mmengine - INFO - Iter(train) [105600/160000] lr: 3.8495e-03 eta: 8:20:59 time: 0.5535 data_time: 0.0071 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.7232 aux.loss_ce: 0.0081 aux.acc_seg: 99.2006 +04/18 18:37:21 - mmengine - INFO - Iter(train) [105650/160000] lr: 3.8464e-03 eta: 8:20:32 time: 0.5552 data_time: 0.0070 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.6098 aux.loss_ce: 0.0080 aux.acc_seg: 98.9934 +04/18 18:37:48 - mmengine - INFO - Iter(train) [105700/160000] lr: 3.8433e-03 eta: 8:20:04 time: 0.5629 data_time: 0.0066 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6754 aux.loss_ce: 0.0084 aux.acc_seg: 99.1598 +04/18 18:38:16 - mmengine - INFO - Iter(train) [105750/160000] lr: 3.8402e-03 eta: 8:19:37 time: 0.5532 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0075 decode.acc_seg: 99.7292 aux.loss_ce: 0.0080 aux.acc_seg: 99.3091 +04/18 18:38:44 - mmengine - INFO - Iter(train) [105800/160000] lr: 3.8371e-03 eta: 8:19:09 time: 0.5552 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7055 aux.loss_ce: 0.0074 aux.acc_seg: 99.2315 +04/18 18:39:12 - mmengine - INFO - Iter(train) [105850/160000] lr: 3.8339e-03 eta: 8:18:42 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6668 aux.loss_ce: 0.0073 aux.acc_seg: 99.1922 +04/18 18:39:40 - mmengine - INFO - Iter(train) [105900/160000] lr: 3.8308e-03 eta: 8:18:14 time: 0.5559 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.8177 aux.loss_ce: 0.0070 aux.acc_seg: 99.5051 +04/18 18:40:07 - mmengine - INFO - Iter(train) [105950/160000] lr: 3.8277e-03 eta: 8:17:47 time: 0.5563 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.6799 aux.loss_ce: 0.0079 aux.acc_seg: 99.0003 +04/18 18:40:35 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:40:35 - mmengine - INFO - Iter(train) [106000/160000] lr: 3.8246e-03 eta: 8:17:19 time: 0.5556 data_time: 0.0063 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7479 aux.loss_ce: 0.0071 aux.acc_seg: 99.2925 +04/18 18:41:03 - mmengine - INFO - Iter(train) [106050/160000] lr: 3.8215e-03 eta: 8:16:52 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7302 aux.loss_ce: 0.0067 aux.acc_seg: 99.1390 +04/18 18:41:31 - mmengine - INFO - Iter(train) [106100/160000] lr: 3.8184e-03 eta: 8:16:24 time: 0.5549 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7599 aux.loss_ce: 0.0074 aux.acc_seg: 99.2908 +04/18 18:41:58 - mmengine - INFO - Iter(train) [106150/160000] lr: 3.8153e-03 eta: 8:15:56 time: 0.5550 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6630 aux.loss_ce: 0.0078 aux.acc_seg: 99.1592 +04/18 18:42:26 - mmengine - INFO - Iter(train) [106200/160000] lr: 3.8122e-03 eta: 8:15:29 time: 0.5537 data_time: 0.0066 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.5579 aux.loss_ce: 0.0074 aux.acc_seg: 99.1849 +04/18 18:42:54 - mmengine - INFO - Iter(train) [106250/160000] lr: 3.8091e-03 eta: 8:15:01 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6932 aux.loss_ce: 0.0078 aux.acc_seg: 99.1446 +04/18 18:43:22 - mmengine - INFO - Iter(train) [106300/160000] lr: 3.8060e-03 eta: 8:14:34 time: 0.5545 data_time: 0.0070 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6707 aux.loss_ce: 0.0074 aux.acc_seg: 99.2505 +04/18 18:43:49 - mmengine - INFO - Iter(train) [106350/160000] lr: 3.8029e-03 eta: 8:14:06 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0080 decode.acc_seg: 99.6784 aux.loss_ce: 0.0084 aux.acc_seg: 99.0918 +04/18 18:44:17 - mmengine - INFO - Iter(train) [106400/160000] lr: 3.7998e-03 eta: 8:13:38 time: 0.5547 data_time: 0.0072 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.5859 aux.loss_ce: 0.0075 aux.acc_seg: 98.9934 +04/18 18:44:45 - mmengine - INFO - Iter(train) [106450/160000] lr: 3.7967e-03 eta: 8:13:11 time: 0.5557 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.8007 aux.loss_ce: 0.0072 aux.acc_seg: 99.3562 +04/18 18:45:13 - mmengine - INFO - Iter(train) [106500/160000] lr: 3.7936e-03 eta: 8:12:43 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6293 aux.loss_ce: 0.0076 aux.acc_seg: 99.0341 +04/18 18:45:40 - mmengine - INFO - Iter(train) [106550/160000] lr: 3.7905e-03 eta: 8:12:16 time: 0.5560 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0078 decode.acc_seg: 99.7142 aux.loss_ce: 0.0072 aux.acc_seg: 99.3312 +04/18 18:46:08 - mmengine - INFO - Iter(train) [106600/160000] lr: 3.7874e-03 eta: 8:11:48 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6775 aux.loss_ce: 0.0080 aux.acc_seg: 99.2909 +04/18 18:46:36 - mmengine - INFO - Iter(train) [106650/160000] lr: 3.7843e-03 eta: 8:11:21 time: 0.5556 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.7286 aux.loss_ce: 0.0074 aux.acc_seg: 99.2894 +04/18 18:47:04 - mmengine - INFO - Iter(train) [106700/160000] lr: 3.7812e-03 eta: 8:10:53 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.7343 aux.loss_ce: 0.0080 aux.acc_seg: 99.1952 +04/18 18:47:32 - mmengine - INFO - Iter(train) [106750/160000] lr: 3.7780e-03 eta: 8:10:25 time: 0.5556 data_time: 0.0064 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.7152 aux.loss_ce: 0.0080 aux.acc_seg: 99.2158 +04/18 18:48:00 - mmengine - INFO - Iter(train) [106800/160000] lr: 3.7749e-03 eta: 8:09:58 time: 0.5551 data_time: 0.0060 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.6323 aux.loss_ce: 0.0079 aux.acc_seg: 99.2817 +04/18 18:48:27 - mmengine - INFO - Iter(train) [106850/160000] lr: 3.7718e-03 eta: 8:09:31 time: 0.5556 data_time: 0.0071 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.6947 aux.loss_ce: 0.0079 aux.acc_seg: 99.2083 +04/18 18:48:55 - mmengine - INFO - Iter(train) [106900/160000] lr: 3.7687e-03 eta: 8:09:03 time: 0.5559 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.6970 aux.loss_ce: 0.0075 aux.acc_seg: 99.2016 +04/18 18:49:23 - mmengine - INFO - Iter(train) [106950/160000] lr: 3.7656e-03 eta: 8:08:35 time: 0.5625 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7646 aux.loss_ce: 0.0072 aux.acc_seg: 99.2825 +04/18 18:49:51 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:49:51 - mmengine - INFO - Iter(train) [107000/160000] lr: 3.7625e-03 eta: 8:08:08 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.7506 aux.loss_ce: 0.0077 aux.acc_seg: 99.2103 +04/18 18:50:18 - mmengine - INFO - Iter(train) [107050/160000] lr: 3.7594e-03 eta: 8:07:40 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.7283 aux.loss_ce: 0.0076 aux.acc_seg: 99.3507 +04/18 18:50:46 - mmengine - INFO - Iter(train) [107100/160000] lr: 3.7563e-03 eta: 8:07:13 time: 0.5571 data_time: 0.0063 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6904 aux.loss_ce: 0.0075 aux.acc_seg: 99.0345 +04/18 18:51:14 - mmengine - INFO - Iter(train) [107150/160000] lr: 3.7532e-03 eta: 8:06:45 time: 0.5549 data_time: 0.0069 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.7509 aux.loss_ce: 0.0077 aux.acc_seg: 99.4362 +04/18 18:51:42 - mmengine - INFO - Iter(train) [107200/160000] lr: 3.7501e-03 eta: 8:06:18 time: 0.5553 data_time: 0.0075 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.5761 aux.loss_ce: 0.0073 aux.acc_seg: 98.9529 +04/18 18:52:10 - mmengine - INFO - Iter(train) [107250/160000] lr: 3.7470e-03 eta: 8:05:50 time: 0.5565 data_time: 0.0061 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.6921 aux.loss_ce: 0.0075 aux.acc_seg: 99.1709 +04/18 18:52:37 - mmengine - INFO - Iter(train) [107300/160000] lr: 3.7438e-03 eta: 8:05:22 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7155 aux.loss_ce: 0.0074 aux.acc_seg: 99.2370 +04/18 18:53:05 - mmengine - INFO - Iter(train) [107350/160000] lr: 3.7407e-03 eta: 8:04:55 time: 0.5540 data_time: 0.0076 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.6866 aux.loss_ce: 0.0079 aux.acc_seg: 99.1728 +04/18 18:53:33 - mmengine - INFO - Iter(train) [107400/160000] lr: 3.7376e-03 eta: 8:04:27 time: 0.5555 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7092 aux.loss_ce: 0.0075 aux.acc_seg: 99.1659 +04/18 18:54:01 - mmengine - INFO - Iter(train) [107450/160000] lr: 3.7345e-03 eta: 8:04:00 time: 0.5558 data_time: 0.0068 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6946 aux.loss_ce: 0.0071 aux.acc_seg: 99.2869 +04/18 18:54:28 - mmengine - INFO - Iter(train) [107500/160000] lr: 3.7314e-03 eta: 8:03:32 time: 0.5542 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7839 aux.loss_ce: 0.0071 aux.acc_seg: 99.3629 +04/18 18:54:56 - mmengine - INFO - Iter(train) [107550/160000] lr: 3.7283e-03 eta: 8:03:05 time: 0.5542 data_time: 0.0071 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7341 aux.loss_ce: 0.0069 aux.acc_seg: 99.3241 +04/18 18:55:24 - mmengine - INFO - Iter(train) [107600/160000] lr: 3.7252e-03 eta: 8:02:37 time: 0.5525 data_time: 0.0068 memory: 7635 loss: 0.0124 decode.loss_ce: 0.0058 decode.acc_seg: 99.7838 aux.loss_ce: 0.0065 aux.acc_seg: 99.1969 +04/18 18:55:51 - mmengine - INFO - Iter(train) [107650/160000] lr: 3.7221e-03 eta: 8:02:09 time: 0.5545 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.6625 aux.loss_ce: 0.0074 aux.acc_seg: 99.0898 +04/18 18:56:19 - mmengine - INFO - Iter(train) [107700/160000] lr: 3.7189e-03 eta: 8:01:42 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7855 aux.loss_ce: 0.0071 aux.acc_seg: 99.3336 +04/18 18:56:47 - mmengine - INFO - Iter(train) [107750/160000] lr: 3.7158e-03 eta: 8:01:14 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6257 aux.loss_ce: 0.0072 aux.acc_seg: 99.2513 +04/18 18:57:15 - mmengine - INFO - Iter(train) [107800/160000] lr: 3.7127e-03 eta: 8:00:47 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7494 aux.loss_ce: 0.0077 aux.acc_seg: 99.2947 +04/18 18:57:43 - mmengine - INFO - Iter(train) [107850/160000] lr: 3.7096e-03 eta: 8:00:19 time: 0.5542 data_time: 0.0062 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7247 aux.loss_ce: 0.0067 aux.acc_seg: 99.3230 +04/18 18:58:11 - mmengine - INFO - Iter(train) [107900/160000] lr: 3.7065e-03 eta: 7:59:52 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.6934 aux.loss_ce: 0.0075 aux.acc_seg: 99.1262 +04/18 18:58:38 - mmengine - INFO - Iter(train) [107950/160000] lr: 3.7034e-03 eta: 7:59:24 time: 0.5556 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7154 aux.loss_ce: 0.0072 aux.acc_seg: 99.3361 +04/18 18:59:06 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:59:06 - mmengine - INFO - Iter(train) [108000/160000] lr: 3.7003e-03 eta: 7:58:56 time: 0.5529 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0072 decode.acc_seg: 99.5666 aux.loss_ce: 0.0079 aux.acc_seg: 98.5047 +04/18 18:59:34 - mmengine - INFO - Iter(train) [108050/160000] lr: 3.6971e-03 eta: 7:58:29 time: 0.5557 data_time: 0.0064 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7347 aux.loss_ce: 0.0075 aux.acc_seg: 99.1542 +04/18 19:00:02 - mmengine - INFO - Iter(train) [108100/160000] lr: 3.6940e-03 eta: 7:58:01 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7680 aux.loss_ce: 0.0069 aux.acc_seg: 99.2745 +04/18 19:00:29 - mmengine - INFO - Iter(train) [108150/160000] lr: 3.6909e-03 eta: 7:57:34 time: 0.5542 data_time: 0.0065 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.6890 aux.loss_ce: 0.0070 aux.acc_seg: 99.1372 +04/18 19:00:57 - mmengine - INFO - Iter(train) [108200/160000] lr: 3.6878e-03 eta: 7:57:06 time: 0.5549 data_time: 0.0074 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7164 aux.loss_ce: 0.0070 aux.acc_seg: 99.0399 +04/18 19:01:25 - mmengine - INFO - Iter(train) [108250/160000] lr: 3.6847e-03 eta: 7:56:39 time: 0.5550 data_time: 0.0064 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7497 aux.loss_ce: 0.0071 aux.acc_seg: 99.2955 +04/18 19:01:53 - mmengine - INFO - Iter(train) [108300/160000] lr: 3.6816e-03 eta: 7:56:11 time: 0.5561 data_time: 0.0063 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0066 decode.acc_seg: 99.7405 aux.loss_ce: 0.0068 aux.acc_seg: 99.2576 +04/18 19:02:20 - mmengine - INFO - Iter(train) [108350/160000] lr: 3.6784e-03 eta: 7:55:43 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7471 aux.loss_ce: 0.0071 aux.acc_seg: 99.3069 +04/18 19:02:48 - mmengine - INFO - Iter(train) [108400/160000] lr: 3.6753e-03 eta: 7:55:16 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7011 aux.loss_ce: 0.0071 aux.acc_seg: 99.3650 +04/18 19:03:16 - mmengine - INFO - Iter(train) [108450/160000] lr: 3.6722e-03 eta: 7:54:48 time: 0.5553 data_time: 0.0065 memory: 7635 loss: 0.0125 decode.loss_ce: 0.0061 decode.acc_seg: 99.7404 aux.loss_ce: 0.0064 aux.acc_seg: 99.4333 +04/18 19:03:44 - mmengine - INFO - Iter(train) [108500/160000] lr: 3.6691e-03 eta: 7:54:21 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7160 aux.loss_ce: 0.0071 aux.acc_seg: 99.0922 +04/18 19:04:12 - mmengine - INFO - Iter(train) [108550/160000] lr: 3.6660e-03 eta: 7:53:53 time: 0.5558 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.6211 aux.loss_ce: 0.0075 aux.acc_seg: 99.1413 +04/18 19:04:39 - mmengine - INFO - Iter(train) [108600/160000] lr: 3.6628e-03 eta: 7:53:26 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.6576 aux.loss_ce: 0.0068 aux.acc_seg: 99.2223 +04/18 19:05:07 - mmengine - INFO - Iter(train) [108650/160000] lr: 3.6597e-03 eta: 7:52:58 time: 0.5551 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.7253 aux.loss_ce: 0.0070 aux.acc_seg: 99.1011 +04/18 19:05:35 - mmengine - INFO - Iter(train) [108700/160000] lr: 3.6566e-03 eta: 7:52:30 time: 0.5552 data_time: 0.0061 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0069 decode.acc_seg: 99.6749 aux.loss_ce: 0.0068 aux.acc_seg: 99.3142 +04/18 19:06:03 - mmengine - INFO - Iter(train) [108750/160000] lr: 3.6535e-03 eta: 7:52:03 time: 0.5544 data_time: 0.0077 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.6973 aux.loss_ce: 0.0067 aux.acc_seg: 99.2664 +04/18 19:06:30 - mmengine - INFO - Iter(train) [108800/160000] lr: 3.6504e-03 eta: 7:51:35 time: 0.5554 data_time: 0.0075 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.8243 aux.loss_ce: 0.0076 aux.acc_seg: 99.4201 +04/18 19:06:58 - mmengine - INFO - Iter(train) [108850/160000] lr: 3.6472e-03 eta: 7:51:08 time: 0.5546 data_time: 0.0074 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6761 aux.loss_ce: 0.0073 aux.acc_seg: 99.2339 +04/18 19:07:26 - mmengine - INFO - Iter(train) [108900/160000] lr: 3.6441e-03 eta: 7:50:40 time: 0.5529 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.6829 aux.loss_ce: 0.0073 aux.acc_seg: 99.0892 +04/18 19:07:54 - mmengine - INFO - Iter(train) [108950/160000] lr: 3.6410e-03 eta: 7:50:13 time: 0.5547 data_time: 0.0073 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7084 aux.loss_ce: 0.0075 aux.acc_seg: 99.1124 +04/18 19:08:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:08:22 - mmengine - INFO - Iter(train) [109000/160000] lr: 3.6379e-03 eta: 7:49:45 time: 0.5546 data_time: 0.0062 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.8028 aux.loss_ce: 0.0067 aux.acc_seg: 99.3844 +04/18 19:08:49 - mmengine - INFO - Iter(train) [109050/160000] lr: 3.6348e-03 eta: 7:49:17 time: 0.5520 data_time: 0.0064 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7956 aux.loss_ce: 0.0071 aux.acc_seg: 99.3993 +04/18 19:09:17 - mmengine - INFO - Iter(train) [109100/160000] lr: 3.6316e-03 eta: 7:48:50 time: 0.5635 data_time: 0.0070 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0064 decode.acc_seg: 99.7819 aux.loss_ce: 0.0068 aux.acc_seg: 99.3765 +04/18 19:09:45 - mmengine - INFO - Iter(train) [109150/160000] lr: 3.6285e-03 eta: 7:48:22 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7561 aux.loss_ce: 0.0077 aux.acc_seg: 99.4558 +04/18 19:10:13 - mmengine - INFO - Iter(train) [109200/160000] lr: 3.6254e-03 eta: 7:47:55 time: 0.5543 data_time: 0.0063 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0071 decode.acc_seg: 99.6473 aux.loss_ce: 0.0070 aux.acc_seg: 99.2697 +04/18 19:10:41 - mmengine - INFO - Iter(train) [109250/160000] lr: 3.6223e-03 eta: 7:47:27 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7118 aux.loss_ce: 0.0068 aux.acc_seg: 99.1602 +04/18 19:11:08 - mmengine - INFO - Iter(train) [109300/160000] lr: 3.6191e-03 eta: 7:47:00 time: 0.5561 data_time: 0.0071 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7101 aux.loss_ce: 0.0072 aux.acc_seg: 99.0958 +04/18 19:11:36 - mmengine - INFO - Iter(train) [109350/160000] lr: 3.6160e-03 eta: 7:46:32 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0079 decode.acc_seg: 99.6615 aux.loss_ce: 0.0075 aux.acc_seg: 99.2090 +04/18 19:12:04 - mmengine - INFO - Iter(train) [109400/160000] lr: 3.6129e-03 eta: 7:46:04 time: 0.5555 data_time: 0.0069 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.6904 aux.loss_ce: 0.0077 aux.acc_seg: 99.2303 +04/18 19:12:32 - mmengine - INFO - Iter(train) [109450/160000] lr: 3.6098e-03 eta: 7:45:37 time: 0.5556 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.7541 aux.loss_ce: 0.0078 aux.acc_seg: 99.2632 +04/18 19:12:59 - mmengine - INFO - Iter(train) [109500/160000] lr: 3.6066e-03 eta: 7:45:09 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0082 decode.acc_seg: 99.7097 aux.loss_ce: 0.0075 aux.acc_seg: 99.2345 +04/18 19:13:27 - mmengine - INFO - Iter(train) [109550/160000] lr: 3.6035e-03 eta: 7:44:42 time: 0.5552 data_time: 0.0069 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0080 decode.acc_seg: 99.7431 aux.loss_ce: 0.0071 aux.acc_seg: 99.4443 +04/18 19:13:55 - mmengine - INFO - Iter(train) [109600/160000] lr: 3.6004e-03 eta: 7:44:14 time: 0.5556 data_time: 0.0078 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6223 aux.loss_ce: 0.0073 aux.acc_seg: 99.1431 +04/18 19:14:23 - mmengine - INFO - Iter(train) [109650/160000] lr: 3.5973e-03 eta: 7:43:47 time: 0.5559 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0076 decode.acc_seg: 99.6944 aux.loss_ce: 0.0072 aux.acc_seg: 99.1576 +04/18 19:14:50 - mmengine - INFO - Iter(train) [109700/160000] lr: 3.5941e-03 eta: 7:43:19 time: 0.5559 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7169 aux.loss_ce: 0.0069 aux.acc_seg: 99.2173 +04/18 19:15:18 - mmengine - INFO - Iter(train) [109750/160000] lr: 3.5910e-03 eta: 7:42:51 time: 0.5562 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6673 aux.loss_ce: 0.0072 aux.acc_seg: 99.1226 +04/18 19:15:46 - mmengine - INFO - Iter(train) [109800/160000] lr: 3.5879e-03 eta: 7:42:24 time: 0.5551 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0068 decode.acc_seg: 99.7271 aux.loss_ce: 0.0067 aux.acc_seg: 99.3597 +04/18 19:16:14 - mmengine - INFO - Iter(train) [109850/160000] lr: 3.5848e-03 eta: 7:41:56 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6505 aux.loss_ce: 0.0075 aux.acc_seg: 99.0623 +04/18 19:16:41 - mmengine - INFO - Iter(train) [109900/160000] lr: 3.5816e-03 eta: 7:41:29 time: 0.5546 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7892 aux.loss_ce: 0.0072 aux.acc_seg: 99.2321 +04/18 19:17:09 - mmengine - INFO - Iter(train) [109950/160000] lr: 3.5785e-03 eta: 7:41:01 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.6477 aux.loss_ce: 0.0079 aux.acc_seg: 99.0071 +04/18 19:17:37 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:17:37 - mmengine - INFO - Iter(train) [110000/160000] lr: 3.5754e-03 eta: 7:40:34 time: 0.5638 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7181 aux.loss_ce: 0.0071 aux.acc_seg: 99.2119 +04/18 19:17:37 - mmengine - INFO - Saving checkpoint at 110000 iterations +04/18 19:17:41 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0464 data_time: 0.0015 memory: 1657 +04/18 19:17:43 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0466 data_time: 0.0015 memory: 1657 +04/18 19:17:46 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0466 data_time: 0.0014 memory: 1657 +04/18 19:17:48 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0453 data_time: 0.0011 memory: 1657 +04/18 19:17:48 - mmengine - INFO - per class results: +04/18 19:17:48 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 98.93 | 99.5 | 99.46 | 99.42 | 99.5 | +| contrast | 76.86 | 86.05 | 86.91 | 87.8 | 86.05 | ++------------+-------+-------+--------+-----------+--------+ +04/18 19:17:48 - mmengine - INFO - Iter(val) [200/200] aAcc: 98.9700 mIoU: 87.8900 mAcc: 92.7800 mFscore: 93.1900 mPrecision: 93.6100 mRecall: 92.7800 data_time: 0.0015 time: 0.0464 +04/18 19:18:16 - mmengine - INFO - Iter(train) [110050/160000] lr: 3.5723e-03 eta: 7:40:06 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7857 aux.loss_ce: 0.0078 aux.acc_seg: 99.4260 +04/18 19:18:44 - mmengine - INFO - Iter(train) [110100/160000] lr: 3.5691e-03 eta: 7:39:39 time: 0.5547 data_time: 0.0073 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0076 decode.acc_seg: 99.6800 aux.loss_ce: 0.0081 aux.acc_seg: 99.0413 +04/18 19:19:12 - mmengine - INFO - Iter(train) [110150/160000] lr: 3.5660e-03 eta: 7:39:11 time: 0.5560 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6807 aux.loss_ce: 0.0074 aux.acc_seg: 99.0971 +04/18 19:19:39 - mmengine - INFO - Iter(train) [110200/160000] lr: 3.5629e-03 eta: 7:38:43 time: 0.5548 data_time: 0.0069 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0066 decode.acc_seg: 99.7349 aux.loss_ce: 0.0066 aux.acc_seg: 99.3257 +04/18 19:20:07 - mmengine - INFO - Iter(train) [110250/160000] lr: 3.5597e-03 eta: 7:38:16 time: 0.5544 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7406 aux.loss_ce: 0.0071 aux.acc_seg: 99.2209 +04/18 19:20:35 - mmengine - INFO - Iter(train) [110300/160000] lr: 3.5566e-03 eta: 7:37:48 time: 0.5554 data_time: 0.0068 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6781 aux.loss_ce: 0.0071 aux.acc_seg: 99.2702 +04/18 19:21:03 - mmengine - INFO - Iter(train) [110350/160000] lr: 3.5535e-03 eta: 7:37:21 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7687 aux.loss_ce: 0.0074 aux.acc_seg: 99.3010 +04/18 19:21:30 - mmengine - INFO - Iter(train) [110400/160000] lr: 3.5504e-03 eta: 7:36:53 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7117 aux.loss_ce: 0.0075 aux.acc_seg: 99.1607 +04/18 19:21:58 - mmengine - INFO - Iter(train) [110450/160000] lr: 3.5472e-03 eta: 7:36:25 time: 0.5547 data_time: 0.0061 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6261 aux.loss_ce: 0.0072 aux.acc_seg: 99.0324 +04/18 19:22:26 - mmengine - INFO - Iter(train) [110500/160000] lr: 3.5441e-03 eta: 7:35:58 time: 0.5656 data_time: 0.0066 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7314 aux.loss_ce: 0.0073 aux.acc_seg: 99.1668 +04/18 19:22:54 - mmengine - INFO - Iter(train) [110550/160000] lr: 3.5410e-03 eta: 7:35:30 time: 0.5550 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.6418 aux.loss_ce: 0.0075 aux.acc_seg: 99.0856 +04/18 19:23:22 - mmengine - INFO - Iter(train) [110600/160000] lr: 3.5378e-03 eta: 7:35:03 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7288 aux.loss_ce: 0.0073 aux.acc_seg: 99.1767 +04/18 19:23:49 - mmengine - INFO - Iter(train) [110650/160000] lr: 3.5347e-03 eta: 7:34:35 time: 0.5557 data_time: 0.0070 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7032 aux.loss_ce: 0.0075 aux.acc_seg: 99.1522 +04/18 19:24:17 - mmengine - INFO - Iter(train) [110700/160000] lr: 3.5316e-03 eta: 7:34:08 time: 0.5549 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.7876 aux.loss_ce: 0.0070 aux.acc_seg: 99.5091 +04/18 19:24:45 - mmengine - INFO - Iter(train) [110750/160000] lr: 3.5284e-03 eta: 7:33:40 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7217 aux.loss_ce: 0.0071 aux.acc_seg: 99.5057 +04/18 19:25:13 - mmengine - INFO - Iter(train) [110800/160000] lr: 3.5253e-03 eta: 7:33:12 time: 0.5557 data_time: 0.0069 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.7141 aux.loss_ce: 0.0069 aux.acc_seg: 99.3195 +04/18 19:25:40 - mmengine - INFO - Iter(train) [110850/160000] lr: 3.5222e-03 eta: 7:32:45 time: 0.5560 data_time: 0.0073 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0068 decode.acc_seg: 99.7230 aux.loss_ce: 0.0077 aux.acc_seg: 99.1342 +04/18 19:26:08 - mmengine - INFO - Iter(train) [110900/160000] lr: 3.5190e-03 eta: 7:32:17 time: 0.5554 data_time: 0.0071 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.6499 aux.loss_ce: 0.0075 aux.acc_seg: 99.1427 +04/18 19:26:36 - mmengine - INFO - Iter(train) [110950/160000] lr: 3.5159e-03 eta: 7:31:50 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7088 aux.loss_ce: 0.0068 aux.acc_seg: 99.2192 +04/18 19:27:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:27:04 - mmengine - INFO - Iter(train) [111000/160000] lr: 3.5128e-03 eta: 7:31:22 time: 0.5542 data_time: 0.0076 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7270 aux.loss_ce: 0.0072 aux.acc_seg: 99.3582 +04/18 19:27:32 - mmengine - INFO - Iter(train) [111050/160000] lr: 3.5096e-03 eta: 7:30:55 time: 0.5552 data_time: 0.0062 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6961 aux.loss_ce: 0.0074 aux.acc_seg: 99.1317 +04/18 19:27:59 - mmengine - INFO - Iter(train) [111100/160000] lr: 3.5065e-03 eta: 7:30:27 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7753 aux.loss_ce: 0.0071 aux.acc_seg: 99.2970 +04/18 19:28:27 - mmengine - INFO - Iter(train) [111150/160000] lr: 3.5034e-03 eta: 7:29:59 time: 0.5545 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7406 aux.loss_ce: 0.0072 aux.acc_seg: 99.2530 +04/18 19:28:55 - mmengine - INFO - Iter(train) [111200/160000] lr: 3.5002e-03 eta: 7:29:32 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.5930 aux.loss_ce: 0.0080 aux.acc_seg: 99.2295 +04/18 19:29:23 - mmengine - INFO - Iter(train) [111250/160000] lr: 3.4971e-03 eta: 7:29:04 time: 0.5539 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.6919 aux.loss_ce: 0.0075 aux.acc_seg: 99.1432 +04/18 19:29:51 - mmengine - INFO - Iter(train) [111300/160000] lr: 3.4940e-03 eta: 7:28:37 time: 0.5560 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.5827 aux.loss_ce: 0.0078 aux.acc_seg: 99.0034 +04/18 19:30:18 - mmengine - INFO - Iter(train) [111350/160000] lr: 3.4908e-03 eta: 7:28:09 time: 0.5544 data_time: 0.0068 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0073 decode.acc_seg: 99.7358 aux.loss_ce: 0.0078 aux.acc_seg: 99.3553 +04/18 19:30:46 - mmengine - INFO - Iter(train) [111400/160000] lr: 3.4877e-03 eta: 7:27:42 time: 0.5548 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7693 aux.loss_ce: 0.0071 aux.acc_seg: 99.3146 +04/18 19:31:14 - mmengine - INFO - Iter(train) [111450/160000] lr: 3.4845e-03 eta: 7:27:14 time: 0.5535 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.7491 aux.loss_ce: 0.0070 aux.acc_seg: 99.2060 +04/18 19:31:42 - mmengine - INFO - Iter(train) [111500/160000] lr: 3.4814e-03 eta: 7:26:46 time: 0.5545 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6525 aux.loss_ce: 0.0073 aux.acc_seg: 99.1815 +04/18 19:32:09 - mmengine - INFO - Iter(train) [111550/160000] lr: 3.4783e-03 eta: 7:26:19 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7223 aux.loss_ce: 0.0074 aux.acc_seg: 99.3914 +04/18 19:32:37 - mmengine - INFO - Iter(train) [111600/160000] lr: 3.4751e-03 eta: 7:25:51 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7206 aux.loss_ce: 0.0069 aux.acc_seg: 99.2196 +04/18 19:33:05 - mmengine - INFO - Iter(train) [111650/160000] lr: 3.4720e-03 eta: 7:25:24 time: 0.5526 data_time: 0.0067 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.6882 aux.loss_ce: 0.0068 aux.acc_seg: 99.2639 +04/18 19:33:33 - mmengine - INFO - Iter(train) [111700/160000] lr: 3.4689e-03 eta: 7:24:56 time: 0.5563 data_time: 0.0067 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.6841 aux.loss_ce: 0.0071 aux.acc_seg: 99.1851 +04/18 19:34:00 - mmengine - INFO - Iter(train) [111750/160000] lr: 3.4657e-03 eta: 7:24:28 time: 0.5552 data_time: 0.0063 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7263 aux.loss_ce: 0.0074 aux.acc_seg: 99.1424 +04/18 19:34:28 - mmengine - INFO - Iter(train) [111800/160000] lr: 3.4626e-03 eta: 7:24:01 time: 0.5550 data_time: 0.0061 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7540 aux.loss_ce: 0.0068 aux.acc_seg: 99.3311 +04/18 19:34:56 - mmengine - INFO - Iter(train) [111850/160000] lr: 3.4594e-03 eta: 7:23:33 time: 0.5552 data_time: 0.0074 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7018 aux.loss_ce: 0.0069 aux.acc_seg: 99.1357 +04/18 19:35:24 - mmengine - INFO - Iter(train) [111900/160000] lr: 3.4563e-03 eta: 7:23:06 time: 0.5551 data_time: 0.0069 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7384 aux.loss_ce: 0.0068 aux.acc_seg: 99.2832 +04/18 19:35:51 - mmengine - INFO - Iter(train) [111950/160000] lr: 3.4532e-03 eta: 7:22:38 time: 0.5549 data_time: 0.0073 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.6992 aux.loss_ce: 0.0069 aux.acc_seg: 99.2122 +04/18 19:36:19 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:36:19 - mmengine - INFO - Iter(train) [112000/160000] lr: 3.4500e-03 eta: 7:22:10 time: 0.5561 data_time: 0.0062 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7575 aux.loss_ce: 0.0069 aux.acc_seg: 99.3261 +04/18 19:36:47 - mmengine - INFO - Iter(train) [112050/160000] lr: 3.4469e-03 eta: 7:21:43 time: 0.5567 data_time: 0.0062 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.6931 aux.loss_ce: 0.0077 aux.acc_seg: 99.0832 +04/18 19:37:15 - mmengine - INFO - Iter(train) [112100/160000] lr: 3.4437e-03 eta: 7:21:15 time: 0.5619 data_time: 0.0068 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7355 aux.loss_ce: 0.0074 aux.acc_seg: 99.2790 +04/18 19:37:43 - mmengine - INFO - Iter(train) [112150/160000] lr: 3.4406e-03 eta: 7:20:48 time: 0.5642 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.6672 aux.loss_ce: 0.0072 aux.acc_seg: 99.2986 +04/18 19:38:11 - mmengine - INFO - Iter(train) [112200/160000] lr: 3.4374e-03 eta: 7:20:20 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.7649 aux.loss_ce: 0.0069 aux.acc_seg: 99.3267 +04/18 19:38:38 - mmengine - INFO - Iter(train) [112250/160000] lr: 3.4343e-03 eta: 7:19:53 time: 0.5560 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.6828 aux.loss_ce: 0.0074 aux.acc_seg: 99.1042 +04/18 19:39:06 - mmengine - INFO - Iter(train) [112300/160000] lr: 3.4312e-03 eta: 7:19:25 time: 0.5541 data_time: 0.0060 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.7375 aux.loss_ce: 0.0071 aux.acc_seg: 99.4389 +04/18 19:39:34 - mmengine - INFO - Iter(train) [112350/160000] lr: 3.4280e-03 eta: 7:18:58 time: 0.5552 data_time: 0.0074 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6963 aux.loss_ce: 0.0072 aux.acc_seg: 99.0375 +04/18 19:40:02 - mmengine - INFO - Iter(train) [112400/160000] lr: 3.4249e-03 eta: 7:18:30 time: 0.5565 data_time: 0.0073 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7343 aux.loss_ce: 0.0070 aux.acc_seg: 99.1905 +04/18 19:40:29 - mmengine - INFO - Iter(train) [112450/160000] lr: 3.4217e-03 eta: 7:18:02 time: 0.5547 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6407 aux.loss_ce: 0.0075 aux.acc_seg: 99.0631 +04/18 19:40:57 - mmengine - INFO - Iter(train) [112500/160000] lr: 3.4186e-03 eta: 7:17:35 time: 0.5556 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7557 aux.loss_ce: 0.0075 aux.acc_seg: 99.4360 +04/18 19:41:25 - mmengine - INFO - Iter(train) [112550/160000] lr: 3.4154e-03 eta: 7:17:07 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6600 aux.loss_ce: 0.0079 aux.acc_seg: 99.1341 +04/18 19:41:53 - mmengine - INFO - Iter(train) [112600/160000] lr: 3.4123e-03 eta: 7:16:40 time: 0.5545 data_time: 0.0075 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0099 decode.acc_seg: 99.6852 aux.loss_ce: 0.0078 aux.acc_seg: 99.3675 +04/18 19:42:20 - mmengine - INFO - Iter(train) [112650/160000] lr: 3.4092e-03 eta: 7:16:12 time: 0.5636 data_time: 0.0067 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.7015 aux.loss_ce: 0.0084 aux.acc_seg: 99.2482 +04/18 19:42:48 - mmengine - INFO - Iter(train) [112700/160000] lr: 3.4060e-03 eta: 7:15:44 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.6842 aux.loss_ce: 0.0070 aux.acc_seg: 99.1373 +04/18 19:43:16 - mmengine - INFO - Iter(train) [112750/160000] lr: 3.4029e-03 eta: 7:15:17 time: 0.5547 data_time: 0.0066 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0089 decode.acc_seg: 99.6509 aux.loss_ce: 0.0085 aux.acc_seg: 99.0849 +04/18 19:43:44 - mmengine - INFO - Iter(train) [112800/160000] lr: 3.3997e-03 eta: 7:14:49 time: 0.5550 data_time: 0.0075 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.6431 aux.loss_ce: 0.0075 aux.acc_seg: 99.1863 +04/18 19:44:12 - mmengine - INFO - Iter(train) [112850/160000] lr: 3.3966e-03 eta: 7:14:22 time: 0.5558 data_time: 0.0062 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0078 decode.acc_seg: 99.6466 aux.loss_ce: 0.0079 aux.acc_seg: 99.2352 +04/18 19:44:39 - mmengine - INFO - Iter(train) [112900/160000] lr: 3.3934e-03 eta: 7:13:54 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.7649 aux.loss_ce: 0.0077 aux.acc_seg: 99.3188 +04/18 19:45:07 - mmengine - INFO - Iter(train) [112950/160000] lr: 3.3903e-03 eta: 7:13:26 time: 0.5555 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7283 aux.loss_ce: 0.0073 aux.acc_seg: 99.2187 +04/18 19:45:35 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:45:35 - mmengine - INFO - Iter(train) [113000/160000] lr: 3.3871e-03 eta: 7:12:59 time: 0.5547 data_time: 0.0071 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0079 decode.acc_seg: 99.6073 aux.loss_ce: 0.0083 aux.acc_seg: 98.9552 +04/18 19:46:03 - mmengine - INFO - Iter(train) [113050/160000] lr: 3.3840e-03 eta: 7:12:31 time: 0.5572 data_time: 0.0074 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7059 aux.loss_ce: 0.0074 aux.acc_seg: 99.1577 +04/18 19:46:30 - mmengine - INFO - Iter(train) [113100/160000] lr: 3.3808e-03 eta: 7:12:04 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.7546 aux.loss_ce: 0.0078 aux.acc_seg: 99.2054 +04/18 19:46:58 - mmengine - INFO - Iter(train) [113150/160000] lr: 3.3777e-03 eta: 7:11:36 time: 0.5567 data_time: 0.0069 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.6556 aux.loss_ce: 0.0077 aux.acc_seg: 99.0924 +04/18 19:47:26 - mmengine - INFO - Iter(train) [113200/160000] lr: 3.3745e-03 eta: 7:11:09 time: 0.5619 data_time: 0.0062 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.6857 aux.loss_ce: 0.0074 aux.acc_seg: 98.9956 +04/18 19:47:54 - mmengine - INFO - Iter(train) [113250/160000] lr: 3.3714e-03 eta: 7:10:41 time: 0.5630 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.6474 aux.loss_ce: 0.0072 aux.acc_seg: 99.1713 +04/18 19:48:22 - mmengine - INFO - Iter(train) [113300/160000] lr: 3.3682e-03 eta: 7:10:13 time: 0.5558 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7250 aux.loss_ce: 0.0073 aux.acc_seg: 99.3504 +04/18 19:48:49 - mmengine - INFO - Iter(train) [113350/160000] lr: 3.3651e-03 eta: 7:09:46 time: 0.5532 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7818 aux.loss_ce: 0.0071 aux.acc_seg: 99.4512 +04/18 19:49:17 - mmengine - INFO - Iter(train) [113400/160000] lr: 3.3619e-03 eta: 7:09:18 time: 0.5560 data_time: 0.0072 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0078 decode.acc_seg: 99.7405 aux.loss_ce: 0.0079 aux.acc_seg: 99.1410 +04/18 19:49:45 - mmengine - INFO - Iter(train) [113450/160000] lr: 3.3588e-03 eta: 7:08:51 time: 0.5569 data_time: 0.0075 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.6954 aux.loss_ce: 0.0075 aux.acc_seg: 99.2711 +04/18 19:50:13 - mmengine - INFO - Iter(train) [113500/160000] lr: 3.3556e-03 eta: 7:08:23 time: 0.5547 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.6919 aux.loss_ce: 0.0072 aux.acc_seg: 99.0643 +04/18 19:50:40 - mmengine - INFO - Iter(train) [113550/160000] lr: 3.3525e-03 eta: 7:07:55 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7746 aux.loss_ce: 0.0073 aux.acc_seg: 99.2249 +04/18 19:51:08 - mmengine - INFO - Iter(train) [113600/160000] lr: 3.3493e-03 eta: 7:07:28 time: 0.5530 data_time: 0.0073 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.6819 aux.loss_ce: 0.0076 aux.acc_seg: 99.1260 +04/18 19:51:36 - mmengine - INFO - Iter(train) [113650/160000] lr: 3.3462e-03 eta: 7:07:00 time: 0.5534 data_time: 0.0077 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.7600 aux.loss_ce: 0.0068 aux.acc_seg: 99.3909 +04/18 19:52:04 - mmengine - INFO - Iter(train) [113700/160000] lr: 3.3430e-03 eta: 7:06:33 time: 0.5542 data_time: 0.0060 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6358 aux.loss_ce: 0.0082 aux.acc_seg: 99.0391 +04/18 19:52:32 - mmengine - INFO - Iter(train) [113750/160000] lr: 3.3399e-03 eta: 7:06:05 time: 0.5547 data_time: 0.0062 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.6324 aux.loss_ce: 0.0081 aux.acc_seg: 99.2223 +04/18 19:52:59 - mmengine - INFO - Iter(train) [113800/160000] lr: 3.3367e-03 eta: 7:05:37 time: 0.5555 data_time: 0.0073 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0109 decode.acc_seg: 99.4678 aux.loss_ce: 0.0094 aux.acc_seg: 98.9538 +04/18 19:53:27 - mmengine - INFO - Iter(train) [113850/160000] lr: 3.3336e-03 eta: 7:05:10 time: 0.5554 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0072 decode.acc_seg: 99.6481 aux.loss_ce: 0.0069 aux.acc_seg: 99.2805 +04/18 19:53:55 - mmengine - INFO - Iter(train) [113900/160000] lr: 3.3304e-03 eta: 7:04:42 time: 0.5567 data_time: 0.0067 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.7268 aux.loss_ce: 0.0076 aux.acc_seg: 99.1993 +04/18 19:54:23 - mmengine - INFO - Iter(train) [113950/160000] lr: 3.3273e-03 eta: 7:04:15 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.7354 aux.loss_ce: 0.0074 aux.acc_seg: 99.4023 +04/18 19:54:50 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:54:50 - mmengine - INFO - Iter(train) [114000/160000] lr: 3.3241e-03 eta: 7:03:47 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6449 aux.loss_ce: 0.0076 aux.acc_seg: 99.0225 +04/18 19:55:18 - mmengine - INFO - Iter(train) [114050/160000] lr: 3.3210e-03 eta: 7:03:19 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6897 aux.loss_ce: 0.0076 aux.acc_seg: 99.1895 +04/18 19:55:46 - mmengine - INFO - Iter(train) [114100/160000] lr: 3.3178e-03 eta: 7:02:52 time: 0.5558 data_time: 0.0070 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.6418 aux.loss_ce: 0.0074 aux.acc_seg: 99.0686 +04/18 19:56:14 - mmengine - INFO - Iter(train) [114150/160000] lr: 3.3147e-03 eta: 7:02:24 time: 0.5549 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.7098 aux.loss_ce: 0.0077 aux.acc_seg: 99.3317 +04/18 19:56:41 - mmengine - INFO - Iter(train) [114200/160000] lr: 3.3115e-03 eta: 7:01:57 time: 0.5560 data_time: 0.0065 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.6345 aux.loss_ce: 0.0081 aux.acc_seg: 98.8980 +04/18 19:57:09 - mmengine - INFO - Iter(train) [114250/160000] lr: 3.3083e-03 eta: 7:01:29 time: 0.5647 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.7429 aux.loss_ce: 0.0075 aux.acc_seg: 99.4370 +04/18 19:57:37 - mmengine - INFO - Iter(train) [114300/160000] lr: 3.3052e-03 eta: 7:01:02 time: 0.5725 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.6933 aux.loss_ce: 0.0079 aux.acc_seg: 99.0668 +04/18 19:58:05 - mmengine - INFO - Iter(train) [114350/160000] lr: 3.3020e-03 eta: 7:00:34 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7735 aux.loss_ce: 0.0069 aux.acc_seg: 99.4714 +04/18 19:58:33 - mmengine - INFO - Iter(train) [114400/160000] lr: 3.2989e-03 eta: 7:00:06 time: 0.5551 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7492 aux.loss_ce: 0.0071 aux.acc_seg: 99.3660 +04/18 19:59:00 - mmengine - INFO - Iter(train) [114450/160000] lr: 3.2957e-03 eta: 6:59:39 time: 0.5549 data_time: 0.0065 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7345 aux.loss_ce: 0.0073 aux.acc_seg: 99.1737 +04/18 19:59:28 - mmengine - INFO - Iter(train) [114500/160000] lr: 3.2926e-03 eta: 6:59:11 time: 0.5561 data_time: 0.0056 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7748 aux.loss_ce: 0.0071 aux.acc_seg: 99.1717 +04/18 19:59:56 - mmengine - INFO - Iter(train) [114550/160000] lr: 3.2894e-03 eta: 6:58:44 time: 0.5544 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7562 aux.loss_ce: 0.0073 aux.acc_seg: 99.1133 +04/18 20:00:24 - mmengine - INFO - Iter(train) [114600/160000] lr: 3.2863e-03 eta: 6:58:16 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.6882 aux.loss_ce: 0.0076 aux.acc_seg: 99.2651 +04/18 20:00:51 - mmengine - INFO - Iter(train) [114650/160000] lr: 3.2831e-03 eta: 6:57:48 time: 0.5543 data_time: 0.0062 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.8008 aux.loss_ce: 0.0068 aux.acc_seg: 99.3563 +04/18 20:01:19 - mmengine - INFO - Iter(train) [114700/160000] lr: 3.2799e-03 eta: 6:57:21 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7588 aux.loss_ce: 0.0070 aux.acc_seg: 99.4811 +04/18 20:01:47 - mmengine - INFO - Iter(train) [114750/160000] lr: 3.2768e-03 eta: 6:56:53 time: 0.5556 data_time: 0.0065 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7975 aux.loss_ce: 0.0071 aux.acc_seg: 99.3069 +04/18 20:02:15 - mmengine - INFO - Iter(train) [114800/160000] lr: 3.2736e-03 eta: 6:56:26 time: 0.5538 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7615 aux.loss_ce: 0.0073 aux.acc_seg: 99.4371 +04/18 20:02:43 - mmengine - INFO - Iter(train) [114850/160000] lr: 3.2705e-03 eta: 6:55:58 time: 0.5564 data_time: 0.0064 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0061 decode.acc_seg: 99.7408 aux.loss_ce: 0.0067 aux.acc_seg: 99.3417 +04/18 20:03:10 - mmengine - INFO - Iter(train) [114900/160000] lr: 3.2673e-03 eta: 6:55:30 time: 0.5568 data_time: 0.0072 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6960 aux.loss_ce: 0.0075 aux.acc_seg: 99.2154 +04/18 20:03:38 - mmengine - INFO - Iter(train) [114950/160000] lr: 3.2641e-03 eta: 6:55:03 time: 0.5526 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.6946 aux.loss_ce: 0.0070 aux.acc_seg: 99.2776 +04/18 20:04:06 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:04:06 - mmengine - INFO - Iter(train) [115000/160000] lr: 3.2610e-03 eta: 6:54:35 time: 0.5558 data_time: 0.0075 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7103 aux.loss_ce: 0.0072 aux.acc_seg: 99.0646 +04/18 20:04:34 - mmengine - INFO - Iter(train) [115050/160000] lr: 3.2578e-03 eta: 6:54:08 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6477 aux.loss_ce: 0.0072 aux.acc_seg: 99.2191 +04/18 20:05:01 - mmengine - INFO - Iter(train) [115100/160000] lr: 3.2547e-03 eta: 6:53:40 time: 0.5549 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7582 aux.loss_ce: 0.0073 aux.acc_seg: 99.3569 +04/18 20:05:29 - mmengine - INFO - Iter(train) [115150/160000] lr: 3.2515e-03 eta: 6:53:13 time: 0.5551 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7608 aux.loss_ce: 0.0072 aux.acc_seg: 99.2516 +04/18 20:05:57 - mmengine - INFO - Iter(train) [115200/160000] lr: 3.2483e-03 eta: 6:52:45 time: 0.5542 data_time: 0.0071 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6777 aux.loss_ce: 0.0076 aux.acc_seg: 99.1147 +04/18 20:06:25 - mmengine - INFO - Iter(train) [115250/160000] lr: 3.2452e-03 eta: 6:52:17 time: 0.5561 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7757 aux.loss_ce: 0.0074 aux.acc_seg: 99.3195 +04/18 20:06:52 - mmengine - INFO - Iter(train) [115300/160000] lr: 3.2420e-03 eta: 6:51:50 time: 0.5568 data_time: 0.0077 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7368 aux.loss_ce: 0.0073 aux.acc_seg: 99.2317 +04/18 20:07:20 - mmengine - INFO - Iter(train) [115350/160000] lr: 3.2388e-03 eta: 6:51:22 time: 0.5553 data_time: 0.0068 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7233 aux.loss_ce: 0.0068 aux.acc_seg: 99.2118 +04/18 20:07:48 - mmengine - INFO - Iter(train) [115400/160000] lr: 3.2357e-03 eta: 6:50:55 time: 0.5643 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7232 aux.loss_ce: 0.0071 aux.acc_seg: 99.2002 +04/18 20:08:16 - mmengine - INFO - Iter(train) [115450/160000] lr: 3.2325e-03 eta: 6:50:27 time: 0.5546 data_time: 0.0077 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7201 aux.loss_ce: 0.0071 aux.acc_seg: 99.2654 +04/18 20:08:44 - mmengine - INFO - Iter(train) [115500/160000] lr: 3.2293e-03 eta: 6:49:59 time: 0.5553 data_time: 0.0068 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0064 decode.acc_seg: 99.7291 aux.loss_ce: 0.0067 aux.acc_seg: 99.1709 +04/18 20:09:12 - mmengine - INFO - Iter(train) [115550/160000] lr: 3.2262e-03 eta: 6:49:32 time: 0.5546 data_time: 0.0062 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0077 decode.acc_seg: 99.6208 aux.loss_ce: 0.0071 aux.acc_seg: 99.1677 +04/18 20:09:39 - mmengine - INFO - Iter(train) [115600/160000] lr: 3.2230e-03 eta: 6:49:04 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6173 aux.loss_ce: 0.0076 aux.acc_seg: 99.1159 +04/18 20:10:07 - mmengine - INFO - Iter(train) [115650/160000] lr: 3.2199e-03 eta: 6:48:37 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7155 aux.loss_ce: 0.0069 aux.acc_seg: 99.2580 +04/18 20:10:35 - mmengine - INFO - Iter(train) [115700/160000] lr: 3.2167e-03 eta: 6:48:09 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0065 decode.acc_seg: 99.7417 aux.loss_ce: 0.0069 aux.acc_seg: 99.3217 +04/18 20:11:03 - mmengine - INFO - Iter(train) [115750/160000] lr: 3.2135e-03 eta: 6:47:41 time: 0.5552 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7369 aux.loss_ce: 0.0072 aux.acc_seg: 99.1931 +04/18 20:11:30 - mmengine - INFO - Iter(train) [115800/160000] lr: 3.2104e-03 eta: 6:47:14 time: 0.5556 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7627 aux.loss_ce: 0.0070 aux.acc_seg: 99.3275 +04/18 20:11:58 - mmengine - INFO - Iter(train) [115850/160000] lr: 3.2072e-03 eta: 6:46:46 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7222 aux.loss_ce: 0.0071 aux.acc_seg: 99.2514 +04/18 20:12:26 - mmengine - INFO - Iter(train) [115900/160000] lr: 3.2040e-03 eta: 6:46:19 time: 0.5558 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7405 aux.loss_ce: 0.0072 aux.acc_seg: 99.2482 +04/18 20:12:54 - mmengine - INFO - Iter(train) [115950/160000] lr: 3.2009e-03 eta: 6:45:51 time: 0.5549 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7407 aux.loss_ce: 0.0076 aux.acc_seg: 99.2984 +04/18 20:13:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:13:22 - mmengine - INFO - Iter(train) [116000/160000] lr: 3.1977e-03 eta: 6:45:24 time: 0.5552 data_time: 0.0065 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6977 aux.loss_ce: 0.0072 aux.acc_seg: 99.2315 +04/18 20:13:49 - mmengine - INFO - Iter(train) [116050/160000] lr: 3.1945e-03 eta: 6:44:56 time: 0.5551 data_time: 0.0061 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.8324 aux.loss_ce: 0.0070 aux.acc_seg: 99.5077 +04/18 20:14:17 - mmengine - INFO - Iter(train) [116100/160000] lr: 3.1913e-03 eta: 6:44:28 time: 0.5574 data_time: 0.0070 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7281 aux.loss_ce: 0.0071 aux.acc_seg: 99.3584 +04/18 20:14:45 - mmengine - INFO - Iter(train) [116150/160000] lr: 3.1882e-03 eta: 6:44:01 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7532 aux.loss_ce: 0.0073 aux.acc_seg: 99.2749 +04/18 20:15:13 - mmengine - INFO - Iter(train) [116200/160000] lr: 3.1850e-03 eta: 6:43:33 time: 0.5565 data_time: 0.0071 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7695 aux.loss_ce: 0.0070 aux.acc_seg: 99.1457 +04/18 20:15:41 - mmengine - INFO - Iter(train) [116250/160000] lr: 3.1818e-03 eta: 6:43:06 time: 0.5553 data_time: 0.0076 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7055 aux.loss_ce: 0.0077 aux.acc_seg: 99.0957 +04/18 20:16:08 - mmengine - INFO - Iter(train) [116300/160000] lr: 3.1787e-03 eta: 6:42:38 time: 0.5552 data_time: 0.0060 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.7753 aux.loss_ce: 0.0076 aux.acc_seg: 99.3449 +04/18 20:16:36 - mmengine - INFO - Iter(train) [116350/160000] lr: 3.1755e-03 eta: 6:42:10 time: 0.5556 data_time: 0.0073 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7267 aux.loss_ce: 0.0076 aux.acc_seg: 99.2901 +04/18 20:17:04 - mmengine - INFO - Iter(train) [116400/160000] lr: 3.1723e-03 eta: 6:41:43 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7842 aux.loss_ce: 0.0074 aux.acc_seg: 99.4181 +04/18 20:17:32 - mmengine - INFO - Iter(train) [116450/160000] lr: 3.1692e-03 eta: 6:41:15 time: 0.5569 data_time: 0.0067 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7127 aux.loss_ce: 0.0068 aux.acc_seg: 99.2112 +04/18 20:18:00 - mmengine - INFO - Iter(train) [116500/160000] lr: 3.1660e-03 eta: 6:40:48 time: 0.5567 data_time: 0.0070 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.7738 aux.loss_ce: 0.0070 aux.acc_seg: 99.2536 +04/18 20:18:28 - mmengine - INFO - Iter(train) [116550/160000] lr: 3.1628e-03 eta: 6:40:20 time: 0.5561 data_time: 0.0078 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7598 aux.loss_ce: 0.0074 aux.acc_seg: 99.3213 +04/18 20:18:55 - mmengine - INFO - Iter(train) [116600/160000] lr: 3.1596e-03 eta: 6:39:53 time: 0.5547 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7788 aux.loss_ce: 0.0071 aux.acc_seg: 99.2798 +04/18 20:19:23 - mmengine - INFO - Iter(train) [116650/160000] lr: 3.1565e-03 eta: 6:39:25 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7314 aux.loss_ce: 0.0073 aux.acc_seg: 99.2580 +04/18 20:19:51 - mmengine - INFO - Iter(train) [116700/160000] lr: 3.1533e-03 eta: 6:38:57 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7270 aux.loss_ce: 0.0072 aux.acc_seg: 99.2430 +04/18 20:20:19 - mmengine - INFO - Iter(train) [116750/160000] lr: 3.1501e-03 eta: 6:38:30 time: 0.5544 data_time: 0.0074 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7726 aux.loss_ce: 0.0073 aux.acc_seg: 99.3933 +04/18 20:20:46 - mmengine - INFO - Iter(train) [116800/160000] lr: 3.1469e-03 eta: 6:38:02 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.7207 aux.loss_ce: 0.0071 aux.acc_seg: 99.1790 +04/18 20:21:14 - mmengine - INFO - Iter(train) [116850/160000] lr: 3.1438e-03 eta: 6:37:35 time: 0.5552 data_time: 0.0065 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.6984 aux.loss_ce: 0.0071 aux.acc_seg: 99.2356 +04/18 20:21:42 - mmengine - INFO - Iter(train) [116900/160000] lr: 3.1406e-03 eta: 6:37:07 time: 0.5557 data_time: 0.0065 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7245 aux.loss_ce: 0.0071 aux.acc_seg: 99.2350 +04/18 20:22:10 - mmengine - INFO - Iter(train) [116950/160000] lr: 3.1374e-03 eta: 6:36:39 time: 0.5650 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7678 aux.loss_ce: 0.0074 aux.acc_seg: 99.3468 +04/18 20:22:38 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:22:38 - mmengine - INFO - Iter(train) [117000/160000] lr: 3.1342e-03 eta: 6:36:12 time: 0.5557 data_time: 0.0061 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.7696 aux.loss_ce: 0.0068 aux.acc_seg: 99.4011 +04/18 20:23:05 - mmengine - INFO - Iter(train) [117050/160000] lr: 3.1311e-03 eta: 6:35:44 time: 0.5540 data_time: 0.0070 memory: 7635 loss: 0.0123 decode.loss_ce: 0.0059 decode.acc_seg: 99.8018 aux.loss_ce: 0.0064 aux.acc_seg: 99.4440 +04/18 20:23:33 - mmengine - INFO - Iter(train) [117100/160000] lr: 3.1279e-03 eta: 6:35:17 time: 0.5555 data_time: 0.0082 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7345 aux.loss_ce: 0.0074 aux.acc_seg: 99.0630 +04/18 20:24:01 - mmengine - INFO - Iter(train) [117150/160000] lr: 3.1247e-03 eta: 6:34:49 time: 0.5568 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7351 aux.loss_ce: 0.0072 aux.acc_seg: 99.3247 +04/18 20:24:29 - mmengine - INFO - Iter(train) [117200/160000] lr: 3.1215e-03 eta: 6:34:21 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7007 aux.loss_ce: 0.0073 aux.acc_seg: 99.0025 +04/18 20:24:56 - mmengine - INFO - Iter(train) [117250/160000] lr: 3.1184e-03 eta: 6:33:54 time: 0.5574 data_time: 0.0077 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0064 decode.acc_seg: 99.6803 aux.loss_ce: 0.0066 aux.acc_seg: 99.1085 +04/18 20:25:24 - mmengine - INFO - Iter(train) [117300/160000] lr: 3.1152e-03 eta: 6:33:26 time: 0.5548 data_time: 0.0070 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.7799 aux.loss_ce: 0.0068 aux.acc_seg: 99.2558 +04/18 20:25:52 - mmengine - INFO - Iter(train) [117350/160000] lr: 3.1120e-03 eta: 6:32:59 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.6716 aux.loss_ce: 0.0072 aux.acc_seg: 99.1094 +04/18 20:26:20 - mmengine - INFO - Iter(train) [117400/160000] lr: 3.1088e-03 eta: 6:32:31 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7077 aux.loss_ce: 0.0075 aux.acc_seg: 99.0832 +04/18 20:26:48 - mmengine - INFO - Iter(train) [117450/160000] lr: 3.1057e-03 eta: 6:32:03 time: 0.5553 data_time: 0.0079 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7317 aux.loss_ce: 0.0075 aux.acc_seg: 99.3379 +04/18 20:27:15 - mmengine - INFO - Iter(train) [117500/160000] lr: 3.1025e-03 eta: 6:31:36 time: 0.5551 data_time: 0.0071 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7749 aux.loss_ce: 0.0070 aux.acc_seg: 99.2544 +04/18 20:27:43 - mmengine - INFO - Iter(train) [117550/160000] lr: 3.0993e-03 eta: 6:31:08 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7262 aux.loss_ce: 0.0073 aux.acc_seg: 99.3453 +04/18 20:28:11 - mmengine - INFO - Iter(train) [117600/160000] lr: 3.0961e-03 eta: 6:30:41 time: 0.5570 data_time: 0.0070 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7079 aux.loss_ce: 0.0073 aux.acc_seg: 99.0181 +04/18 20:28:39 - mmengine - INFO - Iter(train) [117650/160000] lr: 3.0929e-03 eta: 6:30:13 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7430 aux.loss_ce: 0.0072 aux.acc_seg: 99.2660 +04/18 20:29:07 - mmengine - INFO - Iter(train) [117700/160000] lr: 3.0898e-03 eta: 6:29:45 time: 0.5548 data_time: 0.0072 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0059 decode.acc_seg: 99.7495 aux.loss_ce: 0.0068 aux.acc_seg: 99.3814 +04/18 20:29:34 - mmengine - INFO - Iter(train) [117750/160000] lr: 3.0866e-03 eta: 6:29:18 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7581 aux.loss_ce: 0.0074 aux.acc_seg: 99.3503 +04/18 20:30:02 - mmengine - INFO - Iter(train) [117800/160000] lr: 3.0834e-03 eta: 6:28:50 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7300 aux.loss_ce: 0.0069 aux.acc_seg: 99.1374 +04/18 20:30:30 - mmengine - INFO - Iter(train) [117850/160000] lr: 3.0802e-03 eta: 6:28:23 time: 0.5566 data_time: 0.0060 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7112 aux.loss_ce: 0.0074 aux.acc_seg: 99.1974 +04/18 20:30:58 - mmengine - INFO - Iter(train) [117900/160000] lr: 3.0770e-03 eta: 6:27:55 time: 0.5561 data_time: 0.0070 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7927 aux.loss_ce: 0.0077 aux.acc_seg: 99.3387 +04/18 20:31:26 - mmengine - INFO - Iter(train) [117950/160000] lr: 3.0738e-03 eta: 6:27:27 time: 0.5552 data_time: 0.0071 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.7211 aux.loss_ce: 0.0076 aux.acc_seg: 99.2286 +04/18 20:31:53 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:31:53 - mmengine - INFO - Iter(train) [118000/160000] lr: 3.0707e-03 eta: 6:27:00 time: 0.5555 data_time: 0.0066 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.7520 aux.loss_ce: 0.0070 aux.acc_seg: 99.3025 +04/18 20:32:21 - mmengine - INFO - Iter(train) [118050/160000] lr: 3.0675e-03 eta: 6:26:32 time: 0.5552 data_time: 0.0065 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.8034 aux.loss_ce: 0.0069 aux.acc_seg: 99.4008 +04/18 20:32:49 - mmengine - INFO - Iter(train) [118100/160000] lr: 3.0643e-03 eta: 6:26:05 time: 0.5573 data_time: 0.0070 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.7596 aux.loss_ce: 0.0077 aux.acc_seg: 99.3524 +04/18 20:33:17 - mmengine - INFO - Iter(train) [118150/160000] lr: 3.0611e-03 eta: 6:25:37 time: 0.5552 data_time: 0.0068 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.5859 aux.loss_ce: 0.0077 aux.acc_seg: 99.0843 +04/18 20:33:45 - mmengine - INFO - Iter(train) [118200/160000] lr: 3.0579e-03 eta: 6:25:10 time: 0.5553 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7565 aux.loss_ce: 0.0080 aux.acc_seg: 99.3854 +04/18 20:34:12 - mmengine - INFO - Iter(train) [118250/160000] lr: 3.0547e-03 eta: 6:24:42 time: 0.5561 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.7158 aux.loss_ce: 0.0076 aux.acc_seg: 99.0129 +04/18 20:34:40 - mmengine - INFO - Iter(train) [118300/160000] lr: 3.0516e-03 eta: 6:24:14 time: 0.5558 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.6723 aux.loss_ce: 0.0074 aux.acc_seg: 99.1504 +04/18 20:35:08 - mmengine - INFO - Iter(train) [118350/160000] lr: 3.0484e-03 eta: 6:23:47 time: 0.5561 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.7306 aux.loss_ce: 0.0075 aux.acc_seg: 99.3378 +04/18 20:35:36 - mmengine - INFO - Iter(train) [118400/160000] lr: 3.0452e-03 eta: 6:23:19 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.6934 aux.loss_ce: 0.0072 aux.acc_seg: 99.1377 +04/18 20:36:04 - mmengine - INFO - Iter(train) [118450/160000] lr: 3.0420e-03 eta: 6:22:52 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6614 aux.loss_ce: 0.0078 aux.acc_seg: 99.1085 +04/18 20:36:31 - mmengine - INFO - Iter(train) [118500/160000] lr: 3.0388e-03 eta: 6:22:24 time: 0.5557 data_time: 0.0067 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7893 aux.loss_ce: 0.0070 aux.acc_seg: 99.4375 +04/18 20:36:59 - mmengine - INFO - Iter(train) [118550/160000] lr: 3.0356e-03 eta: 6:21:56 time: 0.5642 data_time: 0.0069 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7399 aux.loss_ce: 0.0069 aux.acc_seg: 99.2958 +04/18 20:37:27 - mmengine - INFO - Iter(train) [118600/160000] lr: 3.0324e-03 eta: 6:21:29 time: 0.5631 data_time: 0.0070 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7432 aux.loss_ce: 0.0072 aux.acc_seg: 99.3347 +04/18 20:37:55 - mmengine - INFO - Iter(train) [118650/160000] lr: 3.0293e-03 eta: 6:21:01 time: 0.5561 data_time: 0.0064 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.6925 aux.loss_ce: 0.0068 aux.acc_seg: 99.2470 +04/18 20:38:23 - mmengine - INFO - Iter(train) [118700/160000] lr: 3.0261e-03 eta: 6:20:34 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0102 decode.acc_seg: 99.7601 aux.loss_ce: 0.0081 aux.acc_seg: 99.2406 +04/18 20:38:50 - mmengine - INFO - Iter(train) [118750/160000] lr: 3.0229e-03 eta: 6:20:06 time: 0.5557 data_time: 0.0069 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6594 aux.loss_ce: 0.0077 aux.acc_seg: 99.0275 +04/18 20:39:18 - mmengine - INFO - Iter(train) [118800/160000] lr: 3.0197e-03 eta: 6:19:38 time: 0.5539 data_time: 0.0068 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6863 aux.loss_ce: 0.0078 aux.acc_seg: 99.2388 +04/18 20:39:46 - mmengine - INFO - Iter(train) [118850/160000] lr: 3.0165e-03 eta: 6:19:11 time: 0.5559 data_time: 0.0065 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7490 aux.loss_ce: 0.0073 aux.acc_seg: 99.2550 +04/18 20:40:14 - mmengine - INFO - Iter(train) [118900/160000] lr: 3.0133e-03 eta: 6:18:43 time: 0.5569 data_time: 0.0073 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.6354 aux.loss_ce: 0.0081 aux.acc_seg: 99.0667 +04/18 20:40:42 - mmengine - INFO - Iter(train) [118950/160000] lr: 3.0101e-03 eta: 6:18:16 time: 0.5560 data_time: 0.0078 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.6362 aux.loss_ce: 0.0073 aux.acc_seg: 99.2110 +04/18 20:41:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:41:09 - mmengine - INFO - Iter(train) [119000/160000] lr: 3.0069e-03 eta: 6:17:48 time: 0.5538 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0077 decode.acc_seg: 99.7345 aux.loss_ce: 0.0071 aux.acc_seg: 99.3922 +04/18 20:41:37 - mmengine - INFO - Iter(train) [119050/160000] lr: 3.0037e-03 eta: 6:17:20 time: 0.5547 data_time: 0.0065 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0066 decode.acc_seg: 99.7228 aux.loss_ce: 0.0074 aux.acc_seg: 99.0550 +04/18 20:42:05 - mmengine - INFO - Iter(train) [119100/160000] lr: 3.0006e-03 eta: 6:16:53 time: 0.5646 data_time: 0.0069 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0119 decode.acc_seg: 99.7412 aux.loss_ce: 0.0092 aux.acc_seg: 99.2513 +04/18 20:42:33 - mmengine - INFO - Iter(train) [119150/160000] lr: 2.9974e-03 eta: 6:16:25 time: 0.5574 data_time: 0.0068 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0097 decode.acc_seg: 99.7432 aux.loss_ce: 0.0087 aux.acc_seg: 99.3627 +04/18 20:43:01 - mmengine - INFO - Iter(train) [119200/160000] lr: 2.9942e-03 eta: 6:15:58 time: 0.5562 data_time: 0.0071 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.6814 aux.loss_ce: 0.0071 aux.acc_seg: 99.1776 +04/18 20:43:28 - mmengine - INFO - Iter(train) [119250/160000] lr: 2.9910e-03 eta: 6:15:30 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6810 aux.loss_ce: 0.0072 aux.acc_seg: 99.3422 +04/18 20:43:56 - mmengine - INFO - Iter(train) [119300/160000] lr: 2.9878e-03 eta: 6:15:02 time: 0.5546 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.6921 aux.loss_ce: 0.0077 aux.acc_seg: 99.2339 +04/18 20:44:24 - mmengine - INFO - Iter(train) [119350/160000] lr: 2.9846e-03 eta: 6:14:35 time: 0.5541 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6759 aux.loss_ce: 0.0076 aux.acc_seg: 99.1552 +04/18 20:44:52 - mmengine - INFO - Iter(train) [119400/160000] lr: 2.9814e-03 eta: 6:14:07 time: 0.5553 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0078 decode.acc_seg: 99.7362 aux.loss_ce: 0.0082 aux.acc_seg: 99.2253 +04/18 20:45:20 - mmengine - INFO - Iter(train) [119450/160000] lr: 2.9782e-03 eta: 6:13:40 time: 0.5554 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6972 aux.loss_ce: 0.0071 aux.acc_seg: 99.1246 +04/18 20:45:47 - mmengine - INFO - Iter(train) [119500/160000] lr: 2.9750e-03 eta: 6:13:12 time: 0.5548 data_time: 0.0079 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7215 aux.loss_ce: 0.0071 aux.acc_seg: 99.3957 +04/18 20:46:15 - mmengine - INFO - Iter(train) [119550/160000] lr: 2.9718e-03 eta: 6:12:44 time: 0.5565 data_time: 0.0076 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6366 aux.loss_ce: 0.0073 aux.acc_seg: 99.0820 +04/18 20:46:43 - mmengine - INFO - Iter(train) [119600/160000] lr: 2.9686e-03 eta: 6:12:17 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7691 aux.loss_ce: 0.0069 aux.acc_seg: 99.2675 +04/18 20:47:11 - mmengine - INFO - Iter(train) [119650/160000] lr: 2.9654e-03 eta: 6:11:49 time: 0.5635 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6838 aux.loss_ce: 0.0072 aux.acc_seg: 99.0096 +04/18 20:47:39 - mmengine - INFO - Iter(train) [119700/160000] lr: 2.9622e-03 eta: 6:11:22 time: 0.5639 data_time: 0.0070 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7218 aux.loss_ce: 0.0077 aux.acc_seg: 99.3238 +04/18 20:48:07 - mmengine - INFO - Iter(train) [119750/160000] lr: 2.9590e-03 eta: 6:10:54 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6610 aux.loss_ce: 0.0076 aux.acc_seg: 99.1394 +04/18 20:48:34 - mmengine - INFO - Iter(train) [119800/160000] lr: 2.9558e-03 eta: 6:10:26 time: 0.5548 data_time: 0.0075 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.6144 aux.loss_ce: 0.0074 aux.acc_seg: 98.9707 +04/18 20:49:02 - mmengine - INFO - Iter(train) [119850/160000] lr: 2.9526e-03 eta: 6:09:59 time: 0.5546 data_time: 0.0062 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6242 aux.loss_ce: 0.0080 aux.acc_seg: 98.6984 +04/18 20:49:30 - mmengine - INFO - Iter(train) [119900/160000] lr: 2.9494e-03 eta: 6:09:31 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7492 aux.loss_ce: 0.0072 aux.acc_seg: 99.2558 +04/18 20:49:58 - mmengine - INFO - Iter(train) [119950/160000] lr: 2.9462e-03 eta: 6:09:04 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.6436 aux.loss_ce: 0.0071 aux.acc_seg: 99.0393 +04/18 20:50:25 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:50:25 - mmengine - INFO - Iter(train) [120000/160000] lr: 2.9430e-03 eta: 6:08:36 time: 0.5553 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7734 aux.loss_ce: 0.0074 aux.acc_seg: 99.3732 +04/18 20:50:25 - mmengine - INFO - Saving checkpoint at 120000 iterations +04/18 20:50:29 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0463 data_time: 0.0014 memory: 1657 +04/18 20:50:32 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0465 data_time: 0.0016 memory: 1657 +04/18 20:50:34 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0462 data_time: 0.0014 memory: 1657 +04/18 20:50:36 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0457 data_time: 0.0013 memory: 1657 +04/18 20:50:37 - mmengine - INFO - per class results: +04/18 20:50:37 - mmengine - INFO - ++------------+-------+------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+------+--------+-----------+--------+ +| background | 99.09 | 99.5 | 99.54 | 99.58 | 99.5 | +| contrast | 80.39 | 90.0 | 89.13 | 88.28 | 90.0 | ++------------+-------+------+--------+-----------+--------+ +04/18 20:50:37 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1200 mIoU: 89.7400 mAcc: 94.7500 mFscore: 94.3400 mPrecision: 93.9300 mRecall: 94.7500 data_time: 0.0015 time: 0.0466 +04/18 20:51:04 - mmengine - INFO - Iter(train) [120050/160000] lr: 2.9398e-03 eta: 6:08:08 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7140 aux.loss_ce: 0.0076 aux.acc_seg: 99.1138 +04/18 20:51:32 - mmengine - INFO - Iter(train) [120100/160000] lr: 2.9366e-03 eta: 6:07:41 time: 0.5565 data_time: 0.0075 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.6864 aux.loss_ce: 0.0074 aux.acc_seg: 99.1917 +04/18 20:52:00 - mmengine - INFO - Iter(train) [120150/160000] lr: 2.9334e-03 eta: 6:07:13 time: 0.5557 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7345 aux.loss_ce: 0.0074 aux.acc_seg: 99.0086 +04/18 20:52:28 - mmengine - INFO - Iter(train) [120200/160000] lr: 2.9302e-03 eta: 6:06:46 time: 0.5563 data_time: 0.0072 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.6479 aux.loss_ce: 0.0073 aux.acc_seg: 98.8611 +04/18 20:52:55 - mmengine - INFO - Iter(train) [120250/160000] lr: 2.9270e-03 eta: 6:06:18 time: 0.5556 data_time: 0.0075 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7431 aux.loss_ce: 0.0073 aux.acc_seg: 99.3074 +04/18 20:53:23 - mmengine - INFO - Iter(train) [120300/160000] lr: 2.9238e-03 eta: 6:05:50 time: 0.5546 data_time: 0.0063 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7306 aux.loss_ce: 0.0072 aux.acc_seg: 99.1041 +04/18 20:53:51 - mmengine - INFO - Iter(train) [120350/160000] lr: 2.9206e-03 eta: 6:05:23 time: 0.5562 data_time: 0.0065 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7080 aux.loss_ce: 0.0072 aux.acc_seg: 99.1747 +04/18 20:54:19 - mmengine - INFO - Iter(train) [120400/160000] lr: 2.9174e-03 eta: 6:04:55 time: 0.5540 data_time: 0.0061 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7389 aux.loss_ce: 0.0071 aux.acc_seg: 99.1768 +04/18 20:54:46 - mmengine - INFO - Iter(train) [120450/160000] lr: 2.9142e-03 eta: 6:04:28 time: 0.5533 data_time: 0.0069 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.7629 aux.loss_ce: 0.0068 aux.acc_seg: 99.4340 +04/18 20:55:14 - mmengine - INFO - Iter(train) [120500/160000] lr: 2.9110e-03 eta: 6:04:00 time: 0.5652 data_time: 0.0068 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7408 aux.loss_ce: 0.0072 aux.acc_seg: 99.2261 +04/18 20:55:42 - mmengine - INFO - Iter(train) [120550/160000] lr: 2.9078e-03 eta: 6:03:32 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0073 decode.acc_seg: 99.7856 aux.loss_ce: 0.0071 aux.acc_seg: 99.4425 +04/18 20:56:10 - mmengine - INFO - Iter(train) [120600/160000] lr: 2.9046e-03 eta: 6:03:05 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0062 decode.acc_seg: 99.6795 aux.loss_ce: 0.0068 aux.acc_seg: 98.9835 +04/18 20:56:38 - mmengine - INFO - Iter(train) [120650/160000] lr: 2.9014e-03 eta: 6:02:37 time: 0.5562 data_time: 0.0064 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0060 decode.acc_seg: 99.7681 aux.loss_ce: 0.0067 aux.acc_seg: 99.3935 +04/18 20:57:06 - mmengine - INFO - Iter(train) [120700/160000] lr: 2.8982e-03 eta: 6:02:10 time: 0.5553 data_time: 0.0064 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0061 decode.acc_seg: 99.7891 aux.loss_ce: 0.0068 aux.acc_seg: 99.2044 +04/18 20:57:33 - mmengine - INFO - Iter(train) [120750/160000] lr: 2.8950e-03 eta: 6:01:42 time: 0.5548 data_time: 0.0070 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.7631 aux.loss_ce: 0.0078 aux.acc_seg: 99.3780 +04/18 20:58:01 - mmengine - INFO - Iter(train) [120800/160000] lr: 2.8918e-03 eta: 6:01:14 time: 0.5540 data_time: 0.0077 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7164 aux.loss_ce: 0.0070 aux.acc_seg: 99.3695 +04/18 20:58:29 - mmengine - INFO - Iter(train) [120850/160000] lr: 2.8886e-03 eta: 6:00:47 time: 0.5545 data_time: 0.0072 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.6573 aux.loss_ce: 0.0076 aux.acc_seg: 98.8897 +04/18 20:58:57 - mmengine - INFO - Iter(train) [120900/160000] lr: 2.8854e-03 eta: 6:00:19 time: 0.5555 data_time: 0.0079 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7353 aux.loss_ce: 0.0075 aux.acc_seg: 99.3001 +04/18 20:59:25 - mmengine - INFO - Iter(train) [120950/160000] lr: 2.8822e-03 eta: 5:59:52 time: 0.5547 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7063 aux.loss_ce: 0.0071 aux.acc_seg: 99.1960 +04/18 20:59:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:59:52 - mmengine - INFO - Iter(train) [121000/160000] lr: 2.8790e-03 eta: 5:59:24 time: 0.5553 data_time: 0.0070 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7291 aux.loss_ce: 0.0071 aux.acc_seg: 99.1344 +04/18 21:00:20 - mmengine - INFO - Iter(train) [121050/160000] lr: 2.8758e-03 eta: 5:58:56 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0062 decode.acc_seg: 99.6985 aux.loss_ce: 0.0066 aux.acc_seg: 99.1734 +04/18 21:00:48 - mmengine - INFO - Iter(train) [121100/160000] lr: 2.8726e-03 eta: 5:58:29 time: 0.5558 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.6827 aux.loss_ce: 0.0072 aux.acc_seg: 99.1752 +04/18 21:01:16 - mmengine - INFO - Iter(train) [121150/160000] lr: 2.8694e-03 eta: 5:58:01 time: 0.5560 data_time: 0.0067 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7724 aux.loss_ce: 0.0074 aux.acc_seg: 99.2701 +04/18 21:01:43 - mmengine - INFO - Iter(train) [121200/160000] lr: 2.8662e-03 eta: 5:57:34 time: 0.5554 data_time: 0.0068 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7206 aux.loss_ce: 0.0071 aux.acc_seg: 99.1982 +04/18 21:02:11 - mmengine - INFO - Iter(train) [121250/160000] lr: 2.8630e-03 eta: 5:57:06 time: 0.5551 data_time: 0.0069 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.6980 aux.loss_ce: 0.0070 aux.acc_seg: 99.3225 +04/18 21:02:39 - mmengine - INFO - Iter(train) [121300/160000] lr: 2.8597e-03 eta: 5:56:38 time: 0.5537 data_time: 0.0067 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0119 decode.acc_seg: 99.4873 aux.loss_ce: 0.0094 aux.acc_seg: 99.1308 +04/18 21:03:07 - mmengine - INFO - Iter(train) [121350/160000] lr: 2.8565e-03 eta: 5:56:11 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0086 decode.acc_seg: 99.5501 aux.loss_ce: 0.0082 aux.acc_seg: 98.8340 +04/18 21:03:34 - mmengine - INFO - Iter(train) [121400/160000] lr: 2.8533e-03 eta: 5:55:43 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.7892 aux.loss_ce: 0.0073 aux.acc_seg: 99.3712 +04/18 21:04:02 - mmengine - INFO - Iter(train) [121450/160000] lr: 2.8501e-03 eta: 5:55:15 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0081 decode.acc_seg: 99.6509 aux.loss_ce: 0.0081 aux.acc_seg: 98.9856 +04/18 21:04:30 - mmengine - INFO - Iter(train) [121500/160000] lr: 2.8469e-03 eta: 5:54:48 time: 0.5564 data_time: 0.0072 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.7168 aux.loss_ce: 0.0080 aux.acc_seg: 99.2508 +04/18 21:04:58 - mmengine - INFO - Iter(train) [121550/160000] lr: 2.8437e-03 eta: 5:54:20 time: 0.5551 data_time: 0.0081 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6490 aux.loss_ce: 0.0081 aux.acc_seg: 99.1926 +04/18 21:05:26 - mmengine - INFO - Iter(train) [121600/160000] lr: 2.8405e-03 eta: 5:53:53 time: 0.5552 data_time: 0.0062 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6541 aux.loss_ce: 0.0080 aux.acc_seg: 99.0889 +04/18 21:05:53 - mmengine - INFO - Iter(train) [121650/160000] lr: 2.8373e-03 eta: 5:53:25 time: 0.5557 data_time: 0.0071 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6855 aux.loss_ce: 0.0078 aux.acc_seg: 99.1094 +04/18 21:06:21 - mmengine - INFO - Iter(train) [121700/160000] lr: 2.8341e-03 eta: 5:52:57 time: 0.5566 data_time: 0.0066 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.7393 aux.loss_ce: 0.0074 aux.acc_seg: 99.1827 +04/18 21:06:49 - mmengine - INFO - Iter(train) [121750/160000] lr: 2.8309e-03 eta: 5:52:30 time: 0.5551 data_time: 0.0075 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7639 aux.loss_ce: 0.0071 aux.acc_seg: 99.2971 +04/18 21:07:17 - mmengine - INFO - Iter(train) [121800/160000] lr: 2.8276e-03 eta: 5:52:02 time: 0.5650 data_time: 0.0069 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7086 aux.loss_ce: 0.0070 aux.acc_seg: 99.1890 +04/18 21:07:45 - mmengine - INFO - Iter(train) [121850/160000] lr: 2.8244e-03 eta: 5:51:35 time: 0.5546 data_time: 0.0065 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7795 aux.loss_ce: 0.0077 aux.acc_seg: 99.3040 +04/18 21:08:12 - mmengine - INFO - Iter(train) [121900/160000] lr: 2.8212e-03 eta: 5:51:07 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.7078 aux.loss_ce: 0.0073 aux.acc_seg: 99.0706 +04/18 21:08:40 - mmengine - INFO - Iter(train) [121950/160000] lr: 2.8180e-03 eta: 5:50:39 time: 0.5542 data_time: 0.0067 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7528 aux.loss_ce: 0.0077 aux.acc_seg: 99.2546 +04/18 21:09:08 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:09:08 - mmengine - INFO - Iter(train) [122000/160000] lr: 2.8148e-03 eta: 5:50:12 time: 0.5533 data_time: 0.0069 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7618 aux.loss_ce: 0.0071 aux.acc_seg: 99.5121 +04/18 21:09:36 - mmengine - INFO - Iter(train) [122050/160000] lr: 2.8116e-03 eta: 5:49:44 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0061 decode.acc_seg: 99.7833 aux.loss_ce: 0.0068 aux.acc_seg: 99.3933 +04/18 21:10:03 - mmengine - INFO - Iter(train) [122100/160000] lr: 2.8084e-03 eta: 5:49:17 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.6613 aux.loss_ce: 0.0075 aux.acc_seg: 99.2094 +04/18 21:10:31 - mmengine - INFO - Iter(train) [122150/160000] lr: 2.8051e-03 eta: 5:48:49 time: 0.5537 data_time: 0.0071 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.7586 aux.loss_ce: 0.0072 aux.acc_seg: 99.2103 +04/18 21:10:59 - mmengine - INFO - Iter(train) [122200/160000] lr: 2.8019e-03 eta: 5:48:21 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.6831 aux.loss_ce: 0.0074 aux.acc_seg: 99.1498 +04/18 21:11:27 - mmengine - INFO - Iter(train) [122250/160000] lr: 2.7987e-03 eta: 5:47:54 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7026 aux.loss_ce: 0.0071 aux.acc_seg: 99.1042 +04/18 21:11:54 - mmengine - INFO - Iter(train) [122300/160000] lr: 2.7955e-03 eta: 5:47:26 time: 0.5542 data_time: 0.0062 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7649 aux.loss_ce: 0.0071 aux.acc_seg: 99.4239 +04/18 21:12:22 - mmengine - INFO - Iter(train) [122350/160000] lr: 2.7923e-03 eta: 5:46:58 time: 0.5562 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7177 aux.loss_ce: 0.0075 aux.acc_seg: 99.2822 +04/18 21:12:50 - mmengine - INFO - Iter(train) [122400/160000] lr: 2.7890e-03 eta: 5:46:31 time: 0.5555 data_time: 0.0076 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7313 aux.loss_ce: 0.0076 aux.acc_seg: 99.1595 +04/18 21:13:18 - mmengine - INFO - Iter(train) [122450/160000] lr: 2.7858e-03 eta: 5:46:03 time: 0.5556 data_time: 0.0072 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7914 aux.loss_ce: 0.0073 aux.acc_seg: 99.1714 +04/18 21:13:45 - mmengine - INFO - Iter(train) [122500/160000] lr: 2.7826e-03 eta: 5:45:36 time: 0.5556 data_time: 0.0078 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.8101 aux.loss_ce: 0.0072 aux.acc_seg: 99.4071 +04/18 21:14:13 - mmengine - INFO - Iter(train) [122550/160000] lr: 2.7794e-03 eta: 5:45:08 time: 0.5539 data_time: 0.0069 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0073 decode.acc_seg: 99.6625 aux.loss_ce: 0.0078 aux.acc_seg: 99.1623 +04/18 21:14:41 - mmengine - INFO - Iter(train) [122600/160000] lr: 2.7762e-03 eta: 5:44:40 time: 0.5530 data_time: 0.0062 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.6551 aux.loss_ce: 0.0073 aux.acc_seg: 99.1289 +04/18 21:15:09 - mmengine - INFO - Iter(train) [122650/160000] lr: 2.7730e-03 eta: 5:44:13 time: 0.5644 data_time: 0.0063 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7834 aux.loss_ce: 0.0071 aux.acc_seg: 99.4628 +04/18 21:15:36 - mmengine - INFO - Iter(train) [122700/160000] lr: 2.7697e-03 eta: 5:43:45 time: 0.5541 data_time: 0.0070 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7046 aux.loss_ce: 0.0074 aux.acc_seg: 98.9109 +04/18 21:16:04 - mmengine - INFO - Iter(train) [122750/160000] lr: 2.7665e-03 eta: 5:43:18 time: 0.5557 data_time: 0.0070 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7723 aux.loss_ce: 0.0074 aux.acc_seg: 99.4095 +04/18 21:16:32 - mmengine - INFO - Iter(train) [122800/160000] lr: 2.7633e-03 eta: 5:42:50 time: 0.5546 data_time: 0.0072 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7140 aux.loss_ce: 0.0074 aux.acc_seg: 99.2131 +04/18 21:17:00 - mmengine - INFO - Iter(train) [122850/160000] lr: 2.7601e-03 eta: 5:42:22 time: 0.5548 data_time: 0.0071 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7643 aux.loss_ce: 0.0071 aux.acc_seg: 99.3224 +04/18 21:17:28 - mmengine - INFO - Iter(train) [122900/160000] lr: 2.7568e-03 eta: 5:41:55 time: 0.5552 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.6893 aux.loss_ce: 0.0070 aux.acc_seg: 99.1234 +04/18 21:17:55 - mmengine - INFO - Iter(train) [122950/160000] lr: 2.7536e-03 eta: 5:41:27 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7011 aux.loss_ce: 0.0073 aux.acc_seg: 99.1089 +04/18 21:18:23 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:18:23 - mmengine - INFO - Iter(train) [123000/160000] lr: 2.7504e-03 eta: 5:41:00 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7240 aux.loss_ce: 0.0072 aux.acc_seg: 99.1662 +04/18 21:18:51 - mmengine - INFO - Iter(train) [123050/160000] lr: 2.7472e-03 eta: 5:40:32 time: 0.5555 data_time: 0.0069 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7085 aux.loss_ce: 0.0071 aux.acc_seg: 99.1843 +04/18 21:19:19 - mmengine - INFO - Iter(train) [123100/160000] lr: 2.7440e-03 eta: 5:40:04 time: 0.5564 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7354 aux.loss_ce: 0.0072 aux.acc_seg: 99.2057 +04/18 21:19:47 - mmengine - INFO - Iter(train) [123150/160000] lr: 2.7407e-03 eta: 5:39:37 time: 0.5559 data_time: 0.0064 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7125 aux.loss_ce: 0.0070 aux.acc_seg: 99.1646 +04/18 21:20:14 - mmengine - INFO - Iter(train) [123200/160000] lr: 2.7375e-03 eta: 5:39:09 time: 0.5541 data_time: 0.0064 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7466 aux.loss_ce: 0.0067 aux.acc_seg: 99.2326 +04/18 21:20:42 - mmengine - INFO - Iter(train) [123250/160000] lr: 2.7343e-03 eta: 5:38:41 time: 0.5550 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7112 aux.loss_ce: 0.0074 aux.acc_seg: 99.1482 +04/18 21:21:10 - mmengine - INFO - Iter(train) [123300/160000] lr: 2.7311e-03 eta: 5:38:14 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7950 aux.loss_ce: 0.0073 aux.acc_seg: 99.2402 +04/18 21:21:38 - mmengine - INFO - Iter(train) [123350/160000] lr: 2.7278e-03 eta: 5:37:46 time: 0.5556 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6663 aux.loss_ce: 0.0073 aux.acc_seg: 99.0669 +04/18 21:22:05 - mmengine - INFO - Iter(train) [123400/160000] lr: 2.7246e-03 eta: 5:37:19 time: 0.5565 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.6578 aux.loss_ce: 0.0079 aux.acc_seg: 98.9572 +04/18 21:22:33 - mmengine - INFO - Iter(train) [123450/160000] lr: 2.7214e-03 eta: 5:36:51 time: 0.5557 data_time: 0.0062 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7147 aux.loss_ce: 0.0071 aux.acc_seg: 99.3171 +04/18 21:23:01 - mmengine - INFO - Iter(train) [123500/160000] lr: 2.7181e-03 eta: 5:36:23 time: 0.5556 data_time: 0.0063 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0064 decode.acc_seg: 99.8658 aux.loss_ce: 0.0067 aux.acc_seg: 99.5777 +04/18 21:23:29 - mmengine - INFO - Iter(train) [123550/160000] lr: 2.7149e-03 eta: 5:35:56 time: 0.5555 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7674 aux.loss_ce: 0.0070 aux.acc_seg: 99.3142 +04/18 21:23:57 - mmengine - INFO - Iter(train) [123600/160000] lr: 2.7117e-03 eta: 5:35:28 time: 0.5556 data_time: 0.0074 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.6979 aux.loss_ce: 0.0075 aux.acc_seg: 98.9636 +04/18 21:24:24 - mmengine - INFO - Iter(train) [123650/160000] lr: 2.7085e-03 eta: 5:35:01 time: 0.5562 data_time: 0.0073 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0072 decode.acc_seg: 99.6576 aux.loss_ce: 0.0080 aux.acc_seg: 98.9332 +04/18 21:24:52 - mmengine - INFO - Iter(train) [123700/160000] lr: 2.7052e-03 eta: 5:34:33 time: 0.5555 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7257 aux.loss_ce: 0.0072 aux.acc_seg: 99.1588 +04/18 21:25:20 - mmengine - INFO - Iter(train) [123750/160000] lr: 2.7020e-03 eta: 5:34:05 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.8016 aux.loss_ce: 0.0070 aux.acc_seg: 99.3775 +04/18 21:25:48 - mmengine - INFO - Iter(train) [123800/160000] lr: 2.6988e-03 eta: 5:33:38 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7156 aux.loss_ce: 0.0069 aux.acc_seg: 99.3465 +04/18 21:26:15 - mmengine - INFO - Iter(train) [123850/160000] lr: 2.6955e-03 eta: 5:33:10 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7180 aux.loss_ce: 0.0077 aux.acc_seg: 99.0951 +04/18 21:26:43 - mmengine - INFO - Iter(train) [123900/160000] lr: 2.6923e-03 eta: 5:32:42 time: 0.5556 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7747 aux.loss_ce: 0.0077 aux.acc_seg: 99.3084 +04/18 21:27:11 - mmengine - INFO - Iter(train) [123950/160000] lr: 2.6891e-03 eta: 5:32:15 time: 0.5559 data_time: 0.0072 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7608 aux.loss_ce: 0.0073 aux.acc_seg: 99.3449 +04/18 21:27:39 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:27:39 - mmengine - INFO - Iter(train) [124000/160000] lr: 2.6858e-03 eta: 5:31:47 time: 0.5536 data_time: 0.0061 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.6581 aux.loss_ce: 0.0083 aux.acc_seg: 99.2547 +04/18 21:28:07 - mmengine - INFO - Iter(train) [124050/160000] lr: 2.6826e-03 eta: 5:31:20 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.6129 aux.loss_ce: 0.0075 aux.acc_seg: 99.0871 +04/18 21:28:34 - mmengine - INFO - Iter(train) [124100/160000] lr: 2.6794e-03 eta: 5:30:52 time: 0.5558 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6805 aux.loss_ce: 0.0072 aux.acc_seg: 99.3385 +04/18 21:29:02 - mmengine - INFO - Iter(train) [124150/160000] lr: 2.6761e-03 eta: 5:30:24 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7404 aux.loss_ce: 0.0071 aux.acc_seg: 99.1956 +04/18 21:29:30 - mmengine - INFO - Iter(train) [124200/160000] lr: 2.6729e-03 eta: 5:29:57 time: 0.5558 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6528 aux.loss_ce: 0.0074 aux.acc_seg: 99.0789 +04/18 21:29:58 - mmengine - INFO - Iter(train) [124250/160000] lr: 2.6697e-03 eta: 5:29:29 time: 0.5556 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7506 aux.loss_ce: 0.0070 aux.acc_seg: 99.4198 +04/18 21:30:26 - mmengine - INFO - Iter(train) [124300/160000] lr: 2.6664e-03 eta: 5:29:02 time: 0.5549 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.6850 aux.loss_ce: 0.0072 aux.acc_seg: 99.1245 +04/18 21:30:53 - mmengine - INFO - Iter(train) [124350/160000] lr: 2.6632e-03 eta: 5:28:34 time: 0.5536 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7113 aux.loss_ce: 0.0076 aux.acc_seg: 99.2085 +04/18 21:31:21 - mmengine - INFO - Iter(train) [124400/160000] lr: 2.6600e-03 eta: 5:28:06 time: 0.5555 data_time: 0.0072 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7376 aux.loss_ce: 0.0074 aux.acc_seg: 99.2411 +04/18 21:31:49 - mmengine - INFO - Iter(train) [124450/160000] lr: 2.6567e-03 eta: 5:27:39 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7454 aux.loss_ce: 0.0073 aux.acc_seg: 99.1824 +04/18 21:32:17 - mmengine - INFO - Iter(train) [124500/160000] lr: 2.6535e-03 eta: 5:27:11 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7325 aux.loss_ce: 0.0075 aux.acc_seg: 99.2898 +04/18 21:32:44 - mmengine - INFO - Iter(train) [124550/160000] lr: 2.6503e-03 eta: 5:26:43 time: 0.5555 data_time: 0.0073 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7578 aux.loss_ce: 0.0069 aux.acc_seg: 99.2954 +04/18 21:33:12 - mmengine - INFO - Iter(train) [124600/160000] lr: 2.6470e-03 eta: 5:26:16 time: 0.5555 data_time: 0.0070 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0062 decode.acc_seg: 99.7704 aux.loss_ce: 0.0065 aux.acc_seg: 99.3500 +04/18 21:33:40 - mmengine - INFO - Iter(train) [124650/160000] lr: 2.6438e-03 eta: 5:25:48 time: 0.5545 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.6255 aux.loss_ce: 0.0075 aux.acc_seg: 99.0353 +04/18 21:34:08 - mmengine - INFO - Iter(train) [124700/160000] lr: 2.6405e-03 eta: 5:25:21 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0061 decode.acc_seg: 99.8405 aux.loss_ce: 0.0068 aux.acc_seg: 99.5062 +04/18 21:34:35 - mmengine - INFO - Iter(train) [124750/160000] lr: 2.6373e-03 eta: 5:24:53 time: 0.5538 data_time: 0.0064 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7898 aux.loss_ce: 0.0078 aux.acc_seg: 99.4054 +04/18 21:35:03 - mmengine - INFO - Iter(train) [124800/160000] lr: 2.6341e-03 eta: 5:24:25 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7371 aux.loss_ce: 0.0073 aux.acc_seg: 99.2552 +04/18 21:35:31 - mmengine - INFO - Iter(train) [124850/160000] lr: 2.6308e-03 eta: 5:23:58 time: 0.5554 data_time: 0.0077 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7819 aux.loss_ce: 0.0073 aux.acc_seg: 99.3029 +04/18 21:35:59 - mmengine - INFO - Iter(train) [124900/160000] lr: 2.6276e-03 eta: 5:23:30 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.7383 aux.loss_ce: 0.0074 aux.acc_seg: 99.2082 +04/18 21:36:27 - mmengine - INFO - Iter(train) [124950/160000] lr: 2.6243e-03 eta: 5:23:02 time: 0.5545 data_time: 0.0071 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.7116 aux.loss_ce: 0.0076 aux.acc_seg: 99.3921 +04/18 21:36:55 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:36:55 - mmengine - INFO - Iter(train) [125000/160000] lr: 2.6211e-03 eta: 5:22:35 time: 0.5733 data_time: 0.0073 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.6754 aux.loss_ce: 0.0075 aux.acc_seg: 98.9037 +04/18 21:37:22 - mmengine - INFO - Iter(train) [125050/160000] lr: 2.6179e-03 eta: 5:22:07 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7577 aux.loss_ce: 0.0072 aux.acc_seg: 99.2687 +04/18 21:37:50 - mmengine - INFO - Iter(train) [125100/160000] lr: 2.6146e-03 eta: 5:21:40 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.6872 aux.loss_ce: 0.0075 aux.acc_seg: 99.3011 +04/18 21:38:18 - mmengine - INFO - Iter(train) [125150/160000] lr: 2.6114e-03 eta: 5:21:12 time: 0.5561 data_time: 0.0066 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7501 aux.loss_ce: 0.0075 aux.acc_seg: 99.2669 +04/18 21:38:46 - mmengine - INFO - Iter(train) [125200/160000] lr: 2.6081e-03 eta: 5:20:44 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7517 aux.loss_ce: 0.0072 aux.acc_seg: 99.2521 +04/18 21:39:14 - mmengine - INFO - Iter(train) [125250/160000] lr: 2.6049e-03 eta: 5:20:17 time: 0.5546 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7034 aux.loss_ce: 0.0074 aux.acc_seg: 99.3413 +04/18 21:39:41 - mmengine - INFO - Iter(train) [125300/160000] lr: 2.6016e-03 eta: 5:19:49 time: 0.5565 data_time: 0.0078 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7685 aux.loss_ce: 0.0068 aux.acc_seg: 99.3257 +04/18 21:40:09 - mmengine - INFO - Iter(train) [125350/160000] lr: 2.5984e-03 eta: 5:19:22 time: 0.5566 data_time: 0.0069 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7415 aux.loss_ce: 0.0075 aux.acc_seg: 99.2738 +04/18 21:40:37 - mmengine - INFO - Iter(train) [125400/160000] lr: 2.5952e-03 eta: 5:18:54 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.7213 aux.loss_ce: 0.0076 aux.acc_seg: 99.2002 +04/18 21:41:05 - mmengine - INFO - Iter(train) [125450/160000] lr: 2.5919e-03 eta: 5:18:26 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7778 aux.loss_ce: 0.0072 aux.acc_seg: 99.1993 +04/18 21:41:32 - mmengine - INFO - Iter(train) [125500/160000] lr: 2.5887e-03 eta: 5:17:59 time: 0.5523 data_time: 0.0062 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7118 aux.loss_ce: 0.0072 aux.acc_seg: 99.2544 +04/18 21:42:00 - mmengine - INFO - Iter(train) [125550/160000] lr: 2.5854e-03 eta: 5:17:31 time: 0.5545 data_time: 0.0067 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7169 aux.loss_ce: 0.0072 aux.acc_seg: 99.1631 +04/18 21:42:28 - mmengine - INFO - Iter(train) [125600/160000] lr: 2.5822e-03 eta: 5:17:03 time: 0.5552 data_time: 0.0074 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6750 aux.loss_ce: 0.0071 aux.acc_seg: 99.3027 +04/18 21:42:56 - mmengine - INFO - Iter(train) [125650/160000] lr: 2.5789e-03 eta: 5:16:36 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0062 decode.acc_seg: 99.7605 aux.loss_ce: 0.0066 aux.acc_seg: 99.3504 +04/18 21:43:23 - mmengine - INFO - Iter(train) [125700/160000] lr: 2.5757e-03 eta: 5:16:08 time: 0.5550 data_time: 0.0066 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.7562 aux.loss_ce: 0.0067 aux.acc_seg: 99.3752 +04/18 21:43:51 - mmengine - INFO - Iter(train) [125750/160000] lr: 2.5724e-03 eta: 5:15:41 time: 0.5557 data_time: 0.0062 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7888 aux.loss_ce: 0.0071 aux.acc_seg: 99.4412 +04/18 21:44:19 - mmengine - INFO - Iter(train) [125800/160000] lr: 2.5692e-03 eta: 5:15:13 time: 0.5564 data_time: 0.0068 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7596 aux.loss_ce: 0.0069 aux.acc_seg: 99.4021 +04/18 21:44:47 - mmengine - INFO - Iter(train) [125850/160000] lr: 2.5659e-03 eta: 5:14:45 time: 0.5560 data_time: 0.0066 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.7498 aux.loss_ce: 0.0070 aux.acc_seg: 99.2921 +04/18 21:45:15 - mmengine - INFO - Iter(train) [125900/160000] lr: 2.5627e-03 eta: 5:14:18 time: 0.5558 data_time: 0.0064 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.8037 aux.loss_ce: 0.0071 aux.acc_seg: 99.4242 +04/18 21:45:42 - mmengine - INFO - Iter(train) [125950/160000] lr: 2.5594e-03 eta: 5:13:50 time: 0.5553 data_time: 0.0062 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.7516 aux.loss_ce: 0.0070 aux.acc_seg: 99.4208 +04/18 21:46:10 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:46:10 - mmengine - INFO - Iter(train) [126000/160000] lr: 2.5562e-03 eta: 5:13:22 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.7361 aux.loss_ce: 0.0078 aux.acc_seg: 99.3279 +04/18 21:46:38 - mmengine - INFO - Iter(train) [126050/160000] lr: 2.5529e-03 eta: 5:12:55 time: 0.5562 data_time: 0.0071 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0061 decode.acc_seg: 99.6084 aux.loss_ce: 0.0070 aux.acc_seg: 98.8375 +04/18 21:47:06 - mmengine - INFO - Iter(train) [126100/160000] lr: 2.5497e-03 eta: 5:12:27 time: 0.5555 data_time: 0.0063 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0060 decode.acc_seg: 99.6986 aux.loss_ce: 0.0069 aux.acc_seg: 99.1886 +04/18 21:47:34 - mmengine - INFO - Iter(train) [126150/160000] lr: 2.5464e-03 eta: 5:12:00 time: 0.5556 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7086 aux.loss_ce: 0.0069 aux.acc_seg: 99.1688 +04/18 21:48:01 - mmengine - INFO - Iter(train) [126200/160000] lr: 2.5432e-03 eta: 5:11:32 time: 0.5551 data_time: 0.0073 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.6912 aux.loss_ce: 0.0077 aux.acc_seg: 99.2783 +04/18 21:48:29 - mmengine - INFO - Iter(train) [126250/160000] lr: 2.5399e-03 eta: 5:11:04 time: 0.5544 data_time: 0.0064 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0060 decode.acc_seg: 99.7904 aux.loss_ce: 0.0067 aux.acc_seg: 99.2697 +04/18 21:48:57 - mmengine - INFO - Iter(train) [126300/160000] lr: 2.5367e-03 eta: 5:10:37 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7656 aux.loss_ce: 0.0072 aux.acc_seg: 99.1049 +04/18 21:49:25 - mmengine - INFO - Iter(train) [126350/160000] lr: 2.5334e-03 eta: 5:10:09 time: 0.5549 data_time: 0.0069 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7588 aux.loss_ce: 0.0069 aux.acc_seg: 99.3914 +04/18 21:49:53 - mmengine - INFO - Iter(train) [126400/160000] lr: 2.5302e-03 eta: 5:09:42 time: 0.5555 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7691 aux.loss_ce: 0.0076 aux.acc_seg: 99.2879 +04/18 21:50:20 - mmengine - INFO - Iter(train) [126450/160000] lr: 2.5269e-03 eta: 5:09:14 time: 0.5558 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7457 aux.loss_ce: 0.0072 aux.acc_seg: 99.3004 +04/18 21:50:48 - mmengine - INFO - Iter(train) [126500/160000] lr: 2.5237e-03 eta: 5:08:46 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.6911 aux.loss_ce: 0.0068 aux.acc_seg: 99.1219 +04/18 21:51:16 - mmengine - INFO - Iter(train) [126550/160000] lr: 2.5204e-03 eta: 5:08:19 time: 0.5541 data_time: 0.0068 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7453 aux.loss_ce: 0.0067 aux.acc_seg: 99.2411 +04/18 21:51:44 - mmengine - INFO - Iter(train) [126600/160000] lr: 2.5171e-03 eta: 5:07:51 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.6019 aux.loss_ce: 0.0074 aux.acc_seg: 99.0595 +04/18 21:52:11 - mmengine - INFO - Iter(train) [126650/160000] lr: 2.5139e-03 eta: 5:07:23 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0063 decode.acc_seg: 99.7804 aux.loss_ce: 0.0067 aux.acc_seg: 99.3526 +04/18 21:52:39 - mmengine - INFO - Iter(train) [126700/160000] lr: 2.5106e-03 eta: 5:06:56 time: 0.5556 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7977 aux.loss_ce: 0.0071 aux.acc_seg: 99.4468 +04/18 21:53:07 - mmengine - INFO - Iter(train) [126750/160000] lr: 2.5074e-03 eta: 5:06:28 time: 0.5566 data_time: 0.0073 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7871 aux.loss_ce: 0.0075 aux.acc_seg: 99.3344 +04/18 21:53:35 - mmengine - INFO - Iter(train) [126800/160000] lr: 2.5041e-03 eta: 5:06:01 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.7893 aux.loss_ce: 0.0070 aux.acc_seg: 99.4941 +04/18 21:54:03 - mmengine - INFO - Iter(train) [126850/160000] lr: 2.5008e-03 eta: 5:05:33 time: 0.5545 data_time: 0.0065 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7021 aux.loss_ce: 0.0071 aux.acc_seg: 98.9094 +04/18 21:54:30 - mmengine - INFO - Iter(train) [126900/160000] lr: 2.4976e-03 eta: 5:05:05 time: 0.5569 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7677 aux.loss_ce: 0.0074 aux.acc_seg: 99.2421 +04/18 21:54:58 - mmengine - INFO - Iter(train) [126950/160000] lr: 2.4943e-03 eta: 5:04:38 time: 0.5640 data_time: 0.0070 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7974 aux.loss_ce: 0.0074 aux.acc_seg: 99.5192 +04/18 21:55:26 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:55:26 - mmengine - INFO - Iter(train) [127000/160000] lr: 2.4911e-03 eta: 5:04:10 time: 0.5559 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7417 aux.loss_ce: 0.0075 aux.acc_seg: 99.4516 +04/18 21:55:54 - mmengine - INFO - Iter(train) [127050/160000] lr: 2.4878e-03 eta: 5:03:43 time: 0.5547 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.7052 aux.loss_ce: 0.0075 aux.acc_seg: 99.0387 +04/18 21:56:22 - mmengine - INFO - Iter(train) [127100/160000] lr: 2.4845e-03 eta: 5:03:15 time: 0.5544 data_time: 0.0063 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0064 decode.acc_seg: 99.8090 aux.loss_ce: 0.0074 aux.acc_seg: 99.4081 +04/18 21:56:50 - mmengine - INFO - Iter(train) [127150/160000] lr: 2.4813e-03 eta: 5:02:47 time: 0.5738 data_time: 0.0064 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.8383 aux.loss_ce: 0.0071 aux.acc_seg: 99.4491 +04/18 21:57:17 - mmengine - INFO - Iter(train) [127200/160000] lr: 2.4780e-03 eta: 5:02:20 time: 0.5639 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.7498 aux.loss_ce: 0.0078 aux.acc_seg: 99.1600 +04/18 21:57:45 - mmengine - INFO - Iter(train) [127250/160000] lr: 2.4748e-03 eta: 5:01:52 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7391 aux.loss_ce: 0.0069 aux.acc_seg: 99.3620 +04/18 21:58:13 - mmengine - INFO - Iter(train) [127300/160000] lr: 2.4715e-03 eta: 5:01:24 time: 0.5555 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0074 decode.acc_seg: 99.6553 aux.loss_ce: 0.0080 aux.acc_seg: 98.9337 +04/18 21:58:41 - mmengine - INFO - Iter(train) [127350/160000] lr: 2.4682e-03 eta: 5:00:57 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0080 decode.acc_seg: 99.6821 aux.loss_ce: 0.0083 aux.acc_seg: 99.1216 +04/18 21:59:09 - mmengine - INFO - Iter(train) [127400/160000] lr: 2.4650e-03 eta: 5:00:29 time: 0.5552 data_time: 0.0069 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0060 decode.acc_seg: 99.7319 aux.loss_ce: 0.0067 aux.acc_seg: 99.2224 +04/18 21:59:36 - mmengine - INFO - Iter(train) [127450/160000] lr: 2.4617e-03 eta: 5:00:02 time: 0.5560 data_time: 0.0068 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.7175 aux.loss_ce: 0.0076 aux.acc_seg: 99.2351 +04/18 22:00:04 - mmengine - INFO - Iter(train) [127500/160000] lr: 2.4584e-03 eta: 4:59:34 time: 0.5551 data_time: 0.0071 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7566 aux.loss_ce: 0.0070 aux.acc_seg: 99.3694 +04/18 22:00:32 - mmengine - INFO - Iter(train) [127550/160000] lr: 2.4552e-03 eta: 4:59:06 time: 0.5554 data_time: 0.0069 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0071 decode.acc_seg: 99.6647 aux.loss_ce: 0.0079 aux.acc_seg: 98.8403 +04/18 22:01:00 - mmengine - INFO - Iter(train) [127600/160000] lr: 2.4519e-03 eta: 4:58:39 time: 0.5561 data_time: 0.0074 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7548 aux.loss_ce: 0.0069 aux.acc_seg: 99.2050 +04/18 22:01:27 - mmengine - INFO - Iter(train) [127650/160000] lr: 2.4486e-03 eta: 4:58:11 time: 0.5558 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0068 decode.acc_seg: 99.7610 aux.loss_ce: 0.0079 aux.acc_seg: 99.3730 +04/18 22:01:55 - mmengine - INFO - Iter(train) [127700/160000] lr: 2.4454e-03 eta: 4:57:44 time: 0.5555 data_time: 0.0071 memory: 7635 loss: 0.0122 decode.loss_ce: 0.0060 decode.acc_seg: 99.8035 aux.loss_ce: 0.0062 aux.acc_seg: 99.6104 +04/18 22:02:23 - mmengine - INFO - Iter(train) [127750/160000] lr: 2.4421e-03 eta: 4:57:16 time: 0.5550 data_time: 0.0071 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7979 aux.loss_ce: 0.0074 aux.acc_seg: 99.4246 +04/18 22:02:51 - mmengine - INFO - Iter(train) [127800/160000] lr: 2.4388e-03 eta: 4:56:48 time: 0.5549 data_time: 0.0062 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7128 aux.loss_ce: 0.0078 aux.acc_seg: 99.1030 +04/18 22:03:19 - mmengine - INFO - Iter(train) [127850/160000] lr: 2.4356e-03 eta: 4:56:21 time: 0.5565 data_time: 0.0076 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.6639 aux.loss_ce: 0.0075 aux.acc_seg: 99.2200 +04/18 22:03:47 - mmengine - INFO - Iter(train) [127900/160000] lr: 2.4323e-03 eta: 4:55:53 time: 0.5556 data_time: 0.0066 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.5625 aux.loss_ce: 0.0073 aux.acc_seg: 99.0203 +04/18 22:04:14 - mmengine - INFO - Iter(train) [127950/160000] lr: 2.4290e-03 eta: 4:55:25 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7223 aux.loss_ce: 0.0072 aux.acc_seg: 99.1844 +04/18 22:04:42 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:04:42 - mmengine - INFO - Iter(train) [128000/160000] lr: 2.4258e-03 eta: 4:54:58 time: 0.5557 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7183 aux.loss_ce: 0.0071 aux.acc_seg: 99.1471 +04/18 22:05:10 - mmengine - INFO - Iter(train) [128050/160000] lr: 2.4225e-03 eta: 4:54:30 time: 0.5547 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.6617 aux.loss_ce: 0.0078 aux.acc_seg: 99.0107 +04/18 22:05:38 - mmengine - INFO - Iter(train) [128100/160000] lr: 2.4192e-03 eta: 4:54:03 time: 0.5559 data_time: 0.0068 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.6387 aux.loss_ce: 0.0075 aux.acc_seg: 99.1412 +04/18 22:06:06 - mmengine - INFO - Iter(train) [128150/160000] lr: 2.4159e-03 eta: 4:53:35 time: 0.5531 data_time: 0.0068 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0076 decode.acc_seg: 99.5480 aux.loss_ce: 0.0083 aux.acc_seg: 98.7090 +04/18 22:06:33 - mmengine - INFO - Iter(train) [128200/160000] lr: 2.4127e-03 eta: 4:53:07 time: 0.5544 data_time: 0.0070 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.6588 aux.loss_ce: 0.0074 aux.acc_seg: 99.1114 +04/18 22:07:01 - mmengine - INFO - Iter(train) [128250/160000] lr: 2.4094e-03 eta: 4:52:40 time: 0.5566 data_time: 0.0068 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7232 aux.loss_ce: 0.0073 aux.acc_seg: 99.2478 +04/18 22:07:29 - mmengine - INFO - Iter(train) [128300/160000] lr: 2.4061e-03 eta: 4:52:12 time: 0.5548 data_time: 0.0070 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7443 aux.loss_ce: 0.0073 aux.acc_seg: 99.2952 +04/18 22:07:57 - mmengine - INFO - Iter(train) [128350/160000] lr: 2.4029e-03 eta: 4:51:44 time: 0.5537 data_time: 0.0069 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0072 decode.acc_seg: 99.7016 aux.loss_ce: 0.0082 aux.acc_seg: 99.1817 +04/18 22:08:25 - mmengine - INFO - Iter(train) [128400/160000] lr: 2.3996e-03 eta: 4:51:17 time: 0.5556 data_time: 0.0071 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7628 aux.loss_ce: 0.0071 aux.acc_seg: 99.3367 +04/18 22:08:52 - mmengine - INFO - Iter(train) [128450/160000] lr: 2.3963e-03 eta: 4:50:49 time: 0.5557 data_time: 0.0069 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.8194 aux.loss_ce: 0.0069 aux.acc_seg: 99.4799 +04/18 22:09:20 - mmengine - INFO - Iter(train) [128500/160000] lr: 2.3930e-03 eta: 4:50:22 time: 0.5542 data_time: 0.0076 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0066 decode.acc_seg: 99.7598 aux.loss_ce: 0.0075 aux.acc_seg: 99.1301 +04/18 22:09:48 - mmengine - INFO - Iter(train) [128550/160000] lr: 2.3898e-03 eta: 4:49:54 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6736 aux.loss_ce: 0.0076 aux.acc_seg: 99.2837 +04/18 22:10:16 - mmengine - INFO - Iter(train) [128600/160000] lr: 2.3865e-03 eta: 4:49:26 time: 0.5555 data_time: 0.0073 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0073 decode.acc_seg: 99.7605 aux.loss_ce: 0.0079 aux.acc_seg: 99.3143 +04/18 22:10:44 - mmengine - INFO - Iter(train) [128650/160000] lr: 2.3832e-03 eta: 4:48:59 time: 0.5564 data_time: 0.0064 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.6891 aux.loss_ce: 0.0074 aux.acc_seg: 99.3534 +04/18 22:11:11 - mmengine - INFO - Iter(train) [128700/160000] lr: 2.3799e-03 eta: 4:48:31 time: 0.5561 data_time: 0.0069 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7433 aux.loss_ce: 0.0070 aux.acc_seg: 99.2893 +04/18 22:11:39 - mmengine - INFO - Iter(train) [128750/160000] lr: 2.3766e-03 eta: 4:48:03 time: 0.5550 data_time: 0.0071 memory: 7635 loss: 0.0124 decode.loss_ce: 0.0059 decode.acc_seg: 99.7640 aux.loss_ce: 0.0065 aux.acc_seg: 99.3477 +04/18 22:12:07 - mmengine - INFO - Iter(train) [128800/160000] lr: 2.3734e-03 eta: 4:47:36 time: 0.5568 data_time: 0.0074 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0069 decode.acc_seg: 99.7740 aux.loss_ce: 0.0082 aux.acc_seg: 99.2670 +04/18 22:12:35 - mmengine - INFO - Iter(train) [128850/160000] lr: 2.3701e-03 eta: 4:47:08 time: 0.5557 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.6268 aux.loss_ce: 0.0079 aux.acc_seg: 98.8998 +04/18 22:13:03 - mmengine - INFO - Iter(train) [128900/160000] lr: 2.3668e-03 eta: 4:46:41 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0082 decode.acc_seg: 99.6543 aux.loss_ce: 0.0081 aux.acc_seg: 99.3200 +04/18 22:13:30 - mmengine - INFO - Iter(train) [128950/160000] lr: 2.3635e-03 eta: 4:46:13 time: 0.5560 data_time: 0.0066 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.7135 aux.loss_ce: 0.0077 aux.acc_seg: 99.2747 +04/18 22:13:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:13:58 - mmengine - INFO - Iter(train) [129000/160000] lr: 2.3602e-03 eta: 4:45:45 time: 0.5563 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6754 aux.loss_ce: 0.0071 aux.acc_seg: 98.9764 +04/18 22:14:26 - mmengine - INFO - Iter(train) [129050/160000] lr: 2.3570e-03 eta: 4:45:18 time: 0.5567 data_time: 0.0071 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0075 decode.acc_seg: 99.6849 aux.loss_ce: 0.0080 aux.acc_seg: 99.0967 +04/18 22:14:54 - mmengine - INFO - Iter(train) [129100/160000] lr: 2.3537e-03 eta: 4:44:50 time: 0.5653 data_time: 0.0065 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.7360 aux.loss_ce: 0.0068 aux.acc_seg: 99.1823 +04/18 22:15:22 - mmengine - INFO - Iter(train) [129150/160000] lr: 2.3504e-03 eta: 4:44:23 time: 0.5553 data_time: 0.0071 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7160 aux.loss_ce: 0.0068 aux.acc_seg: 99.1836 +04/18 22:15:49 - mmengine - INFO - Iter(train) [129200/160000] lr: 2.3471e-03 eta: 4:43:55 time: 0.5556 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0066 decode.acc_seg: 99.6051 aux.loss_ce: 0.0075 aux.acc_seg: 98.8124 +04/18 22:16:17 - mmengine - INFO - Iter(train) [129250/160000] lr: 2.3438e-03 eta: 4:43:27 time: 0.5571 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7875 aux.loss_ce: 0.0073 aux.acc_seg: 99.3378 +04/18 22:16:45 - mmengine - INFO - Iter(train) [129300/160000] lr: 2.3405e-03 eta: 4:43:00 time: 0.5565 data_time: 0.0066 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0066 decode.acc_seg: 99.7327 aux.loss_ce: 0.0073 aux.acc_seg: 99.0170 +04/18 22:17:13 - mmengine - INFO - Iter(train) [129350/160000] lr: 2.3373e-03 eta: 4:42:32 time: 0.5650 data_time: 0.0064 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7454 aux.loss_ce: 0.0068 aux.acc_seg: 99.3069 +04/18 22:17:41 - mmengine - INFO - Iter(train) [129400/160000] lr: 2.3340e-03 eta: 4:42:04 time: 0.5561 data_time: 0.0074 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6684 aux.loss_ce: 0.0080 aux.acc_seg: 99.0279 +04/18 22:18:09 - mmengine - INFO - Iter(train) [129450/160000] lr: 2.3307e-03 eta: 4:41:37 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6218 aux.loss_ce: 0.0073 aux.acc_seg: 99.3181 +04/18 22:18:36 - mmengine - INFO - Iter(train) [129500/160000] lr: 2.3274e-03 eta: 4:41:09 time: 0.5562 data_time: 0.0064 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7198 aux.loss_ce: 0.0076 aux.acc_seg: 99.3068 +04/18 22:19:04 - mmengine - INFO - Iter(train) [129550/160000] lr: 2.3241e-03 eta: 4:40:42 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6206 aux.loss_ce: 0.0072 aux.acc_seg: 99.1624 +04/18 22:19:32 - mmengine - INFO - Iter(train) [129600/160000] lr: 2.3208e-03 eta: 4:40:14 time: 0.5543 data_time: 0.0062 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7700 aux.loss_ce: 0.0071 aux.acc_seg: 99.3279 +04/18 22:20:00 - mmengine - INFO - Iter(train) [129650/160000] lr: 2.3175e-03 eta: 4:39:46 time: 0.5557 data_time: 0.0073 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6285 aux.loss_ce: 0.0073 aux.acc_seg: 99.0627 +04/18 22:20:28 - mmengine - INFO - Iter(train) [129700/160000] lr: 2.3143e-03 eta: 4:39:19 time: 0.5566 data_time: 0.0062 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6714 aux.loss_ce: 0.0072 aux.acc_seg: 99.1223 +04/18 22:20:55 - mmengine - INFO - Iter(train) [129750/160000] lr: 2.3110e-03 eta: 4:38:51 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.8057 aux.loss_ce: 0.0070 aux.acc_seg: 99.3267 +04/18 22:21:23 - mmengine - INFO - Iter(train) [129800/160000] lr: 2.3077e-03 eta: 4:38:23 time: 0.5568 data_time: 0.0067 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.7604 aux.loss_ce: 0.0068 aux.acc_seg: 99.4051 +04/18 22:21:51 - mmengine - INFO - Iter(train) [129850/160000] lr: 2.3044e-03 eta: 4:37:56 time: 0.5544 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7201 aux.loss_ce: 0.0074 aux.acc_seg: 99.1375 +04/18 22:22:19 - mmengine - INFO - Iter(train) [129900/160000] lr: 2.3011e-03 eta: 4:37:28 time: 0.5556 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7195 aux.loss_ce: 0.0071 aux.acc_seg: 99.2496 +04/18 22:22:46 - mmengine - INFO - Iter(train) [129950/160000] lr: 2.2978e-03 eta: 4:37:01 time: 0.5555 data_time: 0.0069 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7942 aux.loss_ce: 0.0073 aux.acc_seg: 99.4062 +04/18 22:23:14 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:23:14 - mmengine - INFO - Iter(train) [130000/160000] lr: 2.2945e-03 eta: 4:36:33 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7582 aux.loss_ce: 0.0075 aux.acc_seg: 99.2439 +04/18 22:23:14 - mmengine - INFO - Saving checkpoint at 130000 iterations +04/18 22:23:18 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0461 data_time: 0.0014 memory: 1657 +04/18 22:23:21 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0466 data_time: 0.0017 memory: 1657 +04/18 22:23:23 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0466 data_time: 0.0015 memory: 1657 +04/18 22:23:25 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0454 data_time: 0.0011 memory: 1657 +04/18 22:23:25 - mmengine - INFO - per class results: +04/18 22:23:25 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.08 | 99.52 | 99.54 | 99.56 | 99.52 | +| contrast | 80.15 | 89.42 | 88.98 | 88.55 | 89.42 | ++------------+-------+-------+--------+-----------+--------+ +04/18 22:23:25 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1200 mIoU: 89.6200 mAcc: 94.4700 mFscore: 94.2600 mPrecision: 94.0500 mRecall: 94.4700 data_time: 0.0016 time: 0.0466 +04/18 22:23:53 - mmengine - INFO - Iter(train) [130050/160000] lr: 2.2912e-03 eta: 4:36:05 time: 0.5559 data_time: 0.0062 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.6917 aux.loss_ce: 0.0068 aux.acc_seg: 99.1994 +04/18 22:24:21 - mmengine - INFO - Iter(train) [130100/160000] lr: 2.2879e-03 eta: 4:35:38 time: 0.5559 data_time: 0.0083 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7116 aux.loss_ce: 0.0082 aux.acc_seg: 99.2403 +04/18 22:24:49 - mmengine - INFO - Iter(train) [130150/160000] lr: 2.2846e-03 eta: 4:35:10 time: 0.5563 data_time: 0.0074 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.6255 aux.loss_ce: 0.0075 aux.acc_seg: 99.1024 +04/18 22:25:17 - mmengine - INFO - Iter(train) [130200/160000] lr: 2.2813e-03 eta: 4:34:42 time: 0.5563 data_time: 0.0071 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6431 aux.loss_ce: 0.0081 aux.acc_seg: 99.2937 +04/18 22:25:44 - mmengine - INFO - Iter(train) [130250/160000] lr: 2.2781e-03 eta: 4:34:15 time: 0.5559 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6962 aux.loss_ce: 0.0080 aux.acc_seg: 99.3298 +04/18 22:26:12 - mmengine - INFO - Iter(train) [130300/160000] lr: 2.2748e-03 eta: 4:33:47 time: 0.5547 data_time: 0.0062 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7221 aux.loss_ce: 0.0078 aux.acc_seg: 99.2074 +04/18 22:26:40 - mmengine - INFO - Iter(train) [130350/160000] lr: 2.2715e-03 eta: 4:33:20 time: 0.5566 data_time: 0.0071 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7002 aux.loss_ce: 0.0074 aux.acc_seg: 99.1498 +04/18 22:27:08 - mmengine - INFO - Iter(train) [130400/160000] lr: 2.2682e-03 eta: 4:32:52 time: 0.5557 data_time: 0.0063 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6872 aux.loss_ce: 0.0078 aux.acc_seg: 99.0938 +04/18 22:27:36 - mmengine - INFO - Iter(train) [130450/160000] lr: 2.2649e-03 eta: 4:32:24 time: 0.5570 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7517 aux.loss_ce: 0.0070 aux.acc_seg: 99.2586 +04/18 22:28:04 - mmengine - INFO - Iter(train) [130500/160000] lr: 2.2616e-03 eta: 4:31:57 time: 0.5558 data_time: 0.0067 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.6672 aux.loss_ce: 0.0079 aux.acc_seg: 99.0973 +04/18 22:28:31 - mmengine - INFO - Iter(train) [130550/160000] lr: 2.2583e-03 eta: 4:31:29 time: 0.5561 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.6420 aux.loss_ce: 0.0077 aux.acc_seg: 99.1776 +04/18 22:28:59 - mmengine - INFO - Iter(train) [130600/160000] lr: 2.2550e-03 eta: 4:31:01 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.6999 aux.loss_ce: 0.0074 aux.acc_seg: 99.2559 +04/18 22:29:27 - mmengine - INFO - Iter(train) [130650/160000] lr: 2.2517e-03 eta: 4:30:34 time: 0.5567 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7292 aux.loss_ce: 0.0073 aux.acc_seg: 99.2261 +04/18 22:29:55 - mmengine - INFO - Iter(train) [130700/160000] lr: 2.2484e-03 eta: 4:30:06 time: 0.5557 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.6858 aux.loss_ce: 0.0074 aux.acc_seg: 99.2261 +04/18 22:30:22 - mmengine - INFO - Iter(train) [130750/160000] lr: 2.2451e-03 eta: 4:29:39 time: 0.5547 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7329 aux.loss_ce: 0.0073 aux.acc_seg: 99.4963 +04/18 22:30:50 - mmengine - INFO - Iter(train) [130800/160000] lr: 2.2418e-03 eta: 4:29:11 time: 0.5563 data_time: 0.0070 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.6983 aux.loss_ce: 0.0075 aux.acc_seg: 99.2035 +04/18 22:31:18 - mmengine - INFO - Iter(train) [130850/160000] lr: 2.2385e-03 eta: 4:28:43 time: 0.5548 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7300 aux.loss_ce: 0.0070 aux.acc_seg: 99.1772 +04/18 22:31:46 - mmengine - INFO - Iter(train) [130900/160000] lr: 2.2352e-03 eta: 4:28:16 time: 0.5576 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.8247 aux.loss_ce: 0.0072 aux.acc_seg: 99.4732 +04/18 22:32:14 - mmengine - INFO - Iter(train) [130950/160000] lr: 2.2319e-03 eta: 4:27:48 time: 0.5563 data_time: 0.0066 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7023 aux.loss_ce: 0.0068 aux.acc_seg: 99.1722 +04/18 22:32:42 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:32:42 - mmengine - INFO - Iter(train) [131000/160000] lr: 2.2286e-03 eta: 4:27:20 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7419 aux.loss_ce: 0.0077 aux.acc_seg: 99.1072 +04/18 22:33:09 - mmengine - INFO - Iter(train) [131050/160000] lr: 2.2253e-03 eta: 4:26:53 time: 0.5554 data_time: 0.0062 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.6530 aux.loss_ce: 0.0072 aux.acc_seg: 98.9323 +04/18 22:33:37 - mmengine - INFO - Iter(train) [131100/160000] lr: 2.2220e-03 eta: 4:26:25 time: 0.5562 data_time: 0.0073 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.6785 aux.loss_ce: 0.0070 aux.acc_seg: 99.1549 +04/18 22:34:05 - mmengine - INFO - Iter(train) [131150/160000] lr: 2.2187e-03 eta: 4:25:58 time: 0.5554 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7717 aux.loss_ce: 0.0073 aux.acc_seg: 99.3612 +04/18 22:34:33 - mmengine - INFO - Iter(train) [131200/160000] lr: 2.2154e-03 eta: 4:25:30 time: 0.5556 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.8078 aux.loss_ce: 0.0073 aux.acc_seg: 99.1734 +04/18 22:35:00 - mmengine - INFO - Iter(train) [131250/160000] lr: 2.2120e-03 eta: 4:25:02 time: 0.5584 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0068 decode.acc_seg: 99.7023 aux.loss_ce: 0.0077 aux.acc_seg: 99.0439 +04/18 22:35:28 - mmengine - INFO - Iter(train) [131300/160000] lr: 2.2087e-03 eta: 4:24:35 time: 0.5549 data_time: 0.0073 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7127 aux.loss_ce: 0.0068 aux.acc_seg: 99.1218 +04/18 22:35:56 - mmengine - INFO - Iter(train) [131350/160000] lr: 2.2054e-03 eta: 4:24:07 time: 0.5555 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.7238 aux.loss_ce: 0.0077 aux.acc_seg: 99.0662 +04/18 22:36:24 - mmengine - INFO - Iter(train) [131400/160000] lr: 2.2021e-03 eta: 4:23:39 time: 0.5559 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7224 aux.loss_ce: 0.0074 aux.acc_seg: 99.1974 +04/18 22:36:52 - mmengine - INFO - Iter(train) [131450/160000] lr: 2.1988e-03 eta: 4:23:12 time: 0.5570 data_time: 0.0071 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.7651 aux.loss_ce: 0.0069 aux.acc_seg: 99.3143 +04/18 22:37:20 - mmengine - INFO - Iter(train) [131500/160000] lr: 2.1955e-03 eta: 4:22:44 time: 0.5560 data_time: 0.0073 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7942 aux.loss_ce: 0.0071 aux.acc_seg: 99.2578 +04/18 22:37:48 - mmengine - INFO - Iter(train) [131550/160000] lr: 2.1922e-03 eta: 4:22:17 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.8041 aux.loss_ce: 0.0068 aux.acc_seg: 99.4134 +04/18 22:38:15 - mmengine - INFO - Iter(train) [131600/160000] lr: 2.1889e-03 eta: 4:21:49 time: 0.5573 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7626 aux.loss_ce: 0.0073 aux.acc_seg: 99.3815 +04/18 22:38:43 - mmengine - INFO - Iter(train) [131650/160000] lr: 2.1856e-03 eta: 4:21:21 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0061 decode.acc_seg: 99.8192 aux.loss_ce: 0.0069 aux.acc_seg: 99.4833 +04/18 22:39:11 - mmengine - INFO - Iter(train) [131700/160000] lr: 2.1823e-03 eta: 4:20:54 time: 0.5558 data_time: 0.0061 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0064 decode.acc_seg: 99.7510 aux.loss_ce: 0.0077 aux.acc_seg: 99.0620 +04/18 22:39:39 - mmengine - INFO - Iter(train) [131750/160000] lr: 2.1790e-03 eta: 4:20:26 time: 0.5547 data_time: 0.0062 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0061 decode.acc_seg: 99.7917 aux.loss_ce: 0.0071 aux.acc_seg: 99.4679 +04/18 22:40:07 - mmengine - INFO - Iter(train) [131800/160000] lr: 2.1756e-03 eta: 4:19:58 time: 0.5557 data_time: 0.0064 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7558 aux.loss_ce: 0.0072 aux.acc_seg: 99.3421 +04/18 22:40:34 - mmengine - INFO - Iter(train) [131850/160000] lr: 2.1723e-03 eta: 4:19:31 time: 0.5559 data_time: 0.0067 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7120 aux.loss_ce: 0.0071 aux.acc_seg: 99.1834 +04/18 22:41:02 - mmengine - INFO - Iter(train) [131900/160000] lr: 2.1690e-03 eta: 4:19:03 time: 0.5555 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.6838 aux.loss_ce: 0.0073 aux.acc_seg: 98.8683 +04/18 22:41:30 - mmengine - INFO - Iter(train) [131950/160000] lr: 2.1657e-03 eta: 4:18:35 time: 0.5565 data_time: 0.0063 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7451 aux.loss_ce: 0.0071 aux.acc_seg: 99.2819 +04/18 22:41:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:41:58 - mmengine - INFO - Iter(train) [132000/160000] lr: 2.1624e-03 eta: 4:18:08 time: 0.5566 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7520 aux.loss_ce: 0.0074 aux.acc_seg: 99.1823 +04/18 22:42:25 - mmengine - INFO - Iter(train) [132050/160000] lr: 2.1591e-03 eta: 4:17:40 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7133 aux.loss_ce: 0.0071 aux.acc_seg: 99.2467 +04/18 22:42:53 - mmengine - INFO - Iter(train) [132100/160000] lr: 2.1558e-03 eta: 4:17:13 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0059 decode.acc_seg: 99.7382 aux.loss_ce: 0.0067 aux.acc_seg: 99.0999 +04/18 22:43:21 - mmengine - INFO - Iter(train) [132150/160000] lr: 2.1524e-03 eta: 4:16:45 time: 0.5556 data_time: 0.0065 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.7906 aux.loss_ce: 0.0070 aux.acc_seg: 99.3847 +04/18 22:43:49 - mmengine - INFO - Iter(train) [132200/160000] lr: 2.1491e-03 eta: 4:16:17 time: 0.5571 data_time: 0.0072 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.6938 aux.loss_ce: 0.0074 aux.acc_seg: 99.0777 +04/18 22:44:17 - mmengine - INFO - Iter(train) [132250/160000] lr: 2.1458e-03 eta: 4:15:50 time: 0.5570 data_time: 0.0067 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0071 decode.acc_seg: 99.7658 aux.loss_ce: 0.0080 aux.acc_seg: 99.4457 +04/18 22:44:44 - mmengine - INFO - Iter(train) [132300/160000] lr: 2.1425e-03 eta: 4:15:22 time: 0.5560 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7627 aux.loss_ce: 0.0073 aux.acc_seg: 99.3998 +04/18 22:45:12 - mmengine - INFO - Iter(train) [132350/160000] lr: 2.1392e-03 eta: 4:14:54 time: 0.5563 data_time: 0.0077 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7610 aux.loss_ce: 0.0075 aux.acc_seg: 99.2947 +04/18 22:45:40 - mmengine - INFO - Iter(train) [132400/160000] lr: 2.1359e-03 eta: 4:14:27 time: 0.5549 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7573 aux.loss_ce: 0.0071 aux.acc_seg: 99.2528 +04/18 22:46:08 - mmengine - INFO - Iter(train) [132450/160000] lr: 2.1325e-03 eta: 4:13:59 time: 0.5559 data_time: 0.0075 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.8019 aux.loss_ce: 0.0072 aux.acc_seg: 99.3565 +04/18 22:46:36 - mmengine - INFO - Iter(train) [132500/160000] lr: 2.1292e-03 eta: 4:13:32 time: 0.5549 data_time: 0.0079 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.6260 aux.loss_ce: 0.0075 aux.acc_seg: 99.0879 +04/18 22:47:03 - mmengine - INFO - Iter(train) [132550/160000] lr: 2.1259e-03 eta: 4:13:04 time: 0.5545 data_time: 0.0066 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.7648 aux.loss_ce: 0.0068 aux.acc_seg: 99.2874 +04/18 22:47:31 - mmengine - INFO - Iter(train) [132600/160000] lr: 2.1226e-03 eta: 4:12:36 time: 0.5560 data_time: 0.0077 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.7509 aux.loss_ce: 0.0079 aux.acc_seg: 99.0533 +04/18 22:47:59 - mmengine - INFO - Iter(train) [132650/160000] lr: 2.1193e-03 eta: 4:12:09 time: 0.5552 data_time: 0.0063 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0061 decode.acc_seg: 99.5701 aux.loss_ce: 0.0072 aux.acc_seg: 98.5176 +04/18 22:48:27 - mmengine - INFO - Iter(train) [132700/160000] lr: 2.1159e-03 eta: 4:11:41 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7020 aux.loss_ce: 0.0071 aux.acc_seg: 99.2132 +04/18 22:48:54 - mmengine - INFO - Iter(train) [132750/160000] lr: 2.1126e-03 eta: 4:11:13 time: 0.5557 data_time: 0.0076 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.7324 aux.loss_ce: 0.0074 aux.acc_seg: 99.2095 +04/18 22:49:22 - mmengine - INFO - Iter(train) [132800/160000] lr: 2.1093e-03 eta: 4:10:46 time: 0.5550 data_time: 0.0072 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0060 decode.acc_seg: 99.7230 aux.loss_ce: 0.0069 aux.acc_seg: 99.1921 +04/18 22:49:50 - mmengine - INFO - Iter(train) [132850/160000] lr: 2.1060e-03 eta: 4:10:18 time: 0.5532 data_time: 0.0070 memory: 7635 loss: 0.0125 decode.loss_ce: 0.0061 decode.acc_seg: 99.7101 aux.loss_ce: 0.0064 aux.acc_seg: 99.2487 +04/18 22:50:18 - mmengine - INFO - Iter(train) [132900/160000] lr: 2.1026e-03 eta: 4:09:50 time: 0.5528 data_time: 0.0074 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0062 decode.acc_seg: 99.6808 aux.loss_ce: 0.0071 aux.acc_seg: 99.2229 +04/18 22:50:45 - mmengine - INFO - Iter(train) [132950/160000] lr: 2.0993e-03 eta: 4:09:23 time: 0.5546 data_time: 0.0073 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7187 aux.loss_ce: 0.0074 aux.acc_seg: 99.2344 +04/18 22:51:13 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:51:13 - mmengine - INFO - Iter(train) [133000/160000] lr: 2.0960e-03 eta: 4:08:55 time: 0.5545 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7322 aux.loss_ce: 0.0076 aux.acc_seg: 99.3469 +04/18 22:51:41 - mmengine - INFO - Iter(train) [133050/160000] lr: 2.0927e-03 eta: 4:08:27 time: 0.5533 data_time: 0.0069 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.7278 aux.loss_ce: 0.0079 aux.acc_seg: 99.2898 +04/18 22:52:08 - mmengine - INFO - Iter(train) [133100/160000] lr: 2.0893e-03 eta: 4:08:00 time: 0.5540 data_time: 0.0075 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.7031 aux.loss_ce: 0.0072 aux.acc_seg: 99.2740 +04/18 22:52:36 - mmengine - INFO - Iter(train) [133150/160000] lr: 2.0860e-03 eta: 4:07:32 time: 0.5536 data_time: 0.0074 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7276 aux.loss_ce: 0.0080 aux.acc_seg: 99.3108 +04/18 22:53:04 - mmengine - INFO - Iter(train) [133200/160000] lr: 2.0827e-03 eta: 4:07:04 time: 0.5529 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.6853 aux.loss_ce: 0.0074 aux.acc_seg: 99.2971 +04/18 22:53:32 - mmengine - INFO - Iter(train) [133250/160000] lr: 2.0793e-03 eta: 4:06:37 time: 0.5532 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0085 decode.acc_seg: 99.5847 aux.loss_ce: 0.0087 aux.acc_seg: 99.1069 +04/18 22:53:59 - mmengine - INFO - Iter(train) [133300/160000] lr: 2.0760e-03 eta: 4:06:09 time: 0.5533 data_time: 0.0071 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6610 aux.loss_ce: 0.0075 aux.acc_seg: 99.1081 +04/18 22:54:27 - mmengine - INFO - Iter(train) [133350/160000] lr: 2.0727e-03 eta: 4:05:42 time: 0.5559 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7018 aux.loss_ce: 0.0074 aux.acc_seg: 99.1064 +04/18 22:54:55 - mmengine - INFO - Iter(train) [133400/160000] lr: 2.0694e-03 eta: 4:05:14 time: 0.5542 data_time: 0.0078 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0074 decode.acc_seg: 99.6956 aux.loss_ce: 0.0081 aux.acc_seg: 99.1029 +04/18 22:55:22 - mmengine - INFO - Iter(train) [133450/160000] lr: 2.0660e-03 eta: 4:04:46 time: 0.5521 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.7554 aux.loss_ce: 0.0075 aux.acc_seg: 99.3796 +04/18 22:55:50 - mmengine - INFO - Iter(train) [133500/160000] lr: 2.0627e-03 eta: 4:04:19 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6744 aux.loss_ce: 0.0075 aux.acc_seg: 99.0935 +04/18 22:56:18 - mmengine - INFO - Iter(train) [133550/160000] lr: 2.0594e-03 eta: 4:03:51 time: 0.5545 data_time: 0.0065 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7576 aux.loss_ce: 0.0072 aux.acc_seg: 99.2503 +04/18 22:56:46 - mmengine - INFO - Iter(train) [133600/160000] lr: 2.0560e-03 eta: 4:03:23 time: 0.5538 data_time: 0.0062 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.6710 aux.loss_ce: 0.0078 aux.acc_seg: 99.2356 +04/18 22:57:13 - mmengine - INFO - Iter(train) [133650/160000] lr: 2.0527e-03 eta: 4:02:56 time: 0.5622 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7643 aux.loss_ce: 0.0077 aux.acc_seg: 99.2272 +04/18 22:57:41 - mmengine - INFO - Iter(train) [133700/160000] lr: 2.0494e-03 eta: 4:02:28 time: 0.5530 data_time: 0.0063 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.6926 aux.loss_ce: 0.0069 aux.acc_seg: 99.0005 +04/18 22:58:09 - mmengine - INFO - Iter(train) [133750/160000] lr: 2.0460e-03 eta: 4:02:00 time: 0.5539 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7770 aux.loss_ce: 0.0071 aux.acc_seg: 99.2735 +04/18 22:58:36 - mmengine - INFO - Iter(train) [133800/160000] lr: 2.0427e-03 eta: 4:01:33 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6379 aux.loss_ce: 0.0073 aux.acc_seg: 99.2218 +04/18 22:59:04 - mmengine - INFO - Iter(train) [133850/160000] lr: 2.0393e-03 eta: 4:01:05 time: 0.5537 data_time: 0.0075 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7842 aux.loss_ce: 0.0073 aux.acc_seg: 99.4088 +04/18 22:59:32 - mmengine - INFO - Iter(train) [133900/160000] lr: 2.0360e-03 eta: 4:00:37 time: 0.5528 data_time: 0.0065 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7386 aux.loss_ce: 0.0072 aux.acc_seg: 99.2549 +04/18 22:59:59 - mmengine - INFO - Iter(train) [133950/160000] lr: 2.0327e-03 eta: 4:00:10 time: 0.5541 data_time: 0.0071 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7540 aux.loss_ce: 0.0072 aux.acc_seg: 99.4673 +04/18 23:00:27 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 23:00:27 - mmengine - INFO - Iter(train) [134000/160000] lr: 2.0293e-03 eta: 3:59:42 time: 0.5534 data_time: 0.0068 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7256 aux.loss_ce: 0.0075 aux.acc_seg: 99.2776 +04/18 23:00:55 - mmengine - INFO - Iter(train) [134050/160000] lr: 2.0260e-03 eta: 3:59:14 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.7190 aux.loss_ce: 0.0075 aux.acc_seg: 99.4332 +04/18 23:01:23 - mmengine - INFO - Iter(train) [134100/160000] lr: 2.0226e-03 eta: 3:58:47 time: 0.5530 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7502 aux.loss_ce: 0.0070 aux.acc_seg: 99.2365 +04/18 23:01:50 - mmengine - INFO - Iter(train) [134150/160000] lr: 2.0193e-03 eta: 3:58:19 time: 0.5527 data_time: 0.0074 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7362 aux.loss_ce: 0.0071 aux.acc_seg: 99.1766 +04/18 23:02:18 - mmengine - INFO - Iter(train) [134200/160000] lr: 2.0160e-03 eta: 3:57:51 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7310 aux.loss_ce: 0.0071 aux.acc_seg: 99.1065 +04/18 23:02:46 - mmengine - INFO - Iter(train) [134250/160000] lr: 2.0126e-03 eta: 3:57:24 time: 0.5535 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.8188 aux.loss_ce: 0.0075 aux.acc_seg: 99.2989 +04/18 23:03:13 - mmengine - INFO - Iter(train) [134300/160000] lr: 2.0093e-03 eta: 3:56:56 time: 0.5540 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7257 aux.loss_ce: 0.0070 aux.acc_seg: 99.3756 +04/18 23:03:41 - mmengine - INFO - Iter(train) [134350/160000] lr: 2.0059e-03 eta: 3:56:28 time: 0.5522 data_time: 0.0067 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7658 aux.loss_ce: 0.0071 aux.acc_seg: 99.3269 +04/18 23:04:09 - mmengine - INFO - Iter(train) [134400/160000] lr: 2.0026e-03 eta: 3:56:01 time: 0.5527 data_time: 0.0071 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0064 decode.acc_seg: 99.7603 aux.loss_ce: 0.0068 aux.acc_seg: 99.5281 +04/18 23:04:36 - mmengine - INFO - Iter(train) [134450/160000] lr: 1.9992e-03 eta: 3:55:33 time: 0.5536 data_time: 0.0074 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.8034 aux.loss_ce: 0.0068 aux.acc_seg: 99.4308 +04/18 23:05:04 - mmengine - INFO - Iter(train) [134500/160000] lr: 1.9959e-03 eta: 3:55:05 time: 0.5532 data_time: 0.0074 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7137 aux.loss_ce: 0.0077 aux.acc_seg: 99.3555 +04/18 23:05:32 - mmengine - INFO - Iter(train) [134550/160000] lr: 1.9926e-03 eta: 3:54:38 time: 0.5527 data_time: 0.0071 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0066 decode.acc_seg: 99.7665 aux.loss_ce: 0.0075 aux.acc_seg: 99.2996 +04/18 23:05:59 - mmengine - INFO - Iter(train) [134600/160000] lr: 1.9892e-03 eta: 3:54:10 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.7805 aux.loss_ce: 0.0076 aux.acc_seg: 99.3905 +04/18 23:06:27 - mmengine - INFO - Iter(train) [134650/160000] lr: 1.9859e-03 eta: 3:53:43 time: 0.5613 data_time: 0.0071 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.8212 aux.loss_ce: 0.0066 aux.acc_seg: 99.4777 +04/18 23:06:55 - mmengine - INFO - Iter(train) [134700/160000] lr: 1.9825e-03 eta: 3:53:15 time: 0.5635 data_time: 0.0066 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.6467 aux.loss_ce: 0.0073 aux.acc_seg: 98.8048 +04/18 23:07:22 - mmengine - INFO - Iter(train) [134750/160000] lr: 1.9792e-03 eta: 3:52:47 time: 0.5521 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.6914 aux.loss_ce: 0.0071 aux.acc_seg: 99.1985 +04/18 23:07:50 - mmengine - INFO - Iter(train) [134800/160000] lr: 1.9758e-03 eta: 3:52:20 time: 0.5546 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0066 decode.acc_seg: 99.6202 aux.loss_ce: 0.0072 aux.acc_seg: 99.2083 +04/18 23:08:18 - mmengine - INFO - Iter(train) [134850/160000] lr: 1.9725e-03 eta: 3:51:52 time: 0.5513 data_time: 0.0070 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.7522 aux.loss_ce: 0.0068 aux.acc_seg: 99.2096 +04/18 23:08:45 - mmengine - INFO - Iter(train) [134900/160000] lr: 1.9691e-03 eta: 3:51:24 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7433 aux.loss_ce: 0.0072 aux.acc_seg: 99.1336 +04/18 23:09:13 - mmengine - INFO - Iter(train) [134950/160000] lr: 1.9658e-03 eta: 3:50:57 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7322 aux.loss_ce: 0.0073 aux.acc_seg: 99.1809 +04/18 23:09:41 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 23:09:41 - mmengine - INFO - Iter(train) [135000/160000] lr: 1.9624e-03 eta: 3:50:29 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7094 aux.loss_ce: 0.0071 aux.acc_seg: 99.1610 +04/18 23:10:08 - mmengine - INFO - Iter(train) [135050/160000] lr: 1.9591e-03 eta: 3:50:01 time: 0.5515 data_time: 0.0068 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0065 decode.acc_seg: 99.7212 aux.loss_ce: 0.0069 aux.acc_seg: 99.3560 +04/18 23:10:36 - mmengine - INFO - Iter(train) [135100/160000] lr: 1.9557e-03 eta: 3:49:34 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.6983 aux.loss_ce: 0.0068 aux.acc_seg: 99.0344 +04/18 23:11:04 - mmengine - INFO - Iter(train) [135150/160000] lr: 1.9524e-03 eta: 3:49:06 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7859 aux.loss_ce: 0.0072 aux.acc_seg: 99.3858 +04/18 23:11:31 - mmengine - INFO - Iter(train) [135200/160000] lr: 1.9490e-03 eta: 3:48:38 time: 0.5522 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.6347 aux.loss_ce: 0.0077 aux.acc_seg: 99.1165 +04/18 23:11:59 - mmengine - INFO - Iter(train) [135250/160000] lr: 1.9456e-03 eta: 3:48:11 time: 0.5526 data_time: 0.0061 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7595 aux.loss_ce: 0.0068 aux.acc_seg: 99.3470 +04/18 23:12:27 - mmengine - INFO - Iter(train) [135300/160000] lr: 1.9423e-03 eta: 3:47:43 time: 0.5524 data_time: 0.0076 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.7675 aux.loss_ce: 0.0067 aux.acc_seg: 99.2603 +04/18 23:12:54 - mmengine - INFO - Iter(train) [135350/160000] lr: 1.9389e-03 eta: 3:47:15 time: 0.5531 data_time: 0.0077 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0058 decode.acc_seg: 99.8325 aux.loss_ce: 0.0067 aux.acc_seg: 99.3667 +04/18 23:13:22 - mmengine - INFO - Iter(train) [135400/160000] lr: 1.9356e-03 eta: 3:46:48 time: 0.5520 data_time: 0.0060 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7214 aux.loss_ce: 0.0073 aux.acc_seg: 99.1510 +04/18 23:13:49 - mmengine - INFO - Iter(train) [135450/160000] lr: 1.9322e-03 eta: 3:46:20 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0062 decode.acc_seg: 99.7041 aux.loss_ce: 0.0070 aux.acc_seg: 99.0205 +04/18 23:14:17 - mmengine - INFO - Iter(train) [135500/160000] lr: 1.9289e-03 eta: 3:45:52 time: 0.5527 data_time: 0.0069 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0060 decode.acc_seg: 99.8290 aux.loss_ce: 0.0070 aux.acc_seg: 99.3267 +04/18 23:14:45 - mmengine - INFO - Iter(train) [135550/160000] lr: 1.9255e-03 eta: 3:45:25 time: 0.5524 data_time: 0.0071 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0058 decode.acc_seg: 99.7816 aux.loss_ce: 0.0068 aux.acc_seg: 99.3285 +04/18 23:15:12 - mmengine - INFO - Iter(train) [135600/160000] lr: 1.9221e-03 eta: 3:44:57 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7851 aux.loss_ce: 0.0073 aux.acc_seg: 99.2583 +04/18 23:15:40 - mmengine - INFO - Iter(train) [135650/160000] lr: 1.9188e-03 eta: 3:44:29 time: 0.5526 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.7872 aux.loss_ce: 0.0070 aux.acc_seg: 99.4615 +04/18 23:16:08 - mmengine - INFO - Iter(train) [135700/160000] lr: 1.9154e-03 eta: 3:44:02 time: 0.5614 data_time: 0.0072 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7512 aux.loss_ce: 0.0069 aux.acc_seg: 99.3599 +04/18 23:16:35 - mmengine - INFO - Iter(train) [135750/160000] lr: 1.9121e-03 eta: 3:43:34 time: 0.5516 data_time: 0.0064 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0060 decode.acc_seg: 99.7575 aux.loss_ce: 0.0071 aux.acc_seg: 99.1708 +04/18 23:17:03 - mmengine - INFO - Iter(train) [135800/160000] lr: 1.9087e-03 eta: 3:43:06 time: 0.5609 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7773 aux.loss_ce: 0.0072 aux.acc_seg: 99.3564 +04/18 23:17:31 - mmengine - INFO - Iter(train) [135850/160000] lr: 1.9053e-03 eta: 3:42:39 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.6757 aux.loss_ce: 0.0074 aux.acc_seg: 98.9448 +04/18 23:17:58 - mmengine - INFO - Iter(train) [135900/160000] lr: 1.9020e-03 eta: 3:42:11 time: 0.5522 data_time: 0.0070 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.8109 aux.loss_ce: 0.0072 aux.acc_seg: 99.4719 +04/18 23:18:26 - mmengine - INFO - Iter(train) [135950/160000] lr: 1.8986e-03 eta: 3:41:43 time: 0.5509 data_time: 0.0069 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7066 aux.loss_ce: 0.0069 aux.acc_seg: 99.1639 +04/18 23:18:53 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 23:18:53 - mmengine - INFO - Iter(train) [136000/160000] lr: 1.8952e-03 eta: 3:41:16 time: 0.5521 data_time: 0.0069 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7808 aux.loss_ce: 0.0070 aux.acc_seg: 99.3621 +04/18 23:19:21 - mmengine - INFO - Iter(train) [136050/160000] lr: 1.8919e-03 eta: 3:40:48 time: 0.5526 data_time: 0.0066 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0062 decode.acc_seg: 99.7650 aux.loss_ce: 0.0067 aux.acc_seg: 99.3467 +04/18 23:19:49 - mmengine - INFO - Iter(train) [136100/160000] lr: 1.8885e-03 eta: 3:40:20 time: 0.5530 data_time: 0.0072 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.6713 aux.loss_ce: 0.0073 aux.acc_seg: 99.1525 +04/18 23:20:16 - mmengine - INFO - Iter(train) [136150/160000] lr: 1.8851e-03 eta: 3:39:53 time: 0.5518 data_time: 0.0071 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.7138 aux.loss_ce: 0.0072 aux.acc_seg: 99.2328 +04/18 23:20:44 - mmengine - INFO - Iter(train) [136200/160000] lr: 1.8818e-03 eta: 3:39:25 time: 0.5516 data_time: 0.0071 memory: 7635 loss: 0.0124 decode.loss_ce: 0.0058 decode.acc_seg: 99.8105 aux.loss_ce: 0.0066 aux.acc_seg: 99.4086 +04/18 23:21:12 - mmengine - INFO - Iter(train) [136250/160000] lr: 1.8784e-03 eta: 3:38:57 time: 0.5508 data_time: 0.0069 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7879 aux.loss_ce: 0.0070 aux.acc_seg: 99.4346 +04/18 23:21:39 - mmengine - INFO - Iter(train) [136300/160000] lr: 1.8750e-03 eta: 3:38:30 time: 0.5519 data_time: 0.0076 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6236 aux.loss_ce: 0.0073 aux.acc_seg: 98.8398 +04/18 23:22:07 - mmengine - INFO - Iter(train) [136350/160000] lr: 1.8716e-03 eta: 3:38:02 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0063 decode.acc_seg: 99.7940 aux.loss_ce: 0.0073 aux.acc_seg: 99.3380 +04/18 23:22:34 - mmengine - INFO - Iter(train) [136400/160000] lr: 1.8683e-03 eta: 3:37:34 time: 0.5536 data_time: 0.0069 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.8033 aux.loss_ce: 0.0076 aux.acc_seg: 99.5088 +04/18 23:23:02 - mmengine - INFO - Iter(train) [136450/160000] lr: 1.8649e-03 eta: 3:37:07 time: 0.5525 data_time: 0.0070 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0062 decode.acc_seg: 99.7760 aux.loss_ce: 0.0071 aux.acc_seg: 99.1981 +04/18 23:23:30 - mmengine - INFO - Iter(train) [136500/160000] lr: 1.8615e-03 eta: 3:36:39 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0058 decode.acc_seg: 99.7166 aux.loss_ce: 0.0068 aux.acc_seg: 99.2714 +04/18 23:23:57 - mmengine - INFO - Iter(train) [136550/160000] lr: 1.8582e-03 eta: 3:36:11 time: 0.5515 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.7321 aux.loss_ce: 0.0072 aux.acc_seg: 99.1710 +04/18 23:24:25 - mmengine - INFO - Iter(train) [136600/160000] lr: 1.8548e-03 eta: 3:35:44 time: 0.5525 data_time: 0.0077 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7075 aux.loss_ce: 0.0070 aux.acc_seg: 99.1806 +04/18 23:24:53 - mmengine - INFO - Iter(train) [136650/160000] lr: 1.8514e-03 eta: 3:35:16 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7082 aux.loss_ce: 0.0068 aux.acc_seg: 99.4295 +04/18 23:25:20 - mmengine - INFO - Iter(train) [136700/160000] lr: 1.8480e-03 eta: 3:34:48 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.6579 aux.loss_ce: 0.0070 aux.acc_seg: 99.0955 +04/18 23:25:48 - mmengine - INFO - Iter(train) [136750/160000] lr: 1.8447e-03 eta: 3:34:21 time: 0.5505 data_time: 0.0071 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.7284 aux.loss_ce: 0.0077 aux.acc_seg: 99.1408 +04/18 23:26:16 - mmengine - INFO - Iter(train) [136800/160000] lr: 1.8413e-03 eta: 3:33:53 time: 0.5600 data_time: 0.0066 memory: 7635 loss: 0.0125 decode.loss_ce: 0.0060 decode.acc_seg: 99.7236 aux.loss_ce: 0.0065 aux.acc_seg: 99.3533 +04/18 23:26:43 - mmengine - INFO - Iter(train) [136850/160000] lr: 1.8379e-03 eta: 3:33:25 time: 0.5526 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7611 aux.loss_ce: 0.0072 aux.acc_seg: 99.3172 +04/18 23:27:11 - mmengine - INFO - Iter(train) [136900/160000] lr: 1.8345e-03 eta: 3:32:58 time: 0.5513 data_time: 0.0068 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6988 aux.loss_ce: 0.0072 aux.acc_seg: 99.2083 +04/18 23:27:38 - mmengine - INFO - Iter(train) [136950/160000] lr: 1.8311e-03 eta: 3:32:30 time: 0.5529 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.7701 aux.loss_ce: 0.0075 aux.acc_seg: 99.2968 +04/18 23:28:06 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 23:28:06 - mmengine - INFO - Iter(train) [137000/160000] lr: 1.8278e-03 eta: 3:32:02 time: 0.5530 data_time: 0.0071 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0069 decode.acc_seg: 99.7358 aux.loss_ce: 0.0080 aux.acc_seg: 99.1114 +04/18 23:28:34 - mmengine - INFO - Iter(train) [137050/160000] lr: 1.8244e-03 eta: 3:31:35 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7225 aux.loss_ce: 0.0069 aux.acc_seg: 99.3691 +04/18 23:29:01 - mmengine - INFO - Iter(train) [137100/160000] lr: 1.8210e-03 eta: 3:31:07 time: 0.5517 data_time: 0.0069 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7283 aux.loss_ce: 0.0072 aux.acc_seg: 99.1338 +04/18 23:29:29 - mmengine - INFO - Iter(train) [137150/160000] lr: 1.8176e-03 eta: 3:30:39 time: 0.5522 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.6776 aux.loss_ce: 0.0072 aux.acc_seg: 99.2564 +04/18 23:29:56 - mmengine - INFO - Iter(train) [137200/160000] lr: 1.8142e-03 eta: 3:30:12 time: 0.5502 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7935 aux.loss_ce: 0.0074 aux.acc_seg: 99.3133 +04/18 23:30:24 - mmengine - INFO - Iter(train) [137250/160000] lr: 1.8109e-03 eta: 3:29:44 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7973 aux.loss_ce: 0.0071 aux.acc_seg: 99.4480 +04/18 23:30:52 - mmengine - INFO - Iter(train) [137300/160000] lr: 1.8075e-03 eta: 3:29:16 time: 0.5521 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7424 aux.loss_ce: 0.0073 aux.acc_seg: 99.1555 +04/18 23:31:19 - mmengine - INFO - Iter(train) [137350/160000] lr: 1.8041e-03 eta: 3:28:49 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0059 decode.acc_seg: 99.7989 aux.loss_ce: 0.0072 aux.acc_seg: 99.2894 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 594961 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 594962 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 594963 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 594967 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + losses = self._run_forward(data, mode='loss') + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + results = self(**data, mode=mode) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + output = self.module(*inputs[0], **kwargs[0]) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss + losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy + correct = correct[:, target != ignore_index] +KeyboardInterrupt +Traceback (most recent call last): +Traceback (most recent call last): +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + main() + main() + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch)self.run_iter(data_batch) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step(outputs = self.runner.model.train_step( + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + losses = self._run_forward(data, mode='loss')losses = self._run_forward(data, mode='loss') + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + results = self(**data, mode=mode)results = self(**data, mode=mode) + + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + self.run_iter(data_batch) + output = self.module(*inputs[0], **kwargs[0]) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + output = self.module(*inputs[0], **kwargs[0]) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + outputs = self.runner.model.train_step( return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + +loss_decode = self._decode_head_forward_train(x, data_samples) File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss +losses = self._run_forward(data, mode='loss')loss_decode = self.decode_head.loss(inputs, data_samples, + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss + losses = self.loss_by_feat(seg_logits, batch_data_samples)results = self(**data, mode=mode) + + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl +losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy +loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy + correct = correct[:, target != ignore_index] + KeyboardInterruptcorrect = correct[:, target != ignore_index] + +KeyboardInterrupt + return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + output = self.module(*inputs[0], **kwargs[0]) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss + losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy + correct = correct[:, target != ignore_index] +KeyboardInterrupt +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 594917 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:20:59 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1523973508 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1523973508 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:21:00 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained= + '/workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000_240419' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:21:02 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.cls_token as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1set param backbone.patch_embed.projection.weight as id 0 + +set param backbone.layers.0.gamma_2 as id 1set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1set param backbone.layers.0.gamma_1 as id 1 + +set param backbone.layers.0.attn.relative_position_bias_table as id 1set param backbone.layers.0.gamma_2 as id 1 + +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.0.ln1.weight as id 1 + +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1set param backbone.layers.0.attn.proj.weight as id 1 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.1.gamma_1 as id 2 + +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.1.ln2.bias as id 2set param backbone.layers.2.gamma_2 as id 3 + +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.1.ffn.layers.1.bias as id 2set param backbone.layers.2.attn.qkv.bias as id 3 + +set param backbone.layers.2.attn.proj.weight as id 3set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.2.ln1.weight as id 3 + +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.2.ln1.bias as id 3 + +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.2.ffn.layers.1.weight as id 3set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.3.attn.qkv.bias as id 4 + +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln2.weight as id 4set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.3.ln2.bias as id 4set param backbone.layers.3.ln1.bias as id 4 + +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.3.attn.qkv.bias as id 4set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.3.ln2.weight as id 4set param backbone.layers.4.gamma_2 as id 5 + +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.4.gamma_2 as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln1.weight as id 5set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.4.ln2.bias as id 5set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.4.attn.qkv.weight as id 5set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.4.ln2.bias as id 5 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.5.ln1.weight as id 6 + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7set param backbone.layers.5.attn.relative_position_bias_table as id 6 + +set param backbone.layers.6.gamma_2 as id 7set param backbone.layers.5.attn.qkv.weight as id 6 + +set param backbone.layers.6.ln1.weight as id 7set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.5.attn.proj.weight as id 6set param backbone.layers.6.attn.relative_position_bias_table as id 7 + +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.5.ln2.bias as id 6set param backbone.layers.6.attn.proj.bias as id 7 + +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7set param backbone.layers.6.attn.qkv.weight as id 7 + +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.7.gamma_1 as id 8 + +set param backbone.layers.7.gamma_2 as id 8set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.7.ln1.weight as id 8set param backbone.layers.6.ln2.bias as id 7 + +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.8.gamma_1 as id 9 + +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.8.attn.proj.bias as id 9 + +set param backbone.layers.7.gamma_2 as id 8set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.9.ln1.weight as id 10 + +set param backbone.layers.9.ln1.bias as id 10set param backbone.layers.7.attn.proj.bias as id 8 + +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8set param backbone.layers.9.ln2.weight as id 10 + +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.7.ffn.layers.1.bias as id 8 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 + +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:21:04 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth +04/19 00:21:05 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 00:21:05 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 00:21:05 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/MAE4000_240419. +04/19 00:22:01 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:05:43 time: 1.0235 data_time: 0.0041 memory: 8935 loss: 7.1308 decode.loss_ce: 5.1131 decode.acc_seg: 5.7117 aux.loss_ce: 2.0177 aux.acc_seg: 0.0000 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38950 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38951 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38952 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38953 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) +model = self.train_loop.run() # type: ignore File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.run_iter(data_batch)self.backward(loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward +outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params +torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38950 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38951 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38952 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38953 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 38920 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 38920 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 38920 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:22:46 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 703928584 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 703928584 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:22:46 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000_240419' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:22:49 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} + +set param backbone.layers.0.ln2.bias as id 1 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2set param backbone.cls_token as id 0 + +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.pos_embed as id 0 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.0.gamma_1 as id 1set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.0.ln1.weight as id 1set param backbone.layers.2.ln1.weight as id 3 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3set param backbone.layers.0.attn.relative_position_bias_table as id 1 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.0.attn.qkv.weight as id 1 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.0.attn.qkv.bias as id 1 + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.1.ln1.bias as id 2 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.4.ln1.weight as id 5 + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.1.attn.proj.weight as id 2 + +set param backbone.layers.1.attn.proj.bias as id 2set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.4.ln2.bias as id 5set param backbone.layers.1.ln2.bias as id 2 + +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.2.gamma_2 as id 3set param backbone.layers.5.ln1.bias as id 6 + +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.5.attn.relative_position_bias_table as id 6 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.5.ffn.layers.1.bias as id 6 + +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.3.gamma_1 as id 4set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.4.ln1.bias as id 5set param backbone.layers.7.attn.qkv.weight as id 8 + +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.7.ffn.layers.0.0.weight as id 8 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.8.ln2.bias as id 9 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.5.attn.qkv.weight as id 6set param backbone.layers.8.ffn.layers.1.weight as id 9 + +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.9.ln1.bias as id 10 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6set param backbone.layers.9.attn.qkv.bias as id 10 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6set param backbone.layers.9.attn.proj.weight as id 10 + +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.5.ffn.layers.1.bias as id 6set param backbone.layers.9.ln2.bias as id 10 + +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.6.gamma_1 as id 7set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.6.gamma_2 as id 7set param backbone.layers.9.ffn.layers.1.weight as id 10 + +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11set param backbone.layers.6.attn.relative_position_bias_table as id 7 + +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.10.ln1.bias as id 11 + +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.10.attn.relative_position_bias_table as id 11 + +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.6.ln2.weight as id 7set param backbone.layers.10.attn.proj.bias as id 11 + +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.11.ffn.layers.1.bias as id 12 + +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param backbone.layers.7.gamma_1 as id 8 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.7.gamma_2 as id 8 +set param neck.upsample_2x.0.weight as id 13 +set param backbone.layers.7.ln1.weight as id 8 +set param neck.upsample_2x.0.bias as id 13 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param decode_head.psp_modules.0.1.conv.weight as id 13set param backbone.layers.7.ln2.weight as id 8 + +set param decode_head.psp_modules.0.1.bn.weight as id 13set param backbone.layers.7.ln2.bias as id 8 + +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param backbone.layers.7.ffn.layers.1.bias as id 8 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param backbone.layers.8.gamma_1 as id 9 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param backbone.layers.8.gamma_2 as id 9 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13set param backbone.layers.8.ln1.weight as id 9 + +set param backbone.layers.8.ln1.bias as id 9 +set param decode_head.bottleneck.conv.weight as id 13 +set param backbone.layers.8.attn.relative_position_bias_table as id 9set param decode_head.bottleneck.bn.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param backbone.layers.8.ln2.weight as id 9set param decode_head.lateral_convs.1.bn.bias as id 13 + +set param backbone.layers.8.ln2.bias as id 9 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param decode_head.fpn_convs.0.conv.weight as id 13set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param backbone.layers.9.gamma_1 as id 10 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13set param backbone.layers.9.gamma_2 as id 10 + +set param decode_head.fpn_bottleneck.conv.weight as id 13set param backbone.layers.9.ln1.weight as id 10 + +set param decode_head.fpn_bottleneck.bn.weight as id 13set param backbone.layers.9.ln1.bias as id 10 + +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10set param auxiliary_head.conv_seg.weight as id 13 + +set param auxiliary_head.conv_seg.bias as id 13 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param backbone.layers.9.ln2.weight as id 10 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param backbone.layers.9.ln2.bias as id 10 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:22:50 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Traceback (most recent call last): +Traceback (most recent call last): +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + main() +main()main()main() File "tools/train.py", line 100, in main + + + + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + runner.train() runner.train() +runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + self._init_model_weights() +self._init_model_weights()self._init_model_weights() File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + model.init_weights()model.init_weights() + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights +model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights()m.init_weights()m.init_weights()m.init_weights() + + + + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint)state_dict = self.resize_rel_pos_embed(checkpoint)state_dict = self.resize_rel_pos_embed(checkpoint)state_dict = self.resize_rel_pos_embed(checkpoint) + + + + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() dst_num_pos, _ = self.state_dict()[key].size()dst_num_pos, _ = self.state_dict()[key].size() +dst_num_pos, _ = self.state_dict()[key].size() + + +KeyErrorKeyErrorKeyError: KeyError: : 'backbone.layers.0.attn.relative_position_bias_table': 'backbone.layers.0.attn.relative_position_bias_table''backbone.layers.0.attn.relative_position_bias_table' +'backbone.layers.0.attn.relative_position_bias_table' + + +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 40810) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:22:57 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 40811) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:22:57 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 40812) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:22:57 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 40813) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:22:57 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 40810) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:33:14 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1894895559 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1894895559 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:33:14 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000_240418' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:33:17 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +set param backbone.layers.0.ln2.weight as id 1 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 + +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +set param backbone.layers.11.gamma_2 as id 12 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.11.ffn.layers.1.bias as id 12set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.6.attn.proj.bias as id 7 +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.7.gamma_1 as id 8 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param backbone.layers.7.gamma_2 as id 8 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8set param decode_head.psp_modules.1.1.conv.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.weight as id 13set param backbone.layers.7.attn.qkv.weight as id 8 + +set param decode_head.psp_modules.1.1.bn.bias as id 13set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.7.attn.proj.weight as id 8 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param backbone.layers.7.attn.proj.bias as id 8 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param backbone.layers.7.ln2.weight as id 8set param decode_head.psp_modules.2.1.bn.bias as id 13 + +set param backbone.layers.7.ln2.bias as id 8 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param decode_head.psp_modules.3.1.bn.weight as id 13set param backbone.layers.7.ffn.layers.0.0.bias as id 8 + +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param decode_head.bottleneck.conv.weight as id 13set param backbone.layers.7.ffn.layers.1.bias as id 8 + +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param decode_head.lateral_convs.0.conv.weight as id 13set param backbone.layers.8.ln1.weight as id 9 + +set param decode_head.lateral_convs.0.bn.weight as id 13set param backbone.layers.8.ln1.bias as id 9 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param backbone.layers.8.attn.proj.weight as id 9set param decode_head.lateral_convs.1.bn.bias as id 13 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param backbone.layers.8.ln2.weight as id 9 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param backbone.layers.8.ln2.bias as id 9 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param decode_head.fpn_convs.0.bn.weight as id 13set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param decode_head.fpn_convs.1.conv.weight as id 13set param backbone.layers.8.ffn.layers.1.bias as id 9 + +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.9.gamma_2 as id 10 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param backbone.layers.9.ln1.weight as id 10 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param backbone.layers.9.ln1.bias as id 10 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param auxiliary_head.conv_seg.weight as id 13set param backbone.layers.9.ln2.bias as id 10 + +set param auxiliary_head.conv_seg.bias as id 13 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param auxiliary_head.convs.0.conv.weight as id 13set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param auxiliary_head.convs.0.bn.weight as id 13set param backbone.layers.9.ffn.layers.1.weight as id 10 + +set param auxiliary_head.convs.0.bn.bias as id 13set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + 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"neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:33:18 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint) + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() +KeyError: 'backbone.layers.0.attn.relative_position_bias_table' +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint) + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() +KeyError: 'backbone.layers.0.attn.relative_position_bias_table' +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint) + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() +KeyError: 'backbone.layers.0.attn.relative_position_bias_table' +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint) + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() +KeyError: 'backbone.layers.0.attn.relative_position_bias_table' +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 49439) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:33:20 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 49440) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:33:20 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 49441) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:33:20 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 49445) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:33:20 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 49439) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:36:34 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 135406166 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 135406166 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:36:34 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000_240418' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:36:37 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:36:38 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + self.load_or_resume()main() + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) return CheckpointLoader.load_checkpoint(filename, map_location, logger) +return CheckpointLoader.load_checkpoint(filename, map_location, logger) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError : return checkpoint_loader(filename, map_location) can not be found.return checkpoint_loader(filename, map_location)return checkpoint_loader(filename, map_location) + + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.')raise FileNotFoundError(f'{filename} can not be found.')raise FileNotFoundError(f'{filename} can not be found.') + + +FileNotFoundErrorFileNotFoundError: FileNotFoundError: can not be found.: can not be found. + can not be found. + +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 52295) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:36:40 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 52296) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:36:40 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 52297) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:36:40 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 52298) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:36:40 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 52295) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55354 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55355 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55356 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55357 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 93, in main +Traceback (most recent call last): + File "tools/train.py", line 104, in + runner = Runner.from_cfg(cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg + runner = cls( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 403, in __init__ + main()self._log_env(env_cfg) + + File "tools/train.py", line 93, in main + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2368, in _log_env + runner = Runner.from_cfg(cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg + runner = cls( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 403, in __init__ + self._log_env(env_cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2368, in _log_env + env = collect_env() + File "/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/collect_env.py", line 125, in collect_env + env = collect_env() + File "/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/collect_env.py", line 125, in collect_env +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 93, in main + runner = Runner.from_cfg(cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg + runner = cls( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 403, in __init__ + self._log_env(env_cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2368, in _log_env + env = collect_env() + File "/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/collect_env.py", line 125, in collect_env + cc_info = subprocess.check_output(f'{cc} --version', shell=True)cc_info = subprocess.check_output(f'{cc} --version', shell=True) + + File "/opt/conda/lib/python3.8/subprocess.py", line 415, in check_output + File "/opt/conda/lib/python3.8/subprocess.py", line 415, in check_output +cc_info = subprocess.check_output(f'{cc} --version', shell=True) + File "/opt/conda/lib/python3.8/subprocess.py", line 415, in check_output + return run(*popenargs, stdout=PIPE, timeout=timeout, check=True, + File "/opt/conda/lib/python3.8/subprocess.py", line 493, in run + return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,return run(*popenargs, stdout=PIPE, timeout=timeout, check=True, + + File "/opt/conda/lib/python3.8/subprocess.py", line 493, in run + File "/opt/conda/lib/python3.8/subprocess.py", line 493, in run + with Popen(*popenargs, **kwargs) as process: + File "/opt/conda/lib/python3.8/subprocess.py", line 858, in __init__ + with Popen(*popenargs, **kwargs) as process: + File "/opt/conda/lib/python3.8/subprocess.py", line 858, in __init__ + with Popen(*popenargs, **kwargs) as process: + File "/opt/conda/lib/python3.8/subprocess.py", line 858, in __init__ + self._execute_child(args, executable, preexec_fn, close_fds, + File "/opt/conda/lib/python3.8/subprocess.py", line 1639, in _execute_child + self._execute_child(args, executable, preexec_fn, close_fds, + File "/opt/conda/lib/python3.8/subprocess.py", line 1639, in _execute_child + self._execute_child(args, executable, preexec_fn, close_fds, + File "/opt/conda/lib/python3.8/subprocess.py", line 1639, in _execute_child + self.pid = _posixsubprocess.fork_exec(self.pid = _posixsubprocess.fork_exec( + +self.pid = _posixsubprocess.fork_exec( +KeyboardInterruptKeyboardInterrupt + +KeyboardInterrupt +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 93, in main + runner = Runner.from_cfg(cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg + runner = cls( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 403, in __init__ + self._log_env(env_cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2368, in _log_env + env = collect_env() + File "/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/collect_env.py", line 125, in collect_env + cc_info = subprocess.check_output(f'{cc} --version', shell=True) + File "/opt/conda/lib/python3.8/subprocess.py", line 415, in check_output + return run(*popenargs, stdout=PIPE, timeout=timeout, check=True, + File "/opt/conda/lib/python3.8/subprocess.py", line 493, in run + with Popen(*popenargs, **kwargs) as process: + File "/opt/conda/lib/python3.8/subprocess.py", line 858, in __init__ + self._execute_child(args, executable, preexec_fn, close_fds, + File "/opt/conda/lib/python3.8/subprocess.py", line 1639, in _execute_child + self.pid = _posixsubprocess.fork_exec( +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55354 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55355 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55356 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55357 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 55324 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 55324 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 55324 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:38:45 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1757742453 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1757742453 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:38:45 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:38:48 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14set param backbone.patch_embed.projection.weight as id 0 + +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.cls_token as id 0 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.pos_embed as id 0 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.0.attn.proj.bias as id 1 + +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.0.ffn.layers.0.0.bias as id 1 + +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.0.ffn.layers.1.weight as id 1 + +set param backbone.layers.1.ln2.bias as id 2set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.1.gamma_2 as id 2 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.2.ln2.bias as id 3 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.gamma_1 as id 3set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.2.ffn.layers.1.bias as id 3 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.2.attn.qkv.weight as id 3set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.3.attn.relative_position_bias_table as id 4 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.2.ffn.layers.0.0.weight as id 3 + +set param backbone.layers.3.attn.proj.bias as id 4set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4set param backbone.layers.3.ln1.bias as id 4 + +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.4.attn.qkv.weight as id 5set param backbone.layers.3.ln2.bias as id 4 + +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.4.ln2.bias as id 5 + +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.4.gamma_2 as id 5set param backbone.layers.4.ffn.layers.1.bias as id 5 + +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.4.attn.qkv.bias as id 5 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5set param backbone.layers.5.attn.proj.bias as id 6 + +set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.5.ffn.layers.0.0.weight as id 6 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.5.ffn.layers.1.weight as id 6 + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.7.ln2.bias as id 8 + +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.8.gamma_2 as id 9set param backbone.layers.7.ln1.bias as id 8 + +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.7.attn.relative_position_bias_table as id 8 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.8.attn.relative_position_bias_table as id 9set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.7.attn.proj.weight as id 8 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.8.ln2.bias as id 9 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.8.attn.relative_position_bias_table as id 9set param backbone.layers.9.ln1.weight as id 10 + +set param backbone.layers.9.ln1.bias as id 10set param backbone.layers.8.attn.qkv.weight as id 9 + +set param backbone.layers.8.attn.qkv.bias as id 9set param backbone.layers.9.attn.relative_position_bias_table as id 10 + +set param backbone.layers.8.attn.proj.weight as id 9set param backbone.layers.9.attn.qkv.weight as id 10 + +set param backbone.layers.8.attn.proj.bias as id 9set param backbone.layers.9.attn.qkv.bias as id 10 + +set param backbone.layers.8.ln2.weight as id 9set param backbone.layers.9.attn.proj.weight as id 10 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.9.ffn.layers.0.0.weight as id 10 + +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.10.ln1.bias as id 11 + +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11set param backbone.layers.9.ffn.layers.0.0.weight as id 10 + +set param backbone.layers.10.ln2.bias as id 11set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11set param backbone.layers.10.gamma_1 as id 11 + +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12set param backbone.layers.10.attn.qkv.weight as id 11 + +set param backbone.layers.10.attn.qkv.bias as id 11set param backbone.layers.11.attn.relative_position_bias_table as id 12 + +set param backbone.layers.10.attn.proj.weight as id 11set param backbone.layers.11.attn.qkv.weight as id 12 + +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.10.ln2.weight as id 11set param backbone.layers.11.attn.proj.weight as id 12 + +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12set param backbone.layers.10.ffn.layers.1.weight as id 11 + +set param backbone.layers.11.ffn.layers.0.0.bias as id 12set param backbone.layers.10.ffn.layers.1.bias as id 11 + +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param neck.upsample_4x.1.weight as id 13set param backbone.layers.11.attn.proj.weight as id 12 + +set param neck.upsample_4x.1.bias as id 13set param backbone.layers.11.attn.proj.bias as id 12 + +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.11.ln2.weight as id 12 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.11.ln2.bias as id 12 +set param neck.upsample_2x.0.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12set param neck.upsample_2x.0.bias as id 13 + +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13set param neck.upsample_4x.1.bias as id 13 + +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13set param neck.upsample_2x.0.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13set param decode_head.psp_modules.3.1.conv.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13set param decode_head.psp_modules.0.1.conv.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13set param decode_head.bottleneck.conv.weight as id 13 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13set param decode_head.fpn_convs.1.bn.bias as id 13 + +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13set param decode_head.lateral_convs.2.conv.weight as id 13 + +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13set param decode_head.fpn_convs.0.conv.weight as id 13 + +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13set param auxiliary_head.convs.0.conv.weight as id 13 + +set param auxiliary_head.convs.0.bn.weight as id 13set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param auxiliary_head.convs.0.bn.bias as id 13set param decode_head.fpn_convs.1.bn.weight as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:38:49 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 55633) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:38:51 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 55634) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:38:51 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 55635) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:38:51 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 55636) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:38:51 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 55633) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:41:23 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 856117106 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 856117106 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:41:23 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:41:26 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.cls_token as id 0 + +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.cls_token as id 0 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.pos_embed as id 0 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.1.gamma_2 as id 2 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1set param backbone.layers.1.attn.proj.weight as id 2 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1set param backbone.layers.1.attn.proj.bias as id 2 + +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.1.gamma_1 as id 2 + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3set param backbone.layers.1.ln2.weight as id 2 + +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.1.ffn.layers.0.0.bias as id 2 + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 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"decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:41:27 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 58086) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:41:29 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 58087) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:41:29 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 58088) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:41:29 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 58089) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:41:29 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 58086) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: argument --resume: ignored explicit argument 'auto' +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: argument --resume: ignored explicit argument 'auto' +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: argument --resume: ignored explicit argument 'auto' +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: argument --resume: ignored explicit argument 'auto' +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 2) local_rank: 0 (pid: 59317) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:42:18 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 2 (pid: 59318) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:42:18 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 2 (pid: 59319) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:42:18 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 2 (pid: 59320) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:42:18 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 2 (pid: 59317) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: --load_from= +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: --load_from= +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: --load_from= +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: --load_from= +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 2) local_rank: 0 (pid: 61127) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:44:54 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 2 (pid: 61128) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:44:54 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 2 (pid: 61129) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:44:54 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 2 (pid: 61130) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:44:54 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 2 (pid: 61127) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:45:35 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 172032146 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 172032146 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:45:35 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:45:38 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.layers.2.ln1.weight as id 3 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 + +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4set param backbone.cls_token as id 0 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.pos_embed as id 0 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.cls_token as id 0set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.pos_embed as id 0set param backbone.patch_embed.projection.weight as id 0set param backbone.layers.3.attn.proj.weight as id 4 + + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.patch_embed.projection.weight as id 0set param backbone.layers.3.ffn.layers.0.0.weight as id 4 + +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.gamma_2 as id 1set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.gamma_1 as id 1 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.0.ln1.bias as id 1set param backbone.layers.4.ln1.weight as id 5set param backbone.layers.0.gamma_2 as id 1 + + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.0.attn.relative_position_bias_table as id 1 + +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.layers.0.attn.relative_position_bias_table as id 1 + + +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.0.ln2.weight as id 1set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.0.ln2.bias as id 1 + + +set param backbone.layers.0.attn.proj.bias as id 1set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.0.ln2.weight as id 1set param backbone.layers.5.ln1.weight as id 6 + +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.5.ln1.bias as id 6set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.0.ffn.layers.1.bias as id 1set param backbone.layers.5.ffn.layers.1.weight as id 6 + + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.1.ln1.bias as id 2set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.6.ln1.weight as id 7set param backbone.layers.1.attn.relative_position_bias_table as id 2 + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.6.attn.relative_position_bias_table as id 7 + +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.1.ln1.bias as id 2set param backbone.layers.6.attn.qkv.bias as id 7 + +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.6.attn.proj.weight as id 7set param backbone.layers.1.attn.relative_position_bias_table as id 2 + + +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.1.attn.proj.bias as id 2 + +set param backbone.layers.6.ln2.weight as id 7set param backbone.layers.1.attn.qkv.weight as id 2 + +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.6.ln2.bias as id 7 + +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.2.gamma_2 as id 3set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + + +set param backbone.layers.1.ffn.layers.1.bias as id 2set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.gamma_2 as id 3 + +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.7.ln1.weight as id 8 + +set param backbone.layers.7.ln1.bias as id 8set param backbone.layers.2.ln1.bias as id 3set param backbone.layers.2.attn.proj.weight as id 3 + + +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.2.attn.qkv.bias as id 3 + + +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.2.ffn.layers.0.0.bias as id 3set param backbone.layers.2.attn.proj.bias as id 3 + + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.2.ffn.layers.1.bias as id 3 + +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.3.gamma_1 as id 4set param backbone.layers.8.ln1.weight as id 9 + + +set param backbone.layers.8.ln1.bias as id 9set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.3.ln1.weight as id 4set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.3.ln1.bias as id 4set param backbone.layers.8.attn.qkv.bias as id 9 + +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.8.attn.proj.weight as id 9set param backbone.layers.3.attn.relative_position_bias_table as id 4 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.8.ln2.bias as id 9set param backbone.layers.3.attn.qkv.bias as id 4 + +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.3.ln1.bias as id 4set param backbone.layers.8.ffn.layers.0.0.bias as id 9set param backbone.layers.3.attn.proj.weight as id 4 + + +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.8.ffn.layers.1.bias as id 9 + +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.ln2.weight as id 4set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.9.gamma_2 as id 10set param backbone.layers.3.ln2.bias as id 4 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.3.attn.proj.bias as id 4set param backbone.layers.9.attn.relative_position_bias_table as id 10 + +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 + +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.3.ffn.layers.1.weight as id 4set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.3.ffn.layers.1.bias as id 4set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.3.ffn.layers.0.0.weight as id 4 + + +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.4.gamma_1 as id 5 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.10.gamma_2 as id 11 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.10.ln1.weight as id 11 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.10.attn.qkv.weight as id 11 + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.10.attn.proj.weight as id 11set param backbone.layers.4.attn.relative_position_bias_table as id 5 + + +set param backbone.layers.10.attn.proj.bias as id 11set param backbone.layers.4.attn.proj.bias as id 5 + +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.10.ln2.bias as id 11 + + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11set param backbone.layers.4.attn.proj.bias as id 5 + +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.11.ln1.weight as id 12set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + + +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.5.gamma_1 as id 6 + + +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.4.ffn.layers.1.weight as id 5set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.11.attn.qkv.bias as id 12 + + +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.11.attn.proj.weight as id 12 + +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.5.ln1.bias as id 6set param backbone.layers.11.ln2.weight as id 12set param backbone.layers.5.gamma_1 as id 6 + + +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.5.attn.relative_position_bias_table as id 6set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.5.attn.qkv.weight as id 6set param backbone.layers.5.ln1.weight as id 6 + +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.5.attn.qkv.bias as id 6set param backbone.layers.11.ffn.layers.1.bias as id 12set param backbone.layers.5.ln1.bias as id 6 + + +set param backbone.layers.5.attn.proj.weight as id 6set param backbone.layers.5.attn.relative_position_bias_table as id 6 + +set param backbone.layers.5.attn.proj.bias as id 6 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.5.attn.qkv.weight as id 6 + +set param backbone.layers.5.ln2.weight as id 6set param neck.upsample_4x.0.bias as id 13 + +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param neck.upsample_4x.1.weight as id 13 +set param backbone.layers.5.attn.proj.weight as id 6set param neck.upsample_4x.1.bias as id 13 + +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6set param neck.upsample_4x.3.weight as id 13 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.5.ln2.weight as id 6 +set param neck.upsample_2x.0.weight as id 13set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 + +set param neck.upsample_2x.0.bias as id 13 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param decode_head.conv_seg.weight as id 13set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.6.gamma_2 as id 7set param decode_head.conv_seg.bias as id 13 + +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.ln1.bias as id 7 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.gamma_1 as id 7 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.ln1.weight as id 7set param backbone.layers.6.attn.qkv.bias as id 7set param decode_head.psp_modules.1.1.conv.weight as id 13 + + +set param backbone.layers.6.ln1.bias as id 7set param decode_head.psp_modules.1.1.bn.weight as id 13 + +set param backbone.layers.6.attn.proj.weight as id 7 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param decode_head.psp_modules.2.1.bn.bias as id 13set param backbone.layers.6.ln2.bias as id 7 + +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7set param decode_head.psp_modules.3.1.conv.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param backbone.layers.6.ln2.weight as id 7set param decode_head.psp_modules.3.1.bn.bias as id 13 + +set param backbone.layers.6.ln2.bias as id 7 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param backbone.layers.6.ffn.layers.1.weight as id 7set param decode_head.fpn_convs.0.bn.bias as id 13 + +set param backbone.layers.6.ffn.layers.1.bias as id 7set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param decode_head.fpn_convs.1.bn.weight as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7set param backbone.layers.7.gamma_1 as id 8 +set param decode_head.fpn_convs.2.conv.weight as id 13 + +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param backbone.layers.7.gamma_2 as id 8set param backbone.layers.6.ffn.layers.1.weight as id 7set param decode_head.fpn_convs.2.bn.bias as id 13 + + +set param backbone.layers.6.ffn.layers.1.bias as id 7set param backbone.layers.7.ln1.weight as id 8set param decode_head.fpn_bottleneck.conv.weight as id 13 + + +set param backbone.layers.7.ln1.bias as id 8set param decode_head.fpn_bottleneck.bn.weight as id 13 + +set param decode_head.fpn_bottleneck.bn.bias as id 13set param backbone.layers.7.gamma_1 as id 8 + +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8set param auxiliary_head.conv_seg.weight as id 13 + +set param backbone.layers.7.ln1.weight as id 8 +set param auxiliary_head.conv_seg.bias as id 13set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.7.ln1.bias as id 8 +set param auxiliary_head.convs.0.conv.weight as id 13set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 + +set param auxiliary_head.convs.0.bn.weight as id 13 +set param backbone.layers.7.attn.proj.bias as id 8set param auxiliary_head.convs.0.bn.bias as id 13 + +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.ln2.weight as id 8set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8set param backbone.layers.7.ln2.weight as id 8 + +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9set param backbone.layers.7.ffn.layers.1.weight as id 8 + +set param backbone.layers.7.ffn.layers.1.bias as id 8set param backbone.layers.8.ln1.weight as id 9 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.8.ln1.weight as id 9 + +set param backbone.layers.8.attn.qkv.bias as id 9set param backbone.layers.8.ln1.bias as id 9 + +set param backbone.layers.8.attn.proj.weight as id 9set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.8.ln2.bias as id 9set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.9.ln2.weight as id 10 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10set param backbone.layers.9.ln2.bias as id 10 + +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10set param backbone.layers.10.gamma_1 as id 11 + +set param backbone.layers.9.ffn.layers.1.bias as id 10set param backbone.layers.10.gamma_2 as id 11 + +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ln2.bias as id 11set param backbone.layers.10.ffn.layers.0.0.bias as id 11 + +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11set param backbone.layers.11.gamma_1 as id 12 + +set param backbone.layers.10.ffn.layers.1.bias as id 11set param backbone.layers.11.gamma_2 as id 12 + +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12set param backbone.layers.11.ln1.bias as id 12 + +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12set param backbone.layers.11.attn.proj.weight as id 12 + +set param backbone.layers.11.ln2.bias as id 12set param backbone.layers.11.attn.proj.bias as id 12 + +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12set param backbone.layers.11.ffn.layers.0.0.weight as id 12 + +set param backbone.layers.11.ffn.layers.1.bias as id 12set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13set param neck.upsample_4x.1.weight as id 13 + +set param neck.upsample_4x.3.bias as id 13set param neck.upsample_4x.1.bias as id 13 + +set param neck.upsample_4x.3.weight as id 13set param neck.upsample_2x.0.weight as id 13 + +set param neck.upsample_4x.3.bias as id 13set param neck.upsample_2x.0.bias as id 13 + +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13set param decode_head.psp_modules.1.1.bn.bias as id 13 + +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param decode_head.psp_modules.1.1.bn.bias as id 13 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13set param decode_head.psp_modules.2.1.conv.weight as id 13 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.bottleneck.conv.weight as id 13set param decode_head.psp_modules.3.1.bn.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.bias as id 13set param decode_head.bottleneck.bn.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13set param decode_head.bottleneck.bn.bias as id 13 + +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13set param decode_head.lateral_convs.0.conv.weight as id 13 + +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13set param decode_head.fpn_convs.0.conv.weight as id 13 + +set param decode_head.fpn_convs.0.bn.weight as id 13set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13set param decode_head.fpn_convs.2.conv.weight as id 13 + +set param decode_head.fpn_convs.2.bn.weight as id 13set param decode_head.fpn_bottleneck.conv.weight as id 13 + +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 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+ "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:45:39 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 61752) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:45:41 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 61753) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:45:41 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 61754) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:45:41 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 61755) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:45:41 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 61752) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: auto +train.py: error: unrecognized arguments: auto +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: auto +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: auto +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 2) local_rank: 0 (pid: 62754) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:46:07 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 2 (pid: 62755) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:46:07 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 2 (pid: 62756) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:46:07 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 2 (pid: 62757) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:46:07 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 2 (pid: 62754) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:49:00 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1281961478 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1281961478 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:49:00 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = True +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:49:03 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +set param backbone.layers.0.gamma_1 as id 1 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.cls_token as id 0 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.pos_embed as id 0 + +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.patch_embed.projection.weight as id 0set param backbone.layers.0.attn.proj.weight as id 1 + +set param backbone.patch_embed.projection.bias as id 0set param backbone.layers.0.attn.proj.bias as id 1 + +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.gamma_1 as id 1set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1set param backbone.layers.0.attn.qkv.bias as id 1 + +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1set param backbone.layers.1.gamma_1 as id 2 + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.0.ln2.bias as id 1set param backbone.layers.1.ln1.weight as id 2 + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.0.ffn.layers.0.0.weight as id 1 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.0.ffn.layers.1.weight as id 1 + +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2set param backbone.layers.1.attn.relative_position_bias_table as id 2 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.1.attn.qkv.weight as id 2 + +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.1.ffn.layers.0.0.weight as id 2 + +set param backbone.layers.2.ln1.bias as id 3set param backbone.layers.1.ffn.layers.0.0.bias as id 2 + +set param backbone.layers.2.attn.relative_position_bias_table as id 3set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.1.ffn.layers.1.bias as id 2set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.3.gamma_2 as id 4set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.3.ln1.weight as id 4set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4set param backbone.layers.3.attn.proj.weight as id 4 + +set param backbone.layers.3.attn.qkv.bias as id 4set param backbone.layers.3.attn.proj.bias as id 4 + +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.3.ln2.weight as id 4 + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.gamma_2 as id 5set param backbone.layers.4.ln1.weight as id 5 + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.4.attn.proj.bias as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.4.ln2.bias as id 5set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.5.gamma_1 as id 6set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.4.ffn.layers.1.bias as id 5 + +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.5.attn.relative_position_bias_table as id 6set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6set param backbone.layers.5.attn.proj.bias as id 6 + +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.5.attn.qkv.weight as id 6 + +set param backbone.layers.5.ln2.bias as id 6set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.5.ffn.layers.0.0.weight as id 6set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.5.ffn.layers.1.bias as id 6 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.6.attn.relative_position_bias_table as id 7set param backbone.layers.5.ffn.layers.1.weight as id 6 + +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.5.ffn.layers.1.bias as id 6 + +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7set param backbone.layers.6.gamma_1 as id 7 + +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param backbone.layers.6.ffn.layers.1.bias as id 7set param backbone.layers.7.ffn.layers.0.0.weight as id 8 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8set param backbone.layers.7.gamma_1 as id 8 + +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.7.attn.relative_position_bias_table as id 8 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.7.attn.qkv.bias as id 8set param backbone.layers.8.attn.qkv.weight as id 9 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9set param backbone.layers.7.ln2.weight as id 8 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.8.ffn.layers.1.weight as id 9 + +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.7.ffn.layers.0.0.bias as id 8 + +set param backbone.layers.7.ffn.layers.1.weight as id 8set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.7.ffn.layers.1.bias as id 8set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10set param backbone.layers.8.gamma_1 as id 9 + +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.8.gamma_2 as id 9 + +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.8.ln1.weight as id 9 + +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.8.attn.qkv.weight as id 9 + +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.8.attn.qkv.bias as id 9 + +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.8.ln2.weight as id 9set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11set param backbone.layers.8.ffn.layers.1.weight as id 9 + +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.10.attn.qkv.weight as id 11 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.10.attn.proj.weight as id 11 + +set param backbone.layers.10.attn.proj.bias as id 11set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.10.ln2.weight as id 11set param backbone.layers.9.ln1.weight as id 10 + +set param backbone.layers.10.ln2.bias as id 11set param backbone.layers.9.ln1.bias as id 10 + +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.9.attn.qkv.bias as id 10set param backbone.layers.10.ffn.layers.1.bias as id 11 + +set param backbone.layers.9.attn.proj.weight as id 10set param backbone.layers.11.gamma_1 as id 12 + +set param backbone.layers.9.attn.proj.bias as id 10set param backbone.layers.11.gamma_2 as id 12 + +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.11.attn.relative_position_bias_table as id 12 + +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.11.ln2.weight as id 12set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.10.gamma_1 as id 11set param backbone.layers.11.ffn.layers.0.0.weight as id 12 + +set param backbone.layers.11.ffn.layers.0.0.bias as id 12set param backbone.layers.10.gamma_2 as id 11 + +set param backbone.layers.11.ffn.layers.1.weight as id 12set param backbone.layers.10.ln1.weight as id 11 + +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.10.attn.qkv.weight as id 11 + +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param neck.upsample_4x.1.weight as id 13set param backbone.layers.10.attn.proj.weight as id 11 + +set param neck.upsample_4x.1.bias as id 13 +set param backbone.layers.10.attn.proj.bias as id 11 +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.10.ln2.weight as id 11 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.10.ln2.bias as id 11 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12set param decode_head.psp_modules.0.1.conv.weight as id 13 + +set param backbone.layers.11.ln1.weight as id 12set param decode_head.psp_modules.0.1.bn.weight as id 13 + +set param backbone.layers.11.ln1.bias as id 12set param decode_head.psp_modules.0.1.bn.bias as id 13 + +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12set param decode_head.psp_modules.1.1.conv.weight as id 13 + +set param backbone.layers.11.attn.qkv.bias as id 12set param decode_head.psp_modules.1.1.bn.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.11.attn.proj.weight as id 12 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param backbone.layers.11.attn.proj.bias as id 12 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13set param backbone.layers.11.ln2.weight as id 12 + +set param backbone.layers.11.ln2.bias as id 12 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.bottleneck.conv.weight as id 13set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param decode_head.bottleneck.bn.weight as id 13 +set param backbone.layers.11.ffn.layers.1.weight as id 12set param decode_head.bottleneck.bn.bias as id 13 + +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param neck.upsample_4x.0.bias as id 13set param decode_head.lateral_convs.1.bn.bias as id 13 + +set param decode_head.lateral_convs.2.conv.weight as id 13set param neck.upsample_4x.1.weight as id 13 + +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param neck.upsample_4x.3.bias as id 13set param decode_head.fpn_convs.0.bn.weight as id 13 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13set param auxiliary_head.conv_seg.bias as id 13 + +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 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INFO - Auto resumed from the latest checkpoint /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +04/19 00:49:06 - mmengine - INFO - Load checkpoint from /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +04/19 00:49:06 - mmengine - INFO - resumed epoch: 0, iter: 60000 +04/19 00:49:06 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 00:49:06 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 00:49:06 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/MAE4000. +04/19 00:49:06 - mmengine - WARNING - Advance dataloader 60000 steps to skip data that has already been trained +04/19 01:03:46 - mmengine - INFO - Iter(train) [ 60050/160000] base_lr: 6.3060e-05 lr: 2.3315e-07 eta: 20 days, 9:05:08 time: 0.9809 data_time: 0.0044 memory: 8930 loss: 0.0164 decode.loss_ce: 0.0071 decode.acc_seg: 99.5728 aux.loss_ce: 0.0092 aux.acc_seg: 98.9300 +04/19 01:04:36 - mmengine - INFO - Iter(train) [ 60100/160000] base_lr: 6.3029e-05 lr: 2.3303e-07 eta: 10 days, 18:05:59 time: 0.9869 data_time: 0.0046 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.6572 aux.loss_ce: 0.0076 aux.acc_seg: 98.8302 +04/19 01:05:25 - mmengine - INFO - Iter(train) [ 60150/160000] base_lr: 6.2997e-05 lr: 2.3291e-07 eta: 7 days, 13:07:00 time: 0.9888 data_time: 0.0043 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0062 decode.acc_seg: 99.7871 aux.loss_ce: 0.0071 aux.acc_seg: 99.3629 +04/19 01:06:15 - mmengine - INFO - Iter(train) [ 60200/160000] base_lr: 6.2966e-05 lr: 2.3280e-07 eta: 5 days, 22:38:11 time: 0.9928 data_time: 0.0046 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0060 decode.acc_seg: 99.8487 aux.loss_ce: 0.0078 aux.acc_seg: 99.4499 +04/19 01:07:04 - mmengine - INFO - Iter(train) [ 60250/160000] base_lr: 6.2934e-05 lr: 2.3268e-07 eta: 4 days, 23:33:04 time: 0.9917 data_time: 0.0044 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0056 decode.acc_seg: 99.8344 aux.loss_ce: 0.0069 aux.acc_seg: 99.4980 +04/19 01:07:54 - mmengine - INFO - Iter(train) [ 60300/160000] base_lr: 6.2903e-05 lr: 2.3256e-07 eta: 4 days, 8:09:31 time: 0.9937 data_time: 0.0045 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0060 decode.acc_seg: 99.7702 aux.loss_ce: 0.0072 aux.acc_seg: 99.2840 +04/19 01:08:44 - mmengine - INFO - Iter(train) [ 60350/160000] base_lr: 6.2871e-05 lr: 2.3245e-07 eta: 3 days, 21:09:39 time: 0.9942 data_time: 0.0044 memory: 8457 loss: 0.0135 decode.loss_ce: 0.0062 decode.acc_seg: 99.6994 aux.loss_ce: 0.0073 aux.acc_seg: 99.1688 +04/19 01:09:33 - mmengine - INFO - Iter(train) [ 60400/160000] base_lr: 6.2840e-05 lr: 2.3233e-07 eta: 3 days, 12:54:36 time: 0.9924 data_time: 0.0044 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0061 decode.acc_seg: 99.7244 aux.loss_ce: 0.0076 aux.acc_seg: 99.0751 +04/19 01:10:23 - mmengine - INFO - Iter(train) [ 60450/160000] base_lr: 6.2808e-05 lr: 2.3221e-07 eta: 3 days, 6:29:25 time: 0.9936 data_time: 0.0041 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0061 decode.acc_seg: 99.6300 aux.loss_ce: 0.0072 aux.acc_seg: 98.7514 +04/19 01:11:13 - mmengine - INFO - Iter(train) [ 60500/160000] base_lr: 6.2776e-05 lr: 2.3210e-07 eta: 3 days, 1:21:13 time: 0.9934 data_time: 0.0047 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.6748 aux.loss_ce: 0.0070 aux.acc_seg: 99.3412 +04/19 01:12:02 - mmengine - INFO - Iter(train) [ 60550/160000] base_lr: 6.2745e-05 lr: 2.3198e-07 eta: 2 days, 21:08:55 time: 0.9952 data_time: 0.0043 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.7656 aux.loss_ce: 0.0076 aux.acc_seg: 99.2676 +04/19 01:12:52 - mmengine - INFO - Iter(train) [ 60600/160000] base_lr: 6.2713e-05 lr: 2.3186e-07 eta: 2 days, 17:38:32 time: 0.9947 data_time: 0.0046 memory: 8457 loss: 0.0157 decode.loss_ce: 0.0070 decode.acc_seg: 99.6742 aux.loss_ce: 0.0086 aux.acc_seg: 98.7873 +04/19 01:13:42 - mmengine - INFO - Iter(train) [ 60650/160000] base_lr: 6.2682e-05 lr: 2.3175e-07 eta: 2 days, 14:40:24 time: 0.9943 data_time: 0.0048 memory: 8457 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7372 aux.loss_ce: 0.0075 aux.acc_seg: 99.4942 +04/19 01:14:32 - mmengine - INFO - Iter(train) [ 60700/160000] base_lr: 6.2650e-05 lr: 2.3163e-07 eta: 2 days, 12:07:39 time: 0.9953 data_time: 0.0050 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0057 decode.acc_seg: 99.8459 aux.loss_ce: 0.0069 aux.acc_seg: 99.5266 +04/19 01:15:21 - mmengine - INFO - Iter(train) [ 60750/160000] base_lr: 6.2619e-05 lr: 2.3151e-07 eta: 2 days, 9:55:12 time: 0.9950 data_time: 0.0046 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0062 decode.acc_seg: 99.8405 aux.loss_ce: 0.0066 aux.acc_seg: 99.4726 +04/19 01:16:11 - mmengine - INFO - Iter(train) [ 60800/160000] base_lr: 6.2587e-05 lr: 2.3140e-07 eta: 2 days, 7:59:14 time: 0.9963 data_time: 0.0047 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.6958 aux.loss_ce: 0.0077 aux.acc_seg: 98.9441 +04/19 01:17:01 - mmengine - INFO - Iter(train) [ 60850/160000] base_lr: 6.2556e-05 lr: 2.3128e-07 eta: 2 days, 6:16:51 time: 0.9958 data_time: 0.0045 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0063 decode.acc_seg: 99.7522 aux.loss_ce: 0.0077 aux.acc_seg: 98.9187 +04/19 01:17:51 - mmengine - INFO - Iter(train) [ 60900/160000] base_lr: 6.2524e-05 lr: 2.3116e-07 eta: 2 days, 4:45:41 time: 0.9943 data_time: 0.0047 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0056 decode.acc_seg: 99.8035 aux.loss_ce: 0.0067 aux.acc_seg: 99.2311 +04/19 01:18:40 - mmengine - INFO - Iter(train) [ 60950/160000] base_lr: 6.2493e-05 lr: 2.3105e-07 eta: 2 days, 3:24:03 time: 0.9961 data_time: 0.0044 memory: 8457 loss: 0.0149 decode.loss_ce: 0.0065 decode.acc_seg: 99.6803 aux.loss_ce: 0.0083 aux.acc_seg: 98.6212 +04/19 01:19:30 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 01:19:30 - mmengine - INFO - Iter(train) [ 61000/160000] base_lr: 6.2461e-05 lr: 2.3093e-07 eta: 2 days, 2:10:35 time: 0.9963 data_time: 0.0042 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0057 decode.acc_seg: 99.7381 aux.loss_ce: 0.0067 aux.acc_seg: 99.1875 +04/19 01:20:20 - mmengine - INFO - Iter(train) [ 61050/160000] base_lr: 6.2429e-05 lr: 2.3081e-07 eta: 2 days, 1:03:59 time: 0.9958 data_time: 0.0046 memory: 8457 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.5174 aux.loss_ce: 0.0081 aux.acc_seg: 98.8636 +04/19 01:21:10 - mmengine - INFO - Iter(train) [ 61100/160000] base_lr: 6.2398e-05 lr: 2.3070e-07 eta: 2 days, 0:03:25 time: 0.9957 data_time: 0.0043 memory: 8457 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.6033 aux.loss_ce: 0.0079 aux.acc_seg: 99.1104 +04/19 01:22:00 - mmengine - INFO - Iter(train) [ 61150/160000] base_lr: 6.2366e-05 lr: 2.3058e-07 eta: 1 day, 23:08:02 time: 0.9969 data_time: 0.0045 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0061 decode.acc_seg: 99.7784 aux.loss_ce: 0.0079 aux.acc_seg: 99.1186 +04/19 01:22:50 - mmengine - INFO - Iter(train) [ 61200/160000] base_lr: 6.2335e-05 lr: 2.3046e-07 eta: 1 day, 22:17:13 time: 0.9957 data_time: 0.0048 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0065 decode.acc_seg: 99.7452 aux.loss_ce: 0.0079 aux.acc_seg: 99.3444 +04/19 01:23:39 - mmengine - INFO - Iter(train) [ 61250/160000] base_lr: 6.2303e-05 lr: 2.3035e-07 eta: 1 day, 21:30:22 time: 0.9961 data_time: 0.0044 memory: 8457 loss: 0.0153 decode.loss_ce: 0.0069 decode.acc_seg: 99.7793 aux.loss_ce: 0.0085 aux.acc_seg: 99.4120 +04/19 01:24:29 - mmengine - INFO - Iter(train) [ 61300/160000] base_lr: 6.2272e-05 lr: 2.3023e-07 eta: 1 day, 20:47:05 time: 0.9964 data_time: 0.0050 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0061 decode.acc_seg: 99.7265 aux.loss_ce: 0.0072 aux.acc_seg: 99.2550 +04/19 01:25:19 - mmengine - INFO - Iter(train) [ 61350/160000] base_lr: 6.2240e-05 lr: 2.3011e-07 eta: 1 day, 20:06:58 time: 0.9965 data_time: 0.0042 memory: 8457 loss: 0.0125 decode.loss_ce: 0.0057 decode.acc_seg: 99.7890 aux.loss_ce: 0.0067 aux.acc_seg: 99.2046 +04/19 01:26:09 - mmengine - INFO - Iter(train) [ 61400/160000] base_lr: 6.2209e-05 lr: 2.3000e-07 eta: 1 day, 19:29:38 time: 0.9963 data_time: 0.0046 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0059 decode.acc_seg: 99.7929 aux.loss_ce: 0.0074 aux.acc_seg: 99.2052 +04/19 01:26:59 - mmengine - INFO - Iter(train) [ 61450/160000] base_lr: 6.2177e-05 lr: 2.2988e-07 eta: 1 day, 18:54:50 time: 0.9971 data_time: 0.0044 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7149 aux.loss_ce: 0.0075 aux.acc_seg: 99.1526 +04/19 01:27:49 - mmengine - INFO - Iter(train) [ 61500/160000] base_lr: 6.2146e-05 lr: 2.2976e-07 eta: 1 day, 18:22:20 time: 0.9974 data_time: 0.0043 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0066 decode.acc_seg: 99.7095 aux.loss_ce: 0.0079 aux.acc_seg: 98.9267 +04/19 01:28:39 - mmengine - INFO - Iter(train) [ 61550/160000] base_lr: 6.2114e-05 lr: 2.2965e-07 eta: 1 day, 17:51:51 time: 0.9969 data_time: 0.0050 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0063 decode.acc_seg: 99.7963 aux.loss_ce: 0.0079 aux.acc_seg: 99.4560 +04/19 01:29:28 - mmengine - INFO - Iter(train) [ 61600/160000] base_lr: 6.2082e-05 lr: 2.2953e-07 eta: 1 day, 17:23:13 time: 0.9973 data_time: 0.0046 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.7114 aux.loss_ce: 0.0077 aux.acc_seg: 98.9637 +04/19 01:30:18 - mmengine - INFO - Iter(train) [ 61650/160000] base_lr: 6.2051e-05 lr: 2.2941e-07 eta: 1 day, 16:56:18 time: 0.9978 data_time: 0.0046 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7086 aux.loss_ce: 0.0076 aux.acc_seg: 98.9288 +04/19 01:31:08 - mmengine - INFO - Iter(train) [ 61700/160000] base_lr: 6.2019e-05 lr: 2.2930e-07 eta: 1 day, 16:30:53 time: 0.9968 data_time: 0.0045 memory: 8457 loss: 0.0149 decode.loss_ce: 0.0068 decode.acc_seg: 99.5613 aux.loss_ce: 0.0081 aux.acc_seg: 98.4583 +04/19 01:31:58 - mmengine - INFO - Iter(train) [ 61750/160000] base_lr: 6.1988e-05 lr: 2.2918e-07 eta: 1 day, 16:06:54 time: 0.9974 data_time: 0.0049 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0056 decode.acc_seg: 99.7421 aux.loss_ce: 0.0071 aux.acc_seg: 99.1478 +04/19 01:32:48 - mmengine - INFO - Iter(train) [ 61800/160000] base_lr: 6.1956e-05 lr: 2.2906e-07 eta: 1 day, 15:44:10 time: 0.9975 data_time: 0.0045 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.7810 aux.loss_ce: 0.0070 aux.acc_seg: 99.4858 +04/19 01:33:38 - mmengine - INFO - Iter(train) [ 61850/160000] base_lr: 6.1925e-05 lr: 2.2895e-07 eta: 1 day, 15:22:38 time: 0.9975 data_time: 0.0044 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0060 decode.acc_seg: 99.8119 aux.loss_ce: 0.0072 aux.acc_seg: 99.4720 +04/19 01:34:28 - mmengine - INFO - Iter(train) [ 61900/160000] base_lr: 6.1893e-05 lr: 2.2883e-07 eta: 1 day, 15:02:13 time: 0.9977 data_time: 0.0044 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0059 decode.acc_seg: 99.7007 aux.loss_ce: 0.0075 aux.acc_seg: 99.0156 +04/19 01:35:17 - mmengine - INFO - Iter(train) [ 61950/160000] base_lr: 6.1862e-05 lr: 2.2872e-07 eta: 1 day, 14:42:45 time: 0.9968 data_time: 0.0045 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0060 decode.acc_seg: 99.7168 aux.loss_ce: 0.0066 aux.acc_seg: 99.2653 +04/19 01:36:07 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 01:36:07 - mmengine - INFO - Iter(train) [ 62000/160000] base_lr: 6.1830e-05 lr: 2.2860e-07 eta: 1 day, 14:24:15 time: 0.9968 data_time: 0.0047 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0061 decode.acc_seg: 99.7591 aux.loss_ce: 0.0077 aux.acc_seg: 99.4114 +04/19 01:36:57 - mmengine - INFO - Iter(train) [ 62050/160000] base_lr: 6.1798e-05 lr: 2.2848e-07 eta: 1 day, 14:06:34 time: 0.9956 data_time: 0.0048 memory: 8457 loss: 0.0146 decode.loss_ce: 0.0066 decode.acc_seg: 99.6675 aux.loss_ce: 0.0079 aux.acc_seg: 98.9210 +04/19 01:37:47 - mmengine - INFO - Iter(train) [ 62100/160000] base_lr: 6.1767e-05 lr: 2.2837e-07 eta: 1 day, 13:49:42 time: 0.9973 data_time: 0.0043 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.6946 aux.loss_ce: 0.0073 aux.acc_seg: 99.0738 +04/19 01:38:37 - mmengine - INFO - Iter(train) [ 62150/160000] base_lr: 6.1735e-05 lr: 2.2825e-07 eta: 1 day, 13:33:36 time: 0.9988 data_time: 0.0043 memory: 8457 loss: 0.0121 decode.loss_ce: 0.0057 decode.acc_seg: 99.7206 aux.loss_ce: 0.0064 aux.acc_seg: 99.2146 +04/19 01:39:27 - mmengine - INFO - Iter(train) [ 62200/160000] base_lr: 6.1704e-05 lr: 2.2813e-07 eta: 1 day, 13:18:14 time: 0.9995 data_time: 0.0047 memory: 8457 loss: 0.0125 decode.loss_ce: 0.0057 decode.acc_seg: 99.7284 aux.loss_ce: 0.0068 aux.acc_seg: 99.0816 +04/19 01:40:17 - mmengine - INFO - Iter(train) [ 62250/160000] base_lr: 6.1672e-05 lr: 2.2802e-07 eta: 1 day, 13:03:29 time: 0.9986 data_time: 0.0046 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0056 decode.acc_seg: 99.7137 aux.loss_ce: 0.0073 aux.acc_seg: 99.1442 +04/19 01:41:07 - mmengine - INFO - Iter(train) [ 62300/160000] base_lr: 6.1641e-05 lr: 2.2790e-07 eta: 1 day, 12:49:20 time: 0.9961 data_time: 0.0049 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0055 decode.acc_seg: 99.6380 aux.loss_ce: 0.0065 aux.acc_seg: 98.8453 +04/19 01:41:56 - mmengine - INFO - Iter(train) [ 62350/160000] base_lr: 6.1609e-05 lr: 2.2778e-07 eta: 1 day, 12:35:48 time: 0.9994 data_time: 0.0047 memory: 8457 loss: 0.0160 decode.loss_ce: 0.0076 decode.acc_seg: 99.7267 aux.loss_ce: 0.0084 aux.acc_seg: 99.2651 +04/19 01:42:46 - mmengine - INFO - Iter(train) [ 62400/160000] base_lr: 6.1578e-05 lr: 2.2767e-07 eta: 1 day, 12:22:46 time: 0.9967 data_time: 0.0049 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.6550 aux.loss_ce: 0.0076 aux.acc_seg: 98.7083 +04/19 01:43:36 - mmengine - INFO - Iter(train) [ 62450/160000] base_lr: 6.1546e-05 lr: 2.2755e-07 eta: 1 day, 12:10:14 time: 0.9978 data_time: 0.0043 memory: 8457 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7229 aux.loss_ce: 0.0076 aux.acc_seg: 99.4339 +04/19 01:44:26 - mmengine - INFO - Iter(train) [ 62500/160000] base_lr: 6.1515e-05 lr: 2.2743e-07 eta: 1 day, 11:58:09 time: 0.9980 data_time: 0.0042 memory: 8457 loss: 0.0160 decode.loss_ce: 0.0074 decode.acc_seg: 99.7686 aux.loss_ce: 0.0086 aux.acc_seg: 99.4291 +04/19 01:45:16 - mmengine - INFO - Iter(train) [ 62550/160000] base_lr: 6.1483e-05 lr: 2.2732e-07 eta: 1 day, 11:46:30 time: 0.9972 data_time: 0.0043 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0059 decode.acc_seg: 99.7599 aux.loss_ce: 0.0069 aux.acc_seg: 99.1179 +04/19 01:46:06 - mmengine - INFO - Iter(train) [ 62600/160000] base_lr: 6.1451e-05 lr: 2.2720e-07 eta: 1 day, 11:35:17 time: 0.9975 data_time: 0.0045 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0057 decode.acc_seg: 99.6805 aux.loss_ce: 0.0071 aux.acc_seg: 99.0713 +04/19 01:46:56 - mmengine - INFO - Iter(train) [ 62650/160000] base_lr: 6.1420e-05 lr: 2.2708e-07 eta: 1 day, 11:24:25 time: 0.9953 data_time: 0.0046 memory: 8457 loss: 0.0151 decode.loss_ce: 0.0067 decode.acc_seg: 99.6197 aux.loss_ce: 0.0084 aux.acc_seg: 99.1144 +04/19 01:47:46 - mmengine - INFO - Iter(train) [ 62700/160000] base_lr: 6.1388e-05 lr: 2.2697e-07 eta: 1 day, 11:13:55 time: 0.9955 data_time: 0.0043 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0064 decode.acc_seg: 99.6607 aux.loss_ce: 0.0082 aux.acc_seg: 99.1154 +04/19 01:48:35 - mmengine - INFO - Iter(train) [ 62750/160000] base_lr: 6.1357e-05 lr: 2.2685e-07 eta: 1 day, 11:03:46 time: 0.9950 data_time: 0.0042 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0065 decode.acc_seg: 99.7042 aux.loss_ce: 0.0077 aux.acc_seg: 99.1091 +04/19 01:49:25 - mmengine - INFO - Iter(train) [ 62800/160000] base_lr: 6.1325e-05 lr: 2.2673e-07 eta: 1 day, 10:53:58 time: 0.9973 data_time: 0.0043 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.6748 aux.loss_ce: 0.0077 aux.acc_seg: 99.3170 +04/19 01:50:15 - mmengine - INFO - Iter(train) [ 62850/160000] base_lr: 6.1294e-05 lr: 2.2662e-07 eta: 1 day, 10:44:30 time: 0.9962 data_time: 0.0049 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0060 decode.acc_seg: 99.7328 aux.loss_ce: 0.0077 aux.acc_seg: 99.1205 +04/19 01:51:05 - mmengine - INFO - Iter(train) [ 62900/160000] base_lr: 6.1262e-05 lr: 2.2650e-07 eta: 1 day, 10:35:19 time: 0.9975 data_time: 0.0046 memory: 8457 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+04/19 01:54:24 - mmengine - INFO - Iter(train) [ 63100/160000] base_lr: 6.1136e-05 lr: 2.2603e-07 eta: 1 day, 10:01:24 time: 0.9978 data_time: 0.0045 memory: 8457 loss: 0.0148 decode.loss_ce: 0.0070 decode.acc_seg: 99.5815 aux.loss_ce: 0.0078 aux.acc_seg: 99.1236 +04/19 01:55:14 - mmengine - INFO - Iter(train) [ 63150/160000] base_lr: 6.1104e-05 lr: 2.2592e-07 eta: 1 day, 9:53:32 time: 0.9985 data_time: 0.0044 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0061 decode.acc_seg: 99.7086 aux.loss_ce: 0.0075 aux.acc_seg: 98.6923 +04/19 01:56:04 - mmengine - INFO - Iter(train) [ 63200/160000] base_lr: 6.1073e-05 lr: 2.2580e-07 eta: 1 day, 9:45:53 time: 0.9989 data_time: 0.0046 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0057 decode.acc_seg: 99.8119 aux.loss_ce: 0.0067 aux.acc_seg: 99.3879 +04/19 01:56:54 - mmengine - INFO - Iter(train) [ 63250/160000] base_lr: 6.1041e-05 lr: 2.2568e-07 eta: 1 day, 9:38:26 time: 0.9975 data_time: 0.0048 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0061 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aux.loss_ce: 0.0066 aux.acc_seg: 99.3591 +04/19 02:19:23 - mmengine - INFO - Iter(train) [ 64600/160000] base_lr: 6.0190e-05 lr: 2.2253e-07 eta: 1 day, 7:12:15 time: 0.9984 data_time: 0.0043 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0061 decode.acc_seg: 99.6122 aux.loss_ce: 0.0081 aux.acc_seg: 98.8277 +04/19 02:20:13 - mmengine - INFO - Iter(train) [ 64650/160000] base_lr: 6.0158e-05 lr: 2.2242e-07 eta: 1 day, 7:08:13 time: 0.9981 data_time: 0.0045 memory: 8457 loss: 0.0147 decode.loss_ce: 0.0067 decode.acc_seg: 99.6441 aux.loss_ce: 0.0080 aux.acc_seg: 99.1022 +04/19 02:21:03 - mmengine - INFO - Iter(train) [ 64700/160000] base_lr: 6.0127e-05 lr: 2.2230e-07 eta: 1 day, 7:04:16 time: 1.0002 data_time: 0.0048 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.7303 aux.loss_ce: 0.0076 aux.acc_seg: 99.0334 +04/19 02:21:53 - mmengine - INFO - Iter(train) [ 64750/160000] base_lr: 6.0095e-05 lr: 2.2218e-07 eta: 1 day, 7:00:22 time: 1.0001 data_time: 0.0045 memory: 8457 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INFO - Iter(train) [ 65700/160000] base_lr: 5.9496e-05 lr: 2.1997e-07 eta: 1 day, 5:56:37 time: 1.0004 data_time: 0.0047 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0063 decode.acc_seg: 99.6353 aux.loss_ce: 0.0080 aux.acc_seg: 98.9258 +04/19 02:38:32 - mmengine - INFO - Iter(train) [ 65750/160000] base_lr: 5.9464e-05 lr: 2.1985e-07 eta: 1 day, 5:53:42 time: 0.9989 data_time: 0.0045 memory: 8457 loss: 0.0135 decode.loss_ce: 0.0060 decode.acc_seg: 99.7807 aux.loss_ce: 0.0075 aux.acc_seg: 99.2249 +04/19 02:39:22 - mmengine - INFO - Iter(train) [ 65800/160000] base_lr: 5.9433e-05 lr: 2.1973e-07 eta: 1 day, 5:50:50 time: 0.9999 data_time: 0.0051 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0057 decode.acc_seg: 99.7437 aux.loss_ce: 0.0074 aux.acc_seg: 99.3151 +04/19 02:40:12 - mmengine - INFO - Iter(train) [ 65850/160000] base_lr: 5.9401e-05 lr: 2.1962e-07 eta: 1 day, 5:47:59 time: 1.0005 data_time: 0.0052 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0064 decode.acc_seg: 99.7002 aux.loss_ce: 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mmengine - INFO - Iter(train) [ 66250/160000] base_lr: 5.9149e-05 lr: 2.1868e-07 eta: 1 day, 5:26:26 time: 0.9992 data_time: 0.0045 memory: 8457 loss: 0.0153 decode.loss_ce: 0.0071 decode.acc_seg: 99.7150 aux.loss_ce: 0.0081 aux.acc_seg: 99.1611 +04/19 02:47:42 - mmengine - INFO - Iter(train) [ 66300/160000] base_lr: 5.9117e-05 lr: 2.1857e-07 eta: 1 day, 5:23:52 time: 1.0002 data_time: 0.0047 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0063 decode.acc_seg: 99.7278 aux.loss_ce: 0.0078 aux.acc_seg: 99.1610 +04/19 02:48:32 - mmengine - INFO - Iter(train) [ 66350/160000] base_lr: 5.9086e-05 lr: 2.1845e-07 eta: 1 day, 5:21:20 time: 0.9987 data_time: 0.0044 memory: 8457 loss: 0.0110 decode.loss_ce: 0.0052 decode.acc_seg: 99.8354 aux.loss_ce: 0.0058 aux.acc_seg: 99.5626 +04/19 02:49:22 - mmengine - INFO - Iter(train) [ 66400/160000] base_lr: 5.9054e-05 lr: 2.1833e-07 eta: 1 day, 5:18:49 time: 0.9990 data_time: 0.0042 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.8220 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memory: 8457 loss: 0.0125 decode.loss_ce: 0.0057 decode.acc_seg: 99.7541 aux.loss_ce: 0.0068 aux.acc_seg: 99.2014 +04/19 03:02:42 - mmengine - INFO - Iter(train) [ 67200/160000] base_lr: 5.8549e-05 lr: 2.1647e-07 eta: 1 day, 4:41:52 time: 0.9990 data_time: 0.0045 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.6986 aux.loss_ce: 0.0076 aux.acc_seg: 99.0858 +04/19 03:03:32 - mmengine - INFO - Iter(train) [ 67250/160000] base_lr: 5.8518e-05 lr: 2.1635e-07 eta: 1 day, 4:39:44 time: 1.0010 data_time: 0.0044 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0058 decode.acc_seg: 99.7366 aux.loss_ce: 0.0070 aux.acc_seg: 99.3134 +04/19 03:04:22 - mmengine - INFO - Iter(train) [ 67300/160000] base_lr: 5.8486e-05 lr: 2.1624e-07 eta: 1 day, 4:37:37 time: 1.0003 data_time: 0.0044 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0067 decode.acc_seg: 99.6817 aux.loss_ce: 0.0078 aux.acc_seg: 99.2720 +04/19 03:05:12 - mmengine - INFO - Iter(train) [ 67350/160000] base_lr: 5.8455e-05 lr: 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INFO - Iter(train) [ 67550/160000] base_lr: 5.8328e-05 lr: 2.1565e-07 eta: 1 day, 4:27:17 time: 0.9990 data_time: 0.0042 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0055 decode.acc_seg: 99.7091 aux.loss_ce: 0.0067 aux.acc_seg: 99.1322 +04/19 03:09:22 - mmengine - INFO - Iter(train) [ 67600/160000] base_lr: 5.8297e-05 lr: 2.1554e-07 eta: 1 day, 4:25:16 time: 1.0010 data_time: 0.0051 memory: 8457 loss: 0.0147 decode.loss_ce: 0.0066 decode.acc_seg: 99.6870 aux.loss_ce: 0.0081 aux.acc_seg: 98.9845 +04/19 03:10:12 - mmengine - INFO - Iter(train) [ 67650/160000] base_lr: 5.8265e-05 lr: 2.1542e-07 eta: 1 day, 4:23:16 time: 0.9992 data_time: 0.0044 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0062 decode.acc_seg: 99.6229 aux.loss_ce: 0.0080 aux.acc_seg: 99.0772 +04/19 03:11:02 - mmengine - INFO - Iter(train) [ 67700/160000] base_lr: 5.8234e-05 lr: 2.1530e-07 eta: 1 day, 4:21:16 time: 0.9984 data_time: 0.0047 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0059 decode.acc_seg: 99.8013 aux.loss_ce: 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decode.loss_ce: 0.0066 decode.acc_seg: 99.6967 aux.loss_ce: 0.0079 aux.acc_seg: 98.7911 +04/19 03:15:12 - mmengine - INFO - Iter(train) [ 67950/160000] base_lr: 5.8076e-05 lr: 2.1472e-07 eta: 1 day, 4:11:33 time: 1.0002 data_time: 0.0044 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0059 decode.acc_seg: 99.8369 aux.loss_ce: 0.0071 aux.acc_seg: 99.5193 +04/19 03:16:02 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 03:16:02 - mmengine - INFO - Iter(train) [ 68000/160000] base_lr: 5.8045e-05 lr: 2.1460e-07 eta: 1 day, 4:09:39 time: 0.9999 data_time: 0.0047 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0063 decode.acc_seg: 99.7166 aux.loss_ce: 0.0078 aux.acc_seg: 99.2054 +04/19 03:16:52 - mmengine - INFO - Iter(train) [ 68050/160000] base_lr: 5.8013e-05 lr: 2.1449e-07 eta: 1 day, 4:07:46 time: 1.0013 data_time: 0.0046 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0063 decode.acc_seg: 99.6471 aux.loss_ce: 0.0081 aux.acc_seg: 98.9067 +04/19 03:17:42 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aux.loss_ce: 0.0075 aux.acc_seg: 99.1526 +04/19 03:21:02 - mmengine - INFO - Iter(train) [ 68300/160000] base_lr: 5.7855e-05 lr: 2.1390e-07 eta: 1 day, 3:58:31 time: 1.0000 data_time: 0.0043 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0057 decode.acc_seg: 99.6550 aux.loss_ce: 0.0073 aux.acc_seg: 98.7543 +04/19 03:21:52 - mmengine - INFO - Iter(train) [ 68350/160000] base_lr: 5.7824e-05 lr: 2.1379e-07 eta: 1 day, 3:56:42 time: 1.0000 data_time: 0.0047 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0060 decode.acc_seg: 99.8215 aux.loss_ce: 0.0074 aux.acc_seg: 99.4900 +04/19 03:22:42 - mmengine - INFO - Iter(train) [ 68400/160000] base_lr: 5.7792e-05 lr: 2.1367e-07 eta: 1 day, 3:54:53 time: 0.9983 data_time: 0.0047 memory: 8457 loss: 0.0153 decode.loss_ce: 0.0067 decode.acc_seg: 99.6662 aux.loss_ce: 0.0086 aux.acc_seg: 98.7791 +04/19 03:23:32 - mmengine - INFO - Iter(train) [ 68450/160000] base_lr: 5.7761e-05 lr: 2.1355e-07 eta: 1 day, 3:53:05 time: 1.0010 data_time: 0.0047 memory: 8457 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INFO - Iter(train) [ 69400/160000] base_lr: 5.7161e-05 lr: 2.1134e-07 eta: 1 day, 3:20:57 time: 0.9998 data_time: 0.0048 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0058 decode.acc_seg: 99.7053 aux.loss_ce: 0.0075 aux.acc_seg: 98.9986 +04/19 03:40:12 - mmengine - INFO - Iter(train) [ 69450/160000] base_lr: 5.7130e-05 lr: 2.1122e-07 eta: 1 day, 3:19:21 time: 0.9990 data_time: 0.0045 memory: 8457 loss: 0.0125 decode.loss_ce: 0.0057 decode.acc_seg: 99.7320 aux.loss_ce: 0.0067 aux.acc_seg: 99.3029 +04/19 03:41:02 - mmengine - INFO - Iter(train) [ 69500/160000] base_lr: 5.7098e-05 lr: 2.1110e-07 eta: 1 day, 3:17:45 time: 0.9993 data_time: 0.0045 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0066 decode.acc_seg: 99.7351 aux.loss_ce: 0.0073 aux.acc_seg: 99.2998 +04/19 03:41:52 - mmengine - INFO - Iter(train) [ 69550/160000] base_lr: 5.7067e-05 lr: 2.1099e-07 eta: 1 day, 3:16:10 time: 0.9989 data_time: 0.0048 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0058 decode.acc_seg: 99.8100 aux.loss_ce: 0.0071 aux.acc_seg: 99.4490 +04/19 03:42:42 - mmengine - INFO - Iter(train) [ 69600/160000] base_lr: 5.7035e-05 lr: 2.1087e-07 eta: 1 day, 3:14:35 time: 0.9978 data_time: 0.0046 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0060 decode.acc_seg: 99.7519 aux.loss_ce: 0.0074 aux.acc_seg: 99.4026 +04/19 03:43:32 - mmengine - INFO - Iter(train) [ 69650/160000] base_lr: 5.7004e-05 lr: 2.1075e-07 eta: 1 day, 3:13:00 time: 0.9979 data_time: 0.0044 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0055 decode.acc_seg: 99.7826 aux.loss_ce: 0.0069 aux.acc_seg: 99.2519 +04/19 03:44:22 - mmengine - INFO - Iter(train) [ 69700/160000] base_lr: 5.6972e-05 lr: 2.1064e-07 eta: 1 day, 3:11:25 time: 0.9978 data_time: 0.0045 memory: 8457 loss: 0.0121 decode.loss_ce: 0.0054 decode.acc_seg: 99.7658 aux.loss_ce: 0.0066 aux.acc_seg: 99.1362 +04/19 03:45:12 - mmengine - INFO - Iter(train) [ 69750/160000] base_lr: 5.6940e-05 lr: 2.1052e-07 eta: 1 day, 3:09:52 time: 0.9974 data_time: 0.0046 memory: 8457 loss: 0.0111 decode.loss_ce: 0.0051 decode.acc_seg: 99.7854 aux.loss_ce: 0.0060 aux.acc_seg: 99.4383 +04/19 03:46:02 - mmengine - INFO - Iter(train) [ 69800/160000] base_lr: 5.6909e-05 lr: 2.1040e-07 eta: 1 day, 3:08:18 time: 1.0000 data_time: 0.0047 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7082 aux.loss_ce: 0.0073 aux.acc_seg: 99.2994 +04/19 03:46:51 - mmengine - INFO - Iter(train) [ 69850/160000] base_lr: 5.6877e-05 lr: 2.1029e-07 eta: 1 day, 3:06:45 time: 0.9984 data_time: 0.0047 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0063 decode.acc_seg: 99.6773 aux.loss_ce: 0.0078 aux.acc_seg: 99.0072 +04/19 03:47:41 - mmengine - INFO - Iter(train) [ 69900/160000] base_lr: 5.6846e-05 lr: 2.1017e-07 eta: 1 day, 3:05:12 time: 0.9976 data_time: 0.0046 memory: 8457 loss: 0.0146 decode.loss_ce: 0.0063 decode.acc_seg: 99.7482 aux.loss_ce: 0.0083 aux.acc_seg: 99.1983 +04/19 03:48:31 - mmengine - INFO - Iter(train) [ 69950/160000] base_lr: 5.6814e-05 lr: 2.1005e-07 eta: 1 day, 3:03:40 time: 0.9979 data_time: 0.0051 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0059 decode.acc_seg: 99.7807 aux.loss_ce: 0.0072 aux.acc_seg: 99.3937 +04/19 03:49:21 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 03:49:21 - mmengine - INFO - Iter(train) [ 70000/160000] base_lr: 5.6783e-05 lr: 2.0994e-07 eta: 1 day, 3:02:07 time: 0.9969 data_time: 0.0045 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0059 decode.acc_seg: 99.7444 aux.loss_ce: 0.0075 aux.acc_seg: 99.2407 +04/19 03:49:21 - mmengine - INFO - Saving checkpoint at 70000 iterations +04/19 03:49:32 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:21 time: 0.1162 data_time: 0.0015 memory: 7120 +04/19 03:49:38 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:12 time: 0.1160 data_time: 0.0015 memory: 3999 +04/19 03:49:44 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:06 time: 0.1168 data_time: 0.0015 memory: 3999 +04/19 03:49:50 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1156 data_time: 0.0013 memory: 3999 +04/19 03:49:50 - mmengine - INFO - per class results: +04/19 03:49:50 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.17 | 99.56 | 99.58 | 99.61 | 99.56 | +| contrast | 81.94 | 90.71 | 90.07 | 89.45 | 90.71 | ++------------+-------+-------+--------+-----------+--------+ +04/19 03:49:50 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2000 mIoU: 90.5600 mAcc: 95.1300 mFscore: 94.8300 mPrecision: 94.5300 mRecall: 95.1300 data_time: 0.0024 time: 0.1227 +04/19 03:50:40 - mmengine - INFO - Iter(train) [ 70050/160000] base_lr: 5.6751e-05 lr: 2.0982e-07 eta: 1 day, 3:00:37 time: 0.9962 data_time: 0.0044 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.6668 aux.loss_ce: 0.0070 aux.acc_seg: 99.2702 +04/19 03:51:30 - mmengine - INFO - Iter(train) [ 70100/160000] base_lr: 5.6720e-05 lr: 2.0970e-07 eta: 1 day, 2:59:06 time: 0.9969 data_time: 0.0048 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0059 decode.acc_seg: 99.7742 aux.loss_ce: 0.0071 aux.acc_seg: 99.4482 +04/19 03:52:20 - mmengine - INFO - Iter(train) [ 70150/160000] base_lr: 5.6688e-05 lr: 2.0959e-07 eta: 1 day, 2:57:35 time: 0.9974 data_time: 0.0047 memory: 8457 loss: 0.0112 decode.loss_ce: 0.0050 decode.acc_seg: 99.7908 aux.loss_ce: 0.0062 aux.acc_seg: 99.1558 +04/19 03:53:10 - mmengine - INFO - Iter(train) [ 70200/160000] base_lr: 5.6657e-05 lr: 2.0947e-07 eta: 1 day, 2:56:05 time: 0.9973 data_time: 0.0044 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0060 decode.acc_seg: 99.7467 aux.loss_ce: 0.0072 aux.acc_seg: 99.3105 +04/19 03:53:59 - mmengine - INFO - Iter(train) [ 70250/160000] base_lr: 5.6625e-05 lr: 2.0935e-07 eta: 1 day, 2:54:35 time: 0.9976 data_time: 0.0047 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0066 decode.acc_seg: 99.5974 aux.loss_ce: 0.0079 aux.acc_seg: 99.0288 +04/19 03:54:49 - mmengine - INFO - Iter(train) [ 70300/160000] base_lr: 5.6593e-05 lr: 2.0924e-07 eta: 1 day, 2:53:05 time: 0.9987 data_time: 0.0046 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7795 aux.loss_ce: 0.0073 aux.acc_seg: 99.4055 +04/19 03:55:39 - mmengine - INFO - Iter(train) [ 70350/160000] base_lr: 5.6562e-05 lr: 2.0912e-07 eta: 1 day, 2:51:36 time: 0.9970 data_time: 0.0048 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0058 decode.acc_seg: 99.7595 aux.loss_ce: 0.0069 aux.acc_seg: 99.1049 +04/19 03:56:29 - mmengine - INFO - Iter(train) [ 70400/160000] base_lr: 5.6530e-05 lr: 2.0900e-07 eta: 1 day, 2:50:08 time: 0.9981 data_time: 0.0047 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0051 decode.acc_seg: 99.7854 aux.loss_ce: 0.0064 aux.acc_seg: 99.1919 +04/19 03:57:19 - mmengine - INFO - Iter(train) [ 70450/160000] base_lr: 5.6499e-05 lr: 2.0889e-07 eta: 1 day, 2:48:39 time: 0.9968 data_time: 0.0046 memory: 8457 loss: 0.0157 decode.loss_ce: 0.0073 decode.acc_seg: 99.7282 aux.loss_ce: 0.0084 aux.acc_seg: 99.1796 +04/19 03:58:09 - mmengine - INFO - Iter(train) [ 70500/160000] base_lr: 5.6467e-05 lr: 2.0877e-07 eta: 1 day, 2:47:11 time: 0.9973 data_time: 0.0046 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0064 decode.acc_seg: 99.5211 aux.loss_ce: 0.0079 aux.acc_seg: 98.7547 +04/19 03:58:59 - mmengine - INFO - Iter(train) [ 70550/160000] base_lr: 5.6436e-05 lr: 2.0865e-07 eta: 1 day, 2:45:43 time: 0.9971 data_time: 0.0048 memory: 8457 loss: 0.0152 decode.loss_ce: 0.0066 decode.acc_seg: 99.7805 aux.loss_ce: 0.0086 aux.acc_seg: 99.4310 +04/19 03:59:49 - mmengine - INFO - Iter(train) [ 70600/160000] base_lr: 5.6404e-05 lr: 2.0854e-07 eta: 1 day, 2:44:16 time: 0.9974 data_time: 0.0045 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0064 decode.acc_seg: 99.6746 aux.loss_ce: 0.0079 aux.acc_seg: 98.9529 +04/19 04:00:39 - mmengine - INFO - Iter(train) [ 70650/160000] base_lr: 5.6373e-05 lr: 2.0842e-07 eta: 1 day, 2:42:49 time: 0.9985 data_time: 0.0046 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0054 decode.acc_seg: 99.7784 aux.loss_ce: 0.0066 aux.acc_seg: 99.1545 +04/19 04:01:28 - mmengine - INFO - Iter(train) [ 70700/160000] base_lr: 5.6341e-05 lr: 2.0830e-07 eta: 1 day, 2:41:22 time: 0.9983 data_time: 0.0047 memory: 8457 loss: 0.0119 decode.loss_ce: 0.0054 decode.acc_seg: 99.7101 aux.loss_ce: 0.0064 aux.acc_seg: 99.1886 +04/19 04:02:18 - mmengine - INFO - Iter(train) [ 70750/160000] base_lr: 5.6310e-05 lr: 2.0819e-07 eta: 1 day, 2:39:56 time: 0.9975 data_time: 0.0045 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0058 decode.acc_seg: 99.7566 aux.loss_ce: 0.0071 aux.acc_seg: 99.3814 +04/19 04:03:08 - mmengine - INFO - Iter(train) [ 70800/160000] base_lr: 5.6278e-05 lr: 2.0807e-07 eta: 1 day, 2:38:30 time: 0.9976 data_time: 0.0049 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0056 decode.acc_seg: 99.7635 aux.loss_ce: 0.0074 aux.acc_seg: 99.0618 +04/19 04:03:58 - mmengine - INFO - Iter(train) [ 70850/160000] base_lr: 5.6246e-05 lr: 2.0795e-07 eta: 1 day, 2:37:05 time: 0.9962 data_time: 0.0045 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.6508 aux.loss_ce: 0.0070 aux.acc_seg: 98.9620 +04/19 04:04:48 - mmengine - INFO - Iter(train) [ 70900/160000] base_lr: 5.6215e-05 lr: 2.0784e-07 eta: 1 day, 2:35:40 time: 0.9984 data_time: 0.0044 memory: 8457 loss: 0.0150 decode.loss_ce: 0.0068 decode.acc_seg: 99.5358 aux.loss_ce: 0.0082 aux.acc_seg: 98.5611 +04/19 04:05:38 - mmengine - INFO - Iter(train) [ 70950/160000] base_lr: 5.6183e-05 lr: 2.0772e-07 eta: 1 day, 2:34:15 time: 0.9982 data_time: 0.0047 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0056 decode.acc_seg: 99.7366 aux.loss_ce: 0.0064 aux.acc_seg: 99.2506 +04/19 04:06:28 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 04:06:28 - mmengine - INFO - Iter(train) [ 71000/160000] base_lr: 5.6152e-05 lr: 2.0760e-07 eta: 1 day, 2:32:50 time: 0.9985 data_time: 0.0052 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0059 decode.acc_seg: 99.7271 aux.loss_ce: 0.0072 aux.acc_seg: 99.3591 +04/19 04:07:18 - mmengine - INFO - Iter(train) [ 71050/160000] base_lr: 5.6120e-05 lr: 2.0749e-07 eta: 1 day, 2:31:26 time: 0.9993 data_time: 0.0045 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0061 decode.acc_seg: 99.7278 aux.loss_ce: 0.0077 aux.acc_seg: 99.2346 +04/19 04:08:08 - mmengine - INFO - Iter(train) [ 71100/160000] base_lr: 5.6089e-05 lr: 2.0737e-07 eta: 1 day, 2:30:02 time: 0.9973 data_time: 0.0044 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0054 decode.acc_seg: 99.7286 aux.loss_ce: 0.0066 aux.acc_seg: 99.2502 +04/19 04:08:58 - mmengine - INFO - Iter(train) [ 71150/160000] base_lr: 5.6057e-05 lr: 2.0725e-07 eta: 1 day, 2:28:38 time: 0.9982 data_time: 0.0048 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0063 decode.acc_seg: 99.6756 aux.loss_ce: 0.0082 aux.acc_seg: 99.1711 +04/19 04:09:47 - mmengine - INFO - Iter(train) [ 71200/160000] base_lr: 5.6026e-05 lr: 2.0714e-07 eta: 1 day, 2:27:15 time: 0.9988 data_time: 0.0046 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0058 decode.acc_seg: 99.7915 aux.loss_ce: 0.0074 aux.acc_seg: 99.2517 +04/19 04:10:37 - mmengine - INFO - Iter(train) [ 71250/160000] base_lr: 5.5994e-05 lr: 2.0702e-07 eta: 1 day, 2:25:52 time: 0.9971 data_time: 0.0051 memory: 8457 loss: 0.0117 decode.loss_ce: 0.0056 decode.acc_seg: 99.8142 aux.loss_ce: 0.0061 aux.acc_seg: 99.4389 +04/19 04:11:27 - mmengine - INFO - Iter(train) [ 71300/160000] base_lr: 5.5962e-05 lr: 2.0690e-07 eta: 1 day, 2:24:29 time: 0.9973 data_time: 0.0047 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0062 decode.acc_seg: 99.6574 aux.loss_ce: 0.0074 aux.acc_seg: 99.0965 +04/19 04:12:17 - mmengine - INFO - Iter(train) [ 71350/160000] base_lr: 5.5931e-05 lr: 2.0679e-07 eta: 1 day, 2:23:07 time: 0.9980 data_time: 0.0053 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.6717 aux.loss_ce: 0.0073 aux.acc_seg: 99.0242 +04/19 04:13:07 - mmengine - INFO - Iter(train) [ 71400/160000] base_lr: 5.5899e-05 lr: 2.0667e-07 eta: 1 day, 2:21:45 time: 0.9986 data_time: 0.0048 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0060 decode.acc_seg: 99.8230 aux.loss_ce: 0.0071 aux.acc_seg: 99.4883 +04/19 04:13:57 - mmengine - INFO - Iter(train) [ 71450/160000] base_lr: 5.5868e-05 lr: 2.0655e-07 eta: 1 day, 2:20:23 time: 0.9985 data_time: 0.0050 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.7934 aux.loss_ce: 0.0075 aux.acc_seg: 99.3322 +04/19 04:14:47 - mmengine - INFO - Iter(train) [ 71500/160000] base_lr: 5.5836e-05 lr: 2.0644e-07 eta: 1 day, 2:19:01 time: 0.9977 data_time: 0.0046 memory: 8457 loss: 0.0115 decode.loss_ce: 0.0050 decode.acc_seg: 99.7473 aux.loss_ce: 0.0066 aux.acc_seg: 99.1293 +04/19 04:15:37 - mmengine - INFO - Iter(train) [ 71550/160000] base_lr: 5.5805e-05 lr: 2.0632e-07 eta: 1 day, 2:17:39 time: 0.9969 data_time: 0.0049 memory: 8457 loss: 0.0155 decode.loss_ce: 0.0068 decode.acc_seg: 99.8236 aux.loss_ce: 0.0087 aux.acc_seg: 99.4001 +04/19 04:16:27 - mmengine - INFO - Iter(train) [ 71600/160000] base_lr: 5.5773e-05 lr: 2.0621e-07 eta: 1 day, 2:16:18 time: 0.9972 data_time: 0.0047 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0060 decode.acc_seg: 99.8430 aux.loss_ce: 0.0071 aux.acc_seg: 99.5842 +04/19 04:17:16 - mmengine - INFO - Iter(train) [ 71650/160000] base_lr: 5.5742e-05 lr: 2.0609e-07 eta: 1 day, 2:14:57 time: 0.9974 data_time: 0.0046 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0061 decode.acc_seg: 99.7597 aux.loss_ce: 0.0071 aux.acc_seg: 99.4097 +04/19 04:18:06 - mmengine - INFO - Iter(train) [ 71700/160000] base_lr: 5.5710e-05 lr: 2.0597e-07 eta: 1 day, 2:13:36 time: 0.9976 data_time: 0.0048 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0059 decode.acc_seg: 99.7728 aux.loss_ce: 0.0075 aux.acc_seg: 99.3156 +04/19 04:18:56 - mmengine - INFO - Iter(train) [ 71750/160000] base_lr: 5.5679e-05 lr: 2.0586e-07 eta: 1 day, 2:12:16 time: 0.9975 data_time: 0.0047 memory: 8457 loss: 0.0113 decode.loss_ce: 0.0051 decode.acc_seg: 99.7702 aux.loss_ce: 0.0061 aux.acc_seg: 99.1955 +04/19 04:19:46 - mmengine - INFO - Iter(train) [ 71800/160000] base_lr: 5.5647e-05 lr: 2.0574e-07 eta: 1 day, 2:10:57 time: 0.9991 data_time: 0.0047 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0056 decode.acc_seg: 99.7063 aux.loss_ce: 0.0076 aux.acc_seg: 99.0969 +04/19 04:20:36 - mmengine - INFO - Iter(train) [ 71850/160000] base_lr: 5.5615e-05 lr: 2.0562e-07 eta: 1 day, 2:09:37 time: 0.9976 data_time: 0.0045 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0065 decode.acc_seg: 99.8339 aux.loss_ce: 0.0079 aux.acc_seg: 99.6168 +04/19 04:21:26 - mmengine - INFO - Iter(train) [ 71900/160000] base_lr: 5.5584e-05 lr: 2.0551e-07 eta: 1 day, 2:08:18 time: 0.9980 data_time: 0.0047 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0055 decode.acc_seg: 99.7591 aux.loss_ce: 0.0074 aux.acc_seg: 99.0673 +04/19 04:22:16 - mmengine - INFO - Iter(train) [ 71950/160000] base_lr: 5.5552e-05 lr: 2.0539e-07 eta: 1 day, 2:06:59 time: 0.9987 data_time: 0.0049 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0061 decode.acc_seg: 99.6096 aux.loss_ce: 0.0076 aux.acc_seg: 98.9359 +04/19 04:23:06 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 04:23:06 - mmengine - INFO - Iter(train) [ 72000/160000] base_lr: 5.5521e-05 lr: 2.0527e-07 eta: 1 day, 2:05:40 time: 0.9981 data_time: 0.0044 memory: 8457 loss: 0.0149 decode.loss_ce: 0.0067 decode.acc_seg: 99.6532 aux.loss_ce: 0.0082 aux.acc_seg: 98.9784 +04/19 04:23:56 - mmengine - INFO - Iter(train) [ 72050/160000] base_lr: 5.5489e-05 lr: 2.0516e-07 eta: 1 day, 2:04:22 time: 1.0004 data_time: 0.0043 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0056 decode.acc_seg: 99.7738 aux.loss_ce: 0.0064 aux.acc_seg: 99.4066 +04/19 04:24:46 - mmengine - INFO - Iter(train) [ 72100/160000] base_lr: 5.5458e-05 lr: 2.0504e-07 eta: 1 day, 2:03:04 time: 0.9988 data_time: 0.0048 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0053 decode.acc_seg: 99.7711 aux.loss_ce: 0.0073 aux.acc_seg: 99.2994 +04/19 04:25:36 - mmengine - INFO - Iter(train) [ 72150/160000] base_lr: 5.5426e-05 lr: 2.0492e-07 eta: 1 day, 2:01:47 time: 1.0004 data_time: 0.0052 memory: 8457 loss: 0.0118 decode.loss_ce: 0.0052 decode.acc_seg: 99.7536 aux.loss_ce: 0.0066 aux.acc_seg: 99.1631 +04/19 04:26:26 - mmengine - INFO - Iter(train) [ 72200/160000] base_lr: 5.5395e-05 lr: 2.0481e-07 eta: 1 day, 2:00:29 time: 1.0000 data_time: 0.0046 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0061 decode.acc_seg: 99.7293 aux.loss_ce: 0.0078 aux.acc_seg: 98.9708 +04/19 04:27:16 - mmengine - INFO - Iter(train) [ 72250/160000] base_lr: 5.5363e-05 lr: 2.0469e-07 eta: 1 day, 1:59:12 time: 0.9989 data_time: 0.0044 memory: 8457 loss: 0.0121 decode.loss_ce: 0.0053 decode.acc_seg: 99.8394 aux.loss_ce: 0.0068 aux.acc_seg: 99.4120 +04/19 04:28:06 - mmengine - INFO - Iter(train) [ 72300/160000] base_lr: 5.5332e-05 lr: 2.0457e-07 eta: 1 day, 1:57:55 time: 0.9999 data_time: 0.0048 memory: 8457 loss: 0.0163 decode.loss_ce: 0.0072 decode.acc_seg: 99.6603 aux.loss_ce: 0.0091 aux.acc_seg: 99.1877 +04/19 04:28:56 - mmengine - INFO - Iter(train) [ 72350/160000] base_lr: 5.5300e-05 lr: 2.0446e-07 eta: 1 day, 1:56:38 time: 1.0006 data_time: 0.0048 memory: 8457 loss: 0.0140 decode.loss_ce: 0.0058 decode.acc_seg: 99.7231 aux.loss_ce: 0.0081 aux.acc_seg: 99.1499 +04/19 04:29:46 - mmengine - INFO - Iter(train) [ 72400/160000] base_lr: 5.5268e-05 lr: 2.0434e-07 eta: 1 day, 1:55:22 time: 1.0005 data_time: 0.0049 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0054 decode.acc_seg: 99.6748 aux.loss_ce: 0.0067 aux.acc_seg: 99.1932 +04/19 04:30:36 - mmengine - INFO - Iter(train) [ 72450/160000] base_lr: 5.5237e-05 lr: 2.0422e-07 eta: 1 day, 1:54:06 time: 1.0009 data_time: 0.0045 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0056 decode.acc_seg: 99.7463 aux.loss_ce: 0.0073 aux.acc_seg: 99.0011 +04/19 04:31:26 - mmengine - INFO - Iter(train) [ 72500/160000] base_lr: 5.5205e-05 lr: 2.0411e-07 eta: 1 day, 1:52:50 time: 1.0010 data_time: 0.0047 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.8198 aux.loss_ce: 0.0071 aux.acc_seg: 99.5687 +04/19 04:32:16 - mmengine - INFO - Iter(train) [ 72550/160000] base_lr: 5.5174e-05 lr: 2.0399e-07 eta: 1 day, 1:51:35 time: 0.9999 data_time: 0.0048 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0060 decode.acc_seg: 99.7398 aux.loss_ce: 0.0073 aux.acc_seg: 99.0044 +04/19 04:33:06 - mmengine - INFO - Iter(train) [ 72600/160000] base_lr: 5.5142e-05 lr: 2.0387e-07 eta: 1 day, 1:50:19 time: 0.9998 data_time: 0.0044 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0053 decode.acc_seg: 99.7574 aux.loss_ce: 0.0070 aux.acc_seg: 99.3227 +04/19 04:33:56 - mmengine - INFO - Iter(train) [ 72650/160000] base_lr: 5.5111e-05 lr: 2.0376e-07 eta: 1 day, 1:49:04 time: 1.0023 data_time: 0.0045 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.7295 aux.loss_ce: 0.0077 aux.acc_seg: 99.1224 +04/19 04:34:46 - mmengine - INFO - Iter(train) [ 72700/160000] base_lr: 5.5079e-05 lr: 2.0364e-07 eta: 1 day, 1:47:50 time: 1.0013 data_time: 0.0044 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0061 decode.acc_seg: 99.7032 aux.loss_ce: 0.0075 aux.acc_seg: 98.8962 +04/19 04:35:36 - mmengine - INFO - Iter(train) [ 72750/160000] base_lr: 5.5048e-05 lr: 2.0352e-07 eta: 1 day, 1:46:35 time: 1.0004 data_time: 0.0046 memory: 8457 loss: 0.0117 decode.loss_ce: 0.0053 decode.acc_seg: 99.8070 aux.loss_ce: 0.0064 aux.acc_seg: 99.4915 +04/19 04:36:27 - mmengine - INFO - Iter(train) [ 72800/160000] base_lr: 5.5016e-05 lr: 2.0341e-07 eta: 1 day, 1:45:21 time: 1.0012 data_time: 0.0044 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0062 decode.acc_seg: 99.8421 aux.loss_ce: 0.0073 aux.acc_seg: 99.3183 +04/19 04:37:17 - mmengine - INFO - Iter(train) [ 72850/160000] base_lr: 5.4985e-05 lr: 2.0329e-07 eta: 1 day, 1:44:07 time: 1.0010 data_time: 0.0044 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7499 aux.loss_ce: 0.0073 aux.acc_seg: 99.2165 +04/19 04:38:07 - mmengine - INFO - Iter(train) [ 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memory: 8457 loss: 0.0121 decode.loss_ce: 0.0056 decode.acc_seg: 99.7995 aux.loss_ce: 0.0065 aux.acc_seg: 99.4841 +04/19 04:41:27 - mmengine - INFO - Iter(train) [ 73100/160000] base_lr: 5.4827e-05 lr: 2.0271e-07 eta: 1 day, 1:37:58 time: 1.0006 data_time: 0.0050 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0058 decode.acc_seg: 99.7210 aux.loss_ce: 0.0069 aux.acc_seg: 99.0213 +04/19 04:42:17 - mmengine - INFO - Iter(train) [ 73150/160000] base_lr: 5.4795e-05 lr: 2.0259e-07 eta: 1 day, 1:36:45 time: 1.0021 data_time: 0.0050 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0055 decode.acc_seg: 99.6876 aux.loss_ce: 0.0073 aux.acc_seg: 98.7301 +04/19 04:43:07 - mmengine - INFO - Iter(train) [ 73200/160000] base_lr: 5.4764e-05 lr: 2.0247e-07 eta: 1 day, 1:35:32 time: 0.9996 data_time: 0.0047 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0058 decode.acc_seg: 99.6916 aux.loss_ce: 0.0072 aux.acc_seg: 99.1751 +04/19 04:43:57 - mmengine - INFO - Iter(train) [ 73250/160000] base_lr: 5.4732e-05 lr: 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INFO - Iter(train) [ 73450/160000] base_lr: 5.4606e-05 lr: 2.0189e-07 eta: 1 day, 1:29:28 time: 1.0004 data_time: 0.0049 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0065 decode.acc_seg: 99.7345 aux.loss_ce: 0.0080 aux.acc_seg: 99.1789 +04/19 04:48:07 - mmengine - INFO - Iter(train) [ 73500/160000] base_lr: 5.4574e-05 lr: 2.0177e-07 eta: 1 day, 1:28:16 time: 1.0005 data_time: 0.0044 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7120 aux.loss_ce: 0.0070 aux.acc_seg: 99.2508 +04/19 04:48:57 - mmengine - INFO - Iter(train) [ 73550/160000] base_lr: 5.4543e-05 lr: 2.0166e-07 eta: 1 day, 1:27:04 time: 1.0006 data_time: 0.0044 memory: 8457 loss: 0.0152 decode.loss_ce: 0.0068 decode.acc_seg: 99.7654 aux.loss_ce: 0.0084 aux.acc_seg: 99.1514 +04/19 04:49:47 - mmengine - INFO - Iter(train) [ 73600/160000] base_lr: 5.4511e-05 lr: 2.0154e-07 eta: 1 day, 1:25:52 time: 1.0010 data_time: 0.0045 memory: 8457 loss: 0.0118 decode.loss_ce: 0.0052 decode.acc_seg: 99.7936 aux.loss_ce: 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decode.loss_ce: 0.0056 decode.acc_seg: 99.7301 aux.loss_ce: 0.0066 aux.acc_seg: 99.3900 +04/19 04:53:57 - mmengine - INFO - Iter(train) [ 73850/160000] base_lr: 5.4354e-05 lr: 2.0096e-07 eta: 1 day, 1:19:55 time: 1.0005 data_time: 0.0043 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0060 decode.acc_seg: 99.7335 aux.loss_ce: 0.0073 aux.acc_seg: 99.1028 +04/19 04:54:47 - mmengine - INFO - Iter(train) [ 73900/160000] base_lr: 5.4322e-05 lr: 2.0084e-07 eta: 1 day, 1:18:43 time: 1.0005 data_time: 0.0044 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0058 decode.acc_seg: 99.8169 aux.loss_ce: 0.0069 aux.acc_seg: 99.5962 +04/19 04:55:37 - mmengine - INFO - Iter(train) [ 73950/160000] base_lr: 5.4291e-05 lr: 2.0072e-07 eta: 1 day, 1:17:32 time: 1.0002 data_time: 0.0050 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.7305 aux.loss_ce: 0.0073 aux.acc_seg: 99.1680 +04/19 04:56:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 04:56:27 - mmengine - INFO - Iter(train) [ 74000/160000] base_lr: 5.4259e-05 lr: 2.0061e-07 eta: 1 day, 1:16:21 time: 0.9996 data_time: 0.0045 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0056 decode.acc_seg: 99.7553 aux.loss_ce: 0.0073 aux.acc_seg: 99.1318 +04/19 04:57:17 - mmengine - INFO - Iter(train) [ 74050/160000] base_lr: 5.4227e-05 lr: 2.0049e-07 eta: 1 day, 1:15:11 time: 1.0020 data_time: 0.0044 memory: 8457 loss: 0.0099 decode.loss_ce: 0.0046 decode.acc_seg: 99.7950 aux.loss_ce: 0.0053 aux.acc_seg: 99.3742 +04/19 04:58:07 - mmengine - INFO - Iter(train) [ 74100/160000] base_lr: 5.4196e-05 lr: 2.0037e-07 eta: 1 day, 1:14:00 time: 1.0002 data_time: 0.0046 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0054 decode.acc_seg: 99.7715 aux.loss_ce: 0.0067 aux.acc_seg: 99.3795 +04/19 04:58:57 - mmengine - INFO - Iter(train) [ 74150/160000] base_lr: 5.4164e-05 lr: 2.0026e-07 eta: 1 day, 1:12:50 time: 1.0006 data_time: 0.0045 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0054 decode.acc_seg: 99.7684 aux.loss_ce: 0.0077 aux.acc_seg: 99.3389 +04/19 04:59:47 - mmengine - INFO - Iter(train) [ 74200/160000] base_lr: 5.4133e-05 lr: 2.0014e-07 eta: 1 day, 1:11:40 time: 1.0006 data_time: 0.0046 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0061 decode.acc_seg: 99.7543 aux.loss_ce: 0.0081 aux.acc_seg: 99.1320 +04/19 05:00:37 - mmengine - INFO - Iter(train) [ 74250/160000] base_lr: 5.4101e-05 lr: 2.0002e-07 eta: 1 day, 1:10:30 time: 1.0003 data_time: 0.0051 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0056 decode.acc_seg: 99.8207 aux.loss_ce: 0.0068 aux.acc_seg: 99.3891 +04/19 05:01:27 - mmengine - INFO - Iter(train) [ 74300/160000] base_lr: 5.4070e-05 lr: 1.9991e-07 eta: 1 day, 1:09:20 time: 1.0001 data_time: 0.0048 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0058 decode.acc_seg: 99.7780 aux.loss_ce: 0.0066 aux.acc_seg: 99.2271 +04/19 05:02:17 - mmengine - INFO - Iter(train) [ 74350/160000] base_lr: 5.4038e-05 lr: 1.9979e-07 eta: 1 day, 1:08:10 time: 0.9995 data_time: 0.0049 memory: 8457 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INFO - Iter(train) [ 75300/160000] base_lr: 5.3439e-05 lr: 1.9757e-07 eta: 1 day, 0:46:26 time: 0.9989 data_time: 0.0045 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0064 decode.acc_seg: 99.7427 aux.loss_ce: 0.0080 aux.acc_seg: 98.9557 +04/19 05:18:57 - mmengine - INFO - Iter(train) [ 75350/160000] base_lr: 5.3407e-05 lr: 1.9746e-07 eta: 1 day, 0:45:18 time: 0.9993 data_time: 0.0045 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0063 decode.acc_seg: 99.8064 aux.loss_ce: 0.0082 aux.acc_seg: 99.4492 +04/19 05:19:47 - mmengine - INFO - Iter(train) [ 75400/160000] base_lr: 5.3376e-05 lr: 1.9734e-07 eta: 1 day, 0:44:11 time: 0.9991 data_time: 0.0045 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0050 decode.acc_seg: 99.7866 aux.loss_ce: 0.0066 aux.acc_seg: 99.4564 +04/19 05:20:37 - mmengine - INFO - Iter(train) [ 75450/160000] base_lr: 5.3344e-05 lr: 1.9722e-07 eta: 1 day, 0:43:04 time: 0.9992 data_time: 0.0046 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0065 decode.acc_seg: 99.7326 aux.loss_ce: 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decode.loss_ce: 0.0058 decode.acc_seg: 99.7952 aux.loss_ce: 0.0077 aux.acc_seg: 99.4871 +04/19 05:24:47 - mmengine - INFO - Iter(train) [ 75700/160000] base_lr: 5.3186e-05 lr: 1.9664e-07 eta: 1 day, 0:37:29 time: 1.0001 data_time: 0.0049 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0063 decode.acc_seg: 99.6210 aux.loss_ce: 0.0075 aux.acc_seg: 98.9208 +04/19 05:25:36 - mmengine - INFO - Iter(train) [ 75750/160000] base_lr: 5.3155e-05 lr: 1.9652e-07 eta: 1 day, 0:36:22 time: 0.9981 data_time: 0.0049 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.6876 aux.loss_ce: 0.0077 aux.acc_seg: 99.2889 +04/19 05:26:26 - mmengine - INFO - Iter(train) [ 75800/160000] base_lr: 5.3123e-05 lr: 1.9641e-07 eta: 1 day, 0:35:16 time: 0.9984 data_time: 0.0048 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0056 decode.acc_seg: 99.7414 aux.loss_ce: 0.0067 aux.acc_seg: 99.2846 +04/19 05:27:16 - mmengine - INFO - Iter(train) [ 75850/160000] base_lr: 5.3092e-05 lr: 1.9629e-07 eta: 1 day, 0:34:09 time: 0.9990 data_time: 0.0048 memory: 8457 loss: 0.0163 decode.loss_ce: 0.0072 decode.acc_seg: 99.7507 aux.loss_ce: 0.0091 aux.acc_seg: 99.0150 +04/19 05:28:06 - mmengine - INFO - Iter(train) [ 75900/160000] base_lr: 5.3060e-05 lr: 1.9617e-07 eta: 1 day, 0:33:03 time: 0.9985 data_time: 0.0053 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0053 decode.acc_seg: 99.7759 aux.loss_ce: 0.0069 aux.acc_seg: 99.4860 +04/19 05:28:56 - mmengine - INFO - Iter(train) [ 75950/160000] base_lr: 5.3029e-05 lr: 1.9606e-07 eta: 1 day, 0:31:56 time: 0.9987 data_time: 0.0044 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0055 decode.acc_seg: 99.8390 aux.loss_ce: 0.0072 aux.acc_seg: 99.5131 +04/19 05:29:46 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 05:29:46 - mmengine - INFO - Iter(train) [ 76000/160000] base_lr: 5.2997e-05 lr: 1.9594e-07 eta: 1 day, 0:30:50 time: 0.9990 data_time: 0.0045 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.6550 aux.loss_ce: 0.0074 aux.acc_seg: 98.8285 +04/19 05:30:36 - mmengine - INFO - Iter(train) [ 76050/160000] base_lr: 5.2966e-05 lr: 1.9582e-07 eta: 1 day, 0:29:44 time: 0.9975 data_time: 0.0047 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0061 decode.acc_seg: 99.7934 aux.loss_ce: 0.0073 aux.acc_seg: 99.1962 +04/19 05:31:26 - mmengine - INFO - Iter(train) [ 76100/160000] base_lr: 5.2934e-05 lr: 1.9571e-07 eta: 1 day, 0:28:37 time: 0.9982 data_time: 0.0054 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0061 decode.acc_seg: 99.7311 aux.loss_ce: 0.0076 aux.acc_seg: 99.2887 +04/19 05:32:16 - mmengine - INFO - Iter(train) [ 76150/160000] base_lr: 5.2903e-05 lr: 1.9559e-07 eta: 1 day, 0:27:31 time: 0.9984 data_time: 0.0049 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0061 decode.acc_seg: 99.7318 aux.loss_ce: 0.0075 aux.acc_seg: 99.2664 +04/19 05:33:06 - mmengine - INFO - Iter(train) [ 76200/160000] base_lr: 5.2871e-05 lr: 1.9547e-07 eta: 1 day, 0:26:26 time: 0.9985 data_time: 0.0050 memory: 8457 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decode.acc_seg: 99.7515 aux.loss_ce: 0.0085 aux.acc_seg: 99.0440 +04/19 05:46:24 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 05:46:24 - mmengine - INFO - Iter(train) [ 77000/160000] base_lr: 5.2366e-05 lr: 1.9361e-07 eta: 1 day, 0:09:02 time: 0.9980 data_time: 0.0046 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0057 decode.acc_seg: 99.7608 aux.loss_ce: 0.0072 aux.acc_seg: 99.3055 +04/19 05:47:14 - mmengine - INFO - Iter(train) [ 77050/160000] base_lr: 5.2335e-05 lr: 1.9349e-07 eta: 1 day, 0:07:58 time: 0.9988 data_time: 0.0049 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.7597 aux.loss_ce: 0.0076 aux.acc_seg: 99.3515 +04/19 05:48:04 - mmengine - INFO - Iter(train) [ 77100/160000] base_lr: 5.2303e-05 lr: 1.9338e-07 eta: 1 day, 0:06:53 time: 0.9975 data_time: 0.0045 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0060 decode.acc_seg: 99.7835 aux.loss_ce: 0.0077 aux.acc_seg: 98.8512 +04/19 05:48:54 - mmengine - INFO - 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aux.acc_seg: 99.0391 +04/19 05:52:14 - mmengine - INFO - Iter(train) [ 77350/160000] base_lr: 5.2145e-05 lr: 1.9279e-07 eta: 1 day, 0:01:34 time: 0.9991 data_time: 0.0050 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0056 decode.acc_seg: 99.7997 aux.loss_ce: 0.0067 aux.acc_seg: 99.4102 +04/19 05:53:04 - mmengine - INFO - Iter(train) [ 77400/160000] base_lr: 5.2114e-05 lr: 1.9268e-07 eta: 1 day, 0:00:30 time: 0.9982 data_time: 0.0048 memory: 8457 loss: 0.0110 decode.loss_ce: 0.0051 decode.acc_seg: 99.8072 aux.loss_ce: 0.0059 aux.acc_seg: 99.2752 +04/19 05:53:54 - mmengine - INFO - Iter(train) [ 77450/160000] base_lr: 5.2082e-05 lr: 1.9256e-07 eta: 23:59:26 time: 0.9987 data_time: 0.0050 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0060 decode.acc_seg: 99.6710 aux.loss_ce: 0.0069 aux.acc_seg: 98.9635 +04/19 05:54:43 - mmengine - INFO - Iter(train) [ 77500/160000] base_lr: 5.2051e-05 lr: 1.9244e-07 eta: 23:58:23 time: 0.9988 data_time: 0.0046 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0059 decode.acc_seg: 99.7751 aux.loss_ce: 0.0073 aux.acc_seg: 99.2783 +04/19 05:55:33 - mmengine - INFO - Iter(train) [ 77550/160000] base_lr: 5.2019e-05 lr: 1.9233e-07 eta: 23:57:19 time: 0.9999 data_time: 0.0046 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0054 decode.acc_seg: 99.7700 aux.loss_ce: 0.0068 aux.acc_seg: 99.3414 +04/19 05:56:23 - mmengine - INFO - Iter(train) [ 77600/160000] base_lr: 5.1988e-05 lr: 1.9221e-07 eta: 23:56:16 time: 0.9991 data_time: 0.0045 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0055 decode.acc_seg: 99.7419 aux.loss_ce: 0.0071 aux.acc_seg: 99.2914 +04/19 05:57:13 - mmengine - INFO - Iter(train) [ 77650/160000] base_lr: 5.1956e-05 lr: 1.9209e-07 eta: 23:55:13 time: 1.0000 data_time: 0.0042 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0049 decode.acc_seg: 99.8108 aux.loss_ce: 0.0066 aux.acc_seg: 99.4274 +04/19 05:58:03 - mmengine - INFO - Iter(train) [ 77700/160000] base_lr: 5.1925e-05 lr: 1.9198e-07 eta: 23:54:09 time: 0.9986 data_time: 0.0045 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0056 decode.acc_seg: 99.7299 aux.loss_ce: 0.0071 aux.acc_seg: 99.0665 +04/19 05:58:53 - mmengine - INFO - Iter(train) [ 77750/160000] base_lr: 5.1893e-05 lr: 1.9186e-07 eta: 23:53:06 time: 0.9981 data_time: 0.0050 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0059 decode.acc_seg: 99.6883 aux.loss_ce: 0.0071 aux.acc_seg: 99.0023 +04/19 05:59:43 - mmengine - INFO - Iter(train) [ 77800/160000] base_lr: 5.1862e-05 lr: 1.9174e-07 eta: 23:52:03 time: 0.9973 data_time: 0.0047 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7408 aux.loss_ce: 0.0073 aux.acc_seg: 99.2992 +04/19 06:00:33 - mmengine - INFO - Iter(train) [ 77850/160000] base_lr: 5.1830e-05 lr: 1.9163e-07 eta: 23:51:00 time: 1.0011 data_time: 0.0049 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0057 decode.acc_seg: 99.7818 aux.loss_ce: 0.0066 aux.acc_seg: 99.3597 +04/19 06:01:23 - mmengine - INFO - Iter(train) [ 77900/160000] base_lr: 5.1798e-05 lr: 1.9151e-07 eta: 23:49:57 time: 0.9979 data_time: 0.0047 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0060 decode.acc_seg: 99.7086 aux.loss_ce: 0.0079 aux.acc_seg: 99.0963 +04/19 06:02:13 - mmengine - INFO - Iter(train) [ 77950/160000] base_lr: 5.1767e-05 lr: 1.9139e-07 eta: 23:48:54 time: 0.9972 data_time: 0.0045 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0052 decode.acc_seg: 99.7627 aux.loss_ce: 0.0064 aux.acc_seg: 99.4083 +04/19 06:03:03 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 06:03:03 - mmengine - INFO - Iter(train) [ 78000/160000] base_lr: 5.1735e-05 lr: 1.9128e-07 eta: 23:47:51 time: 0.9979 data_time: 0.0048 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0056 decode.acc_seg: 99.7499 aux.loss_ce: 0.0075 aux.acc_seg: 99.2113 +04/19 06:03:53 - mmengine - INFO - Iter(train) [ 78050/160000] base_lr: 5.1704e-05 lr: 1.9116e-07 eta: 23:46:48 time: 0.9980 data_time: 0.0050 memory: 8457 loss: 0.0121 decode.loss_ce: 0.0054 decode.acc_seg: 99.6878 aux.loss_ce: 0.0067 aux.acc_seg: 99.0467 +04/19 06:04:43 - mmengine - INFO - Iter(train) [ 78100/160000] base_lr: 5.1672e-05 lr: 1.9104e-07 eta: 23:45:45 time: 0.9996 data_time: 0.0046 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0056 decode.acc_seg: 99.7698 aux.loss_ce: 0.0073 aux.acc_seg: 99.2567 +04/19 06:05:33 - mmengine - INFO - Iter(train) [ 78150/160000] base_lr: 5.1641e-05 lr: 1.9093e-07 eta: 23:44:43 time: 0.9991 data_time: 0.0046 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0054 decode.acc_seg: 99.7957 aux.loss_ce: 0.0066 aux.acc_seg: 99.4730 +04/19 06:06:23 - mmengine - INFO - Iter(train) [ 78200/160000] base_lr: 5.1609e-05 lr: 1.9081e-07 eta: 23:43:41 time: 0.9976 data_time: 0.0045 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0055 decode.acc_seg: 99.7253 aux.loss_ce: 0.0065 aux.acc_seg: 99.3870 +04/19 06:07:13 - mmengine - INFO - Iter(train) [ 78250/160000] base_lr: 5.1578e-05 lr: 1.9069e-07 eta: 23:42:39 time: 0.9995 data_time: 0.0048 memory: 8457 loss: 0.0118 decode.loss_ce: 0.0051 decode.acc_seg: 99.7387 aux.loss_ce: 0.0067 aux.acc_seg: 99.0322 +04/19 06:08:03 - mmengine - INFO - Iter(train) [ 78300/160000] base_lr: 5.1546e-05 lr: 1.9058e-07 eta: 23:41:36 time: 0.9991 data_time: 0.0044 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.7467 aux.loss_ce: 0.0077 aux.acc_seg: 99.2725 +04/19 06:08:53 - mmengine - INFO - Iter(train) [ 78350/160000] base_lr: 5.1515e-05 lr: 1.9046e-07 eta: 23:40:34 time: 0.9999 data_time: 0.0051 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0066 decode.acc_seg: 99.7419 aux.loss_ce: 0.0078 aux.acc_seg: 99.2062 +04/19 06:09:43 - mmengine - INFO - Iter(train) [ 78400/160000] base_lr: 5.1483e-05 lr: 1.9034e-07 eta: 23:39:32 time: 0.9978 data_time: 0.0046 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0057 decode.acc_seg: 99.6658 aux.loss_ce: 0.0075 aux.acc_seg: 99.2020 +04/19 06:10:32 - mmengine - INFO - Iter(train) [ 78450/160000] base_lr: 5.1451e-05 lr: 1.9023e-07 eta: 23:38:30 time: 0.9990 data_time: 0.0046 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0059 decode.acc_seg: 99.7873 aux.loss_ce: 0.0075 aux.acc_seg: 99.1709 +04/19 06:11:22 - mmengine - INFO - Iter(train) [ 78500/160000] base_lr: 5.1420e-05 lr: 1.9011e-07 eta: 23:37:28 time: 0.9997 data_time: 0.0048 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0064 decode.acc_seg: 99.7080 aux.loss_ce: 0.0079 aux.acc_seg: 99.2508 +04/19 06:12:12 - mmengine - INFO - Iter(train) [ 78550/160000] base_lr: 5.1388e-05 lr: 1.8999e-07 eta: 23:36:26 time: 0.9989 data_time: 0.0047 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0054 decode.acc_seg: 99.7654 aux.loss_ce: 0.0074 aux.acc_seg: 99.3689 +04/19 06:13:02 - mmengine - INFO - Iter(train) [ 78600/160000] base_lr: 5.1357e-05 lr: 1.8988e-07 eta: 23:35:24 time: 0.9972 data_time: 0.0046 memory: 8457 loss: 0.0155 decode.loss_ce: 0.0067 decode.acc_seg: 99.7011 aux.loss_ce: 0.0087 aux.acc_seg: 98.7347 +04/19 06:13:52 - mmengine - INFO - Iter(train) [ 78650/160000] base_lr: 5.1325e-05 lr: 1.8976e-07 eta: 23:34:22 time: 1.0000 data_time: 0.0047 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0059 decode.acc_seg: 99.6748 aux.loss_ce: 0.0069 aux.acc_seg: 99.0702 +04/19 06:14:42 - mmengine - INFO - Iter(train) [ 78700/160000] base_lr: 5.1294e-05 lr: 1.8964e-07 eta: 23:33:20 time: 0.9996 data_time: 0.0047 memory: 8457 loss: 0.0125 decode.loss_ce: 0.0058 decode.acc_seg: 99.7826 aux.loss_ce: 0.0066 aux.acc_seg: 99.4392 +04/19 06:15:32 - mmengine - INFO - Iter(train) [ 78750/160000] base_lr: 5.1262e-05 lr: 1.8953e-07 eta: 23:32:18 time: 0.9989 data_time: 0.0047 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0056 decode.acc_seg: 99.7278 aux.loss_ce: 0.0070 aux.acc_seg: 99.1493 +04/19 06:16:22 - mmengine - INFO - Iter(train) [ 78800/160000] base_lr: 5.1231e-05 lr: 1.8941e-07 eta: 23:31:17 time: 0.9985 data_time: 0.0046 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0057 decode.acc_seg: 99.7591 aux.loss_ce: 0.0067 aux.acc_seg: 99.1268 +04/19 06:17:12 - mmengine - INFO - Iter(train) [ 78850/160000] base_lr: 5.1199e-05 lr: 1.8929e-07 eta: 23:30:15 time: 0.9996 data_time: 0.0048 memory: 8457 loss: 0.0113 decode.loss_ce: 0.0050 decode.acc_seg: 99.6885 aux.loss_ce: 0.0063 aux.acc_seg: 98.7377 +04/19 06:18:02 - mmengine - INFO - Iter(train) [ 78900/160000] base_lr: 5.1168e-05 lr: 1.8918e-07 eta: 23:29:14 time: 0.9994 data_time: 0.0045 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0056 decode.acc_seg: 99.6813 aux.loss_ce: 0.0068 aux.acc_seg: 99.1846 +04/19 06:18:52 - mmengine - INFO - Iter(train) [ 78950/160000] base_lr: 5.1136e-05 lr: 1.8906e-07 eta: 23:28:13 time: 1.0016 data_time: 0.0043 memory: 8457 loss: 0.0157 decode.loss_ce: 0.0069 decode.acc_seg: 99.7581 aux.loss_ce: 0.0088 aux.acc_seg: 99.3343 +04/19 06:19:42 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 06:19:42 - mmengine - INFO - Iter(train) [ 79000/160000] base_lr: 5.1104e-05 lr: 1.8894e-07 eta: 23:27:12 time: 1.0016 data_time: 0.0051 memory: 8457 loss: 0.0110 decode.loss_ce: 0.0051 decode.acc_seg: 99.8230 aux.loss_ce: 0.0059 aux.acc_seg: 99.5138 +04/19 06:20:32 - mmengine - INFO - Iter(train) [ 79050/160000] base_lr: 5.1073e-05 lr: 1.8883e-07 eta: 23:26:11 time: 1.0001 data_time: 0.0048 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0061 decode.acc_seg: 99.7364 aux.loss_ce: 0.0075 aux.acc_seg: 98.9046 +04/19 06:21:22 - mmengine - INFO - Iter(train) [ 79100/160000] base_lr: 5.1041e-05 lr: 1.8871e-07 eta: 23:25:10 time: 1.0014 data_time: 0.0050 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0058 decode.acc_seg: 99.6956 aux.loss_ce: 0.0075 aux.acc_seg: 99.1325 +04/19 06:22:12 - mmengine - INFO - Iter(train) [ 79150/160000] base_lr: 5.1010e-05 lr: 1.8859e-07 eta: 23:24:09 time: 1.0004 data_time: 0.0058 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0055 decode.acc_seg: 99.7379 aux.loss_ce: 0.0076 aux.acc_seg: 98.9176 +04/19 06:23:02 - mmengine - INFO - Iter(train) [ 79200/160000] base_lr: 5.0978e-05 lr: 1.8848e-07 eta: 23:23:08 time: 1.0009 data_time: 0.0047 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0059 decode.acc_seg: 99.6513 aux.loss_ce: 0.0075 aux.acc_seg: 98.9153 +04/19 06:23:52 - mmengine - INFO - Iter(train) [ 79250/160000] base_lr: 5.0947e-05 lr: 1.8836e-07 eta: 23:22:07 time: 0.9978 data_time: 0.0049 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7013 aux.loss_ce: 0.0073 aux.acc_seg: 99.3008 +04/19 06:24:42 - mmengine - INFO - Iter(train) [ 79300/160000] base_lr: 5.0915e-05 lr: 1.8824e-07 eta: 23:21:06 time: 0.9985 data_time: 0.0046 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.7015 aux.loss_ce: 0.0066 aux.acc_seg: 99.2834 +04/19 06:25:32 - mmengine - INFO - Iter(train) [ 79350/160000] base_lr: 5.0884e-05 lr: 1.8813e-07 eta: 23:20:05 time: 0.9995 data_time: 0.0046 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7406 aux.loss_ce: 0.0072 aux.acc_seg: 99.1667 +04/19 06:26:22 - mmengine - INFO - Iter(train) [ 79400/160000] base_lr: 5.0852e-05 lr: 1.8801e-07 eta: 23:19:04 time: 0.9991 data_time: 0.0047 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0054 decode.acc_seg: 99.8453 aux.loss_ce: 0.0069 aux.acc_seg: 99.5214 +04/19 06:27:12 - mmengine - INFO - Iter(train) [ 79450/160000] base_lr: 5.0821e-05 lr: 1.8789e-07 eta: 23:18:03 time: 0.9974 data_time: 0.0045 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0052 decode.acc_seg: 99.7797 aux.loss_ce: 0.0064 aux.acc_seg: 99.2872 +04/19 06:28:02 - mmengine - INFO - Iter(train) [ 79500/160000] base_lr: 5.0789e-05 lr: 1.8778e-07 eta: 23:17:02 time: 0.9990 data_time: 0.0047 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0054 decode.acc_seg: 99.7972 aux.loss_ce: 0.0073 aux.acc_seg: 99.3031 +04/19 06:28:52 - mmengine - INFO - Iter(train) [ 79550/160000] base_lr: 5.0757e-05 lr: 1.8766e-07 eta: 23:16:01 time: 0.9999 data_time: 0.0048 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0052 decode.acc_seg: 99.8238 aux.loss_ce: 0.0064 aux.acc_seg: 99.3664 +04/19 06:29:42 - mmengine - INFO - Iter(train) [ 79600/160000] base_lr: 5.0726e-05 lr: 1.8754e-07 eta: 23:15:01 time: 0.9977 data_time: 0.0046 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0052 decode.acc_seg: 99.8024 aux.loss_ce: 0.0070 aux.acc_seg: 99.2453 +04/19 06:30:32 - mmengine - INFO - Iter(train) [ 79650/160000] base_lr: 5.0694e-05 lr: 1.8743e-07 eta: 23:14:00 time: 0.9991 data_time: 0.0047 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0059 decode.acc_seg: 99.7225 aux.loss_ce: 0.0082 aux.acc_seg: 98.9305 +04/19 06:31:22 - mmengine - INFO - Iter(train) [ 79700/160000] base_lr: 5.0663e-05 lr: 1.8731e-07 eta: 23:12:59 time: 0.9990 data_time: 0.0048 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0058 decode.acc_seg: 99.6677 aux.loss_ce: 0.0069 aux.acc_seg: 99.2506 +04/19 06:32:12 - mmengine - INFO - Iter(train) [ 79750/160000] base_lr: 5.0631e-05 lr: 1.8719e-07 eta: 23:11:59 time: 1.0000 data_time: 0.0048 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.7698 aux.loss_ce: 0.0077 aux.acc_seg: 99.1795 +04/19 06:33:02 - mmengine - INFO - Iter(train) [ 79800/160000] base_lr: 5.0600e-05 lr: 1.8708e-07 eta: 23:10:59 time: 0.9991 data_time: 0.0052 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0056 decode.acc_seg: 99.6943 aux.loss_ce: 0.0063 aux.acc_seg: 99.2014 +04/19 06:33:52 - mmengine - INFO - Iter(train) [ 79850/160000] base_lr: 5.0568e-05 lr: 1.8696e-07 eta: 23:09:58 time: 0.9993 data_time: 0.0048 memory: 8457 loss: 0.0135 decode.loss_ce: 0.0060 decode.acc_seg: 99.6765 aux.loss_ce: 0.0076 aux.acc_seg: 98.8350 +04/19 06:34:42 - mmengine - INFO - Iter(train) [ 79900/160000] base_lr: 5.0537e-05 lr: 1.8684e-07 eta: 23:08:58 time: 0.9978 data_time: 0.0048 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7200 aux.loss_ce: 0.0073 aux.acc_seg: 99.0034 +04/19 06:35:32 - mmengine - INFO - Iter(train) [ 79950/160000] base_lr: 5.0505e-05 lr: 1.8673e-07 eta: 23:07:57 time: 0.9982 data_time: 0.0043 memory: 8457 loss: 0.0135 decode.loss_ce: 0.0058 decode.acc_seg: 99.7614 aux.loss_ce: 0.0077 aux.acc_seg: 99.4556 +04/19 06:36:22 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 06:36:22 - mmengine - INFO - Iter(train) [ 80000/160000] base_lr: 5.0474e-05 lr: 1.8661e-07 eta: 23:06:57 time: 0.9971 data_time: 0.0045 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0060 decode.acc_seg: 99.7063 aux.loss_ce: 0.0074 aux.acc_seg: 99.2573 +04/19 06:36:22 - mmengine - INFO - Saving checkpoint at 80000 iterations +04/19 06:36:32 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1156 data_time: 0.0014 memory: 3999 +04/19 06:36:37 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1158 data_time: 0.0015 memory: 3999 +04/19 06:36:43 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1158 data_time: 0.0014 memory: 3999 +04/19 06:36:49 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1160 data_time: 0.0016 memory: 3999 +04/19 06:36:49 - mmengine - INFO - per class results: +04/19 06:36:49 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.16 | 99.51 | 99.58 | 99.64 | 99.51 | +| contrast | 81.8 | 91.37 | 89.99 | 88.65 | 91.37 | ++------------+-------+-------+--------+-----------+--------+ +04/19 06:36:49 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1900 mIoU: 90.4800 mAcc: 95.4400 mFscore: 94.7800 mPrecision: 94.1500 mRecall: 95.4400 data_time: 0.0015 time: 0.1160 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64757 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64758 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64759 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64760 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +main()runner.train() + + File "tools/train.py", line 100, in main + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + model = self.train_loop.run() # type: ignoreoptim_wrapper.update_params(parsed_loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + model = self.train_loop.run() # type: ignore + loss.backward(**kwargs) File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter +self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward +loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64757 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64758 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64759 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64760 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 64727 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 64727 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 64727 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 06:46:48 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1862928832 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1862928832 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 06:46:49 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag_10000' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 06:46:51 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + 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+ "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + 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"backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 06:46:52 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +04/19 06:46:53 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 06:46:53 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 06:46:53 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512. +04/19 06:47:50 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:31:49 time: 1.0291 data_time: 0.0041 memory: 8935 loss: 6.8105 decode.loss_ce: 4.8183 decode.acc_seg: 19.2308 aux.loss_ce: 1.9922 aux.acc_seg: 0.0448 +04/19 06:48:41 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 2 days, 0:07:14 time: 1.0283 data_time: 0.0045 memory: 8462 loss: 6.4806 decode.loss_ce: 4.5186 decode.acc_seg: 27.7260 aux.loss_ce: 1.9619 aux.acc_seg: 15.1159 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303901 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303902 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303903 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303904 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +Traceback (most recent call last): + File "tools/train.py", line 104, in + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + main()optim_wrapper.update_params(parsed_loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + File "tools/train.py", line 100, in main + main()self.backward(loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + File "tools/train.py", line 100, in main + loss.backward(**kwargs)runner.train() + + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + runner.train() + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step +self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) +loss.backward(**kwargs) File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303901 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303902 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303903 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303904 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 303871 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 303871 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 303871 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 06:52:22 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1322765965 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1322765965 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 06:52:23 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag_10000' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='./pretrain/mae_pretrain_vit_base_mmcls.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_ade20k-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 06:52:25 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} + +set param backbone.layers.0.gamma_2 as id 1 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.cls_token as id 0 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.pos_embed as id 0 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.0.attn.qkv.weight as id 1 + +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.0.attn.qkv.bias as id 1 + +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.1.attn.proj.bias as id 2 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.2.gamma_1 as id 3set param backbone.layers.0.ffn.layers.1.weight as id 1 + +set param backbone.layers.2.gamma_2 as id 3set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.gamma_1 as id 2set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.2.attn.qkv.bias as id 3 + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.2.ln2.weight as id 3 + +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.2.ln2.bias as id 3 + +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.1.ffn.layers.1.bias as id 2set param backbone.layers.3.ln1.bias as id 4 + +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.2.ln1.bias as id 3 + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.4.gamma_1 as id 5 + +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.2.ffn.layers.1.weight as id 3set param backbone.layers.4.ln1.weight as id 5 + +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.4.attn.qkv.weight as id 5set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.3.attn.relative_position_bias_table as id 4 + +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.4.ffn.layers.1.weight as id 5set param backbone.layers.3.ln2.bias as id 4 + +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.5.attn.qkv.bias as id 6set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.5.attn.proj.bias as id 6set param backbone.layers.4.attn.qkv.weight as id 5 + +set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.6.gamma_1 as id 7 + +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.6.ffn.layers.0.0.bias as id 7set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.6.attn.proj.weight as id 7set param backbone.layers.7.gamma_2 as id 8 + +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9set param backbone.layers.7.gamma_2 as id 8 + +set param backbone.layers.7.ln1.weight as id 8set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.7.ln1.bias as id 8set param backbone.layers.8.ln2.bias as id 9 + +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.7.ln2.weight as id 8 + +set param backbone.layers.9.gamma_2 as id 10set param backbone.layers.7.ln2.bias as id 8 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.9.ln1.bias as id 10 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.9.ffn.layers.1.weight as id 10set param backbone.layers.8.attn.qkv.bias as id 9 + +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9set param backbone.layers.10.gamma_1 as id 11 + +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.10.ln2.weight as id 11 + +set param backbone.layers.9.gamma_2 as id 10set param backbone.layers.10.ln2.bias as id 11 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.10.ffn.layers.0.0.bias as id 11 + +set param backbone.layers.10.ffn.layers.1.weight as id 11set param backbone.layers.9.attn.qkv.weight as id 10 + +set param backbone.layers.10.ffn.layers.1.bias as id 11set param backbone.layers.9.attn.qkv.bias as id 10 + +set param backbone.layers.9.attn.proj.weight as id 10set param backbone.layers.11.gamma_1 as id 12 + +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.11.ln1.weight as id 12 + +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.11.ln1.bias as id 12 + +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.11.attn.qkv.weight as id 12set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.11.attn.proj.weight as id 12set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.10.gamma_1 as id 11set param backbone.layers.11.ln2.weight as id 12 + +set param backbone.layers.10.gamma_2 as id 11set param backbone.layers.11.ln2.bias as id 12 + +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12set param backbone.layers.10.ln1.bias as id 11 + +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.10.ln2.weight as id 11 + +set param neck.upsample_4x.0.bias as id 13set param backbone.layers.10.ln2.bias as id 11 + +set param neck.upsample_4x.1.weight as id 13set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param neck.upsample_4x.1.bias as id 13set param backbone.layers.10.ffn.layers.0.0.bias as id 11 + +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.10.ffn.layers.1.weight as id 11set param neck.upsample_4x.3.bias as id 13 + +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.11.attn.relative_position_bias_table as id 12set param decode_head.conv_seg.bias as id 13 + +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.11.attn.proj.weight as id 12 +set param decode_head.psp_modules.0.1.bn.weight as id 13set param backbone.layers.11.attn.proj.bias as id 12 + +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param backbone.layers.11.ffn.layers.1.bias as id 12 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param neck.upsample_4x.0.weight as id 13set param decode_head.psp_modules.3.1.conv.weight as id 13 + +set param neck.upsample_4x.0.bias as id 13set param decode_head.psp_modules.3.1.bn.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param decode_head.bottleneck.conv.weight as id 13set param neck.upsample_4x.1.bias as id 13 + +set param decode_head.bottleneck.bn.weight as id 13set param neck.upsample_4x.3.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13set param neck.upsample_4x.3.bias as id 13 + +set param neck.upsample_2x.0.weight as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13set param decode_head.psp_modules.0.1.bn.weight as id 13 + +set param decode_head.fpn_convs.0.bn.weight as id 13set param decode_head.psp_modules.0.1.bn.bias as id 13 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param decode_head.fpn_convs.2.bn.weight as id 13 + +set param decode_head.psp_modules.2.1.bn.weight as id 13set param decode_head.fpn_convs.2.bn.bias as id 13 + +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13set param decode_head.fpn_bottleneck.bn.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.weight as id 13set param decode_head.fpn_bottleneck.bn.bias as id 13 + +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13set param auxiliary_head.conv_seg.bias as id 13 + +set param decode_head.bottleneck.bn.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13set param decode_head.lateral_convs.0.conv.weight as id 13 + +set param auxiliary_head.convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 06:52:26 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: ./pretrain/mae_pretrain_vit_base_mmcls.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./pretrain/mae_pretrain_vit_base_mmcls.pth can not be found. +Loads checkpoint by local backend from path: ./pretrain/mae_pretrain_vit_base_mmcls.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./pretrain/mae_pretrain_vit_base_mmcls.pth can not be found. +Loads checkpoint by local backend from path: ./pretrain/mae_pretrain_vit_base_mmcls.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./pretrain/mae_pretrain_vit_base_mmcls.pth can not be found. +Loads checkpoint by local backend from path: ./pretrain/mae_pretrain_vit_base_mmcls.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./pretrain/mae_pretrain_vit_base_mmcls.pth can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 308287) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_06:52:29 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 308288) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_06:52:29 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 308289) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_06:52:29 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 308290) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_06:52:29 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 308287) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 06:57:54 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1351088420 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1351088420 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 06:57:54 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag_10000' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='./mae_pretrain_vit_base.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_ade20k-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 06:57:57 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.layers.0.attn.proj.weight as id 1 + +set param backbone.layers.0.attn.proj.bias as id 1 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.cls_token as id 0 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.pos_embed as id 0set param backbone.layers.1.gamma_2 as id 2 + +set param backbone.layers.1.ln1.weight as id 2 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.layers.1.ln1.bias as id 2 + +set param backbone.patch_embed.projection.weight as id 0set param backbone.layers.1.attn.relative_position_bias_table as id 2 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 + +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1set param backbone.layers.1.ffn.layers.0.0.weight as id 2 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.cls_token as id 0 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.pos_embed as id 0set param backbone.layers.0.attn.qkv.bias as id 1 + +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.2.ln1.weight as id 3 + +set param backbone.layers.0.attn.proj.bias as id 1set param backbone.layers.2.ln1.bias as id 3 + +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.2.attn.relative_position_bias_table as id 3set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.2.attn.qkv.weight as id 3set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.0.gamma_1 as id 1set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.0.gamma_2 as id 1set param backbone.layers.0.ffn.layers.0.0.bias as id 1 + + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.0.ln1.weight as id 1set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.0.ffn.layers.1.weight as id 1 + + +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.0.ln1.bias as id 1set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.0.attn.qkv.weight as id 1 + +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.layers.3.ln1.bias as id 4 + + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.3.attn.qkv.bias as id 4 + + +set param backbone.layers.0.attn.proj.bias as id 1set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.0.ln2.bias as id 1set param backbone.layers.3.ln2.bias as id 4 + +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.0.ffn.layers.1.bias as id 1set param backbone.layers.2.gamma_1 as id 3set param backbone.layers.4.gamma_2 as id 5 + + +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.1.gamma_1 as id 2set param backbone.layers.2.ln1.bias as id 3set param backbone.layers.4.attn.relative_position_bias_table as id 5 + + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.2.attn.relative_position_bias_table as id 3set param backbone.layers.4.attn.qkv.weight as id 5 + + +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.2.attn.qkv.weight as id 3 + + +set param backbone.layers.1.ln1.bias as id 2set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.4.ln2.weight as id 5 + + +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.4.ln2.bias as id 5set param backbone.layers.2.ln2.weight as id 3 + + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.4.ffn.layers.1.bias as id 5 + +set param backbone.layers.3.gamma_1 as id 4set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.3.attn.relative_position_bias_table as id 4set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.2.gamma_1 as id 3set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.3.attn.proj.bias as id 4set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.5.attn.proj.bias as id 6set param backbone.layers.3.ln2.weight as id 4 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.3.ln2.bias as id 4set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.2.attn.qkv.bias as id 3set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.6.gamma_1 as id 7 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.4.gamma_2 as id 5set param backbone.layers.6.gamma_2 as id 7 + + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.4.ln1.weight as id 5set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.4.ln1.bias as id 5set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.6.attn.proj.weight as id 7set param backbone.layers.4.attn.proj.bias as id 5 + + +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.3.gamma_1 as id 4set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.4.ln2.bias as id 5 + +set param backbone.layers.3.gamma_2 as id 4set param backbone.layers.6.ln2.bias as id 7 + +set param backbone.layers.4.ffn.layers.0.0.weight as id 5set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.3.attn.proj.weight as id 4 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.3.ln2.bias as id 4set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.5.attn.proj.bias as id 6 + +set param backbone.layers.6.ffn.layers.1.weight as id 7set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.7.gamma_2 as id 8set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.7.ln1.bias as id 8set param backbone.layers.4.gamma_1 as id 5 + + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.7.attn.qkv.weight as id 8set param backbone.layers.6.gamma_1 as id 7 + +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.7.attn.qkv.bias as id 8set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.7.ln2.weight as id 8set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.4.attn.qkv.bias as id 5 + +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.6.attn.qkv.bias as id 7 + +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.7.ffn.layers.1.weight as id 8 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.7.ffn.layers.1.bias as id 8set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.8.gamma_2 as id 9 + +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.5.attn.qkv.weight as id 6set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.5.attn.proj.bias as id 6set param backbone.layers.6.ffn.layers.1.bias as id 7 + +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.5.ln2.bias as id 6set param backbone.layers.9.ln1.weight as id 10 + +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.7.ln1.bias as id 8 + +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.9.attn.proj.weight as id 10 + +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.5.ffn.layers.1.bias as id 6set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.9.ln2.weight as id 10 + +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.7.ln2.weight as id 8 + +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.9.ffn.layers.1.weight as id 10set param backbone.layers.7.ffn.layers.0.0.bias as id 8 + +set param backbone.layers.6.attn.relative_position_bias_table as id 7set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8set param backbone.layers.6.attn.qkv.weight as id 7 + +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.8.gamma_1 as id 9 + +set param backbone.layers.10.ln1.weight as id 11set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.8.attn.qkv.weight as id 9 + +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.11.gamma_1 as id 12set param backbone.layers.8.ffn.layers.1.bias as id 9 + +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.11.ln1.weight as id 12 + +set param backbone.layers.9.gamma_2 as id 10set param backbone.layers.11.ln1.bias as id 12 + +set param backbone.layers.9.ln1.weight as id 10set param backbone.layers.11.attn.relative_position_bias_table as id 12 + +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.11.attn.proj.bias as id 12 + +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + +set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.11.ln2.bias as id 12 + +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.11.ffn.layers.1.weight as id 12 + +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.11.ffn.layers.1.bias as id 12set param backbone.layers.7.gamma_2 as id 8 + +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.10.gamma_1 as id 11set param backbone.layers.7.ln1.bias as id 8 + +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param neck.upsample_4x.0.weight as id 13 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.7.attn.qkv.weight as id 8set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.10.ln1.bias as id 11 + +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.10.attn.relative_position_bias_table as id 11set param neck.upsample_4x.1.weight as id 13 + +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.10.attn.qkv.weight as id 11 + +set param neck.upsample_4x.1.bias as id 13 +set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.10.attn.qkv.bias as id 11 + +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.7.ln2.weight as id 8set param backbone.layers.10.attn.proj.weight as id 11set param neck.upsample_4x.3.bias as id 13 + + +set param backbone.layers.7.ln2.bias as id 8set param backbone.layers.10.attn.proj.bias as id 11 + +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13set param backbone.layers.10.ln2.weight as id 11 + +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param decode_head.conv_seg.weight as id 13set param backbone.layers.10.ffn.layers.0.0.bias as id 11 + +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.11.gamma_1 as id 12 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.8.attn.relative_position_bias_table as id 9set param backbone.layers.11.gamma_2 as id 12 + +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.11.ln1.weight as id 12 +set param decode_head.psp_modules.0.1.bn.bias as id 13 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.8.attn.proj.bias as id 9set param decode_head.psp_modules.1.1.conv.weight as id 13 + +set param backbone.layers.11.attn.qkv.weight as id 12 +set param decode_head.psp_modules.1.1.bn.weight as id 13set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.11.attn.qkv.bias as id 12 + +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.11.attn.proj.weight as id 12 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param backbone.layers.11.attn.proj.bias as id 12set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9set param backbone.layers.11.ln2.weight as id 12 +set param decode_head.psp_modules.2.1.bn.bias as id 13 + +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.11.ln2.bias as id 12 + +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param decode_head.bottleneck.conv.weight as id 13 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param decode_head.bottleneck.bn.weight as id 13 +set param backbone.layers.9.ln1.weight as id 10set param backbone.layers.11.ffn.layers.1.bias as id 12 + +set param decode_head.bottleneck.bn.bias as id 13 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param decode_head.lateral_convs.0.bn.weight as id 13set param neck.upsample_4x.0.weight as id 13 +set param backbone.layers.9.attn.qkv.bias as id 10 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10set param decode_head.lateral_convs.1.conv.weight as id 13 +set param neck.upsample_4x.1.weight as id 13 + +set param backbone.layers.9.ln2.weight as id 10set param neck.upsample_4x.1.bias as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 + +set param backbone.layers.9.ln2.bias as id 10 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param neck.upsample_4x.3.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param neck.upsample_2x.0.weight as id 13set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param decode_head.lateral_convs.2.bn.bias as id 13 + +set param neck.upsample_2x.0.bias as id 13 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13set param backbone.layers.10.gamma_1 as id 11 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param backbone.layers.10.gamma_2 as id 11 +set param decode_head.fpn_convs.1.conv.weight as id 13set param backbone.layers.10.ln1.weight as id 11 +set param decode_head.conv_seg.weight as id 13 + +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param backbone.layers.10.ln1.bias as id 11set param decode_head.conv_seg.bias as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param backbone.layers.10.attn.qkv.bias as id 11set param decode_head.fpn_convs.2.bn.bias as id 13 + +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.10.attn.proj.weight as id 11 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13set param backbone.layers.10.attn.proj.bias as id 11 + +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13set param backbone.layers.10.ln2.weight as id 11 + +set param backbone.layers.10.ln2.bias as id 11 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11set param decode_head.psp_modules.1.1.bn.weight as id 13 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11set param decode_head.psp_modules.1.1.bn.bias as id 13set param auxiliary_head.conv_seg.weight as id 13 + + +set param auxiliary_head.conv_seg.bias as id 13set param backbone.layers.10.ffn.layers.1.weight as id 11 + +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param backbone.layers.11.gamma_1 as id 12set param auxiliary_head.convs.0.bn.weight as id 13 + +set param backbone.layers.11.gamma_2 as id 12set param auxiliary_head.convs.0.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 + +set param backbone.layers.11.ln1.weight as id 12 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param backbone.layers.11.ln1.bias as id 12 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param decode_head.bottleneck.conv.weight as id 13 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param decode_head.bottleneck.bn.weight as id 13set param backbone.layers.11.attn.qkv.bias as id 12 + +set param decode_head.bottleneck.bn.bias as id 13 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param backbone.layers.11.ln2.weight as id 12 +set param decode_head.lateral_convs.0.bn.weight as id 13set param backbone.layers.11.ln2.bias as id 12 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.lateral_convs.1.conv.weight as id 13set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13set param neck.upsample_4x.1.weight as id 13 + +set param decode_head.fpn_convs.1.bn.weight as id 13set param neck.upsample_4x.1.bias as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13set param neck.upsample_4x.3.bias as id 13 + +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param neck.upsample_2x.0.weight as id 13set param decode_head.fpn_convs.2.bn.bias as id 13 + +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13set param auxiliary_head.convs.0.conv.weight as id 13 + +set param decode_head.psp_modules.0.1.bn.weight as id 13set param auxiliary_head.convs.0.bn.weight as id 13 + +set param decode_head.psp_modules.0.1.bn.bias as id 13set param auxiliary_head.convs.0.bn.bias as id 13 + +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 06:57:58 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: ./mae_pretrain_vit_base.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./mae_pretrain_vit_base.pth can not be found. +Loads checkpoint by local backend from path: ./mae_pretrain_vit_base.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./mae_pretrain_vit_base.pth can not be found. +Loads checkpoint by local backend from path: ./mae_pretrain_vit_base.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./mae_pretrain_vit_base.pth can not be found. +Loads checkpoint by local backend from path: ./mae_pretrain_vit_base.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./mae_pretrain_vit_base.pth can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 312536) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_06:58:00 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 312537) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_06:58:00 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 312538) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_06:58:00 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 312539) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_06:58:00 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 312536) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 06:59:25 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 330997872 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 330997872 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 06:59:25 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag_10000' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained= + '/workspaces/mmsegmentation-1/configs/mae/mae_pretrain_vit_base.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_ade20k-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 06:59:28 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + 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Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 06:59:30 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 06:59:30 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/mae-base_upernet_8xb2-amp-160k_ade20k-512x512. +04/19 07:00:27 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:23:31 time: 1.0255 data_time: 0.0040 memory: 8935 loss: 6.8883 decode.loss_ce: 4.8707 decode.acc_seg: 29.3745 aux.loss_ce: 2.0176 aux.acc_seg: 0.3349 +04/19 07:01:18 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 1 day, 23:58:07 time: 1.0259 data_time: 0.0044 memory: 8462 loss: 6.5402 decode.loss_ce: 4.5599 decode.acc_seg: 36.9930 aux.loss_ce: 1.9803 aux.acc_seg: 2.7327 +04/19 07:02:09 - mmengine - INFO - Iter(train) [ 150/160000] base_lr: 9.9401e-06 lr: 3.6750e-08 eta: 1 day, 23:09:52 time: 1.0258 data_time: 0.0043 memory: 8462 loss: 6.1158 decode.loss_ce: 4.1781 decode.acc_seg: 36.4723 aux.loss_ce: 1.9377 aux.acc_seg: 38.2418 +04/19 07:03:00 - mmengine - INFO - Iter(train) [ 200/160000] base_lr: 1.3276e-05 lr: 4.9083e-08 eta: 1 day, 22:43:21 time: 1.0203 data_time: 0.0039 memory: 8462 loss: 5.6012 decode.loss_ce: 3.7162 decode.acc_seg: 58.7185 aux.loss_ce: 1.8850 aux.acc_seg: 35.2327 +04/19 07:03:51 - mmengine - INFO - Iter(train) [ 250/160000] base_lr: 1.6611e-05 lr: 6.1415e-08 eta: 1 day, 22:24:47 time: 1.0181 data_time: 0.0044 memory: 8462 loss: 5.1370 decode.loss_ce: 3.3229 decode.acc_seg: 60.3662 aux.loss_ce: 1.8141 aux.acc_seg: 37.0596 +04/19 07:04:42 - mmengine - INFO - Iter(train) [ 300/160000] base_lr: 1.9947e-05 lr: 7.3747e-08 eta: 1 day, 22:11:16 time: 1.0159 data_time: 0.0044 memory: 8462 loss: 4.5380 decode.loss_ce: 2.7961 decode.acc_seg: 86.9333 aux.loss_ce: 1.7419 aux.acc_seg: 43.2981 +04/19 07:05:33 - mmengine - INFO - 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aux.acc_seg: 82.0532 +04/19 07:08:55 - mmengine - INFO - Iter(train) [ 550/160000] base_lr: 3.6624e-05 lr: 1.3541e-07 eta: 1 day, 21:31:53 time: 1.0116 data_time: 0.0042 memory: 8462 loss: 1.5581 decode.loss_ce: 0.4585 decode.acc_seg: 97.1006 aux.loss_ce: 1.0996 aux.acc_seg: 89.4524 +04/19 07:09:46 - mmengine - INFO - Iter(train) [ 600/160000] base_lr: 3.9960e-05 lr: 1.4774e-07 eta: 1 day, 21:26:46 time: 1.0079 data_time: 0.0042 memory: 8462 loss: 1.2520 decode.loss_ce: 0.3212 decode.acc_seg: 97.2136 aux.loss_ce: 0.9308 aux.acc_seg: 95.9038 +04/19 07:10:36 - mmengine - INFO - Iter(train) [ 650/160000] base_lr: 4.3296e-05 lr: 1.6007e-07 eta: 1 day, 21:21:33 time: 1.0020 data_time: 0.0038 memory: 8462 loss: 1.0009 decode.loss_ce: 0.2412 decode.acc_seg: 96.5857 aux.loss_ce: 0.7596 aux.acc_seg: 95.6675 +04/19 07:11:26 - mmengine - INFO - Iter(train) [ 700/160000] base_lr: 4.6631e-05 lr: 1.7240e-07 eta: 1 day, 21:16:14 time: 1.0006 data_time: 0.0042 memory: 8462 loss: 0.7967 decode.loss_ce: 0.1877 decode.acc_seg: 97.4550 aux.loss_ce: 0.6090 aux.acc_seg: 96.2496 +04/19 07:12:16 - mmengine - INFO - Iter(train) [ 750/160000] base_lr: 4.9967e-05 lr: 1.8474e-07 eta: 1 day, 21:11:03 time: 0.9981 data_time: 0.0041 memory: 8462 loss: 0.5978 decode.loss_ce: 0.1384 decode.acc_seg: 97.7348 aux.loss_ce: 0.4594 aux.acc_seg: 97.1413 +04/19 07:13:06 - mmengine - INFO - Iter(train) [ 800/160000] base_lr: 5.3302e-05 lr: 1.9707e-07 eta: 1 day, 21:06:08 time: 0.9967 data_time: 0.0040 memory: 8462 loss: 0.5116 decode.loss_ce: 0.1474 decode.acc_seg: 95.7693 aux.loss_ce: 0.3642 aux.acc_seg: 95.3093 +04/19 07:13:56 - mmengine - INFO - Iter(train) [ 850/160000] base_lr: 5.6638e-05 lr: 2.0940e-07 eta: 1 day, 21:01:39 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.3884 decode.loss_ce: 0.1213 decode.acc_seg: 98.1377 aux.loss_ce: 0.2671 aux.acc_seg: 96.9400 +04/19 07:14:45 - mmengine - INFO - Iter(train) [ 900/160000] base_lr: 5.9973e-05 lr: 2.2173e-07 eta: 1 day, 20:57:36 time: 0.9968 data_time: 0.0042 memory: 8462 loss: 0.3336 decode.loss_ce: 0.1094 decode.acc_seg: 97.8647 aux.loss_ce: 0.2242 aux.acc_seg: 96.1260 +04/19 07:15:35 - mmengine - INFO - Iter(train) [ 950/160000] base_lr: 6.3309e-05 lr: 2.3407e-07 eta: 1 day, 20:53:56 time: 0.9966 data_time: 0.0043 memory: 8462 loss: 0.2584 decode.loss_ce: 0.0931 decode.acc_seg: 97.6976 aux.loss_ce: 0.1653 aux.acc_seg: 97.0474 +04/19 07:16:25 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_065922 +04/19 07:16:25 - mmengine - INFO - Iter(train) [ 1000/160000] base_lr: 6.6644e-05 lr: 2.4640e-07 eta: 1 day, 20:50:32 time: 0.9976 data_time: 0.0051 memory: 8462 loss: 0.2484 decode.loss_ce: 0.1043 decode.acc_seg: 96.5818 aux.loss_ce: 0.1441 aux.acc_seg: 96.0867 +04/19 07:17:15 - mmengine - INFO - Iter(train) [ 1050/160000] base_lr: 6.9980e-05 lr: 2.5873e-07 eta: 1 day, 20:47:24 time: 0.9968 data_time: 0.0044 memory: 8462 loss: 0.2135 decode.loss_ce: 0.0918 decode.acc_seg: 97.1264 aux.loss_ce: 0.1217 aux.acc_seg: 96.5210 +04/19 07:18:05 - mmengine - INFO - Iter(train) [ 1100/160000] base_lr: 7.3316e-05 lr: 2.7106e-07 eta: 1 day, 20:44:25 time: 0.9965 data_time: 0.0043 memory: 8462 loss: 0.2053 decode.loss_ce: 0.0867 decode.acc_seg: 96.3804 aux.loss_ce: 0.1187 aux.acc_seg: 95.7396 +04/19 07:18:55 - mmengine - INFO - Iter(train) [ 1150/160000] base_lr: 7.6651e-05 lr: 2.8339e-07 eta: 1 day, 20:41:40 time: 0.9979 data_time: 0.0045 memory: 8462 loss: 0.1674 decode.loss_ce: 0.0742 decode.acc_seg: 97.7476 aux.loss_ce: 0.0932 aux.acc_seg: 96.7743 +04/19 07:19:45 - mmengine - INFO - Iter(train) [ 1200/160000] base_lr: 7.9987e-05 lr: 2.9573e-07 eta: 1 day, 20:39:08 time: 0.9967 data_time: 0.0042 memory: 8462 loss: 0.1592 decode.loss_ce: 0.0774 decode.acc_seg: 98.5477 aux.loss_ce: 0.0818 aux.acc_seg: 97.7438 +04/19 07:20:34 - mmengine - INFO - Iter(train) [ 1250/160000] base_lr: 8.3322e-05 lr: 3.0806e-07 eta: 1 day, 20:36:46 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.1593 decode.loss_ce: 0.0811 decode.acc_seg: 97.6845 aux.loss_ce: 0.0782 aux.acc_seg: 96.8628 +04/19 07:21:24 - mmengine - INFO - Iter(train) [ 1300/160000] base_lr: 8.6658e-05 lr: 3.2039e-07 eta: 1 day, 20:34:27 time: 0.9981 data_time: 0.0043 memory: 8462 loss: 0.1575 decode.loss_ce: 0.0764 decode.acc_seg: 98.2693 aux.loss_ce: 0.0811 aux.acc_seg: 97.4092 +04/19 07:22:14 - mmengine - INFO - Iter(train) [ 1350/160000] base_lr: 8.9993e-05 lr: 3.3272e-07 eta: 1 day, 20:32:15 time: 0.9968 data_time: 0.0042 memory: 8462 loss: 0.1320 decode.loss_ce: 0.0655 decode.acc_seg: 98.1331 aux.loss_ce: 0.0666 aux.acc_seg: 97.2891 +04/19 07:23:04 - mmengine - INFO - Iter(train) [ 1400/160000] base_lr: 9.3329e-05 lr: 3.4506e-07 eta: 1 day, 20:30:10 time: 0.9974 data_time: 0.0048 memory: 8462 loss: 0.1308 decode.loss_ce: 0.0717 decode.acc_seg: 98.7215 aux.loss_ce: 0.0591 aux.acc_seg: 97.6765 +04/19 07:23:54 - mmengine - INFO - Iter(train) [ 1450/160000] base_lr: 9.6664e-05 lr: 3.5739e-07 eta: 1 day, 20:28:10 time: 0.9978 data_time: 0.0047 memory: 8462 loss: 0.1409 decode.loss_ce: 0.0761 decode.acc_seg: 97.6967 aux.loss_ce: 0.0648 aux.acc_seg: 96.7949 +04/19 07:24:44 - mmengine - INFO - Iter(train) [ 1500/160000] base_lr: 1.0000e-04 lr: 3.6972e-07 eta: 1 day, 20:26:20 time: 0.9988 data_time: 0.0042 memory: 8462 loss: 0.1213 decode.loss_ce: 0.0638 decode.acc_seg: 97.8680 aux.loss_ce: 0.0575 aux.acc_seg: 97.0081 +04/19 07:25:34 - mmengine - INFO - Iter(train) [ 1550/160000] base_lr: 9.9969e-05 lr: 3.6961e-07 eta: 1 day, 20:24:31 time: 0.9977 data_time: 0.0038 memory: 8462 loss: 0.1194 decode.loss_ce: 0.0642 decode.acc_seg: 98.4535 aux.loss_ce: 0.0553 aux.acc_seg: 96.6642 +04/19 07:26:24 - mmengine - INFO - Iter(train) [ 1600/160000] base_lr: 9.9938e-05 lr: 3.6949e-07 eta: 1 day, 20:22:50 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.1192 decode.loss_ce: 0.0650 decode.acc_seg: 98.8091 aux.loss_ce: 0.0542 aux.acc_seg: 97.6425 +04/19 07:27:14 - mmengine - INFO - Iter(train) [ 1650/160000] base_lr: 9.9906e-05 lr: 3.6937e-07 eta: 1 day, 20:21:09 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.1103 decode.loss_ce: 0.0602 decode.acc_seg: 97.7407 aux.loss_ce: 0.0500 aux.acc_seg: 96.1254 +04/19 07:28:04 - mmengine - INFO - Iter(train) [ 1700/160000] base_lr: 9.9874e-05 lr: 3.6926e-07 eta: 1 day, 20:19:34 time: 0.9983 data_time: 0.0041 memory: 8462 loss: 0.0946 decode.loss_ce: 0.0512 decode.acc_seg: 98.4478 aux.loss_ce: 0.0435 aux.acc_seg: 98.2590 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314189 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314190 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314191 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314192 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314189 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314190 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314191 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314192 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 314155 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 314155 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 314155 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 07:32:08 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 930945155 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 930945155 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 07:32:09 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained= + '/workspaces/mmsegmentation-1/configs/mae/mae_pretrain_vit_base.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 07:32:12 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.layers.0.ln1.bias as id 1 + +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.cls_token as id 0set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.pos_embed as id 0 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.ln1.weight as id 1set param backbone.layers.1.gamma_2 as id 2 + +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2set param backbone.layers.0.attn.relative_position_bias_table as id 1 + +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.0.ffn.layers.0.0.bias as id 1 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.2.attn.qkv.bias as id 3 + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2set param backbone.layers.2.ffn.layers.0.0.weight as id 3 + +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.3.ln1.weight as id 4set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.3.attn.qkv.weight as id 4set param backbone.layers.2.gamma_2 as id 3 + +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3set param backbone.layers.3.attn.proj.weight as id 4 + +set param backbone.layers.3.attn.proj.bias as id 4set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.3.ln2.weight as id 4set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.3.ln2.bias as id 4set param backbone.layers.2.attn.qkv.bias as id 3 + +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.4.attn.qkv.weight as id 5set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.3.attn.relative_position_bias_table as id 4set param backbone.layers.4.attn.proj.bias as id 5 + +set param backbone.layers.3.attn.qkv.weight as id 4set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.3.attn.qkv.bias as id 4set param backbone.layers.4.ln2.bias as id 5 + +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5set param backbone.layers.3.attn.proj.bias as id 4 + +set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.3.ln2.weight as id 4 + +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.5.attn.proj.weight as id 6set param backbone.layers.4.attn.relative_position_bias_table as id 5 + +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.4.attn.qkv.bias as id 5 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5set param backbone.layers.5.ffn.layers.1.bias as id 6 + +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.7.gamma_2 as id 8 + +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9set param backbone.layers.7.gamma_1 as id 8 + +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.7.attn.qkv.bias as id 8set param backbone.layers.8.attn.proj.bias as id 9 + +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.8.ln2.bias as id 9 + +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9set param backbone.layers.7.ln2.bias as id 8 + +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.9.attn.qkv.bias as id 10 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.8.attn.proj.weight as id 9set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.8.ln2.weight as id 9set param backbone.layers.9.ln2.bias as id 10 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.10.ln1.weight as id 11set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.9.attn.relative_position_bias_table as id 10 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.10.ffn.layers.1.weight as id 11set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12set param backbone.layers.10.attn.qkv.weight as id 11 + +set param backbone.layers.11.attn.proj.weight as id 12set param backbone.layers.10.attn.qkv.bias as id 11 + +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.11.ln2.weight as id 12set param backbone.layers.10.attn.proj.bias as id 11 + +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param backbone.layers.11.gamma_2 as id 12 +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param neck.upsample_4x.1.weight as id 13 +set param backbone.layers.11.attn.relative_position_bias_table as id 12set param neck.upsample_4x.1.bias as id 13 + +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param neck.upsample_2x.0.weight as id 13set param backbone.layers.11.attn.proj.weight as id 12 + +set param neck.upsample_2x.0.bias as id 13set param backbone.layers.11.attn.proj.bias as id 12 + +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13set param neck.upsample_4x.1.weight as id 13 + +set param neck.upsample_4x.1.bias as id 13set param decode_head.psp_modules.1.1.bn.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.bias as id 13set param neck.upsample_4x.3.weight as id 13 + +set param neck.upsample_4x.3.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param neck.upsample_2x.0.weight as id 13 + +set param decode_head.psp_modules.2.1.bn.weight as id 13set param neck.upsample_2x.0.bias as id 13 + +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13set param decode_head.psp_modules.0.1.bn.weight as id 13 + +set param decode_head.lateral_convs.0.bn.weight as id 13set param decode_head.psp_modules.0.1.bn.bias as id 13 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13set param decode_head.psp_modules.2.1.conv.weight as id 13 + +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13set param decode_head.fpn_convs.0.bn.bias as id 13 + +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.bottleneck.conv.weight as id 13set param decode_head.fpn_convs.1.bn.weight as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13set param decode_head.bottleneck.bn.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13set param decode_head.fpn_convs.2.conv.weight as id 13 + +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13set param decode_head.fpn_bottleneck.conv.weight as id 13 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 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Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 07:32:14 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 07:32:14 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000. +04/19 07:33:10 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:26:12 time: 1.0282 data_time: 0.0044 memory: 8935 loss: 6.8929 decode.loss_ce: 4.8994 decode.acc_seg: 13.5880 aux.loss_ce: 1.9936 aux.acc_seg: 0.0000 +04/19 07:34:02 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 2 days, 0:05:00 time: 1.0281 data_time: 0.0044 memory: 8462 loss: 6.5884 decode.loss_ce: 4.6293 decode.acc_seg: 34.7708 aux.loss_ce: 1.9591 aux.acc_seg: 11.9486 +04/19 07:34:53 - mmengine - INFO - Iter(train) [ 150/160000] base_lr: 9.9401e-06 lr: 3.6750e-08 eta: 1 day, 23:14:54 time: 1.0264 data_time: 0.0038 memory: 8462 loss: 6.0648 decode.loss_ce: 4.1524 decode.acc_seg: 36.6734 aux.loss_ce: 1.9124 aux.acc_seg: 23.0017 +04/19 07:35:45 - mmengine - INFO - Iter(train) [ 200/160000] base_lr: 1.3276e-05 lr: 4.9083e-08 eta: 1 day, 22:47:50 time: 1.0232 data_time: 0.0040 memory: 8462 loss: 5.5490 decode.loss_ce: 3.6809 decode.acc_seg: 73.1098 aux.loss_ce: 1.8681 aux.acc_seg: 61.9604 +04/19 07:36:35 - mmengine - INFO - Iter(train) [ 250/160000] base_lr: 1.6611e-05 lr: 6.1415e-08 eta: 1 day, 22:28:20 time: 1.0183 data_time: 0.0051 memory: 8462 loss: 5.0071 decode.loss_ce: 3.2241 decode.acc_seg: 74.2155 aux.loss_ce: 1.7830 aux.acc_seg: 79.6803 +04/19 07:37:26 - mmengine - INFO - Iter(train) [ 300/160000] base_lr: 1.9947e-05 lr: 7.3747e-08 eta: 1 day, 22:14:09 time: 1.0157 data_time: 0.0040 memory: 8462 loss: 4.4336 decode.loss_ce: 2.7250 decode.acc_seg: 81.5327 aux.loss_ce: 1.7086 aux.acc_seg: 69.2970 +04/19 07:38:17 - mmengine - INFO - Iter(train) [ 350/160000] base_lr: 2.3282e-05 lr: 8.6079e-08 eta: 1 day, 22:02:23 time: 1.0125 data_time: 0.0042 memory: 8462 loss: 3.8821 decode.loss_ce: 2.2462 decode.acc_seg: 86.4904 aux.loss_ce: 1.6358 aux.acc_seg: 69.9341 +04/19 07:39:08 - mmengine - INFO - Iter(train) [ 400/160000] base_lr: 2.6618e-05 lr: 9.8412e-08 eta: 1 day, 21:52:57 time: 1.0134 data_time: 0.0043 memory: 8462 loss: 3.1226 decode.loss_ce: 1.6190 decode.acc_seg: 94.7613 aux.loss_ce: 1.5036 aux.acc_seg: 83.1755 +04/19 07:39:58 - mmengine - INFO - Iter(train) [ 450/160000] base_lr: 2.9953e-05 lr: 1.1074e-07 eta: 1 day, 21:45:08 time: 1.0109 data_time: 0.0041 memory: 8462 loss: 2.5017 decode.loss_ce: 1.1098 decode.acc_seg: 96.2904 aux.loss_ce: 1.3919 aux.acc_seg: 81.9899 +04/19 07:40:49 - mmengine - INFO - Iter(train) [ 500/160000] base_lr: 3.3289e-05 lr: 1.2308e-07 eta: 1 day, 21:38:21 time: 1.0099 data_time: 0.0041 memory: 8462 loss: 1.9654 decode.loss_ce: 0.7101 decode.acc_seg: 98.1623 aux.loss_ce: 1.2554 aux.acc_seg: 88.5754 +04/19 07:41:39 - mmengine - INFO - Iter(train) [ 550/160000] base_lr: 3.6624e-05 lr: 1.3541e-07 eta: 1 day, 21:32:18 time: 1.0070 data_time: 0.0040 memory: 8462 loss: 1.5712 decode.loss_ce: 0.4877 decode.acc_seg: 98.3290 aux.loss_ce: 1.0835 aux.acc_seg: 93.5476 +04/19 07:42:30 - mmengine - INFO - Iter(train) [ 600/160000] base_lr: 3.9960e-05 lr: 1.4774e-07 eta: 1 day, 21:26:52 time: 1.0063 data_time: 0.0044 memory: 8462 loss: 1.2105 decode.loss_ce: 0.3179 decode.acc_seg: 96.7295 aux.loss_ce: 0.8926 aux.acc_seg: 95.1063 +04/19 07:43:20 - mmengine - INFO - Iter(train) [ 650/160000] base_lr: 4.3296e-05 lr: 1.6007e-07 eta: 1 day, 21:21:34 time: 1.0028 data_time: 0.0043 memory: 8462 loss: 0.9846 decode.loss_ce: 0.2559 decode.acc_seg: 96.0199 aux.loss_ce: 0.7287 aux.acc_seg: 95.8773 +04/19 07:44:10 - mmengine - INFO - Iter(train) [ 700/160000] base_lr: 4.6631e-05 lr: 1.7240e-07 eta: 1 day, 21:16:11 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.7521 decode.loss_ce: 0.1831 decode.acc_seg: 97.2086 aux.loss_ce: 0.5690 aux.acc_seg: 96.8592 +04/19 07:45:00 - mmengine - INFO - Iter(train) [ 750/160000] base_lr: 4.9967e-05 lr: 1.8474e-07 eta: 1 day, 21:11:06 time: 1.0000 data_time: 0.0042 memory: 8462 loss: 0.6118 decode.loss_ce: 0.1588 decode.acc_seg: 97.6543 aux.loss_ce: 0.4529 aux.acc_seg: 97.6311 +04/19 07:45:50 - mmengine - INFO - Iter(train) [ 800/160000] base_lr: 5.3302e-05 lr: 1.9707e-07 eta: 1 day, 21:06:10 time: 0.9954 data_time: 0.0041 memory: 8462 loss: 0.4727 decode.loss_ce: 0.1355 decode.acc_seg: 96.8500 aux.loss_ce: 0.3372 aux.acc_seg: 96.2122 +04/19 07:46:39 - mmengine - INFO - Iter(train) [ 850/160000] base_lr: 5.6638e-05 lr: 2.0940e-07 eta: 1 day, 21:01:45 time: 0.9967 data_time: 0.0042 memory: 8462 loss: 0.4170 decode.loss_ce: 0.1385 decode.acc_seg: 98.6187 aux.loss_ce: 0.2784 aux.acc_seg: 97.8769 +04/19 07:47:29 - mmengine - INFO - Iter(train) [ 900/160000] base_lr: 5.9973e-05 lr: 2.2173e-07 eta: 1 day, 20:57:43 time: 0.9982 data_time: 0.0044 memory: 8462 loss: 0.3119 decode.loss_ce: 0.1100 decode.acc_seg: 96.6267 aux.loss_ce: 0.2019 aux.acc_seg: 96.7194 +04/19 07:48:19 - mmengine - INFO - Iter(train) [ 950/160000] base_lr: 6.3309e-05 lr: 2.3407e-07 eta: 1 day, 20:54:01 time: 0.9974 data_time: 0.0042 memory: 8462 loss: 0.2706 decode.loss_ce: 0.1000 decode.acc_seg: 97.2006 aux.loss_ce: 0.1706 aux.acc_seg: 96.2858 +04/19 07:49:09 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_073206 +04/19 07:49:09 - mmengine - INFO - Iter(train) [ 1000/160000] base_lr: 6.6644e-05 lr: 2.4640e-07 eta: 1 day, 20:50:37 time: 0.9962 data_time: 0.0046 memory: 8462 loss: 0.2227 decode.loss_ce: 0.0922 decode.acc_seg: 98.8609 aux.loss_ce: 0.1306 aux.acc_seg: 97.1130 +04/19 07:49:59 - mmengine - INFO - Iter(train) [ 1050/160000] base_lr: 6.9980e-05 lr: 2.5873e-07 eta: 1 day, 20:47:26 time: 0.9968 data_time: 0.0046 memory: 8462 loss: 0.2316 decode.loss_ce: 0.0922 decode.acc_seg: 97.4508 aux.loss_ce: 0.1394 aux.acc_seg: 96.8807 +04/19 07:50:49 - mmengine - INFO - Iter(train) [ 1100/160000] base_lr: 7.3316e-05 lr: 2.7106e-07 eta: 1 day, 20:44:28 time: 0.9962 data_time: 0.0041 memory: 8462 loss: 0.1786 decode.loss_ce: 0.0795 decode.acc_seg: 97.5788 aux.loss_ce: 0.0991 aux.acc_seg: 97.0448 +04/19 07:51:39 - mmengine - INFO - Iter(train) [ 1150/160000] base_lr: 7.6651e-05 lr: 2.8339e-07 eta: 1 day, 20:41:37 time: 0.9960 data_time: 0.0041 memory: 8462 loss: 0.1799 decode.loss_ce: 0.0788 decode.acc_seg: 96.7302 aux.loss_ce: 0.1011 aux.acc_seg: 95.9280 +04/19 07:52:28 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_073206 +04/19 07:52:28 - mmengine - INFO - Iter(train) [ 1200/160000] base_lr: 7.9987e-05 lr: 2.9573e-07 eta: 1 day, 20:39:01 time: 0.9964 data_time: 0.0042 memory: 8462 loss: 0.1665 decode.loss_ce: 0.0772 decode.acc_seg: 98.2353 aux.loss_ce: 0.0893 aux.acc_seg: 97.0778 +04/19 07:53:18 - mmengine - INFO - Iter(train) [ 1250/160000] base_lr: 8.3322e-05 lr: 3.0806e-07 eta: 1 day, 20:36:34 time: 0.9961 data_time: 0.0044 memory: 8462 loss: 0.1596 decode.loss_ce: 0.0793 decode.acc_seg: 98.0207 aux.loss_ce: 0.0804 aux.acc_seg: 96.4180 +04/19 07:54:08 - mmengine - INFO - Iter(train) [ 1300/160000] base_lr: 8.6658e-05 lr: 3.2039e-07 eta: 1 day, 20:34:14 time: 0.9980 data_time: 0.0041 memory: 8462 loss: 0.1510 decode.loss_ce: 0.0773 decode.acc_seg: 96.0957 aux.loss_ce: 0.0737 aux.acc_seg: 95.7628 +04/19 07:54:58 - mmengine - INFO - Iter(train) [ 1350/160000] base_lr: 8.9993e-05 lr: 3.3272e-07 eta: 1 day, 20:32:00 time: 0.9967 data_time: 0.0043 memory: 8462 loss: 0.1365 decode.loss_ce: 0.0683 decode.acc_seg: 97.9652 aux.loss_ce: 0.0682 aux.acc_seg: 97.5304 +04/19 07:55:48 - mmengine - INFO - Iter(train) [ 1400/160000] base_lr: 9.3329e-05 lr: 3.4506e-07 eta: 1 day, 20:29:56 time: 0.9980 data_time: 0.0042 memory: 8462 loss: 0.1231 decode.loss_ce: 0.0644 decode.acc_seg: 97.4878 aux.loss_ce: 0.0587 aux.acc_seg: 95.0882 +04/19 07:56:38 - mmengine - INFO - Iter(train) [ 1450/160000] base_lr: 9.6664e-05 lr: 3.5739e-07 eta: 1 day, 20:27:59 time: 0.9969 data_time: 0.0040 memory: 8462 loss: 0.1321 decode.loss_ce: 0.0677 decode.acc_seg: 96.7474 aux.loss_ce: 0.0644 aux.acc_seg: 95.5687 +04/19 07:57:28 - mmengine - INFO - Iter(train) [ 1500/160000] base_lr: 1.0000e-04 lr: 3.6972e-07 eta: 1 day, 20:26:09 time: 0.9976 data_time: 0.0041 memory: 8462 loss: 0.1305 decode.loss_ce: 0.0714 decode.acc_seg: 97.9761 aux.loss_ce: 0.0591 aux.acc_seg: 96.3438 +04/19 07:58:18 - mmengine - INFO - Iter(train) [ 1550/160000] base_lr: 9.9969e-05 lr: 3.6961e-07 eta: 1 day, 20:24:27 time: 0.9994 data_time: 0.0040 memory: 8462 loss: 0.1054 decode.loss_ce: 0.0567 decode.acc_seg: 97.9790 aux.loss_ce: 0.0487 aux.acc_seg: 96.3348 +04/19 07:59:08 - mmengine - INFO - Iter(train) [ 1600/160000] base_lr: 9.9938e-05 lr: 3.6949e-07 eta: 1 day, 20:22:48 time: 0.9983 data_time: 0.0041 memory: 8462 loss: 0.1074 decode.loss_ce: 0.0558 decode.acc_seg: 97.5245 aux.loss_ce: 0.0516 aux.acc_seg: 97.0989 +04/19 07:59:57 - mmengine - INFO - Iter(train) [ 1650/160000] base_lr: 9.9906e-05 lr: 3.6937e-07 eta: 1 day, 20:21:15 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.1062 decode.loss_ce: 0.0588 decode.acc_seg: 98.6660 aux.loss_ce: 0.0473 aux.acc_seg: 97.1249 +04/19 08:00:47 - mmengine - INFO - Iter(train) [ 1700/160000] base_lr: 9.9874e-05 lr: 3.6926e-07 eta: 1 day, 20:19:43 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.1060 decode.loss_ce: 0.0581 decode.acc_seg: 98.2771 aux.loss_ce: 0.0478 aux.acc_seg: 97.3316 +04/19 08:01:37 - mmengine - INFO - Iter(train) [ 1750/160000] base_lr: 9.9843e-05 lr: 3.6914e-07 eta: 1 day, 20:18:12 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.1101 decode.loss_ce: 0.0612 decode.acc_seg: 98.4524 aux.loss_ce: 0.0489 aux.acc_seg: 97.2885 +04/19 08:02:27 - mmengine - INFO - Iter(train) [ 1800/160000] base_lr: 9.9811e-05 lr: 3.6902e-07 eta: 1 day, 20:16:44 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.1074 decode.loss_ce: 0.0601 decode.acc_seg: 98.3191 aux.loss_ce: 0.0473 aux.acc_seg: 97.3679 +04/19 08:03:17 - mmengine - INFO - Iter(train) [ 1850/160000] base_lr: 9.9780e-05 lr: 3.6891e-07 eta: 1 day, 20:15:20 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.1145 decode.loss_ce: 0.0646 decode.acc_seg: 96.8958 aux.loss_ce: 0.0499 aux.acc_seg: 95.6266 +04/19 08:04:07 - mmengine - INFO - Iter(train) [ 1900/160000] base_lr: 9.9748e-05 lr: 3.6879e-07 eta: 1 day, 20:13:58 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0987 decode.loss_ce: 0.0551 decode.acc_seg: 98.0410 aux.loss_ce: 0.0437 aux.acc_seg: 97.2126 +04/19 08:04:57 - mmengine - INFO - Iter(train) [ 1950/160000] base_lr: 9.9717e-05 lr: 3.6867e-07 eta: 1 day, 20:12:40 time: 1.0001 data_time: 0.0042 memory: 8462 loss: 0.1058 decode.loss_ce: 0.0595 decode.acc_seg: 98.5418 aux.loss_ce: 0.0463 aux.acc_seg: 96.3310 +04/19 08:05:47 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_073206 +04/19 08:05:47 - mmengine - INFO - Iter(train) [ 2000/160000] base_lr: 9.9685e-05 lr: 3.6856e-07 eta: 1 day, 20:11:20 time: 1.0007 data_time: 0.0041 memory: 8462 loss: 0.1130 decode.loss_ce: 0.0679 decode.acc_seg: 96.9311 aux.loss_ce: 0.0452 aux.acc_seg: 95.4798 +04/19 08:06:37 - mmengine - INFO - Iter(train) [ 2050/160000] base_lr: 9.9654e-05 lr: 3.6844e-07 eta: 1 day, 20:10:02 time: 0.9987 data_time: 0.0041 memory: 8462 loss: 0.0946 decode.loss_ce: 0.0536 decode.acc_seg: 98.1184 aux.loss_ce: 0.0410 aux.acc_seg: 97.6820 +04/19 08:07:27 - mmengine - INFO - Iter(train) [ 2100/160000] base_lr: 9.9622e-05 lr: 3.6832e-07 eta: 1 day, 20:08:43 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0973 decode.loss_ce: 0.0559 decode.acc_seg: 98.9077 aux.loss_ce: 0.0414 aux.acc_seg: 97.5845 +04/19 08:08:17 - mmengine - INFO - Iter(train) [ 2150/160000] base_lr: 9.9591e-05 lr: 3.6821e-07 eta: 1 day, 20:07:30 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0883 decode.loss_ce: 0.0493 decode.acc_seg: 98.4428 aux.loss_ce: 0.0390 aux.acc_seg: 97.5756 +04/19 08:09:07 - mmengine - INFO - Iter(train) [ 2200/160000] base_lr: 9.9559e-05 lr: 3.6809e-07 eta: 1 day, 20:06:16 time: 1.0000 data_time: 0.0041 memory: 8462 loss: 0.0858 decode.loss_ce: 0.0488 decode.acc_seg: 98.9340 aux.loss_ce: 0.0370 aux.acc_seg: 98.4314 +04/19 08:09:57 - mmengine - INFO - Iter(train) [ 2250/160000] base_lr: 9.9527e-05 lr: 3.6797e-07 eta: 1 day, 20:05:02 time: 0.9992 data_time: 0.0041 memory: 8462 loss: 0.0857 decode.loss_ce: 0.0492 decode.acc_seg: 97.9370 aux.loss_ce: 0.0365 aux.acc_seg: 96.0619 +04/19 08:10:47 - mmengine - INFO - Iter(train) [ 2300/160000] base_lr: 9.9496e-05 lr: 3.6786e-07 eta: 1 day, 20:03:48 time: 0.9986 data_time: 0.0042 memory: 8462 loss: 0.0937 decode.loss_ce: 0.0543 decode.acc_seg: 99.1913 aux.loss_ce: 0.0394 aux.acc_seg: 97.6168 +04/19 08:11:37 - mmengine - INFO - Iter(train) [ 2350/160000] base_lr: 9.9464e-05 lr: 3.6774e-07 eta: 1 day, 20:02:34 time: 0.9974 data_time: 0.0042 memory: 8462 loss: 0.0839 decode.loss_ce: 0.0480 decode.acc_seg: 96.4069 aux.loss_ce: 0.0359 aux.acc_seg: 95.8021 +04/19 08:12:27 - mmengine - INFO - Iter(train) [ 2400/160000] base_lr: 9.9433e-05 lr: 3.6762e-07 eta: 1 day, 20:01:23 time: 0.9996 data_time: 0.0042 memory: 8462 loss: 0.0740 decode.loss_ce: 0.0406 decode.acc_seg: 98.9935 aux.loss_ce: 0.0333 aux.acc_seg: 97.8920 +04/19 08:13:17 - mmengine - INFO - Iter(train) [ 2450/160000] base_lr: 9.9401e-05 lr: 3.6751e-07 eta: 1 day, 20:00:12 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0819 decode.loss_ce: 0.0465 decode.acc_seg: 97.6427 aux.loss_ce: 0.0354 aux.acc_seg: 95.5507 +04/19 08:14:07 - mmengine - INFO - Iter(train) [ 2500/160000] base_lr: 9.9370e-05 lr: 3.6739e-07 eta: 1 day, 19:59:02 time: 0.9996 data_time: 0.0041 memory: 8462 loss: 0.0806 decode.loss_ce: 0.0457 decode.acc_seg: 98.3541 aux.loss_ce: 0.0350 aux.acc_seg: 97.8451 +04/19 08:14:57 - mmengine - INFO - Iter(train) [ 2550/160000] base_lr: 9.9338e-05 lr: 3.6727e-07 eta: 1 day, 19:57:55 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0901 decode.loss_ce: 0.0521 decode.acc_seg: 96.8052 aux.loss_ce: 0.0380 aux.acc_seg: 95.4477 +04/19 08:15:47 - mmengine - INFO - Iter(train) [ 2600/160000] base_lr: 9.9307e-05 lr: 3.6716e-07 eta: 1 day, 19:56:46 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0891 decode.loss_ce: 0.0503 decode.acc_seg: 98.3551 aux.loss_ce: 0.0388 aux.acc_seg: 96.2929 +04/19 08:16:37 - mmengine - INFO - Iter(train) [ 2650/160000] base_lr: 9.9275e-05 lr: 3.6704e-07 eta: 1 day, 19:55:38 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0795 decode.loss_ce: 0.0454 decode.acc_seg: 97.8434 aux.loss_ce: 0.0341 aux.acc_seg: 97.0446 +04/19 08:17:27 - mmengine - INFO - Iter(train) [ 2700/160000] base_lr: 9.9244e-05 lr: 3.6692e-07 eta: 1 day, 19:54:33 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0840 decode.loss_ce: 0.0501 decode.acc_seg: 98.5741 aux.loss_ce: 0.0339 aux.acc_seg: 96.9194 +04/19 08:18:17 - mmengine - INFO - Iter(train) [ 2750/160000] base_lr: 9.9212e-05 lr: 3.6681e-07 eta: 1 day, 19:53:27 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0900 decode.loss_ce: 0.0518 decode.acc_seg: 98.2058 aux.loss_ce: 0.0381 aux.acc_seg: 96.4069 +04/19 08:19:07 - mmengine - INFO - Iter(train) [ 2800/160000] base_lr: 9.9180e-05 lr: 3.6669e-07 eta: 1 day, 19:52:24 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0752 decode.loss_ce: 0.0434 decode.acc_seg: 98.1392 aux.loss_ce: 0.0318 aux.acc_seg: 97.6873 +04/19 08:19:57 - mmengine - INFO - Iter(train) [ 2850/160000] base_lr: 9.9149e-05 lr: 3.6657e-07 eta: 1 day, 19:51:18 time: 0.9991 data_time: 0.0041 memory: 8462 loss: 0.0800 decode.loss_ce: 0.0476 decode.acc_seg: 98.0701 aux.loss_ce: 0.0324 aux.acc_seg: 95.6417 +04/19 08:20:47 - mmengine - INFO - Iter(train) [ 2900/160000] base_lr: 9.9117e-05 lr: 3.6646e-07 eta: 1 day, 19:50:15 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0739 decode.loss_ce: 0.0421 decode.acc_seg: 98.0650 aux.loss_ce: 0.0317 aux.acc_seg: 97.2357 +04/19 08:21:37 - mmengine - INFO - Iter(train) [ 2950/160000] base_lr: 9.9086e-05 lr: 3.6634e-07 eta: 1 day, 19:49:14 time: 1.0000 data_time: 0.0042 memory: 8462 loss: 0.0739 decode.loss_ce: 0.0434 decode.acc_seg: 98.9803 aux.loss_ce: 0.0306 aux.acc_seg: 97.1418 From f4cf0dab20d025c43df3e93c45785449b1c1804c Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Tue, 23 Apr 2024 05:56:10 +0000 Subject: [PATCH 19/24] 2024.04.22 --- nohup.out | 2 ++ 1 file changed, 2 insertions(+) diff --git a/nohup.out b/nohup.out index 7c03785cbd..c2a6817ea9 100644 --- a/nohup.out +++ b/nohup.out @@ -43296,3 +43296,5 @@ Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/config 04/19 08:19:57 - mmengine - INFO - Iter(train) [ 2850/160000] base_lr: 9.9149e-05 lr: 3.6657e-07 eta: 1 day, 19:51:18 time: 0.9991 data_time: 0.0041 memory: 8462 loss: 0.0800 decode.loss_ce: 0.0476 decode.acc_seg: 98.0701 aux.loss_ce: 0.0324 aux.acc_seg: 95.6417 04/19 08:20:47 - mmengine - INFO - Iter(train) [ 2900/160000] base_lr: 9.9117e-05 lr: 3.6646e-07 eta: 1 day, 19:50:15 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0739 decode.loss_ce: 0.0421 decode.acc_seg: 98.0650 aux.loss_ce: 0.0317 aux.acc_seg: 97.2357 04/19 08:21:37 - mmengine - INFO - Iter(train) [ 2950/160000] base_lr: 9.9086e-05 lr: 3.6634e-07 eta: 1 day, 19:49:14 time: 1.0000 data_time: 0.0042 memory: 8462 loss: 0.0739 decode.loss_ce: 0.0434 decode.acc_seg: 98.9803 aux.loss_ce: 0.0306 aux.acc_seg: 97.1418 +04/19 08:22:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_073206 +04/19 08:22:27 - mmengine - INFO - Iter(train) [ 3000/160000] base_lr: 9.9054e-05 lr: 3.6622e-07 eta: 1 day, 19:48:15 time: 1.0009 data_time: 0.0045 memory: 8462 loss: 0.0741 decode.loss_ce: 0.0436 decode.acc_seg: 99.0244 aux.loss_ce: 0.0305 aux.acc_seg: 98.1958 From ba4ef193feb791e1321f35f5aa997f120a7f890b Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Tue, 23 Apr 2024 05:56:33 +0000 Subject: [PATCH 20/24] 2024.04.22 --- a.ipynb | 87 ++++++++++++++++++++++++++---------------------- test_commands.sh | 48 ++++++++++++++++++++++++++ 2 files changed, 95 insertions(+), 40 deletions(-) create mode 100644 test_commands.sh diff --git a/a.ipynb b/a.ipynb index fa322284ef..20239d3c28 100644 --- a/a.ipynb +++ b/a.ipynb @@ -2,12 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[ WARN:0@36.485] global loadsave.cpp:248 findDecoder imread_('/workspaces/mmsegmentation-1/OUPUT/MAEOUT_240422/00000.png'): can't open/read file: check file path/integrity\n" + ] + }, { "data": { - "image/png": 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", + "image/png": "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", "text/plain": [ "
" ] @@ -24,7 +31,7 @@ "import numpy as np\n", "\n", "# Define the paths to the images\n", - "image_path_output = '/workspaces/mmsegmentation-1/MAEOUT_240416/00000.png'\n", + "image_path_output = '/workspaces/mmsegmentation-1/OUPUT/MAEOUT_240422/00000.png'\n", "image_path_img = '/workspaces/mmsegmentation-1/data/cag/images/test/00000.png'\n", "image_path_label = '/workspaces/mmsegmentation-1/data/cag/annotations/test/00000.png'\n", "\n", @@ -60,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -68,15 +75,15 @@ "output_type": "stream", "text": [ "00000.png\n", - "IoU: 88.77\n", - "F1 Score: 94.05\n", - "Precision: 92.45\n", - "Recall: 95.71\n" + "IoU: 86.47\n", + "F1 Score: 92.74\n", + "Precision: 90.84\n", + "Recall: 94.73\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -91,15 +98,15 @@ "output_type": "stream", "text": [ "00001.png\n", - "IoU: 83.04\n", - "F1 Score: 90.73\n", - "Precision: 92.86\n", - "Recall: 88.71\n" + "IoU: 75.86\n", + "F1 Score: 86.27\n", + "Precision: 84.14\n", + "Recall: 88.52\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -114,15 +121,15 @@ "output_type": "stream", "text": [ "00002.png\n", - "IoU: 76.92\n", - "F1 Score: 86.96\n", - "Precision: 81.38\n", - "Recall: 93.35\n" + "IoU: 83.50\n", + "F1 Score: 91.01\n", + "Precision: 88.15\n", + "Recall: 94.06\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -137,15 +144,15 @@ "output_type": "stream", "text": [ "00003.png\n", - "IoU: 73.53\n", - "F1 Score: 84.75\n", - "Precision: 80.49\n", - "Recall: 89.48\n" + "IoU: 77.12\n", + "F1 Score: 87.08\n", + "Precision: 85.08\n", + "Recall: 89.19\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -160,15 +167,15 @@ "output_type": "stream", "text": [ "00004.png\n", - "IoU: 88.14\n", - "F1 Score: 93.69\n", - "Precision: 97.29\n", - "Recall: 90.36\n" + "IoU: 88.68\n", + "F1 Score: 94.00\n", + "Precision: 96.41\n", + "Recall: 91.70\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -183,15 +190,15 @@ "output_type": "stream", "text": [ "00005.png\n", - "IoU: 90.04\n", - "F1 Score: 94.76\n", - "Precision: 96.10\n", - "Recall: 93.46\n" + "IoU: 84.01\n", + "F1 Score: 91.31\n", + "Precision: 94.36\n", + "Recall: 88.45\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -205,10 +212,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average IoU: 83.41\n", - "Average F1 Score: 90.82\n", - "Average Precision: 90.09\n", - "Average Recall: 91.85\n" + "Average IoU: 82.61\n", + "Average F1 Score: 90.40\n", + "Average Precision: 89.83\n", + "Average Recall: 91.11\n" ] } ], @@ -220,7 +227,7 @@ "\n", "iou_list, f1_score_list, precision_list, recall_list = [],[],[],[]\n", "\n", - "file_name_list = os.listdir('/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240418/')\n", + "file_name_list = os.listdir('/workspaces/mmsegmentation-1/OUTPUT/R101OUT_240419/')\n", "file_name_list = sorted(file_name_list)\n", "\n", "def evaluate_segmentation(label, output):\n", @@ -251,7 +258,7 @@ "\n", "\n", "for file_name in file_name_list[0:6]:\n", - " image_path_output = f'/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240418/{file_name}'\n", + " image_path_output = f'/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422/{file_name}'\n", " image_path_img = f'/workspaces/mmsegmentation-1/data/cag/images/test/{file_name}'\n", " image_path_label = f'/workspaces/mmsegmentation-1/data/cag/annotations/test/{file_name}'\n", "\n", diff --git a/test_commands.sh b/test_commands.sh new file mode 100644 index 0000000000..c920d5bce0 --- /dev/null +++ b/test_commands.sh @@ -0,0 +1,48 @@ +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_10000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_20000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_30000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_40000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_50000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_60000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_70000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_80000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_90000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_100000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_110000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_120000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_130000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_140000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_150000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_160000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_10000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_20000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_30000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_40000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py 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/workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_80000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_90000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_100000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_110000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_120000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_130000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_140000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_150000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_160000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_10000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_20000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_30000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_40000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_50000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_60000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_70000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_80000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_90000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_100000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_110000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_120000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_130000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_140000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_150000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch +nohup python -m torch.distributed.launch --nproc_per_node=4 tools/test.py /workspaces/mmsegmentation-1/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000/iter_160000.pth --out /workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240422 --launcher pytorch From 336b71d6545468ce12bf1c538df65837a61113ad Mon Sep 17 00:00:00 2001 From: jaeofbum Date: Tue, 23 Apr 2024 06:04:05 +0000 Subject: [PATCH 21/24] Committing changes to .gitignore and a.ipynb --- .gitignore | 5 +- a.ipynb | 143 ++++++++++++------ configs/_base_/models/upernet_mae.py | 4 +- ...-base_upernet_8xb2-amp-160k_cag-512x512.py | 2 +- feature_map_visual.py | 137 +++++++++++++++++ mmseg/datasets/transforms/formatting.py | 6 +- 6 files changed, 241 insertions(+), 56 deletions(-) create mode 100644 feature_map_visual.py diff --git a/.gitignore b/.gitignore index ce225418b5..85baaed25c 100644 --- a/.gitignore +++ b/.gitignore @@ -30,7 +30,7 @@ MANIFEST # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec - +nohup.out # Installer logs pip-log.txt pip-delete-this-directory.txt @@ -62,7 +62,8 @@ instance/ # Scrapy stuff: .scrapy - +wandb +test_commands.sh # Sphinx documentation docs/en/_build/ docs/zh_cn/_build/ diff --git a/a.ipynb b/a.ipynb index 817ad0b913..1bf7fb24c8 100644 --- a/a.ipynb +++ b/a.ipynb @@ -60,23 +60,69 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "00285.png\n", - "IoU: 94.63\n", - "F1 Score: 97.24\n", - "Precision: 96.41\n", - "Recall: 98.09\n" + "00009.png\n", + "IoU: 19.76\n", + "F1 Score: 33.00\n", + "Precision: 32.68\n", + "Recall: 33.33\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "00072.png\n", + "IoU: 28.44\n", + "F1 Score: 44.28\n", + "Precision: 51.37\n", + "Recall: 38.91\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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" ] @@ -90,16 +136,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "00316.png\n", - "IoU: 94.75\n", - "F1 Score: 97.31\n", - "Precision: 97.20\n", - "Recall: 97.41\n" + "00473.png\n", + "IoU: 41.94\n", + "F1 Score: 59.09\n", + "Precision: 43.62\n", + "Recall: 91.59\n" ] }, { "data": { - "image/png": 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RTTP0pfPa1f72+6UJmDJSOlp4ASYnJyshOKqy+DYk4ITzKJ7ryIZhYOhEmzIilISUhYtQk1Fx28aZpNZ3ai9JbY18ASO3UbBAzMFeHGW3YSADjMmMstk/ly6syDgtEzieu6Gooa7XkGfYIPm9c1kzuyZacN6uczN35FCmI0ZIzmHLXNMR2JHVaZKNkIumCC47RGToNKNcC5fTDFmRq/NKry/PoC4KKjG7XZI/u6Vztd/v+ZukJkhDPwvOuReL5wZph3HDPZdJSmUo2brhcFh5OedKjBHYUeZjGCS9nDgbNiIbeg+Hw6yvr1fJuu/hDpXBYFAz7lI5nDMxfhszckHBk9R6W1lkRwdHf5dL8MClQ+Dfu0FqNjvze2TLnE26MSazu9RYgZ5EA683czNJxbxwBna4jIVyVTLq0jJU575ESORqObqWbGKKZ1tHie7A5jKgOE0xFC1JPOaBvlm2DmY2TnMQe11P3DJW3ny/PBJlSb4IczxoBG2FKyMABuiFBtszHmqY/gxRhZ5SlI3flx7em2eNBszIoZhusGeMyMWw1kqxW2vgxsZGVbO0MjF2w86NjY0vOLZer5f19fVaxOb+rv2iuKQcm5ubtWI5hgmRYjSBvMp8uNkc7cMlCgGHDcvd5eT0xA7UZJRRgCH8YDAqf7g8BtuLHLkf6+q81mtoWIrjoITF2N1gz/hM6DgFcfknqRNAtqFWq1Vr1CjJvd2uJ5ZSMAaiIYqCV/ODmJA9ieGIjZlwz2LgEctuEQY/NTVVi5DcD+PCi9uTs8AsVsnKOqrbgAyBMB7nNfyOzzpCOZrZm/Kdra2tqqXNxss4LAt7f7PbnGBAicilj0ajUTWRA79QQvgAjNLKCJpgzN71Qh5nYoT6HySP+2NLpfROFnQKR4fjoAHeTDLP9DoCsUEA6CZOxA0icAt2OtwfvbbOovPl/xlfvz/a5A134Q3oRk1OZ6zvTiesp1/aOG0EZWRCoVBOh3EreIm1+YyZRMM0EyCuAxk2IzQrJWNBkQ0hMQwLAs9uKE0NjdzHY7Y3Rw7ch/nzTMbFMRaMl8uwivtYaRqNRlZXV2t9pC7jJKPaLX8zZpQIB8A9QRhmbDGY9fX1amzMwxC61WpVR5i4w8tEitODMv8m8pkscXRxRPK4nGObBLOjAI5jKEBj9BJ5luUVQ1NDcn++5AesvwSDra2tateJA4ADDXMwjLes97qeCGutEMCaMpfxYjJAhFtCIyZI4o2QjckNmw2L8ZY+0QAY5+jHwhMNmIcJDxeneZY7kUqiy8aHYfM9Kx4RiY21fM7zZ3EdVXEUQFmODTFCQbmIVjyLCAvkZC1oaURWhu8muLyWzl9xhL1er9qowOWc0kQW6YeV2PCW8TNedAFl9/3cJ+yoS9BwbmfdKdMung3hY3hc9nBzmZPA2JrNZqWDk5OT1QFqHPwFL0KN1+UwE1jMC7Sw1/UzRU4L1NjaSa2jpskMjARhGfIhbH+fRTYjhoKY9TMxY6jt3NeRn+fzrKTeVcTPWUQUxVvNEKxJLHIu54fck7KB50rTg3M2R3ig2NzcXGXwVjYTU3wWGRtWEtWAp84Fve3JsNxN4M69gMHAY5QcXQBqopCUiZg3Ds/lq42NjYqMQXldHiNlseNFqcvNDF5brpJsQW7oFs91jbZsHkAX2f86NTVVQwmMgWhppw6sdiAz/8H/nzrnXF9fr0UMQ1gmgMez0Zp9K9kv/m9Py4CBCf5OyUga8vo+binkc1bsZMTs2jC4P4rDOMvGg1KYRHPnRVYAL4KNkD/ssGcOKLxzbkcR7yhhzlaAEuWg7MjB+ZbXw061lAHR0gSPHTD/N/lh4zBZluQL93AEc1qS1Flvdy0Z/exGuuzWKOH8j7VHzsgEgzcsdb7ogOG1Rc+999e6x32QmY03GaUlu11PJIRQVHsYEzteqGTk3SzEUmBAFrxVSTTZ45ZJdUnqMGGEZ+qdqGYI5vFxD8bEwlXCUdcIC+NclQV2bkMEM7vH1ev10u12q0bv8fHx+Fzg0mj4DmvBs52H+ZkYAnAVeG2ZOtJYhm4C4CiR8vRBvketjzU1CZTsMNLk8IzPa+FcriTmcCLkfF5/R/+k3tVU8gQ2WDtMZISOONCsr6/XiKrt7e0aGihTNcvfe2b9WdbQp2/wvBK5lde+J75b6Zzsc2MriYWMEPw58igMkwVEGX0mrIXI/czKMh4n4HyO6ItQDEHtNe0sUG5KAigi8AwBlqUBlMNK0mw2KyLHTOjKyko1x8nJydrpBD7BAKNxLdmKYCfm8gCR1xDNEd9RzqUF0I0dGjCOP6wRYzWaQDbIAadDfmZEZQdkxtIGbIYagzPiQqfYQWPnXKIkzwt5ICfrrkkhjIecnRTAYzTJaG7DOmWuhXnwTPSCsex1PfH0PQZqSOHIWWJoh3aH8b3IFCf+GJujgT08BmvGD4FAjgAX3Rdq3O9Fds6BY3Aeh9HxDOaPAvFdfs7nUGba35aXl9Nq7RxaTcGf8RKVy0Uy6eFUgXmYXSTKsFYoDtHNhmS4RYRHxh6/ZZvUz5NyZDe77T2O4+PjtfOLcLA4HpNhJr3KoymdMnHZQJifiTfW1RsdymhnRMW8jBIgflxbx0jd+I6eoteWm0/G4DnOmR1IdrueSAjZyu157cV4gKMfC4IXtfAwGI5IBIoZfrKAGEK5iBiwiSGUyvlnySay6CY0HAnwyO42KQ3VY0JxreAmVjh/BniJfLhMJJToo6TiS8QCImH3h6O3SSArB4SG13O3z6J4QFzngzg2Ow46hpCveQfuWzZ+k9PyPObk/bU2QncEufWz2+3Wms/RXSMLvm/m1frmsdKwAdGFTL3m/HtsbCzdbrdm6F5DEADr6Z5d555f2jj9vggvvOEtyub3k9izl3DV0cYTcl6BwTiaQpxgKAidhUUhKLKXrJ7heZkbOQIxV+cqzuf4vT2hZWE4hSF4EzMOxzm054lSOiIzX+SPgRhJ7AYxMSrmBSrgOSALRwWvC6lEkgrqcm5QCeGpgVIHpZEBx7O6ulrpkEsKJRuN7MrymOEhnyvTGpNNJr5cckGOPJ91Ng9BxCyjqnXCqREycGNEMtrR4kht0s2pyZc2zhLHG66y2MAA6kJMjsXkPoaLCGu34yiT0dlF7uxIUtWFzLqxQCwci5eMcjF7Sjw0Skv5g0VD4fGkXhAbTa/XqxFdhu6gg16vV+2ON0HGopg9Jpq73a98FobcbrdrZ9OQJ0JKlPI0d4BcnELgYEqm3E4nSdVuyJg7nU7VRmiYiPG4xDAzM1ONB1k7wvMsopUjkPXRZJudLdGPZxp627B4bqMxOmXQqZlTB3SR+XM/cxkm6RxQmKNhsJ22O6L2up64n9ODL3NGnxzuNjdPCqWkU8b5mskfC805lheaMVA+MAQuvZMZNxYHGOznYqQYmMkfGxzjwzgYM5E6GZUi/F2iI3PxW7a2t7erU8MhgKwMyM4OyRCXCxkwJhNX/MzGjpMkJ3SeSiOFIyjjI72hdurxMlZeboSuGJlAjgEfYUFLaInBmUG2w6W0xM9NvDAfohboxQbFZ5w+2Njs3NBb1oMUzoHDdVCvlff+es2T1NbiqYyzJHVKoTnP4mF+OJ8p6WwL1FEAATnHY8HwmCiEu3HK8dDCxQIyxjJ6oIRm0IhohtKuiaFMKJH/DAaDKgfkaA1DVS+yIbANxErnaGtnhkwdiQxxPVfkSTQHWfi8H2SM3BqNRi3PnJ2dreZn9tKOExhL+yCRicjqdAdDoQOKSOw5lywnzprISNQxHHcK4lSJ9XLOXKYlyWjjN46c+VnmOAnGxu/daFPqmskzR1VzD1/aOFdXV6sHMVEXhMuoYkaVAdhzO3eyAA1xWRwmZe9rYVLmsGLyGRspCmaDcA5iYqDf79deJ1f21zIWQ2TQAxfvMGGnBB6UfxvqJ6N9rs61De9LtGJUgRzW19e/0N3iqMkzgVcoCnAZ71/WK4nGzglBD2a0y5yvRCLkbxgajtUdQDYqvmdj8DMxNlKHsjPHhKDzv1L3fPF51zbLUhRO3A6Pe+IEMEBkUB79QjDabQzlta9x0t9Zkg0mJZiYjdWezCEe5SjbmgxlDGMcFX0AcQnTOFEAhaDzBuNDMGWHjPNRL5yhWJKqLru9vV2RO4ydxfLce73RSeeMyXmoITxKgHI6EhA9DDvtea3sZVmE//t4F0ctfu9WNtbcDSIomeXd6XRqh2eZoDOpRpRm7qyJ18o1WjtAs72loqNTJp+YA3N1LsvcjXDsBHbLhbmYh/Ndv8SK59HeyCl8zBdS1YjRRmnH/qWM0yWUUnGdZJuEMb5GufBswEjjdgsJZS4hMApgRWRiGJ9bBTFE8iSXNqzwjIP5MTaXMAzdOSfW5QGe78Xrdru1vZNELhNi1GK9SHZ2wCuMwovKWBkHcnKpws7JZSkbInLgcyY1YFqT1Iy1hHGUW4xscJB2orvlxDglO02XuNAD56Z2/uYebPR8100GZSupuQ100TqFEwbG22gnJydrsjcCMyIjdSr1FLk5ADyVcTpfcIRgIs5ZbFTG026rGw6HtXd+sLjcL6nDSQyABcBLrq2tVbXDZAR7Go1G5dlYpEajUYMSzj2SUa3RCoYCUe/k94ZXQCucBrkQc3WBGdTQarW+8GIbFMCRHO+PAhtyWhZmN3kOEcbEDgrOz1kn5DAcDquN4D7lAAfD90um1KUQE0eWFZAbGG3IiU6sra3VDnh2CcLRxTqIvEwEAhvNHqNTZamqjKY2Mn7vXJLPMnZ00ftZ0Z+ylZA5oW/7RUyuL1VKYXGBI56M4Qbf9eIYTlrJrHxOzu1trAA+yjIZMZrOd1AC52Y4AYQ0GAyqbT/eIeGEntIECML3s+fkclsizzGTaoIMmI8iWCYoBkaOQSAXIw9DMuTjsoEhIuPlJANHqGSUSjhKGhbyfdYJ8oc1tEG7a8lRx2y9u4Fwnu415t/dbrcan3XDegoy43kYq1MUG4WbGJiTCTQcmscPpMV5W8fRPQJa2XaI/exGVO11/UzGyYP4mRWJCaMoZvmshK5dWRm4er3RAc4IH6VFGLCp3hSNMrvYn6R2+LAXAkOH8nekQ+GZMwyuDcYwxzmy2cgy6rKwbIAu82bLG0fAwnNPk2+sB/ey4QPl3E5nB+VSD4bkvK3T6VQwGGhKRDRRxN/dbreKisi/zP2YA+/gNLFkVOI/hu3WN8bgcgr3RN/8XJfDzAo7QlqX0UtHSeRlyA46w0GZfMRguUAAhude86cyTibsRNaeBWX24hpzQxw4R7RSlzjdta6SELIHsvE5Ny13idDDSn5jWMu4DAkdCc1sMm+EbKGS0xIRMBzekubFoVvKkcQ5VMkA7iYr58GGdigpMjSks2JZ3kBcHAiMLT3BOEPm4DzOebRZcNbDRsmaGgW5waMkEY2o0AvDUGC7n+eoiuOxk8OpM0eeicNApzwOk1Ow7yAsE25eD55rvUIedqSuqT+VcfrdhVZWFKRsHrZXKAmYUpjkbBgIuQJGgnCYHJETJU9SnY9r54EyMUbDPAzUObJhCTAL40Z4hkQ8y72kRIrNzc3qyA+znMkIjpPb+SWshls2QJQN43PZAXkko8K2jYA/hppOU0x4YYiQLOYLUGiiEXDVelFGDKIkYzSERZG9tsjJ7LY3LuzmcMwdME4QEc8dDoe1iN/v92vv2XQ+azKJq0ydbOy2Dee6PMN6joMy0cf9jB7L64mR03kghubODT6HwAxpXLsi8qFAW1tbef/99/ODH/wghw8fzrlz59LpdCqyhChoiNvtdnP16tW8++67OX78eL75zW9WQnXjO+MD+qLYnGnqnIExMzfniGUUc0Q3zEIp1tbWsrW1lfn5+ZqDwUCcW2Eczpmdi5SOArkzDsNVvl/yA1zkmYzHDClMqg3chkMJCUVHJ5Az33OUoznEDtnjxaExFjd5OPIR0a1fJiCNHrjK55SEJnwJ+okznJqaqtbFvyeIONLxPBNMOFi+hwFjpCakWB87y92uJ24ZswdnwfAGGIMF4/8zULfQcY/V1dU8ePAgi4uLuXLlSr797W9XhjE7O5v5+fkafMK47t69m7m5uWxtbeXu3bs5cuRILXczU9ZutzM7O1uD1a6pOidhrkQbL1S/38/a2tquBJWNq9/vZ3Z2trZgNnravJxPl+1dOBoW08paRkQzsSita3J8v4ygFNldqkGByTlRcMPAkpixbvCHKGlG05EeZWVN7Cg8Vhsuc+HzRD6XeMocHthuA0YGJeJwnyvrgBN1Pm9j4rkcLWpCsjQ4O2fswo51r+uJB3w5cS4ZKJSuZLfKCIqwNjc3s7y8nOXl5dy+fTu3bt36QmRdW1vL8vJyrl+/nmZzVJwmZ4B0uHv3bmWgzWazOuFtamoq09PT1Wvy2u127WU+FrDZShQDeOeFx4C9eMyNXI3czVDMeQvkinM2PoszIer4VQvIBXk7f2EM7oE2JKbbhVQAg6TcQXpAzozR2rDd3eJ5e8eFFZifNZs7WwKdOyJHHBbfs7GSXiALz9+OM0ltz6XJK5xMiUZYSyMBdIP1Q1eTkePk+zh+1qBkgPkcPdO2H3/fDsO17i9lnEQLBGPvmNTbu/yHAXS73Tx+/Dj379/P4uJi7t69m5WVlSQ7DdIU652jJCM4bSXEuwOzJicn8/nnn6fb7WZqaioHDx5Mv9/P48ePK3YRJZycnMzCwkIOHjyYmZmZ2gKboMHDoXxcOCEuzoUFtkBQUJoxEcCCoIgotRs8WFjmy/jJ14BFGKgVyP82cQJcLVsHrWD8fHNzs9pb65PkvBYQRThJl6r4HOMDIm5vb2d2drYW5eEBgJcYAS+bMpoxUsDoHaVMIGEsPiHBxg8J6OM1XeJzHkhubRhrbqLME80Mc+4W+myyzMQR6/bUm62BNMbTeA1PDCFwOtn9+/dz/fr13Lp1K0tLS1lcXKxg0czMTNXpz/csUBbPnth518bGRubn5yuDO3ToUKX0x44dy4EDB7KxsZHV1dUsLi5mZWUlN2/ezGeffZYDBw7kxIkTOXbsWJXncISlyRsEyfzsXfG2eF+ijYkHjNwRDDk5kuAkMEoTZ+Q5GH75c+dfGJhLVoZ3jvhESfJxXkFBiYDIxfh40VGrNXqVOmM2/C75AcNNHDakCUpebkywswG6Mh+eDZTEaFzKczMC43GZJUnlrC1/pwIYD2toxOix8fuSSDKsd3oF4vDYjDa/tHHiwewVUSSEQ7fP1tZWrl27lnfeeSc/+clP0u12s7CwkJMnT+b555/P6dOns7S0VDGaV65cqQyeaIcHtUdBEfzujPn5+Zw6dSoLCwsVBN3e3s6dO3cyOTmZw4cP5/jx4+n3d86B+eyzz3Lnzp08fPgwjx8/zq1bt3L27NmcOHEiyag53RHP3pHoRfQ2IeQclh30XESqwWC0tYzcg1cycDFf5GAviyEYtuK4DCNZJ9cvHUGSHYabN1Q7ErDWsJkciG3EQPRzyYoxE+0Zu8kUw01DPDtfX27aMFrg/0R39BA0YnjNs1xDxaicn5aQmbXmmXwG+TsFMqvtwHXo0KEam8v3mXfZ/vlUxokycnMWCC/IhO7fv5+3334777//fra2tnLq1Kk8++yzOXv2bHX+ar/fz4kTJ7K5uVmdo/PWW2/l/v37VUNAo9HIgQMHcvjw4coTekN2t9vNnTt3Ku907NixLC4uptFoVG1nfNZn2xw+fDizs7Ppdru5e/duPv/886ysrGR5eTnnzp1Lq9WqjQHF9uZjRwZyTQgLPKUNy4ZmZTCkArbiPVEM/jZ5AnvrvMikE89j/CZicBBmtv1mLzs+G3rpDFzqIu3AQbvB3sQHa0CZxspspeVn3NfIwCUk/rYczDyjt4zTXVSOWo6YTtXMDruEQ65qfXDENamFs3P6slvJ0Y78Sxun4YvDL4swHA7zzjvv5M0338ytW7dy4cKFfPOb38zp06e/QGrwZ3p6OuPj43n22Wfz0Ucf5cyZM7l8+XJmZmYqwofTA0q40O/3c//+/XzyySc5fPhwDh06lPn5+Qous1PfSoDwYVIPHDiQ48eP5/33388777yTpaWlvPTSS5mfn68tGjmlczgES+RYWVnJ7OxsrQ0PRccLEz2AxysrKxkOhxVhxcJCfqDMViSXGDBCoo7RjdfMbXLb29tVIzsnP1ipHTmS1EoAzr3dmeR2R5wUzx8MRmdHOWLwXXZuWFEd+U0i7nYaPPqHTpAHG44a3bhrCFRkRMHvSA3smNyOWDoC0ghkwwn76AjOyYQSF7Jhw/uXNk5Dh5LKb7fbee+99/KHf/iHSZK//Jf/cl5//fXMzs5WQkhS0fI20MFgkMOHD+fv/t2/W8szUbyyvujC94kTJ3Lq1Klannbw4MEvNM/jvVBeNv62Wq2cPHkyMzMz+fDDD3Pnzp00Go1q7Dgf5252FFa0brdbORsckRv5LT8vDgrlUxTod3VZhPmVb9ECGuMAqJ8yb+ddLm+RwxF1MAjnjf4OCoaSsialEzHvYOWnIQADdr68vr5ec34YV0nYYBAm8Uq43u/3qzIPY/JYmRPRu8wxMVBy7vX19RqB12g0KsLMz3f+7FTMkBXdd82dgMfzn9o4MRBHjYmJidy8eTN/9Ed/lM3Nzfz2b/92Ll26VPOCzpfs1bmf257Kbg17POcIRHAglBXZY7RRoCwsPkY7NzeXV155JRsbG7lz504OHjyY559/vgbv/GyUkfGZaXQOglF5AVgoyIi1tbVqjB4fuTs5KgaP83K+450lJhiI/IbljuweL2vixgxHOwyf+fsNW64dM04bqqEbMkdufNalN5OAyNmogPkAzYGMOByiLGjFXUnWRf7NfE0GIhPn00mqVMzEElcJV73+BDJsCXnxcx+g9qWNE8GQ7DPZ7e3tvPfee3n8+HH+2l/7a7l06VLN45b5mT0c0cwLDKQrcwLDNSuX8xf3f0KXG46UibvP8Jmens7LL7+c73//+/n4449z5MiRzM3NVaUSbxfD87IYND7bKKz8XnQU3s3RdmJ4T9foHLUbjUbtbWVElcFgUHl0jrBkfpQNSvhKsR2ZE3UdrU3C2Hmg0LCUzrX8DIyA32PUjN9obHp6uqbEwFJHeBwTTsqOFz11ZMVwkSFR32tkPYNtx4k4197e3s7y8nIN7npHkkknYDHtmUZu1h102QjzSxsnBIsnOTExkdXV1dy9ezeTk5O5fPlyrWBv+MAEqU2Wk0dZTY748sCZlL2tyQ3T3RhtWfZh/ycKs7W1lSNHjuTZZ5/N22+/ndu3b2d+fr6CpSYDWERHRGCfDc51OFKBJFXuh3Ia7vpZ9uzIgM+T16FIGOnWVv1N3aurq9UxILDfzKnX62V5ebk6Aa4kOBin4TFjZiwuW7DeGAWbDVqtVlVa6/V6lTMzOWNHA3RnrkZOzJtnEZUYt9fdLHW3260ZJwbumiakz/b2drrdbqUX3IOT+r2rBGfLnNH3RqNRISCfpYyBmqhjTPy927WvcXr7l5N1lHRqaqoSCJFhfX29OuSKwdo77dYza5iRjI7KKA3RsIufm9YnSkDCGG6ZyCiV/MyZM3nnnXfy8OHDGuOK4eC5KV3Qi4ozMqtInsXFPUgHQALIqCzh8PyyjGWq3hGXuUCKuFMJI+W+vBLDsNoGMzY2Vh0XSdSykwVuo/CGtEQQk1bdbrdWA8UB0ITAPCCXOF4GnXA91sSUx4PeYLBcpSE6R/ZOHMNz8kaiPjxAo9Go+rJL6G522e/pLEk6/lhXS/j8pYwTRfegMDgEy+TW19ezvLxcy3umpqYqgeOFHQFYXEorGDEwCpgGzESZiBKwXSYWzOIBJZyTAOt4Fko9MzNTeUlyFwuY7o+pqakqKs3Pz9f2d5IPO5p6cfxsvC2dMexk4F48G6jvkoTv49wNedsgUR73BnNKHp8hCpJXIVsikpW+rKvilL2edjSGrEQMnjkcDmu7eCBicPomZlxPZm0Zq/NixktzCQ4hqR80DbR3TmsozrNN+pT8gmEpP+c+GC7rWcJnl7meyjidMyIYhHfs2LFcv349d+7cyenTpyuDcQ7hiFV6EucePIcIbXKFOqKjLTDZnhqFREDOEUoShOcQufr9nf16GIqbrR0Zm81mlpaWsrW1lYWFhVrUs3LwXObnfYJEDnJ4nA/H/7sNrVx0O0TmyvNQfhMq3tyM8ZGjk6d2u93q804RzDA68tkpGKHsltsxRqIvczQfkSQrKyvZ2tqq0g6iKPeyI8WQGaPhN06POSB/EJNbAJEp8ve+YwzfKIkc0WchOZXw1jp0FK7GCBF9cW/yUxknCo6FU7tqNBo5depU7ty5k6tXr+bIkSMZHx/P7OxsZVhELZc8jNn5uReRhXQHv5k0wyDyHrZCJTuednV1Nb1er3o/CYaTjF66Y0rcxItLMjwPyAIkbLV2znGdnp7+QmQp2UYTADwfj+otbhsbG1lcXKxInEZjpy95YmKi6gUG5tlRIkOXuSCDklFtmLUD5mK0Vi4Uj5PjyK3ZsWHZoRPO+b3OOE0Mh3UE1pus4nLqYh6i5BaszDiFEl2UJCSfRe4YBQ7YMNMMOfc3jCa1Yb3L11EY/bl/mvtwLyPSpzZOJoOSwpIeOXIkhw8fzv379/Po0aOcO3eu+gzC9kKiQCisJ29G1krmxXP0ZVwQCfyOhep2uxURAaVONOTFQq3W6MTzbreb9fX1HDt2rHZqN4qxvr5e5WkoFkrnck9JUIAauA8/5/44FhaUyDYxMVE5QkcqM34Ytkk0nJ+fi7PBMEwWoVykEfybP0BD7uXuJ9IE562Gj6wr0M7RFtlwHxNAICBkY4dnEs0IC1mYwHJExdE652SMJaQ3KYdRYfzooWEt9/HBaDY8y4WgVea2T2WcLiMgaG569OjRnD9/Pu+++24VPel4QVguDVihklS1TXtXe2cmwf3ct+ld7ywWCzEzM1NjMGEK+SzCpAlga2srt2/fTpKqod49qwh0YmKigih4cDsEYDKK5/kit07nQcbGlvLgQSOrq6tZWFhIsmPg09PTWVtbq3IdlBWCyPQ7huFTHpyC8HmUjrSAvyGuILUc4e0Ekb9ZdcZhhSOqGdYzdyAe30WP2FSBXGE9HU1M6HGhU87tkJfHDMQ0LMUJJqNGE+s2qZINHUcBgjCRY4fp70N4Ab2RM7KxvvLzL22cNsbhcFjR8kSXCxcupNvt5t69e/n0009z+fLlmtdhcCySoSoDNcxku01ZazI04N/OdfBQLme02+3aJtrt7e2srq5WEIzPLC4u5tq1a5mbm8vs7Gy2tnZedEtk5u3TZlJR5pLxrCj94SD35j9L5847aSx+mn/nn+wY+PHjn+VPzif/183XMrd6KEeOHKna+MbGxjI3N5dOp1MdBGZHR66KHIgwyBijRX7O3zEKnBufWV1drYzCUZ+1M2HGOJzDouQ2Sq+VmwnsOJyLoazOCc1BIAcbBlAeVOTUy+uDTOxY/HsbLJ8jdTG5Uzaxl4QPDpbvMBfQFp9F92mmKBnmL2WcCGt7ezuPHz9Ou93OgQMHqofNzMzkpZdeyubmZq5evZrZ2dmcP3++GgT5h+FcMkq4USoue0A+Z4Hg7Si8462AbCiKoTOemSjqt581m818/PHHefDgQQ4fPpx+v5/V1dU0GiNanM8RQb0wVs7BYJCp/mZO3H87Z37n7WxtvJuLH2+ls51UWdK7yS8cTrb+t7cy9smJCmbj7cfHx6sX7Pp5jvbJqAxTkmqgFJQNmXIPntNq7ezswTlxH8pjrJ1rxzhADIo5sy5mItEZ/vh75g5YP+7hsTuKGnbaSHkuDoooWZKYhuLefM69uXD2oLpk1CCDk0G3fNCX50BwwH6c/ti5ElSeGtbCxtG3ODc3l4mJiSoh7nQ6VRH//fffz4cffpiJiYkcPXq0+r4hXpkrsEh4Qk+Q7yNcd3WYhh4MdvYkcuw9giyfS2Qi4mxububu3bu5cuVK5ubmcujQoQoCHzlypCptkJ9xX6IPY02S8dXVnPzOd/LiH/xe5hfvpNnd+/iJA0l+K8mfb2/XlNvUvLc5mTl2qjA+Pp5ut1udNgCsNDzF8aGkXPy/0+lkbm4ui4uLlUKzLklqc+T/jlDIA0NKRsdU+pmsC3CzZGsxstKJuAvMTtB5HZ+j+cN6W6ZWGDbriRMxv4HskIPb8Fxx4AVNfMe657KPKxat1ugMZKeJe11P3JXCoVULCwtVDc2M22AwyNmzZ7OxsZEPP/ww77//fsbGxnLo0KEaBe7o44U3Lc3kzAaS35UEkRWHZglv0fF9WPjJycmqa+X27dt5//33MxwO89xzz+XkyZMZGxvL4uJi7t+/n4WFhWrcLvCjSM1mM2Pdbk7/yZ/k+d/7vczfuZPmcG+j5GotJ7+++SjfHexEcson1B7tlTmhL6mfz8Siw8r6zc7IE0Ml+nAfyxFkMTU1lbW1tYrJJQo5QpbND871kvo7VRuNUfO4Ixvrwr1x+qQYdgw8z6ww60hPLXIxO+30wg6Pe2P4EJ0Yu/NSw2I7FnJHthe6xkupyOtj4xwMRr3J3kD/1LCWJoDJyclKICTxLADe5cKFC1lfX8/169fz9ttv54033sjCwkK1gO6XBOq6LuZJ8Me1UkdhN8DzGQwIBwDchRQgCrJt69q1a+l2uzl37lwOHjxYefKDBw9WZxzNzMzkxIkTVSQjqm1lNe1PvpO/+l/9buZu387elapdrq3k2Y9XMje+lHbnaEUw8XJZe2iUmsXHEFAWxjQ+Pl6tBZ+3V09GJA2QDWOwE7AB87c5ASMd8wEYMX+zRrQBVsqmjhobPBHO73DFMM2wGmUlqVIPIy+TYRgAaYJzc5NDzNOyw6m43OQSTdn55LmYrTaaod7rEyUsn/La1ziXl5czMTFReykNC46nhJnqdDq5dOlSBoNBPv3003znO9/JL//yL+fgwYNJRiyta0d4rhKCspBEECuJFZCFoLC9vLxcO0ZkbW0tc3NzlRcDCbz//vt58OBBzp49m/Pnz9ccQqMx6tZhYXEOnUYj63f+IM//9/9DXvvOrUytPTlS7nYd+hfb+av/wU/y+73zmZmZyeHDhytZUviempqqHUxtVtAk22AwqLbD0TJZ5jRmB8mH/PNms5kDBw6k2WzWYCf3ceHdmwAw0iQ1WMraNBqNakyMvTRiDMv/t7MGInJuEbVbjAVoyFvPXMdEzzBSynDmC1wmAgnCAyArtxsmI0TmmiX3oLuIAMYckMfMzExl0M5Jd7ueeDQmCtpsNquCOIuLFzRev3TpUpLk3XffzXe/+9288cYbOXLkSAXXYHy5hw8xLvM6apEsCgbmMgD5Bd7WzC2wCaN7+PBh3n333dy/fz/PPvtszp07V92Pg8JYzJmZmUqZ2p12VtuPMv/tf5R/979+O52t3peLlsXV+MXka//+Yr73T1s5dOhQlS5AXrnZw+wo83DtLBmdWzMq2dRrr8jAzK6N15CNNMIsr5vVDftQ7qSe19p4DSkZE4bjn7ntkHE6mqPMGJNJMt+P+9OgYmbZ+aoNjSiOXNAznzFMmQyZgViIrtZP7sd+XdbECAzZPbVxUhczzkcJuCneymWMF198MUly5cqVvPXWW3n99ddz4sSJCoa6VuUFt3dDKbz1xySQFcMMHYs9NTVV5QZbW1u5detW3n///SwuLubMmTN57rnn0m6Pzix6+PBhFbGIFv1+P61GI7fW/ii//v/4b3P5+w8ztnf+/uRrLNl8rZkH/9lEPlk5moMHR6fjucheEjluLHBN1Zt7XZcl+jhfI99K6mcmGcq6pu3PkhsTSeAHXBf0/lyvr3d54MzLDibYdMbAGhoi2vhtPMyfv/me81fGbNjJZRk4rzdyQP9L2O4aKbI2K01Vgc/Stmn0Zx6lvH6mE98RJnkbhmDIBRTBQJ9//vkkydWrV/P2229na2srp0+frrB+2ZdqofAsDJZ/8ywiChfPJpfBm9IYf/v27Xz44YdZWlrKmTNncvbs2cqTwSB2u92srq5mfn6+QgOdjY2c+/M/z9/55/9tZh9vfKloWYn8TLKco+l/q5NPn3sx35mdzPf/h0aazQN55pnDNeizGyvtxcNoyk4c7mHFhpxwZElG0c+/dzsahu46H46KtedyZPahXN5HSlQZDAbVoWE8h5quc+dmc7SxHN1gTjhs10QZBzJBdswVx2Z9AaK6tAFiwllwOTcnGPi0SPd2T01N1RwUTg3UiBzsHJ4658RbcxMimw2Fn3O5eH7u3Lk0Go18+OGH+e53v5tHjx7l0qVLlbdF2RggBoaX5FnANeCFt4OB7fme4cXm5mauX7+ea9euZXNzM2fOnKkcBM+dnJysGs+Xl5erhvaDrVt54x/9s5x7+0c/EwubJMNmMphuZfHSmdz6tefz2daLabzSyOrEi1ldb6Q/HGa4tpEXXlitGErkaHnYKF1rNKxlTVA8wydgqvN8votxW1Y4Yf7tGh6Ok2fwXPMGGDzknPeDsg44AU54SFLVQJmP4bPHU0Ysnm0uhNTHBsM9kjqJhhzsaBzN7Jww1jJCln3iJjG5F3PlM8ibz1KX3ev6mXJOQ1h3jHiyKA8eCs/8zDPPpN1u5+OPP85HH32UVquVF198sSoh0IliYTk3IaoYcpQEkhWSxV1ZWckHH3xQnRH0wgsv5OjRo7VyCIZv5q7XW80b597J6//Vv8z0D5aeJJ4kSXeqkR9961imX3ojn730i1k6cCyLSz/NC3tJc23k0MbHx2tnAgFR3S1ipbJioqyQJNyDq9FoVOSF5YQTMOzn86UROFc1nDS0Bvm4EZ3x47BZMxyOySLujxHzXTsUopN/bxTltjwiKfrqq2SMXc8tmxUcoclXW61WjWjku1QdmAfkHePG+SM3b/h24HnqnNMh2FHUTBlGRbEe74C3bLfbOXv2bA4cOJAPP/ww7777brrdbl5++eXqQC23wWGsKGkJ91xbslI5At+7dy8ffvhhHj58mNnZ2Zw9ezaHDh2qHAae0FuBms1mFhYm8isn/mW+9o/+NM3/cf9oOUzSHxvLD//SqXz3r/5ituZey4GZYxUMGgyWqzE6vyBXMxnDApEXoZA4OiCRoSYNE8iGNUDpkCERy7nbbojHBsQcWF9y93J/LzrB57zRnXVFwTEC1y9p9Pch4yXhxDOAh9yXy0x8UmdWnROSxpTlExx72VnFmOxsQAb8fGVlpZIHu3nMobAWJrCQkx3mXtcTIyfCB4paqTAWBuNIZNiUJIcOHco3vvGNXLlyJR9//HGWl5dz+fLlnDx5svLGFrLzUNPvbClzy14yIhc++uij3L59O1tbWzl+/HhOnjxZkRLJKG9jLpBUM5Pt/LX2v87X/vPvp3lvf8Pc6iTf/7WTufrX/1aGh7+V3tJyWoP6i3MRvqNHqdzNZrPaTwkcQ2EGg51TB+izdS5u6J+MDqS2YlsxkafzJiubYSRGgIwc8cyGOjUwoWKWlu+weXp5ebnWEMGcqZ/bOfjkAxyamV7LkIsxm7jC2Xs8wE9yc/JPnuW1azR2Smscacrc/apBR0FDdAyV9fJeVnQCPme364mHSgOL8IAlXi7brlyvMsRl58Ubb7yR6enp/PjHP86bb76Z559/Ps8//3yNUXSOY0XAE7nnFUV79OhRPvjgg9y7dy8HDx7MpUuXcvDgwayurtZqXTCMRPft7e0caT7Ir/z5n+b1t95Nc3l/wxy0Wvmzf+/X86Pf/K1Mrc5n8fMH2djYyOzsbK2pGmXxwjkfBxqiRERNCCrkv1tZhMVGqbzDx6SRUxI7zWSEfoxM+J0bRuiZLQ2kLK+4xuj2TsYCg45iI5/SeZRHfHpzuMkynsPG6RKeMjb01g6Nn/Ns8ko7tDIH5T6MlT/Ov/ksa2Eoy9owb8ax38HST9zPiVCcm7DwwE57Wbdh4QFZZKLvSy+9lMnJybz99tv54Q9/mEePHuUb3/hGZmdnK2VCcVlA1+MQKEpy7dq1XL16Nf1+Py+99FLtFHfmwDxY4MXFxdy6dyvTJ67nf/Pf/DjH7j1KY2/4nyS5fq6VH/wv/242n/+bWdgaZq23ls8//zyNxs5J9c7BTWYhI0MzFtlkj+EY3p06M7k+Rsln2Lt64MCBqoiOvEtOwErF78vtTCAJfkZUpfXMDoB1Zy2oWTPOspeVmiD3wsnzOZ7JmMwF8FwObi4dii+jBFcSLDdHey5+B+Pc643eP2rU4ECVjFKxUUpT3/Rt8o+UJRm1G+51PRHWYnTeuW+vZm9sJWDAJTyFwDl//nwmJiby3nvv5fr161lZWck3vvGNnDhxouZhYPsYh5uhHz58mI8++igPHz7MgQMH8uyzz+bEiRM1Cp9658rKSrrdbubm5tLr9fL+o/dz5fL38o//6zs5dme4b5lkmKR79FA++D/99azP/Y0MtnY+zy4SZEHh2S/acT6GDB3FiArT09O1z/r1efbgXlRkzCkUXnzWAEPCwJEhqYGPgDHHYCSEE3HHllMQHInzP+f0jhSGq4bVjkreF2xYTYM7ukHNHYfjPLhkee3ky5psMtq7bKYfJ4ETaDZHL90ywmHNyuiOXNwJ5N6B/Qwz+RnqnGawDHHJWQwPyr5Gvgv2h1jgOwsLC/mFX/iFzMzMVC1/ly9fzoULFyovjqIDLaiNXbt2LZ999lkGg0HOnTtXvZeFz+EtqT2tr69Xwu4Ne/n477+b/+R3bufyh9m/ftlMumeP5g//w/9dHo9fSHO77kXn5uZy7969Ksdw7WwwGFQLSD+lCSgU27CVRSwjFgqH06GBYnt7O3NzczVCDEXFCJ2WkMN6HVi3kpV1eQLFLskY1tYMLQqLDgGL7bSIKKwp/y+PQ7FMTECaNCqbUFzbtVyAtj7BgbX0XB3FmSc/x+htG9zHny3LZE4ZWEfPbbfribtSDGVYdMMNBmTqHKFzD3tUaGX+PTExkVdeeSXT09P54IMP8qd/+qd59OhRXnrppQqmGZ/fvXs3n376aZaXl3Po0KGcP38+R48ereUK5Bim+tka1ev10j3czf/i92/mP/u/J619oOyg0ci1X/+lfPgf/U/zcOtIBj/NcRxhaBMDrkHwgBBg/QzBSmjpEoSjyNbWVrU1CbhpAmVtbS2Tk5NVKcE7N5wTuWBf5lLcm7Xg36Xyel8u64qTsQGwDqAtR36X4PieU4Cy+cGssOXD85GlSRXzHsnovGDeo8MzGQPPpFOMSOl1MmzHkEsEYW7AXI35AdsOb3DbDZZz/UwdQoYWTN6REcHbA1cPENZGITAaBt/r9apX+r377rt57733cuvWrbzyyis5fPhwms1mVlZW8tFHH+XTTz9Nu93OCy+8kLNnz1aFXu5rKJOk1hOJsizcfzf/6/9ma1/D3JiZyX/3n/9GcvbfSTKVVnuQjZ++Oq/RaFQn112/fr2WX+EVl5eXMz8/X+tE4neWJy2DeFIIHmS5urqaJLXWR5cg/K5JnjExMVGxiaUCNJvNWgum19CGYJbRxA0XObKfbyOzfjgiO19zSuRjTmyYSf21iOiQkQd6ZoaUMXOfslPKzoR5gBBwmv6c65V0OdlZueaJzhtJ2vEwJxOEu11PPEOI19s54iEAF4YRqiEYwnM+kaRGW6Ns/X4/8/Pzef3113Po0KG89957+e53v5uLFy+m3W7no48+yv3793PmzJm8+OKLOXbsWKU8ZmNRcH7GGJrNZubm5tJfepjf+P0/y7E9yiXDJJ+fms0H/95/mrz4StY3NtL4KRwnWqEon332Wfr9fs6ePVsRHxBg7P+kcZ88ygtqYsSdJSgXm6EbjUatKL6xsREO6GZuyBZSAuiL49je3q69LIrfW0bkYvyf+dqZuIwGTDZbb6Xm36urq5menq415QNRiV4Y6/j4eDU/R3xvL+RZOGTXxsuSnOHnbkxuSThxGQX2+zsnZExOTlZnVDkft0EzbxpNQJUmOI0OHMi+YH97/uanymNmzgQQkwae2SsY6rhPEeWxArJI3Ht6ejoXLlxIp9PJlStX8u1vfzvNZjNHjhzJG2+8kVOnTlV7H82+mT2m4bjValVnw/Z6vbQajfzWv/rXufzWoz3n/PBbp/Lf/Sf/cebGnk2zNdq5Tq5CXrK6upqlpaWcPn068/Pz1fEnHAYFcYCX944LF6Bd4sFQrRzIxoQLskpSKwGYAALiOr/l97ymgOc4PyuhGc9CCXHSfh6OCJ1xOYW6oyMKBmF2n40V/Bt0YOIGSOngAJpAniaHzFmgl+S4ODWcDWkQ9/a7TtBLM8AgMZwEa0NwAgbjABzk7Dj+QichOI/DYziP8OLC6DlJ5ufeLMvn/Q4V7ktk4ITysbGxrKysVMpvr+N7EdWJXCZZWJTnV3+cF959Z08CaONXZ/L2f/r30+mezubW6OT2ZOS5Ufrl5eWMj49nYWGh5mVhimnpQg5lGuAIaiIFJUFR8cDkVY5a7DtEFkB4jMC1YjPHZja9s4RICGQzr0Ck8D1wNkQw2FSMju/YOFHaZMTW8izuaxbWfAdy8IuOycG5F8HAEZT5msDCGTo6u+7pSGv9pDwHcjGMRm42VHQG/acWvB+c5Xri6XvG/2akDCk9IQ+URU3qfZwuGQCvENCjR49y5cqV3Lx5M/Pz8/nmN7+ZtbW13Lx5Mz/84Q8zHA7z7LPPZnp6uhonntC0tfOnTqeTdpIX/scfZGxzd0+1Pj2dP/zF/zB31i5VHUVAFd7B6XY/mFSzonZMg8GgMmAM2yxhCWeoZ/LziYmJrK+vV1DQ/aV4e6KHISRrY6UujdGGZxSEgwMdsC48i799cpxZUxsmciB62Fip93K8DFDXhzIbbvtvDNF1SMbPnHH66IbzcTspR3M7MP7vUiCRFtkRPJyXm311XuwSjZng/Zja5Geoc9rru1/TmNneCS+KoniSKDe1Ju7F569du5ZPPvkkKysrOX78eF588cXqNLpjx47lrbfeyo9//OPcu3cvr732WnViHmPAs9lJVPT/+g9y5t0ru85x0Eje+7VfzY3zL6fR66fZHHXvwMZ6i5v3mxrOl57WC8uC7Ra9+E75MzyuYTByTVLtykBhgFqQQd6xY7IFmWBEKJi7W5z/MnYrqLtmWF+cgss3fNbbDp2v4Uidqxp5eS1xTD5lwPL19jS+78sEkaEtn7eRIWc3c5Tw2RHdcJbPOEAhG5wLCI9S227XE2EtiuYakL0Vl0sZGK9pb/A5B4aB/albfvjhh9Xxmq+//npOnTpVO+rw6NGj+eVf/uW8++67uXnzZt58881cvnw5p0+frpEVwCgLKsvL+fv/5R9mYn13KPHeawv5s7/7lzPeb9UMwck7JZISEWAcLLoPQHMJiaYCFsnsrrcNsfBlg4e9rMsl7rxKRt01nJnq/N8w3/Iq0xDOjYKEKg2mjISO1Mno3TBJqiYQzwVl5378zfjcrMF8yUkxZMZgA8PgcTzojh0TRuRuMdYIJt2klO9tvsBEkvNOIndpvA5mZR691/VEQojL3R7G1YYwHgADJSl31OE+vV4vN27cqKLlxYsX8+yzz1ZtfM5Btra2Mjc3l2984xs5fPhw3nnnnfzpn/5pXn311Vy8eLFqNuAdI3jRycnJXPjRj3Jkn4O4fud/fiRH+wu1Vq2k/q4TlxgMc4C0LLyVlIXodDrVghNtSvhm4gbFLPNUvxWLz/A6Cu4BtMZgHXmcC1nhcQZcGICdM7mv4XP5fyKNv+9OJhjhMk0C0jNuFNdklFlp1w6JptzHaRYy4bNuoMAYzaLy93A4rI4gMVvt0oflZZhvx8qY+YxTLkPgva4nbhkjnKOADLSs8bDILIyL6wjU3mZpaSkffvhhbt26lYWFhXzzm9/M6dOnq+8T9s2k8XLT559/PtPT03n33Xfz1ltvZXFxMZcuXcqBAweqxWUh22tr+dq3v73vhunz9y5ndX4UxezxzM4h1K2trWxsbFSFZPKtsmxkwgrjcL9yt9ut8jwUwzIzMdFqtarWQJMJjsRsLSMfZqycQpiMjhgxwwqCgbgDdaB0hor8DTJw7ktEB2UwF9aRz/HKC+vYboSZdc7liN2INebHmGjHc9tiaTCkJvzMTiwZvYGAe6JXdkCgBLgFHKjLieiwIb2Rzl7XE098t8cyeYDgMVzDHhQGAfMZlOWzzz7LJ598ksFgkJdeeinnz5+vNb1zXyaP1yFK9Pv9qoTx9ttv59q1a1lcXMxLL72Us2fPpt1uV50zl5feyrG7d/acY7+ZzG0eyuL26GhFEyksmg0h2Yli09PTNWjF3J13oDD2vCiOoZrzUZMxyJHxWKFZeJervBUM8obP4Ujcowq5sra2VsvfMVQUMdkxClCJjZHxoR8YrhXd5ZGyfGCCCznhPFz+MERFZs7/cMygmWQEsY0iDI0Nt9FfN8f41YAlGnCKZxTgKgU/c2TlmRj1XtcTCSF7fcM8BgrrxkTphnFhlkk/ePAgH3zwQRYXF3Pw4MG89NJLOXLkSKWsvV6vdkaRa0MIDUG0Wq3Mzc3lW9/6VtVZ9Gd/9mdZXV3N5cuXf7o9aSsXfvf9NPbZBfbw8FSuXDqWqYycjo0AxdqW8S4vL2c4HGZ2drZS6N2K30l9V42NhnoonydHKhXO0A1EAUPs5zAGM8ZAMozdczLc9Bo5kpdG52M1XHR3voaMeC4R2QrpaM/lNIZWSHI8Rx0TWfzO+a9JH0dEWu/QT4zMjtOG6Tom+uByltfCBo4zxQnZ8FkndKGE2uX1RFjrUgfJrpUMxSKfsCdBmNvb2/nkk0/yySefpNfbOYD60qVLmZ+fr5QXBbCnLPsVvciwshMTE3nttddy8ODBvPXWW3nnnXeysbGRr33taznS3MzpDz/cb4q5f3Iqs8MjaY6NCBhHPDpgnHs+ePCgWjxerQ4ZxAI6b0V2LsUko137dgIofRlNHM2toCixIyz5Gz9zcb1kSVE28meMns8xF5TZl+F4t9ut+oiNInaTgzkBIxSU10iAE+swPKcWZUOCO5DQK2TgmrFJMOfAZmXRcTfV2/gdqJAPDha5uWONzxhVOe/e7XqicYLzHRkYlI0Tz4kxs/DLy8u5cuVKrl27lvn5+bzxxhu5ePFi5VEZLAtPLZAiL14N5bDH9oTPnj2bmZmZvPfee/n444/T7XbzH51bztjdjf2mmHe+eSLDybG0Uoc6CJC8Cwizvb1dRT0n+ORRjjosGBDNhu9CNApakjD8zotqRpMohbwNaWkzdDTHWCxzpyZra2s1mOWOKP54g4MjEk6CMhmGYIRAx5nzUOdyhs98hvmWJ+4ZlltHfD8TS84jkTc/d8rAGrlOaqfAc8j/LX+cG87EaMHsPLpv1nq364mlFCsgpIMhiKEQXo8I++mnn+YnP/lJVlZWcunSpbzwwgs5dOhQWq2d7hYL34sDu1lS/igFC+McYnx8PEePHs2RI0cyNzeXK+/+KC999MM0Nvee/PpE8uDixUw3xqvyC07AwnV+R4Q0UcSFsZGnME4Umqjm6Fl2XWHQROOSAXW5xs+2o/N6eK1wMoarVkinFzzX5B8wzV04jkysHY7TRX47dUNPnIqJOB+tyf3QF0dv573WBwyPn4P20CfrFt/zJm5+ZvbXTpGuJBsqMsUwfVSKUyWnO/u17iU/45YxHooxmWlL6rvJW61W7t+/n6tXr+b69euZmZnJL/3SL+XcuXNV7gYJgQDKnfMWmqMkntA7GBin26ouX76cl87fy4n/2/5R886psQyOvpJOu1PBj9L7JqOoztxnZ2fz2WefZXJyMsePH69kYPgFoTI5Obkn42mSJqk3MfBzxoJhmBzBYMvX2aE8GAcRinVDppR2DFlROL/w1g3mrBlGynNhKk3cYBC8ksFMtPNdDAp9I9qS87mJxTyEda5EH95nasKmZK6NbtAxIxfWku8TQd1YYV0lWDgqcs8yupf6Xl5PPEMIYbvDw/DDA97a2srVq1fzwQcfZH19PWfOnMnLL7+cAwcOVF4Wz4MXcjJv4iUZRRzXj8oeV4gnBLQDW5K/8eInmVjbv4709tfHsz02l/52HSaXdSgb09bWVo4dO5Zms5l79+6l0WhUJ/uNjY1lfX292kZFVLHyoUAYIJ8xC2kyJdnxtjTzIxtyKCsNBoB8nYqQGyejoj6OkrX0OqBMRDXnukYTzMk8hHM2xm9EAnPrWiZjLXNG7s2/6avlYv7ASUdt5uUcm3Xh2cgSPUJHMUATaXYkdCmRDnA0JqSmx04+z/8tt6dufPfknB852ebP48ePqyNHpqam8vWvfz3PPPPMF46SwFMxUCfVKKgbHsxamkDg526iXl9fz/j4eA4dXM/xf3ozebj3vAaN5NaF5zK+OJ7M1LfysFg2Gj+z0+nk5MmTmZiYyJ07d9Lv93Ps2LFqTu5MKftVUQbDJwyVCOt0wZufUR5v1HV0ox6MMZgXcHqCUvF8eAKXOZA3nTImjzBQR0ijGUdIl4dM+uDwndZgnI66rIOdgElCvgdUxZl4zHzeTgkEwPrYUSAbE0X8QRcZExwEJZt+f9Qcb6LUztqIwwjgSxmn4RbCRUA+ke3atWt55513srq6mlOnTuXy5cu1Lh/nFyTQKBCDtkculdHKAHxyrjY1NZXPP/88a2trOXDgQBYefJ6p39n/QOjFA42sHHwt7e5a+r3Rqd5Jav2sLhmVudv8/HyazWZu376dJFlYWKjqssiGBcP4MCITFSaSkjqMRkExGJ/5k9RzOX/eY7WBmLCy4qEkICCU2L3PzhedN/NvR1bG7HzWSMSIxySJx4dDA5pjsMwNvSQa+4hNl2DMnjpAOOB4a6AjHI0ILrMYknJvylYem+eHLuFEPP+9rp8p57QimTZeXV3Nu+++mw8++CBJ8sorr+TixYu13QmGEkSI0sPasxp2WAj2WigTTcO9Xi9Hjx6tFuqFzg/TWN4f0t4+fSKNxsXMzMxUC4sz6PVGr6d3PoKA+ZPsnA106tSp3Lt3L/fu3aua8WlKJyeE0TR54oiBM3KJisteGQVjLIamSWrOEwVw/sWzcT5WaF98l7mXnUfOIa3UlhMMJvPDeD1PIxVkAYnChb5sbGxUO1r8effI7pbD4yhtHHxmbGystvXOhI1zfCK1CSh0NEmVg/r7ML22BeaCse93/UwHfKEsZgjv3buX733ve7l161aOHTuW119/PUeOHKkmgTdy5CwJj2S0f9GsJEIBNpiwYOKu9TknbjZXcuqffZjGPtvlhkne+UuvZCz1JgciBceG8IdIWjZkJ6nqe88880y175RdIY3GiJ11uxykRLm52ERQyZia1OBzZa7GHEqFdGRFfmaLnbZgwGtra0lSlUiIssyfezgSlAQXDGuZt3ltnSOjzCiux8s6udPGEY75Uf8GYZQR1y2Lg8GgdnaQZWkojaPi/5B9yIv1KA8o8z5Ty7rUob2uJ0ZO4/pkx0N89NFH1XmzL7zwQl5//fXqLB8bDCysKXOzYHgls2Om+5k8MAsPx2FNfN4dKaenfpAD372576TXZmezefyXK/LB+StjxBB9eDasI4l/ktoCwDAiJz7D8Zl2WOwHdA6HUZawnosxmlXkHiZjGINhpNGLnZ5TDBQfFDE7O5tWq1XVP03GAbFLA2JtvTPFeWOZe9KWyedAMR4TusEcbZCWnfN45G2CipzPRo2j8f5LOy4cAuNFt3E0HgucgZvid0vVkDl6ste1r3F6QslOLvbOO+/kBz/4QQaDQb71rW/lxRdfrEEgL5QTdzB+SbDgpZgshAT5mXG8IdVgMKh6bRFMo5FMf3g/zVv7Q9qNN2ayOj1eLSjfN1LwKQIoBWwsEc9bmJLUOlaIPobLKAPygjQgwrm2iaGWvb7IFyUh8phE4e92u11FEpcVkDHGYQKLqOCIipPleE8rq2ujzgHN8Jp1T0YtboaFzmeRv52Pa57IANlZ79xwwBiRjxFIiRyQNfpJYw3rhS7gKOx4uBcyZOysDffz/JHLftD2Z36RUaPRyJtvvpnvfe97abfb+St/5a/k4sWLNVyPgMpalOEXikUu44mjRGZlXZNyMu7ySrO5U0vb3lzNM1feTPYpbw6bybXnXs9ab5jJzoiV5H6bm5uVcvI7nmsjw7j4PZ8FWjo/hCxgvPa2Zm7ZVYKisHAlw+oIbAIFeRoW4lSAW8wRRcaYnOeyTqXXN9vc6/WqQ6mJwk5p7DBQWOYOV0DUxAiQR8kvcOFEWBue64hJFHTeiRNzRDUrbrLPTCw6aQLJu3EGg52zpEAJZqjLFARHAlpEx/aDt0+MnFw//vGP83u/93uZmprKb/7mb+bZZ5+twSjX2XgoSoTgPHn+z2JbuNzLNSAiJ56a/GJra6s6XPnA43cy98/u7zel9KY6+eSFlzPsjyIdG4uZM4c5eVO4lTtJdfykj9bgPZ+OOsiARoH19fXKowKDm81mZTAuDZgpBkkkI7iFIWDEyNdlGxusIR71OOqDrAF5Nu1pROES6vX7Oyee45AcWSBHuC/OmLmUzfA2BMN45uRolqRCA+gStU/IN+e9OIiSpS6N0sZcciwmtIh4XBim+3q5V0lsgQpwvsn/B94y1mq18r3vfS83btzI3/7bfztHjx6tWD5P2AtpNqpsUTNDZ8G12+1KeZ1PEIlgaDc2NrK0tJT19fXKMGdmZnJp8Z20P9+fnn746vHcb8+l0xwdBOaFw9k4qrBQhtn8zN7XdTWu9fX1jI2N1U4BbDQamZ2drRXgDblQNB9GZdiFMdP7ynfsoZGfT8VDkc3YElWJQii+czFHTW/BYh0dadwhtLq6WoPozAGjsLK6fRKldb5nB8HPWb+VlZUab4H83ctrfXSaxd+sS6nDzlfLdj7Ga+fHZVTk3xtFGEbvdv1Mdc5eb+e0uX6/n08//TRHjhzJhQsXMjc3Vxkmny+hVonNDT2YKLXLXq+XlZWV9Hq9aiPy5uZmVldXa4c2cY2NjWV6enqnn3Z2Ns/9vxb3m06GSR7/xrFs9iYyOTleE5gdhAXunBmlKIvy7mrxApETY7hzc3PVayGAz8mozmcYRyscBszfTjNKz+7yggkI52w2FiKNoyjQCwUqYb8JD57vPY8mxwxxjUwcJQ3/0RV6VR1hDEVp08SYWCMjMuYPd+DPISvGgCFiKK1Wq5Z+AGeNhLwW6LZZfea1vb1dnVm82wFoJo/K64mbrRnsr/7qr+bRo0e5ceNGPv/8851i/8JCBV/YRULeZLbOuZvJD5TDuY1LJkBMIs/8/HwOHTqUubm5TExMZHJyMlNTU2m1Wpm4dSunbu69qTpJhoeb+fjwN9LojSAMV0laubTgRWOxWXzneJRDMFYWxATM1NRUVfdE+aw4QCGea9iJQphY43PeR8tYG42dNjMc2+rqajVns8lJam9A49xdv4GbcbDePBvuwCkA47dhgzw8Nhs9czALz++YM8bI893hw9oxJtCHYT7wmrXwv5Et2974Dp1PZZrA91qtViVfkIu5CY+FcZKvl/peXk8spTDo1157LUeOHMmnn36apaWlrK2tZW1tLd1uN91ut5YveVBjY2NV6aPdbleN4Pyu3W5Xb8miVolys1j8QTHMUCLYY/kgY5t7n2SWJA+fOZnb4y+l0xhBKjsQjBKPai+LkpCfkkOZQXZXkZlTFLLMhYbD+tu/TJQZ0rOA3JcF5jNmm03Ro1CTk5O1pgTDUpNvJj9oEcS5UDpx4zjzgjkv8yiXPqgf+0VP5PJ2KBiCCS8jBJ5tqO0cHN3wVkf3hZcIxWyySZter1dtA7SDcDXBNpKkmptr4ug6a87xNnbme11PNE4E22g0cvTo0Rw7dqxSNCuZqXFgjT1oMtoFjvCcIwDBgCIsrD0WhsLPRx61lxPffieN/j5HHownV3/rlSyvblYRHo9G/ZT5OIfyz/l3p9OptXUBg8xO+rvUgMs6G3mgu3xQRmTEM6xY9bnX98MS7XAu/gxOrmTFGQMGPDMzU9sGVdakuRfjBWbirFyrNty30/HRqHaUJWtcOg2vBzm5o1Kpc+U8klHvNnPg88hhYmKihn4YO7oIkiD6QfSVzKvJMu4BQsF5PvXLc1lgIKhbvRjoxMREDdogHEMG/mYgwENgHMJ3tLIhmqBxdOJe42uLOf6D9/abSraOjeX28y8m91IxjTgevDIekl0FFrYJH55rthHlMtnCvA3bMTxqXLC+eFu3mXEvOzKXdlgjl06QjY3OhuGuIV45kCQrKysVyWR4bSRhZ+mo5+jDZbIKQ8IgrewuOyBTjNtkIU7PNVYMzxDWh5IxDjswDLUsI1mOjMcN6+g3c5qamqqlCH45FI7CuTS6ZeRkO9nt2tc46a63Vy/3YzI4M7Tu+mHBHKGIClY+d4KgHI64VkQzuYPBIMduvJWx+939ppLNvz6Ve6vHqnKM4SAGZFLCLCYwizm56G2DY4y7wUdkwDPcyuVjEyFSTOygxGW3TBlRvejOkz0vFDkZRYBkx5n4FRgm7hqNRq37yfMwfEMhXY+1jEpmstPp1M7XLUtAGCdQs0QMLjV53GXphM/aWLgH4wNJOD+048DJmfz0PGHjYcAdlNCZ4XBYzXfnjKsvvr3N1xNhLQbKAMn5MForOvDGykoHSuk1UVyXVBAagmSRMFb+j4G32+20GsOceP+7aWzsD2nvfOOlDLYmMj09Xd3PhFBZk8KZIGDGywJhKG4UMLyihELdLRmdz+qSBz93pw4em4hpcso7ZqysMLKsDV6ey0aODHmmr83Nzayvr1fOivuhXMBbZFPyA05zWCfGiAMqySJk6tIDY7UusBbIwkgDAytTqJIzMMz3Wropge+XPATO0jpv+M4zgavk50Yi/EE21sHy2tc4IWxYyLGxserISaIkA8aIytfM4Q2dZ9qzOT/ACWA4eFEUq8zZhsNhOrfu5MC/uLbfNDKcTJYPHk3n4eQXnmGlYDyus5LTYGD01zpyAPuBPt4qRpQ0WQARZMIJWOS8zMaOwri84oXFeyc7xs44LTuzpaUjYo7OXRmnldpRmPWAdOH/ECk8F9iLojN2HBy6wj3dxOHyG+sCJGUu5LCGrUYw6JCjLOttCOw6sptO7Jy5l/NqnoX8+Cw2gSMhOnMP2kH3up5Y57SH6HQ6tWSZxWCALCSKhxdBoAiDBTNEBWI592LhUFpPnu9cuPv7mVjaf+9m/1fGcqf1S1UjNzU858jOqfg3i1S+YMfePhkdnclcGR/PMRQFDnF/fu6WMx8I7TZHap82lGR0/CZMONCOMQBJQQMmi4g8bvh3xKCWbC/PM7jnYDCoCDLn6nYKOEM+b3RgRXaEsjEko9KeSxVcyMs5JQbkHNJNGui4o175ljyTnC7JgC48fh8XA7R1fg36Q/ecw+92PZEQMpSipujJePOwDw02tCVHc4mhZESZlGtICIIcF2MBYs1ML+b8m3+852sWuO4vns/a5rHKUJxDlrkcZJHzYZ5XsonAfkNwRzU8vL20YR0ysBMDpSRffPEql7uFXN9zDsZ68PuHDx/W4CMQ0FvyHNkhzBiPnSTzcnN6q9WqpUBOa4ygTDBC3jAeHC73RtcwqDJfdnM9c7CD9Jqy7sjadUajHKcajtzmWHhFJTrE9zxGBy4/m3WbmJio6d1u177GSYSkEQC4gUCJdiYsTGZU0LMzOl7QJRJ7cNfv6FoBwvFM7o0hb998nLG3VvY1zGGS98dP5cGDbubnR32hS0tL2djYqObS7++86m9qaqpWeJ6cnKwgCZAViA/kxIHZS5vBpRuICOs6GEYNoWCyqCxwW3YscJLaxm5T/xAQeGw7SZcV7MGRMVCViIEhYzB2vq1Wq2JOkQ/EFmM182nkYxmtra1VDSyMzRAVR20yzMiOz3H5OThkIh2NANZhfu6gw7W6ulozJGqVXBg648eJ8Tt0hjFi2E/N1loQhj6wrsADokSZh1rR3PLm/MORBgPHAPmZmTp31LQ/+rNkZf9Nq9vtdt49eTJLDx5k6afwlwVIRnsku91uNjY2srq6mk6nUwnfHnl9fb1qhnDuTARL6tu5cCoQRO4UMrzFyxumml31hdPCCHAaGL/Xx9BrfHy8ymsN04G6yN6tZ/AHPAfngSPlPhgqzqpMRxxlyYXLORIpB4NBut0d5t0kk7uSiE7In3wbWfukwLLkg4G5NGf4WdZoS1Z6Y2OjOqvKyI7dNTj0pP6aCY8ZvfAe0t2uJxoni0bvJQvgxNdFeJ+fgzc2oeILr2hGkkkaljj3RXn7k71sP3w7rf23bubapUtZfe65jHVGeydpNaRGu729naWlpSwvL1eHMbPTwdCFWh0KQrHeJANyKTt2KPDbkTmyMNfhcFg1Ldgx8nsTVWVE4vtmQtmDaSfDGMmJk5HRdzqdLCws1BhyE0ZEMDOcjUajOhGCMbkXFfYSx2B4imyQtevYJlaQA3KzQXr8hpAYkBlYO0OnU9Y1dN8phtcB4+P+llMyenEuaYVLkeZf0JO9rn2Nkx5QexIuhI8XNdvJAiYjBtbKw8VkUBzT9QgY4/XLcba3t9PeXMsv/ev9G923x8fz4W/+ZuYWFqpoiOC90XU43HnvycTERFZWVmoREYaaMZCPrq2tZXV1ter5ZRHsHZm3z6ixcRC5iISNRqN6VT3eHyPHOaJYGKfJH/4N/CaqlihkamqqckKu89Kex9qRd1Fwt0GhAxipoyk1vGRHUSm7sObD4bDaC4pcGKOjm5EXsrSzc65oB+R6rL9jNpyUwbppVpi1JMVhHIa7TrNAFTDmW1tb1dZDE4yG4ujhXtcTXzuPMTjxt6CcY5YPLssCZS7KfTxwlMzMIiyrI8lPhm/l11eX9xt+Vv7S4Sw+eyb9br3hGu8FDDIkJ5JiuGVBeWFh5z2ea2trWV9fz9LSUh49epROp1Pt0vFCY0CGiciCOigKYRjH51yYx5joZQay+jvmA8xWkvcY/hq6TU9P12q/jN+5HPJHFnzf688eV+6BwQLj+G6/368dRwKyMMPtxhaTSc4jkRPjMXmXjPJWdM4Iws9Ar82YexO45VKiPWTr3N/Ig1zTYyih/W7XzwRr7VkcoomYXjx+ZijKojhxR5hMHmOAPHG5AaNiMo1WI8//yc0c2CdwDhqNfHDqf5KtjKXTSXUI8OrqaqampmoNzvauKEOpRBguwu50OpmZmami6fr6eu7fv5+xsbGK1UZOJlU2NjZqpJPLJm7ABknwfFKLkvDw+D0HjMb1VIwZhQYSs9b83LkcOajvWRbR+ePxJqOjPnBQ09PTFYx0uaJU1t3gphsc/EyjB0Nf/5sAwbOS0SHUzNl6iCPh/8wVZ0egMGS2PMyEt9vt2vtKS5va73riZmsmw4MpHRj7I2TKDngacjTyNzc0uB2OQbs/E7hnSDaqXfXTufGjfUsog5l27rz6Ui03TuqN4UmqaOZc2tDTHtU1RuCN2VwYx263Wy2Oi87ch9c0cG8WDLIERSRfNKQsjQSl4m9kCrmFcdiBouTONzFe1zshlYjqPM85dplvM0dIKl/IE4dmuTAG9MdMJgbpvJL78bfLSY7qTjUcZHxwG1vhMEocngOI0w9+hz7YuEEprsWaJEWHnSvvdT3ROJMRvEU5EBxeiYdjfAiLBQfacC+8iOt+hhemzJ1QV+PZ3M7Xv7s/E/TopVPpHprN+qP1Wu7G698ZF2xeWYdCiCgAUDGp51HMGcdE9ABCojAmD0wsmcwxyQFr2WzubFND5hhmMiIkvMDIyqUnN0iUEZ0LpfTZq/yeHlicBQqY1E8ftHyQG9GevJ1xmLRy/zbrBGx0M4HHjoxZA9bIMNsRnjE7AhslWV+ZS3niIOMDWqP3Tvm8nozDV9nGut/1M+WcTKLRaNSKp3h1exOTQQiLqIKhodAsHnWxZITpicTcn7Hs5FQ3s9W7u+e4h0luH3gh65vt9HrdSomJwmZOUSzDJPKWpN7g7KNZ8MiG83ye3R0+ra7Z3CnZzM7O1pyD5wvp5dx4ZWWlVmOGpDAJhFNhDhjJ1NRUHj16VJ1thMH6hUOsk9lX5GPewM7Ufc7OxeyUnSe6D7fX61V1c/SIPNkoycy8ERrOaLfSHbJ0zso9TIwZAeEkDIeB1ugd6QHG5/2h2AdR3uUynJSjvQkwHOde1xONE8Pz4ldGUMAgFtgQxXS0I6thI8ozPT1dPReYhxEQhcbGxvLce8s5dnv/16d9dulSBckMbbjwpE7KMWDyO8MSvlMyiMAz53dl2YTF4Vmzs7OVYtAgDzxORrtUMEAijMeKEjr/MbQCok1PT1flqY2NjdqrF5mjW8ncCME62slifC4HmCXFIDc3NyvW1k6KezAH72Utc2PP2akGn2FNrUvomF/yXJYuXPZznmj5YcwEFueUSWpjw0BdJ0ZmNkoM0nn3ftHziUdjlnklN+TBpvBN3gDdnL8AU0wSDIfDzM3NZXV1taozci8misJSm3zmj/94X8NcOXAgD44cqfIJM3NWEhbSOZkp8zIamWhAeZ3ou2Ok3+9nZWWlBp9RJJMwREW+j8Lx2cnJyaoxomQzXXP02vR6X+xTJjJ7q9LS0tIX0JEJEMZjiG84Z+PAye7GOgPLTZ4AtUuCBdmi4HweyM/PHPVMJmE0lKTQtSS1NcOBOK/kng4g6AcdT8vLy1WvMgYMdHdNlHGXrLMjpfPSL22cKLcfVn1RdbayWdqe3nASRbI3xivNzMxUMILPu9Oiglhraxm/e3dfMmjt1dmsTE5kc7VbjcHsowUPueWcB+Vynk0u5kic1EstoAiUa3p6ulJCFIEo4m4SjBNHhrKRH1t2ZjlZG7f3meFE0WBNMS7nVOUhY8jeMklSvW7QKY2NA6KHezFew30MmvQI1MGpAozV8NiOz86g5AUYA7Igehr1OA1Ap22AzJkjV/mONzZglDhLHE2rNTpi1Y6KtbRT4c9fKOeE2eRCEGUj9mAwekcFE7TBmvBw3Q/Is7y8XE0cUsXeEAENBoO0Hz7M/I0bew+6lVx5/vWsrK1XzzQji0DtJZ37Ufop4ZujHxHL3h4vmYxeP5B88bXoyIDnclnRHTF4ppvhQTSO7nzXZzoZSvEs5Mz6GlVgsL5nWWIxYqIcMTExUYPhLseUhsxniILAz/L+zuWJoswB3sMydW7qlIUxOhcvIacJKCK8c1lyRz/H5cEyUrJDx3Zih0MA+wvBWkdHBsji8TMmyGCBnyXjZ/oYYSOEubm5qkHbibSFxHV88G6a2Zt+Hh5O1n6lmc2rm1UNkWcRwXyYs5lioqi9nJ9voeKpDf1brVYFJycnJ6veVucw3Bcn5vv7//bw5O+rq6u1SLS1tVWdJk9EZIMvDg8FskLxM7qmOp1Otcl6a2vrC1vPUGbk5LXDMTNOdqYYBvM9EyNmz92J5rXmc87vyNH5PFGvLHMl9c4fzxt9RdY4MacbSWpnS/EdnuV1t+Oz8zN6RP8cOVnDva59jXN5ebmWU3mXiBXMcJGBkeegsAwMoXFfPOLMzEzVgE0dFMU2NT75h/fS2N57m836X57MB/dOVc+ycJzD2qvj1cj/EHT5GS8UiksEcF3WpSUMGqTAyX2MATkCTzFM5EQvsAkt52M+Wc8KxJpx3Mpu+Q3wstVqVTXRZJSnmZlGBp3OzikIdqAwuHbKlj3jJtoS0ZwLOp8nxzOfYZkwNr/l2p8ruQXmALnj4MDam7UtDchR2mlHWdopiSHGi6zNYdhp7XU9sc4JLCqVFWVwrsRiuMSA0HfzHl5gogPvYOR+9tzNbGW+8cneg20kg7OtbGxPVIppAy83OTN+N+tDuqA0jNFMnuEwi4DSuY8UpUNhUCLDGTOgkC0uV2AY6+vrlWLhaCYnJ6vOJxS11+tVr6j3nMuXQKEwKF+n08nU1FQ1Vozba5988fwg1yoNP31v1tH5naEuyMIlrJIHcMRGPh6LSyWsMXCd+cLeO+dkPcrITekpyRcckfkIPous+BxowpscyE/L3Tx7XT/Tie8MnujiQVjY0PclZmdgeDeM3AuOEEw9u8jbbrfTf7iUuT++tfeAO8nNb55PPpqpDAuIidJhiPzc5I6NiLkxV0fPkuSy8qJMJSFWKqTZWH7uqG5qn3twQDSsdbPZrGC6ZQ5ba4bUzoWxOfci/2c7FJeji+G2CRF0hZ/ZkVuGSf19oIzBDG2Z6+Ek7CBtwN40gA6xloyr3+/XjjL195EJBs6a8W/Gj8xMgGHMPgyOOXJ/Pm9CEadZkm7l9TMbJxHFuxZQMCfvzr+8QM7b/BkiFguF8aOUKPtgMMjq2P1sD/c+53OYZHH5YFqtyarxwAIm13Tua2jpXMIwhd+7F5P8mJ+j5K7joXjO45znoIRWAH5XMrMYn3NUdwwZOmIkJbNq52L4B2NuAhCHUJJ6/NzRg1weMo/uKOZl4gudsWNDrv4cMmD9zN7CmAJtHVVZO6cAOGPmgEGvr6/X4CYO3esEmrLOmCjzpvt+v1+9y9TO2acrYj+M046wvJ4Ia53MU8hGKXwObQlXHFkQvH+Gh/eC2YjtLfnd89//SWaW9j4Qqf9sM+/cfykTkxO1iMF9USigbAlvbRg4DxQUxtS7HDCsMjqilM47uC//RrlQXhNNzkMd1ZMv9pOiyCZ/gE/8m/my3c1Q3zk96YnPGTLp4hwJY+Re7MjZzRjKVkfkjRGbjXc6xL1Rbi4grJFWSUCZZ8CRonPOTfkbUgxHQCBi/ZgvLX04Su5fsvvcB9uwU0HXTELtdj1xV4oJhsFgUOUzMzMzNe/HQtiYTRCVTJUxN8ZDzjYzM/MFONZoNJLu5r6bq4fPNLM13UoGqaIH3tjHXOIMaINjUXmeFRPD9Dy90CYALA/gpx2Ac25yam/U9c+ck0IKYXRGNFZi508YJLkoOT0KaeILeSSpvL5fumsjMTRGdqQzLinZqEyilLkmObYbOrx2zlud86GfRlgl4nEpw80fwFanZt1ut4qG3oljMo+xOHd2tAfiWmcxcGyHZgZk99RsbaX0w9HbmmyECAilbDab1XsdobzN9hrmAvksNOCNcxwWpNVp5fsXvp9f22ectx6dzurq4bTbOxtdiTwIzQqMciAo5snvHWl3UywMJEnV1eRFctSxHO2kkhGln4wgGHLlZ26YKMeD7MvIzR+3wQ2Hw+qoFTsYRyyUEqUy9LajxVG4/ESE4PJ9fYKE0yIfBG252nBBGTyTcZUw1qcpAr17vV7V4FCiKObrn5UIxcej8HMfTLa2tlZbc+99hpxzauTfJ6NdUbtdT2zfQ6lYBArA3vZkA0tSIws8WSt/ydQ6irp2VwlmMMzX39z7VPdhksXfns/mZjIYbFX4HsiRjKA1C48iGV6YYUUGzv+4F907jijIyopug+KygaNww+GwxuI5MpeQsszjTVC5ZkhOY7k7+jN/FK40DH6HgRqqGlInqUXVklCxbJg3ZA/foQECWTgylaURZGu+w+vtiIdB2IGhz8iurGWXaRc64v2vloOdO0iH9XFq1GyO3sZGffepIyftc/aIVhSEaGInSQXnSgPFKE0u2IugXCUd3263k8Egl9/bp2lvNvlo7Y20ZkZKOjMzU2M8GSsG4Ehmr+n2MOd9jniGOcyF/5d5kxUzGVHzZR3OuxhYeJ4DcYCielyQESw67WeOIjg+oLNPZUhSewMYCgU8B2qi6I7Ehm5mutvtdtU0YWfIPIG6NmI6h1gnjKqcJ06NNWEe/nnJwnttuL+JuzJFQM50P3nNBoNB7SwkHCLHkni8jMc/S1LV9MsuPF/7GieFZbxXGbLX19drNUn3ZjIIDMBe394b4fM3uaAj2HA4TLu5d7E2SYanm2m9PpPWtZ1F9Z7JEv5xlSUF55n+juETl+EpioVyQm4YauJsyH2JLiZlXM/j3owXWRChHEkZl5ULxUEWODzyY0Mt5uj9oY7sNA9gpHa2NlQ7WtbaDRfIgvwZZ2AC0c4tSU0GQHJ6ch0YcERuG3UKxfjQK35eNjo4vwVN0FYKmUQTRmn8pGrMyWkdjggHhz7MzMzsqdNPPFSaaMfk+Tlh3vsCfX6LWUlHG/4GziEUoq5fHWiH0Ol00mzsHTmXlo5mefnFTExsVkedUFtCqCV0Y3EdWUtHgrd2bo1yljU77oVh2BuzaL6Pa15mPktoxXcsu7LpnctcAMoIqnFbm+uDeH+3wDnXdd7GGEyAGZo5z3Z0woghqGBAk1GN1GiGNQAiTk1NVe8A3W1zPBGI/NTwGPk5Z8ZJlsiI+znXtgMr5W4mmHvxGTsq1sXOwc/e7XrirhQu2sTM9vlIB3t+50AMBPYPIXgRuZ+Fws8reNnau1ibJMNznbTHJjLeS3UMxsTERLVHFMMw+YCwWaSknkvhSRkLc7EXTEaRsmTq+B7zxAPbm/NvL7wdF79zKmCn5zKWkYGZVp4P/OKeSSoHhhHa8XgNjCKSensmaYRzPOYDgqHnGmdhOIuxOsIxF5wAa1iSZkRT8xMmz9DjsoOJV8Eb1XB/yxq54RB7vV5taxxr7sBl/gV9QgasH7ZhAy6vJ7523gNw8g0MMhR0rRBhtts7r5bDOH1cIMSSo4nJJxau1Wql3+tlMNy7JvTps19PWjvvu1xaWqopCfsJDeNM8JjE8OKblEBxmJejjA2QBcJxmRiwMzJkpDGiJGpMNPC3owCw1U7HYy+ZQa8Tz/Jn3R/rfHo4rL+ewZHGRuWxc79ktHmcy6ymjwC18SMjRxmeDaJivhg7CMTphnM9H5DGmpsMcqQk18SomOf29naFIspcmTGbVzFKarVa1TE0ltNe1xNfx8DADJcQkMN9MuoxNBtrbM9AzSqiBCZr7KX58/n8/TyYfpBDe4y115tKv78jhAMHDuTBgwfp9/vVUY02iL2ID8bKs820ooDONdi1MDU1VWMyUTSipHMZ36/T2XlDNjkIUZwygD9bGpMJHj7jbVulUyDX41gY1pfLZx85Elh5k9TWEgdkyOgmDe9uaTQaFQvvMdIc4WjJhUGUrX0YIWiLNUV+JvYg0xgHsjCs5N4mrobDUceU0RRn+5Im9Hq96hBucwXMxzl1szliayGV9rue2L6HMHiwi8corOGvDZIFdp2Ty8JBEGYHXerY2tpKe+lxxrqruw90PMnZ+uFjQJDl5eXq3FuQgAkf50cYEgvMvzc2NvL48eNqIVkwGza9rnhSlKssGdlzG0r5aBM+704UHAbenoU2BAauO7+1zB1pbAyMl7dsG3ozV9YbBYdJJX+lrFS26pktt5y5t3XHqRFrz1xNpvnnLm2ZRPJao29lkwMGu1tqYIdnHXdpCqfgfuRGo1Grq+II+TfOlLXa73pinRMoQL5gAcDSesERpPE+QsdgDcNccjDxgodhos9dGebcT3Yf5/bkWB6/dCr97dEmZ473oAhM83syeo27654sFlGC1jKint/6bNLK5AkLgAGxVQsDQC4gB8sCBeJvG4bzRmSd1F8RYHLHuRuK0O12a6UOxsXWLO/8N1R3exosLr3CjAn9wBgNqbkPzypZbIw3GeW5jmKlIZMGME87KfQHY3Pk9TrxHa6ypIWROu81ojOktQNA/zFmdIK0hWf7FRnM/UsbpxcbYdkASwaLyFqyj+vr6zWK2rkRwilPV/AilNG5vHq9sTx8eDKDmUE1FnZwtNs7zdHz8/NptVqVwdkjG3p625hzLggxSkcol/eIwgKykMzBiojC7rZh2IuMDMzGeh3wyDiU0vH5nJuKVGvWtzyxXigIxrO2tla9R8Zb4lB8K7HJjd2K9DCadsLlH8bmTdolskHPTLZx8QzneSVqYC122zmCjhPZLGd0gVzTkQ/5m3DyM5NUe6AZF2vE8x8/frynXj+xlILilEQPXhKhm6Vygdm1PzyUBcyiulTDojtP+Hx8n61i55NM1Fve2u129e4T7slWK8aDk+h2u1W3xurqas2b8U4P3hdSRjWXkjB4nunGd0fH4XB0fAZjc6O1Iyif9XiRiyGZWVqnFZaJ0YvXFWPF4FyiMClXMrf8zeXcGEdHXlmWpMz2mnC0LpnMYW5GCyAA57HOJz0uI7iJiYnqeZSQWK8SPZg0csDBKVENsIPFCH0GFs6G9cKZlEHJ1xMjJ+16TNqtSQiSBbUBO2RbGbwAKAsRxwvBpFDgN/7ogz3H+ei5U+lPTWTYG1aRkcXqdDpZWlrK4uJi5ubmKk89HO60CQ6Hw+rIyCTVhuNkJ5K4iI6HtJGgcMwThXPu5oiIwdmZkbvYmJ2/EtUdSWFAdyPldivzmMxC0fiu4S5rYAeAEjuvNdrh/y6vYdSsJycIIrder1ex6GbsmbfZcH6OfvkVFsgP9MAYgfkEAcYBOYThG3Ia5Tg4AMmZOw7NbL0JOusDFQlewYGBmjjb63oiW2vIxmWyx7Q6SueDdb0H0pM1oVKeb8vC2jNPLw/2PHHv8eNT6fXGarDKnTBJsri4WO0KcG6DgMfHxyvo6gsFLcdkhWHMFjhygLHkc95e5DINuRxMoGGhlaXM340ynGvhBFyicFNHSfKw1svLy5VsuIf3aeI4kDFz51lEPAgy1g+CzruVvDm7hLA2YGTP7y0fZIRsubw+ZX7H30ZvNmIHjTL94W/GQ75O8wPf4SpRpfNt/3y362dia/EYLIIhCMK2Qpjw4fOOvo4IDA4FsgAqIqSZbDf2zjmbEyPhAwEZ06FDh6ojJ8jD3MGBkjgich97RsaF1yxJDUdkZGcK3pGMCIMy2XjZUIDcLItS6ZrNUadKCff4fdkt41IBBu1am+uEhqhAO5Rxbm6uKst4O55JPH+vjBBlQ4uV34ZhVrRMh5JRnRCoWpJAJYQmn+b33Jfv2QGxnjiZ0kkiH8Zpx+5IzXdBErvlzbtdP9OWMZMGGF/ZtsQDSapZLBQCRfNEmICNmvs5sg43H6a19NGuYxu0Grn5y6drW4msDGNjYzl79myVHxqqA9mSUbtbMoqWLnTzGedqXggTO3ZWg8GgluM4JaC26TE7QphF5HtJ/VQ5K7XLADybnzn3d1cQzgJZlE0nzltN1qC0kDSMBYjKubQ4GTs3lBTG2kaM/jjHBco2m80vdJrxqj5aAc3kktezxqyV32GDEwD1OOoZFpeBBYdH6uTardMVozme5f7q/a4nNr4bGgETyXn4N4vnjcAYqHMUe89eb3RgtaOtI071rHu9nPnJ7pFzmGYWN89UUAmB47Fc9jCjyKI7Z3a0dn5kFpBxWS5uAcT4YYZhjO2gkIshMt1WNkZHRDsJE0XIy/Jm4RkDzsERHMdDpBgfH69enMRnHY3sVFxGcl2Svyknkc85SnIPy8uODRiJk2J+7oRqNBo1QoU8EnnsVvoAUlu+OCyMBYdi8scOkjHjCHDgRGoTSRgwzgfd5OhQ7Gu/64lsrXMlfsbC83vnoIZ8yWg7jg0EgQwGg2pztvNLFoB7bzW2srNjc5drOCqIQ/2zSPZkRCoXlu3N/X+usuDMohp6m+QyUmCh6AAy5LETsOE70iFDH2tR5k48C5kh72Rk/CVhZ8Xf2trK1NRUBY/LFMMsJw7ZNVGXDgzDuV/Z5ZSMTiRwhDdE5PN+wRVOAGMpERun5jvFcO4NzEa+oBWcj9MYDNsEj+fo9cPwsAvknozyYpcEmTObMoDre9rfnr/JyLvgBV205/cIiYGUEBhhOcLyWSukSYLyunHkx+k19+hBbDRqhpCM8D4Qir/tofmc9066jIDyMd6ynGCl4nvOUfkeXt5Oivv7e7sZ2m7e32sDscb3zPbipYfDYbW/0w6PqEMND0Z2dXW1Wh+jDN732e12ay1+JtRcAzQLa8deRt6G1g9jAoG5U8moB2NNUjWImCCzgzWXwdoNBoPqAG2+xztkXBbDuThoAJeZB2kAOgQ5NBwOKyfg11/igEzI7XU9sfGdQVnhESy7DkxCOBpYMCiUO2ZMXPBvGwCQ4rd+ZzsTG7uPEaXkHq7RGT6ZnLHCMM8SCTi/4DMoLUphz+mDn+y1O51ObQub63Le94lS+ER6EIEjOGtgB2CZ2QCSUc8s42Ut3chgKOadK8A2DMgpCASbUwN2a6AXzBOW2OuELBmzmwKQI/J13sc9yxqoAwW6Nz4+Xsk8Sc1R82yjBEdmE5oYU3murufuphJ0nPfcMDYcIXPz+He7nrhljIdDFbv2xERsgEn9dQtMDMEYYiSpQdDBYPRaOnvHRm/vMgoL4bzLHs95hZ2Hozr3cDRLRm1+jNUHBVsRfA/DNhu8n2/0gdLasJybuLaGETrd4N9EOqMTRwpDeLe78czt7e3q8DZHBuRAp5HRDpHZaQhjxVBdVishO7Ir04KSdLKTsGEawrsk5M4s9za7S61ME8yN0OdNtEaeTodw1hzqzfNxNNwPR8DfjA/YTulqt2tf46ShF0v3QiajaFEyjigAZABMm/NNR1iUEONiklDe+3VRdKeT/nS75hUxVPJLjwslKU83Z4FQJLch4lQc+aw8kCCekxWBlzVRjEYx7cF5LtGa6GMHaKfDZ1HSMg+2ozJx1Gw2vxCJMTYbEfdCDjRVGJ34oCtk7tTAhoRs7fhwcjZCy5D5ufTjsRKtTEQiKwcIcwBOFexgy/GXTpE5eVcSek5e7PsbXXLKI45vbW2t2tViCF5eT2xCQNGgrIkmTnKBK3hBitCunzkfM+tWdtEYNprA2eu6c+F0uqePZ3qw00zsLhgziCy2F8b5iYkLE0A+SoTPG+LzM7yuIzA5OPmHHQjzZRsXCsicTZJhlKAAGyP3cteLoZJrkKxpOSfqwO7uQVntdIgayAtZcfG51dXVikMgD8NAjMRQbjeLGPbhQMo3cfMsl2K4h1s+7fx8Ch7OCwiPjEtDNruODJ0CsQa+QAu+l6E9Gyocffe69jVOFgID8uZZlKfT6eTatWtZWVnJhQsXanvgDNNgqXq9Xu7fv5/vfe976ff7+ZVf+ZUcPny4RtpgACX03O2a6c1mpjmbQXaUxAXwpH4ciet2PI+xGgbzWSAYC+q8FS9pRXcUZPwoSDkPd+sk9c3FdmI4Q0O3ErIyB0eP3fbWOlIZ3nW73YpV5vvD4c4G68FgUJWGnIt7F4y/w5rNzMyk1WpVSMEoBDmg6Pwfx8H6WaalEzCfgRNxtPLaeL0Zi9eJOaDfpBqdTqd20gJpDQwrqJLvUz+HPUYuW1s7h7G7TdC5717XEyMnN7cSManNzc0sLi7mj/7oj/KTn/wkBw4cyMWLF3P8+PHMzc1VSkaReGlpKVevXs2tW7fS7/dz5syZXLlypRIE9/ZeweFwmI31vU95dxJvQ2L8KAyCMfQzy+Z80dGBfxveYAhEQ+7hMRjqtlqt6j0kHJ9iKGdWGAPYLWrZ2XW73VqjvJ2hXyfPnMycmiFkfYgelh8RyM4OxwnJhDzNeiejlwYxR+eZyajHFflR1mHdgaxEcOqYGLmRieXkiGfDMcphg3cZoc2Wk/PjZHFURlWkbSbQyrZFUJCjKfr+F2JrHX3wvExgeXk5jx49yo0bN6pI8tFHH+Xq1au1JJjFA0JAKx84cCDb29u5evVq5VUQgOum09ON/NbRH+45xn/yt/o58FPY7TyB57jLh4V3FEDYXkQzhCgOFwtsj0/fqWEOSs2CohRJql0whoooE7mLobHzfeA+bxhzKQUIiAwxTO4J4eNanB2Tt4i5zxkDpq2QqAk8NEtJTus+asN47mndAspi0EkqZEae1263aw0lyYigc6qR1F9ebAfm57pc5oYTHIGJN2TsVMsOxyQfezXZVIHTQB/n5uYquF8e31JeT9xs7VwLo1tdXc2tW7eyuLiYxcXFrKysZHt7uxoYymH4x5arfn/08hai19zcXObn52vfRXAT41s5dOP39hzj+63lbP+bf5PTp0/n6NGjmZ+frx26ZHIDxWKxWBC8G57Nr09PRiSDcyDn28kIZVBrLaE50cCwDoiFsmDAzntBESZ0Zmdnq5xpONypYxIZDU8N13bz0u12O91utzrKxeUA8qPJycmas8ToDf+ZS+kU3MaZjFokyzTBhX8+jwxs+HzeJ+fxbAykLL04upvoo/mCZ9pJY6TAV0N4AhTra4cPmgSJmFdwGkUOzXj2up7Y+F4KNElmZmZy7ty5HDx4sML3q6urFZRjQq77QfM7WrneOT09nYWFhcpIK8JhaSnt/3LvYT73vefzJxOPcvPmzUxNTeXEiRN55plncvr06aqFD+ETQVAI/k+kdV8ohAUkjeEMczDL6yiKIpk04Ll02TjvcZ+mz/JpNBrVGam0qeEAkGtSJ9uAZC638DmUh8gAOYE8xsbGqu/yM5dmzPqy3csMuyOnDYsDqzEyCDJHepyHyULkg7NwuYTLv+O5oIySQcVQDVVBVURwxoABLi8vV+to0sgEJgbNupX95W6g59Ud09PTf7H2vXJxzAqurKzkwYMHuX37du7evVuFcS/C9vZ2VldXa7BtamqqNqmtra3cuXMnDx48yJkzZ3L58uUaudFutdLYO/Ln8nMvZ/LFF3P79u3cvn07169fz7Vr13Ly5Mm8+uqrOXHiRC0HQLA4EhbHxuR+WoRaNtaj3EAdE1/+jJsA/BzDZqITC0vpiUja7XarPHNmZqb2El0fzIyRu3ncrCxoxixys7mzORyHBLNJbucGgiQVqjCU6/VGpyg6n2I8yJM52cmZTANa+wgX5sk9afskn/YWQBwaa0UkxZnwPGCqiUqgJjAdYsrNGiAQ5OQ9u+5yQk8gkqheJKkQpN8O/lTGae+8sbGRxcXF3LlzJ59//nmWlpaqkwZceMazcQiwPRatX5yUNjs7m2PHjuXChQvVa+fX19czMzMzgpDNzfQbe3Tvt5Px+fHMzs7mhRdeyPPPP59Hjx7lypUr+fjjj3Pz5s1cunQpr732WmZmZr5AxxuWucBsZ+KmAuAcikXU9BEebl8saXIbqA2Zz9tjE+VarVZVT15fX69OsKOh3crOPSCTiHQosAmh8tXxKBy5P0ra6/UqCIhy42DL4j/PcT6djDrMMGQjCqce3kHCuJ2XE4UZN6wo0c6oCKdlviPZcS4lnMZ5ou8mGRk/bYLN5qjxALLUMJo5tdvtzM7O1th81sSk0FOXUoBe169fz5UrV3Lnzp20Wq0cOXIkFy5cyPHjx/Pw4cN88MEHWV1dzeHDh/Pqq6/m1KlTmZ+f/8LugG63mwcPHuTevXu5ceNGtra2KniMITx69Kga+HA4zHvH3stfmVzK3C7j653tZPiLs2ksjvpPDx8+nOPHj+fixYv5/ve/nytXrqTZbOZrX/tarc7GAjmCmFjBc9vz2zMaPmGkZW5mWOXc158xaWACAqV00TsZsaA4Aj7HvXbrS/Va9vv9qq+UYzT4t6MVio9Rcy9+5shZ1roxKqc2OOnSSDFI56jI13CdHBfnakfmPZOsEZEwGW3GMNKxc3Dut7GxUaUxREx+NzMzU2OMXRLBufN2u+np6UouJkYZM3N46sjZ7Xbz53/+5/n4448zHA5z/vz5vPTSSzl79mwFxU6cOJFGo5HTp0/n1VdfzcGDByvv69wF73nkyJFcunQpq6s7x1weOHCg1ii8tLSUbrdbCfzcxzdy5PM9xvfXDudh92KazdHLbJKdiHXmzJlMTEzkD//wD/PRRx/l0KFDeemllyr4XbKHLCK5EtAGAUISIFQUCKXygrJgKPVueSvPNQzCuGj14zMwe2Ue1mjUd1aYaPGZR2ZjWY/Jycl0Op3qfaicTojhUNs0erDxcC9yVOaGwWEERBbgOnLAMIjgZoqnp6crqOzN52awkYFzRmQEOvArJNykYEdlfoB83Kc2eEM8B7wNh8PqADSTRIwJ1EgLI/PHkbHe+5FByROM8/d///dz7dq1HD9+PN/4xjdy8eLFTE9P1+Du1NRUfuEXfqFG20NH22MY5mCIJOVMDJi8traWsbGxfJabufDpdzK+R97cOziZNOsbixFkkhw6dCjf+ta38q/+1b/Khx9+mPPnz2dmZqZaHMaDpzVxRV3NjQUoQFI/PNkQlyiA0bHgPIO2PJTDNV13ADkdqPLv9ug1Atvb2+l2u7uSdoagNgSfh+RTAil9MA+UlR5TnKuhJp+DiLGDMtuMrsDk73ZW0l5jRpcwItbWNUKQhhtYcCpwCuVpBC4/YVzeoQLy4PPk2TgQQ3n02KjKpBbyspFa9546cn7wwQe5fPlyfv3Xfz2HDh2qWTs3NcXNZMvknyTcuQPGRI6GQGj7665186Pz387f+97S7oObTq6d/Ho2NvoZDEZOAdiBUJ955pmcPXs2169fz/3792vNEV5YPBr38G55nAmRwErgaG2v6HmyuGaAvZsH5SPyojit1ujlsiUEcx5jyIgciXgoO8bBOPmb/L/ValUliunp6epAKta3zO2Yl/M3E19EKjsE/s/64PR8CqFzfDtzum9wCGaFCQRu0Lcs3BLYbDYrWO+cm7FZbxuNnbeAGYVwzpMhqclBE4k+DmcwGNRSEjdKPJVxzs3N5Wtf+1oVot2uhPXjKY2/XaNyNLMxIBTugQdiX9291t1c+OD7eWmPQ/f6F1v5/OCB9Nb6lZIACcl7eNYzzzyT999/P/fv38+zzz5beTyUHoYZJSfXQIGJihgL+QVzd07l75itNDnhYrdJERMThmQogHNSG3oJw3u9XlZWVmolDUdATil/9OhRpehEZbqZkCcOh3kD7/gM815eXq4hDFCRO79wGmyGsIyQMTAZp8gWOgweVhmD5fNuuXPrH44DMobIRXpDlPXmC7aaJakgd7LzBnPrP6dcDAaDquQyNzeXlZWVzM3NVd/HefhUeJzVU0fOEydOZH5+vlpYooFhBkaKEjp/c0LP5PmsvUeZi3U6nWwMf5T/w/95afetYs1k/T+ey42ls+l2H2d6erq2b6/MASYnJ7O9vV0xyyy+oY77T1kcYAhjc/8mSuvivks0yANvT3QyaUSO6b2PnIMK/DP8YmFZi+SLGwRoSPDZPjwLRdrY2MjKykpNHkSOI0eOZHZ2tvqZywes73A4rJh3xu6Iidzcl+o0AYIGx9ZsNrO0tFSx9OS3/r0NkIiUjNAF7aGtVqt6dw0GnqTqPjP5R24/OTmZmZmZWv7vlAP5s0aw5qAD8lL0ECdiO3FaZxj+1B1CZYHWTKKhIfQ5EZBFNOSiHGBPBZQzYdRut7Pd3Mrzb76TA4/2GFgjuXPi1QwfHEi7vV29xQzDcq6bfLHlDkFj0GZgWRzXd83u4uUxVLOqdjCGZcDFUp5mJIn0KLmdBRDSxmFixU6Rv3m+ia/t7Z39g0tLS1VuTxpBxH38+HGmpqYyNTWV9fX1anzMm/Nn+T+RjW1ojgqzs7NVlCfymyj0OUCG7W5mQZntbI0EzKJPT09X60SUNMlGHglygdDhBbasP10+wPvSuVImGQ53mm8mJiaqPyBA8np0hPubZWb99rr2Nc7Hjx+n2+1mbm6u8p4sPEJDWKbWUSyiB4YIOWCjxctjBJ1OJ+1eL7/2Bw/33mA9TKY/fpCp6ZUsDbay3Uk6Y520pn66obsxUSljr9fL4uJiWq1WBTU6nU766WfYXc5Y/6e9ntvbaTSbGQ4GSaORRpLlqWG2O8OM98bTaYw2RKNAjJ+fe0M1OQfzZkFwDkBfIgTe3I0CdgJEKH6GYgHjeQYytkJgsN1uN0tLS1XOBaQ3HF9dXc3q6mqmpqZqdUSUnbN/UVQTMRgJMmGtMUDPySUi2OGVlZWsr69nbm6uduoAuR46Yp1ijZEbTsKOjJ/TBTYxMVGdUQwR5Lx1amoqBw4cqJ6dpBZQmB9phJl09GJxcbGWczJH5PQXhrXLy8u5efNmXn755epnjp54MBTCMCT5YsO4IyhF4t3Y3PQGOfhg721iGSQn/ov38/fG/ov8/q8lt06NCsBHFo/n3MNzO0ozNczw3x3m+Nlb+cbpxzn/x3+c+R/9KMMkqxMrOfXDd3LwUZLhaPeJJpo//I3k9snk6P2zOdU9lXdeTb77i40cWj6UhccXstqeyHxjvlIw8jETQIPB6K3LzNWvfsOwiaru16TMYJkCt1FWooPTDrPGjUajgnT379/P6upqFQl6vV663W5lYDgM8tXZ2dkqulPWcLcQUBjHxxrAT3Cejh0Ahka+hhFBwBlRMC9kQxMFMgR5IS+zyskoFWB9aPw/ePBg+v1+VbKzo8SJmr13lHULo8s1BBuf7FHW1dfW1qp6cllZ2O3a1zjHx8fz4x//OEePHs3Ro0drLGzpNXkgEMlQiX87H3PrHMZdwccn1H+SpLE+zNT6av7Wvyh/s5xEZ9z+i+TVRpJekv5nT7yvr9/+p/zrcZIf5YXfTf52O2kMG7lxtpXlhWOZXT+Qd197Lf1vnMjnndNptkfn8xhKG7Z5rs7jvU3MpQRkZCNCdsBhYJRzWvJP4KgPtcL47EiSVGtEzy25OfA22UEkMJazs7NVzoZz9l5SdIL7wMhTBiLq2bmVZQ9kx75I58BGBmVKwGn7nOgBOnGHDs9mDUzo0SY5NTVVg94cn+mNHu68wnEQjTkNwUigrHrsdu1rnM8//3w+/vjjfPe73823vvWtHD16tArdREwXdpPRThMUzAk90XNycrLWu4gScK9+O/nkuU6+/r0vYUl7Xfv3Fn+pq93f+ZMM89zHvSS3MsytPPfuu+n9QSdLjcO5d/ZsPj9zJp+99FJ6hxayMTWR1qBVKajJGxQCZcRogU8utSBTOzpfKEilFI3tDB8u5+DURCYPPsz379Vrayj/gQMHqhwLmMb4qHOiQD6f18TM9vZ27t+/X62xd9+YLbZjIWcDJpplZZ4+CRBE0W63q8OqkQn3x1hci5ycnKxyR76PA3BdmXkhS96fw3N9vtL6+nrVCYQzMNFoZ2QbSFI1JvA8cwJf0Lf9lPGFF17I5uZmPv3003z/+9/PG2+8UUXQZPTeD876IVTz4LIUYLbLUcUU/GAwyKDRyuLZb2X45/8yjRJu/pxd+L3O3e0czp0cvnMnwzffzObUVJbmW7ny+oVMDA/md367k+dvv56N45O5f2zHy89szWRqZapyeCwWkddHuLi1MPlpN9PEICtzKzlwcysHFodZeubH+Rv/3XI6q72cOLCUM+88TKPXyKNvDfLt515O5yc77wXv9XpVe5nZcqKMc2v6aIGMJfxGyemJNquJkaH8RGCU2Pd0kzgQmtIKRuIoZUNC/0BzRHXySgzZLL0NA4O3Y8GQme/m5mY2NjYqI7VzZfyUVBYWFpLssLfefO1obtS01/XEQ6Vfe+21jI+P5/r16/nBD36QV155JcePH68Vb9fX12uTd6HY5RMnwCXR4Zxp0B/k+q/8YpZ6f5b5f7oPMfRzejWSTKytZWItOXZnJ8d9/Q+S1uAPcvOZ5MoLO5/7yYWFXHnlaJKfbjqeejatmdNptVp5/sf9nLw3IlmQzdbmZp5/++1Mrq5mbWItnx35LM9/NMwzN5JBa5DOdr4gr7E3kzO/cCM/+XgmjYyatJMR4cFa2ICc2/KdMhfGsKempipyyjkwUdn6QpkF40R3SkVut9uZnp6ujZWo43ZKnBdtfkBQIr2didOp0gk5wgHrCUAYNc9JUmP7l5aWqs+6sYOxY8CM/0lMbfIE46Tf8pVXXsn4+Hg++eSTvP3227l48WLOnz9fK/TjHckzvZ+NhSFHQNAQCv4Z9Pjy2jD/8mv/Qf7Gg3+che/fT3P95zuC7nc1koxtJ0k/536S6g3dwzxM8rD63J0T7+Xxws6/T95O5vdojvKSvuxf7LF5Z2Iz+aUbK3n7Vx5l4U8OVEpoKEuqAapBeRqNRsXWu01wamqqqumNj49XMM9KZ2bWDgYo6Lyb1AidwHCHw2H10lozp0RQDJDcjgiLXiajw569od5bxIiWLhf2+zsbBMghCTTuqfWxn6R61msIJsbBGEqkuNf1xAO+IA+ee+65zM3N5dNPP80nn3ySx48f54UXXqgo536/X71awflS9SAVo70gGLULvijFg9mz+Sf/s/99Xvgrb+Xlu3+exj/vZ725lJO3umnIVv//LbJyleM+eWfnz/83rl/78Vb+j/+XD9O5/WoO3jhYQVIMk5MQHj58WDnRZrOZmZmZHDhwoGZYJqrofHE7Ig4W47My4rxdF92tnMbvDPlheLnKLq9kVE3A2DA0IhddUIzDDCsEDjCd0lhZ6zWySEbnNGO0vGzZtWZqntzf5OBe177G6YJuq9XKqVOnMjs7mxs3buTu3bt56623cvHixZw9e7aqGQEnPJky6YUIKne0mwxAWOlM593Dv5H3Dv9aBs8PstK6n2c/XMxYs53NqY2cvPKDnPpsO58c/ST94XZ+8U+HmV6rvyGq9uzRA6ufPQlejD6YNBu9VB/fzp7R6ufteulK8gtvdtKdmq6VPYg07XY7CwsLmZubq22LojaXjNbIOeT6+nqtTtpoNLK6ulrrqXW3WFLfuE/EgyX2afeUbZrNZvV6dkcnoqBzWJc4MHTgclI/3QODx1hNdiITNre7nAO8xbjpmcVJuYyFMTu9wyDtuHa7nrifEyMD4s7Pz+fSpUtZWFjItWvX8sknn2RpaSnPPfdcDh4ceWQzgyXGN7Zn8bwrwGyv2a92u52JHM2NiztHaU5MTGTpuV/Ih+1G7k7czbvvv5P/Z/9uzh85lbNnz1aK5VKN55SM2t+IBhSizajx+VZrK6dP/1maP31vy9ifreT4o7cz/vsraT4eJnt1NP1bvjaPtfPw78zkN669nA9WL2bs8Kgx3jm/31sJZCNHxEhcPoDFZK1QcmAgTt3lFKAn605fs6HfyspKrexGZKS9z4eU4+T5uaGxA4NbKdGr4XBYdRVR92VeloWdi/NYjKvcs0kpypupjST5HI5kr+uJB3yRT5rubzabOXv2bA4ePJhPP/00d+7cyb179/LMM8/kwoULVfG69B4usJMzMEkG7VY0/m+WEOEbFq0urebTb3+ax7eXcurMs1l49dUM5+ayNhydEo5xEtW5vxuiodmh3V1P5POfr/29qkc0L/fTbv29nPib72Sqey/nf/J2GklmvvMgYzfXkttJ7v//FnaDBwaHmrnXPpgfXriQ93/heG72D6b7XiuNRr92QBbOyftIybcmJycrGbAmkH8ub0D8rK2tZWZmpjIMRxccr/t/0Q2jLtIjnHWZ/7IXlYjrxnleaEQnEGPAAdCq6L5ljG9tbS1ra2tJRsfzYEDoPLpCDRQiDP3mD85te3v7C2/mLue03/XEozGBGiyiPcHc3FwuXbqUY8eO5erVq7l+/XoePnyYM2fO5OTJk7VJ2pNCGDnvSUaH+RraJvUTA9y50Ww28+DBg7z//vtZWVnJmTNnqiNJyiIvz8P4zaK56AwpkqQqHtvRlF0y4+Pjuf/5y2m3X8uPDv+NDAaDHPr3P8/UZDedW2s533kzza1Bmv+4kYnhag7Nfpr8cZKtpDXczj4v7P7i1UgynsraewutDL8J5Gzk6tUTeXBpKn++9WyGJ8fyWedYNreH2VyGhR3tZGGHRavVqkE7ICo7kMq9uY6Q5KQodZIqktrhIv/hsN4wzysuQEHuyWZsECuukaIvdDehq5RcDHExQFAC9zF0pi2R+foMIyOuXm90ZAv6ip6CBsozsugKorvJ+a+h/m7XE2EtMGZHAepvbk52vO+JEycyNTWV+/fv5/bt27l69Wru3r1bHVcJ5jcbhmHivZNUmJ2Fd27B5Mzs3rx5M1evXk2z2czFixfz4osvZmJiIqurqxVxQFRA+Mb4vV6v2oGB8jEeogHN3xioWelWq5Vut5vBYPDT3fs7i3ln62AajUNZX1/PB+PPp91uZ/x/NZ52eysTnUdp/9VOGv1Bzq78IJ0fdNPb3s5gOEwfIk3ztyPJbDL4O4MMmztR7Ort81mcmvtpa9p67p7vZH39p+NLK9trW1leXq7VE+0kIeXcVtlsNqsN9awxzrksXdjw5uZ2xkFDvRlZlB5CxpsUiDzlTo6ZmZkKXQEzca6ukboTB90CmWEA6KybLJaXl7O8vJxWa2cn0PT0dFWnpdWv2+3W9sEyZ56DbtH0jmzdAYRc7aAwyv1a955onHiDpM6Cscg8fDDY2TXBeUA3b97Mw4cPqz2UZ86cyeHDhyvBeJe5yQG8imEMRox3SnZ6fm/cuJFbt25lamoqL7/8ck6ePFnLZQxHEALtXVYcv83LEYFtZsAvlNK1MpTQW5S4J0ZM5O12u0mS1dZCGpM7srw3+xvpHa2f3oeMmTuOKNlxlus3RiePb29vZ/vBdra3G9ncHLWb+XLUMYPKfdlVkqSKGKwn7Gi7vXOWL3VMoi3Rr9PppNvt7qzP8eT9X3g/jw8+ztjDsRz+3uHc+tVb6U308szVZ3Lpzy9ldna24gO8LY7SDmtNFHfjBcynW+4wBvaZukMIuVoP3MgwNzeXmZmZL0Rcv5CXziu4EW9ts8MD2XnDNw4E7sLr+hcqpbhH1B4Lb44xuHY1NzeXl19+OcvLy7l+/XoWFxfz+PHjLCws5Jlnnsnhw4drxV/6ERm46WaMEkNqNBr5/PPPc+PGjSwtLeXAgQN58cUXc+zYsSSpyCNDZyh2Gz0OAaGyaBipa2fuwcTDA7mokeE9gTEU2VEMFnl6erpWnHehm3sC05zrshfVR4uUu0m8VY/vMtbV1dXK4JAThI/7eb2pGANwo4BZSKDacDjMo8Gj3HnxTsb6Yzl341wu/emlfOe3vpP3fvu9DP/msILhU8OprP/xeraaW+mt9dLb6lUR1DqE8btX1mN1vZx1nJiYqMoXzJ3xlkd8bm5uZm5urlobHBVygcixM/YZyG6qgNFG95IRl2HHh6yMQv33UxknX7YQnXfyMBSfrTELCwuZnp7O559/Xp1L++jRo5w4cSJnz56tHX8JZHAdCSGgTBsbG7l7927u3r2bra2tnDp1KufOncvc3FxtLyVjQsAosAXEWF0SMFuIoXgx+H7pkEyLO18z80d0RaG3t7erg5ZRQgyV+4MyvLODcbXb7epYfxQBSh+FBer5HCQrqImUsbGxzMzMVHmft6HxDOuBi/j9fj9j/bGcWjyV/mY/rWErnUed/Oo//9WcvHYyjw883jGQ9lgufPdCkuTGMzcy9/FcxtbGKjIItpX0odwIDrGztbVVfQ5ngkHgZPk8MsT5u47KBX/h0hJy98/c0IDTMieCbAxd0SEMl7UFlYEin8o48QRETCKBk2SgBv2JLCRGevbs2Rw7dix3797N7du3c+fOnTx69CgnT57MwYMHq/NZvIuD5B0FX15ezv379/P5559neno6Fy9ezMmTJ6tT2jBGjMbssKGcd65jsMAPiAhvJDZLVzoQe1Yfc2JSxXDGi8Y9GB8GCmFAnsuFs4M8saHABqK0yIPNxexjBC6zZYz5Uc+cm5urHIbXpDzQy7VOxjLsDTP5cLLm8Ka3p3Ppe5eqcTabO22evU4vx987vgP/t0Yw2ZyEUwnQCQ4G9OFggBxxKM4LWRcivysQGBhHrpCmMDccgh28a7Gs49bW6GjRsbGxyg6IyugF+mZHi419aeNMRl0dWD8dICgXnsb5HAIDviXJqVOnMjc3l4cPH+bu3bv58MMPc+jQoRw5cqTKRxmoWbtbt27l7t272dzczMmTJ3Pu3LksLCzUNim7jsrfw+GwOuTa+Zybmb1hNxnlz8yPfJSFd1mH+znqYnw811AdA6YTB3LCXt1N2ii+T3YAdvNsl5SQA84UA2u1djYaPH78uJovCkI9EQNfXl6ulBammueiZDgQooRJHwyKExDQH/7Q9I3j9YHkKPP4+HgFE8vjJ5NRXowBkbsSLLzrw44XQ7Izx3i5Go1G7fgRO1enHaw7TDKyQjbYBWvB+tNsgdNxK9+XNk4GgpLg5Zg4E3WdB2/k/G4wGFR1s/Pnz+fIkSO5f/9+7t69mytXruT69es5ceJEFR22trYqI15bW8v09HTOnz9fMb8oP6QFrV+QAowPIy29KT93o0Gv16u9Vo9xs8DJiDo3s8sfnul6GjDTBJpb0Ny+6LqX36ZFPunn8W8cJgQEUIlnJTsweWJiotb7ajYRFtsK6FozOuBxmRvg81NTU1VnEJ+xcuPYk53DmVkLv32O+0EGJan6Z5kvztYdN2VjCd+zgRCNna4k9WNPQCaMfTcn6wjvBgggqp0/0BYZGYEZmT2VcXpQ/J8J8QdPZiUkIpBcE0mJsmNjYzlx4kRmZ2crI7169Wq1xYdtQq1WKydOnMixY8cq5bLQiT6M0eNrNBo1AsQMIEJzbrBbKQWU4Chgr8g8Xfbwwtso+b0jriEZUdENECamfEwJimhSjnzMpSevDwQWhuJzc5wn2xkgz9JRYDieC2O2MbPeZnqJ0siKEgR64rVxyceOxz2wJsA8Fp5BPsu8mRs6geEyBpw/6+10xbyCZcSYidysbZJq3yrOE97BNvZUxumCKYoE3EP4LDjMFA/mO45e9lLAgvPnz2dqaio3btzI7du3qwOljx49mrNnz+bIkSNf8LwskBsSyrIPisf/DWsxFoge75pghz7HT2CkJbuGd7VBG+6jUMwVBOLN0tD+znHtaDA2v/eDZ3tcpvWRD4aL7Ni8TB7K/Bkzisd3WIdkFL243JhQ7tAw7EPWfg09zDZ5mUsxXE4n7ASY+14En5/N703aOErReIEczafwPOTtcTAvjM/vq3FE9bpjQ+ikc/P9ap1PzDmZZCkklzzKsE+EMdQFY6OAKOODBw9y586dNBqNnDhxomoiHg6HuXPnTnq9Xg4ePFjzuHhYR0gScxMtJlUQ+vj4eCYnJ6uuFkcfvKdJDxS2rD+y8E76UQovLh6TnAnjcI7GvVECFt+no7PgzrOZF4uMV8fZWImBae7MIleC9OCz8AQm10wG4RTcyuc0AEUlzUBu3Btoj5HbIXl+JSOOkpcIwuScZYn8cKRGIJYD32UtvJ6Oji6xYIiQoi7vmA0vTz6A38CxPXXO6eMAubm9tUkcGKyylshA/WrAtbW1LC0t5fPPP8/jx48zGAxy/PjxKqfc3NzM/fv389lnn+XBgweZnJzM8ePHc+LEiUqxyek8ccbDGFEUIivQjVfqlfmiPTGKgcET/TESIJIJGuBfSTJ5n6Hhs3M8kxp+HsrjqObLUZk58B0/hzIJysafubm5qoEABfO6IRM4g0ajUSEnnBeGbUPHoKwPGBPHSeJUqBXzqozdUAKG5Z/boNxEgHOw4zREBXZSFsKQnJNb7kZQJuO4LzrHzw2FcYJ2lMnorXZPbZxMxF4mqXtUhGzPioL59L1er1e9BfvBgwfVgcNHjhzJwYMHs7CwUHnTycnJHDhwIKdPn85PfvKT3LlzJx9//HFu376d06dP5/Dhw5VhOD8BVrtOh3ImqaC3IRf3sGEalvIZM3xlbsRn/HtyC0MgjMPEgckTEz1OI1AkEyImW3CeOAdyTdfRxsfHq2YErxVGZYOAAOS+JuFQOqAuzCzQme9D1CFTIqbHz2UUZhTj5hMQUqfTqcpEoAoM2oQOjpq5Os/FCDnms0R+ZuDp+GFcyMJQlnm6Awm0xBo6tUJXsLGnMk63weENbLDcGHhnaIFH7fV6WV1dzeLiYtX5Pz4+nlOnTuXAgQMVMcHkzKxOTU3l1VdfzTPPPJObN2/m9u3b+eSTT3L79u0cPXo0p06dqrb8MIby33hd53ssPE3MQDCzaBgOlyNqyQgjA4zH+wdplCe/5LPAbLyuy0ilPEqWku/byyNrlzXMcKJgGAZyYi1Nspg4cccMkYFSSKs1amG0w7NBOW8zErCh8Hu+m3zx9YisBfVzDIlDrm1Aw+GwKnGYncbB4tSsz+Vl6Gvj4+cQgcyJq3TYyJR7GvqabNzV/vaxzSwuLla5D7DUyoVHd/2MUN7r9arTxfHiBw8ezIEDB6pNvYZpCNR5HJ5zdnY2zz77bI4dO5bPP/88d+/ezaeffppbt27l1KlTuXDhwhfeJGVoxb8bjUbtmMSSxHA0YlwYjY3EnhjDarVGB0s538XQzSYbRhE1TehYBiisNwRgZNyfMWxublbOymvCMxxhUSITZPbiw+HODgvWHwUDLrpziDVHPia/IAtRaBsZfaqlzL1+OEM7TCu0TwgEKTjiGoJyf5oGmCfGUtZsIbrcBOGdL86J+Y6Zfp7ltTabXq5zee1rnL/7u79bTciFWdPt7s5gF8fa2lrt6Mvx8fEsLCxUsGZ5eblmlFDTzWazaiKHrGAxgQKnTp3KzMxM1RL4zjvv5NNPP82FCxdy8eLFqhTBmFhoY363sDlnxZsT4VAmohzfN5EAyeSXBgGnXDw3IkBpzeZhhDzLUYAxWLG5H5AeOdoRcS8UAaKH4j7wn+c57012nATdMyacUFQf7AZ8J4rZ8VAmImpAygAp3RFkwo/1NwHoi0hmZIfjI5fluYa7XKRBOJ6SRGS+3BsyD8iepBa0cEDJaLsheuRqgtf2qSPniRMnqu58YIKFyCG/6+vrlYCnp6czOztblQro+LcnAi7hkYm0/X4/Dx8+rDGGTNDehn2ANC8/ePAgd+/ezccff5xvfvObOXfuXBXFDXW5b2ko0OouxiNkK7jZQPIJd6xwYWQcP2kWtYT/ZhNRXhsKYzAzyeJiNCVJYuY1qbPBzgE9fiMBR1Puj2KaRYZBp2XNTKsjkYkwM/YgF57Lszc2NqqTCEg5+L7/ADuRHeiCgIB8DK+JvobhfAZozP9BKsiWC1jd7XarMUAc4nBKYrRERnbET2WcX//616uHUGBdX1/P6upqBVephzWbzRw8eDCHDh3K3NxctU/SwkHZGKybi/l9t9utmr37/X51VqgPPnaTOFFoa2sr9+7dy3vvvZezZ8/WyikuQeCRS0aPHJTIajjqXI45IJck1QuBbMzOv/jbMJOFM7VflqxwMEQh7oHCoFhEThupf070dsR3JGPOzJGoWinJT8krn2BHruxoZcfL2GFPyWVdluF7PvqEA6NdSmm32xUiMioCrfG53d4uxho7xzYysU46n3d5BZmzRhBDW1tb6Xa7mZ6errXnEQTI10uW3bzIU7O1/+bf/JtaIdo5F7Dk4MGDOX78ePVmKrfvcVSDE2OSdBJtd8H0+/2K/WMCVlSOt0Dx6EIit221Wrl48WIt30GYjLnZbFZMsXOEJFW5wHkU0NxR1UZfwhPDF+AnRsviA/tMqnmRkI+ZYxTGkTYZ5UpAUTw10c0Ei+GxoxIGyXY3NwC4NsjlSGcjwygYG80OGBTOaGNjo3o5L2vEZ8xuIl/yQyu05UUv7nA4rJhUnJphN86E9kqTf47wOCu6oTxXoiM5OQGrhM9l7bckUF1leCrj7Pf7tXcssg2MNjsfxIsSOody6aBkAxkgjKVf0WCm0nkZz7YHRJmB3mZf7RRcOuE7JkbM3KF8fkGsIQn3cQ7G94FTRBiMn+hleOVIx9/cz2UJ5smcUCicH78v/3ZZBkeHAgEBcRhEOe7fbDarpgXGxh7PVqtV2yFTdoeZfOH/KLbrwUYjGLg3GuDYTdSxlkRDI5QSwtphklJ55w7yRZ7oIt9HVugcuozj4ihY59Im/mwPyA19Yu382fLa1zi/9a1vVRPAa5umJ4pZ4REg7CXK5ZyChQcatVqj3RlgfqAUxrK1tfO+RGAPcMUeCK9mz2rWFIO05yohzvr6eu1IDJTLeaebLVgUs7TIy3UtkzZWGntkDND5ChHF7CU/88JyHzcalHkPF59zuYjcsUQdJp9YT9IKZOP8uDxzyWSOZcb8rbBljbM0IH5fwlP33RrCOyqZ6TU0LmXlSJqMyBwOrWON0BHaFwlkRiLJ6OwqvssmfJ711Mbp4/ERBlibCWEkpvdRQAwAz1cKJ0nVRuc3M3nwyagvEmHjvYjIKCALaUbOZYsyd3M3iefJ4tpwuT+MHB7bJISjlCMaC4BCYATMi3FbyZm3lY3PlewzcrPT8f5FNzMwZuRrY3Q6AhKxDJwKuDEC2bEeKKqL8MwBHfCm6RLxWO/I2/g+jrI0fMbGelv5/cd7ee2wzMw758W4hsNhtWPK8JwgRM7JuOAh0CN0Ixk5JTvH3a4nHo1p40MBURIWg44NJoySu+zCvYhczkOS1BTOEIzn8zMMDMMy42bjcw5RLrLJFjOHjM3UOnPjPjyHBUMBDRlZEO5jhtdsq+drmfFZlM2wkO9h8Ga7WRPutRtMdAM73zWb6LdwW8nL+WPAjMMphp0Va1aWhvi9yR0rqtlZBwf+70gIHCePRD/RDZyhYakRn9MAvuto2mq1srS0lPn5+cohuQnBY8H4WA/4APgW8wf7MbVPNE4eVDYJO6rR0G5vzN8M0qwgE3adDgHxHW+SNjQ22YIRGfo4EtnIWEDnO1z2wuRNRCdHvpLB4zKpxJzYxwm54u+yOMjDxmav77nhXFAA5sGiW4mIQMAn/m9CzM+0Qvb7/WpHf7u9c/YQ87KT4JkmlBgT84V0SUYbpPm+iSATSYalfh6/895dzwn0gIxMhDk1Yj0dIJCx0zUbD89nXN7JxJZEjsN0qgbCcmXBu4FsvHva3p6/yYiAIOm1QPE6TNRvjXIuweLZOzv6umjrGpQjCx7Rns4QwkpmyMrPTeLYmKwU9vQshmEfsNz3s6HwfOdPPM+1L5M0QPN2u13lzEQL7o23RQ7IyrDZ5E4y2jJmJXXEd05XOhzXUFkzz9OGXUYYIhJox69ZZ9wcHmaozJp7Fw5jcQQHpdgZouyOil53ggrOzPtGS9RDREffcUBAdi6ChqOvdR5nymsF7fSxD+vYUxmnBetE3ZjcUANlNtT1K9aJZDDAvg8LiqKZjXWEdRQCzhBpHR3woDgHFsTUtpXLZAsKD0VPos983IzB/Fk0nkMagFE5OjI35oSyMS/P2wwnz7Dc7cRcRnBuz7iIDjgNz9dydpTh9/58s9ms1pWxl+kEMiWVwfCQDWtXrq2Vl5MpcH40QNjB7pY/IiMu65eJMBN5jUa9QQIZ0npKyYpxOLVCD+z8kTe/RxcMy73Wu11PLKXYm5VeiIViokzcYX04HFYlCRTSDFVp7MkIKvN7RyZHF6JNMmIJESLfHQwGFTQuc14Wgfu6VNHr9arDqVECwxDfxzmR8+MkVa7re/h3OBMbJvPDGbKoRF1HPyIiio3siMoem5UDJfPPjIj4GZDWR4yWqQWRnMjFWIhA8A5AQu5NZMcQvM7oELL0xgDOGWKdQEqlfHEi1CJ5EwCO12uAjHYrgWFYPN9rw/0xcKCsjZFxmjcxL7DX9URCCNiQpGoYR0kNbVAcbxNi0jBczkNQLHIQn/K2vb1dY9WYJM+kvauEmI1Go3odAEwZXttJu5U/+SIqwGhd3DfxguLwWWCq62yl8fu5yJZnG+6ayOB3JopwhqyNO2/4v+XC4rufdTisHy+zW95mpwJMZx4YKX/zb2RkR2Q0xJoyL1hbnARyMfHoXN0QH+eMXpqXMBGFbHxqPRs5HHSGw2EN5bnvGjTBDpySoPT8k9RSIuaLPF2SMx+z2/XEN1sjCLxXye45kllB7Y1h96wshjAWiqOqIYMP9XXemoxOCeDf5U5+Fs4RrCRCmKNhiD0x3rGcA0riziHG1uv1ao3jRAsUBJnxPRfLnd85tymJKpcTSvbbCIF7kgeaJTUM516OoP7b38NZusCOfjAG3kxtHgC9wfF5fT1/HEar1aqaO3BArAGOiN+hpxgN+a0dvWuTNpiSQwAGExh8JCZjx+EYNThg2VFjvLahp4a1nPNSQiqiHJGSbgcGxmJawVl4PJbzUzwcXhSShOclqb0QxgrN315019swtNKgUQpyAxu+DY/5+rvIZXp6Okn9fRiG5nyf+5K/2ohcJyUK2tCIxO6WMdlT5rwoqlENZInH46jvxg8z8nh3xm8nzR/kVSo56AIZIyeebeLJUREZMCecFJ/lvj410Lk182UuPNO5rFMIkElSf5W9oTZjNtFGOytyQ58MWcv0DRm6RfKpYa0jIF4Gb4S3wCgI3UQHvg+EarVaFaPrBBzjdpSzYSFY/rZ3QgBWNDN1JQHAfcs8BsjnDhQvDN6csaO0dBOheCYeGNtuxE1JItgIMDIuM9tENJxbCXl5Lvc0VLWTheTwIWZGRZazqX7L1eM3+UGEYb5lPdDMu+EnZBbH0Lhcg3y5MCL37lr2ZQRjLYiojJ/1RK9xFLvpWclNGLKi46wPsi6fia2YS3hq42QwJWPI9jFDQmCEE+Ayqk1NTdVgH96OBcLTQEEjNLOjeFUbwWAwqI64574k88noeBLnex4vY0BYXhzgiNsCW62dEwBMGpSRjr9RJuYI/HE6kIzqZ2U0MEzk75LFLuEvciEikfezDq4xA/EcLf18ckaaz01k8LnBYFAZV8kr+AiU3XJuHAljJiVpNHZ2mTBuUqbd0hHrHPe0jhFYnGaxviVRg/GQg7LGJvxMxNlZOW0iKjNv5uw0wUTTbtfeJ9r+9EKIKIoXl/+j4F4IBoknMwQD1tjASyOHQieh598oGJEXoTo6mNAxs+m8F8EZMhHBTYlbDqUzYgtbmcNx8XPYRRbNntfRg2e6ocNOovw/ykI05bISc0yKz2tljs5XUWofaGZ2FWVD0dlgjqz5YwNEV+w8bAAwoVZul+icVjHXJDWlRmbATc/RMBdHhHzQA2TH9xmz67DsLS2dr+fs32EzTjkwXOTouude1xNLKeRwPMCTtSEwaRSNyTukoySmkLmnYYEp/t2ICUcpYKW9Yumx7OHx0nwHaFfWoQxzh8NhdbyJvSxzWllZyczMzBd6dZPUlJI82ukCl6M346D/FOdkx2FUggxNfDgftwzJl2j6YD7Iww6OMRPVXLZhzJOTk1XEc2SAgTfZ5G1aHif6BLnCuEq2lGd6/s7xzJDbmIzoLHcTNuiA83r0jEiMfjtAuWzisVhnmWOJbMyY73Y98YAvb5w1HOT3ZthYLARgWAhLyWBRBpg66pP2fMnI47gcYphtB2II6OjNOLwYjuZltOP3XiTG4VoccxgOdwrmtHEloyjEPR2x+L9zYENYFA+FZj7Od7gM3xwtIJaA5Biv5VAaLgpaOjOQB7/jXs6zDbWdQzuaGcrxeUgjZMnl7zFOR2WTTXYm6IflU9Zg0QcTYDg/BxyOJGHtgOxef+bLeJ2j4gjNmZTko9HZF+xvz9+kXouz1eOBS4/pSGPCxsrohcdgXR9zHRF2l03Zw+Gw2mNoWA3ENEFgT2lDtWMxVHPUZaHdYuftRzDKfr9mozE68tLR25HBDgf58Rw3Y9gAuL9JiZKoQAFtaO12u7ap3Kci8Pkyj8bIWV+MBuMDZcBOe9sen0cOOBFyRWTu6IahueaIgyiRkB0+Sl4iNBTehslzHZn5v1MbOzQMfGVlpQZHDV+dC1O9sINAJ3kGdXenEDbUL22cbljGk3Q6ndy+fTuLi4tVJJuamqq98wIFptRC5GPxHUGBJiiOd4cAvcriM5d3qiNsFB7BeUcC8zA54sVlodyWBZnivM0K7RwC6OtXEzpPtlExF0MfHz6N08A5IBN7YZevyqjoI0OIwqQOhmHMl/shd6+LHW+/3682XZtY4SKSWqaG4DgqPsvfbl5hfnZWKDTRjO+xxugchl4+000z3JN54xx5rlGG81WjFP6PXGhQcImEuZcR2w7qqSPnbu8LefDgQd58883Mzc1Vh3/dv38/SbKwsJDjx49nYWGhmgBwlWiBJ0QhfciX4Q2GQ2uYhVnmncmoaaBk38oknj84DyfnpcBspCiPIY3lwvhNPKFQJr/KHs9kdAocPafMwRHH0NB5YqlQOA6iHbLjM6WRIDM7pZKwQqGd97Gvl7H64rNu+C+RgteHnyFTRy8ir52zIbIV340q3MPpGGgHlOF0h3kZdTlKkzq52sC6UmazfsFnlCmaEZnTsN2uJ5ZSDHfwuhMTE5mYmMjly5ergd+7dy/3799Po9H4wivnDHnK3AuvBswsqX6Mx0ed2FP72AiP19DIrJkjq+FekmrTrBWY6IhzwHOXgmXBgXsopp9lp+O6maMqBsxiuj/WeR7HUjJ25st2MTdzGGq50O4UAMVHpt4lgxITKTz3sq5n54ne4CDIoQ1RQVJ2Er4n8jEUdrRGb4CryNQoA+ht6GnjIAVgPOgBMmHs6KF1mTkC7e2szB3YsZsXsJMtr4YV/avrq+ur6+fnemKd86vrq+ur69/O9ZVxfnV9df2cXl8Z51fXV9fP6fWVcX51fXX9nF5fGedX11fXz+n1lXF+dX11/Zxe/2+jLmvewkVgPwAAAABJRU5ErkJggg==", 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jSprlIeeuOAa8+PT0dHESU1NTDU+MnB3t9vb2GgrhEgwRmO12Lq0wH0M51oZ7IF83IDjHqsdkx2FDRCmTMZHomuj+/n4h8gxr7ex4FrJkrnymds7oI/czcmE8bsnz9jAbtucNjDUSI3LXcJh54TAYe52z38h1rMZ3uiwMvfiTjOtiCwsLRdERLorc7XYLtKybrYlS7fZBHyQ5J1GNqGKoiVCIqs5ZMEC3oJH8M24zfSgEClATKDybRXTpoM51zYDyc9hnvm9oC4QqC/TsGIHl9vJEIkfpOq9F+ZEtc3fkQeFYW+ZsNOJ0hUhqWMe4/DPG4DTE27Vgchm7dQRHR4RNDt9UjY4YdvPHDRxOkYy+mMdh7XaGveYdIC9r6GoSi/+bdHtOc06EZ1qdqIXBGo4wyZrZstAxIvIxDBNlMS3Oz00csfj8ARLZQzpHtoD4ucmnOhdyzbVmou0xDWlMcBhWMQ/quh6LDblucvCz+K7n4NyRz7MW1I2B6kQAHIojAt93wd7OwOiiJpH4mY3W62c51QaBkTiy8nmgKGtymJK71GU4i+zRM57tKFazwk4rHOnsrL03s85DSb+I8qwhf45yHavOycNZ4P39/QJVNzY2ShkDOOAI1O/3G9umiKCGEhBICKDf72dtba0s4NzcXGllQ2huELBxEzlwGDUctkd1ecTRyvd2tHTTg+GcFx2FAwEwXsN3FB7Gz7kT96oNlntAilgB8fZ83yiEZzkfxIgNwfnd7u5uaY6oy0o4H5CJo7RzNebDHPm+u7p4LuNNxgjMKASHWTs357+GyKAU9uUOh8NSb7cT8XONApPmXt+aBHLpxfPms17r5xzWGuLAataRBkVIxi1eZvAsGLwm+d3u7m7m5uaKBwLzoywYMgpIEu/ozfOcT7KYNiRHOBMLPJdIRUQmz0vGxWuzvnzP82JxiFrQ/N5pwcIb7tZ0vSEZziXJdXNEQa3w/N4Q1pAW57Ozs9NwiIacODbQDbms19bMd+3MGEuShkOzE3HexqkG/j5lEc+F5zOvJA2D8Xq5zZKonIzzaDsBG6cjnllbxuH5mWzCORNsePaNXsc2Trx0HRHKjVWywCvDyDrqEp3w1g7/jkau7RlOurgOlCNvorZ62MZnIhuLRC7htj6PzwgAJTU084Iclm/ybBYMQ3RunIwVF4dXG5bh2GGkUzKu22LQKKUhuQ3IOSG5O/eua8U4DgwL+TMvtylyD9YJJIPBIzfnoYaYda7G3NE9LhuKm058P9CGP1vfw87D64yMuL+5iRpm12Qpz6uj8Y1cx9pszcBhw1hsBpfkusmzCM4RmQTKCrNmZpRdLNyzJlEQmhe1XihYUQTkiGACx2QQvz8sN2NsXCilDYfLOaFh7s7OTmOjOQvJ2KxANo464jliouTIpYa4wDs+xxp4rIZ5o9FBiQv4THRE5m7LQ16GcPwxA2xo6ohbK24NY2k+NwQ10YVOsH7ID6P0d3G2liV6yD08htrgh8Nh2ejPs0ygsWbcz0HrKNeRjZNFtHG4lAKedz8rSre/v18ipwULIdTtdnP16tVMTk5menq6RLA6f0Xg5LsIyEQUOTC5hpXAkYYCN0rbarWKIZNrJU1vXUc7Kx73d0mljqQmTcjpMFTLGWMix8KwnR/jbGycjAlnZLjrHB0lck7ocgxpAuNzOoKzdYT3HwxrcnKykVI4YtfkEJ9xVJqYmMj8/Px1+bPv9dU2PViG/ByCz0ZZj5tcl7GYoTb6si6hY/yc+VHnryH+jVxHrnPiEfBc/NzbqcwUtlqtkmdB/vh+KM7Ozk4xCBtPfZqCF54oi8HaOMy82atbKTEWDM9eM0lZ1M3NzXQ6nSwsLDSSfsMVkzhGElYU/t3r9RpnBlGbZS6gBZNWblfjfoyFuYM8yPOTA4WB+udenqMjt+VjlhlHiWIjO5+L4+jJ2Hi+0U5NxPE7R1xkMTc3V4yO9ff37YhqUorf26Hh4HmG83IbHve0Thi12TkwZ/SA+yJD2Oaa/b2R61jxtsb8XmALgnzExobng/AhwSfaMrHt7e3rapo2BARo40Iw29vb5d/8jr+JBj4hoM7VWq1Wo7/UORVzJJKBBlzC8XiIZi6a8/uZmZmCOtbX1xtHh5DPmcFlbDijZIwIvNOGn9NAXrO+IALWB0dCh5ajPz83GgDq82ynFzZcQ0lHpTp6JuM0oTZSy6HOP3meDdWkjx10u92+bt+w81qnIta3w8pWGKNb8jBmEAWfR8bPOSHEAIiIRBYTH3R9YDSuT/LvZHycJItRd9t4uw/P3dnZKQphIzFRAcxyY8BhZIqhKbll7dH5v+uS1GNRFI/R5JRhFXn5xMREOdplZmamHK9h4gBSzDDTf9iFwRj5XrfbbWxs53ekBXyOTe4YC//GYaFUrAEKiTxwXHzuMLLGeS9rY/2po6vzSPMaGLeNlPUybHdfLWy+u708Ltaa3znft/Pn3+Y06sjHmFzC8sUYSBHccXUj17HqnPTM0qVvIQBD8S5ERCuDhY+h1GUNRww+Q2F5YWGhwDhfKKNb4Wroy1hxGEnzbCFHAf9+e3u7UQskArj+aIjG9/g/+1n39vYaW9JwTMmBgs/OzjaICwzmwx/+cM6ePZvXve515Z6OPIyZFMIn0g2Hw0ZdGOOx/EzKcC8bPwp8GNttB+pnOJLzfX+OZ/I7IzLGgizcxmlH4fXCgaEv5M/O8XGW/i7Oy6Qg62E98XiQAQHKdmD4ayRz1OtYHULekEw0Q5g+BtPQz/kK3o4oYkKA+xtuEV2siJARKAlK7lqn4aoX34aH4tbe2jkORssWMe/mByomzX2NJhjw6jCPnuvU1FTm5+fT7XYLI+lN6Rjn5z//+czMzBTU8tVKGNzfik89z1Gz9uLkwjXMtMH5iI6amLHzdbT1z5EL5FXSPFmPn4ME6rEZGtc5nA/hcvM6KQUOFKfqkw8IIu5QYjygNOZIVAbe+wBwxloTbvz8Oc85DdmMwZOUhgSinBeo1To4GAnhMFHO/gQK16SDI4TzHozJSsY9IUZwInhDC9/jquElhkMEJgc0o4gsaMRHeTwWFgnFYVM5nx8MBpmZmcns7GxRcuAX7HVycBTHmTNncvvtt+fSpUu5ePFier1ebr311txyyy2NufNcRwHk5WM9k2a3Dg6Ln/F7f989yUmzo4a1MDS0E/TnavbTuuXIiiFwT+ZmIhHdMfNbk4E8c29vL1tbWw0jcWSt0wXnv46IrJMdjLkCp2t1enSU68illF6vl62trUZ0cK6UjJNyLzhext7T92AyrsHxPXt655pACT7PAgB9MA4blBcEJo2EnXvYg0NsMB+8LkprcgqlNZTBECCMvCGXqAiBBe0+MTFRSJvZ2dk888wzOXPmTPb29vL+978/n/3sZzMxMZEXvvCF+Y7v+I4sLS2VedT7IRlHMj7smwiDUvN55+/MEQNxbzI5uHNbjJe1qbdH2cGabPHn6qjjisBh7KhzdX7GmJ3GsL6bm5tZW1srY+x2xy/Kqp0E8gKy2jFbD1qtVnmhlrvAkLH3BR/1OlYppXz5WWV0Jw9F693d3bIrheiSjGExys9CG2ZgJCgzC2dCyUrBvSFeqIvVEBOIgoJB1fO8ra2t6wwGJXAzgoVtJjFJYQQZN3mdmWAUiPu7jWxvb6+xi2dzczMXL17M8vJypqen8/DDD2dnZydbW1v5+Mc/ntOnT+dFL3pRTpw40TglzrKsywjIDVYV+GyI7GjB34byfNfyx9AdcU20YOhEKQyaewIvcZCOPKQHpEp8x5UA57f8DKewvb2d9fX1YkAmg0CA/p0v0gTmWOehzqe53+zsbImwdvBHuY5VSmHCbDwejUalFra2ttZgQ/EukEcIDkPy0SVJylEfOzs7jXdmIBj+4ARYbJ4/MzPTEDyL7nGz0OQvdSQHnvtYf+fKJpCS5kl2fu2hoasb1IlMKNP09HR2dnayurqa0WhUFnZzczPXrl3LcDgsDmdpaSmbm5tJUvpbP/vZz2Z5eTlnz57NyspK2Q+ajIkLG4h3pzB+y/kww3LeZqdofgDyz2QXRm5UhXNkPIfxAc75GV9N6uEUTRLxbHf1DAaD4mxnZmbK2GdmZrKwsFDKR86rWRvnsCAT93rTHur0zs0vHrtz0Bu5jtVba2rZLCWCr70YR4zwc8gMfkYUdQNCt9stueBh3UCMYWJiInNzcyVv8zP4bs042rAcgVkUnIIPIzYx1GqNu4f4HQrCz5IDZ7CwsFDGykKZSUb5fGLBaDTK1tZWaX645ZZb0u/3c/bs2bzyla9Mq9XK5uZm7r777jKmK1euZGdnJ5ubmzl37lx5nSKwbHt7O1tbW40jWNzBBRuNkrKOrHnNOjpiOq/i+452rLd1yOSRz/m1cTo/9hjgNVhXjNY6x71wkC6HJQdBYGlpqcGozs7OFr1hPlx2GO12u3Aldj7MCWacecNbWEdu5DpW5MSg7G35GbB1dXU129vbBf8vLCxkY2OjEaWIQnzfLKtzshru2puRd/pcGJ+wYHKJaOHieM0Eky8bGgG3TJSgDDWcdW4KzKzhtb09kWZ6erqcNri+vp61tbUkB0o0Pz+fK1eu5MqVK7nrrrsyMzOT9fX1JGPjY0xra2vp9XpZXl7OyspK2u12Ll++nMuXL2dtba0BsxcWFnL69OmsrKwkOYDydiKWH/Mw1PUfl0DsgJBFUTgZCMpuR+0/vod/X7PCTjPsLPk/z8VZQLYRvQkK9bisH+jd7OxsIyrTBYQM7PS9B/aoTG1yzMhpgoGBwl6Rb7DIKB/7Me1B68Lw4uJiY7cDWJ39n0RKEnoL37mh4RkLzHj4Y9hBY0FNCpCHGJ7VEcWfZYE59Y3vJ+O3JSMTcjzKOBsbG3nmmWeytbVVjg9dXl7OaDTK1atXc+XKlayurmZra6tsndrd3S3GTFnGNP/Vq1eLYpjYoBa6urpa4PTJkydLjdVOKGk2Gxg5WR71Ori84uiCvjii+bV5TiPMrCNfEJUjm42f9WbNSJsYx9TUVDkFwvwDEc792Z47/IchOb/DMQOLa+IK2Gzdv5HrWJETw6i3is3Ozpafzc/PN8ogg8GgEV0Qjtmvra2tBizFe1Of47tJ80wgLyhKiBGgVBgJ+zJ9P57HZ/f29squA6IGC+yI7UXAyMit2X7GeIiSLC75+vb2djY2NrK5uVkWklyI566trRXiBjYQzw6EWl9fz/b2djG+VquVK1euZHNzs3QWwQgvLS1lYWGhKOLGxkY2NjYyPz+f06dPl7nXBuPIgPK6DuiyTO3IDDtpJkE2tfJaJ6xvyTha1jpo52CCzvoBWjMhxnPQOzsdAoWjOEjC8N5OnZSL9Qap+Q3bN2xnR/r0s5e9WTLeKcDk8YwoqiOTIadZxfqgLb6D53cdKhnT3FaeOpeEQjcMtWGyAHyenxGtYTExbNfWzFCTp7qUgBfu9w9Oh0AeHH6GYbBoGPjKykqWlpaKUkxMTGR2djZzc3O5cuVKkZmNhB5d7v/UU09ldXW1yBooNxwOs7KyUv6emppqvAz26tWrWV9fz+23356zZ89ex0wjH8NLPmOoZ1jJd1gn54E4DNbUZBMOxjL3qX7ohp0qzzFasgHWeWjSZIkNPdEXE3k1nDb3Yn3iZ6A/Q/GjXEeuczqcG8oyeffVcko2Lw71SXMoJxPjexhSr9fL9PR0ZmdnC2tqdtZCT1JOy8OTG/pSRiCS83yMwlDIUQLoi5GbWbYn7vf7hT324vd6vWxvbzfqi1YeYOb8/HxmZmZy4sSJzM3NFXgEDIP0gtXt9XpZX18vCkxUpcuI71y7di3t9kFpaW1treT8KNqdd95Z8lGMYHNzM5/85Cdz/vz53HfffYX9domhbrvDibiU4AiC0brRvt5t4vzUTs+Mq9fIxlobDPXTugvIAYHv2LjRK1KDZJy6mcipYWuNFhhfneIdNe88cp3T0az2ZBaYi/SGd0yM/ZYIHqOibxdhUANE6GbyEJQbCcy+MjZ7M59NxMIbCnH/TqdTnk2hOknDKIGYm5ub2dnZKawokJVIhuJhjAsLC8VoaN/jM2tra6W5HRZ1ZWWlzGFycjIrKys5efJkOZqFcZCXEk1XVlbKsRntdrtxyPTq6mr29/dz+vTpTE9P5+rVq6VEMxqN8thjj+WZZ57Jq1/96iwtLTUUFnLEjK0VFkV1vRR5jEaj0q7Is7hQXpdQrNzcGyOyk7DOcflEDD4HCkImOAkjERwj36kJHUdCHwSALvl5jBP+5CjXsWEtwmTRbQju/6QGxHtREMCpU6capQQiUKvVPErTuYpzGITnd12gLLTMOYI792H8PiKF8eJkMCQMwjkMb7ZiA/nW1lbJL9gKR84M3CePg4afnp4u0RAHRi5q2OpCPznn9vZ2aQUEAuPUTp8+XRRjZmYmOzs7uXr1ak6cOJHZ2dlsbm5mcXExZ8+ezdraWlqtVlZWVnLHHXfk6aefLjKAMPrIRz6S17/+9Y3arSOfoxFGWacakFbD4bA4JsNMnoduIGcz5qw3sqkjN8b51ZofnA65EwrYiX44vbG+M4aafHKkdIrDujuHPep1ZOOsqXYeSjShUAusszElydraWlHuc+fOFQH58GMUEUG4vokiuAaJ0MH4GAjGSV7JGPw97uujS0zfUxPc39/PxsZGrl69mmvXrjUcQTImItgJYdKC57Lvc2FhIdPT02VRrXzsuAESI1OiJ+N1x49JIEidqampXLhwIY8//niJ5t1uN+fPn8+LXvSiTExM5OrVqwW5nDx5MufOncv09HQeffTRUpve3t7OI488khe96EVF9t6RY5kiBxSfiE6JZnFxsTgiQ7/6HsjMf+p1s1z9PJM67igisvE957J2LPW84Em82b1mr9GZZLxbiLTqsKBwo9exXztviDMYDLKzs1NySmAAngx2EWJjd3e35D9nzpwphukmZJ7BxIF5Sa4r2+AlvXF7NBqVd7VQb/WYkjS2VZmJNBu3t7eX1dXVbGxsHHpeDmhhdna2dAL52Ixer5fFxcWSV5ptTpov6k3SgP6Li4vlfleuXCks8NTUVPb394shJ2mgF3bPPPHEE6XuORgMsrS0lJe85CUFRp86darA2ytXrpT1eelLX5qHH364GO/q6mouXbpUSCLWxjtF3JbHvFBS5uEcD+P0uhyWHyIXDIzv0rvqPtxkbASOtKQqrpOzdjXbap1DF5y68RmMn8/wbIzbfAhO4Tk1TuN7vDWGZzKFyRi7U1ppt9tZXl4uOcjFixeTHJRhiMZEMvJIJ+TOT3mOIYSJIzpg8Phmd0ejUXnnCI0MyTg/GQwGWV9fb0BWHAD/T1IMc3FxsRisFe/kyZO57bbbGrt0UFrmY2WC4DGch9Elv52bmytwjM+ahd7e3s5wOMz6+noDBdx7771ZWFhonAaxvLxc3l1DhJ2fn8/tt99eIvRwOMxDDz2U7e3tnD9/vgHx3OdKzdJO1utnpbZRW+kNGxk39/D6IWN/z7mgHQAGWJNU3J+fMQejKL5nwqvOX42Q6gYXgsFzTgghMLwz5/54wBiP32doQoJ/D4fDQgxsbW2VPMYLxIKg2HVRHM+IcngRTGfze8/Bu/75DPW3Xq+Xq1evNuASn7VzGA6HmZ2dzcrKSuOkgp2dnWxsbGRnZyfT09PlPZL1GOouHO5vmIVTAPoPhwf1YGDW8vJyGef+/n7m5ubK1ijKJL1eL6dPn87i4mJ54aw3B3NfGsOvXbtWWtlarVaefvrpXLx4MVevXs3999+f5z3veWUudsZ2EoZ1IA47T3dOkXrYMJERY6wZdBOC3PewrWNudMFwHBkxGObjVM3P4KrhrNlfOwmTWPV8bvQ6docQXqLdbvZGJuOXw0LW8BkM2EJk8VxbRFhmzoiiSQozSgTCEK1wdOE4p+S+9orJ+OBryJ3V1dXG6yWS5gHB1CRPnz6dW265pfTZmvq/ePFiNjY2sre3l0uXLhXm1Dm6WWdvDLaRuie03W43TjTY2NjI9vZ2IYVQcEd2K0syfkX6+vp6KY6TI/Nv8kTW5uLFi7lw4UI2NjYKpDx37ly5P/c1F1FHOaIoY3PZyySQFdiOl3m4Md2GRZTk/jwTozTio8toa2urYZTmCnxvPuPOIO7DGvlyuaiG1ke5jlxKod1rfn6+0V8L3MPbwzq6fQrCA2YRo9ze3k6SbG5uXkeRu5DLThWTCqbVGSPPgzTh986LcAwYeK/XK8pOUzO7D5Jx+YBSyIkTJ7K4uHgdqQC0X1hYaGxRsjHyanYrr5WA37kxg8+5/tlqtYqCzc7OFgQCDLRh9Hq9koviXFBAatBJsry8XJzpzs5OLl++nOFwmMXFxYJsvvjFL2Z7e7ucRri9vZ3Lly+n1Wrl9OnTefGLX3zdFjkMgHUikpqosSI7D+V0Qr7niAts7Pf7jTQrSYH/NXPb7/ezublZmmR4Hg6jbifk94zdcJ7fIxs7Aj5X56DPiXEmKbCn1+s12uqAS+QI/X4/8/PzpZbJwjApIh45Xa/Xa/SGulEAKINgTRJ5h8JoNCrHStasIf/n8xgMpyVAnCTjMggbyycmJrK4uJjZ2dnSKECh24ZpheI7q6urpaQEk723t5eVlZVGGaeGSZ6royoKihOgHxkZ7u3tlbNwiXTIHgOsDYaNCkRN1gmCq9Vq5ezZs5mamsrCwkKuXbuWBx98MCsrK3nBC16QEydOlF00zzzzTD7zmc/kvvvuKw0VRFHgrIvzXyuPxGhxyBiaz3LiPkTMdnv88tz6BcigN0pghq2sC9+t1wTnyphIF3CG5hv4fM0nGK09J8ZJvrm/v5+lpaVGYowwMEC8GJ7dJ9VBQKA4PigJeIpiOpIuLi42NmrjELjM9jJevJlPO0DY29vbuXr1amOjLU5hdna2EFNTU1M5ceJElpeXS6S3x2eRMaJWq5WlpaXS0cP9acMbDAZZXl4uc3UebbiLsaIkIBQ2hsPeUmPleE0aOSiJbGxsZGtrqwHVyTUxSJSKrXq0BZ4/fz5TU1O55ZZb0u12c/HixWxvb+fKlSvZ29vLG97whtx///157LHHsrm5mV6vl0ceeSS33XZbaYLAKDAyIyrnrTYUPmsI6UO70BUQnBtEbOzIFM7DLCo6yjhc30bu6LBRmXNWQ+caAjOf+s3iN3IdK+fEa6yurqbVauX8+fM5ceJEIxIlaUBblMUtdVZKU9sU+ultRSgcuwlB4v7Wmq3jWfzOzGy73S77G7e2thqtfJATKO7Zs2eLU1ldXS335HAyR2h7RYyb7V4YAUw1hBoe1rmoPTBwChlxuT5IDoRCsg7tdjurq6uZm5tr9DrzfcuLZ21vb2dtbS1TU1PZ2dnJwsJC7rnnngyHw5Jn3nbbbbl48WIpk124cCF33HFH7rrrrly9ejU7OzvZ29vLV77ylZw/f/66pm/QEMZkyArjC7Q2R+F/c/Edow3XRZEHhm7uA33DIHEC6BTrAIJhDjgy9KZmhA8ru/Cso5BCRy6lLCws5Pz583n66aeL4WxubmZmZqZECryclQvv4zdZISQ8jwmemoll4ggKVtBG4dwWz4ygYU2BM7SyGaaYWGq324XhBB34tDXgqwkrxs0C9/sHR6Gsr6+X2u6JEycyPz9fWNeaHAOSw2QPBoPGie08A2dnQqZuEgfKjUajxjk5bBmzcwCagWrgDYDOyGB6ejoPPPBAVlZW8tRTT2VhYSFLS0t5/PHHs7i4mNOnTzeK/5cvX87y8nKWl5fL2jEHIpCfjZ5xqgX/Z37kiHZWhpDkniYHTfwhYyIzMNivfLDeOS0yC8zvrAc8j2jqs5Cf85yTaHTmzJmMRqM888wzpfi9vr5elAYI58FsbGyUwRPiTeYAIVmwwWBQtjXZmC0IDMNN0i59YCTUB7e2toqw/Do/GyZR/tSpU0mab7DiXu61deREKYHC7fbBWTLLy8u5evVqRqODntZbbrmlKDzOjBye1MDHf8D8ISvmZ4XG0EzyXLlypXTnuE3Nnp+9sm49wzmBfDAOatNJcu7cudx6660lSk5NTeWpp57K9vZ2br311szOzmZjYyP9fr/skDlx4kTDsbm8AZICsvozGJRJM3SESMnvzNoiG98Po8VhJeMaOxfGh5MnBULvivGoAYI1co6LTvH957SUgjK3Wq2cO3cup06dysWLFzMYDMoRGBglE0d4wEqXWZgg0NP4vq4ZcdmQ8L7kgOQd7XY7CwsbWVzs5a75B7P/e5u5+jt7+ZUfGmRvKtnN6SxdOJnF9cVMd6cbB4MtLS0VQ/BCTk9PZ2trKxsbGxmNRmVHvHNDRwVH0Pn5+dKQTvQE9pIHwrD6+7QDmlhAcfyqiu3t7Wxubpa2QOqcbNpmvJR8MID9/f1i1NwHttoQDLRBVEf5u91u5ubmSm569erVXLp0KcPhwY6XEydOZGdnJ2tra6W3en5+PrOzs2VuRD1aLrlwinZ+dtDI3H/bsBm/iUhOfzTUJRozP/SV+7hOyUkVfqa71YDkrhyQzjiFuNHryDknORlGc+rUqQJHmaAJIjwdRXgEgWL5ZagogYWJ8PA8eD8fvuUtZfPzE7n33ofzvd/zrzL/S9vp/NV+Wo8lw17y5o8k/W7yO69vZW+ynfe/+lwuzt+RE2svz9nJ85mdmc3u7m4xdqJKMm6I6Ha7ZRsVZYzhcPxWM5rsyfsoBc3NzeXy5ctFERcWFso8kE+SxtGeNdxLxifOzc7OFuJmamoq58+fz8zMTMnlrly5UhRtf3+/GK0b8i1/ouhweNCMYBhpg4R86nQ65SialZWVAl2feOKJbGxs5OLFi7nttttKaQaoTG+y96niVJNxvRdn5HzOJw7UW84wYtdbfVl/LE87VOeqdk4mllgP/g8yMWLBSXAfN108p2ytJ4fh0fMJ+0VOx8QxIpMfFgTwFCHMz8+XnMLlEiaK0pEX0US+vr6e2267mre+9V9kZmI/rXcm+eLBuNtJZg4QWb73naOMMshb/+VXcuH8V/LonZ/JB77zm/OFM3fk5Prp7G3vNSKnWTiY3RMnThThU7eEmcWATU6srKzk8uXL6fV62dzczJkzZzIcDkuezjw4eBtjmZmZaTQvoEicjQqRgdGDLBYXF3Pq1KlywoE9NylIvX2OUwlo/8PhsqasCUqKobIndGZmptRDacRYWVkpKIMcn1dTwJRjiHNzc+U41WRcF0WGOAkM0AwqxmLH711HODsbvo3K30+uf9cnOmtm3jmpjdn1UMZjctPR/2tdRzZOsLexNIKlbY2JbWxslA3TGKxzODcDcO9kfGwmAqm7QkzC4C2JIp/61EJ++7dvy/d//yPJ9yX5zSSHoAn8160XklsvXMtLPv3O/OX/12Q+85Ln5+5r357Tq7dm2BuWcaP4U1NT2djYyO7ubmlkpxGdhd7e3i4wHkUG+gHzyNVBBZBNwKJut5vNzc2yV5RnEcF9ip4Zx62trVIHfNGLXpRPfOITmZ+fz97eXq5evVoiNqcV+j6OCDhLGjLMZjPG/f39bG5uFmi3vLxc5gmB9thjj2VpaSmnT58uOoIC83wiOvns0tJSwwiZDzDRxujx4piS619/gDN3+kE0q+dtYsowuCaFMDq3LHIv1ippdrsd5ToyIQRcY8I8mOjZ6XRKZ8zExETZgX/69OlC+BjuMlkM2h6e/1PvxAsxYRgxb2FaW1vL299+Pq9//Vey8oP7yX+VZO3rz215Lfm5n9xPv/tQ/tGf/nze+eY/lPsuvyzPW31euhNjzwe51G63S5G/0+kUhUrSYJLd2LC7u5u1tbVsbW3l1KlTOX36dIkknPUDeQNRNDExUZSWeyYpe0GtqCg5LO0dd9yRL3/5y0WZeK0DSsgrIlhH5AxBQpcQa2DSzaWtqampnDp1qpRrut1ulpeXy86jJ598Mjs7O7n11ltL9EBXIMXoF7527Vqmp6cbZzCNRqOCTtAV9A/HxmVHz/35eV0+qXPAwyIxskGfnea0Wq1SdwdeI3/f+7AofSPXDRunk3LyPg+EiMY5OFtbW6URm72QCBYjb7fbhbSwQOzZkjTOkzEFb4aRjpiDnRUr+fmff0H+0z/1qXTuTvLxajIrOcC5q0l0INpE/+DPT/39Xvrd385f+6u/k4WN07n/M/fnJU++JMs7y+kMOo1SR7/fLxuJl5aWyoK5TolHxzBbrVapmTJPjvTHIE1usW3MeZP7bZHJcDgsNc1Wq9XI4VFWYDO5qSPH3t5eo648Pz9fmkNQMiIa9yAnxUAoEVFiMUH15JNPZmlpKUtLS0kOEMbc3FyWl5eztLSUS5culZz07NmzjVSIcp2hqSEqZYs66oGsjNAgvJyb1qka61ZXC4zokAWf8xgYd5JSyjtqI8ING6cZMhM/7uP0C4ROnDiRa9eulWI9ykVzta9+v19yDfI17o3iWQERNAKmlMMWqcFgkA984FR+4idmcsv37SQ7yVZ7JlvfN5dPP3JPtr59Pp2FTrq/PcqLZx7K1K/2snx5Nd1nO5u6g+TP/91kY2E/f/WvPpknzz+Z9+y9J9N703n+w89P9ObBVquV/qCf0XBUIB3kxkxvJg988YFkcKAQ+5399Kf7mX/mYPvc3NxcabgA8mLc3tzrjpidnZ1CFKFs/I5clSi8t7eXpaWl8nOM/ur01ay31ovxDofDLOwsZG53rjgcnKjzJxs9fIAdEFCXfBmy55Zbbink2sbGRimTjUYHLw3m0DEiE/dwdIPpBpKzzklK/m1mNRm/ehEeAP31H7d6JuPqQZ2D4myRN5GSdIqc3fYCGqG54Tlv37OntmcjtJtEoIgMnOh2uyWfdNnAzccuoziPM62ON5ycnCy9uXQrcSbpYNDOpz51R07/N5/Pp3/oZXnf+741l9cWs/+CZHTh2SNKznXyvolXJD8xyp1rl3L+qQ/mu37zi+n2R+kOkj/9j5L/+U8lTzwv2Z3eze70bj7yyo/cuLBGyW++/DcbP2r1Wznz8JlMtacyNzeXxf3FvPLhV2Y0GKW91c7pR0+nPd1ON9200y5lEJSS0os7WZLxC1ppbKCvuTvRzXZvO8+ceybv+Y73ZHRulMcXH8/q9GpjXKdWT+WNn3tj7l+9P7dcvqXIm80IRAlyRVBR0qz3UbPd2NgoZTMcLmQTxBmQHcTFZ2uU4Boh+b/7mx2hiKRuEjCM534YjWvDPMNRlr9dk3XDATuaaibWjo3vPKcdQlxuT6K2iXE6l8SjLC0tlWb50WhUvKgLvExuODzogeQM3HoLkhlioBU1P9fBTp48nQcffF6efvrleeyxu7O7S+1q7OkPPO7BvR8+cS5PnPwP84EH3p2/+Pc/klNXklOXk+Vr3Tx1S9KfPNrr25IkrWTQqQ4S7iQXXnKh8aPfved3kyTdQTdLVw/eGPbSZ16aOzbvyMueeFlGg1FmhjONshSRDI+8u7ubzc3NzM7OZnZuNjuzO3lq/6l84Js/kIde9FCunrmavem9MRNWXRdPXswvv+6X8+u7v563/P5b8trHXpvZndkidxwosNy8AYbJeMjLO51Oaetk7RcWFsoOmeHw4H0w5OjsAXb5hgjlqMNJizSDuBsMvUvGZxzbwPjbB25hnOiPNxoYsTmAcF/INDqxapbXxsz3bvQ6Vp2zxtYwcHg/IidejLoc+wSZKF0xeC/uZwEnaXhPFq7f75eoiadFYIuLizl58mRWV7tZW4OoGRQGFdYRej15dufD9HQ2F16T3/jez+SP/e8HJ9E98MmX54FPvThPzT2Vi7dczNN3Pd3wfqPWKNdOXcuw8zUo4ST5Wg7z2c/1u/1cOXMlSXLxxMVklMy+dDZLu0t58dMvTqfTyUsuvCTbe9sZjoY5s30mk8PJ8Vm4y/08ev7RfPiOD+eh2x7K5sRmdiZ3vqpBHjaO9Zn1/NI3/1Le84L35I/8zh/JbVu3pdvqFmjsWp97Wu2gvda1snJoNvAZJpYD0zjjd25uLqdPny5r7pIFhggshz1mXbwh29GMaIkOYYzoWBGDEAmGydxMiiUHhg25hc5CODFe0MZzbpxgayfKsG9Mpk62yQfm5uYaOQr1PRadXBMjNxxAWBg+3TrtdruUBZI0jvBAmCY/uI/zJdPvc5vz+fLds0k20xoltz6zn5XWS/PCx1+Y4WeHyfuS3Z3dDIYH6GFnfyfrL1hPa0ZKO+znky/4ZLbnt9PutNNpd3L3hbsz+/nZ8o6Tzc3NnD59OlvfvJVLZy5lL3t5+OzD6bf7Y0NvJdtT29me2s5TS08lSd5197vSetbablu9LQt7C2UNVudWc3HpYoat4fUGOUrafZ0ENzq4f0ZJv9NvfH7UHuXJ5Sfzt7/3b+cHfu8H8j2f+55iJEDXZNz95bIWNVZgNpu6V1ZWGjwBRkvnEiUbGO1Pf/rTueeee8rRnTZKw3ry24mJ8eHlhrJJ80QFgkWS0uTC7w1X+VlduuECPRq64oScs5Ke1e8AvZHr2LAWD4qXcpcGAgQGEZ1oU+MzhgObm5vF0+J16u4P4AZHgLDYboLAq7Fwbm5wjZTPuAFiMBhkbmkuE/sH453oJ3/5v/t8fuOHPpsn7ro3o+EonXTSGrQyNzV3cNLB2l7u+spdRblYjLu+cFchBIChjz76aJZaB+2Bly5dyrlz5/KiPHuqXXuUS/OX8lj3sXzuBZ/LQ3c8lI25jesi8rA9/v9jJx77umvV6XVy9uGzOfupszn/kfO59fytOXfuXMm3+oN+Hrz9wVyav5RP3PWJbM1sHRhqK+l1e/nVb/7VfP55n88ff98fz4ntEyVaeqM969Jutxtv5HapZHV1NSsrK42Ii6OH/KO1b3d3N5cuXcpDDz2UU6dO5c4770ySEiHrNUVPCBLoW9I0NMgfggpGWkdEVwP4vWue/MEWkubJDIwHpOBmmueUEMLwfLAXSmj2C9jqmg/HkLCI7B7AY5HX+A/5huEwsBrhYfCuk3p/ZpICoQwtgB0Y/2AwyP7Ofl7xqRdklK+kleTUlf388Lv+j7z9L/3hPLp2VyPHhazBM1JiYH6cMNBut8tLhYgUJ06caORfnU4n8xvzuXfv3rz86Zfne1vfmy/d8aWsdlbzhed9IRduv5BrC9eeXYSvs0ijpD1s544n7si3f+zbM//h+eyvH+Tv3Se66Wx1cvsdtxfFOf/p8xm1RnnyoSfzS6//pTx87uHiFAadQT5z/jP5uW//ufyR3/0jue3KbWm3xucVQwpS5KdOSkcQpNHe3l4uX76ckydPNgwYJwr51+12c8stt2Q4HObixYt5+umnMzExkdOnT5dthNR4XcZjDDhrM8h8jjV2s4I7gOxsTDw6Crp8UzfRYBsu6bgBBZ27YVv7Wh9utVrX/fLVr351Xv/61zeMj0n7ZT5ETm/N8YZjanc7OztZX18vJRY24nq/H4vuwjcJvZvD3ebnumjdiOyeUxwLCtPtdjPRfjh/+ef+RVZWx4a8+ab5vOMHfywPXz5fNok75z516lS5v2uQ9OpevHgx7XY7J0+eLGM4c+ZMMW6fGuETIZKDvHZrYSuXT1zO5+76XJ5ZeiZPLz6dy0uXy/jOPnE2CzsL6U5087IvvSzLV5Zzx+U7Mts5KDtcuHChFPvZOH3rrbem2+02to7tDHbyG6/4jbz7m9/djNqjZKo3lR9774/l5Z99eVqjVnGurhHaSdY7PZA37DPGQxpDHZDXSVy8eDFPPvlkut1uTp06lZMnT5b9whw1CoNs9ONWOS5yZhuqKw7uscWB28kTNTFMDNJNGRBEsMjkoGwQf/e7352f/umfvq5TaDQaHepuj3USAlAUA3LYR6mIeC508zkaFDhkGQ/rgjYTdiM9uyAwYhJ8QwsE4h3teMq6ewOPi5Pg+M7W5D1537felrf82mMlSM3/1mZes/CuPPLSt6bXG3egbGxslFyaMbtRf25urrS44UFpccTZgBCc1/GHktH8+nzOjc7lVZuvymg0yqXhpVzuH7zjZDgYpn2xndnMlqi1ubmZUWuUwfSgRCN2EFEiuXjxYs6fP19kOBqNMtudzVs++ZZMTEzkX73iXzXy373JvfzyG345T809le/+7HdnujV+gbCjCWiorhVSSuEt00kaTC2v5xsMDvaXnjx5MtPT07l06VKuXbuWvb29nD59unShYZTe+F4c2rNkn8tyjNE5qPNGDIqf1Q0J3kmFUzI65P+QXc5hacaou5K+1nVk4yRaUQznPZEM3rvDOXCJASbjV9Yl413sNEzPzMwUqISy4ATwuMBiQ1iiYL3x2ZQ4G2zxYhS9WRh2TXS73Yz2O3n3H3ptXvCFi3nhF8atc/f+1pfzlrt/O/9i6a25em2tlJFQRhwVfbPMExhM04FfQ+8yAe1f7Ovk9xg98mi1WlnqLaW90S77YEfz4yjBTgmMmw6Vs2fPpt1ul1fZr66upt1ul1c4IIvZ9mx+6KEfSiedvOdF78nm7GaB0ntTe3nna9+Zi2cu5od/74dzevV0WV/XFpExKAhmlTVkHhwUZ9lx9tBwOCzvDb106VI5SAyU0el0curUqQZzi8xpeXTrJ5ehrJ0q8mUd+L4bMuxsnMaZnUUePuSM7x7J1o706aQBGZMU9pHfsTAYJcYExneXCVDWXgfPQ7M2jB4GypEXPpENgSMUe0agNmMG+hClaQ2s89GZK8/Lb7/2jTnzzG/kBKneRvLiv/PZfPH77s5H7rmnPJ+dGSABFA0IRr7Mjg3OT6LTJTmc/nf+TtRFmVh0HCLj572eHPMJN8Dv6XHGIJ5++ukkKb2xPKsz7OQHP/GDeeGXX5iff9PP59rStbEStJJP3POJPHLukfzQh34or7nwmrS3xycCOodDDo6edqoYMOkJUchohy4jTqDnzd7Xrl0re1hBbYa5OEVkWp9i4VKQDRV9ZH1x+jY21opAYpLR0Ttpvnz4KNeR34VtxWdC7i9Mxopm5gz8bhzunBGBOGLMzMyUPX+cqE6UJhowefLNJI3XHvhAYzNyOAvnGignkOTRO1+cX/3e+zOUYCf3e/nB3/iNvPzRRwucRS7Ue13TWl9fz+rqammsMNXOeIlwRh04KZAGBXeUidKBGUqU3ewlSrqxsZH19fVyMBvdVVtbW7l27VpxIEY8w8EwL9x4Yf7su/5s7n/4/rScGrWS9bn1/O/f8b/nb775b+bLJ76cYXv85jaz7IaTpDYYjY2DORGF/F0MeG5urmzwZsOA06q6DIKDox6Jg0NOjMW/A11heP7jPDUZVyCYt1sGkSfrcRRImxzROBGgW+rcvI0ioXSGOHUzu09YY2I+HQ0Gj4kCL9zeR1MBY/HeRgyYfIP74FlNEpnUIi/o9XqZ6E3msXvelF/7gZUMpZfT+/t5wwc+kLlr1zIajcrm7NHo4P0sV65cydbWVjmljt8hK5TX/bNEWIr35EhEDiA6YyTv93tBS0/vzEwpS4AK/K7TdrudM2fOZH5+vkDtJMXJctYS47z72t152wfelh95349kZnem0VAxbA/z+KnH87ff+rfz6y/69axOrBZ5ck4TpJi7yNAHG0Qyrh9ubm6Wt6AR6ZI0nDVb5NwcUZNDOBtgsOvcGCRBwtEPQzdfgtPGiTsi4kRcT7eR85mjXEc2Ttd7GAxR0I3P5KKugbn5156TC0OpW/qSFAYYnM8+QOeWLpLXdSn3NhIlMRB6PZkjHm9nZyfbe+18+AXfn2vP7qTguv3pp/M9739H9k7sNork1Fx5DwxkhUkXoJ3PkR2NRqUFjy6Z4XBYmro5LRADxRMTUVE8lwEwSLgBWPKpqaksLi5meXm5wQGwc8IQmvHNjebyPV/+nvyVX/sree1nXpuJnjYvPEsW/eq3/Gr+/hv/ftYW19KZ6JRnEhENC1kXxjMYDMrrI4bDYWlI4TxhmlM4NX5ycjKLi4tJUhha3xu9wuHgIHBqGJfTrjr4JGOEZ1iKvuE8a5baBBKONmmeqHAj15FhbZJGuAeO+dBkR1ND2SSFXud33tbE5ICl3NukiAv7HBdyWHsV3hMohKGymC57ELn4vAvtm5ub2eqdyP/2J74nV1f0AtUkZ595MpfOvzv7w/Fh2jMzM2VDM46Ls4m8ibzb7RZlQUa8qQwEYBg0HB5sxeIIESI9Y4ZxNhR07W40GpV9qNQgybdxbOztBELu7u5mdXU1165dy/r6egb9Qe7avCtve/Bt+eP/5o/n/DPnm4rRSh4+93D+2+//b/PRuz6aTrdTIhRKThmMcTMeRyB6r6kVb25uHjSIPOtcCAow05zrhGHU+R9Rj2M/6es1ZObZJrb8M5OYSUqqcOXKlVy7di1Xr14tWwLRU0hIyEZD9Ru5jmScbgIgYuH5+D+Gh6G4jxFvkzTfO+H+WA4KA37UR1iywM7bvP8RR8HfCJtIYmbO0DYZkxVAYD7b6XSyvnBfPvnAGzKQVz33VPLf/T++mMmpz+Xs2bOl1ukd/Ry5CJLAeZFXcaoCcsLrJmMK32UHIqbLNsjEP0OR+AODi1OjDEaUsHO9du1atra20m6PX/TbbrcLkbW1vpVXPvLK/NRv/VTe8PE3pD1oj6FuK1mdX83/8pr/JR+7+2NFxjgbE1Ssh/NtjBUZ7e3tZW1trQHnk3Er3szMTHknjcko8kJIqomJiczPzzd2QLmTyJDXHUKGtyAu3rNCpGd+vod5GPT7qLD2D3RMCdEJKMDbtFA08p06Spk4wus7au3v75dyAgvk/XqcEIfhYajkJs4j3QOMQaBoCI7cwCw07B7QbDRK3vu616c9Sl7/ofelsztMK8k9jwzzX/zDj+Wf/kd35+nTpzPKuDyC9xwMBiWqTUxMlNIRNT8YaFP+Lpp7nyMywPvzzhkjBuftPLvdHr+mwJ08ficKY0PB6z88m3N4l/vL+bGP/1juv3B/funbfinX5seM7s7kTn72m342o+Eor3rsVclofLYOsjE5CLJhzkZblH1Go1EpuRm58fKplZWVcn/+NhmDrqB7IB07ceevRDkcJPI0bDZRxL0JBnze63kU1vbIsBYFdjeFj7Pg4UROF6hRPmpPLuImY4Jpe3u7HHXCz4lom5sHu0Xq+ikEEeQRuZRhneFxcuDt3NQAvGKzM9Gj1HFHo7zn9a/Pb/75H8y15eWD5yc5c+1afuT/+KeZGz6UCbHYOCWiG22PRI+FhYWyLxEIixL5b15QBCFGZOaltOT6bl4gCmLcLmcgz9nZ2cYrJ3hNBBEVJNHvH5z2wEkTOOW1tbVkP3nx4y/OT77rJ3P/l+5vkEU7Ezv5B6/8B/nw7R9Oq90qh5XVdV3IH9YPfSASkTezVsiJ/LzT6eTy5cvFGPhjpGHD5/kgLDrMXAXAYaBjIDjkx9iQPaiI4MI5yURpxvycNiGYGWQhUUJgi2t4KGddTPcmWaKg604oJUbEMzFslN/srMdoz8xneZeIC9SG2exqYKcMx2xQX+31etlrtfKxmQfylf/gZP7jf/7Ps7y6etCDe/Va/uLf+rU8dtun8rkXvjBXXrSS3fv20u10srPTz1e+cjZJ83UCwEx2VRDtcSY4C6CUYRqeuCZuXJ7hlRXc2/Ilp1teXs7a2lqGw2E5buX06dNFlm72YPM8ZZ6tra1y1tHZx8/mJy7+RP7pt//TfOT5HylNC9uT2/lHr/5HGQ6Hed2F12VqYqoYjlGBa5UudXlORKTkINouLCzkmWeeKVGVxnhIrWTcNENAsWxarVZ5URUGZ2bVTs4bJOAS0Gt0ku8hW5ycKwlHiZzHOhrTXo86Jx4DIaH4eHuUwhuE+/1+2bHBpAaDQYFbbrfz9/H8dOLwPfJKCA/XL1lcvFm9R9CwgyhF7yZel3tdvXo1Gysr+fk/+kfzxve9L6/41KfSHo0yvT/ICx55JM9/5JHkg0me96zMhq1ceuZELr96Ie/tviKXnv+8dLtzjecaTnnOg8GgUbjnj2l7yA4MB/hqRtH1WGTIM+fn58v3OTbz/PnzDdjsxm0UzJ08g8Eg86P5vO0Tb0u3280H7/5gRs+2Zu9M7uQfveYf5fce+b386Ad/NGemzhQnQ1qDkzW7nqQ0bfDyrJMnTxZnNj8/X3bYPP7447lw4UI5wIyINTMzU9YROfA7DhFDZp6bSR0cJlHWXWbcx28xcGpkyOsc9zkxTgaOtzBsAJ5RW8JzwKSieEAwPAqdGyyG6182HisVntDOgVwSxSRvREBuQGAcrsPiocmTXcsySuB+V+bm8o7v+I58ZXEx3/Wlj2buqWdLI0ly7dk/SVoZ5ZZcyS3/8kqe3/1K1l65lC+/9SX5xNQL8un1qRLVffRGMj52w0QFigLzSxTC6+OhgXj8G+UCHbifdHl5ubxLZm9vLxsbG1ldXS0nE6B0njt6QB5Ns/7U9lR+4kM/kdH+KA/e82D2Jg/Sj73JvXz4BR/O7nA3P/7Qj+feiXsbimuDxCBwSjTK8zMMam1tLZOTkzl37lw6nYNDrqempjI/P5+tra0888wzpeTCoWJAVyIk80KnvOY4ezcreOcKP+cezlNNTLmWepTrSLtSWq1W7r///rz2ta8ti41BIFAGvbCwUMK7259cc2SRmVDdeWHmFUEAr9rtdlFK8kzmgleDIaWID8T2Toper9coW2DU7NZ37ao0J4hZHQ6H6fd6uW95IxMPPZOF3U/nmx4aZNge5slbL2Si182ZS2dy4bYLedFn+zl1eZTOMBl1k/1bJ/L733JXPnjva/Lk/i2FkGG+KAtbpOjiYT50USEXKxJNHqAQnJ2jHjKEHFpbW8uVK1dKa+XZs2cL+eI8jboi6wZMBP4tLCxk1Bnl987+Xn7h238hO1N61cIoObF9Ij/1Oz+VF1x5QVqjsRwxCG8JxJhAXOhGcgC5OasKRhSDMxlDbdnvamE+XPVGCeTr9Ahj88+AuCbOCFAQUuSx73rXu/LTP/3TjRTs2fH9wXelOOr4IF08qGtVrhk5b3BBHuHidfFeSYqCug5GmYI8wFuScBRe0LrkwD3d10sjAuUCjI9/G5IDS0z2DIfDpNXK47unM7jrREaj5+cTL+1m1Brl8anHMz8xn/lr87l64mpW/tB6lvKJ/NTPPpZTVwaZeqyXVz/2hbzs/OP5rXtenU898ED2O+P3YyInIHySgiDcOsicqf/iJN2OCPScmpoq9UyXHiYmJkotlhcPXbt2raAfyLE6QrjWDZEyGAzSGrby6guvTvc93fz6A7+eL5/9ctnEfXXuav7Hb/sf8/rPvj5v/vSbc3JwMlPdqUZJiyiKjhRH+Gz0x1Bgsck1MRSMiZcC7+7u5urVqwUlgOTca+sdJRidicYkxdiQIXrA7xkb0d124eaGG7mOlXOacSJsGxqYZmYyde3NzBmeBQE5Z3A05G9DLL9bxDmD74VguC/3gnDCIbjc4KIzY8ETuusJL1gTXvv7+1leXS6Ra/aJ2Ywml3Ote2v+n//XJ/Nn/8WHc/cjT6a1n8xc2M0PXnh/XvepT+Wf/dAP5fFz5xrdNBxYbWWYmZl59ozegw4lzqs1hMWLI6d2u12iB/dhTqCQ5eXlwsRyyPPKykrDATvHd7Qw9GdL4Msef1lWrqzk577v5/LM8jNjomh6O+9++bvzoRd8KN/14HflNU+/JncO78xEd6K0ZdapCDlkkoaxrK+vF3RAXdYnU7Tb7bId8OrVqzl9+nQDnh4WEZG/SU3maR3jb1cEinF1x/uJ60h9I9exGt+BMV4kjKzVGh+fj2ez9zBGNzRMUjaqcg8+A+w0DCZXJaf0WUQoBzCLSOvICvvpjhN+x3cYM3mMf8Y4bBAmALz7nZxsMBhkf6ufwf4d+YW3fn9+6z/59ly5ZSnD1sFCnFpdzY/+6ttzz/TlklMbHSD/ZPzCI+fyjImzmZIU0mx+fr6cZQv8w6kCbZH7qVOnihJi2LDvRMl63YGSNdpJkvOb5/MX/vlfyH1P3peMkjMbZ9IetjNsD7Mxu5F3vO4d+es/8NfzG7f9Rh6ff7zMmfVjg0OSRr2R9lBKQgsLC6UhxqUPs6u9Xi+XLl0qJBNlO+TpYznNVTh3NKNOdQJdpNc5GXcTOVgd5ToWIZSMYSR0uFvtMErDSwZZU/M14zU1NZXV1dUiVARF9KXRGcXkXsnYUxH1UD43PzA2KyYML8IDKjkXJvfl98A4noWRAGsoFfA7up6oS27uz+WdeUXe90fvyus/8v68+aNfSnt7lOVra/kPP/Er+Sff/mP5yqWT18Gt+vR7HA9rQp6O0XgewDtquLzQlvkC1YhQNJWQfyNzs9zMj+cTgY0ipqens7Kzkld95lWZ7c3mT3z0T+SRc4/kY2c+lg/f8+H0O/1sT2/nF1/3i/m1rV/LDz/0w3ndk6/L/P584/44XJpT7Chxssid4EBENK/A/Dm138w/c0OfmIt/5xKe66F2XKATorGD0I1exzpDyAQLC2S2czAYNM4LcqhnwaDhzW7hjRcXF4sHYkJAL0NdQ1DvhwT6EtEMSSgWo7xuBTT0RamS8cZdGzvPcjTGiOpIguFubW2VIjyefzS5lHff/6as3fvZfP8/+XAWtrez/Ftrec23fTBPtn6gjMcyNERCzhsbG4XJ9AkVsLMoGIV8Ws/4Q9TA8BcWFoqT9CsLbZigEZcoTDyZSJqcnMz9X7w/dz19Vya3JvPawWvzysdemXarnQ/e/cH02/2Sj/6vr/5f864XvitvfvjNefHFF+fE1RMNeAjCYo7MgXWG/W+324VxJh3BICG2+Jy7ipw2QRTxnTp1cjpV653z+qPmm8kfIHICPZxMOy9zvcfYezQalU79uh5kuEBijyMgcrpsYtIDIsIChsFEmLC6RGkMDeG5NISnHQwGJXLY4Bg744fEsKGyk4IjKpI0ojd56szMTD7Uflk2f2guf/Jf/Xo6V0c588vPpPWmcZsZBp6Mf0ZNGEeZjMkq1oH14R7IE9Rg5GGSz6cxEGlgRYnirLcJJyMq5+jtdjtzw7lMX53O7mi3vPvkzzz4Z/KaL70m//g1/zgXli8krYNtaI8tPZZ/+E3/MDP7M7nj6h2Z2Z/J6x9/fZ43fF7u3rw7E5koDo4mEaI7a4XTYY5eM4zO8sSxuucaw3MgMtfglj2XXDBGxgj0Pcp1bOP0QNxri8JjaJBELBLnx7iIbvYQ5SD/wbuxMwHs76hrAeLlrMwo597eXtlmBAuIY0GRGDesLTVQPocBYxzkasy93h3jLh8bCTKkja/f7+fh2+7Ox17+QL75fR9P6/FLuXj7J3L6Kw80HBdjhEHE8MkHkStzclQEvvr5KBjRhzUlR6P+yrE0GKZTFZwuYyR/85EykG28pp6T9OYn5/PNG9+ccx86l7e/9O356C0fze7EAVIZtUbZntrOZ899Nkny4B0PZqY3k/Ob5/PmR96cVz72ykwPx2+S5pBxnAqwlpy53T7o/7527VpOnTrVIBdN5qA7rks6p8eAzTPYQeLMaIQhjXpOYW1N6qBc/N81w5o8cGEc4aFA7uBBAdkuxL3o5oAA4nMk/4zBBIprZTyXCEfeUBeTzczVrXB+vT33Bba6MQKngeOy4iIT7sM4D+qE/fybb3tFXrL0uZx7x05e8ql35vdfNcrpp16a9tqY2ndJZ3d3t8yJqNztdssbvJAjpQY7tWTMtrslEvmeOHEiFy9eLGO0fA3vuOpNDqypHSgvMOr3++UNc+12O8/bel7+0if+Uh5efDi/dvev5bPLn83l+WdPF9RjdiZ28qWVL+VnX/GzufWOW/PSiy/Ndz36XTm/cf469hk9MpoBFdDEYFKQ8bJmlIbMylrv0WeehSPilENyd9oJHblv5DpynZPBtFqtUmv0SdsoydTUVCma+0zXiYmJ0ijc6XRKs4LrWm4wMCwD5vJ9nwnjuiAKAsyyU/FxmDwLpef+XFZEL4yjn3fgOBLVUYxyEc/x4WCUQzqdTp7ZXM57X/+6fPen/03+3N/r57Hn/UZ+6zs/ncfu++5MT9yTVr9V5tp5tiaapCiRjw5F8fr9fjnag64aQ3rmiHLyMyA5iKfb7ebEiRNJ0oiczrNwcqQPSRrRibY7XqS8tbVVmODWfiv3Xb0vf3n9L+da51oeXn4477/9/bk4ezGPLD+SXrtXDHXYHuaJU0/kiZNP5Hfu+Z381Id/Kt+y8S1pDcYvU07GfAE6gJNymkHNG3KQsbL7iUPJiJYYpTu5nJK12+PzlEAP/062jLmUYhLILJdZPZQIbww09ITx+C6pAE8went7lG92drYcf4HBoQxEcKCYx0VEM3xhEVFOjBvog1F5nCY+/H8u/s8c6aLBafCSWDw1kecjm69N3pa86R/8m9z58CB/+n95LB/6ll/Mv/wPXpblvTdmebud7sROnj//uWx/YD+//7o7M1y4NbNTs+nvjtcDRfJrB1wCMbnmlIL5Qb4QcXCIyNW1RhQwGXdo2eE6/6QeCftK9Ka+OT09ndnRbF6x84p809PflL2JvVyYvpAP3vLBPD73eB4++3A2pzbT6x4Y68b0Rv7Oa/9OPvb4x/InP/0nM92eLgEBEot5m1vAcEz8mKklwtuZo9fe5oauOMJipGwMcNXiRq9jvSsFY2NRGRgLRoQi6vlQLiChew35XE1SmKXkdASEg+IQfVFwHALRqe7rRUGAr1YeoglETTKG2i7JHFaEZwH5272vrqGixBMTE6XLp9vtlv2lB/KdzEc2X5trf3g5r/oX78u9n38mr/3Qfl7w+Y9lY+FTueViK63WKBPt/Yx2kje+t5uPvKabR154bx6+7a7088JMjaYbxuCcynkisgQi24g7nU7m5+cLc856Aasxcm+Gh5zzZ9AP5AhSSMZNBGx/oz5J9B4OhxlsD3J+83x+5NqPpDXZynpvPQ+feDifOf+ZvO/O92V1ejW7k7t59z3vzt1P3Z03XnhjWq1WafM02oHHmJubK7rGKYWsn/f3ElBgsrnsxI2qbKzON133vdHrWFvGTIgk47Yk99DybzNUblQwLE7G8Ij7sDDc3yf64fEhJmraG6Ws61zknjZYns3zqKMZHgOlmROCr6Mk43XbH3CVcg8wF8XxhRLRXvjFvDSf/xN354+9/1/mnl//XE5cS5ZX9/Pu72pnfbGbV360m1OX+5nc7+fb3tvPG977ULbmHsrPvu1D+eKLX5e7N1+Tue5cyUtZA+ebtcLY8SDjubm58jPXDIkerufx/kwM17kpRosTQq79fj8bGxtZWloqKIcXCvP6ioJ6+t3MD+fzqtVX5XXbr8ubvvymvP2Fb89v3/XbGbVG+cev/sc586EzeeXqK0u5B0NDF1g7HPiVK1cavbeMn24k8mN0AeLPpRTWHb3jBAz07DgGemRCCGjghUYRMVZgA80FkDnOw7xoGBsTseG7Rsf/IWKIkhiFjZUI5++RF7ENCjjNGOp783OMkv+7vEFeQZTmmZAOfA/Y7HEmYw/M50kDIK8Gg+l86C0/lBOv/l9z4oMXM3oy+cCf/P4Mtx7I77/mUp669ZG89OPb6ex/Nt/xrtW88HPD/Gc/80y+fNev5hf+b48nK2/J/LVTDTabiOFaHM82OkgOUoyFhYVcvny5lAXYXuUcu97tYlKQyIwOEZ3m5+fTarXKifh1I8RwOCzcBGiL58KIrnRX8rbPvi2PrjyaL618KZvTm3n7i96eBx58IO3d8ekQQFyjKMovGGOv1yvoAU7Ab2KvN7QzL5fw3GlVR1Lr1Y1cxzrgy7S76WjXmRCAcw28l/E7C8nkXHZIxqSM2/D8e9cUDceSJllhYsjkEcLj+Xh2/o+3s5fk/8yH9jdHSdcKyS+RC1ugcFze5U/JgwjV7Xbz5JXlfPD21yd/M2m9o5W79u7K4rXFzPTvyK1feF2uzn9n1u7+S/m5//yP5L//6efl6onkri+P8l/9lx/P83/tZ9PPw2lPthsFfEMyuAMbAcqIokEiERmTXIdgkHktI34OZCXXJJ/j/+vr6yXvdK5vNt11aBjQmeFM3vKlt6QzPFj3J5aeyOZwszgNcwXoIrk4EHtxcbHk/JCc5Ix2VknK0TWupfttBEmTgGLdj9qIcKRPI3hHJUcHft5uH7zNmvdmIkwfBwH8ZRHpWAEyOOp5p4iNlu+7bQ9lgbm00WBwjJUFw/M5B7OCMV5HFcNzFozv28OTi09OTjZe1uQySzLO1TweO4/9Xx0mP5Nsn5jPzrNvnMbY2mlntJHc+tRLMnHqP8lf+x++M+9/w3xao+SH/8mlvOVn/l6eXv97WZ25nFa7ecwMUJ51Ad2QduD4yPlphienNjHoMhQKyvq4S4tnsqGeUka/3y+Hi9GoAhRlszUBgTVGJ1711Kty59U7kySbk5t5533vzOTc5HVHcpqsgYXG0F168bhZJ+Cp0Q05K04HPXQwQJesL//WjROBu9bjwaKkTA5FRkGZDF6WxJuDlrg/i4di8DNvwzFMJeLhoYAvxvjksdRJLUjnnDZSb/lxjcp0OwgiGR91AtlhqEiBHOLFxyUa/k1OThaCgud2u92DTctvT66+91TW1pbKOpC7oQCT/cncu/WW/O4P/fn8w7/w0lw51c3djwzyX/+Xn0nn4b+VL578SCamxvtRkYl39yMDowbnp/1+vxiPz+mpyQ/Wwvez7EE0bqHj/B3GhN7AAxBtnUr1er1M7U/lez7/PWkNWxm2h/mVe38lf+v5fyu/cNsv5AudL+Ta5LWCDMiNOd7EbLorDDwLeG3eAT135xj6T+9v0nyl/VFh7bF6ax0teDCTdNnEJwrYqC1cw06ExGedx/LaPQTouhqGZ2jtlj57LX6GQhkCA0fdFMGiubfVhBU5VLd78H4XoFCr1SqvX7CBo2SHkUuQCRAVECFpJYPFQbKatP6nVjrfPW6AQEYo8Wg0yqA/yFz3lgzu+FP5pf/m0Tz/vb+VN73jC/nLf3M9v/9N/5/8zF/7XF7x5FuzMrFSlBEY7Rc6sd0tOYBny8vLBY4Cbd1xhQKSP/rVCsyLc4fQJTP629vbpUnB5BOyBi2Qcxr9dLvdvPrpV+eDX/lgPn77xzNoD/Kvb/vXySj5lft+JSu7K3nR5RflRz/9o1ncWyxdQ97bCRmHo0QXTHwSxZkXckc/YHaNKLz54CjXsXJOX+RJbkqvvRwe0yUKYJ9zEzyuIwELbljo+wClXCrhviaKDssvWXggJrmIyQ7DYBhjkxMsEDmlHYhz7GTsRXkuylbX4iATSndVdyInN08eyO2LY3YQWblsQfTqdDrJoJXZ6Zfkwnf9ubzjbX86H33NSl78qd38jZ96f0ZP/mJWu1fL95CZc2aiGWhjamoqKysrxZA2NjbKhuwaMjI/1jpJaTyg/OJi/uzsbBYXF7OwsJAk5W3YHIRtIrHTGW8qJ3ccjUZZ6a7kJz/yk3nlE6/M9P70wUmAz76h+9L8pbz3jvfmn9/6z3PhqQvldYgYEYGDtaghrnkLDJDxgLC4QAfI1O9hec7YWhSMCfA3HpsIw4CJrKabXVN03ZJo6HKK64OOcs75uGyUjrg8p1YYvot3dBeLoY4hsqGK2c86n8LREGG9O5/vgxjcZF3PBaX17xYXr2Z66kp2dk8Uo6Bk5UI640iSYaebJ1/4onzqW96Wf/3Uv8p/8jc/mT/ztz6Zv/HX/2bWbvmJ3L754nIPIhby7na7jR0/J0+eTLfbzeXLlwtzzamGOGpYTr7D2qLcnGyPU+n1esVIOp1O2WuZpBwzg8MxO4p+bW5uZnV19eBEvumF/MUH/2KemngqD97yYLbb23nwrgfT7zybQuwe1NIH/UE5TpN3y+BYMSxyTfTDZRlgPE7UDHOts+TmRyWEjtUhRFRAIShNYAhAI863ScYvHAWeIGTfz4whnsZdJkka+Qpw0fkvxo4BO8pubW2VRD5pniPq5m0grxfB8Ir7w7Z6gRgjC4nR43Gpb7o3syarXFctDezPjnNxYT1LS3vZ2283vDuysuPzs7vdbk6t35bByT+Rn/sv/0ne8osfyn/7Vy7nvd/18/nwH/uzObv1/ALXkvFGchQWpdzfP3ir9HA4LK+GWFtbKyUIuIPhcNjoowWyIiPkjVLbuTEnaotbW1uFTcXYcRhJigPDUUyPpnPv6N6c/9z5DIaD/NijP5bpmYMjTrfXtrO5slnWGCPc2Ngo28vQWZhznD1/SAP4DHqFEzaKAwkxz+c050R5wOJ4CSfGeE1PxMk0C+9Qz6AxOuefCJCL+5kqN8xkjK61ISQig/NQ7sNODwzbRgNZwXecY/B5nBXelsXmszRnsLBJs9XLJQ1qp4W4YVE/kXS+2E77xIEx0tnCeJGXkYPXpr3TztzKj+a/+LuD/Pn//qP57ndupDX6B/ngH/1TObF/bykL1XmwiQ0aE/wZ1oj6IMem8LuTJ0+WnUEovw3VtW5k1W63y1Gm6+vr5bXzw+GwHEOCbFF+8j2g5+zsbBanF9NKK/1RP8tnlzM6M66V8ioHpyg+FYFyDZyHS3ZuxiBqIn/mAgeDkzzKdez2PStokobHJRogMLeQodxuMDdjSkRlQjYQDBKl4HN4LMMPQwu6ig5rMMCQXAaoa1WGoIbMvshXgds+MMy5Bs/GIQDbyFNdM6YpfmlpKf3Z2Qza7bS2RpnJbvks93XnE07PLYOsR6vVymBzkG//3H+Uv/dftNIefSTf+VvrSf7nvO/H/i85tX5fGbPrvE4zXFo4ffp0aU7g57wtjTIGkc1Emru5XDfH+HGoBIJer1dOb8AQDOudk5oZ7XQ6pWnfh5/xXJOZ6Bm6aTTkY3kc6YHmHr9TI9vNUa9jRU4zqhgbhE8y3r3CYvgz5KgMmM8iSHtXExOGiXgjlN/3JneyohueuFTA+MmZDaExcBs93tIe0ItNGcHwmGfVm8FxYCYTHPmSA4XgqJAn7rkn2/Pzmd9cz71TH8+jrXvK4tvwDblZK9eW6X5pbbXyhz73H+bv/tdJa/iRvPG31rOw8c/z4R/8qUxNTRen590vdmxOK9gSNRwOG4dbg6iQJzyFy2CG3ozdbDsn7jM/EzHMnXn5BIh2u13q7EDzpLnrxu2jjGNtba3UdH2MTj1Wj8GkoY3QbYpu1LjR68jte6bFGSA5lllK0/N4sjryOIIh4GT8ljI3JTg35d8sOt8lOhsq1/Cbz/peGKKbAKzofN85KJ6RudS7NAaDQTn2E6NFhlxAX+Ayi+f7l1bD+ak8/PyJvOKjSecdexn96CCtZ+XEuGlFo1UQp8NzHamGw2Fm+7P5ts/8R/mZ/2qYYftj+c53P5nffd0/zvbtP5Lu/mTDGBgXMkQONXk0MzOTra2t7O/vl/eqOMJ5HdEhl0SA9kRWUhgUnTnhhLk3DtKpCm2jfB9ZEU1xmt4dZfRj3oMgwXNcnzW8NjtvQ/bPb/Q6cqx1tz6KaGjBv/Gy5JoonmtGRLLDogcC9rk2hiooHTtTzIZhDLBwkDZOyBEoC4QhMw6iMfdhflZGvLIbq83Que67vb3daJrwPF1Xc0G+0+mUPtbdfvKVe16aYSu563Ofy9LFi4UUwXkhJ55JtEJmKB9dMa1WK5O9ybz282/Nf/Z3zuRd35X8yX/w6Uxe+D/SH46hq1/DiAxdQ3Z3EQ0U9Apz6gHrh+xZQ/fe2ikxXt6QZpLMesXlVMA59mg0KswwJ+RhmKQTdJBNTEyUN2Yn44YV8k/m6Y0T8AOU0ng2jTWgo+NEziO37/kBQLM65PNZG5nJn9ozuc4IjW3vY8KEf2OUyfhwMZMtJnx4FmMluphFtVPhnmaUXdvCCbBwLJpLOcn41XZ4Z76HIgLX/T3LjXHMzc1la3Mrn3/RC9Ob7GZmezsPfOAD6WqeKBt5KjJAgRyBgKs8b2W4kjc++KP5c//Tcn7v1clf+Nufyysf+myi0hCOx0V1rzWkzWg0ypkzZ3Ly5MmCbOiZRWZ8xxdyMmzGiHjt32g0Ksec4CAsb8YBUuNZOJGZmZns7OwcvAj4WdicpKHT3AM99bp6jXi2yyjJmKl3KnHYfG/kOnLkdJM5gyUiGEL5WAYn0CipiSPatFqtVmNiJmHwPs7l8I54MpM7RF1yLv4mP+JvvLgL6SwWsMoRySUOFo7WQWBPkkYELMJuj1/pkDR32HiniPMvHMvExER2h2fzyN33JUle8Xu/l+c//HCScR6FXMjTIGncGkiTN+fX0pZ2x6U78uYP/kT+339xJRvzvXznP/tnuftDH8rOs8dtAJ3rhgMiJRu65+bmypgmJydL4/jW1lZprsBoPH+TgDYQHBzOFQTkPM/BwY6W3zutQoe5h8kjdNekEmjD3Eg9Vj6PLibNY1xqvuNGr2P11tqrW5j2MvybwSEoPK8nd9ipZERUK6rPtjV09GKRO3C+rUmZ4XBY2FHYVEcFYKIFSfR0/yjGCrHF4rH4PMdywNtbsbkfEcOLTlSFiJmcnMzm3l4++NrXpt/tpjMY5Fs/8o6cmNku3zWBZgNNUhyVkQzyZXfG7U/fnnu2/nj+ys+sZHNuP9/9jnfkhQ8+mOFwkM29zTyx9EQ+dvvH8itv/JV86M4P5dLspYIo6CtmjsjyySefzMbGRnq9XjY3N69DUThbk2Xcz2jHuZ0JPi4bjw3Fjh7UAPz0GtuImYMZ6yQNZAanYPRoVIge+MBu8w03ch1rP6eFincjmgKXMAoGxL68wWBQXtbjzhN7fzw7E/TkDSn5WU3FAzsQrJ0Gz8WIzRhzQWQ5N2LRMWQTX14M5ujvWwHMyPJsygVuJatLICCOx289n4+9+my+5YNPZvlTl/PiX/9gPvDG70xPhoeScjIfDsHPZVycPoCTuuOZOzM88cfy1/7Gv8z//acfyZve/vZ84FW/l1/+jwd58tyTB+uVVj78gg9nZmcmP/6OH8/zrzy/HGHCq91JNU6cOFH+vb6+ntnZ2aIzfv2jmzKSlAhsPmN6ejobGxvZ2NjIaDQq+TjryBzQCyKy95oeVvJwysQ6W9dY22RM+FjvCDYYpnkWH91y1OvIB3yhyGY8mYTfNo2SJinHTiAIXliajFuwMBzyCjxSjdlRQNg6Q+TRaFym4TsWHBeQzq1uRD7IBxTKUASPzzzrnLUmyRivc1DLDHk42kMuWHGS8d7S3mCQL7z++/JNH/uFTO7v56XvfV+2X9rJ79/yLdnvjfNrZM/pe87hMX5HJX43HAxz/sKt2e/+ifz9//RX89b/7+/nPd/6SB67PVlZW8mPv+vHs7K1kmuda/mlH/ilfO7ez+UFV19Q2t7c8jYajXLPPfeUnSa93sEbyFBo8m3m1+mMTxAw2UMUxRi3trbKG60h61hf57ReW36GIdMsg6xwADivmh9gjEY7/Nt5Od9BzugMenqU64aN0/ByMBif6A6EYPJEDf7vojW1wPLwbvPQKRaV6FoTDjVpUjOodZ5h+O0c18QB5QXfy7AMo7CnRQ6OoI7i3l1iyp2xMXZ7fOqPTgcsb0eD1TP35Yk/9KLc/du/n9nNzbzhf/61rPznl/P+mR/OcNhurIkJIe5dK7LzLJRpojeZ1twfzv/2516R265czm2/0cqdV+7MHat3pN/vZyUrObF+Io/d+lj22nuZGI5POwe5MFdOpMcp1nxEjTCMWEzGwFlAFvGi39nZ2YLiuIcDCIgFJ+oSIOuL7vF95mEH5rTGTRLImMjv+RMwHHFv9Lph48T71DmRjeww3O/XAUCq1MaGZ+Sz7tRBmBgr5QEu5yz2UEAMxgMpQj2WdjvIgSRlPiY/WCxH0Nqp8BmXUgzDayPgsofFaIBitXK6VtbvdPLRH39zTq1+JYsfu5L25VHu/x8+lK3/YTm/+8wbG4rq5nmTHCgQ0ctb6QoEH3Vybv2+3LJ2b1HEYZ7N0zrtvOwLL8u7v/nd2WvtpTsaGxswmY4c15B5tYONxDJE15A9TsYohnsRkYHIjrZ2apY9UJoSDbJwvzVrfBhnwnwYt52ff+6dRhBadZ789a4jE0J4Gu/uB1agnBjK4uLidQpr44WIoJfREzQE8aJ5ks4hnOQ7iqL8eEpyTo7D4Hc+QY5/MzZgNqwp33EUtaPh/44C9c9qeh4ZWlnxxnzODPPV3Tvyr9/0tmycOnWgfM+M8sAvvj93zD1R2uYoFdAQwGn7KB0RnhqxGU+U3yijwXqOkuXt5WzObebT9326gXzqVISuITsjIplRFSmIITg6w0W+Oj8/X7aXbW1tlUOckVkyrsm7Jo0OTUxMlLeLra2tFWN1dDNUJcDUiIn1BR25zuzxG17f6HXsVwA6MjjHwBNZCTEcf98QijYramFeQARj5tX3YRFGo1FjV36d8DNWnmk6Pxlv6yIKkz/ByqHczt/wmkkazGrNMNo46+8ZcdhYgXHMG/YRg9vb28szt9+ed/+ZP5OHX3w6w1Yy+57NvOLn/026SgfIqVAaDIe5UW4xtHWJA3myxpwTPBwOM7s/m86wk/d/0/uz2dksyIa/MTaaxiGF0BFk4bzXGwpYo5obwAg48DkZbxvj+zZ0cwHMaXp6OgsLC2VHjUsgtcNlrZwWIEuvkQ2SZ/E5l1lu9DqycTpvZAF8ernzGYyFaMWiIzR7G4gWNuEySeApCm3vjTIjLDxlv98v9bvhsFlXRLEwRuczVkQuOwSUmajrvJKFYC4mCtifWHe08F07C3teK6xTCDdgXL7jjrzrz/6ZrJ8+lYySOz712bQvfbjI1fOBmWUN+b3Zy3oPqlMVs6mDwSD3PHFPvvUT35qnTj+Vf/qmf5rdmWb5iGaP+fn5zM3NlbnSOWSOgbpo0mRona5AxDAfQ+ZWq5Vr166VN6O5xGOYDycA3D979mw2Njayurp63Xydp/tv1gEykM+63u5n8fdRryMbp/M60/FOti1gL6yjmg0Wg7SyJM0iNd91Y4FpcTcbcJmc4ln2aO53tZEydjsA7kFta29vL+vr6+UFS0RJOxIgmOt1NcFBmxctZnXjPPcz0jBa6fV66c7ckQ//yI9m0Omk2+vlgQ/8dnoTYyhJmxp/TJLQfWNIZvn7QuELOz1q5bs/9d259/F789ALH8pvv/y3s7O7c13jB8oK3CYntWPlMtNcow6cr9fF7KkjHSmAG1zsPBnbzMxMzp49W+bGcx1JWTOvz2EMLQ7ZDs8M9pFt7ahfsCEgDHKKJI3dEPZcRC236NnrIfzDemjrSOT2PxQIQdmbJin5IsVvohJeri5p1DkH+TBjNNyywLknUZu9jUB1FMsLy/Pt2Q9jCU1cEJmYb79/cEzHYy98fr74sjNpJfmmjzyTa50HG86CuYEwcJg4QOTJEZ01r5Ac1B55JypjnF2bzR975x/L/V+6Px/7po/lnd/6zvT6B++GmZ+fz9LSUuldtRNHyX3hcD0enAnjTsYdZ7UDtW7xe+SK7hoeb29vl7eonT59unzWesLnnY8CfTFExuTe2iQNx3Kc68hbxoy1nWvyM78OHUMjQtUs52HkCIyqGVaMFIXByCneO780MeFxMW7fD8NBCYfDYdnJULco8l13BfV6vUNLPigd36fOixycc8GSsvCwiUR9FNm7b3gOyjAYDDJod/PYA9+UF37i19Ptj7J88emMlsbjT8bNHiaEjAxYX4yXrU7Ou+z4GNstrVvyk+/5yXx59svZXT+Y615vL61hK7OZLVAc5wXSqPNc9KB+LrJwh5N1EHnxe5wZJ/ixfiYYcbKuXfJ855wOCF5TPmfCzqjNjt/3O8p1rP2cLC6e12e2GoMn48K9Iw6C8KlkZlth0/i5o2idR5jEqY2RmpiFyPMdjVF+arL8G2XFMBF2XbqxJ6+JHUe7usEeD0tZp9PplDd+87skjWjjvlAv/Gg0yu6JF6Q3+a8zsb+f7/rVz+Q9//Gb05mcb0BAt0QiC+bkaGYnytxxBBxJgkOhoeOOtTuSJPtz+1kfreeJuSfy6OlH87prrysHMVO+4vV/OGLWhvIOa+/jVHHITnOMoBy5vHvKUdS6QKnH+SiyRxY2Su7tCgHywkEwF9aPnzmi3uh1rKMxeQhd/vZybJO5evVqMRK+Z0IHA6eAzKT5jou67lk1W2aCgkWEaDBxYW9pyIpy+TXlvV6veHkXyusIkqTxNmVHfhsn0d3Hl9Qoge/3euN3RtpwHWWNNFh0Isi1W27JYGIiE/v7ecEXenkwk9lRacFRwP2pPKOGi0mKPJCloTZzqEm2drudyeFkJtcmc6l/Kb3OuCljZmYmV69eTa/Xy5kzZxonZDh6+nUGrK332/IZj7ve2oesrLuWfd2k4NShUe+t6t21XlufTCTVa3XUvPPYB3x5sVig0WjUOI/FzCXezGQOEcAMF7CpzufM8BpuOGLV8CRpwjSztiyaDQGiwkSUlb9mcusohiNxzgH8xRBYSE4qJKq6xDMajd/Yxsbl5eXlci8bPC2M7XY7g8lhRvck+WQyzCj72c9wOG6PZEzIfGdnp4wdOVD7xCAhtNya6fOIuaehJdfM5EzObpxNb67XYOapqyIHnJjhJxGRMgzr7LQEfcRYSDEMcXkWjpA1sgOtnVHdmuf6O3rjvP0wh28nYod9lOtYbxkzVsfLGHPDgtmbMDATMzC1dQM0XpMJG0KwEE7ea4NigZLxi4fsxbrd8eFhjA+n4tyvzhdGo1F58arLLiZekIGdBkrkGrDzLi7uwedNRKBQzunx7smzTfdL+8mrknwy2ZrZyifPfTLPf+ZbkqSRgyVj9rrT6ZQ18No6l8cpYDDIFTnXOasb6RmblRrnwqvna8LLUNpdYziEuiLAeOAL0EH0whEPeMx32QiOjPjbTL45C5xXfRkVsmaso/Pio1zHOuDLdL+9mgkewxxHWy8Uk7cHNktpmAvMJN/EqKzwzjFRGBSYSM54iFy8Hs+L7AjAWEAG5DW+FxHWuXG/Pz7e3w4Mpbazskfmnpubm43c25DeUQPYedA4kWyvLGSmtZWJ3iinrzV30ThPJX+34vszKCGfc0+0z+phvI5W7tSqCbJ2u525ublSb/Up79YT1sw5JT8DkiND5kAjy+LiYoHuPnMIJ+A1wrmDXphLncKQdiCLmiiqgwJzMIo76nXsN1sjoLqswE71paWlouBuTDDkPVCo8ZuNmUi96CyKSRkEwn1MEpTJPUug1A6CBU7GHs89wFz2uvaEzKVuNeR+Zq6BcyyQ4bFzHXt5YGW3e3B2K4wuhuj5O4r0+3N59IEX5sT001m5ltz36afz9L17GQ2vbxe0s+D+dWmK8ToK8m+nJtR+7SBxZltbW+UewFecMG2F7A6p2xeTZl3da46x8DvqiZSx7HC8DjjM/f3xe1jRYzsyb1Kv52udtLNEvkYc5jyOeh3pG14w5ypEO6JWeceHLucllEKcr7o9D7jLRmFDFcMEe1sbHmNKmicS2IujSIzbNama1LGyMC7DFKIGkAkW2rVNe2I8bg19mDvjNjrhvR71Z52DDYfD/P72y7P38qm0klyb+VKGo+bB3VYaPL1PO+TeKPpoNGq86NfGakdp9rZuCKFHmZ5m5JMc9MVevXq1GLS7cw5zyIaydhTIHZIOo2DM7tJxNLb8/R2jQDtsl0dINwyZCRQ1ajDUvdHrho2ThXXCyyTdDWIvYZqez8/MzBTjxVsyQRoXyFl8RL+3oRlScB8WivtwWTENv5PxAU54XguW3+FFyTOB9fayOAAvsEkiw18rjD9PwR15ujfURmkFsBJz38ur5/KB274voyQ//M+uJp3LRYlMqIBmWFvkgYzIBT0WZE0E4vJniWacOjgzM9PYz1nniKPRQTPA2tpaOS3Bp1iwd5OGAbdCEnntLDlDiZ/zTMue+SNz5GKmt91uN95s7ZcfY/zWB6dDDhDIyqnTjV5H2jKGEnPUB5ABY6E1i4mzCEQkzpNxJxDey9AFgULmmIVDIV1PxBhR8rrZoc4f6stRk++gfDgXFtSkk8fsRoLDciKgYx0NgGlJ8xDrZLyFzekDn4O08NgO5jzKh157Z17ygZnc9sROvuV3HsnH33BbI3+382AeTil4PhDQxJOL996obJIuGZ+Xy64lFHx//+AUdncfbW1tZW1trThciK+aaWd9qUN73P3++PUfHMmZjF+e1GqNX4vgtAld9VE5djwYGX+s+86LWWejNMaLbJ5TQmg0GjXYNZJuRzKTCckY8rHD37mHX5xDJMIoYRDxrmY3PUkUHMVxRDEVjqBRGoSGcOtaXbfbLe8OMUSxoXH5fiYAGINZQCMNs3t8x2ylkQiftVLQ4YSjSw4Mej5nMpw8k1Yeyx0PPZSPvfrV2ZbyOgIaJns+ThvcEOK1dW6FkRshIDtYVMM9vjM3N1caS3jlgiE4DsX1ZL8kyetdE3UmFb35Gb1hHOiF9djdRCbJQFW9Xi/z8/MNlh+0RWDiHt6QcZTryISQ2S7ndqPRqHFOED/z4VV1jRKB+zNc9jZ8F+GaJbYX9HsSDWEM+8y+YijOC3y+kGGLiRIbkiONGVX+7dMi6jEn4wPJ7CzcFOAT61h0R3Y8P+OemJjIoNPJFx94Wc4/9lhuffTRzK9fy/7SiUZqAYphXjY4Kz1rbFac+VjRkYEjBvdBtl5/mNSZmZnMzc1lc3Mzu7u7WVtby/LycmO9+b7TJLPjrAGdVkBJoDPpEd+rCcYkDeN1VxkyMatuFj4ZH88D/DfTzfexnaNcxz7gywVxs1l4fRQf5rGmmIEZPi+I+7gDyMZQt/s5obcBJ80ShaMzC+LaJ0pv2OxSiO/nFjrDOUNSFg4YjOzsyJz3GQ5yuSXRxBOyI4LYyA1RL3/L2fTf1U32ehlsfiLDhW9v5FMenwvpbntD2ZiXSSyctOFyDdFdP3TuZ1LJOXO3283GxkaSZHl5uWEoJpwcVevUCH1E/4bDg2YLdpRYf1gL1qM+DYI58TlkznfZBOAthIyRsXPyhkm1G72OvWXM9Z26c8XECn9sAIPB+MhGG8tgcHBSGq97437kICy6IxBe2OMiMh4G2VBCfmcW0HNkJ0UNzxmzPaOZZkdo6pT28s7Ha6NEbk4Fvlo+h8z5v71zv9/Pk63bsvHixUz0k+/7tS9mZtR8nXq9jY114VQBZOoXyBq6ukTkQ6pNqli+GAcyMkliBwC83d7eLvKAzQdaOsVIxo0NfB54y9qhO4wTaErZDx3mBAwTlEYMdox1Rxfr6UjPmtq5HOU68onvVqJ6gTx4YIuF6HzJNSkEYihk4ohnwph6LERFaHoOksI4necAOfD6LCy/MyOajHOGw/JAb571ZxwdKBlxn8FgULws6MPP5hn8jj+OSEnzhTl2Mlbijf5EPvaGV2bYaeXWJ57IvV/6UiMn5rnb29uNaGYlRBYue5n8YBzcE6cDC2pEwXfqnyXj09673XGjO6ca1Dk362LuwDmmUyTkxnE5zA80Nzc31+BByH2ZA8605jkIAgQZM8ecKA+8pgTG/I9yHbnOiReoX0BriARDRsnEimvoQbR0z22ShiGxGPZEUPN+7dzOzk42NzcbpQaXP+gIcp5hgWEEGCCR3ZHXeYZJJP6PLKwsNexkoW3cODkTPZRQnO/h9Q3j+JtnEdWHw2E+NPstefSOu9IdDPLK331vOq39xhiBnjYYQ3/mi4HC1Hs7HjAPMsTECtGHNWf9kJcdDP8mwuzs7GRra+u6DdM4DFIoOzDuVZNSGCA6Y+dgx0swwHgNpT1mE5DOT/n5cDg+VBwHcFRImxwzcgIHXH9E8ZkUHs2MrKMdUIRF5LhEvuvOC3dqACfxdPypoxsR2dDDBmVIR+QwK8nvXO9MxjDFSowiOwq6FAP0dh7pXNa/R/EcjS0//21Dd+5WlG9yMh/51m9Nv9PJLZe+nDNrH7+OsaybIcx+us6cjAkPyxGDZczcz/PjlH4Ye6KX9QT0RLQZDAZZXV3Nzs5OMUIaGag7g9Ds2HEYjnxmfNERH9xm1MCzMG4cDvMBztYsvo17YWGhvIoCFOf8/UavIxNCzq9QMiuGP8dg7Xnq/ALl4iTww3YOAJmJyBgETBwK5VwYQ6FkwxhNneMMmAtjJKIQ3RlvzWTCtPI8kwFJGsZLDmdo46MwkjGbh8MgQtV1Msbm8gKOEEXk3o/eeWfWTpzIiWeeyf3rn8xjZ16edrvZY8u8GQP/9lrVCgxSopRQ51QuUzFHF+tRcI4u4fC0paWlsiPn2rVruXDhQk6cOFH0hflTK7Vz9HzMyOJQ+Ozm5maSlLdvm+1FzsiBe1jfO52DHTCu1/N7UgBkh57ViO1GriPXOYGwwMTRaFReMlrXGe3RrWDuVnF+BluGd0M5bejehY9QnbDzf3A+fZWM27DPC+lcirnWTsLGyvwZi6FSnZv75yw0ymsiyc6NqI0RohjIjbEyLjsoR//O1FQ+86pX5Vt/8zfzvAe/lOU/fCkbO7c28mmP16jB6IX78lyeVcuLOcFqE2n4DBsYnKYkKdCTEg/1z93d3Vy9ejVLS0vFmDngzLmwmXEHC6cr6N/MzEwDXsM7GNbauPm/956i26w9DpW1MYL8Wqz817qOdTSm4ZsTbXIk8iYE7dMRUAbDO+cTLD7eaXp6ungpJ9aONgjO5IChbk00EY2dT+AtnWd4bhiaHQDzSpqkEN7a9Ly9ME6OsbFB3Yyz63HObVAM3uyFMdDy5mg1HA7TarfziW9+IE+dn870h3byivxuBoP9BvuM4lh56tzTTpd7G/qZlTRU5W/Lh+/jzLk3jpfXddxyyy3lAOrhcFh4Dt5s5vySubP2OA0cuqNqrcushWvb/N7Rzutr5t5GbaRQVyye81KKH47CocA1NDQEdsKM13FnifOq4XB8IjfC4Dl8h7/xuC4Akwu4KcLn0rK4yThS2oB9CgKfwbAMm3k2zgQFcN4FnGUhuY+jlhuy7QAMax3dvSfSHSoYtZVxf38/qzMzefBVr062khf/1u9nae7pRo4Nyca6Gs2Y4DFsxyBwQmZQne/XuoNTTMZ7ShmzUQLrQkOC78caIQunHCAL53uWnY0GHUK+/G1jcm2UmqXtwI7BP3O05rnPuXHyIA/CMAgavSZmKKu4GM0knS+iGCZHXOMy/uecWxQNoziMGUxSorEVzEjA+STHYDrPsCNiDnYK/L4+Kd2OzIuPE2PRYLrtyEx6cA8chGuVfL8uMfT7/Uy1pvLoS/9Q1lZWMvPu7bxy4/1pZbdxoLTlzd+OiFZCZIms6yMjuYeZ1trBGGnhmImOKysr5QVMyH9jY+M66AhJ5HyfNQd1jUaj8hJeAog5CKcK1gUz4jWCMMx1vdMQn7/t2J9zWGuPaW9gKIkHNx5HmIYXVlxvEcM4vRPAsNReDSXnVHZvR7Jw8ZDJ+LwhDN9OxPmQo5sXzcbJUZJ1Qd81M3tfKzD3MGFBFLFx+Hk4PUfkOjdiTox/YmIi+ydP5ve+4zvS2+nmgX/xYJ5/35fLs2ZnZxvN6DVCsBNhvUw8MU9vlwN+O60xCnBtEwTg9XJe6RMX0Q0f9GbW3mgsScNJwdBubW0VJ2jHQGNHHYXNLDutqXkL1siRtHbER7mOfcCXFatuBGdwQAAbFIKyFzJk4efUzpyTOjohEN/HnR2GjDgHwzN6Uf18571WRBTUzNxwOGyUS/gZygDstDxsYCgyCsv3bdBAW0d6xsImdZrzDdvtDLhHu93Ox1/76uwOP5M3/5vPZWPw0ezs3ZFOa9x7bCfkHHgwGFx3nIh3brgckRzsBGG7FZvFucwV8N2kmeOaDDx16lQ2NjYKBLZB46Rg7Hk+m9KdB6KDEEqG0+7g4vk1w0rt2dC6LmuZxcXgqbXjVI5yHeuYkhqWIjiOOrTXAvfX/bV8t845ETxeCY9qWGk8X/+c77lgb5hhphilraM5iuKoAVRmLCgxxuMd/syJOfMzPusUwLKAjHC9D7hpeMT3UK46gppYohg+Go3SaU/ksZf+4fx3L//l/Mybvpw/culKzq2eK8ZZOyKYbuZa558mh1BonAHHeeAI6+jhdAhjQU9gqZeXlzM5OZlr1641cnjDTOZed/LgVDAeQ1rmVztkR3fu59wf5+TIaIfN+tcpHbZw1Oh5pM3WybhDh/Yk4I+9l/MxFA7Pj+FZsQzpagNut9uNs0yZoAmKZFwgR5GBQSiCoTXRHkKn3W436nUcBWJGjgW0R/UfFMBMoXM1oirMLIvqfBeva6dTQ02gFZGsrh1aiZk33nswGGR3aSm9Ez+a06u35X3f+r7s9nfL3Bhzr3dw6h8bljudTmZnZxuQH3TCXC1br40VnX+z7sgbJWetOp2DfZObm5vZ2NgoeajrnTZMmhOcD7L2foMd+uRoOhqNykkNdtTcE70jh0X+rEW/3y/kExCb+7nUhu1Y1l/vOvL7Oa28hh/Q3AjWDfD1rngbMZEFj0hnBb/v9/ul/9NGCbuIMvjf/HF3CMZmSEQUIrc1rPT9gO011ERJXPYwdIcwsGeG1GJMJhNcQvF9kAuK42jGvJCL0wfGjAExn5XeSv7ov/yjWdtdy3B/mN2MT54w6TMcDkuJhjyQz9SRsK751k6Xzxg1kPsz3yRlBwm60uv1Sh2d0wPJHSEBuT/5LmmCy33orj9vzoK1Yg4YM9+nad7kmaFt7ZiNjGqS6UavY+3ndJQhYUdBrFBJ883KLAxGwOK4uGvlM+TAyPb29sr2G5S1NkQbo3PHmtBIxqwhsG17e7u8bNYvZMVozPDyLEdKf88/J6/xPcxgGvIipxrCokjMjWjMZafk/JZ1cPlqYm8ip3Iqg9nxm9MoX/Favfn5+ULWsLGbsgkXZI03kfNsN3/Y8WGwOCpgLc0BnKgxHA4zOztbggFREAPb29trwMXDUBXyNPoAdbCO7t2l66vOgf1M55mgAPMnLiHagJ2X38h1rNcxOOdAOezVyfdMnQPxjPPJS1BUIDOezXmNczJ7a+eGQCSTKGY8GaPHcFh7lZvSWWBgO4aFUbMIKBbOxL2Xhpv8wUh8bs9weFDf5V7I2S11tbdmrBghn3EEcS7F+HimncvOzk5RXiBqMobyyXjPpFlI/vZcGRNwkfuwJoalGD4Ia3V1Nd1ut6QArJVRiE9DYK4+mX5vby/z8/ONNbczdAoFXEW/cBY1j2Gd8ZoQsU1GmlvwSfXPCazlQqCzs7MN5rImVtz6ZaLBBmaSwfmbo5wVy0SAIyACMSlgKGNyAGUEkluxrNDJwRuZrYgmwyC/8KogA77v8bihIUmBYx4XcuSzODP/jvuyBjiieocQyug8HnIHZYdBHQwGpd7IaygYo50webzXqC7zJOOjWLwVD6jqNIBxEkEZ58TERJaXlxvzsPzt/Hge97Dx1ojCWwjJM1utVpFd/RoPOxnWw6WaiYmJ8v5RQ+B+v5/Z2dmSktUnMxzlOta5tUSLiYmJhoK6WcAbcJ0TOZkHTtYGZuPDmJy3cbFYZgpRGBsoi2vMjzHiIGZnZ0skh2Wdn58vBmPjxugYj+Ek47L3dDR3nmYjckkBRUQWjqyOJHYAzuMdJU3EudxSK7+dgD/L+vAMDMnzhlBDRi4hmfxhXo7sZpMpP9U6YEifHEQ68xKsK+SVIxz1UjtjdIN3eNbrx3NqJtgQfTAYlFopkRejd9pC9cI9xjd6HSty1u/KgJlyjogA+Jwn7sX2aQdWXEcjFsBkjCOof2aohtHjxYiCJmx4xubmZsnxMFjmSb5hiMQCoHTOQVEqy8Cw2PMyUrCM+eP8h9+haFYofs/nD8s9eYYjkGXkMpNzMrgB2h6dBtCEYORB7socubiPobQbV5wmmWRz6cLPSsYnLGAMJnPcdO5y1s7OTubn5xunPFh/XXc3P8Gct7e3C1HF2jEuB5wkZeeUd2jZTr7WdazIaeEiSLOf/tnOzk5h+Zz72YuNRqNGvoBwgSwoD0oKlLChsIAIxwvrIr1zHZSM77OJ2xEBhWFx68hn54EMTGbVbCwLzt91OlA7OJMvMJbkZo6QzIkIxAXD6ZouSlbnUuS8RiFOJ2ZnZxtvIXcZCEX32hre1o7Tjou5UbZwGgAEZ3zUvev6K//nxMZkjELqFGJiYqLxEmDrHWPks3WzhVskLbfkwOnBLLsCYJmwRjdyHYsQInwjRLxC/YoDjArPYsE7j2Bh/Fry4XC8C8Hw03CR+yAk57s8j32izimIVqbeUQKP1d02QLfDIB2GYUjF/1m8mtXl3zX89veBQ/bEKBmRmJ8hJ98bWfD8r8YHuPQwNzdXZG3SCceLLD1nOy70wevmdbYjQb6Oel8N/o9Go8LO4iCc6uDgzZ5irG6GIF1Bz5iLoyOycD7ttXaZJxmfc2tHV5e+HOFv9DpW+x4L4D2NKK7hIgSDveVgMD460PmVc0t/3uQDRzritQyR8Ghzc3ON5gHnKoZr/Cy5np3FiK2cSRrGYI/Pv33YsZWrJn7MaNcdJ9wTRfX3MVY7oxom4qXJjX0/5IhTshI772ONLDs+Z4dsUsjKjEOg3t3v97OwsFBKVMwJp2GEgOPlPnaOXk87FQg61phxAVeTcZvg/Px80VeXfnx/foZuOJ3h/jgKEz1EVBs1cuOU+qNcxz4a08YD5CFJJxJagP6uj3Q0TGBBiGKOrsAWkwvJ9QaxubmZ+fn5ck8rMeNxnogTsSCdr/F9KwIdS1Zwe3/n2iycSRDDSe6FQgPDjBIMWYHZVpjDchgK4mY1uQdG6HujhOyfvXr1avkszDzPJnVImufU1nqyv79fTnKnrIHzxlGSIzMPxl6TiUnz8GznnbChzK8o97Nr6K4w0B2GRQmFq36eZca6uUvI3zHX4bOKnH45KHy968iRE2Ox96YxgEVhYGaoGDwwzvU5exof81B3zJAPOd8w8TAaHbzVCghWJ/UImD8WJs+zwmLcHFRGFD2sYR3lJGoRDQ1168Wx4+Dvw/ISw0HnubWRGs7akVnuzJfLJ0UQfTDI3d3dXLhwIbfddluDXEKmODG/xSsZvzuFyHX27NnG+z8N1V2fBC3wfX6GbFx24bIzrOftnSvUpEFgXgucBXm5yTUTOfwO+bA2JgdBdjgMG+iNEkFcRz6mhEHbq9e7/e2Vne+4hgQmx5Cg95MU4flPuz0u+tMYjVGw0ES1ra2tnDhxoiw2QnUib6N3N4dZVCsSC48iWciGsGZr+RwycFcMLDVOwZGV7+LtKUk43wV+1TkwiuXCt5nU2oM7xzZSmJubS6fTydLSUhYWFgoRU8Nv5nyYw0hSDvTCqFhrZIqjNavKM1hXk4eGlMzVaYiNlLXBUXPvOg9HZ10HxritK3Wqgs46/YKsc32YZ5tcupHrWLDWSkqtE4VG+M4dXM+y1+PfVlp7dnJM5w4sPgu9tbV13cK22+2sr6+XNj9gEp9BkHUUMExMUlq+TAiYtMDgrdT2jnh/Q1bDeRsU93K5B49siJ00DR5j8/0cLWsSzHkiio9cmYtJlenp6cb7N7lQfuuG2VCIE45TQT+cJmC0dmREeMbrGiPOyIjAKYohqY2INIGUAd1hHmZh7ZwZU412GJvXl+/4zeM82/tAj3IdOXLaE7EYrgHihc08GuohaC9mreh4UiuWSRc8kmEj40vGUWN/fz9zc3MNBWZLUu3hDU35P7+zkhDNTBixyDZeRzCe55P8HPHNUJKDJeN9icjGjsJKW7OJ9WYE2MPD4DEnDjj387EpyMLRHCXn/zZ2OwV0A7ltbW01GFUiIfrkI0V4zmAwKBCS+eLskKcNkjHg5JCjDcrHqPCZGmIbCbCmPJv2RnSPaOlI6yhrp3qU61h1zpqYYIHwZF4gJmooQTTEo3FfImhtvDVtbk8I/EAxnUf0+/1sbm5mbm6uYexWfhTDhmhviedzjo3SAFXNNHJvYM/W1laSZsRGMVEAk2F2eiZOuD9jdST3mA0Z7ZT8PZ7rMTv18FY6ZAynYCjOs51z4YBxLKCP0Wi8NQt4ZwTjSGMnBbuMvmHMRijoi40TmbsMVZes0Cfn+bVsiKhEbbdzooOG6tYLQ3RH3xu9jl3nNISof0dLH/CXPLHfP9j1gWAdZVGM2riSXOeNk3GnDORMkhIBpqenizdjx8Xs7GwxRDOtjMPG4zzQ0dQowbCJaJqMI4vLGSgr3/M7UHzVuWCSMh/PH1nVn0cOjNHGW3MAdVTkO4Z8bPPjPs6luJwn2yGTw/H/TqdT+pFxpnXKgB7YgdQdPsPhuBHBhm3oaIfd6XTKqQw+s8i8iB09c3PTiskukCBrg47CjvN9UJpffHxUIz0WW4vwW61Wg/b2bhAbDhPzfjhDUryxTxh3Xmc4VjOWpug7nU5pVncU6PV6WV9fz8LCQhnLcDjeogSRZNKBaAYTSbklSWOhWDxHKyAlcNSXmUV7bL/jwzn9cDgsSMPQNRmXE/gZ9yOa+9lu9zNvgHJD2CBrHIi5BGRUcwq14tl5eb6MjbHyXe4PVPV80Ad/37KEWEvG5A+fY11xMMiF2iuOyCkGsNfOlb9dj+UajUaFoDQh5tdJLC4u/rtpfOcygVMn0s4JzeKaIDJMBMoe5tnd9+lFcOsd/2+326V/044CBeY8G3pm+a4ZWZwHJyVsbW1dNw8M11CLhfI8nDvbMJLm4WbOMzF2G7ajiA2G8TqHtZdOxhHHTg3o5dyOPNpG4rEk159mCIHlCO3fOw2ou5KQn++TNN/OZXKI+ddse7fbLV1gfIcOHrrYCCI8h2qAeYM6nWGslM74PCQZgcNozkeg8Pmtra3yndqwv9517P2chivOpZx7GOPjfRC8N2z7+3hkGzQLC1RotVpFwIbXRGMXhYGxJksgQoA0NiDDK5+HhCI52hkd+LtJSjuju01QyJp4wSCMMurGBIyJuQGjuZfP+TWsTsYHjBmJ4MAMgR3hQDM+job1cETzdxmf+2mB8R4z8mRtTfoBfZ3iOG8eDMbtdz4YHHn6FD/P0WmCc+Y6/0S/DT+Zs52NzxWi1gvkr9MJ8manYDdyHTtyJuO+yrp462NAWAgmYnaMBWAC3BNB4LUpxbD5Fm/mKGx2DzIFj+k6bJJy3i2vhuMePifV0dGQ2nQ+C8qY7eXNrvKZw2RgJ4QyebuWP2+G0BAsyXVjQjZGG2bHHUlNlDmq8exer1e2z/F7LsbBsSFJ80hR/s9Y7ZyIdKAnFJ2mD8bqiLa0tFQIPueczhUNs80x1IiMZ5qgMrnHPYHDyIMcl3F7nnzHHAM577+TyMniMzkebNjh3A5WlB0VHmSdv/GHhSLnMpRjv2VN7NgZANGAXUBvck2gm5UcRcCYGacV2dCtJl1YJMNg5+iOMkYSzkH9OWSKkfmoUSu5Cbja+F2HtoOo+4nr19QhV3LfZNwwkDSdaDImUHiu15E0gbmDFHieGWlHKX5Ozj8/P1/miTyMdBg3cqgdGWuIjvjfjmhOzWqE43QMXWItPQ44FIgxz+tGr2MZJ4tuT5w0TzpzH6SFbU9jIVnJWSjnonUUtrC4P/DNwmMha0NyXkCR3dvWut2DYyUxlmTMDtaQHaM1pMWZeDyOXjgAlBzYaO/O/YHqPMsbDpyXcn+e77oqY6l7kY0ouI9fk+B6cA3fUVqTS3wO+Xh9qCO6Jlg7ZTtmPu+dJcD6fr9/3ZvbcNhGDsjYSM4pFHqDnGzc9TtWnB5xmdV1msYh3eghUP0o17HqnB6MG5nr6FXXwlh8BATb5vd0IBjua6Nj46p3azhXMkxzfmbB27snzQOr/Aq9pPlOEudsQCI8u40XYzOJYXkRNWyYzrlAF44s3Jv7QQAxZ/5tA3VubHk6d0UmRETu5doea4BRG6kY9Zit5jK0BwUwTjs3kA7fhWF1ZAQS17kza+77OWra2EhdMHwTl9Zh5sEcyRep3VKqMyxHVuTXrLl1+yhRMzlm5DSUcNOwsT775bj4TA0ngMaE//qcGvIPvueFSMYlAiIAJI29JDkDn3GERXAuv7hn0h6cMRN1Tag458Og6/IFRo0R+Tvc24RN0iywOz91jsjzD7sPRsHzTVAZylMDRGZWdpNmdpjIirKRIyf3MNQmgphL4BnMYWFhoTQ8JM1DwZyj82wTaP68eQLGW7+qA91FH+vcmDEZjZhVZx1g9u2s7aycYvn5X+86lnE6nzIz1+12s7OzUzwmi4ThutPHiTxCMtRB+e0EHBmYOPcyY4gyOIqjLPa0RBQSe35v2IoDabVaDcWs4a4RhSM29/KrJWyUjnBWFP7tZzIvGyo/g1V1jyyfxdkwB+TldIBnQbaBapAdB15jrL4XMsWh1iQJc62V3MgK2bhR3lwBcuZv18zNJFu/uD/OoN7GiJwtR68Dc3X+m6RAZ9KL7e3tMp8a7ZiI8u9u5Dp2+x4TcN+sqfeaTCGBpwF+bm6u0N78jAlQ8LZSscj8jGhoqOqOIudiMLZ17sG9EbzhqAmu0WhUNstCkpj44vOGfIyD51BbtRH74j6uVaLEdXnCimkm0EpN/mXSpc6PGZ93vJjYQS5+XbwNntzKiMi1P5wq98M4eR5G475foo4RhFEMcjos6llHne/bAWDEyMzciA3INd3aiTAHZMP8t7e3izzdZYVxutvsRq5jnYRQF5RtrGbIMF4EDibnqlv0EJKbjGvhOac0hGDRiACUX+xlGXNN56Nodb7sXlcrpCl4H+iEEtoAnfMgEy7XCmvPj0xxesB+b842e2pWmu/XxsjnzS4SHbgHBu115m/XECE76jSEOdv4GYvzZeYCHORFy4zFNXGeTc6HsdpgbFjOh607hsi1MVOnrEsxJuOYj8t3/rfTOOB+vZniKNex2vf29/cbBx97v13S9GLJmFjhd87/aP+zkfKcXq933fm4ZjOtjKayrTQ1i+koaSjGzhEue87k+u4fFH19fb0R4VhgR2KgY91wYSLEkM2y9hgZB//nd4dFbX+PUgb3tyNjHM43D3OChuoeix2TS2msgd/oxRu+QEZuJbQxcV/v1oGP8MuC7dzQoTqdwJnyWXS1doxOD2CpmSNzdkrBs/b398v5tZxt1O8fHMtSpzPPeZ2TXAQv4bwQT+N8kok6t0rGZ4GapTS7yWKh+CaDgKf8vt/vlxP5UDJ7chYWRTDsYqHx1IwBRbEiGs7yfDOtQG1DUuZqj43i2kido6MMddTlHt5yRVRlLex4LEfvaXU0NRS1kdCwzedxLGZ5zTmYtGu1WuX7OFMcI4bJeB2lkDPjAh0gU0c39ATijXIOkZm80Hpnx+Tn2PFysb6GtJYPDsFzgLxkjNZTENpRrmPlnJQyTD0n40OTDNFYQARsssUtZlZM/9wsHB4raUYGFALIV7OAjIlDh3keC+X8xC/wtfNAuWrSyQ3NzhlZFO6b5DrlqAmeJEXZ7aRMNBie10bPfFFQ/9+QnPzX87YT9Pi83n5+7eScDzqPZ16029W1Q68F9zdpgwz4GT93mcbzsCO3k6rz/doY7ZBd+2VOjNPpFwbq0gzOEnbYjs1E1I1cxzLO4XBYdnuYskeYSa57Y9X+/n5j0ywwEM8zGo3K8Zh4Szy9c0p+5vzQSsRCejENK12g5+deQCuGWUDnnCaOnLthrM4t7FDot0Xh3DVjOO7FN0RGrozRyIDnOMcynOffNm47BZNlyIVnm8m2snIP5IbhoYisDZEEGIgyJ+PoWTsKj585kkqhT47KSfPoEFInO4g6z+TynO1guI/nDJxF75kLEdMpmElDB58bvY79ZmuTFy5gW7jOAWAPUXjnBNwLYyZ3MXmBcA1nISjcbmdFNgTzqwgMHevIzaIgYIwYUqnVahVvyXtKPWdHRHtgLis6G4+RY03x42zs9Q3//POk2QOKHM3yIjeUmLHWKYafw339M+bG9+10kanze+771erMyMvzN6IAClMN4F67u7uNg8Ps9F0Kst567nZ+g8Gg1FdrAqgm8ggSLtW4vOL51ijjKNexck4rFGGc13J74Uaj8evwmEzSPCqQ+pO7dJiQ6XR7KJ5hiGPY4OfbmB1teC4XSumOHiJkzdJhtCxkDWu4X9J0APwfj4uBJmkQZpyC51PpTHglzTdbMVegLMbPv1FWpxxAL0N/yjKOsBi2Ix4Gze/8hjVverYcPS7D8jontONEt4wG3CziKN3r9YqxWi6Mt3bCZsK5d00i1qww9+EzXnscITrKPCGIkn8HbC0KiOJ0u91sbW0VoqLetMvCYgyOvHU+YYMwyWCBOKom4xeyukmBxSLaUVap54Gn8wLU5FatLFyMEZKohmX1AmFgLKKVlIiAMtLTy3Ot1HWEMyRstVrXbQr3nCx3nCDzds5Wk2mG5ibc+D9zA6q3WgfHo7L2liGGath8WGSqHY8voHCd5/Id5FFHMcbA2OtmkLrvGKOzoboBw2gPw+VZh63Bc26c5EUMAs8PtJmZmfmqCbc9qJsU5ubmSpHf92cByB3ddHCYgibjKMq/PW6ExPftRJyXIFwUzPDPUNnRh+8YEdSQ1pDKW7m4N83R3e74LFiaNzqdTokKNRpwScb5v4kg5FkTUMgEh+Zc3+w4RpuMt2UZARi+1pffRIe8XTP1+ng8Vn7noDhW5lAfYYnhOD3B2EivbEx8jjk6OpuYYpx1zs7/XfqxfFnfw3Tia13HPvHdOSOslL2rowICctKNIA2DeQYKaU9jZebndcudO2ecB2I4EE51E3UdlfxMF5ptpM6fagbYz3ZDBYbsntzDoj6f93yt0ORTrt/VOS//thPB2B0Rbbg8jwiBU6gNsHaOdnA4BOfjJpb8HDvxWl+cr9sIrHd2OK5N2gGxzjUKOiyAME7GWj8f2aNL6JsdI5cNO2k6oRu9jnU0pnNHPJl/hgc2JMNAaqVDQEA+6pXb29uF8k9Sjpxw3mRDSZrHeTAOvCCLw30MRWqmmVzEZwxZObmcozIf5l0bfKvVanhsLj+fMbpk4Oeh9H4FY6vVyuzsbAPe873aaXQ6B4ddOZpyD6Iayo8BmECzjNyiVjtYz93ztwxtQDgsP4Px2Zlwbz5vx1yX9dAF7m94beISx4Ue+3tJGmma9RcHZseNHBi7kcVh5ODXu44dORFcXT5ASIZI7i+0xzNRhGKwEBg8xeza85CfYZimsU30ULg282a4a8MD9jjPMLxh7szN0JhF554slNv7iJD1Io9GYyLF3hdD5UJWhqej0aicaDgYNPeu+lBsHB/ytYMjutYkiyMP6YrX2PC8hnqGubXCm1nl3B0ruA2adbbhb29vZzA4OM8W+dsp1WmGv8vc+b/z2hre8hkTjiYobWz182r9+WqQ/2tdR46cde5iFtCLNjMzk+3t7WK87JU0vY9C4tWcE2LUvN8TSMbfLILhY51422OjRPZ2GJe9dk1AOYLZ29dMY52/+DsskovTdTTwMR04kBpCmohzXoYhOAon4xxob28v29vbDdTC8yG0IKHof+b3LhnYiIwomIOJtXpTPXJhPuYQkubLci1X5IjCk5d7Defm5sraOTdEnr6XnUgxgm63sdZ+vnfx1I6xdlJwMM5/zZ9YXjdyHau3FmU2LHXpA4EyeL9a3IMjDzOkoIuEBQEKkisidKIsHtMRanZ2tiyUBe1dCNwfp+HT55IxC+wcu86DzRLawyYp8JxXEhry+PNmb63A9vCGtl5gHCM/p8zFc+xQTGi57omyH7ZejM0bi1utVimb2cidcnDyHXP1aQHO3ex4+D9kE/c0qYN8FxcXGzrm/JtxIBujFOse80cXakLL86lzeK85c6FkWJOIThE8/xu5jtWE4D9MnomY6HEOx9GAhkosig/WqutE3Hs4HJZXKVgoCBfjQQBEZ55Jwf+wsgkR3HsIa8bSizsajV9vQOQ1G+naJZ8lmuDIUBCgPWNxwwWognE4WtpbGzKiDEYhKDDlLpwQDDHn+VqGw+HB2U3OpTAmQ38rLM8wFMfJWOlZNwzO9Vm+X9dE+a7zyJp/8BpB1ljmdoqM11ftDGuoCzpgLI7Sto2kCXNt3Ee5jlXn9G4RBOhtWTVBwOSYAF6EA6BRMNcRmTRKCFTGKMx48jOe6/yQYyOIGtwPw7LwbOzJ2HgYsxWJg55Z/GTcIF6XPKwwdR7Gv3FSbt8jl+IZRiaWM8/lPiZL+NvRkwjFv5GR0wx+75qyt+A5+iJzGyzN7fXJ6XZM/G0ZoCd2Boa+6ETN1joi2bG22+3SU438DF99Pztt5MoY3ODu9I6xMg5HexNqR33DWPIHiJzb29tlUigQC2eFYIJAYUeuur2MVzY4J+WiDxdFQhCtVqu8IMe0v1v5DPnIYXmPortw+LzHYHhEJHS9EwOw4XA/kyjM3X22ztlrr14rkRUfxXbO6c3ckEH28kRNxo8yI0++y3dYR8bN791rSt7o3zvHRp7T09MNmfn9pnWaUeeN3qyNY63ZZKcDfM7rgE7gMG3YfJ7x1DDbQaNGjPyMMdcOxUZr3bjR61jGyaKxNYcJmzZGoYB9zsfsdVFWU9aGl84p+C5/+xkYE8JB6CToKCcejIWuC9jALr/ByjmfoyAG5PzU3h3FtfGitM6NUPZk3LLHfA3jnC5w/36/X/LxdrtdNi/biRiuHbZOftuzI7QZcZMiPtDZ0NlchFlYGzSR2Bvv6/yPfwPbjcr8Bz1y04UjF+tRX3YkjoqOfPy7Juasw6wLn2U+7t3l+TVBdiPXsXelJCnnySIYlNrsLYwrngtFYnsXsMeK5AVCECgmdUoruDs66rqSFYjfJ+N3gjpJR+HtEFgYnIe9r1vQELyjgD1vDcVcD3W+aIiGA2HuKAfdQygEv3c5xvfGmSJfOwyMxXOokc5oNGq86YtTED12one9i8MchHUHZpPoaEjrtMRr7+153MtGVed+rFEdSfm/548uGYEYite5o8fmiOu15qrz8Bu9jh05nWvVl3MXDDMZ5x9ARwzFisoz+NtRw3mFFRcBo0SGFiyeIzfOYHFxMdvb240zRW3EzpFQMntMEwA4I+fNVkb/nmfUpEcN130/ID5KWNeOGR95PHDVa+Zn7e3tlQZ7f98yxuCRLY0HrVar5J81uUc0t9O2U7Lhokc+dtIyMPyu820QTnL4GwX4nNfKRmMYiqPmZ+ZAagjrsTsnJQJb70wEWq43eh2bEDL5woLv7u42DNDKyf5O4JCL+DVLaOhlqOGEHGF5AZJmTQxBI2TgJ4s+MTGR2dnZtNvjd41ALqG8NZlBjk30S5pwzPmiFZbP2YuzqD78Gfkia+ea3APF5PtAWZNuVjDD2omJiRJ5WcM612Zujv6zs7PXESDeOM1a2TmxFm4u8bolKZHKr6BImjCQchrpi1lwR+9kbJDI3CScc0Y/CwO1Uzfa4uJ7XoukubsGvaKl1Y0jNeH59a5jHY15WFLun5vtsreEEOHfMGk+RNn1M0PK0WhUXve3vb3dOBKC+3lDr4VR54yQQggbBYGQ4rkuY3hxDSshPGpSwMcw2gtbkZCXI7yjQu2EkjHr7V0rPKfVGrfy1Tk+KQcyM9mGQ3AHkw01ScPAfPi2yRbky7/taFH8GtriELk/LDHbCO2QOaOHjijQA/s9HXEtN6cEXM4LGYOjoJlc5mN9Mhx2dCcAkLaRhrnJ4kavP9CJ73goBud6YZ0HWFj2ICiJvVFtnLWx8DwmjaDZ/eC8zmN2/RIoS4GdXTUspDdw+8TAmqDxcwx7kIUhew1x63kmBwtMHsl4+CzjrndN4LxcPzTUHI1GjS4r2t+oG7tLyNC7honO92oCrkZL3hZnw2SOyZgMrHuL7fytX24CYSx1KybbvjBYy7fWRSJmjQIPq7GyzugQ9zRb7ciKo9ne3i5wt04bvt51rMjp5NZeknwEr2Eig1IIEY8JJuMIwuBRIl8QNHhv39ftfz7fiHvxOecLKDdjYlFhCC1ERwUitCOXSyGHedtaQSFP3LxBJLBzcsRJxh1VKB9Ig3vg9KamprK/v19a9mA+fU9HSfc4A7ORn3O9WhFtcKwdY2d/6mBwsIEAB2cnwroTAbm3SSXk5d8xByMTf344HGZzc7PIjDW3ztXf4XI0dVrlNImLsZhcMzoiHTAncpTrWJGzprBdrzK7mKThQZPrC9au67mDiHsYZhEBuB+dOAiK/JHPIzAvtpsKICOAIS792MubQXWjvhWNRWG8djQ1LCX3cysdciUdIGqgKDhElA3FqXMg4B/ytwEib5MXh3VCtVoHhM/+/n42NjayuLhYUg9HVPJynulcDYNKxmWyw55Tox1QCxefcesmOoSuGM2ZMDPCc/5IpHRUNRFEymMDxpH6zXS8tNfOu4b1PIPfH+U69mvnWYik+eYvQysmwd6+iYmDtwqzO8IY3rsbOMuF+9srOUfl95yFWnsn5w6MuVYEFspCR8FslGZGmbPzQrw6hEWdc9vjklPhzWuvbMNyBDc0wjECRV139NzsiJwrHdZqd9gWqCQlx2PeScqbv5xX2jEbVpsUNJuOU9rZ2Sl5u7fD2fFRgvMaYkzIn79xZobZdsr+jlMtggYIBX3iwGueXVcGWOukuXPI+g0kPsp1bOP0AjgKOufywnjBgZN+HRzKQv3LAmTBeIaZODy8oREXkZjFsgNwbsFcMC6/noD7MAfndDXFXhNHfo5rkLUHpbBfK6Q3/5osS1JyRJ5B9K9zekf0ZNx7S+Q2VHREA4pyL0dxkInJJaclyTind15sOdWRi2Z673mtCUUupw/cBx1ElowRB4DMfVyljZvv4fj94ioYbrPqjLnmHwz3LbvjXEc2TiaD8tWe1lC3PERdM+RB1NgQFpdzGOCJPWUyjijOP0yUYEAsls+y9XO4vztE7HyAYkQIRzmUpts9OEPJhuoasHtYUSK3FrKYRBWfZMdio0jkmjs7O9d19biEhZw8Rhue624mXUw+eY5mdjEauANSAyMGLrOpg8EgGxsbmZmZaTCXPtojaXaA8bd3CqFPDgboHes4NTXVqDGS3xvd4TRcHnMUrPmAqampxlsOPD4jLWTMVQeLo1zH7q11Zz+eI2lG1n7/4EU7lDZQRBTPBA3fseA9MX7H5mkcg6Gs34XorhN7ST5vh2ByhPyJk+gY62E7R3AYEEr1YptprB2GI6JzFmSJ0uHNkeX09HSWl5ezu7ubnZ2d8j3nP3aUztcPK5XUpS2fGOCI5Hyd39WEjKOkj5Uh58QxuVjPZwxzMQCTXjigzc3NoguMlzmxznWfdzJ29vwbHWq1xm/1tjOE+fV6W3cdtc2ZIEccaZJDA9aNXMd+yxiDNCvrhnEMjFcCIjzDKbNuGJ6fQS7FItD2ZwhsZWQRESywzNHA7VwYE8oyMzOTnZ2dshjktwjYeSjzcK7BAiILe2vnqnVtENkAbfkduTCycpoA7ATmoyxEMkNGOx4/1+vgfBtGGhmNRqNS22V97CTrnN4bjE0Uer42Ou5LCc6pjMsuIC7kYvRkdtl1ThOLrJudgnWSKFjDVGRiZOE80kZNmpCMmxP4vGuiN3Idq5TCoiFoFAZSwK1mDJTanT2Pc0I8OJOtmxlqosaeC+W2YDqdTmll8/gsdO7Fzw3XECbzo9RCPmPanHFRNnCk4vcYkvNKt6z1+/3GVjMMhAVFKep8F4Ux5Gq326W+hvLV+Trrh6EdtjYYneWA4nsu/uPiPvfHUdgpej3tNHCUNmYTcSYH+b+ZeRw3SIYm/Vp3kpSISbRD9vU2QM+BZ1sPrE8mg/y9fr/f2It8I9eRjZNoSG6E8Ow1DUfxnq59wsoZ+jnHMrTjHuSTvV6vdKgQGTGuJGU3+vLy8nWCs8BQkJmZmQZRxMJ4Mbg33ppWNsP6pNlM74U0LHLuZ4WDQOGV5tzDymXFqAkWSBzyo/n5+TIu2PKtra1GDgr5hgOoD/R251RdGzbyQXZOVZgXOkHOnjSPWGEezIE/hsUYHD+DoXaExHmwA4rngCJ82DSGQvmNz1qPjchYN3Ma1nvsgu8wb9ac+Zrdv5HryAd8mf63N/Wi1l7eiophYlQotOtT+/v7jbdi1UrP81lEs4GMByaNKICR2ehteDXbiINxTuWczdHE5QlDtxq28R1+noxzN14Zx3dHo1GDPDFTiyxQxOnp6UL386Jbk2abm5ulHOK83/e1Q7SRMEYUzT9nPR1dMAKTQyAF1tXrQH7K851rHvY+lG6320AJjIvfs1UNBzscDkvftE9z5Pf+vJsinDtyUT9utcbdakYLyNtrbP0/agP8kQ/4MnYmLzHpgKdg4eujNDAY3xPD5juGOyhgDbHa7fZ179S0R8aROMfD+J2z8p1kvIcSoduDm/Ry7lXPI7m+vlr/zeZjjMzR0SiA59jpOScy0Ubzu+EY0WVxcbGgjWR8hAete3ye5zif5g8kWZ3fETFYFzs81tykkvNwCC0UljTIUcgyxijQEx9Mzdw8f+SJswIB0FXGeOEyrMu1k3b+6O4w20W95ob6biW90euGjdMh2zkJUJFOESuzYQ4Lj4fiM0mznc4EjiEp33PuYEhnRbGnRZBE61pB7fkYh3NgnsWpBigulx0WY3G7FwblaIFHJw+3c2KuPu+IBQWOOSej+QBHxZxQMuQ/NzdXDBp5sAZWOhhPnBprhhIbonEhQ+7jt08byjnX9NoDeQ0jHX0cqd3fasW3Q2Ds9ba5Wi8tq9pRO2I7v6/RG3I+jIOws/I8bvS6YeOsLd6slXM1e7yazYMsssfZ2dlpKIXvaRbM98XQUFCf4s53eTfK/Px8EWx9josNwawo3tmGbkfA2JmjIa3H7aZ+R4/DcphOp3MdIcS9TALRJGEo5chhmOm8Lrn+XF0iFYrEeICAbq2bm5trOEfGWG9GACo6ncBQjI64p42Y76MntRMwsqrzVjfLm6WvEZARGI7N0bbWN+bmNIYIbu4Ch2lkh514fY5yHetQaSuFa1tEAy+iJ+0SCwP3Blvub4YW5XdR3GQB9wO6sAAYhxc7SUNBWFQinxeZ+zIenu1cETaVqGEDNunilq46N8EL21B8uoJzd57HGNy37Dos8qPMgMyAtty3djDuzHIO6IK787wkxaE4f2V+Rk41bLYBOJqa2UR2/HxmZqYRABg3egh0nZ6eLtvLZmdnG6mM9YmUqc7rifw4fJf/HJR86gdGyRYxdMRE2FGvY7G1GIF3jyMwBtxqtUpOgfHg9XwvlA+vYyhIXZLcCWEjXJTEUIdyBl7di5eMi+J+r+ZwOD6EinEZimAsVkzGiEI70rKAPJvF9ndqD20Kn/v3+833lfi8I8N9iDVvBEjG0R35mElHnkQAZMP8PC7kx1x8vAgtg9625miCPJw/85yaqTesZ6xObRzJ7eA8DqOMra2tJAdOjGiKPJ0bGm5ivMiu7iyyLJyy1SdDGNIzTlcxbuQ6snFubm7mwoULjQ3GToQZBIuP10Ax7X1sqFZe55T2SmbPMDIL154UoTjPM0SyIhqGOFd0pHMeTFTk9/6uSR1DRObHuDF634PnWD7AZdoMMUzn2owHReGe/AERuIEbY/fc61wW4zGUdIR3B475Ahwma2LHxc+Scb6OwmNwjKeG7jXLXa+BnYqRiZFRHbUdCJiT80UjCGRkJMW/TWI6RcMu2u12HnnkkSPZWutrsUetVmukfzeUufpcw/vW//bPDvvODQ201aT1D/uZPeC/jXt/tTF/vbF8rcT/34YsvtrY6+fWcvla472RMaGQX22OX00Hvtr46u/W8v1a3zvsc37e1/tMPTfmddi9vp5eHHWetf2MRqNDv3Bk47xRJfqDKNxzcR1lPF/vs/+u5vaNJsM/6HXc+fz7Jof6+gMb583r5nXzem6ur2ach59tefO6ed28/k+/bhrnzevm9Q16fU1Ye/O6ed28/s+7bkbOm9fN6xv0ummcN6+b1zfoddM4b143r2/Q66Zx3rxuXt+g103jvHndvL5Br5vGefO6eX2DXv8/kknrRysws98AAAAASUVORK5CYII=", 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" ] @@ -113,16 +159,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "00365.png\n", - "IoU: 94.87\n", - "F1 Score: 97.37\n", - "Precision: 97.84\n", - "Recall: 96.90\n" + "00492.png\n", + "IoU: 31.08\n", + "F1 Score: 47.42\n", + "Precision: 38.47\n", + "Recall: 61.80\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -136,16 +182,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "00452.png\n", - "IoU: 94.78\n", - "F1 Score: 97.32\n", - "Precision: 96.21\n", - "Recall: 98.46\n" + "00954.png\n", + "IoU: 34.70\n", + "F1 Score: 51.52\n", + "Precision: 52.61\n", + "Recall: 50.47\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -159,10 +205,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average IoU: 82.83\n", - "Average F1 Score: 90.30\n", - "Average Precision: 90.21\n", - "Average Recall: 90.89\n" + "Average IoU: 83.11\n", + "Average F1 Score: 90.48\n", + "Average Precision: 90.45\n", + "Average Recall: 91.03\n" ] } ], @@ -174,7 +220,7 @@ "\n", "iou_list, f1_score_list, precision_list, recall_list = [],[],[],[]\n", "\n", - "file_name_list = os.listdir('/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240417/')\n", + "file_name_list = os.listdir('/workspaces/mmsegmentation/OUTPUT/MAEOUT_240422/')\n", "file_name_list = sorted(file_name_list)\n", "\n", "def evaluate_segmentation(label, output):\n", @@ -205,9 +251,9 @@ "\n", "\n", "for file_name in file_name_list:\n", - " image_path_output = f'/workspaces/mmsegmentation-1/OUTPUT/MAEOUT_240417/{file_name}'\n", - " image_path_img = f'/workspaces/mmsegmentation-1/data/cag/images/test/{file_name}'\n", - " image_path_label = f'/workspaces/mmsegmentation-1/data/cag/annotations/test/{file_name}'\n", + " image_path_output = f'/workspaces/mmsegmentation/OUTPUT/MAEOUT_240422/{file_name}'\n", + " image_path_img = f'/workspaces/mmsegmentation/data/cag/images/test/{file_name}'\n", + " image_path_label = f'/workspaces/mmsegmentation/data/cag/annotations/test/{file_name}'\n", "\n", " label_img = cv2.imread(image_path_label, 0)\n", " img = cv2.imread(image_path_img)\n", @@ -220,7 +266,7 @@ " precision_list.append(precision)\n", " recall_list.append(recall)\n", " \n", - " if f1_score > 0.972 and f1_score < 0.975:\n", + " if f1_score < 0.6:\n", " # Create a mask for pixels where label and output are both 1\n", " overlap_mask = (label_img == 1) & (output_img == 1)\n", "\n", @@ -313,15 +359,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "4262\n", - "2400\n" + "4262\n" ] } ], @@ -330,20 +375,20 @@ "#annotations\n", "# images\n", "# Directory containing the images\n", - "directory = '/workspaces/mmsegmentation-1/data/cag/annotations/training'\n", - "print(len(os.listdir('/workspaces/mmsegmentation-1/data/cag/annotations/training')))\n", + "directory = '/workspaces/mmsegmentation/data/cag/annotations/training'\n", + "print(len(os.listdir('/workspaces/mmsegmentation/data/cag/annotations/training')))\n", "# Iterate over the files in the directory\n", - "for filename in os.listdir(directory):\n", - " # Check if the filename is in the range to be deleted\n", - " if filename.endswith('.png'):\n", - " file_number = int(filename.split('.')[0])\n", - " if file_number >= 2400:\n", - " # Construct the full file path\n", - " file_path = os.path.join(directory, filename)\n", - " # Delete the file\n", - " os.remove(file_path)\n", + "# for filename in os.listdir(directory):\n", + "# # Check if the filename is in the range to be deleted\n", + "# if filename.endswith('.png'):\n", + "# file_number = int(filename.split('.')[0])\n", + "# if file_number >= 2400:\n", + "# # Construct the full file path\n", + "# file_path = os.path.join(directory, filename)\n", + "# # Delete the file\n", + "# os.remove(file_path)\n", " \n", - "print(len(os.listdir('/workspaces/mmsegmentation-1/data/cag/annotations/training')))" + "# print(len(os.listdir('/workspaces/mmsegmentation-1/data/cag/annotations/training')))" ] }, { diff --git a/configs/_base_/models/upernet_mae.py b/configs/_base_/models/upernet_mae.py index b833b67645..9ea5cda984 100644 --- a/configs/_base_/models/upernet_mae.py +++ b/configs/_base_/models/upernet_mae.py @@ -1,8 +1,8 @@ norm_cfg = dict(type='SyncBN', requires_grad=True) data_preprocessor = dict( type='SegDataPreProcessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], + # mean=[123.675, 116.28, 103.53], + # std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) diff --git a/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py b/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py index 0225c1e9f7..e28a03ec88 100644 --- a/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py +++ b/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py @@ -6,7 +6,7 @@ data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, - pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + pretrained='/workspaces/mmsegmentation/converted_model.pth', backbone=dict( type='MAE', img_size=(512, 512), diff --git a/feature_map_visual.py b/feature_map_visual.py new file mode 100644 index 0000000000..266ceee01c --- /dev/null +++ b/feature_map_visual.py @@ -0,0 +1,137 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from argparse import ArgumentParser +from typing import Type + +import mmcv +import torch +import torch.nn as nn + +from mmengine.model import revert_sync_batchnorm +from mmengine.structures import PixelData +from mmseg.apis import inference_model, init_model +from mmseg.structures import SegDataSample +from mmseg.utils import register_all_modules +from mmseg.visualization import SegLocalVisualizer + + +class Recorder: + """record the forward output feature map and save to data_buffer.""" + + def __init__(self) -> None: + self.data_buffer = list() + + def __enter__(self, ): + self._data_buffer = list() + + def record_data_hook(self, model: nn.Module, input: Type, output: Type): + self.data_buffer.append(output) + + def __exit__(self, *args, **kwargs): + pass + + +def visualize(args, model, recorder, result): + seg_visualizer = SegLocalVisualizer( + vis_backends=[dict(type='WandbVisBackend')], + save_dir='temp_dir', + alpha=0.5) + seg_visualizer.dataset_meta = dict( + classes=model.dataset_meta['classes'], + palette=model.dataset_meta['palette']) + + image = mmcv.imread(args.img, 'gray') + + seg_visualizer.add_datasample( + name='predict', + image=image, + data_sample=result, + draw_gt=False, + draw_pred=True, + wait_time=0, + out_file=None, + show=False) + + # add feature map to wandb visualizer + for i in range(len(recorder.data_buffer)): + feature = recorder.data_buffer[i][0] # remove the batch + drawn_img = seg_visualizer.draw_featmap( + feature, image, channel_reduction='select_max') + seg_visualizer.add_image(f'feature_map{i}', drawn_img) + + if args.gt_mask: + sem_seg = mmcv.imread(args.gt_mask, 'unchanged') + sem_seg = torch.from_numpy(sem_seg) + gt_mask = dict(data=sem_seg) + gt_mask = PixelData(**gt_mask) + data_sample = SegDataSample() + data_sample.gt_sem_seg = gt_mask + + seg_visualizer.add_datasample( + name='gt_mask', + image=image, + data_sample=data_sample, + draw_gt=True, + draw_pred=False, + wait_time=0, + out_file=None, + show=False) + + seg_visualizer.add_image('image', image) + + +def main(): + parser = ArgumentParser( + description='Draw the Feature Map During Inference') + parser.add_argument('img', help='Image file') + parser.add_argument('config', help='Config file') + parser.add_argument('checkpoint', help='Checkpoint file') + parser.add_argument('--gt_mask', default=None, help='Path of gt mask file') + parser.add_argument('--out-file', default=None, help='Path to output file') + parser.add_argument( + '--device', default='cuda:0', help='Device used for inference') + parser.add_argument( + '--opacity', + type=float, + default=0.5, + help='Opacity of painted segmentation map. In (0, 1] range.') + parser.add_argument( + '--title', default='result', help='The image identifier.') + args = parser.parse_args() + + register_all_modules() + + # build the model from a config file and a checkpoint file + model = init_model(args.config, args.checkpoint, device=args.device) + if args.device == 'cpu': + model = revert_sync_batchnorm(model) + + # show all named module in the model and use it in source list below + for name, module in model.named_modules(): + print(name) + + source = [ + 'decode_head.fusion.stages.0.query_project.activate', + 'decode_head.context.stages.0.key_project.activate', + 'decode_head.context.bottleneck.activate' + ] + source = dict.fromkeys(source) + + count = 0 + recorder = Recorder() + # registry the forward hook + for name, module in model.named_modules(): + if name in source: + count += 1 + module.register_forward_hook(recorder.record_data_hook) + if count == len(source): + break + + with recorder: + # test a single image, and record feature map to data_buffer + result = inference_model(model, args.img) + + visualize(args, model, recorder, result) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/mmseg/datasets/transforms/formatting.py b/mmseg/datasets/transforms/formatting.py index ef9a783f6b..07a58321c8 100644 --- a/mmseg/datasets/transforms/formatting.py +++ b/mmseg/datasets/transforms/formatting.py @@ -66,11 +66,13 @@ def transform(self, results: dict) -> dict: if len(img.shape) < 3: img = np.stack([img] * 3, axis=-1) if not img.flags.c_contiguous: - # img = (img - np.min(img))/np.max(img) + img = (img - np.min(img)) + img = img /np.max(img) img = to_tensor(np.ascontiguousarray(img.transpose(2, 0, 1))) else: img = img.transpose(2, 0, 1) - # img = (img - np.min(img))/np.max(img) + img = (img - np.min(img)) + img = img /np.max(img) img = to_tensor(img).contiguous() packed_results['inputs'] = img data_sample = SegDataSample() From c5bbc5ee16b1ae20166eea54232109ea9a4f784e Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Tue, 23 Apr 2024 06:06:42 +0000 Subject: [PATCH 22/24] 2024.04.23 --- .gitignore | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 88f4e10013..9f4acc0a01 100644 --- a/.gitignore +++ b/.gitignore @@ -30,7 +30,7 @@ MANIFEST # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec - +nohup.out # Installer logs pip-log.txt pip-delete-this-directory.txt @@ -62,7 +62,8 @@ instance/ # Scrapy stuff: .scrapy - +wandb +test_commands.sh # Sphinx documentation docs/en/_build/ docs/zh_cn/_build/ From 576b2dfaa481c932546b649168caa75005f21883 Mon Sep 17 00:00:00 2001 From: jaeofbum Date: Tue, 23 Apr 2024 06:08:34 +0000 Subject: [PATCH 23/24] 2024.04.24 --- nohup.out | 33871 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 33871 insertions(+) diff --git a/nohup.out b/nohup.out index 353438b038..c2a6817ea9 100644 --- a/nohup.out +++ b/nohup.out @@ -9427,3 +9427,33874 @@ Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/conver 04/17 08:31:37 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/MAE4000. 04/17 08:32:34 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:06:51 time: 1.0239 data_time: 0.0042 memory: 8935 loss: 7.0101 decode.loss_ce: 5.0132 decode.acc_seg: 1.2255 aux.loss_ce: 1.9970 aux.acc_seg: 0.1034 04/17 08:33:25 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 1 day, 23:47:20 time: 1.0232 data_time: 0.0044 memory: 8462 loss: 6.7600 decode.loss_ce: 4.7872 decode.acc_seg: 22.1193 aux.loss_ce: 1.9728 aux.acc_seg: 13.3171 +04/17 08:34:16 - mmengine - INFO - Iter(train) [ 150/160000] base_lr: 9.9401e-06 lr: 3.6750e-08 eta: 1 day, 22:59:29 time: 1.0228 data_time: 0.0041 memory: 8462 loss: 6.2894 decode.loss_ce: 4.3502 decode.acc_seg: 42.1999 aux.loss_ce: 1.9392 aux.acc_seg: 23.7051 +04/17 08:35:07 - mmengine - INFO - Iter(train) [ 200/160000] base_lr: 1.3276e-05 lr: 4.9083e-08 eta: 1 day, 22:33:58 time: 1.0184 data_time: 0.0040 memory: 8462 loss: 5.7614 decode.loss_ce: 3.8749 decode.acc_seg: 58.2272 aux.loss_ce: 1.8865 aux.acc_seg: 34.2005 +04/17 08:35:58 - mmengine - INFO - Iter(train) [ 250/160000] base_lr: 1.6611e-05 lr: 6.1415e-08 eta: 1 day, 22:15:08 time: 1.0145 data_time: 0.0039 memory: 8462 loss: 5.0487 decode.loss_ce: 3.2347 decode.acc_seg: 78.0340 aux.loss_ce: 1.8141 aux.acc_seg: 43.1890 +04/17 08:36:49 - mmengine - INFO - Iter(train) [ 300/160000] base_lr: 1.9947e-05 lr: 7.3747e-08 eta: 1 day, 22:00:43 time: 1.0089 data_time: 0.0040 memory: 8462 loss: 4.3936 decode.loss_ce: 2.6770 decode.acc_seg: 85.6838 aux.loss_ce: 1.7167 aux.acc_seg: 64.1836 +04/17 08:37:39 - mmengine - INFO - Iter(train) [ 350/160000] base_lr: 2.3282e-05 lr: 8.6079e-08 eta: 1 day, 21:48:21 time: 1.0050 data_time: 0.0044 memory: 8462 loss: 3.7508 decode.loss_ce: 2.1300 decode.acc_seg: 87.5259 aux.loss_ce: 1.6208 aux.acc_seg: 62.4052 +04/17 08:38:29 - mmengine - INFO - Iter(train) [ 400/160000] base_lr: 2.6618e-05 lr: 9.8412e-08 eta: 1 day, 21:38:10 time: 1.0046 data_time: 0.0045 memory: 8462 loss: 3.1693 decode.loss_ce: 1.6545 decode.acc_seg: 95.9000 aux.loss_ce: 1.5148 aux.acc_seg: 74.7307 +04/17 08:39:19 - mmengine - INFO - Iter(train) [ 450/160000] base_lr: 2.9953e-05 lr: 1.1074e-07 eta: 1 day, 21:29:51 time: 1.0036 data_time: 0.0041 memory: 8462 loss: 2.5232 decode.loss_ce: 1.1461 decode.acc_seg: 96.0243 aux.loss_ce: 1.3771 aux.acc_seg: 65.2546 +04/17 08:40:10 - mmengine - INFO - Iter(train) [ 500/160000] base_lr: 3.3289e-05 lr: 1.2308e-07 eta: 1 day, 21:23:02 time: 1.0035 data_time: 0.0040 memory: 8462 loss: 2.0180 decode.loss_ce: 0.7713 decode.acc_seg: 95.5585 aux.loss_ce: 1.2467 aux.acc_seg: 72.2454 +04/17 08:41:00 - mmengine - INFO - Iter(train) [ 550/160000] base_lr: 3.6624e-05 lr: 1.3541e-07 eta: 1 day, 21:17:08 time: 1.0037 data_time: 0.0041 memory: 8462 loss: 1.6391 decode.loss_ce: 0.5415 decode.acc_seg: 97.0821 aux.loss_ce: 1.0976 aux.acc_seg: 81.3814 +04/17 08:41:50 - mmengine - INFO - Iter(train) [ 600/160000] base_lr: 3.9960e-05 lr: 1.4774e-07 eta: 1 day, 21:11:56 time: 1.0025 data_time: 0.0044 memory: 8462 loss: 1.2957 decode.loss_ce: 0.3839 decode.acc_seg: 96.9400 aux.loss_ce: 0.9119 aux.acc_seg: 88.7630 +04/17 08:42:40 - mmengine - INFO - Iter(train) [ 650/160000] base_lr: 4.3296e-05 lr: 1.6007e-07 eta: 1 day, 21:07:17 time: 1.0012 data_time: 0.0042 memory: 8462 loss: 1.0201 decode.loss_ce: 0.2598 decode.acc_seg: 99.0055 aux.loss_ce: 0.7604 aux.acc_seg: 94.6356 +04/17 08:43:30 - mmengine - INFO - Iter(train) [ 700/160000] base_lr: 4.6631e-05 lr: 1.7240e-07 eta: 1 day, 21:02:57 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.8337 decode.loss_ce: 0.2176 decode.acc_seg: 96.4949 aux.loss_ce: 0.6161 aux.acc_seg: 95.5870 +04/17 08:44:20 - mmengine - INFO - Iter(train) [ 750/160000] base_lr: 4.9967e-05 lr: 1.8474e-07 eta: 1 day, 20:58:43 time: 0.9966 data_time: 0.0043 memory: 8462 loss: 0.6015 decode.loss_ce: 0.1586 decode.acc_seg: 97.6515 aux.loss_ce: 0.4429 aux.acc_seg: 97.1811 +04/17 08:45:10 - mmengine - INFO - Iter(train) [ 800/160000] base_lr: 5.3302e-05 lr: 1.9707e-07 eta: 1 day, 20:54:39 time: 0.9973 data_time: 0.0043 memory: 8462 loss: 0.4766 decode.loss_ce: 0.1337 decode.acc_seg: 98.6265 aux.loss_ce: 0.3429 aux.acc_seg: 97.3650 +04/17 08:46:00 - mmengine - INFO - Iter(train) [ 850/160000] base_lr: 5.6638e-05 lr: 2.0940e-07 eta: 1 day, 20:50:56 time: 0.9974 data_time: 0.0039 memory: 8462 loss: 0.3757 decode.loss_ce: 0.1175 decode.acc_seg: 98.1531 aux.loss_ce: 0.2582 aux.acc_seg: 96.8962 +04/17 08:46:50 - mmengine - INFO - Iter(train) [ 900/160000] base_lr: 5.9973e-05 lr: 2.2173e-07 eta: 1 day, 20:47:32 time: 0.9970 data_time: 0.0040 memory: 8462 loss: 0.3494 decode.loss_ce: 0.1149 decode.acc_seg: 97.5227 aux.loss_ce: 0.2345 aux.acc_seg: 96.7276 +04/17 08:47:39 - mmengine - INFO - Iter(train) [ 950/160000] base_lr: 6.3309e-05 lr: 2.3407e-07 eta: 1 day, 20:44:24 time: 0.9968 data_time: 0.0040 memory: 8462 loss: 0.2647 decode.loss_ce: 0.0933 decode.acc_seg: 98.1697 aux.loss_ce: 0.1714 aux.acc_seg: 97.2790 +04/17 08:48:29 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 08:48:29 - mmengine - INFO - Iter(train) [ 1000/160000] base_lr: 6.6644e-05 lr: 2.4640e-07 eta: 1 day, 20:41:36 time: 0.9979 data_time: 0.0043 memory: 8462 loss: 0.2273 decode.loss_ce: 0.0908 decode.acc_seg: 98.6879 aux.loss_ce: 0.1365 aux.acc_seg: 97.6452 +04/17 08:49:19 - mmengine - INFO - Iter(train) [ 1050/160000] base_lr: 6.9980e-05 lr: 2.5873e-07 eta: 1 day, 20:38:59 time: 0.9989 data_time: 0.0049 memory: 8462 loss: 0.2224 decode.loss_ce: 0.0969 decode.acc_seg: 97.7600 aux.loss_ce: 0.1255 aux.acc_seg: 96.2608 +04/17 08:50:09 - mmengine - INFO - Iter(train) [ 1100/160000] base_lr: 7.3316e-05 lr: 2.7106e-07 eta: 1 day, 20:36:31 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.1755 decode.loss_ce: 0.0823 decode.acc_seg: 97.8956 aux.loss_ce: 0.0933 aux.acc_seg: 96.8731 +04/17 08:50:59 - mmengine - INFO - Iter(train) [ 1150/160000] base_lr: 7.6651e-05 lr: 2.8339e-07 eta: 1 day, 20:34:13 time: 0.9981 data_time: 0.0045 memory: 8462 loss: 0.1492 decode.loss_ce: 0.0711 decode.acc_seg: 97.3215 aux.loss_ce: 0.0781 aux.acc_seg: 95.2385 +04/17 08:51:49 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 08:51:49 - mmengine - INFO - Iter(train) [ 1200/160000] base_lr: 7.9987e-05 lr: 2.9573e-07 eta: 1 day, 20:32:04 time: 0.9979 data_time: 0.0046 memory: 8462 loss: 0.1411 decode.loss_ce: 0.0710 decode.acc_seg: 98.7997 aux.loss_ce: 0.0700 aux.acc_seg: 97.9790 +04/17 08:52:39 - mmengine - INFO - Iter(train) [ 1250/160000] base_lr: 8.3322e-05 lr: 3.0806e-07 eta: 1 day, 20:30:02 time: 0.9994 data_time: 0.0047 memory: 8462 loss: 0.1245 decode.loss_ce: 0.0608 decode.acc_seg: 98.9174 aux.loss_ce: 0.0637 aux.acc_seg: 98.4625 +04/17 08:53:29 - mmengine - INFO - Iter(train) [ 1300/160000] base_lr: 8.6658e-05 lr: 3.2039e-07 eta: 1 day, 20:28:03 time: 0.9979 data_time: 0.0041 memory: 8462 loss: 0.1184 decode.loss_ce: 0.0597 decode.acc_seg: 97.2700 aux.loss_ce: 0.0587 aux.acc_seg: 96.9965 +04/17 08:54:19 - mmengine - INFO - Iter(train) [ 1350/160000] base_lr: 8.9993e-05 lr: 3.3272e-07 eta: 1 day, 20:26:10 time: 0.9970 data_time: 0.0039 memory: 8462 loss: 0.1053 decode.loss_ce: 0.0539 decode.acc_seg: 98.1895 aux.loss_ce: 0.0514 aux.acc_seg: 97.5515 +04/17 08:55:09 - mmengine - INFO - Iter(train) [ 1400/160000] base_lr: 9.3329e-05 lr: 3.4506e-07 eta: 1 day, 20:24:20 time: 0.9986 data_time: 0.0044 memory: 8462 loss: 0.0978 decode.loss_ce: 0.0511 decode.acc_seg: 98.8268 aux.loss_ce: 0.0467 aux.acc_seg: 98.3084 +04/17 08:55:58 - mmengine - INFO - Iter(train) [ 1450/160000] base_lr: 9.6664e-05 lr: 3.5739e-07 eta: 1 day, 20:22:34 time: 0.9980 data_time: 0.0045 memory: 8462 loss: 0.1061 decode.loss_ce: 0.0579 decode.acc_seg: 97.1462 aux.loss_ce: 0.0482 aux.acc_seg: 95.9591 +04/17 08:56:48 - mmengine - INFO - Iter(train) [ 1500/160000] base_lr: 1.0000e-04 lr: 3.6972e-07 eta: 1 day, 20:20:56 time: 0.9979 data_time: 0.0038 memory: 8462 loss: 0.1053 decode.loss_ce: 0.0602 decode.acc_seg: 97.4928 aux.loss_ce: 0.0451 aux.acc_seg: 97.0434 +04/17 08:57:38 - mmengine - INFO - Iter(train) [ 1550/160000] base_lr: 9.9969e-05 lr: 3.6961e-07 eta: 1 day, 20:19:24 time: 0.9988 data_time: 0.0041 memory: 8462 loss: 0.0964 decode.loss_ce: 0.0526 decode.acc_seg: 98.3452 aux.loss_ce: 0.0438 aux.acc_seg: 97.5035 +04/17 08:58:28 - mmengine - INFO - Iter(train) [ 1600/160000] base_lr: 9.9938e-05 lr: 3.6949e-07 eta: 1 day, 20:17:50 time: 0.9984 data_time: 0.0041 memory: 8462 loss: 0.0870 decode.loss_ce: 0.0482 decode.acc_seg: 97.9443 aux.loss_ce: 0.0388 aux.acc_seg: 97.1024 +04/17 08:59:18 - mmengine - INFO - Iter(train) [ 1650/160000] base_lr: 9.9906e-05 lr: 3.6937e-07 eta: 1 day, 20:16:19 time: 0.9976 data_time: 0.0046 memory: 8462 loss: 0.0931 decode.loss_ce: 0.0529 decode.acc_seg: 98.5298 aux.loss_ce: 0.0402 aux.acc_seg: 98.1544 +04/17 09:00:08 - mmengine - INFO - Iter(train) [ 1700/160000] base_lr: 9.9874e-05 lr: 3.6926e-07 eta: 1 day, 20:14:52 time: 0.9971 data_time: 0.0042 memory: 8462 loss: 0.0831 decode.loss_ce: 0.0484 decode.acc_seg: 97.0835 aux.loss_ce: 0.0347 aux.acc_seg: 96.7350 +04/17 09:00:58 - mmengine - INFO - Iter(train) [ 1750/160000] base_lr: 9.9843e-05 lr: 3.6914e-07 eta: 1 day, 20:13:28 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.0656 decode.loss_ce: 0.0356 decode.acc_seg: 98.7150 aux.loss_ce: 0.0301 aux.acc_seg: 97.9824 +04/17 09:01:48 - mmengine - INFO - Iter(train) [ 1800/160000] base_lr: 9.9811e-05 lr: 3.6902e-07 eta: 1 day, 20:12:06 time: 0.9982 data_time: 0.0041 memory: 8462 loss: 0.0693 decode.loss_ce: 0.0407 decode.acc_seg: 99.0469 aux.loss_ce: 0.0285 aux.acc_seg: 98.2542 +04/17 09:02:38 - mmengine - INFO - Iter(train) [ 1850/160000] base_lr: 9.9780e-05 lr: 3.6891e-07 eta: 1 day, 20:10:46 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0593 decode.loss_ce: 0.0350 decode.acc_seg: 98.9889 aux.loss_ce: 0.0243 aux.acc_seg: 98.6946 +04/17 09:03:28 - mmengine - INFO - Iter(train) [ 1900/160000] base_lr: 9.9748e-05 lr: 3.6879e-07 eta: 1 day, 20:09:26 time: 0.9981 data_time: 0.0044 memory: 8462 loss: 0.0596 decode.loss_ce: 0.0332 decode.acc_seg: 98.8941 aux.loss_ce: 0.0264 aux.acc_seg: 98.0751 +04/17 09:04:18 - mmengine - INFO - Iter(train) [ 1950/160000] base_lr: 9.9717e-05 lr: 3.6867e-07 eta: 1 day, 20:08:11 time: 0.9993 data_time: 0.0047 memory: 8462 loss: 0.0573 decode.loss_ce: 0.0316 decode.acc_seg: 99.2884 aux.loss_ce: 0.0257 aux.acc_seg: 98.5992 +04/17 09:05:08 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 09:05:08 - mmengine - INFO - Iter(train) [ 2000/160000] base_lr: 9.9685e-05 lr: 3.6856e-07 eta: 1 day, 20:06:58 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0635 decode.loss_ce: 0.0379 decode.acc_seg: 99.3744 aux.loss_ce: 0.0256 aux.acc_seg: 98.4859 +04/17 09:05:58 - mmengine - INFO - Iter(train) [ 2050/160000] base_lr: 9.9654e-05 lr: 3.6844e-07 eta: 1 day, 20:05:42 time: 0.9965 data_time: 0.0040 memory: 8462 loss: 0.0711 decode.loss_ce: 0.0426 decode.acc_seg: 98.8665 aux.loss_ce: 0.0285 aux.acc_seg: 98.4669 +04/17 09:06:48 - mmengine - INFO - Iter(train) [ 2100/160000] base_lr: 9.9622e-05 lr: 3.6832e-07 eta: 1 day, 20:04:29 time: 0.9983 data_time: 0.0042 memory: 8462 loss: 0.0604 decode.loss_ce: 0.0348 decode.acc_seg: 98.1258 aux.loss_ce: 0.0257 aux.acc_seg: 97.6543 +04/17 09:07:38 - mmengine - INFO - Iter(train) [ 2150/160000] base_lr: 9.9591e-05 lr: 3.6821e-07 eta: 1 day, 20:03:15 time: 0.9988 data_time: 0.0042 memory: 8462 loss: 0.0644 decode.loss_ce: 0.0382 decode.acc_seg: 97.8350 aux.loss_ce: 0.0261 aux.acc_seg: 97.0947 +04/17 09:08:28 - mmengine - INFO - Iter(train) [ 2200/160000] base_lr: 9.9559e-05 lr: 3.6809e-07 eta: 1 day, 20:02:05 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0556 decode.loss_ce: 0.0321 decode.acc_seg: 98.8342 aux.loss_ce: 0.0235 aux.acc_seg: 98.3250 +04/17 09:09:18 - mmengine - INFO - Iter(train) [ 2250/160000] base_lr: 9.9527e-05 lr: 3.6797e-07 eta: 1 day, 20:00:56 time: 0.9994 data_time: 0.0040 memory: 8462 loss: 0.0595 decode.loss_ce: 0.0345 decode.acc_seg: 99.4347 aux.loss_ce: 0.0250 aux.acc_seg: 98.9706 +04/17 09:10:07 - mmengine - INFO - Iter(train) [ 2300/160000] base_lr: 9.9496e-05 lr: 3.6786e-07 eta: 1 day, 19:59:48 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0528 decode.loss_ce: 0.0305 decode.acc_seg: 99.5253 aux.loss_ce: 0.0223 aux.acc_seg: 98.7959 +04/17 09:10:57 - mmengine - INFO - Iter(train) [ 2350/160000] base_lr: 9.9464e-05 lr: 3.6774e-07 eta: 1 day, 19:58:40 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0578 decode.loss_ce: 0.0343 decode.acc_seg: 97.5035 aux.loss_ce: 0.0235 aux.acc_seg: 97.0406 +04/17 09:11:47 - mmengine - INFO - Iter(train) [ 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decode.acc_seg: 98.5094 aux.loss_ce: 0.0220 aux.acc_seg: 98.0539 +04/17 09:18:27 - mmengine - INFO - Iter(train) [ 2800/160000] base_lr: 9.9180e-05 lr: 3.6669e-07 eta: 1 day, 19:49:10 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0552 decode.loss_ce: 0.0334 decode.acc_seg: 98.9201 aux.loss_ce: 0.0217 aux.acc_seg: 98.0875 +04/17 09:19:17 - mmengine - INFO - Iter(train) [ 2850/160000] base_lr: 9.9149e-05 lr: 3.6657e-07 eta: 1 day, 19:48:11 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0417 decode.loss_ce: 0.0238 decode.acc_seg: 99.2218 aux.loss_ce: 0.0179 aux.acc_seg: 99.0133 +04/17 09:20:07 - mmengine - INFO - Iter(train) [ 2900/160000] base_lr: 9.9117e-05 lr: 3.6646e-07 eta: 1 day, 19:47:10 time: 0.9996 data_time: 0.0044 memory: 8462 loss: 0.0500 decode.loss_ce: 0.0298 decode.acc_seg: 99.1209 aux.loss_ce: 0.0201 aux.acc_seg: 98.5458 +04/17 09:20:57 - mmengine - INFO - Iter(train) [ 2950/160000] base_lr: 9.9086e-05 lr: 3.6634e-07 eta: 1 day, 19:46:10 time: 0.9993 data_time: 0.0043 memory: 8462 loss: 0.0529 decode.loss_ce: 0.0313 decode.acc_seg: 99.5939 aux.loss_ce: 0.0216 aux.acc_seg: 99.2641 +04/17 09:21:47 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 09:21:47 - mmengine - INFO - Iter(train) [ 3000/160000] base_lr: 9.9054e-05 lr: 3.6622e-07 eta: 1 day, 19:45:09 time: 0.9983 data_time: 0.0041 memory: 8462 loss: 0.0457 decode.loss_ce: 0.0268 decode.acc_seg: 98.7343 aux.loss_ce: 0.0190 aux.acc_seg: 98.1049 +04/17 09:22:37 - mmengine - INFO - Iter(train) [ 3050/160000] base_lr: 9.9023e-05 lr: 3.6611e-07 eta: 1 day, 19:44:11 time: 1.0002 data_time: 0.0042 memory: 8462 loss: 0.0476 decode.loss_ce: 0.0275 decode.acc_seg: 99.1837 aux.loss_ce: 0.0200 aux.acc_seg: 98.4442 +04/17 09:23:27 - mmengine - INFO - Iter(train) [ 3100/160000] base_lr: 9.8991e-05 lr: 3.6599e-07 eta: 1 day, 19:43:12 time: 0.9995 data_time: 0.0047 memory: 8462 loss: 0.0473 decode.loss_ce: 0.0283 decode.acc_seg: 98.4861 aux.loss_ce: 0.0190 aux.acc_seg: 97.4447 +04/17 09:24:17 - mmengine - INFO - Iter(train) [ 3150/160000] base_lr: 9.8960e-05 lr: 3.6587e-07 eta: 1 day, 19:42:14 time: 1.0001 data_time: 0.0043 memory: 8462 loss: 0.0475 decode.loss_ce: 0.0282 decode.acc_seg: 98.9435 aux.loss_ce: 0.0193 aux.acc_seg: 98.5840 +04/17 09:25:07 - mmengine - INFO - Iter(train) [ 3200/160000] base_lr: 9.8928e-05 lr: 3.6576e-07 eta: 1 day, 19:41:16 time: 0.9992 data_time: 0.0039 memory: 8462 loss: 0.0488 decode.loss_ce: 0.0294 decode.acc_seg: 98.9069 aux.loss_ce: 0.0194 aux.acc_seg: 98.5401 +04/17 09:25:57 - mmengine - INFO - Iter(train) [ 3250/160000] base_lr: 9.8897e-05 lr: 3.6564e-07 eta: 1 day, 19:40:18 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0468 decode.loss_ce: 0.0273 decode.acc_seg: 99.3895 aux.loss_ce: 0.0195 aux.acc_seg: 99.0913 +04/17 09:26:47 - mmengine - INFO - Iter(train) [ 3300/160000] base_lr: 9.8865e-05 lr: 3.6552e-07 eta: 1 day, 19:39:20 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0460 decode.loss_ce: 0.0271 decode.acc_seg: 97.9965 aux.loss_ce: 0.0189 aux.acc_seg: 97.4051 +04/17 09:27:37 - mmengine - INFO - Iter(train) [ 3350/160000] base_lr: 9.8833e-05 lr: 3.6541e-07 eta: 1 day, 19:38:21 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0462 decode.loss_ce: 0.0276 decode.acc_seg: 99.5575 aux.loss_ce: 0.0186 aux.acc_seg: 99.0801 +04/17 09:28:27 - mmengine - INFO - Iter(train) [ 3400/160000] base_lr: 9.8802e-05 lr: 3.6529e-07 eta: 1 day, 19:37:25 time: 1.0004 data_time: 0.0042 memory: 8462 loss: 0.0479 decode.loss_ce: 0.0294 decode.acc_seg: 99.4705 aux.loss_ce: 0.0185 aux.acc_seg: 98.8924 +04/17 09:29:17 - mmengine - INFO - Iter(train) [ 3450/160000] base_lr: 9.8770e-05 lr: 3.6517e-07 eta: 1 day, 19:36:28 time: 0.9993 data_time: 0.0047 memory: 8462 loss: 0.0443 decode.loss_ce: 0.0267 decode.acc_seg: 99.0513 aux.loss_ce: 0.0176 aux.acc_seg: 98.5983 +04/17 09:30:07 - mmengine - INFO - Iter(train) [ 3500/160000] base_lr: 9.8739e-05 lr: 3.6506e-07 eta: 1 day, 19:35:31 time: 1.0001 data_time: 0.0043 memory: 8462 loss: 0.0527 decode.loss_ce: 0.0326 decode.acc_seg: 99.3343 aux.loss_ce: 0.0202 aux.acc_seg: 98.6254 +04/17 09:30:57 - mmengine - INFO - Iter(train) [ 3550/160000] base_lr: 9.8707e-05 lr: 3.6494e-07 eta: 1 day, 19:34:35 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0407 decode.loss_ce: 0.0241 decode.acc_seg: 99.0374 aux.loss_ce: 0.0166 aux.acc_seg: 98.3089 +04/17 09:31:47 - mmengine - INFO - Iter(train) [ 3600/160000] base_lr: 9.8676e-05 lr: 3.6482e-07 eta: 1 day, 19:33:38 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0448 decode.loss_ce: 0.0264 decode.acc_seg: 98.7450 aux.loss_ce: 0.0184 aux.acc_seg: 98.2246 +04/17 09:32:37 - mmengine - INFO - Iter(train) [ 3650/160000] base_lr: 9.8644e-05 lr: 3.6471e-07 eta: 1 day, 19:32:43 time: 1.0006 data_time: 0.0043 memory: 8462 loss: 0.0418 decode.loss_ce: 0.0246 decode.acc_seg: 99.4370 aux.loss_ce: 0.0172 aux.acc_seg: 98.7982 +04/17 09:33:27 - mmengine - INFO - Iter(train) [ 3700/160000] base_lr: 9.8613e-05 lr: 3.6459e-07 eta: 1 day, 19:31:47 time: 1.0014 data_time: 0.0041 memory: 8462 loss: 0.0480 decode.loss_ce: 0.0291 decode.acc_seg: 98.6782 aux.loss_ce: 0.0189 aux.acc_seg: 98.5292 +04/17 09:34:17 - mmengine - INFO - Iter(train) [ 3750/160000] base_lr: 9.8581e-05 lr: 3.6447e-07 eta: 1 day, 19:30:50 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0477 decode.loss_ce: 0.0289 decode.acc_seg: 99.2256 aux.loss_ce: 0.0188 aux.acc_seg: 98.6662 +04/17 09:35:07 - mmengine - INFO - Iter(train) [ 3800/160000] base_lr: 9.8550e-05 lr: 3.6436e-07 eta: 1 day, 19:29:53 time: 0.9986 data_time: 0.0039 memory: 8462 loss: 0.0446 decode.loss_ce: 0.0276 decode.acc_seg: 99.1703 aux.loss_ce: 0.0170 aux.acc_seg: 98.7638 +04/17 09:35:57 - mmengine - INFO - Iter(train) [ 3850/160000] base_lr: 9.8518e-05 lr: 3.6424e-07 eta: 1 day, 19:28:56 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0389 decode.loss_ce: 0.0235 decode.acc_seg: 99.4406 aux.loss_ce: 0.0154 aux.acc_seg: 98.9601 +04/17 09:36:47 - mmengine - INFO - Iter(train) [ 3900/160000] base_lr: 9.8486e-05 lr: 3.6412e-07 eta: 1 day, 19:28:01 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0435 decode.loss_ce: 0.0262 decode.acc_seg: 98.1987 aux.loss_ce: 0.0173 aux.acc_seg: 97.4186 +04/17 09:37:37 - mmengine - INFO - Iter(train) [ 3950/160000] base_lr: 9.8455e-05 lr: 3.6401e-07 eta: 1 day, 19:27:06 time: 1.0012 data_time: 0.0041 memory: 8462 loss: 0.0486 decode.loss_ce: 0.0304 decode.acc_seg: 99.3372 aux.loss_ce: 0.0182 aux.acc_seg: 99.0168 +04/17 09:38:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 09:38:27 - mmengine - INFO - Iter(train) [ 4000/160000] base_lr: 9.8423e-05 lr: 3.6389e-07 eta: 1 day, 19:26:10 time: 0.9985 data_time: 0.0041 memory: 8462 loss: 0.0411 decode.loss_ce: 0.0260 decode.acc_seg: 99.3818 aux.loss_ce: 0.0151 aux.acc_seg: 98.6591 +04/17 09:39:17 - mmengine - INFO - Iter(train) [ 4050/160000] base_lr: 9.8392e-05 lr: 3.6377e-07 eta: 1 day, 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0.0042 memory: 8462 loss: 0.0353 decode.loss_ce: 0.0215 decode.acc_seg: 99.1190 aux.loss_ce: 0.0139 aux.acc_seg: 98.4636 +04/17 09:52:37 - mmengine - INFO - Iter(train) [ 4850/160000] base_lr: 9.7887e-05 lr: 3.6191e-07 eta: 1 day, 19:10:46 time: 0.9983 data_time: 0.0041 memory: 8462 loss: 0.0354 decode.loss_ce: 0.0206 decode.acc_seg: 99.3814 aux.loss_ce: 0.0148 aux.acc_seg: 98.8951 +04/17 09:53:27 - mmengine - INFO - Iter(train) [ 4900/160000] base_lr: 9.7856e-05 lr: 3.6179e-07 eta: 1 day, 19:09:53 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0361 decode.loss_ce: 0.0214 decode.acc_seg: 98.6313 aux.loss_ce: 0.0147 aux.acc_seg: 98.2702 +04/17 09:54:17 - mmengine - INFO - Iter(train) [ 4950/160000] base_lr: 9.7824e-05 lr: 3.6168e-07 eta: 1 day, 19:08:59 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0347 decode.loss_ce: 0.0205 decode.acc_seg: 99.5302 aux.loss_ce: 0.0142 aux.acc_seg: 99.1716 +04/17 09:55:07 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 09:55:07 - mmengine - INFO - Iter(train) [ 5000/160000] base_lr: 9.7792e-05 lr: 3.6156e-07 eta: 1 day, 19:08:07 time: 1.0003 data_time: 0.0046 memory: 8462 loss: 0.0372 decode.loss_ce: 0.0226 decode.acc_seg: 99.1566 aux.loss_ce: 0.0146 aux.acc_seg: 98.8195 +04/17 09:55:57 - mmengine - INFO - Iter(train) [ 5050/160000] base_lr: 9.7761e-05 lr: 3.6144e-07 eta: 1 day, 19:07:15 time: 1.0014 data_time: 0.0046 memory: 8462 loss: 0.0359 decode.loss_ce: 0.0222 decode.acc_seg: 99.2836 aux.loss_ce: 0.0138 aux.acc_seg: 98.8277 +04/17 09:56:47 - mmengine - INFO - Iter(train) [ 5100/160000] base_lr: 9.7729e-05 lr: 3.6133e-07 eta: 1 day, 19:06:21 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0186 decode.acc_seg: 99.5804 aux.loss_ce: 0.0127 aux.acc_seg: 99.2104 +04/17 09:57:37 - mmengine - INFO - Iter(train) [ 5150/160000] base_lr: 9.7698e-05 lr: 3.6121e-07 eta: 1 day, 19:05:29 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0433 decode.loss_ce: 0.0268 decode.acc_seg: 99.1711 aux.loss_ce: 0.0166 aux.acc_seg: 98.8379 +04/17 09:58:27 - mmengine - INFO - Iter(train) [ 5200/160000] base_lr: 9.7666e-05 lr: 3.6109e-07 eta: 1 day, 19:04:35 time: 0.9999 data_time: 0.0041 memory: 8462 loss: 0.0383 decode.loss_ce: 0.0227 decode.acc_seg: 99.3835 aux.loss_ce: 0.0156 aux.acc_seg: 98.8422 +04/17 09:59:17 - mmengine - INFO - Iter(train) [ 5250/160000] base_lr: 9.7635e-05 lr: 3.6098e-07 eta: 1 day, 19:03:44 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0380 decode.loss_ce: 0.0231 decode.acc_seg: 99.4253 aux.loss_ce: 0.0149 aux.acc_seg: 98.9494 +04/17 10:00:07 - mmengine - INFO - Iter(train) [ 5300/160000] base_lr: 9.7603e-05 lr: 3.6086e-07 eta: 1 day, 19:02:51 time: 1.0010 data_time: 0.0041 memory: 8462 loss: 0.0373 decode.loss_ce: 0.0229 decode.acc_seg: 98.6557 aux.loss_ce: 0.0145 aux.acc_seg: 98.1289 +04/17 10:00:57 - mmengine - INFO - Iter(train) [ 5350/160000] base_lr: 9.7572e-05 lr: 3.6074e-07 eta: 1 day, 19:01:58 time: 1.0000 data_time: 0.0040 memory: 8462 loss: 0.0357 decode.loss_ce: 0.0208 decode.acc_seg: 98.8726 aux.loss_ce: 0.0148 aux.acc_seg: 98.3179 +04/17 10:01:47 - mmengine - INFO - Iter(train) [ 5400/160000] base_lr: 9.7540e-05 lr: 3.6063e-07 eta: 1 day, 19:01:06 time: 1.0000 data_time: 0.0042 memory: 8462 loss: 0.0377 decode.loss_ce: 0.0226 decode.acc_seg: 99.2432 aux.loss_ce: 0.0151 aux.acc_seg: 98.8401 +04/17 10:02:37 - mmengine - INFO - Iter(train) [ 5450/160000] base_lr: 9.7509e-05 lr: 3.6051e-07 eta: 1 day, 19:00:14 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0334 decode.loss_ce: 0.0194 decode.acc_seg: 99.0490 aux.loss_ce: 0.0141 aux.acc_seg: 98.4489 +04/17 10:03:27 - mmengine - INFO - Iter(train) [ 5500/160000] base_lr: 9.7477e-05 lr: 3.6039e-07 eta: 1 day, 18:59:21 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0403 decode.loss_ce: 0.0243 decode.acc_seg: 99.5821 aux.loss_ce: 0.0160 aux.acc_seg: 98.7394 +04/17 10:04:17 - mmengine - INFO - Iter(train) [ 5550/160000] base_lr: 9.7445e-05 lr: 3.6028e-07 eta: 1 day, 18:58:29 time: 1.0009 data_time: 0.0042 memory: 8462 loss: 0.0361 decode.loss_ce: 0.0219 decode.acc_seg: 99.4492 aux.loss_ce: 0.0143 aux.acc_seg: 98.9521 +04/17 10:05:07 - mmengine - INFO - Iter(train) [ 5600/160000] base_lr: 9.7414e-05 lr: 3.6016e-07 eta: 1 day, 18:57:36 time: 1.0006 data_time: 0.0040 memory: 8462 loss: 0.0342 decode.loss_ce: 0.0203 decode.acc_seg: 99.4709 aux.loss_ce: 0.0139 aux.acc_seg: 99.1426 +04/17 10:05:57 - mmengine - INFO - Iter(train) [ 5650/160000] base_lr: 9.7382e-05 lr: 3.6004e-07 eta: 1 day, 18:56:45 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0376 decode.loss_ce: 0.0223 decode.acc_seg: 98.4533 aux.loss_ce: 0.0153 aux.acc_seg: 97.8525 +04/17 10:06:47 - mmengine - INFO - Iter(train) [ 5700/160000] base_lr: 9.7351e-05 lr: 3.5993e-07 eta: 1 day, 18:55:53 time: 1.0012 data_time: 0.0042 memory: 8462 loss: 0.0358 decode.loss_ce: 0.0211 decode.acc_seg: 99.3319 aux.loss_ce: 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decode.loss_ce: 0.0230 decode.acc_seg: 99.0547 aux.loss_ce: 0.0146 aux.acc_seg: 99.2113 +04/17 10:10:57 - mmengine - INFO - Iter(train) [ 5950/160000] base_lr: 9.7193e-05 lr: 3.5934e-07 eta: 1 day, 18:51:35 time: 1.0009 data_time: 0.0045 memory: 8462 loss: 0.0346 decode.loss_ce: 0.0212 decode.acc_seg: 99.1407 aux.loss_ce: 0.0134 aux.acc_seg: 98.7675 +04/17 10:11:47 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 10:11:47 - mmengine - INFO - Iter(train) [ 6000/160000] base_lr: 9.7161e-05 lr: 3.5923e-07 eta: 1 day, 18:50:43 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0308 decode.loss_ce: 0.0179 decode.acc_seg: 99.2178 aux.loss_ce: 0.0129 aux.acc_seg: 98.6378 +04/17 10:12:37 - mmengine - INFO - Iter(train) [ 6050/160000] base_lr: 9.7130e-05 lr: 3.5911e-07 eta: 1 day, 18:49:51 time: 1.0007 data_time: 0.0044 memory: 8462 loss: 0.0298 decode.loss_ce: 0.0170 decode.acc_seg: 99.5426 aux.loss_ce: 0.0128 aux.acc_seg: 98.9246 +04/17 10:13:27 - mmengine - INFO - Iter(train) [ 6100/160000] base_lr: 9.7098e-05 lr: 3.5899e-07 eta: 1 day, 18:49:00 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0350 decode.loss_ce: 0.0213 decode.acc_seg: 99.6117 aux.loss_ce: 0.0137 aux.acc_seg: 99.2361 +04/17 10:14:17 - mmengine - INFO - Iter(train) [ 6150/160000] base_lr: 9.7067e-05 lr: 3.5888e-07 eta: 1 day, 18:48:09 time: 1.0003 data_time: 0.0042 memory: 8462 loss: 0.0323 decode.loss_ce: 0.0194 decode.acc_seg: 99.6401 aux.loss_ce: 0.0129 aux.acc_seg: 99.0118 +04/17 10:15:07 - mmengine - INFO - Iter(train) [ 6200/160000] base_lr: 9.7035e-05 lr: 3.5876e-07 eta: 1 day, 18:47:18 time: 1.0015 data_time: 0.0045 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0145 decode.acc_seg: 99.0974 aux.loss_ce: 0.0099 aux.acc_seg: 98.7240 +04/17 10:15:57 - mmengine - INFO - Iter(train) [ 6250/160000] base_lr: 9.7004e-05 lr: 3.5864e-07 eta: 1 day, 18:46:25 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0372 decode.loss_ce: 0.0233 decode.acc_seg: 99.3490 aux.loss_ce: 0.0139 aux.acc_seg: 98.9359 +04/17 10:16:47 - mmengine - INFO - Iter(train) [ 6300/160000] base_lr: 9.6972e-05 lr: 3.5853e-07 eta: 1 day, 18:45:34 time: 1.0004 data_time: 0.0047 memory: 8462 loss: 0.0360 decode.loss_ce: 0.0216 decode.acc_seg: 99.5167 aux.loss_ce: 0.0144 aux.acc_seg: 99.0215 +04/17 10:17:37 - mmengine - INFO - Iter(train) [ 6350/160000] base_lr: 9.6941e-05 lr: 3.5841e-07 eta: 1 day, 18:44:43 time: 1.0010 data_time: 0.0049 memory: 8462 loss: 0.0320 decode.loss_ce: 0.0187 decode.acc_seg: 99.2607 aux.loss_ce: 0.0133 aux.acc_seg: 98.5270 +04/17 10:18:27 - mmengine - INFO - Iter(train) [ 6400/160000] base_lr: 9.6909e-05 lr: 3.5829e-07 eta: 1 day, 18:43:52 time: 1.0014 data_time: 0.0045 memory: 8462 loss: 0.0435 decode.loss_ce: 0.0272 decode.acc_seg: 98.5983 aux.loss_ce: 0.0163 aux.acc_seg: 98.1480 +04/17 10:19:17 - mmengine - INFO - Iter(train) [ 6450/160000] base_lr: 9.6878e-05 lr: 3.5818e-07 eta: 1 day, 18:42:59 time: 0.9994 data_time: 0.0048 memory: 8462 loss: 0.0364 decode.loss_ce: 0.0225 decode.acc_seg: 99.4532 aux.loss_ce: 0.0139 aux.acc_seg: 98.9351 +04/17 10:20:07 - mmengine - INFO - Iter(train) [ 6500/160000] base_lr: 9.6846e-05 lr: 3.5806e-07 eta: 1 day, 18:42:08 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0339 decode.loss_ce: 0.0201 decode.acc_seg: 99.1266 aux.loss_ce: 0.0138 aux.acc_seg: 98.5552 +04/17 10:20:57 - mmengine - INFO - Iter(train) [ 6550/160000] base_lr: 9.6814e-05 lr: 3.5794e-07 eta: 1 day, 18:41:16 time: 0.9999 data_time: 0.0044 memory: 8462 loss: 0.0331 decode.loss_ce: 0.0196 decode.acc_seg: 98.7511 aux.loss_ce: 0.0135 aux.acc_seg: 98.2067 +04/17 10:21:47 - mmengine - INFO - Iter(train) [ 6600/160000] base_lr: 9.6783e-05 lr: 3.5783e-07 eta: 1 day, 18:40:24 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0330 decode.loss_ce: 0.0203 decode.acc_seg: 99.3629 aux.loss_ce: 0.0127 aux.acc_seg: 98.8394 +04/17 10:22:37 - mmengine - INFO - Iter(train) [ 6650/160000] base_lr: 9.6751e-05 lr: 3.5771e-07 eta: 1 day, 18:39:32 time: 0.9991 data_time: 0.0041 memory: 8462 loss: 0.0314 decode.loss_ce: 0.0184 decode.acc_seg: 99.6651 aux.loss_ce: 0.0130 aux.acc_seg: 99.2207 +04/17 10:23:27 - mmengine - INFO - Iter(train) [ 6700/160000] base_lr: 9.6720e-05 lr: 3.5759e-07 eta: 1 day, 18:38:41 time: 1.0009 data_time: 0.0047 memory: 8462 loss: 0.0288 decode.loss_ce: 0.0163 decode.acc_seg: 99.4678 aux.loss_ce: 0.0125 aux.acc_seg: 98.8937 +04/17 10:24:17 - mmengine - INFO - Iter(train) [ 6750/160000] base_lr: 9.6688e-05 lr: 3.5748e-07 eta: 1 day, 18:37:50 time: 1.0009 data_time: 0.0044 memory: 8462 loss: 0.0323 decode.loss_ce: 0.0190 decode.acc_seg: 99.3738 aux.loss_ce: 0.0133 aux.acc_seg: 98.7343 +04/17 10:25:07 - mmengine - INFO - Iter(train) [ 6800/160000] base_lr: 9.6657e-05 lr: 3.5736e-07 eta: 1 day, 18:36:59 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0381 decode.loss_ce: 0.0228 decode.acc_seg: 99.1611 aux.loss_ce: 0.0153 aux.acc_seg: 98.4055 +04/17 10:25:57 - mmengine - INFO - Iter(train) [ 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memory: 8462 loss: 0.0356 decode.loss_ce: 0.0214 decode.acc_seg: 99.3029 aux.loss_ce: 0.0143 aux.acc_seg: 98.7858 +04/17 10:29:17 - mmengine - INFO - Iter(train) [ 7050/160000] base_lr: 9.6499e-05 lr: 3.5678e-07 eta: 1 day, 18:32:42 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0337 decode.loss_ce: 0.0205 decode.acc_seg: 99.5481 aux.loss_ce: 0.0132 aux.acc_seg: 99.1684 +04/17 10:30:07 - mmengine - INFO - Iter(train) [ 7100/160000] base_lr: 9.6467e-05 lr: 3.5666e-07 eta: 1 day, 18:31:51 time: 1.0014 data_time: 0.0043 memory: 8462 loss: 0.0319 decode.loss_ce: 0.0187 decode.acc_seg: 99.1455 aux.loss_ce: 0.0132 aux.acc_seg: 98.8676 +04/17 10:30:57 - mmengine - INFO - Iter(train) [ 7150/160000] base_lr: 9.6436e-05 lr: 3.5654e-07 eta: 1 day, 18:31:00 time: 1.0008 data_time: 0.0043 memory: 8462 loss: 0.0319 decode.loss_ce: 0.0187 decode.acc_seg: 98.9529 aux.loss_ce: 0.0131 aux.acc_seg: 98.1979 +04/17 10:31:47 - mmengine - INFO - Iter(train) [ 7200/160000] base_lr: 9.6404e-05 lr: 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decode.loss_ce: 0.0193 decode.acc_seg: 99.3982 aux.loss_ce: 0.0131 aux.acc_seg: 98.7888 +04/17 10:41:48 - mmengine - INFO - Iter(train) [ 7800/160000] base_lr: 9.6026e-05 lr: 3.5503e-07 eta: 1 day, 18:20:00 time: 1.0006 data_time: 0.0040 memory: 8462 loss: 0.0360 decode.loss_ce: 0.0224 decode.acc_seg: 99.0635 aux.loss_ce: 0.0136 aux.acc_seg: 98.8470 +04/17 10:42:38 - mmengine - INFO - Iter(train) [ 7850/160000] base_lr: 9.5994e-05 lr: 3.5491e-07 eta: 1 day, 18:19:10 time: 1.0003 data_time: 0.0044 memory: 8462 loss: 0.0255 decode.loss_ce: 0.0145 decode.acc_seg: 99.1734 aux.loss_ce: 0.0110 aux.acc_seg: 98.4362 +04/17 10:43:28 - mmengine - INFO - Iter(train) [ 7900/160000] base_lr: 9.5963e-05 lr: 3.5479e-07 eta: 1 day, 18:18:19 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0285 decode.loss_ce: 0.0167 decode.acc_seg: 99.1795 aux.loss_ce: 0.0118 aux.acc_seg: 98.7524 +04/17 10:44:18 - mmengine - INFO - Iter(train) [ 7950/160000] base_lr: 9.5931e-05 lr: 3.5468e-07 eta: 1 day, 18:17:27 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0262 decode.loss_ce: 0.0151 decode.acc_seg: 99.2907 aux.loss_ce: 0.0111 aux.acc_seg: 98.7049 +04/17 10:45:08 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 10:45:08 - mmengine - INFO - Iter(train) [ 8000/160000] base_lr: 9.5900e-05 lr: 3.5456e-07 eta: 1 day, 18:16:36 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0325 decode.loss_ce: 0.0191 decode.acc_seg: 99.4884 aux.loss_ce: 0.0134 aux.acc_seg: 98.7900 +04/17 10:45:58 - mmengine - INFO - Iter(train) [ 8050/160000] base_lr: 9.5868e-05 lr: 3.5444e-07 eta: 1 day, 18:15:44 time: 0.9992 data_time: 0.0045 memory: 8462 loss: 0.0257 decode.loss_ce: 0.0149 decode.acc_seg: 99.4730 aux.loss_ce: 0.0108 aux.acc_seg: 99.1138 +04/17 10:46:48 - mmengine - INFO - Iter(train) [ 8100/160000] base_lr: 9.5837e-05 lr: 3.5433e-07 eta: 1 day, 18:14:52 time: 0.9996 data_time: 0.0044 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0180 decode.acc_seg: 98.8304 aux.loss_ce: 0.0131 aux.acc_seg: 97.3690 +04/17 10:47:38 - mmengine - INFO - Iter(train) [ 8150/160000] base_lr: 9.5805e-05 lr: 3.5421e-07 eta: 1 day, 18:14:01 time: 0.9994 data_time: 0.0043 memory: 8462 loss: 0.0284 decode.loss_ce: 0.0165 decode.acc_seg: 99.4873 aux.loss_ce: 0.0118 aux.acc_seg: 99.1148 +04/17 10:48:28 - mmengine - INFO - Iter(train) [ 8200/160000] base_lr: 9.5773e-05 lr: 3.5409e-07 eta: 1 day, 18:13:10 time: 1.0002 data_time: 0.0042 memory: 8462 loss: 0.0321 decode.loss_ce: 0.0184 decode.acc_seg: 99.5493 aux.loss_ce: 0.0136 aux.acc_seg: 99.0458 +04/17 10:49:18 - mmengine - INFO - Iter(train) [ 8250/160000] base_lr: 9.5742e-05 lr: 3.5398e-07 eta: 1 day, 18:12:18 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0264 decode.loss_ce: 0.0148 decode.acc_seg: 99.6235 aux.loss_ce: 0.0116 aux.acc_seg: 99.0587 +04/17 10:50:08 - mmengine - INFO - Iter(train) [ 8300/160000] base_lr: 9.5710e-05 lr: 3.5386e-07 eta: 1 day, 18:11:27 time: 0.9996 data_time: 0.0042 memory: 8462 loss: 0.0318 decode.loss_ce: 0.0187 decode.acc_seg: 99.6046 aux.loss_ce: 0.0132 aux.acc_seg: 99.2018 +04/17 10:50:58 - mmengine - INFO - Iter(train) [ 8350/160000] base_lr: 9.5679e-05 lr: 3.5374e-07 eta: 1 day, 18:10:35 time: 1.0004 data_time: 0.0048 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0187 decode.acc_seg: 99.0238 aux.loss_ce: 0.0125 aux.acc_seg: 98.4592 +04/17 10:51:48 - mmengine - INFO - Iter(train) [ 8400/160000] base_lr: 9.5647e-05 lr: 3.5363e-07 eta: 1 day, 18:09:44 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0297 decode.loss_ce: 0.0168 decode.acc_seg: 99.4942 aux.loss_ce: 0.0129 aux.acc_seg: 99.0360 +04/17 10:52:38 - mmengine - INFO - Iter(train) [ 8450/160000] base_lr: 9.5616e-05 lr: 3.5351e-07 eta: 1 day, 18:08:53 time: 1.0005 data_time: 0.0047 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0164 decode.acc_seg: 99.4204 aux.loss_ce: 0.0121 aux.acc_seg: 98.8857 +04/17 10:53:28 - mmengine - INFO - Iter(train) [ 8500/160000] base_lr: 9.5584e-05 lr: 3.5339e-07 eta: 1 day, 18:08:02 time: 1.0005 data_time: 0.0050 memory: 8462 loss: 0.0294 decode.loss_ce: 0.0174 decode.acc_seg: 99.3834 aux.loss_ce: 0.0120 aux.acc_seg: 98.7679 +04/17 10:54:18 - mmengine - INFO - Iter(train) [ 8550/160000] base_lr: 9.5553e-05 lr: 3.5328e-07 eta: 1 day, 18:07:11 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0322 decode.loss_ce: 0.0191 decode.acc_seg: 99.6374 aux.loss_ce: 0.0131 aux.acc_seg: 99.1426 +04/17 10:55:08 - mmengine - INFO - Iter(train) [ 8600/160000] base_lr: 9.5521e-05 lr: 3.5316e-07 eta: 1 day, 18:06:20 time: 0.9991 data_time: 0.0048 memory: 8462 loss: 0.0302 decode.loss_ce: 0.0176 decode.acc_seg: 99.0908 aux.loss_ce: 0.0126 aux.acc_seg: 98.6433 +04/17 10:55:58 - mmengine - INFO - Iter(train) [ 8650/160000] base_lr: 9.5490e-05 lr: 3.5304e-07 eta: 1 day, 18:05:28 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0315 decode.loss_ce: 0.0184 decode.acc_seg: 99.4024 aux.loss_ce: 0.0130 aux.acc_seg: 98.8716 +04/17 10:56:48 - mmengine - INFO - Iter(train) [ 8700/160000] base_lr: 9.5458e-05 lr: 3.5293e-07 eta: 1 day, 18:04:36 time: 1.0000 data_time: 0.0043 memory: 8462 loss: 0.0312 decode.loss_ce: 0.0186 decode.acc_seg: 99.1198 aux.loss_ce: 0.0126 aux.acc_seg: 98.9529 +04/17 10:57:38 - mmengine - INFO - Iter(train) [ 8750/160000] base_lr: 9.5426e-05 lr: 3.5281e-07 eta: 1 day, 18:03:45 time: 0.9992 data_time: 0.0045 memory: 8462 loss: 0.0291 decode.loss_ce: 0.0169 decode.acc_seg: 98.7726 aux.loss_ce: 0.0121 aux.acc_seg: 97.8586 +04/17 10:58:28 - mmengine - INFO - Iter(train) [ 8800/160000] base_lr: 9.5395e-05 lr: 3.5269e-07 eta: 1 day, 18:02:53 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0293 decode.loss_ce: 0.0173 decode.acc_seg: 99.5106 aux.loss_ce: 0.0119 aux.acc_seg: 99.0864 +04/17 10:59:18 - mmengine - INFO - Iter(train) [ 8850/160000] base_lr: 9.5363e-05 lr: 3.5258e-07 eta: 1 day, 18:02:02 time: 0.9989 data_time: 0.0044 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0170 decode.acc_seg: 98.9819 aux.loss_ce: 0.0117 aux.acc_seg: 98.1556 +04/17 11:00:08 - mmengine - INFO - Iter(train) [ 8900/160000] base_lr: 9.5332e-05 lr: 3.5246e-07 eta: 1 day, 18:01:11 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0326 decode.loss_ce: 0.0199 decode.acc_seg: 99.2109 aux.loss_ce: 0.0127 aux.acc_seg: 98.8785 +04/17 11:00:58 - mmengine - INFO - Iter(train) [ 8950/160000] base_lr: 9.5300e-05 lr: 3.5234e-07 eta: 1 day, 18:00:19 time: 1.0001 data_time: 0.0045 memory: 8462 loss: 0.0297 decode.loss_ce: 0.0174 decode.acc_seg: 99.3111 aux.loss_ce: 0.0123 aux.acc_seg: 98.6567 +04/17 11:01:48 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 11:01:48 - mmengine - INFO - Iter(train) [ 9000/160000] base_lr: 9.5269e-05 lr: 3.5223e-07 eta: 1 day, 17:59:29 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0280 decode.loss_ce: 0.0168 decode.acc_seg: 99.1098 aux.loss_ce: 0.0111 aux.acc_seg: 98.6355 +04/17 11:02:38 - mmengine - INFO - Iter(train) [ 9050/160000] base_lr: 9.5237e-05 lr: 3.5211e-07 eta: 1 day, 17:58:37 time: 0.9995 data_time: 0.0041 memory: 8462 loss: 0.0279 decode.loss_ce: 0.0163 decode.acc_seg: 99.2556 aux.loss_ce: 0.0117 aux.acc_seg: 98.5273 +04/17 11:03:28 - mmengine - INFO - Iter(train) [ 9100/160000] base_lr: 9.5206e-05 lr: 3.5199e-07 eta: 1 day, 17:57:46 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0171 decode.acc_seg: 99.2605 aux.loss_ce: 0.0112 aux.acc_seg: 98.7671 +04/17 11:04:18 - mmengine - INFO - Iter(train) [ 9150/160000] base_lr: 9.5174e-05 lr: 3.5188e-07 eta: 1 day, 17:56:54 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0292 decode.loss_ce: 0.0174 decode.acc_seg: 99.2044 aux.loss_ce: 0.0118 aux.acc_seg: 98.6469 +04/17 11:05:08 - mmengine - INFO - Iter(train) [ 9200/160000] base_lr: 9.5143e-05 lr: 3.5176e-07 eta: 1 day, 17:56:03 time: 0.9999 data_time: 0.0041 memory: 8462 loss: 0.0288 decode.loss_ce: 0.0166 decode.acc_seg: 99.1257 aux.loss_ce: 0.0122 aux.acc_seg: 98.5540 +04/17 11:05:58 - mmengine - INFO - Iter(train) [ 9250/160000] base_lr: 9.5111e-05 lr: 3.5164e-07 eta: 1 day, 17:55:12 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0361 decode.loss_ce: 0.0221 decode.acc_seg: 99.4940 aux.loss_ce: 0.0140 aux.acc_seg: 99.1802 +04/17 11:06:47 - mmengine - INFO - Iter(train) [ 9300/160000] base_lr: 9.5079e-05 lr: 3.5153e-07 eta: 1 day, 17:54:20 time: 0.9994 data_time: 0.0041 memory: 8462 loss: 0.0266 decode.loss_ce: 0.0153 decode.acc_seg: 99.6422 aux.loss_ce: 0.0114 aux.acc_seg: 99.3658 +04/17 11:07:37 - mmengine - INFO - Iter(train) [ 9350/160000] base_lr: 9.5048e-05 lr: 3.5141e-07 eta: 1 day, 17:53:29 time: 0.9984 data_time: 0.0042 memory: 8462 loss: 0.0290 decode.loss_ce: 0.0168 decode.acc_seg: 99.5070 aux.loss_ce: 0.0122 aux.acc_seg: 99.0034 +04/17 11:08:27 - mmengine - INFO - Iter(train) [ 9400/160000] base_lr: 9.5016e-05 lr: 3.5130e-07 eta: 1 day, 17:52:37 time: 0.9990 data_time: 0.0046 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0172 decode.acc_seg: 99.3139 aux.loss_ce: 0.0114 aux.acc_seg: 99.0477 +04/17 11:09:17 - mmengine - INFO - Iter(train) [ 9450/160000] base_lr: 9.4985e-05 lr: 3.5118e-07 eta: 1 day, 17:51:46 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0280 decode.loss_ce: 0.0161 decode.acc_seg: 99.5781 aux.loss_ce: 0.0120 aux.acc_seg: 99.3572 +04/17 11:10:07 - mmengine - INFO - Iter(train) [ 9500/160000] base_lr: 9.4953e-05 lr: 3.5106e-07 eta: 1 day, 17:50:55 time: 0.9997 data_time: 0.0042 memory: 8462 loss: 0.0311 decode.loss_ce: 0.0183 decode.acc_seg: 99.4741 aux.loss_ce: 0.0128 aux.acc_seg: 98.6847 +04/17 11:10:57 - mmengine - INFO - Iter(train) [ 9550/160000] base_lr: 9.4922e-05 lr: 3.5095e-07 eta: 1 day, 17:50:04 time: 0.9996 data_time: 0.0047 memory: 8462 loss: 0.0341 decode.loss_ce: 0.0208 decode.acc_seg: 99.3027 aux.loss_ce: 0.0133 aux.acc_seg: 98.7852 +04/17 11:11:47 - mmengine - INFO - Iter(train) [ 9600/160000] base_lr: 9.4890e-05 lr: 3.5083e-07 eta: 1 day, 17:49:12 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0271 decode.loss_ce: 0.0162 decode.acc_seg: 99.4505 aux.loss_ce: 0.0109 aux.acc_seg: 98.7850 +04/17 11:12:37 - mmengine - INFO - Iter(train) [ 9650/160000] base_lr: 9.4859e-05 lr: 3.5071e-07 eta: 1 day, 17:48:21 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0422 decode.loss_ce: 0.0263 decode.acc_seg: 97.1151 aux.loss_ce: 0.0158 aux.acc_seg: 97.0732 +04/17 11:13:27 - mmengine - INFO - Iter(train) [ 9700/160000] base_lr: 9.4827e-05 lr: 3.5060e-07 eta: 1 day, 17:47:29 time: 0.9986 data_time: 0.0044 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0143 decode.acc_seg: 99.3326 aux.loss_ce: 0.0102 aux.acc_seg: 98.6275 +04/17 11:14:17 - mmengine - INFO - Iter(train) [ 9750/160000] base_lr: 9.4796e-05 lr: 3.5048e-07 eta: 1 day, 17:46:38 time: 0.9988 data_time: 0.0042 memory: 8462 loss: 0.0313 decode.loss_ce: 0.0178 decode.acc_seg: 99.2683 aux.loss_ce: 0.0135 aux.acc_seg: 98.4152 +04/17 11:15:07 - mmengine - INFO - Iter(train) [ 9800/160000] base_lr: 9.4764e-05 lr: 3.5036e-07 eta: 1 day, 17:45:46 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0285 decode.loss_ce: 0.0174 decode.acc_seg: 99.5544 aux.loss_ce: 0.0111 aux.acc_seg: 99.2006 +04/17 11:15:57 - mmengine - INFO - Iter(train) [ 9850/160000] base_lr: 9.4732e-05 lr: 3.5025e-07 eta: 1 day, 17:44:55 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0149 decode.acc_seg: 99.2363 aux.loss_ce: 0.0118 aux.acc_seg: 98.6767 +04/17 11:16:47 - mmengine - INFO - Iter(train) [ 9900/160000] base_lr: 9.4701e-05 lr: 3.5013e-07 eta: 1 day, 17:44:03 time: 0.9983 data_time: 0.0044 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0154 decode.acc_seg: 98.6429 aux.loss_ce: 0.0113 aux.acc_seg: 98.1266 +04/17 11:17:37 - mmengine - INFO - Iter(train) [ 9950/160000] base_lr: 9.4669e-05 lr: 3.5001e-07 eta: 1 day, 17:43:11 time: 0.9989 data_time: 0.0045 memory: 8462 loss: 0.0329 decode.loss_ce: 0.0202 decode.acc_seg: 99.0934 aux.loss_ce: 0.0127 aux.acc_seg: 98.5346 +04/17 11:18:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 11:18:27 - mmengine - INFO - Iter(train) [ 10000/160000] base_lr: 9.4638e-05 lr: 3.4990e-07 eta: 1 day, 17:42:20 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0318 decode.loss_ce: 0.0197 decode.acc_seg: 99.1579 aux.loss_ce: 0.0122 aux.acc_seg: 98.8224 +04/17 11:18:27 - mmengine - INFO - Saving checkpoint at 10000 iterations +04/17 11:18:38 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:21 time: 0.1155 data_time: 0.0014 memory: 7125 +04/17 11:18:44 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:13 time: 0.1156 data_time: 0.0014 memory: 4004 +04/17 11:18:50 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:06 time: 0.1157 data_time: 0.0014 memory: 4004 +04/17 11:18:56 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1154 data_time: 0.0012 memory: 4004 +04/17 11:18:56 - mmengine - INFO - per class results: +04/17 11:18:56 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.08 | 99.54 | 99.54 | 99.54 | 99.54 | +| contrast | 80.1 | 89.03 | 88.95 | 88.87 | 89.03 | ++------------+-------+-------+--------+-----------+--------+ +04/17 11:18:56 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1200 mIoU: 89.5900 mAcc: 94.2800 mFscore: 94.2400 mPrecision: 94.2100 mRecall: 94.2800 data_time: 0.0025 time: 0.1230 +04/17 11:19:46 - mmengine - INFO - Iter(train) [ 10050/160000] base_lr: 9.4606e-05 lr: 3.4978e-07 eta: 1 day, 17:41:31 time: 0.9982 data_time: 0.0045 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0127 decode.acc_seg: 99.5184 aux.loss_ce: 0.0101 aux.acc_seg: 98.6654 +04/17 11:20:36 - mmengine - INFO - Iter(train) [ 10100/160000] base_lr: 9.4575e-05 lr: 3.4966e-07 eta: 1 day, 17:40:38 time: 0.9976 data_time: 0.0043 memory: 8462 loss: 0.0265 decode.loss_ce: 0.0152 decode.acc_seg: 99.2083 aux.loss_ce: 0.0113 aux.acc_seg: 98.8071 +04/17 11:21:26 - mmengine - INFO - Iter(train) [ 10150/160000] base_lr: 9.4543e-05 lr: 3.4955e-07 eta: 1 day, 17:39:46 time: 0.9978 data_time: 0.0042 memory: 8462 loss: 0.0320 decode.loss_ce: 0.0193 decode.acc_seg: 99.5743 aux.loss_ce: 0.0127 aux.acc_seg: 98.9920 +04/17 11:22:16 - mmengine - INFO - Iter(train) [ 10200/160000] base_lr: 9.4512e-05 lr: 3.4943e-07 eta: 1 day, 17:38:55 time: 0.9988 data_time: 0.0042 memory: 8462 loss: 0.0263 decode.loss_ce: 0.0156 decode.acc_seg: 99.2430 aux.loss_ce: 0.0108 aux.acc_seg: 98.4165 +04/17 11:23:05 - mmengine - INFO - Iter(train) [ 10250/160000] base_lr: 9.4480e-05 lr: 3.4931e-07 eta: 1 day, 17:38:03 time: 0.9993 data_time: 0.0048 memory: 8462 loss: 0.0263 decode.loss_ce: 0.0153 decode.acc_seg: 99.4232 aux.loss_ce: 0.0110 aux.acc_seg: 98.7839 +04/17 11:23:55 - mmengine - INFO - Iter(train) [ 10300/160000] base_lr: 9.4449e-05 lr: 3.4920e-07 eta: 1 day, 17:37:12 time: 0.9985 data_time: 0.0049 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0137 decode.acc_seg: 99.3811 aux.loss_ce: 0.0103 aux.acc_seg: 98.7280 +04/17 11:24:45 - mmengine - INFO - Iter(train) [ 10350/160000] base_lr: 9.4417e-05 lr: 3.4908e-07 eta: 1 day, 17:36:20 time: 0.9986 data_time: 0.0046 memory: 8462 loss: 0.0265 decode.loss_ce: 0.0156 decode.acc_seg: 99.4926 aux.loss_ce: 0.0109 aux.acc_seg: 99.3280 +04/17 11:25:35 - mmengine - INFO - Iter(train) [ 10400/160000] base_lr: 9.4385e-05 lr: 3.4896e-07 eta: 1 day, 17:35:29 time: 0.9999 data_time: 0.0047 memory: 8462 loss: 0.0279 decode.loss_ce: 0.0161 decode.acc_seg: 99.0896 aux.loss_ce: 0.0118 aux.acc_seg: 98.7675 +04/17 11:26:25 - mmengine - INFO - Iter(train) [ 10450/160000] base_lr: 9.4354e-05 lr: 3.4885e-07 eta: 1 day, 17:34:38 time: 0.9985 data_time: 0.0045 memory: 8462 loss: 0.0307 decode.loss_ce: 0.0181 decode.acc_seg: 99.5108 aux.loss_ce: 0.0127 aux.acc_seg: 99.0753 +04/17 11:27:15 - mmengine - INFO - Iter(train) [ 10500/160000] base_lr: 9.4322e-05 lr: 3.4873e-07 eta: 1 day, 17:33:46 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.0291 decode.loss_ce: 0.0175 decode.acc_seg: 99.3355 aux.loss_ce: 0.0116 aux.acc_seg: 98.9653 +04/17 11:28:05 - mmengine - INFO - Iter(train) [ 10550/160000] base_lr: 9.4291e-05 lr: 3.4861e-07 eta: 1 day, 17:32:55 time: 1.0003 data_time: 0.0043 memory: 8462 loss: 0.0286 decode.loss_ce: 0.0176 decode.acc_seg: 99.3546 aux.loss_ce: 0.0111 aux.acc_seg: 98.6609 +04/17 11:28:55 - mmengine - INFO - Iter(train) [ 10600/160000] base_lr: 9.4259e-05 lr: 3.4850e-07 eta: 1 day, 17:32:03 time: 0.9968 data_time: 0.0045 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0156 decode.acc_seg: 99.4614 aux.loss_ce: 0.0119 aux.acc_seg: 98.6715 +04/17 11:29:45 - mmengine - INFO - Iter(train) [ 10650/160000] base_lr: 9.4228e-05 lr: 3.4838e-07 eta: 1 day, 17:31:11 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0164 decode.acc_seg: 99.5975 aux.loss_ce: 0.0119 aux.acc_seg: 99.0761 +04/17 11:30:35 - mmengine - INFO - Iter(train) [ 10700/160000] base_lr: 9.4196e-05 lr: 3.4826e-07 eta: 1 day, 17:30:20 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0154 decode.acc_seg: 99.5754 aux.loss_ce: 0.0113 aux.acc_seg: 99.1217 +04/17 11:31:25 - mmengine - INFO - Iter(train) [ 10750/160000] base_lr: 9.4165e-05 lr: 3.4815e-07 eta: 1 day, 17:29:28 time: 0.9996 data_time: 0.0049 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0124 decode.acc_seg: 99.7696 aux.loss_ce: 0.0100 aux.acc_seg: 99.5125 +04/17 11:32:15 - mmengine - INFO - Iter(train) [ 10800/160000] base_lr: 9.4133e-05 lr: 3.4803e-07 eta: 1 day, 17:28:37 time: 0.9997 data_time: 0.0041 memory: 8462 loss: 0.0295 decode.loss_ce: 0.0172 decode.acc_seg: 99.4450 aux.loss_ce: 0.0124 aux.acc_seg: 98.9927 +04/17 11:33:05 - mmengine - INFO - Iter(train) [ 10850/160000] base_lr: 9.4102e-05 lr: 3.4791e-07 eta: 1 day, 17:27:46 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0328 decode.loss_ce: 0.0201 decode.acc_seg: 99.3118 aux.loss_ce: 0.0128 aux.acc_seg: 99.0999 +04/17 11:33:55 - mmengine - INFO - Iter(train) [ 10900/160000] base_lr: 9.4070e-05 lr: 3.4780e-07 eta: 1 day, 17:26:54 time: 0.9985 data_time: 0.0047 memory: 8462 loss: 0.0293 decode.loss_ce: 0.0176 decode.acc_seg: 99.5304 aux.loss_ce: 0.0117 aux.acc_seg: 99.0887 +04/17 11:34:45 - mmengine - INFO - Iter(train) [ 10950/160000] base_lr: 9.4038e-05 lr: 3.4768e-07 eta: 1 day, 17:26:03 time: 0.9986 data_time: 0.0042 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0135 decode.acc_seg: 99.4892 aux.loss_ce: 0.0107 aux.acc_seg: 98.9141 +04/17 11:35:35 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 11:35:35 - mmengine - INFO - Iter(train) [ 11000/160000] base_lr: 9.4007e-05 lr: 3.4756e-07 eta: 1 day, 17:25:11 time: 0.9977 data_time: 0.0049 memory: 8462 loss: 0.0291 decode.loss_ce: 0.0168 decode.acc_seg: 99.1938 aux.loss_ce: 0.0123 aux.acc_seg: 98.6153 +04/17 11:36:25 - mmengine - INFO - Iter(train) [ 11050/160000] base_lr: 9.3975e-05 lr: 3.4745e-07 eta: 1 day, 17:24:20 time: 0.9982 data_time: 0.0048 memory: 8462 loss: 0.0313 decode.loss_ce: 0.0184 decode.acc_seg: 99.2914 aux.loss_ce: 0.0129 aux.acc_seg: 98.7675 +04/17 11:37:14 - mmengine - INFO - Iter(train) [ 11100/160000] base_lr: 9.3944e-05 lr: 3.4733e-07 eta: 1 day, 17:23:29 time: 0.9997 data_time: 0.0051 memory: 8462 loss: 0.0285 decode.loss_ce: 0.0160 decode.acc_seg: 99.6382 aux.loss_ce: 0.0124 aux.acc_seg: 99.2136 +04/17 11:38:04 - mmengine - INFO - Iter(train) [ 11150/160000] base_lr: 9.3912e-05 lr: 3.4721e-07 eta: 1 day, 17:22:37 time: 0.9979 data_time: 0.0044 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0143 decode.acc_seg: 99.3307 aux.loss_ce: 0.0109 aux.acc_seg: 98.5315 +04/17 11:38:54 - mmengine - INFO - Iter(train) [ 11200/160000] base_lr: 9.3881e-05 lr: 3.4710e-07 eta: 1 day, 17:21:46 time: 0.9979 data_time: 0.0042 memory: 8462 loss: 0.0291 decode.loss_ce: 0.0178 decode.acc_seg: 99.6355 aux.loss_ce: 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decode.loss_ce: 0.0177 decode.acc_seg: 99.1589 aux.loss_ce: 0.0124 aux.acc_seg: 98.4291 +04/17 11:43:04 - mmengine - INFO - Iter(train) [ 11450/160000] base_lr: 9.3723e-05 lr: 3.4651e-07 eta: 1 day, 17:17:27 time: 0.9990 data_time: 0.0048 memory: 8462 loss: 0.0251 decode.loss_ce: 0.0149 decode.acc_seg: 99.0963 aux.loss_ce: 0.0102 aux.acc_seg: 98.6616 +04/17 11:43:54 - mmengine - INFO - Iter(train) [ 11500/160000] base_lr: 9.3691e-05 lr: 3.4640e-07 eta: 1 day, 17:16:36 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0246 decode.loss_ce: 0.0141 decode.acc_seg: 99.2422 aux.loss_ce: 0.0105 aux.acc_seg: 98.6483 +04/17 11:44:44 - mmengine - INFO - Iter(train) [ 11550/160000] base_lr: 9.3660e-05 lr: 3.4628e-07 eta: 1 day, 17:15:45 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0138 decode.acc_seg: 99.5718 aux.loss_ce: 0.0108 aux.acc_seg: 98.8657 +04/17 11:45:34 - mmengine - INFO - Iter(train) [ 11600/160000] base_lr: 9.3628e-05 lr: 3.4616e-07 eta: 1 day, 17:14:53 time: 0.9977 data_time: 0.0041 memory: 8462 loss: 0.0267 decode.loss_ce: 0.0157 decode.acc_seg: 99.7395 aux.loss_ce: 0.0110 aux.acc_seg: 99.3423 +04/17 11:46:24 - mmengine - INFO - Iter(train) [ 11650/160000] base_lr: 9.3597e-05 lr: 3.4605e-07 eta: 1 day, 17:14:02 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0138 decode.acc_seg: 99.1066 aux.loss_ce: 0.0108 aux.acc_seg: 98.3194 +04/17 11:47:14 - mmengine - INFO - Iter(train) [ 11700/160000] base_lr: 9.3565e-05 lr: 3.4593e-07 eta: 1 day, 17:13:11 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0288 decode.loss_ce: 0.0169 decode.acc_seg: 98.5687 aux.loss_ce: 0.0119 aux.acc_seg: 98.0122 +04/17 11:48:03 - mmengine - INFO - Iter(train) [ 11750/160000] base_lr: 9.3534e-05 lr: 3.4581e-07 eta: 1 day, 17:12:19 time: 0.9987 data_time: 0.0043 memory: 8462 loss: 0.0266 decode.loss_ce: 0.0157 decode.acc_seg: 99.4154 aux.loss_ce: 0.0109 aux.acc_seg: 98.7604 +04/17 11:48:53 - mmengine - INFO - Iter(train) [ 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aux.acc_seg: 98.7490 +04/17 11:52:13 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 11:52:13 - mmengine - INFO - Iter(train) [ 12000/160000] base_lr: 9.3376e-05 lr: 3.4523e-07 eta: 1 day, 17:08:02 time: 0.9975 data_time: 0.0043 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0151 decode.acc_seg: 98.8495 aux.loss_ce: 0.0110 aux.acc_seg: 98.4133 +04/17 11:53:03 - mmengine - INFO - Iter(train) [ 12050/160000] base_lr: 9.3344e-05 lr: 3.4511e-07 eta: 1 day, 17:07:10 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0136 decode.acc_seg: 99.4600 aux.loss_ce: 0.0097 aux.acc_seg: 99.2146 +04/17 11:53:53 - mmengine - INFO - Iter(train) [ 12100/160000] base_lr: 9.3313e-05 lr: 3.4500e-07 eta: 1 day, 17:06:19 time: 0.9968 data_time: 0.0051 memory: 8462 loss: 0.0287 decode.loss_ce: 0.0161 decode.acc_seg: 99.6464 aux.loss_ce: 0.0126 aux.acc_seg: 99.0677 +04/17 11:54:43 - mmengine - INFO - Iter(train) [ 12150/160000] base_lr: 9.3281e-05 lr: 3.4488e-07 eta: 1 day, 17:05:27 time: 0.9973 data_time: 0.0045 memory: 8462 loss: 0.0291 decode.loss_ce: 0.0168 decode.acc_seg: 99.0984 aux.loss_ce: 0.0123 aux.acc_seg: 98.5958 +04/17 11:55:33 - mmengine - INFO - Iter(train) [ 12200/160000] base_lr: 9.3250e-05 lr: 3.4476e-07 eta: 1 day, 17:04:35 time: 0.9968 data_time: 0.0042 memory: 8462 loss: 0.0271 decode.loss_ce: 0.0160 decode.acc_seg: 99.2754 aux.loss_ce: 0.0111 aux.acc_seg: 98.7045 +04/17 11:56:22 - mmengine - INFO - Iter(train) [ 12250/160000] base_lr: 9.3218e-05 lr: 3.4465e-07 eta: 1 day, 17:03:43 time: 0.9983 data_time: 0.0043 memory: 8462 loss: 0.0274 decode.loss_ce: 0.0156 decode.acc_seg: 99.5762 aux.loss_ce: 0.0118 aux.acc_seg: 98.3437 +04/17 11:57:12 - mmengine - INFO - Iter(train) [ 12300/160000] base_lr: 9.3187e-05 lr: 3.4453e-07 eta: 1 day, 17:02:52 time: 0.9974 data_time: 0.0045 memory: 8462 loss: 0.0259 decode.loss_ce: 0.0148 decode.acc_seg: 99.4024 aux.loss_ce: 0.0111 aux.acc_seg: 98.6334 +04/17 11:58:02 - mmengine - INFO - Iter(train) [ 12350/160000] base_lr: 9.3155e-05 lr: 3.4441e-07 eta: 1 day, 17:02:01 time: 0.9980 data_time: 0.0047 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0144 decode.acc_seg: 99.4419 aux.loss_ce: 0.0107 aux.acc_seg: 98.6288 +04/17 11:58:52 - mmengine - INFO - Iter(train) [ 12400/160000] base_lr: 9.3124e-05 lr: 3.4430e-07 eta: 1 day, 17:01:09 time: 0.9977 data_time: 0.0047 memory: 8462 loss: 0.0268 decode.loss_ce: 0.0153 decode.acc_seg: 99.0204 aux.loss_ce: 0.0115 aux.acc_seg: 98.3780 +04/17 11:59:42 - mmengine - INFO - Iter(train) [ 12450/160000] base_lr: 9.3092e-05 lr: 3.4418e-07 eta: 1 day, 17:00:18 time: 0.9977 data_time: 0.0048 memory: 8462 loss: 0.0252 decode.loss_ce: 0.0142 decode.acc_seg: 99.4844 aux.loss_ce: 0.0110 aux.acc_seg: 98.9780 +04/17 12:00:32 - mmengine - INFO - Iter(train) [ 12500/160000] base_lr: 9.3061e-05 lr: 3.4406e-07 eta: 1 day, 16:59:26 time: 0.9965 data_time: 0.0042 memory: 8462 loss: 0.0316 decode.loss_ce: 0.0184 decode.acc_seg: 99.4486 aux.loss_ce: 0.0132 aux.acc_seg: 98.5220 +04/17 12:01:22 - mmengine - INFO - Iter(train) [ 12550/160000] base_lr: 9.3029e-05 lr: 3.4395e-07 eta: 1 day, 16:58:35 time: 0.9981 data_time: 0.0044 memory: 8462 loss: 0.0269 decode.loss_ce: 0.0160 decode.acc_seg: 99.4404 aux.loss_ce: 0.0109 aux.acc_seg: 99.0669 +04/17 12:02:12 - mmengine - INFO - Iter(train) [ 12600/160000] base_lr: 9.2997e-05 lr: 3.4383e-07 eta: 1 day, 16:57:43 time: 0.9981 data_time: 0.0044 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0137 decode.acc_seg: 99.5785 aux.loss_ce: 0.0108 aux.acc_seg: 99.0877 +04/17 12:03:02 - mmengine - INFO - Iter(train) [ 12650/160000] base_lr: 9.2966e-05 lr: 3.4371e-07 eta: 1 day, 16:56:52 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0250 decode.loss_ce: 0.0136 decode.acc_seg: 99.3414 aux.loss_ce: 0.0114 aux.acc_seg: 98.4335 +04/17 12:03:52 - mmengine - INFO - Iter(train) [ 12700/160000] base_lr: 9.2934e-05 lr: 3.4360e-07 eta: 1 day, 16:56:01 time: 0.9980 data_time: 0.0045 memory: 8462 loss: 0.0310 decode.loss_ce: 0.0179 decode.acc_seg: 99.0231 aux.loss_ce: 0.0131 aux.acc_seg: 98.6908 +04/17 12:04:42 - mmengine - INFO - Iter(train) [ 12750/160000] base_lr: 9.2903e-05 lr: 3.4348e-07 eta: 1 day, 16:55:09 time: 0.9977 data_time: 0.0047 memory: 8462 loss: 0.0256 decode.loss_ce: 0.0142 decode.acc_seg: 99.5392 aux.loss_ce: 0.0114 aux.acc_seg: 99.0322 +04/17 12:05:31 - mmengine - INFO - Iter(train) [ 12800/160000] base_lr: 9.2871e-05 lr: 3.4336e-07 eta: 1 day, 16:54:18 time: 0.9983 data_time: 0.0048 memory: 8462 loss: 0.0259 decode.loss_ce: 0.0146 decode.acc_seg: 99.4265 aux.loss_ce: 0.0113 aux.acc_seg: 98.6423 +04/17 12:06:21 - mmengine - INFO - Iter(train) [ 12850/160000] base_lr: 9.2840e-05 lr: 3.4325e-07 eta: 1 day, 16:53:26 time: 0.9972 data_time: 0.0046 memory: 8462 loss: 0.0269 decode.loss_ce: 0.0159 decode.acc_seg: 99.3744 aux.loss_ce: 0.0110 aux.acc_seg: 98.5023 +04/17 12:07:11 - mmengine - INFO - Iter(train) [ 12900/160000] base_lr: 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13450/160000] base_lr: 9.2461e-05 lr: 3.4185e-07 eta: 1 day, 16:43:09 time: 0.9979 data_time: 0.0045 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0135 decode.acc_seg: 99.5245 aux.loss_ce: 0.0105 aux.acc_seg: 98.8018 +04/17 12:17:10 - mmengine - INFO - Iter(train) [ 13500/160000] base_lr: 9.2430e-05 lr: 3.4173e-07 eta: 1 day, 16:42:18 time: 0.9969 data_time: 0.0043 memory: 8462 loss: 0.0278 decode.loss_ce: 0.0156 decode.acc_seg: 99.4808 aux.loss_ce: 0.0123 aux.acc_seg: 98.9613 +04/17 12:18:00 - mmengine - INFO - Iter(train) [ 13550/160000] base_lr: 9.2398e-05 lr: 3.4161e-07 eta: 1 day, 16:41:26 time: 0.9970 data_time: 0.0047 memory: 8462 loss: 0.0221 decode.loss_ce: 0.0125 decode.acc_seg: 98.9977 aux.loss_ce: 0.0096 aux.acc_seg: 98.8420 +04/17 12:18:50 - mmengine - INFO - Iter(train) [ 13600/160000] base_lr: 9.2367e-05 lr: 3.4150e-07 eta: 1 day, 16:40:35 time: 0.9968 data_time: 0.0048 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0115 decode.acc_seg: 99.4091 aux.loss_ce: 0.0095 aux.acc_seg: 98.9174 +04/17 12:19:40 - mmengine - INFO - Iter(train) [ 13650/160000] base_lr: 9.2335e-05 lr: 3.4138e-07 eta: 1 day, 16:39:43 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0118 decode.acc_seg: 99.1947 aux.loss_ce: 0.0101 aux.acc_seg: 98.6771 +04/17 12:20:29 - mmengine - INFO - Iter(train) [ 13700/160000] base_lr: 9.2303e-05 lr: 3.4126e-07 eta: 1 day, 16:38:52 time: 0.9994 data_time: 0.0046 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0123 decode.acc_seg: 99.5081 aux.loss_ce: 0.0096 aux.acc_seg: 98.7627 +04/17 12:21:19 - mmengine - INFO - Iter(train) [ 13750/160000] base_lr: 9.2272e-05 lr: 3.4115e-07 eta: 1 day, 16:38:00 time: 0.9972 data_time: 0.0048 memory: 8462 loss: 0.0248 decode.loss_ce: 0.0138 decode.acc_seg: 99.3843 aux.loss_ce: 0.0110 aux.acc_seg: 98.3892 +04/17 12:22:09 - mmengine - INFO - Iter(train) [ 13800/160000] base_lr: 9.2240e-05 lr: 3.4103e-07 eta: 1 day, 16:37:09 time: 0.9985 data_time: 0.0048 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0167 decode.acc_seg: 99.5398 aux.loss_ce: 0.0116 aux.acc_seg: 99.1119 +04/17 12:22:59 - mmengine - INFO - Iter(train) [ 13850/160000] base_lr: 9.2209e-05 lr: 3.4091e-07 eta: 1 day, 16:36:18 time: 0.9953 data_time: 0.0045 memory: 8462 loss: 0.0256 decode.loss_ce: 0.0142 decode.acc_seg: 98.9668 aux.loss_ce: 0.0114 aux.acc_seg: 98.2203 +04/17 12:23:49 - mmengine - INFO - Iter(train) [ 13900/160000] base_lr: 9.2177e-05 lr: 3.4080e-07 eta: 1 day, 16:35:26 time: 0.9975 data_time: 0.0042 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0139 decode.acc_seg: 99.4198 aux.loss_ce: 0.0104 aux.acc_seg: 98.5403 +04/17 12:24:39 - mmengine - INFO - Iter(train) [ 13950/160000] base_lr: 9.2146e-05 lr: 3.4068e-07 eta: 1 day, 16:34:35 time: 0.9975 data_time: 0.0045 memory: 8462 loss: 0.0241 decode.loss_ce: 0.0139 decode.acc_seg: 99.3166 aux.loss_ce: 0.0102 aux.acc_seg: 98.9058 +04/17 12:25:29 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 12:25:29 - mmengine - INFO - Iter(train) [ 14000/160000] base_lr: 9.2114e-05 lr: 3.4056e-07 eta: 1 day, 16:33:43 time: 0.9971 data_time: 0.0042 memory: 8462 loss: 0.0258 decode.loss_ce: 0.0149 decode.acc_seg: 99.5991 aux.loss_ce: 0.0108 aux.acc_seg: 99.1245 +04/17 12:26:19 - mmengine - INFO - Iter(train) [ 14050/160000] base_lr: 9.2083e-05 lr: 3.4045e-07 eta: 1 day, 16:32:52 time: 0.9998 data_time: 0.0049 memory: 8462 loss: 0.0270 decode.loss_ce: 0.0141 decode.acc_seg: 99.3965 aux.loss_ce: 0.0128 aux.acc_seg: 98.5229 +04/17 12:27:09 - mmengine - INFO - Iter(train) [ 14100/160000] base_lr: 9.2051e-05 lr: 3.4033e-07 eta: 1 day, 16:32:01 time: 0.9974 data_time: 0.0043 memory: 8462 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.5802 aux.loss_ce: 0.0090 aux.acc_seg: 99.3126 +04/17 12:27:58 - mmengine - INFO - Iter(train) [ 14150/160000] base_lr: 9.2020e-05 lr: 3.4022e-07 eta: 1 day, 16:31:10 time: 0.9978 data_time: 0.0044 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0126 decode.acc_seg: 99.6016 aux.loss_ce: 0.0102 aux.acc_seg: 98.9491 +04/17 12:28:48 - mmengine - INFO - Iter(train) [ 14200/160000] base_lr: 9.1988e-05 lr: 3.4010e-07 eta: 1 day, 16:30:18 time: 0.9979 data_time: 0.0048 memory: 8462 loss: 0.0248 decode.loss_ce: 0.0138 decode.acc_seg: 99.4162 aux.loss_ce: 0.0109 aux.acc_seg: 98.5853 +04/17 12:29:38 - mmengine - INFO - Iter(train) [ 14250/160000] base_lr: 9.1956e-05 lr: 3.3998e-07 eta: 1 day, 16:29:27 time: 0.9979 data_time: 0.0042 memory: 8462 loss: 0.0226 decode.loss_ce: 0.0123 decode.acc_seg: 99.3723 aux.loss_ce: 0.0103 aux.acc_seg: 98.7762 +04/17 12:30:28 - mmengine - INFO - Iter(train) [ 14300/160000] base_lr: 9.1925e-05 lr: 3.3987e-07 eta: 1 day, 16:28:35 time: 0.9959 data_time: 0.0042 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0114 decode.acc_seg: 99.5321 aux.loss_ce: 0.0096 aux.acc_seg: 98.9382 +04/17 12:31:18 - mmengine - INFO - Iter(train) [ 14350/160000] base_lr: 9.1893e-05 lr: 3.3975e-07 eta: 1 day, 16:27:44 time: 0.9986 data_time: 0.0045 memory: 8462 loss: 0.0255 decode.loss_ce: 0.0144 decode.acc_seg: 99.4427 aux.loss_ce: 0.0111 aux.acc_seg: 98.8909 +04/17 12:32:08 - mmengine - INFO - Iter(train) [ 14400/160000] base_lr: 9.1862e-05 lr: 3.3963e-07 eta: 1 day, 16:26:53 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0243 decode.loss_ce: 0.0139 decode.acc_seg: 99.5283 aux.loss_ce: 0.0105 aux.acc_seg: 99.2180 +04/17 12:32:58 - mmengine - INFO - Iter(train) [ 14450/160000] base_lr: 9.1830e-05 lr: 3.3952e-07 eta: 1 day, 16:26:02 time: 0.9968 data_time: 0.0044 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0125 decode.acc_seg: 99.5022 aux.loss_ce: 0.0101 aux.acc_seg: 98.7011 +04/17 12:33:48 - mmengine - INFO - Iter(train) [ 14500/160000] base_lr: 9.1799e-05 lr: 3.3940e-07 eta: 1 day, 16:25:10 time: 0.9973 data_time: 0.0047 memory: 8462 loss: 0.0254 decode.loss_ce: 0.0139 decode.acc_seg: 99.5682 aux.loss_ce: 0.0115 aux.acc_seg: 99.0284 +04/17 12:34:37 - mmengine - INFO - Iter(train) [ 14550/160000] base_lr: 9.1767e-05 lr: 3.3928e-07 eta: 1 day, 16:24:19 time: 0.9985 data_time: 0.0046 memory: 8462 loss: 0.0246 decode.loss_ce: 0.0136 decode.acc_seg: 99.6719 aux.loss_ce: 0.0110 aux.acc_seg: 99.3185 +04/17 12:35:27 - mmengine - INFO - Iter(train) [ 14600/160000] base_lr: 9.1736e-05 lr: 3.3917e-07 eta: 1 day, 16:23:28 time: 0.9979 data_time: 0.0049 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0125 decode.acc_seg: 99.6122 aux.loss_ce: 0.0099 aux.acc_seg: 99.1463 +04/17 12:36:17 - mmengine - INFO - Iter(train) [ 14650/160000] base_lr: 9.1704e-05 lr: 3.3905e-07 eta: 1 day, 16:22:37 time: 0.9967 data_time: 0.0044 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0127 decode.acc_seg: 99.5100 aux.loss_ce: 0.0098 aux.acc_seg: 99.2855 +04/17 12:37:07 - mmengine - INFO - Iter(train) [ 14700/160000] base_lr: 9.1673e-05 lr: 3.3893e-07 eta: 1 day, 16:21:46 time: 0.9975 data_time: 0.0043 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0120 decode.acc_seg: 99.6143 aux.loss_ce: 0.0102 aux.acc_seg: 99.1068 +04/17 12:37:57 - mmengine - INFO - Iter(train) [ 14750/160000] base_lr: 9.1641e-05 lr: 3.3882e-07 eta: 1 day, 16:20:55 time: 0.9978 data_time: 0.0043 memory: 8462 loss: 0.0283 decode.loss_ce: 0.0164 decode.acc_seg: 99.5216 aux.loss_ce: 0.0120 aux.acc_seg: 98.9532 +04/17 12:38:47 - mmengine - INFO - Iter(train) [ 14800/160000] base_lr: 9.1609e-05 lr: 3.3870e-07 eta: 1 day, 16:20:04 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0275 decode.loss_ce: 0.0154 decode.acc_seg: 99.6338 aux.loss_ce: 0.0122 aux.acc_seg: 99.1535 +04/17 12:39:37 - mmengine - INFO - Iter(train) [ 14850/160000] base_lr: 9.1578e-05 lr: 3.3858e-07 eta: 1 day, 16:19:13 time: 0.9978 data_time: 0.0044 memory: 8462 loss: 0.0235 decode.loss_ce: 0.0129 decode.acc_seg: 99.4799 aux.loss_ce: 0.0106 aux.acc_seg: 98.4684 +04/17 12:40:27 - mmengine - INFO - Iter(train) [ 14900/160000] base_lr: 9.1546e-05 lr: 3.3847e-07 eta: 1 day, 16:18:22 time: 0.9977 data_time: 0.0044 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0132 decode.acc_seg: 99.5035 aux.loss_ce: 0.0108 aux.acc_seg: 99.1489 +04/17 12:41:17 - mmengine - INFO - Iter(train) [ 14950/160000] base_lr: 9.1515e-05 lr: 3.3835e-07 eta: 1 day, 16:17:31 time: 0.9975 data_time: 0.0046 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0135 decode.acc_seg: 99.4625 aux.loss_ce: 0.0105 aux.acc_seg: 99.0116 +04/17 12:42:06 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 12:42:06 - mmengine - INFO - Iter(train) [ 15000/160000] base_lr: 9.1483e-05 lr: 3.3823e-07 eta: 1 day, 16:16:40 time: 0.9989 data_time: 0.0046 memory: 8462 loss: 0.0261 decode.loss_ce: 0.0151 decode.acc_seg: 99.4242 aux.loss_ce: 0.0111 aux.acc_seg: 99.0814 +04/17 12:42:56 - mmengine - INFO - Iter(train) [ 15050/160000] base_lr: 9.1452e-05 lr: 3.3812e-07 eta: 1 day, 16:15:48 time: 0.9970 data_time: 0.0042 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0142 decode.acc_seg: 99.3740 aux.loss_ce: 0.0098 aux.acc_seg: 99.0076 +04/17 12:43:46 - mmengine - INFO - 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decode.loss_ce: 0.0126 decode.acc_seg: 99.6105 aux.loss_ce: 0.0108 aux.acc_seg: 98.9338 +04/17 12:50:25 - mmengine - INFO - Iter(train) [ 15500/160000] base_lr: 9.1168e-05 lr: 3.3707e-07 eta: 1 day, 16:08:08 time: 0.9982 data_time: 0.0046 memory: 8462 loss: 0.0237 decode.loss_ce: 0.0137 decode.acc_seg: 99.3944 aux.loss_ce: 0.0101 aux.acc_seg: 98.6105 +04/17 12:51:15 - mmengine - INFO - Iter(train) [ 15550/160000] base_lr: 9.1136e-05 lr: 3.3695e-07 eta: 1 day, 16:07:17 time: 0.9970 data_time: 0.0041 memory: 8462 loss: 0.0255 decode.loss_ce: 0.0140 decode.acc_seg: 99.3401 aux.loss_ce: 0.0115 aux.acc_seg: 99.0372 +04/17 12:52:05 - mmengine - INFO - Iter(train) [ 15600/160000] base_lr: 9.1105e-05 lr: 3.3683e-07 eta: 1 day, 16:06:26 time: 0.9961 data_time: 0.0042 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0133 decode.acc_seg: 99.5707 aux.loss_ce: 0.0103 aux.acc_seg: 99.0843 +04/17 12:52:55 - mmengine - INFO - Iter(train) [ 15650/160000] base_lr: 9.1073e-05 lr: 3.3672e-07 eta: 1 day, 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memory: 8462 loss: 0.0224 decode.loss_ce: 0.0126 decode.acc_seg: 98.7751 aux.loss_ce: 0.0098 aux.acc_seg: 98.1810 +04/17 12:59:34 - mmengine - INFO - Iter(train) [ 16050/160000] base_lr: 9.0821e-05 lr: 3.3578e-07 eta: 1 day, 15:58:45 time: 0.9982 data_time: 0.0048 memory: 8462 loss: 0.0217 decode.loss_ce: 0.0117 decode.acc_seg: 99.1167 aux.loss_ce: 0.0099 aux.acc_seg: 98.3572 +04/17 13:00:24 - mmengine - INFO - Iter(train) [ 16100/160000] base_lr: 9.0789e-05 lr: 3.3567e-07 eta: 1 day, 15:57:54 time: 0.9979 data_time: 0.0042 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0111 decode.acc_seg: 99.3414 aux.loss_ce: 0.0094 aux.acc_seg: 98.5754 +04/17 13:01:14 - mmengine - INFO - Iter(train) [ 16150/160000] base_lr: 9.0758e-05 lr: 3.3555e-07 eta: 1 day, 15:57:03 time: 0.9976 data_time: 0.0043 memory: 8462 loss: 0.0244 decode.loss_ce: 0.0141 decode.acc_seg: 99.2956 aux.loss_ce: 0.0103 aux.acc_seg: 99.0616 +04/17 13:02:03 - mmengine - INFO - Iter(train) [ 16200/160000] base_lr: 9.0726e-05 lr: 3.3543e-07 eta: 1 day, 15:56:12 time: 0.9976 data_time: 0.0039 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0127 decode.acc_seg: 99.5726 aux.loss_ce: 0.0100 aux.acc_seg: 99.1488 +04/17 13:02:53 - mmengine - INFO - Iter(train) [ 16250/160000] base_lr: 9.0695e-05 lr: 3.3532e-07 eta: 1 day, 15:55:21 time: 0.9975 data_time: 0.0045 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0120 decode.acc_seg: 99.4024 aux.loss_ce: 0.0100 aux.acc_seg: 98.6151 +04/17 13:03:43 - mmengine - INFO - Iter(train) [ 16300/160000] base_lr: 9.0663e-05 lr: 3.3520e-07 eta: 1 day, 15:54:30 time: 0.9963 data_time: 0.0044 memory: 8462 loss: 0.0241 decode.loss_ce: 0.0135 decode.acc_seg: 99.3757 aux.loss_ce: 0.0107 aux.acc_seg: 98.7549 +04/17 13:04:33 - mmengine - INFO - Iter(train) [ 16350/160000] base_lr: 9.0631e-05 lr: 3.3508e-07 eta: 1 day, 15:53:39 time: 0.9961 data_time: 0.0043 memory: 8462 loss: 0.0276 decode.loss_ce: 0.0151 decode.acc_seg: 99.6042 aux.loss_ce: 0.0125 aux.acc_seg: 99.2022 +04/17 13:05:23 - mmengine - INFO - Iter(train) [ 16400/160000] base_lr: 9.0600e-05 lr: 3.3497e-07 eta: 1 day, 15:52:48 time: 0.9965 data_time: 0.0045 memory: 8462 loss: 0.0253 decode.loss_ce: 0.0138 decode.acc_seg: 99.4143 aux.loss_ce: 0.0114 aux.acc_seg: 98.7661 +04/17 13:06:13 - mmengine - INFO - Iter(train) [ 16450/160000] base_lr: 9.0568e-05 lr: 3.3485e-07 eta: 1 day, 15:51:57 time: 0.9980 data_time: 0.0046 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0107 decode.acc_seg: 99.5127 aux.loss_ce: 0.0087 aux.acc_seg: 98.9428 +04/17 13:07:03 - mmengine - INFO - Iter(train) [ 16500/160000] base_lr: 9.0537e-05 lr: 3.3473e-07 eta: 1 day, 15:51:06 time: 0.9970 data_time: 0.0043 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0118 decode.acc_seg: 99.8001 aux.loss_ce: 0.0096 aux.acc_seg: 99.4225 +04/17 13:07:53 - mmengine - INFO - Iter(train) [ 16550/160000] base_lr: 9.0505e-05 lr: 3.3462e-07 eta: 1 day, 15:50:15 time: 0.9988 data_time: 0.0047 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0119 decode.acc_seg: 99.6180 aux.loss_ce: 0.0100 aux.acc_seg: 99.0452 +04/17 13:08:42 - mmengine - INFO - Iter(train) [ 16600/160000] base_lr: 9.0474e-05 lr: 3.3450e-07 eta: 1 day, 15:49:25 time: 0.9987 data_time: 0.0050 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0127 decode.acc_seg: 99.4692 aux.loss_ce: 0.0104 aux.acc_seg: 99.0362 +04/17 13:09:32 - mmengine - INFO - Iter(train) [ 16650/160000] base_lr: 9.0442e-05 lr: 3.3438e-07 eta: 1 day, 15:48:34 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0117 decode.acc_seg: 99.4780 aux.loss_ce: 0.0104 aux.acc_seg: 98.8773 +04/17 13:10:22 - mmengine - INFO - Iter(train) [ 16700/160000] base_lr: 9.0411e-05 lr: 3.3427e-07 eta: 1 day, 15:47:44 time: 0.9975 data_time: 0.0045 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0137 decode.acc_seg: 99.4667 aux.loss_ce: 0.0107 aux.acc_seg: 98.9105 +04/17 13:11:12 - mmengine - INFO - Iter(train) [ 16750/160000] base_lr: 9.0379e-05 lr: 3.3415e-07 eta: 1 day, 15:46:53 time: 0.9969 data_time: 0.0043 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0121 decode.acc_seg: 99.6586 aux.loss_ce: 0.0101 aux.acc_seg: 99.2302 +04/17 13:12:02 - mmengine - INFO - Iter(train) [ 16800/160000] base_lr: 9.0348e-05 lr: 3.3403e-07 eta: 1 day, 15:46:02 time: 0.9977 data_time: 0.0043 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0122 decode.acc_seg: 99.7305 aux.loss_ce: 0.0101 aux.acc_seg: 99.3217 +04/17 13:12:52 - mmengine - INFO - Iter(train) [ 16850/160000] base_lr: 9.0316e-05 lr: 3.3392e-07 eta: 1 day, 15:45:11 time: 0.9971 data_time: 0.0045 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0124 decode.acc_seg: 99.4852 aux.loss_ce: 0.0104 aux.acc_seg: 98.7169 +04/17 13:13:42 - mmengine - INFO - Iter(train) [ 16900/160000] base_lr: 9.0284e-05 lr: 3.3380e-07 eta: 1 day, 15:44:20 time: 0.9984 data_time: 0.0043 memory: 8462 loss: 0.0221 decode.loss_ce: 0.0115 decode.acc_seg: 99.1451 aux.loss_ce: 0.0106 aux.acc_seg: 97.9866 +04/17 13:14:32 - mmengine - INFO - Iter(train) [ 16950/160000] base_lr: 9.0253e-05 lr: 3.3368e-07 eta: 1 day, 15:43:30 time: 0.9992 data_time: 0.0050 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0122 decode.acc_seg: 99.4707 aux.loss_ce: 0.0099 aux.acc_seg: 98.7959 +04/17 13:15:22 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 13:15:22 - mmengine - INFO - Iter(train) [ 17000/160000] base_lr: 9.0221e-05 lr: 3.3357e-07 eta: 1 day, 15:42:39 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0238 decode.loss_ce: 0.0127 decode.acc_seg: 99.7061 aux.loss_ce: 0.0111 aux.acc_seg: 99.3080 +04/17 13:16:12 - mmengine - INFO - Iter(train) [ 17050/160000] base_lr: 9.0190e-05 lr: 3.3345e-07 eta: 1 day, 15:41:48 time: 0.9982 data_time: 0.0047 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0107 decode.acc_seg: 99.4898 aux.loss_ce: 0.0079 aux.acc_seg: 99.2535 +04/17 13:17:02 - mmengine - INFO - Iter(train) [ 17100/160000] base_lr: 9.0158e-05 lr: 3.3333e-07 eta: 1 day, 15:40:58 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0125 decode.acc_seg: 99.1219 aux.loss_ce: 0.0097 aux.acc_seg: 98.7671 +04/17 13:17:51 - mmengine - INFO - Iter(train) [ 17150/160000] base_lr: 9.0127e-05 lr: 3.3322e-07 eta: 1 day, 15:40:07 time: 0.9980 data_time: 0.0045 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0113 decode.acc_seg: 99.5983 aux.loss_ce: 0.0102 aux.acc_seg: 99.0562 +04/17 13:18:41 - mmengine - INFO - Iter(train) [ 17200/160000] base_lr: 9.0095e-05 lr: 3.3310e-07 eta: 1 day, 15:39:17 time: 0.9983 data_time: 0.0044 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0116 decode.acc_seg: 99.5598 aux.loss_ce: 0.0103 aux.acc_seg: 98.6620 +04/17 13:19:31 - mmengine - INFO - Iter(train) [ 17250/160000] base_lr: 9.0064e-05 lr: 3.3298e-07 eta: 1 day, 15:38:26 time: 0.9996 data_time: 0.0041 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0135 decode.acc_seg: 99.4719 aux.loss_ce: 0.0107 aux.acc_seg: 99.0494 +04/17 13:20:21 - mmengine - INFO - Iter(train) [ 17300/160000] base_lr: 9.0032e-05 lr: 3.3287e-07 eta: 1 day, 15:37:36 time: 0.9993 data_time: 0.0048 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0120 decode.acc_seg: 99.6330 aux.loss_ce: 0.0100 aux.acc_seg: 99.3477 +04/17 13:21:11 - mmengine - INFO - Iter(train) [ 17350/160000] base_lr: 9.0001e-05 lr: 3.3275e-07 eta: 1 day, 15:36:45 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0114 decode.acc_seg: 99.5409 aux.loss_ce: 0.0104 aux.acc_seg: 98.9855 +04/17 13:22:01 - mmengine - INFO - Iter(train) [ 17400/160000] base_lr: 8.9969e-05 lr: 3.3263e-07 eta: 1 day, 15:35:55 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0126 decode.acc_seg: 99.4459 aux.loss_ce: 0.0094 aux.acc_seg: 98.9763 +04/17 13:22:51 - mmengine - INFO - Iter(train) [ 17450/160000] base_lr: 8.9937e-05 lr: 3.3252e-07 eta: 1 day, 15:35:05 time: 0.9987 data_time: 0.0049 memory: 8462 loss: 0.0242 decode.loss_ce: 0.0139 decode.acc_seg: 99.3793 aux.loss_ce: 0.0102 aux.acc_seg: 98.7186 +04/17 13:23:41 - mmengine - INFO - Iter(train) [ 17500/160000] base_lr: 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13:27:01 - mmengine - INFO - Iter(train) [ 17700/160000] base_lr: 8.9780e-05 lr: 3.3193e-07 eta: 1 day, 15:30:52 time: 0.9986 data_time: 0.0044 memory: 8462 loss: 0.0241 decode.loss_ce: 0.0135 decode.acc_seg: 99.1665 aux.loss_ce: 0.0106 aux.acc_seg: 98.5540 +04/17 13:27:51 - mmengine - INFO - Iter(train) [ 17750/160000] base_lr: 8.9748e-05 lr: 3.3182e-07 eta: 1 day, 15:30:02 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0111 decode.acc_seg: 99.6120 aux.loss_ce: 0.0103 aux.acc_seg: 98.6225 +04/17 13:28:41 - mmengine - INFO - Iter(train) [ 17800/160000] base_lr: 8.9717e-05 lr: 3.3170e-07 eta: 1 day, 15:29:12 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0101 decode.acc_seg: 99.6502 aux.loss_ce: 0.0085 aux.acc_seg: 99.2655 +04/17 13:29:31 - mmengine - INFO - Iter(train) [ 17850/160000] base_lr: 8.9685e-05 lr: 3.3158e-07 eta: 1 day, 15:28:21 time: 0.9986 data_time: 0.0048 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0132 decode.acc_seg: 99.6231 aux.loss_ce: 0.0113 aux.acc_seg: 99.1957 +04/17 13:30:20 - mmengine - INFO - Iter(train) [ 17900/160000] base_lr: 8.9654e-05 lr: 3.3147e-07 eta: 1 day, 15:27:31 time: 0.9986 data_time: 0.0046 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0108 decode.acc_seg: 99.6162 aux.loss_ce: 0.0091 aux.acc_seg: 99.0400 +04/17 13:31:10 - mmengine - INFO - Iter(train) [ 17950/160000] base_lr: 8.9622e-05 lr: 3.3135e-07 eta: 1 day, 15:26:40 time: 0.9975 data_time: 0.0043 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0121 decode.acc_seg: 99.8589 aux.loss_ce: 0.0097 aux.acc_seg: 99.5493 +04/17 13:32:00 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 13:32:00 - mmengine - INFO - Iter(train) [ 18000/160000] base_lr: 8.9590e-05 lr: 3.3123e-07 eta: 1 day, 15:25:50 time: 0.9972 data_time: 0.0043 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0125 decode.acc_seg: 99.6740 aux.loss_ce: 0.0104 aux.acc_seg: 99.0997 +04/17 13:32:50 - mmengine - INFO - 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decode.loss_ce: 0.0151 decode.acc_seg: 99.4741 aux.loss_ce: 0.0126 aux.acc_seg: 99.0673 +04/17 13:39:30 - mmengine - INFO - Iter(train) [ 18450/160000] base_lr: 8.9307e-05 lr: 3.3018e-07 eta: 1 day, 15:18:17 time: 0.9995 data_time: 0.0050 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0097 decode.acc_seg: 99.5771 aux.loss_ce: 0.0091 aux.acc_seg: 98.8585 +04/17 13:40:20 - mmengine - INFO - Iter(train) [ 18500/160000] base_lr: 8.9275e-05 lr: 3.3007e-07 eta: 1 day, 15:17:27 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0111 decode.acc_seg: 99.6395 aux.loss_ce: 0.0102 aux.acc_seg: 99.1505 +04/17 13:41:10 - mmengine - INFO - Iter(train) [ 18550/160000] base_lr: 8.9243e-05 lr: 3.2995e-07 eta: 1 day, 15:16:37 time: 0.9982 data_time: 0.0044 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0100 decode.acc_seg: 99.6601 aux.loss_ce: 0.0095 aux.acc_seg: 99.2523 +04/17 13:42:00 - mmengine - INFO - Iter(train) [ 18600/160000] base_lr: 8.9212e-05 lr: 3.2983e-07 eta: 1 day, 15:15:47 time: 1.0000 data_time: 0.0051 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0120 decode.acc_seg: 99.4564 aux.loss_ce: 0.0099 aux.acc_seg: 98.5744 +04/17 13:42:50 - mmengine - INFO - Iter(train) [ 18650/160000] base_lr: 8.9180e-05 lr: 3.2972e-07 eta: 1 day, 15:14:56 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0111 decode.acc_seg: 99.4984 aux.loss_ce: 0.0102 aux.acc_seg: 98.9391 +04/17 13:43:40 - mmengine - INFO - Iter(train) [ 18700/160000] base_lr: 8.9149e-05 lr: 3.2960e-07 eta: 1 day, 15:14:06 time: 0.9989 data_time: 0.0046 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0109 decode.acc_seg: 99.7986 aux.loss_ce: 0.0092 aux.acc_seg: 99.4448 +04/17 13:44:29 - mmengine - INFO - Iter(train) [ 18750/160000] base_lr: 8.9117e-05 lr: 3.2948e-07 eta: 1 day, 15:13:16 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0116 decode.acc_seg: 99.5569 aux.loss_ce: 0.0097 aux.acc_seg: 99.1232 +04/17 13:45:19 - mmengine - INFO - Iter(train) [ 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aux.acc_seg: 98.5264 +04/17 13:48:39 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 13:48:39 - mmengine - INFO - Iter(train) [ 19000/160000] base_lr: 8.8960e-05 lr: 3.2890e-07 eta: 1 day, 15:09:04 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0222 decode.loss_ce: 0.0120 decode.acc_seg: 99.6391 aux.loss_ce: 0.0103 aux.acc_seg: 99.2020 +04/17 13:49:29 - mmengine - INFO - Iter(train) [ 19050/160000] base_lr: 8.8928e-05 lr: 3.2879e-07 eta: 1 day, 15:08:14 time: 0.9976 data_time: 0.0042 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0114 decode.acc_seg: 99.5697 aux.loss_ce: 0.0102 aux.acc_seg: 98.9531 +04/17 13:50:19 - mmengine - INFO - Iter(train) [ 19100/160000] base_lr: 8.8896e-05 lr: 3.2867e-07 eta: 1 day, 15:07:23 time: 0.9977 data_time: 0.0042 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0113 decode.acc_seg: 99.6140 aux.loss_ce: 0.0091 aux.acc_seg: 99.0669 +04/17 13:51:09 - mmengine - INFO - Iter(train) [ 19150/160000] base_lr: 8.8865e-05 lr: 3.2855e-07 eta: 1 day, 15:06:33 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0126 decode.acc_seg: 99.3668 aux.loss_ce: 0.0098 aux.acc_seg: 98.8646 +04/17 13:51:59 - mmengine - INFO - Iter(train) [ 19200/160000] base_lr: 8.8833e-05 lr: 3.2844e-07 eta: 1 day, 15:05:43 time: 0.9993 data_time: 0.0044 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0120 decode.acc_seg: 99.4331 aux.loss_ce: 0.0108 aux.acc_seg: 98.8338 +04/17 13:52:49 - mmengine - INFO - Iter(train) [ 19250/160000] base_lr: 8.8802e-05 lr: 3.2832e-07 eta: 1 day, 15:04:53 time: 0.9982 data_time: 0.0043 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0117 decode.acc_seg: 99.3053 aux.loss_ce: 0.0102 aux.acc_seg: 98.6570 +04/17 13:53:39 - mmengine - INFO - Iter(train) [ 19300/160000] base_lr: 8.8770e-05 lr: 3.2820e-07 eta: 1 day, 15:04:03 time: 0.9992 data_time: 0.0043 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0118 decode.acc_seg: 99.6469 aux.loss_ce: 0.0102 aux.acc_seg: 99.2630 +04/17 13:54:29 - mmengine - INFO - Iter(train) [ 19350/160000] base_lr: 8.8739e-05 lr: 3.2809e-07 eta: 1 day, 15:03:13 time: 0.9986 data_time: 0.0042 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0125 decode.acc_seg: 99.3605 aux.loss_ce: 0.0102 aux.acc_seg: 98.8811 +04/17 13:55:19 - mmengine - INFO - Iter(train) [ 19400/160000] base_lr: 8.8707e-05 lr: 3.2797e-07 eta: 1 day, 15:02:22 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0126 decode.acc_seg: 99.1262 aux.loss_ce: 0.0106 aux.acc_seg: 98.6568 +04/17 13:56:09 - mmengine - INFO - Iter(train) [ 19450/160000] base_lr: 8.8676e-05 lr: 3.2785e-07 eta: 1 day, 15:01:32 time: 1.0000 data_time: 0.0043 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0112 decode.acc_seg: 99.6866 aux.loss_ce: 0.0107 aux.acc_seg: 99.1751 +04/17 13:56:59 - mmengine - INFO - Iter(train) [ 19500/160000] base_lr: 8.8644e-05 lr: 3.2774e-07 eta: 1 day, 15:00:42 time: 0.9976 data_time: 0.0044 memory: 8462 loss: 0.0247 decode.loss_ce: 0.0137 decode.acc_seg: 99.2142 aux.loss_ce: 0.0111 aux.acc_seg: 98.6185 +04/17 13:57:49 - mmengine - INFO - Iter(train) [ 19550/160000] base_lr: 8.8613e-05 lr: 3.2762e-07 eta: 1 day, 14:59:52 time: 0.9992 data_time: 0.0041 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0116 decode.acc_seg: 99.1375 aux.loss_ce: 0.0091 aux.acc_seg: 98.7877 +04/17 13:58:39 - mmengine - INFO - Iter(train) [ 19600/160000] base_lr: 8.8581e-05 lr: 3.2750e-07 eta: 1 day, 14:59:02 time: 0.9983 data_time: 0.0042 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0093 decode.acc_seg: 99.5197 aux.loss_ce: 0.0089 aux.acc_seg: 99.0948 +04/17 13:59:28 - mmengine - INFO - Iter(train) [ 19650/160000] base_lr: 8.8549e-05 lr: 3.2739e-07 eta: 1 day, 14:58:11 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0097 decode.acc_seg: 99.7725 aux.loss_ce: 0.0090 aux.acc_seg: 99.3301 +04/17 14:00:18 - mmengine - INFO - Iter(train) [ 19700/160000] base_lr: 8.8518e-05 lr: 3.2727e-07 eta: 1 day, 14:57:21 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0210 decode.loss_ce: 0.0114 decode.acc_seg: 99.7171 aux.loss_ce: 0.0096 aux.acc_seg: 99.4770 +04/17 14:01:08 - mmengine - INFO - Iter(train) [ 19750/160000] base_lr: 8.8486e-05 lr: 3.2715e-07 eta: 1 day, 14:56:31 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0104 decode.acc_seg: 99.5567 aux.loss_ce: 0.0091 aux.acc_seg: 99.2126 +04/17 14:01:58 - mmengine - INFO - Iter(train) [ 19800/160000] base_lr: 8.8455e-05 lr: 3.2704e-07 eta: 1 day, 14:55:41 time: 0.9990 data_time: 0.0044 memory: 8462 loss: 0.0245 decode.loss_ce: 0.0133 decode.acc_seg: 99.5886 aux.loss_ce: 0.0112 aux.acc_seg: 98.8911 +04/17 14:02:48 - mmengine - INFO - Iter(train) [ 19850/160000] base_lr: 8.8423e-05 lr: 3.2692e-07 eta: 1 day, 14:54:51 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0230 decode.loss_ce: 0.0129 decode.acc_seg: 99.3370 aux.loss_ce: 0.0101 aux.acc_seg: 98.9658 +04/17 14:03:38 - mmengine - INFO - Iter(train) [ 19900/160000] base_lr: 8.8392e-05 lr: 3.2680e-07 eta: 1 day, 14:54:01 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0098 decode.acc_seg: 99.7274 aux.loss_ce: 0.0091 aux.acc_seg: 99.2981 +04/17 14:04:28 - mmengine - INFO - Iter(train) [ 19950/160000] base_lr: 8.8360e-05 lr: 3.2669e-07 eta: 1 day, 14:53:11 time: 0.9984 data_time: 0.0044 memory: 8462 loss: 0.0244 decode.loss_ce: 0.0134 decode.acc_seg: 98.7705 aux.loss_ce: 0.0110 aux.acc_seg: 98.3341 +04/17 14:05:18 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 14:05:18 - mmengine - INFO - Iter(train) [ 20000/160000] base_lr: 8.8329e-05 lr: 3.2657e-07 eta: 1 day, 14:52:21 time: 0.9999 data_time: 0.0046 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0089 decode.acc_seg: 99.6473 aux.loss_ce: 0.0090 aux.acc_seg: 99.0765 +04/17 14:05:18 - mmengine - INFO - Saving checkpoint at 20000 iterations +04/17 14:05:28 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1155 data_time: 0.0014 memory: 4004 +04/17 14:05:34 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1158 data_time: 0.0015 memory: 4004 +04/17 14:05:40 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1156 data_time: 0.0014 memory: 4004 +04/17 14:05:46 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1153 data_time: 0.0013 memory: 4004 +04/17 14:05:46 - mmengine - INFO - per class results: +04/17 14:05:46 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.14 | 99.55 | 99.57 | 99.58 | 99.55 | +| contrast | 81.18 | 89.96 | 89.61 | 89.28 | 89.96 | ++------------+-------+-------+--------+-----------+--------+ +04/17 14:05:46 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1700 mIoU: 90.1600 mAcc: 94.7500 mFscore: 94.5900 mPrecision: 94.4300 mRecall: 94.7500 data_time: 0.0015 time: 0.1160 +04/17 14:06:36 - mmengine - INFO - Iter(train) [ 20050/160000] base_lr: 8.8297e-05 lr: 3.2645e-07 eta: 1 day, 14:51:31 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0221 decode.loss_ce: 0.0120 decode.acc_seg: 99.3975 aux.loss_ce: 0.0101 aux.acc_seg: 98.7370 +04/17 14:07:26 - mmengine - INFO - Iter(train) [ 20100/160000] base_lr: 8.8266e-05 lr: 3.2634e-07 eta: 1 day, 14:50:41 time: 0.9985 data_time: 0.0046 memory: 8462 loss: 0.0203 decode.loss_ce: 0.0107 decode.acc_seg: 99.5319 aux.loss_ce: 0.0096 aux.acc_seg: 98.8304 +04/17 14:08:16 - mmengine - INFO - Iter(train) [ 20150/160000] base_lr: 8.8234e-05 lr: 3.2622e-07 eta: 1 day, 14:49:51 time: 0.9998 data_time: 0.0048 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0112 decode.acc_seg: 99.5188 aux.loss_ce: 0.0113 aux.acc_seg: 98.8132 +04/17 14:09:06 - mmengine - INFO - Iter(train) [ 20200/160000] base_lr: 8.8202e-05 lr: 3.2610e-07 eta: 1 day, 14:49:01 time: 1.0004 data_time: 0.0047 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0116 decode.acc_seg: 99.5768 aux.loss_ce: 0.0099 aux.acc_seg: 98.7598 +04/17 14:09:55 - mmengine - INFO - Iter(train) [ 20250/160000] base_lr: 8.8171e-05 lr: 3.2599e-07 eta: 1 day, 14:48:11 time: 0.9997 data_time: 0.0050 memory: 8462 loss: 0.0212 decode.loss_ce: 0.0112 decode.acc_seg: 99.4320 aux.loss_ce: 0.0100 aux.acc_seg: 98.8493 +04/17 14:10:45 - mmengine - INFO - Iter(train) [ 20300/160000] base_lr: 8.8139e-05 lr: 3.2587e-07 eta: 1 day, 14:47:21 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0124 decode.acc_seg: 99.3404 aux.loss_ce: 0.0112 aux.acc_seg: 98.4577 +04/17 14:11:35 - mmengine - INFO - Iter(train) [ 20350/160000] base_lr: 8.8108e-05 lr: 3.2575e-07 eta: 1 day, 14:46:31 time: 0.9991 data_time: 0.0045 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0104 decode.acc_seg: 99.5899 aux.loss_ce: 0.0093 aux.acc_seg: 99.0639 +04/17 14:12:25 - mmengine - INFO - Iter(train) [ 20400/160000] base_lr: 8.8076e-05 lr: 3.2564e-07 eta: 1 day, 14:45:41 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0110 decode.acc_seg: 99.4804 aux.loss_ce: 0.0101 aux.acc_seg: 98.7062 +04/17 14:13:15 - mmengine - INFO - Iter(train) [ 20450/160000] base_lr: 8.8045e-05 lr: 3.2552e-07 eta: 1 day, 14:44:51 time: 0.9996 data_time: 0.0051 memory: 8462 loss: 0.0206 decode.loss_ce: 0.0105 decode.acc_seg: 99.5016 aux.loss_ce: 0.0100 aux.acc_seg: 98.5600 +04/17 14:14:05 - mmengine - INFO - Iter(train) [ 20500/160000] base_lr: 8.8013e-05 lr: 3.2540e-07 eta: 1 day, 14:44:01 time: 0.9985 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0106 decode.acc_seg: 99.5970 aux.loss_ce: 0.0094 aux.acc_seg: 98.8459 +04/17 14:14:55 - mmengine - INFO - Iter(train) [ 20550/160000] base_lr: 8.7982e-05 lr: 3.2529e-07 eta: 1 day, 14:43:11 time: 1.0003 data_time: 0.0043 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0110 decode.acc_seg: 99.0553 aux.loss_ce: 0.0095 aux.acc_seg: 98.2803 +04/17 14:15:45 - mmengine - INFO - Iter(train) [ 20600/160000] base_lr: 8.7950e-05 lr: 3.2517e-07 eta: 1 day, 14:42:21 time: 0.9983 data_time: 0.0047 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0100 decode.acc_seg: 99.6210 aux.loss_ce: 0.0100 aux.acc_seg: 99.0175 +04/17 14:16:35 - mmengine - INFO - Iter(train) [ 20650/160000] base_lr: 8.7919e-05 lr: 3.2505e-07 eta: 1 day, 14:41:31 time: 0.9992 data_time: 0.0042 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0135 decode.acc_seg: 98.8693 aux.loss_ce: 0.0104 aux.acc_seg: 98.3368 +04/17 14:17:25 - mmengine - INFO - Iter(train) [ 20700/160000] base_lr: 8.7887e-05 lr: 3.2494e-07 eta: 1 day, 14:40:41 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0225 decode.loss_ce: 0.0122 decode.acc_seg: 99.4473 aux.loss_ce: 0.0103 aux.acc_seg: 98.7219 +04/17 14:18:15 - mmengine - INFO - Iter(train) [ 20750/160000] base_lr: 8.7855e-05 lr: 3.2482e-07 eta: 1 day, 14:39:51 time: 0.9991 data_time: 0.0046 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0098 decode.acc_seg: 99.6065 aux.loss_ce: 0.0086 aux.acc_seg: 99.1484 +04/17 14:19:05 - mmengine - INFO - Iter(train) [ 20800/160000] base_lr: 8.7824e-05 lr: 3.2470e-07 eta: 1 day, 14:39:01 time: 0.9999 data_time: 0.0046 memory: 8462 loss: 0.0219 decode.loss_ce: 0.0115 decode.acc_seg: 99.6258 aux.loss_ce: 0.0105 aux.acc_seg: 99.0911 +04/17 14:19:55 - mmengine - INFO - Iter(train) [ 20850/160000] base_lr: 8.7792e-05 lr: 3.2459e-07 eta: 1 day, 14:38:11 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0227 decode.loss_ce: 0.0121 decode.acc_seg: 99.5569 aux.loss_ce: 0.0106 aux.acc_seg: 99.1344 +04/17 14:20:45 - mmengine - INFO - Iter(train) [ 20900/160000] base_lr: 8.7761e-05 lr: 3.2447e-07 eta: 1 day, 14:37:21 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0117 decode.acc_seg: 99.2662 aux.loss_ce: 0.0106 aux.acc_seg: 98.4653 +04/17 14:21:35 - mmengine - INFO - Iter(train) [ 20950/160000] base_lr: 8.7729e-05 lr: 3.2435e-07 eta: 1 day, 14:36:31 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0111 decode.acc_seg: 99.4967 aux.loss_ce: 0.0105 aux.acc_seg: 98.8319 +04/17 14:22:25 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 14:22:25 - mmengine - INFO - Iter(train) [ 21000/160000] base_lr: 8.7698e-05 lr: 3.2424e-07 eta: 1 day, 14:35:41 time: 0.9986 data_time: 0.0046 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0109 decode.acc_seg: 99.6061 aux.loss_ce: 0.0099 aux.acc_seg: 99.0068 +04/17 14:23:15 - mmengine - INFO - Iter(train) [ 21050/160000] base_lr: 8.7666e-05 lr: 3.2412e-07 eta: 1 day, 14:34:51 time: 0.9994 data_time: 0.0047 memory: 8462 loss: 0.0223 decode.loss_ce: 0.0108 decode.acc_seg: 99.4978 aux.loss_ce: 0.0115 aux.acc_seg: 99.0391 +04/17 14:24:05 - mmengine - INFO - Iter(train) [ 21100/160000] base_lr: 8.7635e-05 lr: 3.2400e-07 eta: 1 day, 14:34:01 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0109 decode.acc_seg: 99.3053 aux.loss_ce: 0.0095 aux.acc_seg: 98.8251 +04/17 14:24:55 - mmengine - INFO - Iter(train) [ 21150/160000] base_lr: 8.7603e-05 lr: 3.2389e-07 eta: 1 day, 14:33:11 time: 1.0008 data_time: 0.0047 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0116 decode.acc_seg: 99.6618 aux.loss_ce: 0.0101 aux.acc_seg: 99.1035 +04/17 14:25:45 - mmengine - INFO - Iter(train) [ 21200/160000] base_lr: 8.7572e-05 lr: 3.2377e-07 eta: 1 day, 14:32:21 time: 1.0010 data_time: 0.0047 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0108 decode.acc_seg: 99.4436 aux.loss_ce: 0.0094 aux.acc_seg: 98.6851 +04/17 14:26:35 - mmengine - INFO - Iter(train) [ 21250/160000] base_lr: 8.7540e-05 lr: 3.2365e-07 eta: 1 day, 14:31:31 time: 0.9999 data_time: 0.0046 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0099 decode.acc_seg: 99.5758 aux.loss_ce: 0.0095 aux.acc_seg: 99.0086 +04/17 14:27:25 - mmengine - INFO - Iter(train) [ 21300/160000] base_lr: 8.7508e-05 lr: 3.2354e-07 eta: 1 day, 14:30:41 time: 0.9984 data_time: 0.0044 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0109 decode.acc_seg: 99.5089 aux.loss_ce: 0.0102 aux.acc_seg: 98.9325 +04/17 14:28:15 - mmengine - INFO - Iter(train) [ 21350/160000] base_lr: 8.7477e-05 lr: 3.2342e-07 eta: 1 day, 14:29:50 time: 0.9979 data_time: 0.0044 memory: 8462 loss: 0.0240 decode.loss_ce: 0.0124 decode.acc_seg: 99.1619 aux.loss_ce: 0.0117 aux.acc_seg: 97.6984 +04/17 14:29:05 - mmengine - INFO - Iter(train) [ 21400/160000] base_lr: 8.7445e-05 lr: 3.2330e-07 eta: 1 day, 14:29:00 time: 1.0005 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0106 decode.acc_seg: 99.6611 aux.loss_ce: 0.0095 aux.acc_seg: 99.1283 +04/17 14:29:55 - mmengine - INFO - Iter(train) [ 21450/160000] base_lr: 8.7414e-05 lr: 3.2319e-07 eta: 1 day, 14:28:10 time: 0.9994 data_time: 0.0043 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0095 decode.acc_seg: 99.4682 aux.loss_ce: 0.0091 aux.acc_seg: 98.8892 +04/17 14:30:45 - mmengine - INFO - Iter(train) [ 21500/160000] base_lr: 8.7382e-05 lr: 3.2307e-07 eta: 1 day, 14:27:20 time: 0.9987 data_time: 0.0045 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0114 decode.acc_seg: 99.5121 aux.loss_ce: 0.0091 aux.acc_seg: 99.0715 +04/17 14:31:35 - mmengine - INFO - Iter(train) [ 21550/160000] base_lr: 8.7351e-05 lr: 3.2295e-07 eta: 1 day, 14:26:30 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0211 decode.loss_ce: 0.0112 decode.acc_seg: 99.4154 aux.loss_ce: 0.0099 aux.acc_seg: 99.1652 +04/17 14:32:25 - mmengine - INFO - Iter(train) [ 21600/160000] base_lr: 8.7319e-05 lr: 3.2284e-07 eta: 1 day, 14:25:40 time: 1.0001 data_time: 0.0053 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0113 decode.acc_seg: 99.2371 aux.loss_ce: 0.0102 aux.acc_seg: 98.2595 +04/17 14:33:15 - mmengine - INFO - Iter(train) [ 21650/160000] base_lr: 8.7288e-05 lr: 3.2272e-07 eta: 1 day, 14:24:50 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0088 decode.acc_seg: 99.5977 aux.loss_ce: 0.0086 aux.acc_seg: 98.9861 +04/17 14:34:05 - mmengine - INFO - Iter(train) [ 21700/160000] base_lr: 8.7256e-05 lr: 3.2260e-07 eta: 1 day, 14:24:00 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0099 decode.acc_seg: 99.7166 aux.loss_ce: 0.0093 aux.acc_seg: 99.2840 +04/17 14:34:55 - mmengine - INFO - Iter(train) [ 21750/160000] base_lr: 8.7225e-05 lr: 3.2249e-07 eta: 1 day, 14:23:10 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0120 decode.acc_seg: 99.5123 aux.loss_ce: 0.0109 aux.acc_seg: 98.7047 +04/17 14:35:45 - mmengine - INFO - Iter(train) [ 21800/160000] base_lr: 8.7193e-05 lr: 3.2237e-07 eta: 1 day, 14:22:20 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0218 decode.loss_ce: 0.0117 decode.acc_seg: 99.4455 aux.loss_ce: 0.0101 aux.acc_seg: 98.8354 +04/17 14:36:35 - mmengine - INFO - Iter(train) [ 21850/160000] base_lr: 8.7161e-05 lr: 3.2225e-07 eta: 1 day, 14:21:30 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0099 decode.acc_seg: 99.6090 aux.loss_ce: 0.0091 aux.acc_seg: 99.1480 +04/17 14:37:25 - mmengine - INFO - Iter(train) [ 21900/160000] base_lr: 8.7130e-05 lr: 3.2214e-07 eta: 1 day, 14:20:40 time: 0.9999 data_time: 0.0048 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0112 decode.acc_seg: 99.3156 aux.loss_ce: 0.0103 aux.acc_seg: 98.5327 +04/17 14:38:15 - mmengine - INFO - Iter(train) [ 21950/160000] base_lr: 8.7098e-05 lr: 3.2202e-07 eta: 1 day, 14:19:50 time: 0.9998 data_time: 0.0047 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0095 decode.acc_seg: 99.6925 aux.loss_ce: 0.0090 aux.acc_seg: 99.2367 +04/17 14:39:05 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 14:39:05 - mmengine - INFO - Iter(train) [ 22000/160000] base_lr: 8.7067e-05 lr: 3.2190e-07 eta: 1 day, 14:19:00 time: 0.9991 data_time: 0.0045 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0087 decode.acc_seg: 99.7269 aux.loss_ce: 0.0086 aux.acc_seg: 99.2006 +04/17 14:39:55 - mmengine - INFO - Iter(train) [ 22050/160000] base_lr: 8.7035e-05 lr: 3.2179e-07 eta: 1 day, 14:18:10 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0239 decode.loss_ce: 0.0123 decode.acc_seg: 99.4081 aux.loss_ce: 0.0116 aux.acc_seg: 98.3019 +04/17 14:40:45 - mmengine - INFO - Iter(train) [ 22100/160000] base_lr: 8.7004e-05 lr: 3.2167e-07 eta: 1 day, 14:17:21 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0233 decode.loss_ce: 0.0126 decode.acc_seg: 99.6626 aux.loss_ce: 0.0107 aux.acc_seg: 99.1590 +04/17 14:41:35 - mmengine - INFO - Iter(train) [ 22150/160000] base_lr: 8.6972e-05 lr: 3.2155e-07 eta: 1 day, 14:16:31 time: 1.0003 data_time: 0.0047 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0105 decode.acc_seg: 99.7681 aux.loss_ce: 0.0093 aux.acc_seg: 99.4343 +04/17 14:42:25 - mmengine - INFO - Iter(train) [ 22200/160000] base_lr: 8.6941e-05 lr: 3.2144e-07 eta: 1 day, 14:15:41 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0085 decode.acc_seg: 99.6647 aux.loss_ce: 0.0089 aux.acc_seg: 99.1859 +04/17 14:43:15 - mmengine - INFO - Iter(train) [ 22250/160000] base_lr: 8.6909e-05 lr: 3.2132e-07 eta: 1 day, 14:14:51 time: 0.9980 data_time: 0.0045 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0112 decode.acc_seg: 99.6481 aux.loss_ce: 0.0101 aux.acc_seg: 99.2300 +04/17 14:44:05 - mmengine - INFO - Iter(train) [ 22300/160000] base_lr: 8.6878e-05 lr: 3.2120e-07 eta: 1 day, 14:14:01 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6572 aux.loss_ce: 0.0080 aux.acc_seg: 99.1638 +04/17 14:44:55 - mmengine - INFO - Iter(train) [ 22350/160000] base_lr: 8.6846e-05 lr: 3.2109e-07 eta: 1 day, 14:13:11 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0098 decode.acc_seg: 99.5817 aux.loss_ce: 0.0102 aux.acc_seg: 99.0046 +04/17 14:45:45 - mmengine - INFO - Iter(train) [ 22400/160000] base_lr: 8.6814e-05 lr: 3.2097e-07 eta: 1 day, 14:12:21 time: 1.0010 data_time: 0.0053 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0114 decode.acc_seg: 99.5935 aux.loss_ce: 0.0100 aux.acc_seg: 98.8609 +04/17 14:46:35 - mmengine - INFO - Iter(train) [ 22450/160000] base_lr: 8.6783e-05 lr: 3.2085e-07 eta: 1 day, 14:11:31 time: 1.0001 data_time: 0.0044 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0114 decode.acc_seg: 99.6431 aux.loss_ce: 0.0095 aux.acc_seg: 99.1547 +04/17 14:47:25 - mmengine - INFO - Iter(train) [ 22500/160000] base_lr: 8.6751e-05 lr: 3.2074e-07 eta: 1 day, 14:10:41 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0192 decode.loss_ce: 0.0099 decode.acc_seg: 99.6481 aux.loss_ce: 0.0093 aux.acc_seg: 98.8970 +04/17 14:48:15 - mmengine - INFO - Iter(train) [ 22550/160000] base_lr: 8.6720e-05 lr: 3.2062e-07 eta: 1 day, 14:09:51 time: 1.0005 data_time: 0.0049 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0107 decode.acc_seg: 99.6973 aux.loss_ce: 0.0092 aux.acc_seg: 98.9571 +04/17 14:49:05 - mmengine - INFO - Iter(train) [ 22600/160000] base_lr: 8.6688e-05 lr: 3.2050e-07 eta: 1 day, 14:09:01 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.5554 aux.loss_ce: 0.0086 aux.acc_seg: 99.0030 +04/17 14:49:55 - mmengine - INFO - Iter(train) [ 22650/160000] base_lr: 8.6657e-05 lr: 3.2039e-07 eta: 1 day, 14:08:12 time: 1.0015 data_time: 0.0052 memory: 8462 loss: 0.0217 decode.loss_ce: 0.0113 decode.acc_seg: 99.2714 aux.loss_ce: 0.0104 aux.acc_seg: 98.3213 +04/17 14:50:45 - mmengine - INFO - Iter(train) [ 22700/160000] base_lr: 8.6625e-05 lr: 3.2027e-07 eta: 1 day, 14:07:22 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0232 decode.loss_ce: 0.0122 decode.acc_seg: 99.4961 aux.loss_ce: 0.0110 aux.acc_seg: 99.1377 +04/17 14:51:34 - mmengine - INFO - Iter(train) [ 22750/160000] base_lr: 8.6594e-05 lr: 3.2015e-07 eta: 1 day, 14:06:32 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0087 decode.acc_seg: 99.7097 aux.loss_ce: 0.0078 aux.acc_seg: 99.4293 +04/17 14:52:24 - mmengine - INFO - Iter(train) [ 22800/160000] base_lr: 8.6562e-05 lr: 3.2004e-07 eta: 1 day, 14:05:42 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0096 decode.acc_seg: 99.4398 aux.loss_ce: 0.0094 aux.acc_seg: 99.2588 +04/17 14:53:14 - mmengine - INFO - Iter(train) [ 22850/160000] base_lr: 8.6531e-05 lr: 3.1992e-07 eta: 1 day, 14:04:51 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0119 decode.acc_seg: 99.4925 aux.loss_ce: 0.0105 aux.acc_seg: 98.6650 +04/17 14:54:04 - mmengine - INFO - Iter(train) [ 22900/160000] base_lr: 8.6499e-05 lr: 3.1980e-07 eta: 1 day, 14:04:02 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0105 decode.acc_seg: 99.4282 aux.loss_ce: 0.0103 aux.acc_seg: 98.7370 +04/17 14:54:54 - mmengine - INFO - Iter(train) [ 22950/160000] base_lr: 8.6467e-05 lr: 3.1969e-07 eta: 1 day, 14:03:11 time: 0.9989 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.5348 aux.loss_ce: 0.0084 aux.acc_seg: 98.6467 +04/17 14:55:44 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 14:55:44 - mmengine - INFO - Iter(train) [ 23000/160000] base_lr: 8.6436e-05 lr: 3.1957e-07 eta: 1 day, 14:02:21 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0089 decode.acc_seg: 99.5611 aux.loss_ce: 0.0087 aux.acc_seg: 99.0721 +04/17 14:56:34 - mmengine - INFO - Iter(train) [ 23050/160000] base_lr: 8.6404e-05 lr: 3.1945e-07 eta: 1 day, 14:01:32 time: 1.0006 data_time: 0.0043 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0100 decode.acc_seg: 99.4980 aux.loss_ce: 0.0097 aux.acc_seg: 98.8644 +04/17 14:57:24 - mmengine - INFO - Iter(train) [ 23100/160000] base_lr: 8.6373e-05 lr: 3.1934e-07 eta: 1 day, 14:00:42 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.5207 aux.loss_ce: 0.0088 aux.acc_seg: 98.8394 +04/17 14:58:14 - mmengine - INFO - Iter(train) [ 23150/160000] base_lr: 8.6341e-05 lr: 3.1922e-07 eta: 1 day, 13:59:52 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0097 decode.acc_seg: 99.5272 aux.loss_ce: 0.0097 aux.acc_seg: 98.7566 +04/17 14:59:04 - mmengine - INFO - Iter(train) [ 23200/160000] base_lr: 8.6310e-05 lr: 3.1910e-07 eta: 1 day, 13:59:02 time: 0.9996 data_time: 0.0041 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0103 decode.acc_seg: 99.6855 aux.loss_ce: 0.0099 aux.acc_seg: 99.0540 +04/17 14:59:54 - mmengine - INFO - Iter(train) [ 23250/160000] base_lr: 8.6278e-05 lr: 3.1899e-07 eta: 1 day, 13:58:12 time: 0.9996 data_time: 0.0041 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0088 decode.acc_seg: 99.7057 aux.loss_ce: 0.0089 aux.acc_seg: 99.1768 +04/17 15:00:44 - mmengine - INFO - Iter(train) [ 23300/160000] base_lr: 8.6247e-05 lr: 3.1887e-07 eta: 1 day, 13:57:22 time: 0.9991 data_time: 0.0051 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0100 decode.acc_seg: 99.4694 aux.loss_ce: 0.0090 aux.acc_seg: 99.0191 +04/17 15:01:34 - mmengine - INFO - Iter(train) [ 23350/160000] base_lr: 8.6215e-05 lr: 3.1875e-07 eta: 1 day, 13:56:32 time: 0.9996 data_time: 0.0047 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.7654 aux.loss_ce: 0.0079 aux.acc_seg: 99.2838 +04/17 15:02:24 - mmengine - INFO - Iter(train) [ 23400/160000] base_lr: 8.6184e-05 lr: 3.1864e-07 eta: 1 day, 13:55:42 time: 1.0003 data_time: 0.0049 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0108 decode.acc_seg: 99.2485 aux.loss_ce: 0.0105 aux.acc_seg: 98.4438 +04/17 15:03:14 - mmengine - INFO - Iter(train) [ 23450/160000] base_lr: 8.6152e-05 lr: 3.1852e-07 eta: 1 day, 13:54:52 time: 1.0005 data_time: 0.0043 memory: 8462 loss: 0.0228 decode.loss_ce: 0.0119 decode.acc_seg: 99.4175 aux.loss_ce: 0.0108 aux.acc_seg: 98.8270 +04/17 15:04:04 - mmengine - INFO - Iter(train) [ 23500/160000] base_lr: 8.6120e-05 lr: 3.1840e-07 eta: 1 day, 13:54:03 time: 0.9993 data_time: 0.0049 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0110 decode.acc_seg: 99.1472 aux.loss_ce: 0.0099 aux.acc_seg: 97.9836 +04/17 15:04:54 - mmengine - INFO - Iter(train) [ 23550/160000] base_lr: 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decode.acc_seg: 99.6197 aux.loss_ce: 0.0095 aux.acc_seg: 99.0349 +04/17 15:11:34 - mmengine - INFO - Iter(train) [ 23950/160000] base_lr: 8.5837e-05 lr: 3.1736e-07 eta: 1 day, 13:46:33 time: 0.9994 data_time: 0.0050 memory: 8462 loss: 0.0224 decode.loss_ce: 0.0117 decode.acc_seg: 99.5115 aux.loss_ce: 0.0107 aux.acc_seg: 98.7293 +04/17 15:12:24 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 15:12:24 - mmengine - INFO - Iter(train) [ 24000/160000] base_lr: 8.5805e-05 lr: 3.1724e-07 eta: 1 day, 13:45:43 time: 0.9991 data_time: 0.0049 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0102 decode.acc_seg: 99.6090 aux.loss_ce: 0.0096 aux.acc_seg: 99.2201 +04/17 15:13:14 - mmengine - INFO - Iter(train) [ 24050/160000] base_lr: 8.5773e-05 lr: 3.1712e-07 eta: 1 day, 13:44:53 time: 1.0017 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0087 decode.acc_seg: 99.5626 aux.loss_ce: 0.0091 aux.acc_seg: 98.9571 +04/17 15:14:04 - mmengine - INFO - Iter(train) [ 24100/160000] base_lr: 8.5742e-05 lr: 3.1701e-07 eta: 1 day, 13:44:03 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0099 decode.acc_seg: 99.3309 aux.loss_ce: 0.0092 aux.acc_seg: 98.7541 +04/17 15:14:54 - mmengine - INFO - Iter(train) [ 24150/160000] base_lr: 8.5710e-05 lr: 3.1689e-07 eta: 1 day, 13:43:13 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0236 decode.loss_ce: 0.0123 decode.acc_seg: 99.6435 aux.loss_ce: 0.0113 aux.acc_seg: 98.7827 +04/17 15:15:44 - mmengine - INFO - Iter(train) [ 24200/160000] base_lr: 8.5679e-05 lr: 3.1677e-07 eta: 1 day, 13:42:23 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0103 decode.acc_seg: 99.6401 aux.loss_ce: 0.0101 aux.acc_seg: 98.8926 +04/17 15:16:34 - mmengine - INFO - Iter(train) [ 24250/160000] base_lr: 8.5647e-05 lr: 3.1666e-07 eta: 1 day, 13:41:33 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0092 decode.acc_seg: 99.5783 aux.loss_ce: 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decode.loss_ce: 0.0104 decode.acc_seg: 99.5043 aux.loss_ce: 0.0092 aux.acc_seg: 98.9340 +04/17 15:20:44 - mmengine - INFO - Iter(train) [ 24500/160000] base_lr: 8.5489e-05 lr: 3.1607e-07 eta: 1 day, 13:37:23 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0098 decode.acc_seg: 99.3469 aux.loss_ce: 0.0096 aux.acc_seg: 98.7484 +04/17 15:21:34 - mmengine - INFO - Iter(train) [ 24550/160000] base_lr: 8.5458e-05 lr: 3.1596e-07 eta: 1 day, 13:36:34 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0102 decode.acc_seg: 99.5737 aux.loss_ce: 0.0099 aux.acc_seg: 98.8726 +04/17 15:22:24 - mmengine - INFO - Iter(train) [ 24600/160000] base_lr: 8.5426e-05 lr: 3.1584e-07 eta: 1 day, 13:35:44 time: 1.0013 data_time: 0.0045 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0093 decode.acc_seg: 99.5886 aux.loss_ce: 0.0093 aux.acc_seg: 98.9346 +04/17 15:23:14 - mmengine - INFO - Iter(train) [ 24650/160000] base_lr: 8.5395e-05 lr: 3.1572e-07 eta: 1 day, 13:34:54 time: 0.9982 data_time: 0.0043 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0089 decode.acc_seg: 99.7517 aux.loss_ce: 0.0087 aux.acc_seg: 99.3788 +04/17 15:24:04 - mmengine - INFO - Iter(train) [ 24700/160000] base_lr: 8.5363e-05 lr: 3.1561e-07 eta: 1 day, 13:34:04 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0090 decode.acc_seg: 99.5827 aux.loss_ce: 0.0095 aux.acc_seg: 98.7396 +04/17 15:24:54 - mmengine - INFO - Iter(train) [ 24750/160000] base_lr: 8.5332e-05 lr: 3.1549e-07 eta: 1 day, 13:33:14 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0124 decode.acc_seg: 99.6279 aux.loss_ce: 0.0105 aux.acc_seg: 98.9002 +04/17 15:25:44 - mmengine - INFO - Iter(train) [ 24800/160000] base_lr: 8.5300e-05 lr: 3.1537e-07 eta: 1 day, 13:32:24 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0100 decode.acc_seg: 99.5020 aux.loss_ce: 0.0099 aux.acc_seg: 98.9683 +04/17 15:26:34 - mmengine - INFO - Iter(train) [ 24850/160000] base_lr: 8.5269e-05 lr: 3.1526e-07 eta: 1 day, 13:31:34 time: 1.0006 data_time: 0.0050 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0103 decode.acc_seg: 99.4736 aux.loss_ce: 0.0099 aux.acc_seg: 98.9622 +04/17 15:27:24 - mmengine - INFO - Iter(train) [ 24900/160000] base_lr: 8.5237e-05 lr: 3.1514e-07 eta: 1 day, 13:30:44 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0099 decode.acc_seg: 99.5302 aux.loss_ce: 0.0102 aux.acc_seg: 98.9445 +04/17 15:28:14 - mmengine - INFO - Iter(train) [ 24950/160000] base_lr: 8.5206e-05 lr: 3.1502e-07 eta: 1 day, 13:29:54 time: 0.9981 data_time: 0.0046 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0095 decode.acc_seg: 99.5508 aux.loss_ce: 0.0089 aux.acc_seg: 98.8819 +04/17 15:29:04 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 15:29:04 - mmengine - INFO - Iter(train) [ 25000/160000] base_lr: 8.5174e-05 lr: 3.1491e-07 eta: 1 day, 13:29:04 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.0216 decode.loss_ce: 0.0109 decode.acc_seg: 99.5039 aux.loss_ce: 0.0106 aux.acc_seg: 98.5380 +04/17 15:29:54 - mmengine - INFO - Iter(train) [ 25050/160000] base_lr: 8.5142e-05 lr: 3.1479e-07 eta: 1 day, 13:28:14 time: 1.0018 data_time: 0.0046 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0091 decode.acc_seg: 99.5274 aux.loss_ce: 0.0087 aux.acc_seg: 98.6612 +04/17 15:30:44 - mmengine - INFO - Iter(train) [ 25100/160000] base_lr: 8.5111e-05 lr: 3.1467e-07 eta: 1 day, 13:27:24 time: 0.9999 data_time: 0.0042 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0108 decode.acc_seg: 99.4074 aux.loss_ce: 0.0105 aux.acc_seg: 98.5809 +04/17 15:31:34 - mmengine - INFO - Iter(train) [ 25150/160000] base_lr: 8.5079e-05 lr: 3.1456e-07 eta: 1 day, 13:26:34 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0084 decode.acc_seg: 99.6000 aux.loss_ce: 0.0086 aux.acc_seg: 98.8234 +04/17 15:32:24 - mmengine - INFO - Iter(train) [ 25200/160000] base_lr: 8.5048e-05 lr: 3.1444e-07 eta: 1 day, 13:25:44 time: 0.9993 data_time: 0.0043 memory: 8462 loss: 0.0209 decode.loss_ce: 0.0106 decode.acc_seg: 99.3948 aux.loss_ce: 0.0103 aux.acc_seg: 98.6862 +04/17 15:33:14 - mmengine - INFO - Iter(train) [ 25250/160000] base_lr: 8.5016e-05 lr: 3.1432e-07 eta: 1 day, 13:24:54 time: 0.9995 data_time: 0.0048 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0102 decode.acc_seg: 99.6029 aux.loss_ce: 0.0104 aux.acc_seg: 98.9231 +04/17 15:34:04 - mmengine - INFO - Iter(train) [ 25300/160000] base_lr: 8.4985e-05 lr: 3.1421e-07 eta: 1 day, 13:24:04 time: 1.0018 data_time: 0.0043 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0096 decode.acc_seg: 99.5554 aux.loss_ce: 0.0091 aux.acc_seg: 98.9813 +04/17 15:34:54 - mmengine - INFO - Iter(train) [ 25350/160000] base_lr: 8.4953e-05 lr: 3.1409e-07 eta: 1 day, 13:23:14 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0104 decode.acc_seg: 99.7545 aux.loss_ce: 0.0093 aux.acc_seg: 99.3500 +04/17 15:35:44 - mmengine - INFO - Iter(train) [ 25400/160000] base_lr: 8.4922e-05 lr: 3.1397e-07 eta: 1 day, 13:22:24 time: 1.0003 data_time: 0.0056 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0096 decode.acc_seg: 99.7293 aux.loss_ce: 0.0092 aux.acc_seg: 99.2653 +04/17 15:36:34 - mmengine - INFO - Iter(train) [ 25450/160000] base_lr: 8.4890e-05 lr: 3.1386e-07 eta: 1 day, 13:21:34 time: 0.9994 data_time: 0.0042 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0091 decode.acc_seg: 99.5956 aux.loss_ce: 0.0086 aux.acc_seg: 99.1735 +04/17 15:37:24 - mmengine - INFO - Iter(train) [ 25500/160000] base_lr: 8.4859e-05 lr: 3.1374e-07 eta: 1 day, 13:20:45 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0101 decode.acc_seg: 99.7757 aux.loss_ce: 0.0098 aux.acc_seg: 99.2811 +04/17 15:38:14 - mmengine - INFO - Iter(train) [ 25550/160000] base_lr: 8.4827e-05 lr: 3.1362e-07 eta: 1 day, 13:19:55 time: 0.9994 data_time: 0.0044 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0105 decode.acc_seg: 99.6950 aux.loss_ce: 0.0096 aux.acc_seg: 99.2168 +04/17 15:39:04 - mmengine - INFO - Iter(train) [ 25600/160000] base_lr: 8.4795e-05 lr: 3.1351e-07 eta: 1 day, 13:19:05 time: 0.9999 data_time: 0.0043 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.5350 aux.loss_ce: 0.0084 aux.acc_seg: 99.0034 +04/17 15:39:54 - mmengine - INFO - Iter(train) [ 25650/160000] base_lr: 8.4764e-05 lr: 3.1339e-07 eta: 1 day, 13:18:15 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0090 decode.acc_seg: 99.5932 aux.loss_ce: 0.0091 aux.acc_seg: 98.7549 +04/17 15:40:44 - mmengine - INFO - Iter(train) [ 25700/160000] base_lr: 8.4732e-05 lr: 3.1327e-07 eta: 1 day, 13:17:25 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0090 decode.acc_seg: 99.7034 aux.loss_ce: 0.0087 aux.acc_seg: 99.1829 +04/17 15:41:34 - mmengine - INFO - Iter(train) [ 25750/160000] base_lr: 8.4701e-05 lr: 3.1316e-07 eta: 1 day, 13:16:35 time: 1.0003 data_time: 0.0053 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0100 decode.acc_seg: 99.6637 aux.loss_ce: 0.0095 aux.acc_seg: 99.0105 +04/17 15:42:24 - mmengine - INFO - Iter(train) [ 25800/160000] base_lr: 8.4669e-05 lr: 3.1304e-07 eta: 1 day, 13:15:45 time: 1.0000 data_time: 0.0048 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0085 decode.acc_seg: 99.7545 aux.loss_ce: 0.0085 aux.acc_seg: 99.1781 +04/17 15:43:14 - mmengine - INFO - Iter(train) [ 25850/160000] base_lr: 8.4638e-05 lr: 3.1292e-07 eta: 1 day, 13:14:55 time: 1.0000 data_time: 0.0044 memory: 8462 loss: 0.0217 decode.loss_ce: 0.0107 decode.acc_seg: 99.2132 aux.loss_ce: 0.0110 aux.acc_seg: 97.7404 +04/17 15:44:04 - mmengine - INFO - Iter(train) [ 25900/160000] base_lr: 8.4606e-05 lr: 3.1281e-07 eta: 1 day, 13:14:05 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0100 decode.acc_seg: 99.7927 aux.loss_ce: 0.0090 aux.acc_seg: 99.4165 +04/17 15:44:54 - mmengine - INFO - Iter(train) [ 25950/160000] base_lr: 8.4575e-05 lr: 3.1269e-07 eta: 1 day, 13:13:15 time: 1.0007 data_time: 0.0047 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0091 decode.acc_seg: 99.4757 aux.loss_ce: 0.0088 aux.acc_seg: 98.9252 +04/17 15:45:44 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 15:45:44 - mmengine - INFO - Iter(train) [ 26000/160000] base_lr: 8.4543e-05 lr: 3.1257e-07 eta: 1 day, 13:12:25 time: 0.9984 data_time: 0.0046 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0089 decode.acc_seg: 99.5583 aux.loss_ce: 0.0088 aux.acc_seg: 99.0234 +04/17 15:46:34 - mmengine - INFO - Iter(train) [ 26050/160000] base_lr: 8.4512e-05 lr: 3.1246e-07 eta: 1 day, 13:11:35 time: 1.0002 data_time: 0.0043 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0104 decode.acc_seg: 99.3654 aux.loss_ce: 0.0095 aux.acc_seg: 98.8035 +04/17 15:47:24 - mmengine - INFO - Iter(train) [ 26100/160000] base_lr: 8.4480e-05 lr: 3.1234e-07 eta: 1 day, 13:10:45 time: 1.0006 data_time: 0.0042 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0095 decode.acc_seg: 99.3250 aux.loss_ce: 0.0105 aux.acc_seg: 98.2485 +04/17 15:48:14 - mmengine - INFO - Iter(train) [ 26150/160000] base_lr: 8.4448e-05 lr: 3.1222e-07 eta: 1 day, 13:09:55 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.6895 aux.loss_ce: 0.0086 aux.acc_seg: 99.3401 +04/17 15:49:04 - mmengine - INFO - Iter(train) [ 26200/160000] base_lr: 8.4417e-05 lr: 3.1211e-07 eta: 1 day, 13:09:05 time: 0.9983 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0078 decode.acc_seg: 99.5541 aux.loss_ce: 0.0083 aux.acc_seg: 98.9290 +04/17 15:49:54 - mmengine - INFO - Iter(train) [ 26250/160000] base_lr: 8.4385e-05 lr: 3.1199e-07 eta: 1 day, 13:08:15 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.5800 aux.loss_ce: 0.0084 aux.acc_seg: 99.3288 +04/17 15:50:44 - mmengine - INFO - Iter(train) [ 26300/160000] base_lr: 8.4354e-05 lr: 3.1187e-07 eta: 1 day, 13:07:25 time: 0.9991 data_time: 0.0048 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0084 decode.acc_seg: 99.5434 aux.loss_ce: 0.0086 aux.acc_seg: 99.2088 +04/17 15:51:34 - mmengine - INFO - Iter(train) [ 26350/160000] base_lr: 8.4322e-05 lr: 3.1176e-07 eta: 1 day, 13:06:35 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0215 decode.loss_ce: 0.0101 decode.acc_seg: 99.6191 aux.loss_ce: 0.0114 aux.acc_seg: 99.0993 +04/17 15:52:24 - mmengine - INFO - Iter(train) [ 26400/160000] base_lr: 8.4291e-05 lr: 3.1164e-07 eta: 1 day, 13:05:45 time: 0.9989 data_time: 0.0047 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0098 decode.acc_seg: 99.5804 aux.loss_ce: 0.0103 aux.acc_seg: 98.5632 +04/17 15:53:14 - mmengine - INFO - Iter(train) [ 26450/160000] base_lr: 8.4259e-05 lr: 3.1152e-07 eta: 1 day, 13:04:55 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0096 decode.acc_seg: 99.6672 aux.loss_ce: 0.0091 aux.acc_seg: 99.0217 +04/17 15:54:04 - mmengine - INFO - Iter(train) [ 26500/160000] base_lr: 8.4228e-05 lr: 3.1141e-07 eta: 1 day, 13:04:06 time: 1.0003 data_time: 0.0049 memory: 8462 loss: 0.0220 decode.loss_ce: 0.0103 decode.acc_seg: 99.5424 aux.loss_ce: 0.0117 aux.acc_seg: 98.9143 +04/17 15:54:54 - mmengine - INFO - Iter(train) [ 26550/160000] base_lr: 8.4196e-05 lr: 3.1129e-07 eta: 1 day, 13:03:16 time: 1.0008 data_time: 0.0050 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0094 decode.acc_seg: 99.3586 aux.loss_ce: 0.0102 aux.acc_seg: 98.1108 +04/17 15:55:44 - mmengine - INFO - Iter(train) [ 26600/160000] base_lr: 8.4165e-05 lr: 3.1117e-07 eta: 1 day, 13:02:26 time: 1.0004 data_time: 0.0048 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.6861 aux.loss_ce: 0.0083 aux.acc_seg: 99.3816 +04/17 15:56:34 - mmengine - INFO - Iter(train) [ 26650/160000] base_lr: 8.4133e-05 lr: 3.1106e-07 eta: 1 day, 13:01:36 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0097 decode.acc_seg: 99.6843 aux.loss_ce: 0.0091 aux.acc_seg: 98.8846 +04/17 15:57:24 - mmengine - INFO - Iter(train) [ 26700/160000] base_lr: 8.4101e-05 lr: 3.1094e-07 eta: 1 day, 13:00:46 time: 0.9990 data_time: 0.0043 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0096 decode.acc_seg: 99.5705 aux.loss_ce: 0.0097 aux.acc_seg: 98.9902 +04/17 15:58:14 - mmengine - INFO - Iter(train) [ 26750/160000] base_lr: 8.4070e-05 lr: 3.1082e-07 eta: 1 day, 12:59:56 time: 1.0011 data_time: 0.0043 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0092 decode.acc_seg: 99.7662 aux.loss_ce: 0.0090 aux.acc_seg: 99.4053 +04/17 15:59:04 - mmengine - INFO - Iter(train) [ 26800/160000] base_lr: 8.4038e-05 lr: 3.1071e-07 eta: 1 day, 12:59:06 time: 1.0011 data_time: 0.0044 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0091 decode.acc_seg: 99.5886 aux.loss_ce: 0.0089 aux.acc_seg: 99.1833 +04/17 15:59:54 - mmengine - INFO - Iter(train) [ 26850/160000] base_lr: 8.4007e-05 lr: 3.1059e-07 eta: 1 day, 12:58:16 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0100 decode.acc_seg: 99.6778 aux.loss_ce: 0.0103 aux.acc_seg: 99.0946 +04/17 16:00:44 - mmengine - INFO - Iter(train) [ 26900/160000] base_lr: 8.3975e-05 lr: 3.1047e-07 eta: 1 day, 12:57:27 time: 1.0005 data_time: 0.0048 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0086 decode.acc_seg: 99.6935 aux.loss_ce: 0.0088 aux.acc_seg: 99.0488 +04/17 16:01:34 - mmengine - INFO - Iter(train) [ 26950/160000] base_lr: 8.3944e-05 lr: 3.1036e-07 eta: 1 day, 12:56:37 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0113 decode.acc_seg: 99.6801 aux.loss_ce: 0.0101 aux.acc_seg: 98.8554 +04/17 16:02:24 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 16:02:24 - mmengine - INFO - Iter(train) [ 27000/160000] base_lr: 8.3912e-05 lr: 3.1024e-07 eta: 1 day, 12:55:47 time: 1.0002 data_time: 0.0042 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0090 decode.acc_seg: 99.6613 aux.loss_ce: 0.0093 aux.acc_seg: 99.0273 +04/17 16:03:14 - mmengine - INFO - Iter(train) [ 27050/160000] base_lr: 8.3881e-05 lr: 3.1012e-07 eta: 1 day, 12:54:57 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0095 decode.acc_seg: 99.5941 aux.loss_ce: 0.0098 aux.acc_seg: 98.9958 +04/17 16:04:04 - mmengine - INFO - Iter(train) [ 27100/160000] base_lr: 8.3849e-05 lr: 3.1001e-07 eta: 1 day, 12:54:07 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0085 decode.acc_seg: 99.6609 aux.loss_ce: 0.0084 aux.acc_seg: 99.1459 +04/17 16:04:54 - mmengine - INFO - Iter(train) [ 27150/160000] base_lr: 8.3818e-05 lr: 3.0989e-07 eta: 1 day, 12:53:17 time: 0.9974 data_time: 0.0046 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0095 decode.acc_seg: 99.3881 aux.loss_ce: 0.0093 aux.acc_seg: 98.5313 +04/17 16:05:44 - mmengine - INFO - Iter(train) [ 27200/160000] base_lr: 8.3786e-05 lr: 3.0977e-07 eta: 1 day, 12:52:27 time: 0.9983 data_time: 0.0045 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.6912 aux.loss_ce: 0.0084 aux.acc_seg: 99.0955 +04/17 16:06:34 - mmengine - INFO - Iter(train) [ 27250/160000] base_lr: 8.3754e-05 lr: 3.0966e-07 eta: 1 day, 12:51:38 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0101 decode.acc_seg: 99.5813 aux.loss_ce: 0.0093 aux.acc_seg: 98.8226 +04/17 16:07:24 - mmengine - INFO - Iter(train) [ 27300/160000] base_lr: 8.3723e-05 lr: 3.0954e-07 eta: 1 day, 12:50:48 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0098 decode.acc_seg: 99.5985 aux.loss_ce: 0.0092 aux.acc_seg: 98.8377 +04/17 16:08:14 - mmengine - INFO - Iter(train) [ 27350/160000] base_lr: 8.3691e-05 lr: 3.0942e-07 eta: 1 day, 12:49:58 time: 0.9998 data_time: 0.0051 memory: 8462 loss: 0.0199 decode.loss_ce: 0.0100 decode.acc_seg: 99.6555 aux.loss_ce: 0.0099 aux.acc_seg: 98.7318 +04/17 16:09:04 - mmengine - INFO - Iter(train) [ 27400/160000] base_lr: 8.3660e-05 lr: 3.0931e-07 eta: 1 day, 12:49:08 time: 0.9989 data_time: 0.0041 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0084 decode.acc_seg: 99.7126 aux.loss_ce: 0.0085 aux.acc_seg: 99.1102 +04/17 16:09:54 - mmengine - INFO - Iter(train) [ 27450/160000] base_lr: 8.3628e-05 lr: 3.0919e-07 eta: 1 day, 12:48:18 time: 1.0001 data_time: 0.0043 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6378 aux.loss_ce: 0.0082 aux.acc_seg: 98.9553 +04/17 16:10:44 - mmengine - INFO - Iter(train) [ 27500/160000] base_lr: 8.3597e-05 lr: 3.0907e-07 eta: 1 day, 12:47:28 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0093 decode.acc_seg: 99.6639 aux.loss_ce: 0.0086 aux.acc_seg: 99.1831 +04/17 16:11:34 - mmengine - INFO - Iter(train) [ 27550/160000] base_lr: 8.3565e-05 lr: 3.0896e-07 eta: 1 day, 12:46:38 time: 0.9997 data_time: 0.0050 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0103 decode.acc_seg: 99.5102 aux.loss_ce: 0.0095 aux.acc_seg: 98.8453 +04/17 16:12:24 - mmengine - INFO - Iter(train) [ 27600/160000] base_lr: 8.3534e-05 lr: 3.0884e-07 eta: 1 day, 12:45:48 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0200 decode.loss_ce: 0.0097 decode.acc_seg: 99.6437 aux.loss_ce: 0.0103 aux.acc_seg: 98.9790 +04/17 16:13:14 - mmengine - INFO - Iter(train) [ 27650/160000] base_lr: 8.3502e-05 lr: 3.0872e-07 eta: 1 day, 12:44:58 time: 0.9998 data_time: 0.0048 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0109 decode.acc_seg: 99.1842 aux.loss_ce: 0.0098 aux.acc_seg: 98.6662 +04/17 16:14:04 - mmengine - INFO - Iter(train) [ 27700/160000] base_lr: 8.3471e-05 lr: 3.0861e-07 eta: 1 day, 12:44:09 time: 1.0016 data_time: 0.0046 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0091 decode.acc_seg: 99.5279 aux.loss_ce: 0.0093 aux.acc_seg: 98.8819 +04/17 16:14:54 - mmengine - INFO - Iter(train) [ 27750/160000] base_lr: 8.3439e-05 lr: 3.0849e-07 eta: 1 day, 12:43:19 time: 1.0006 data_time: 0.0050 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0081 decode.acc_seg: 99.5584 aux.loss_ce: 0.0084 aux.acc_seg: 99.1430 +04/17 16:15:44 - mmengine - INFO - Iter(train) [ 27800/160000] base_lr: 8.3407e-05 lr: 3.0837e-07 eta: 1 day, 12:42:29 time: 1.0014 data_time: 0.0045 memory: 8462 loss: 0.0201 decode.loss_ce: 0.0102 decode.acc_seg: 99.7395 aux.loss_ce: 0.0099 aux.acc_seg: 99.1167 +04/17 16:16:34 - mmengine - INFO - Iter(train) [ 27850/160000] base_lr: 8.3376e-05 lr: 3.0826e-07 eta: 1 day, 12:41:39 time: 1.0003 data_time: 0.0048 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0093 decode.acc_seg: 99.6017 aux.loss_ce: 0.0096 aux.acc_seg: 98.9437 +04/17 16:17:24 - mmengine - INFO - Iter(train) [ 27900/160000] base_lr: 8.3344e-05 lr: 3.0814e-07 eta: 1 day, 12:40:49 time: 1.0001 data_time: 0.0044 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0093 decode.acc_seg: 99.5377 aux.loss_ce: 0.0087 aux.acc_seg: 98.9826 +04/17 16:18:14 - mmengine - INFO - Iter(train) [ 27950/160000] base_lr: 8.3313e-05 lr: 3.0802e-07 eta: 1 day, 12:39:59 time: 1.0013 data_time: 0.0044 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0089 decode.acc_seg: 99.6466 aux.loss_ce: 0.0099 aux.acc_seg: 99.0154 +04/17 16:19:04 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 16:19:04 - mmengine - INFO - Iter(train) [ 28000/160000] base_lr: 8.3281e-05 lr: 3.0791e-07 eta: 1 day, 12:39:09 time: 0.9994 data_time: 0.0043 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0094 decode.acc_seg: 99.5798 aux.loss_ce: 0.0093 aux.acc_seg: 98.9143 +04/17 16:19:54 - mmengine - INFO - Iter(train) [ 28050/160000] base_lr: 8.3250e-05 lr: 3.0779e-07 eta: 1 day, 12:38:19 time: 0.9998 data_time: 0.0042 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0086 decode.acc_seg: 99.4314 aux.loss_ce: 0.0087 aux.acc_seg: 98.5657 +04/17 16:20:44 - mmengine - INFO - Iter(train) [ 28100/160000] base_lr: 8.3218e-05 lr: 3.0767e-07 eta: 1 day, 12:37:29 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.4890 aux.loss_ce: 0.0080 aux.acc_seg: 98.7194 +04/17 16:21:34 - mmengine - INFO - Iter(train) [ 28150/160000] base_lr: 8.3187e-05 lr: 3.0756e-07 eta: 1 day, 12:36:40 time: 1.0013 data_time: 0.0045 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0078 decode.acc_seg: 99.6967 aux.loss_ce: 0.0080 aux.acc_seg: 99.5455 +04/17 16:22:24 - mmengine - INFO - Iter(train) [ 28200/160000] base_lr: 8.3155e-05 lr: 3.0744e-07 eta: 1 day, 12:35:50 time: 0.9999 data_time: 0.0048 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0088 decode.acc_seg: 99.4530 aux.loss_ce: 0.0092 aux.acc_seg: 98.8672 +04/17 16:23:14 - mmengine - INFO - Iter(train) [ 28250/160000] base_lr: 8.3124e-05 lr: 3.0732e-07 eta: 1 day, 12:35:00 time: 1.0004 data_time: 0.0048 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0109 decode.acc_seg: 99.3597 aux.loss_ce: 0.0105 aux.acc_seg: 98.2992 +04/17 16:24:04 - mmengine - INFO - Iter(train) [ 28300/160000] base_lr: 8.3092e-05 lr: 3.0721e-07 eta: 1 day, 12:34:10 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0213 decode.loss_ce: 0.0109 decode.acc_seg: 99.7747 aux.loss_ce: 0.0104 aux.acc_seg: 99.4843 +04/17 16:24:54 - mmengine - INFO - Iter(train) [ 28350/160000] base_lr: 8.3060e-05 lr: 3.0709e-07 eta: 1 day, 12:33:20 time: 0.9990 data_time: 0.0047 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0096 decode.acc_seg: 99.6977 aux.loss_ce: 0.0092 aux.acc_seg: 98.8693 +04/17 16:25:44 - mmengine - INFO - Iter(train) [ 28400/160000] base_lr: 8.3029e-05 lr: 3.0697e-07 eta: 1 day, 12:32:30 time: 1.0008 data_time: 0.0045 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0092 decode.acc_seg: 99.7694 aux.loss_ce: 0.0090 aux.acc_seg: 99.2901 +04/17 16:26:34 - mmengine - INFO - Iter(train) [ 28450/160000] base_lr: 8.2997e-05 lr: 3.0686e-07 eta: 1 day, 12:31:40 time: 1.0002 data_time: 0.0048 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0089 decode.acc_seg: 99.5073 aux.loss_ce: 0.0089 aux.acc_seg: 98.7480 +04/17 16:27:24 - mmengine - INFO - Iter(train) [ 28500/160000] base_lr: 8.2966e-05 lr: 3.0674e-07 eta: 1 day, 12:30:51 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0098 decode.acc_seg: 99.4968 aux.loss_ce: 0.0095 aux.acc_seg: 99.0419 +04/17 16:28:14 - mmengine - INFO - Iter(train) [ 28550/160000] base_lr: 8.2934e-05 lr: 3.0663e-07 eta: 1 day, 12:30:01 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0091 decode.acc_seg: 99.6040 aux.loss_ce: 0.0100 aux.acc_seg: 98.9010 +04/17 16:29:04 - mmengine - INFO - Iter(train) [ 28600/160000] base_lr: 8.2903e-05 lr: 3.0651e-07 eta: 1 day, 12:29:11 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0100 decode.acc_seg: 99.6107 aux.loss_ce: 0.0104 aux.acc_seg: 98.8239 +04/17 16:29:54 - mmengine - INFO - Iter(train) [ 28650/160000] base_lr: 8.2871e-05 lr: 3.0639e-07 eta: 1 day, 12:28:21 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.7017 aux.loss_ce: 0.0081 aux.acc_seg: 99.3147 +04/17 16:30:44 - mmengine - INFO - Iter(train) [ 28700/160000] base_lr: 8.2840e-05 lr: 3.0628e-07 eta: 1 day, 12:27:31 time: 1.0018 data_time: 0.0045 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0098 decode.acc_seg: 99.6914 aux.loss_ce: 0.0093 aux.acc_seg: 99.1346 +04/17 16:31:34 - mmengine - INFO - Iter(train) [ 28750/160000] base_lr: 8.2808e-05 lr: 3.0616e-07 eta: 1 day, 12:26:41 time: 1.0001 data_time: 0.0045 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0093 decode.acc_seg: 99.6403 aux.loss_ce: 0.0093 aux.acc_seg: 99.2172 +04/17 16:32:24 - mmengine - INFO - Iter(train) [ 28800/160000] base_lr: 8.2777e-05 lr: 3.0604e-07 eta: 1 day, 12:25:51 time: 1.0006 data_time: 0.0051 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0086 decode.acc_seg: 99.6933 aux.loss_ce: 0.0085 aux.acc_seg: 99.2430 +04/17 16:33:14 - mmengine - INFO - Iter(train) [ 28850/160000] base_lr: 8.2745e-05 lr: 3.0593e-07 eta: 1 day, 12:25:02 time: 1.0003 data_time: 0.0049 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0070 decode.acc_seg: 99.7858 aux.loss_ce: 0.0081 aux.acc_seg: 99.3725 +04/17 16:34:04 - mmengine - INFO - Iter(train) [ 28900/160000] base_lr: 8.2713e-05 lr: 3.0581e-07 eta: 1 day, 12:24:12 time: 0.9995 data_time: 0.0047 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0087 decode.acc_seg: 99.5235 aux.loss_ce: 0.0087 aux.acc_seg: 99.2687 +04/17 16:34:54 - mmengine - INFO - Iter(train) [ 28950/160000] base_lr: 8.2682e-05 lr: 3.0569e-07 eta: 1 day, 12:23:22 time: 1.0003 data_time: 0.0051 memory: 8462 loss: 0.0207 decode.loss_ce: 0.0106 decode.acc_seg: 99.6569 aux.loss_ce: 0.0102 aux.acc_seg: 99.1526 +04/17 16:35:44 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 16:35:44 - mmengine - INFO - Iter(train) [ 29000/160000] base_lr: 8.2650e-05 lr: 3.0558e-07 eta: 1 day, 12:22:32 time: 1.0016 data_time: 0.0044 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0084 decode.acc_seg: 99.5972 aux.loss_ce: 0.0085 aux.acc_seg: 99.0278 +04/17 16:36:34 - mmengine - INFO - Iter(train) [ 29050/160000] base_lr: 8.2619e-05 lr: 3.0546e-07 eta: 1 day, 12:21:42 time: 1.0009 data_time: 0.0047 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0086 decode.acc_seg: 99.5779 aux.loss_ce: 0.0092 aux.acc_seg: 99.0356 +04/17 16:37:24 - mmengine - INFO - Iter(train) [ 29100/160000] base_lr: 8.2587e-05 lr: 3.0534e-07 eta: 1 day, 12:20:52 time: 1.0013 data_time: 0.0050 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0094 decode.acc_seg: 99.5415 aux.loss_ce: 0.0091 aux.acc_seg: 99.1438 +04/17 16:38:14 - mmengine - INFO - Iter(train) [ 29150/160000] base_lr: 8.2556e-05 lr: 3.0523e-07 eta: 1 day, 12:20:02 time: 1.0008 data_time: 0.0043 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0093 decode.acc_seg: 99.7322 aux.loss_ce: 0.0095 aux.acc_seg: 99.3235 +04/17 16:39:04 - mmengine - INFO - Iter(train) [ 29200/160000] base_lr: 8.2524e-05 lr: 3.0511e-07 eta: 1 day, 12:19:13 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.7124 aux.loss_ce: 0.0080 aux.acc_seg: 99.0248 +04/17 16:39:54 - mmengine - INFO - Iter(train) [ 29250/160000] base_lr: 8.2493e-05 lr: 3.0499e-07 eta: 1 day, 12:18:23 time: 1.0018 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0090 decode.acc_seg: 99.5857 aux.loss_ce: 0.0088 aux.acc_seg: 99.1066 +04/17 16:40:44 - mmengine - INFO - Iter(train) [ 29300/160000] base_lr: 8.2461e-05 lr: 3.0488e-07 eta: 1 day, 12:17:33 time: 1.0012 data_time: 0.0046 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0086 decode.acc_seg: 99.6841 aux.loss_ce: 0.0089 aux.acc_seg: 99.0004 +04/17 16:41:34 - mmengine - INFO - Iter(train) [ 29350/160000] base_lr: 8.2430e-05 lr: 3.0476e-07 eta: 1 day, 12:16:43 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0090 decode.acc_seg: 99.5378 aux.loss_ce: 0.0092 aux.acc_seg: 98.6506 +04/17 16:42:24 - mmengine - INFO - Iter(train) [ 29400/160000] base_lr: 8.2398e-05 lr: 3.0464e-07 eta: 1 day, 12:15:53 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0090 decode.acc_seg: 99.7160 aux.loss_ce: 0.0092 aux.acc_seg: 99.1240 +04/17 16:43:14 - mmengine - INFO - Iter(train) [ 29450/160000] base_lr: 8.2366e-05 lr: 3.0453e-07 eta: 1 day, 12:15:03 time: 0.9994 data_time: 0.0048 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0098 decode.acc_seg: 99.4558 aux.loss_ce: 0.0090 aux.acc_seg: 98.5292 +04/17 16:44:04 - mmengine - INFO - Iter(train) [ 29500/160000] base_lr: 8.2335e-05 lr: 3.0441e-07 eta: 1 day, 12:14:13 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0084 decode.acc_seg: 99.6563 aux.loss_ce: 0.0093 aux.acc_seg: 99.0377 +04/17 16:44:54 - mmengine - INFO - Iter(train) [ 29550/160000] base_lr: 8.2303e-05 lr: 3.0429e-07 eta: 1 day, 12:13:24 time: 1.0001 data_time: 0.0042 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.6290 aux.loss_ce: 0.0083 aux.acc_seg: 98.9460 +04/17 16:45:44 - mmengine - INFO - Iter(train) [ 29600/160000] base_lr: 8.2272e-05 lr: 3.0418e-07 eta: 1 day, 12:12:34 time: 0.9990 data_time: 0.0045 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0096 decode.acc_seg: 99.5298 aux.loss_ce: 0.0101 aux.acc_seg: 98.7080 +04/17 16:46:34 - mmengine - INFO - Iter(train) [ 29650/160000] base_lr: 8.2240e-05 lr: 3.0406e-07 eta: 1 day, 12:11:44 time: 1.0004 data_time: 0.0044 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0092 decode.acc_seg: 99.6342 aux.loss_ce: 0.0092 aux.acc_seg: 98.8804 +04/17 16:47:24 - mmengine - INFO - Iter(train) [ 29700/160000] base_lr: 8.2209e-05 lr: 3.0394e-07 eta: 1 day, 12:10:54 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.6902 aux.loss_ce: 0.0082 aux.acc_seg: 99.1652 +04/17 16:48:14 - mmengine - INFO - Iter(train) [ 29750/160000] base_lr: 8.2177e-05 lr: 3.0383e-07 eta: 1 day, 12:10:04 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0082 decode.acc_seg: 99.6937 aux.loss_ce: 0.0089 aux.acc_seg: 99.2622 +04/17 16:49:04 - mmengine - INFO - Iter(train) [ 29800/160000] base_lr: 8.2146e-05 lr: 3.0371e-07 eta: 1 day, 12:09:14 time: 0.9988 data_time: 0.0046 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0089 decode.acc_seg: 99.6332 aux.loss_ce: 0.0088 aux.acc_seg: 98.9525 +04/17 16:49:54 - mmengine - INFO - Iter(train) [ 29850/160000] base_lr: 8.2114e-05 lr: 3.0359e-07 eta: 1 day, 12:08:24 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0190 decode.loss_ce: 0.0094 decode.acc_seg: 99.7751 aux.loss_ce: 0.0096 aux.acc_seg: 99.3799 +04/17 16:50:44 - mmengine - INFO - Iter(train) [ 29900/160000] base_lr: 8.2083e-05 lr: 3.0348e-07 eta: 1 day, 12:07:34 time: 1.0004 data_time: 0.0047 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.7232 aux.loss_ce: 0.0084 aux.acc_seg: 99.4005 +04/17 16:51:34 - mmengine - INFO - Iter(train) [ 29950/160000] base_lr: 8.2051e-05 lr: 3.0336e-07 eta: 1 day, 12:06:45 time: 0.9991 data_time: 0.0042 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0084 decode.acc_seg: 99.7128 aux.loss_ce: 0.0083 aux.acc_seg: 99.5285 +04/17 16:52:24 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 16:52:24 - mmengine - INFO - Iter(train) [ 30000/160000] base_lr: 8.2019e-05 lr: 3.0324e-07 eta: 1 day, 12:05:55 time: 1.0000 data_time: 0.0048 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0087 decode.acc_seg: 99.6927 aux.loss_ce: 0.0086 aux.acc_seg: 99.1001 +04/17 16:52:24 - mmengine - INFO - Saving checkpoint at 30000 iterations +04/17 16:52:34 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1160 data_time: 0.0015 memory: 4004 +04/17 16:52:40 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1161 data_time: 0.0016 memory: 4004 +04/17 16:52:46 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1158 data_time: 0.0013 memory: 4004 +04/17 16:52:51 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1155 data_time: 0.0013 memory: 4004 +04/17 16:52:52 - mmengine - INFO - per class results: +04/17 16:52:52 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.17 | 99.57 | 99.58 | 99.6 | 99.57 | +| contrast | 81.89 | 90.44 | 90.05 | 89.66 | 90.44 | ++------------+-------+-------+--------+-----------+--------+ +04/17 16:52:52 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2000 mIoU: 90.5300 mAcc: 95.0000 mFscore: 94.8200 mPrecision: 94.6300 mRecall: 95.0000 data_time: 0.0018 time: 0.1163 +04/17 16:53:42 - mmengine - INFO - Iter(train) [ 30050/160000] base_lr: 8.1988e-05 lr: 3.0313e-07 eta: 1 day, 12:05:05 time: 0.9991 data_time: 0.0046 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0093 decode.acc_seg: 99.6229 aux.loss_ce: 0.0092 aux.acc_seg: 98.9380 +04/17 16:54:32 - mmengine - INFO - Iter(train) [ 30100/160000] base_lr: 8.1956e-05 lr: 3.0301e-07 eta: 1 day, 12:04:15 time: 0.9996 data_time: 0.0051 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0094 decode.acc_seg: 99.5686 aux.loss_ce: 0.0089 aux.acc_seg: 98.9799 +04/17 16:55:22 - mmengine - INFO - Iter(train) [ 30150/160000] base_lr: 8.1925e-05 lr: 3.0289e-07 eta: 1 day, 12:03:25 time: 0.9991 data_time: 0.0043 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0087 decode.acc_seg: 99.5405 aux.loss_ce: 0.0086 aux.acc_seg: 98.7236 +04/17 16:56:12 - mmengine - INFO - Iter(train) [ 30200/160000] base_lr: 8.1893e-05 lr: 3.0278e-07 eta: 1 day, 12:02:35 time: 1.0004 data_time: 0.0050 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0088 decode.acc_seg: 99.7475 aux.loss_ce: 0.0087 aux.acc_seg: 99.2769 +04/17 16:57:02 - mmengine - INFO - Iter(train) [ 30250/160000] base_lr: 8.1862e-05 lr: 3.0266e-07 eta: 1 day, 12:01:46 time: 0.9994 data_time: 0.0045 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0089 decode.acc_seg: 99.4925 aux.loss_ce: 0.0094 aux.acc_seg: 98.6217 +04/17 16:57:52 - mmengine - INFO - Iter(train) [ 30300/160000] base_lr: 8.1830e-05 lr: 3.0254e-07 eta: 1 day, 12:00:56 time: 0.9999 data_time: 0.0049 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0087 decode.acc_seg: 99.6794 aux.loss_ce: 0.0085 aux.acc_seg: 99.3116 +04/17 16:58:42 - mmengine - INFO - Iter(train) [ 30350/160000] base_lr: 8.1799e-05 lr: 3.0243e-07 eta: 1 day, 12:00:06 time: 1.0018 data_time: 0.0048 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0086 decode.acc_seg: 99.7377 aux.loss_ce: 0.0087 aux.acc_seg: 99.4810 +04/17 16:59:32 - mmengine - INFO - Iter(train) [ 30400/160000] base_lr: 8.1767e-05 lr: 3.0231e-07 eta: 1 day, 11:59:16 time: 0.9999 data_time: 0.0043 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0082 decode.acc_seg: 99.5684 aux.loss_ce: 0.0086 aux.acc_seg: 98.9161 +04/17 17:00:22 - mmengine - INFO - Iter(train) [ 30450/160000] base_lr: 8.1736e-05 lr: 3.0219e-07 eta: 1 day, 11:58:26 time: 1.0013 data_time: 0.0044 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0092 decode.acc_seg: 99.7215 aux.loss_ce: 0.0092 aux.acc_seg: 99.3488 +04/17 17:01:12 - mmengine - INFO - Iter(train) [ 30500/160000] base_lr: 8.1704e-05 lr: 3.0208e-07 eta: 1 day, 11:57:36 time: 1.0001 data_time: 0.0043 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0091 decode.acc_seg: 99.6208 aux.loss_ce: 0.0088 aux.acc_seg: 98.4980 +04/17 17:02:02 - mmengine - INFO - Iter(train) [ 30550/160000] base_lr: 8.1672e-05 lr: 3.0196e-07 eta: 1 day, 11:56:46 time: 0.9996 data_time: 0.0046 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.7467 aux.loss_ce: 0.0076 aux.acc_seg: 99.0271 +04/17 17:02:52 - mmengine - INFO - Iter(train) [ 30600/160000] base_lr: 8.1641e-05 lr: 3.0184e-07 eta: 1 day, 11:55:57 time: 0.9997 data_time: 0.0043 memory: 8462 loss: 0.0229 decode.loss_ce: 0.0121 decode.acc_seg: 99.5672 aux.loss_ce: 0.0108 aux.acc_seg: 98.9258 +04/17 17:03:42 - mmengine - INFO - Iter(train) [ 30650/160000] base_lr: 8.1609e-05 lr: 3.0173e-07 eta: 1 day, 11:55:07 time: 0.9990 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0086 decode.acc_seg: 99.5903 aux.loss_ce: 0.0092 aux.acc_seg: 98.6874 +04/17 17:04:32 - mmengine - INFO - Iter(train) [ 30700/160000] base_lr: 8.1578e-05 lr: 3.0161e-07 eta: 1 day, 11:54:17 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0085 decode.acc_seg: 99.6534 aux.loss_ce: 0.0083 aux.acc_seg: 99.3263 +04/17 17:05:22 - mmengine - INFO - Iter(train) [ 30750/160000] base_lr: 8.1546e-05 lr: 3.0149e-07 eta: 1 day, 11:53:27 time: 1.0014 data_time: 0.0044 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.6407 aux.loss_ce: 0.0082 aux.acc_seg: 98.9790 +04/17 17:06:12 - mmengine - INFO - Iter(train) [ 30800/160000] base_lr: 8.1515e-05 lr: 3.0138e-07 eta: 1 day, 11:52:37 time: 0.9999 data_time: 0.0047 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0099 decode.acc_seg: 99.6201 aux.loss_ce: 0.0105 aux.acc_seg: 99.0280 +04/17 17:07:02 - mmengine - INFO - Iter(train) [ 30850/160000] base_lr: 8.1483e-05 lr: 3.0126e-07 eta: 1 day, 11:51:47 time: 1.0010 data_time: 0.0048 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0082 decode.acc_seg: 99.6662 aux.loss_ce: 0.0084 aux.acc_seg: 99.2002 +04/17 17:07:52 - mmengine - INFO - Iter(train) [ 30900/160000] base_lr: 8.1452e-05 lr: 3.0114e-07 eta: 1 day, 11:50:57 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0089 decode.acc_seg: 99.5846 aux.loss_ce: 0.0087 aux.acc_seg: 99.1982 +04/17 17:08:42 - mmengine - INFO - Iter(train) [ 30950/160000] base_lr: 8.1420e-05 lr: 3.0103e-07 eta: 1 day, 11:50:07 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.6296 aux.loss_ce: 0.0081 aux.acc_seg: 99.0252 +04/17 17:09:32 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 17:09:32 - mmengine - INFO - Iter(train) [ 31000/160000] base_lr: 8.1389e-05 lr: 3.0091e-07 eta: 1 day, 11:49:17 time: 0.9979 data_time: 0.0043 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0093 decode.acc_seg: 99.6519 aux.loss_ce: 0.0086 aux.acc_seg: 99.0602 +04/17 17:10:22 - mmengine - INFO - Iter(train) [ 31050/160000] base_lr: 8.1357e-05 lr: 3.0079e-07 eta: 1 day, 11:48:28 time: 1.0004 data_time: 0.0044 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0080 decode.acc_seg: 99.6960 aux.loss_ce: 0.0083 aux.acc_seg: 99.1999 +04/17 17:11:12 - mmengine - INFO - Iter(train) [ 31100/160000] base_lr: 8.1325e-05 lr: 3.0068e-07 eta: 1 day, 11:47:38 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0094 decode.acc_seg: 99.5914 aux.loss_ce: 0.0101 aux.acc_seg: 98.2475 +04/17 17:12:02 - mmengine - INFO - Iter(train) [ 31150/160000] base_lr: 8.1294e-05 lr: 3.0056e-07 eta: 1 day, 11:46:48 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.7179 aux.loss_ce: 0.0084 aux.acc_seg: 99.2775 +04/17 17:12:52 - mmengine - INFO - Iter(train) [ 31200/160000] base_lr: 8.1262e-05 lr: 3.0044e-07 eta: 1 day, 11:45:58 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0089 decode.acc_seg: 99.5459 aux.loss_ce: 0.0086 aux.acc_seg: 98.5708 +04/17 17:13:42 - mmengine - INFO - Iter(train) [ 31250/160000] base_lr: 8.1231e-05 lr: 3.0033e-07 eta: 1 day, 11:45:08 time: 1.0007 data_time: 0.0043 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0086 decode.acc_seg: 99.5615 aux.loss_ce: 0.0088 aux.acc_seg: 98.9775 +04/17 17:14:32 - mmengine - INFO - Iter(train) [ 31300/160000] base_lr: 8.1199e-05 lr: 3.0021e-07 eta: 1 day, 11:44:18 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0081 decode.acc_seg: 99.6435 aux.loss_ce: 0.0083 aux.acc_seg: 99.1495 +04/17 17:15:22 - mmengine - INFO - Iter(train) [ 31350/160000] base_lr: 8.1168e-05 lr: 3.0009e-07 eta: 1 day, 11:43:28 time: 1.0012 data_time: 0.0050 memory: 8462 loss: 0.0205 decode.loss_ce: 0.0106 decode.acc_seg: 99.4677 aux.loss_ce: 0.0099 aux.acc_seg: 98.7720 +04/17 17:16:12 - mmengine - INFO - Iter(train) [ 31400/160000] base_lr: 8.1136e-05 lr: 2.9998e-07 eta: 1 day, 11:42:38 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0084 decode.acc_seg: 99.6706 aux.loss_ce: 0.0094 aux.acc_seg: 98.9902 +04/17 17:17:02 - mmengine - INFO - Iter(train) [ 31450/160000] base_lr: 8.1105e-05 lr: 2.9986e-07 eta: 1 day, 11:41:49 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0087 decode.acc_seg: 99.6580 aux.loss_ce: 0.0087 aux.acc_seg: 99.0391 +04/17 17:17:52 - mmengine - INFO - Iter(train) [ 31500/160000] base_lr: 8.1073e-05 lr: 2.9974e-07 eta: 1 day, 11:40:59 time: 1.0016 data_time: 0.0048 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0087 decode.acc_seg: 99.5750 aux.loss_ce: 0.0094 aux.acc_seg: 98.9014 +04/17 17:18:42 - mmengine - INFO - Iter(train) [ 31550/160000] base_lr: 8.1042e-05 lr: 2.9963e-07 eta: 1 day, 11:40:09 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.6256 aux.loss_ce: 0.0081 aux.acc_seg: 99.2733 +04/17 17:19:32 - mmengine - INFO - Iter(train) [ 31600/160000] base_lr: 8.1010e-05 lr: 2.9951e-07 eta: 1 day, 11:39:19 time: 1.0005 data_time: 0.0043 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0088 decode.acc_seg: 99.6853 aux.loss_ce: 0.0094 aux.acc_seg: 99.0355 +04/17 17:20:22 - mmengine - INFO - Iter(train) [ 31650/160000] base_lr: 8.0978e-05 lr: 2.9939e-07 eta: 1 day, 11:38:29 time: 1.0009 data_time: 0.0042 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0089 decode.acc_seg: 99.7139 aux.loss_ce: 0.0087 aux.acc_seg: 99.0431 +04/17 17:21:12 - mmengine - INFO - Iter(train) [ 31700/160000] base_lr: 8.0947e-05 lr: 2.9928e-07 eta: 1 day, 11:37:39 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0087 decode.acc_seg: 99.5094 aux.loss_ce: 0.0090 aux.acc_seg: 98.4970 +04/17 17:22:02 - mmengine - INFO - Iter(train) [ 31750/160000] base_lr: 8.0915e-05 lr: 2.9916e-07 eta: 1 day, 11:36:50 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0093 decode.acc_seg: 99.6027 aux.loss_ce: 0.0088 aux.acc_seg: 99.1896 +04/17 17:22:52 - mmengine - INFO - Iter(train) [ 31800/160000] base_lr: 8.0884e-05 lr: 2.9904e-07 eta: 1 day, 11:36:00 time: 1.0007 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0083 decode.acc_seg: 99.6967 aux.loss_ce: 0.0095 aux.acc_seg: 99.1680 +04/17 17:23:42 - mmengine - INFO - Iter(train) [ 31850/160000] base_lr: 8.0852e-05 lr: 2.9893e-07 eta: 1 day, 11:35:10 time: 1.0009 data_time: 0.0046 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0094 decode.acc_seg: 99.6681 aux.loss_ce: 0.0093 aux.acc_seg: 99.1896 +04/17 17:24:32 - mmengine - INFO - Iter(train) [ 31900/160000] base_lr: 8.0821e-05 lr: 2.9881e-07 eta: 1 day, 11:34:20 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0083 decode.acc_seg: 99.6717 aux.loss_ce: 0.0083 aux.acc_seg: 99.2178 +04/17 17:25:22 - mmengine - INFO - Iter(train) [ 31950/160000] base_lr: 8.0789e-05 lr: 2.9869e-07 eta: 1 day, 11:33:30 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0093 decode.acc_seg: 99.6681 aux.loss_ce: 0.0093 aux.acc_seg: 99.0532 +04/17 17:26:12 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 17:26:12 - mmengine - INFO - Iter(train) [ 32000/160000] base_lr: 8.0758e-05 lr: 2.9858e-07 eta: 1 day, 11:32:40 time: 1.0000 data_time: 0.0049 memory: 8462 loss: 0.0195 decode.loss_ce: 0.0096 decode.acc_seg: 99.5377 aux.loss_ce: 0.0099 aux.acc_seg: 98.8344 +04/17 17:27:02 - mmengine - INFO - Iter(train) [ 32050/160000] base_lr: 8.0726e-05 lr: 2.9846e-07 eta: 1 day, 11:31:51 time: 1.0012 data_time: 0.0048 memory: 8462 loss: 0.0214 decode.loss_ce: 0.0103 decode.acc_seg: 99.5535 aux.loss_ce: 0.0112 aux.acc_seg: 98.8245 +04/17 17:27:52 - mmengine - INFO - Iter(train) [ 32100/160000] base_lr: 8.0695e-05 lr: 2.9834e-07 eta: 1 day, 11:31:01 time: 1.0007 data_time: 0.0048 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0091 decode.acc_seg: 99.5520 aux.loss_ce: 0.0096 aux.acc_seg: 98.9317 +04/17 17:28:42 - mmengine - INFO - Iter(train) [ 32150/160000] base_lr: 8.0663e-05 lr: 2.9823e-07 eta: 1 day, 11:30:11 time: 0.9988 data_time: 0.0045 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0085 decode.acc_seg: 99.6653 aux.loss_ce: 0.0082 aux.acc_seg: 98.9891 +04/17 17:29:32 - mmengine - INFO - Iter(train) [ 32200/160000] base_lr: 8.0631e-05 lr: 2.9811e-07 eta: 1 day, 11:29:21 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0074 decode.acc_seg: 99.6592 aux.loss_ce: 0.0084 aux.acc_seg: 98.9397 +04/17 17:30:22 - mmengine - INFO - Iter(train) [ 32250/160000] base_lr: 8.0600e-05 lr: 2.9799e-07 eta: 1 day, 11:28:31 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0079 decode.acc_seg: 99.7044 aux.loss_ce: 0.0087 aux.acc_seg: 99.1220 +04/17 17:31:12 - mmengine - INFO - Iter(train) [ 32300/160000] base_lr: 8.0568e-05 lr: 2.9788e-07 eta: 1 day, 11:27:42 time: 0.9999 data_time: 0.0046 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0083 decode.acc_seg: 99.5981 aux.loss_ce: 0.0089 aux.acc_seg: 99.0562 +04/17 17:32:02 - mmengine - INFO - Iter(train) [ 32350/160000] base_lr: 8.0537e-05 lr: 2.9776e-07 eta: 1 day, 11:26:52 time: 0.9999 data_time: 0.0047 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0072 decode.acc_seg: 99.7723 aux.loss_ce: 0.0081 aux.acc_seg: 99.3151 +04/17 17:32:52 - mmengine - INFO - Iter(train) [ 32400/160000] base_lr: 8.0505e-05 lr: 2.9764e-07 eta: 1 day, 11:26:02 time: 1.0009 data_time: 0.0047 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.6283 aux.loss_ce: 0.0080 aux.acc_seg: 99.1644 +04/17 17:33:42 - mmengine - INFO - Iter(train) [ 32450/160000] base_lr: 8.0474e-05 lr: 2.9753e-07 eta: 1 day, 11:25:12 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0083 decode.acc_seg: 99.5544 aux.loss_ce: 0.0087 aux.acc_seg: 98.7215 +04/17 17:34:32 - mmengine - INFO - Iter(train) [ 32500/160000] base_lr: 8.0442e-05 lr: 2.9741e-07 eta: 1 day, 11:24:22 time: 1.0011 data_time: 0.0049 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.6866 aux.loss_ce: 0.0082 aux.acc_seg: 99.3580 +04/17 17:35:22 - mmengine - INFO - Iter(train) [ 32550/160000] base_lr: 8.0411e-05 lr: 2.9729e-07 eta: 1 day, 11:23:32 time: 1.0015 data_time: 0.0049 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0083 decode.acc_seg: 99.6582 aux.loss_ce: 0.0091 aux.acc_seg: 99.2222 +04/17 17:36:12 - mmengine - INFO - Iter(train) [ 32600/160000] base_lr: 8.0379e-05 lr: 2.9718e-07 eta: 1 day, 11:22:43 time: 1.0007 data_time: 0.0048 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0102 decode.acc_seg: 99.6645 aux.loss_ce: 0.0095 aux.acc_seg: 98.9851 +04/17 17:37:03 - mmengine - INFO - Iter(train) [ 32650/160000] base_lr: 8.0348e-05 lr: 2.9706e-07 eta: 1 day, 11:21:53 time: 1.0004 data_time: 0.0047 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0081 decode.acc_seg: 99.8064 aux.loss_ce: 0.0086 aux.acc_seg: 99.3704 +04/17 17:37:52 - mmengine - INFO - Iter(train) [ 32700/160000] base_lr: 8.0316e-05 lr: 2.9694e-07 eta: 1 day, 11:21:03 time: 1.0001 data_time: 0.0044 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0092 decode.acc_seg: 99.6901 aux.loss_ce: 0.0090 aux.acc_seg: 99.2725 +04/17 17:38:43 - mmengine - INFO - Iter(train) [ 32750/160000] base_lr: 8.0284e-05 lr: 2.9683e-07 eta: 1 day, 11:20:13 time: 1.0008 data_time: 0.0051 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.5844 aux.loss_ce: 0.0074 aux.acc_seg: 98.8190 +04/17 17:39:33 - mmengine - INFO - Iter(train) [ 32800/160000] base_lr: 8.0253e-05 lr: 2.9671e-07 eta: 1 day, 11:19:23 time: 1.0012 data_time: 0.0043 memory: 8462 loss: 0.0198 decode.loss_ce: 0.0101 decode.acc_seg: 99.6080 aux.loss_ce: 0.0098 aux.acc_seg: 99.1676 +04/17 17:40:23 - mmengine - INFO - Iter(train) [ 32850/160000] base_lr: 8.0221e-05 lr: 2.9659e-07 eta: 1 day, 11:18:34 time: 0.9995 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0079 decode.acc_seg: 99.6521 aux.loss_ce: 0.0082 aux.acc_seg: 98.9561 +04/17 17:41:13 - mmengine - INFO - Iter(train) [ 32900/160000] base_lr: 8.0190e-05 lr: 2.9648e-07 eta: 1 day, 11:17:44 time: 1.0006 data_time: 0.0048 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.7070 aux.loss_ce: 0.0081 aux.acc_seg: 99.1634 +04/17 17:42:03 - mmengine - INFO - Iter(train) [ 32950/160000] base_lr: 8.0158e-05 lr: 2.9636e-07 eta: 1 day, 11:16:54 time: 1.0020 data_time: 0.0045 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7488 aux.loss_ce: 0.0072 aux.acc_seg: 99.3393 +04/17 17:42:53 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 17:42:53 - mmengine - INFO - Iter(train) [ 33000/160000] base_lr: 8.0127e-05 lr: 2.9624e-07 eta: 1 day, 11:16:04 time: 0.9996 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0079 decode.acc_seg: 99.7297 aux.loss_ce: 0.0088 aux.acc_seg: 99.3464 +04/17 17:43:43 - mmengine - INFO - Iter(train) [ 33050/160000] base_lr: 8.0095e-05 lr: 2.9613e-07 eta: 1 day, 11:15:14 time: 1.0004 data_time: 0.0044 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0090 decode.acc_seg: 99.6449 aux.loss_ce: 0.0096 aux.acc_seg: 99.0391 +04/17 17:44:33 - mmengine - INFO - Iter(train) [ 33100/160000] base_lr: 8.0064e-05 lr: 2.9601e-07 eta: 1 day, 11:14:25 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0091 decode.acc_seg: 99.4652 aux.loss_ce: 0.0098 aux.acc_seg: 98.3170 +04/17 17:45:23 - mmengine - INFO - Iter(train) [ 33150/160000] base_lr: 8.0032e-05 lr: 2.9589e-07 eta: 1 day, 11:13:35 time: 1.0021 data_time: 0.0044 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0073 decode.acc_seg: 99.7093 aux.loss_ce: 0.0081 aux.acc_seg: 99.1882 +04/17 17:46:13 - mmengine - INFO - Iter(train) [ 33200/160000] base_lr: 8.0001e-05 lr: 2.9578e-07 eta: 1 day, 11:12:45 time: 1.0014 data_time: 0.0044 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0077 decode.acc_seg: 99.7759 aux.loss_ce: 0.0083 aux.acc_seg: 99.1198 +04/17 17:47:03 - mmengine - INFO - Iter(train) [ 33250/160000] base_lr: 7.9969e-05 lr: 2.9566e-07 eta: 1 day, 11:11:55 time: 1.0003 data_time: 0.0047 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.7992 aux.loss_ce: 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decode.loss_ce: 0.0083 decode.acc_seg: 99.6145 aux.loss_ce: 0.0085 aux.acc_seg: 99.1199 +04/17 17:51:13 - mmengine - INFO - Iter(train) [ 33500/160000] base_lr: 7.9811e-05 lr: 2.9508e-07 eta: 1 day, 11:07:46 time: 1.0003 data_time: 0.0050 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0082 decode.acc_seg: 99.7141 aux.loss_ce: 0.0098 aux.acc_seg: 98.9956 +04/17 17:52:03 - mmengine - INFO - Iter(train) [ 33550/160000] base_lr: 7.9780e-05 lr: 2.9496e-07 eta: 1 day, 11:06:56 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.5331 aux.loss_ce: 0.0078 aux.acc_seg: 99.1173 +04/17 17:52:53 - mmengine - INFO - Iter(train) [ 33600/160000] base_lr: 7.9748e-05 lr: 2.9485e-07 eta: 1 day, 11:06:07 time: 1.0011 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0089 decode.acc_seg: 99.6397 aux.loss_ce: 0.0087 aux.acc_seg: 99.3166 +04/17 17:53:43 - mmengine - INFO - Iter(train) [ 33650/160000] base_lr: 7.9717e-05 lr: 2.9473e-07 eta: 1 day, 11:05:17 time: 1.0014 data_time: 0.0045 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0092 decode.acc_seg: 99.5447 aux.loss_ce: 0.0092 aux.acc_seg: 98.8676 +04/17 17:54:33 - mmengine - INFO - Iter(train) [ 33700/160000] base_lr: 7.9685e-05 lr: 2.9461e-07 eta: 1 day, 11:04:27 time: 1.0010 data_time: 0.0041 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0079 decode.acc_seg: 99.6780 aux.loss_ce: 0.0085 aux.acc_seg: 98.8388 +04/17 17:55:23 - mmengine - INFO - Iter(train) [ 33750/160000] base_lr: 7.9653e-05 lr: 2.9450e-07 eta: 1 day, 11:03:37 time: 1.0008 data_time: 0.0049 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0085 decode.acc_seg: 99.7696 aux.loss_ce: 0.0092 aux.acc_seg: 99.3156 +04/17 17:56:13 - mmengine - INFO - Iter(train) [ 33800/160000] base_lr: 7.9622e-05 lr: 2.9438e-07 eta: 1 day, 11:02:48 time: 1.0008 data_time: 0.0048 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0086 decode.acc_seg: 99.5960 aux.loss_ce: 0.0091 aux.acc_seg: 98.7976 +04/17 17:57:03 - mmengine - INFO - Iter(train) [ 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memory: 8462 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.5853 aux.loss_ce: 0.0082 aux.acc_seg: 98.8203 +04/17 18:00:24 - mmengine - INFO - Iter(train) [ 34050/160000] base_lr: 7.9464e-05 lr: 2.9380e-07 eta: 1 day, 10:58:38 time: 1.0006 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0084 decode.acc_seg: 99.6889 aux.loss_ce: 0.0095 aux.acc_seg: 99.3422 +04/17 18:01:14 - mmengine - INFO - Iter(train) [ 34100/160000] base_lr: 7.9433e-05 lr: 2.9368e-07 eta: 1 day, 10:57:49 time: 1.0003 data_time: 0.0047 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0077 decode.acc_seg: 99.5663 aux.loss_ce: 0.0086 aux.acc_seg: 98.7095 +04/17 18:02:04 - mmengine - INFO - Iter(train) [ 34150/160000] base_lr: 7.9401e-05 lr: 2.9356e-07 eta: 1 day, 10:56:59 time: 1.0005 data_time: 0.0044 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0078 decode.acc_seg: 99.5920 aux.loss_ce: 0.0082 aux.acc_seg: 99.0913 +04/17 18:02:54 - mmengine - INFO - Iter(train) [ 34200/160000] base_lr: 7.9370e-05 lr: 2.9345e-07 eta: 1 day, 10:56:09 time: 0.9992 data_time: 0.0045 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.5230 aux.loss_ce: 0.0081 aux.acc_seg: 98.9002 +04/17 18:03:44 - mmengine - INFO - Iter(train) [ 34250/160000] base_lr: 7.9338e-05 lr: 2.9333e-07 eta: 1 day, 10:55:19 time: 1.0003 data_time: 0.0044 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0078 decode.acc_seg: 99.7005 aux.loss_ce: 0.0083 aux.acc_seg: 99.4404 +04/17 18:04:34 - mmengine - INFO - Iter(train) [ 34300/160000] base_lr: 7.9306e-05 lr: 2.9321e-07 eta: 1 day, 10:54:29 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7816 aux.loss_ce: 0.0077 aux.acc_seg: 99.3183 +04/17 18:05:24 - mmengine - INFO - Iter(train) [ 34350/160000] base_lr: 7.9275e-05 lr: 2.9310e-07 eta: 1 day, 10:53:39 time: 0.9992 data_time: 0.0046 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0084 decode.acc_seg: 99.7252 aux.loss_ce: 0.0095 aux.acc_seg: 99.2121 +04/17 18:06:14 - mmengine - INFO - Iter(train) [ 34400/160000] base_lr: 7.9243e-05 lr: 2.9298e-07 eta: 1 day, 10:52:50 time: 1.0001 data_time: 0.0047 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0094 decode.acc_seg: 99.7040 aux.loss_ce: 0.0097 aux.acc_seg: 99.1589 +04/17 18:07:04 - mmengine - INFO - Iter(train) [ 34450/160000] base_lr: 7.9212e-05 lr: 2.9286e-07 eta: 1 day, 10:52:00 time: 1.0013 data_time: 0.0046 memory: 8462 loss: 0.0193 decode.loss_ce: 0.0094 decode.acc_seg: 99.3542 aux.loss_ce: 0.0099 aux.acc_seg: 98.7295 +04/17 18:07:54 - mmengine - INFO - Iter(train) [ 34500/160000] base_lr: 7.9180e-05 lr: 2.9275e-07 eta: 1 day, 10:51:10 time: 0.9988 data_time: 0.0043 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0089 decode.acc_seg: 99.7618 aux.loss_ce: 0.0091 aux.acc_seg: 99.5354 +04/17 18:08:44 - mmengine - INFO - Iter(train) [ 34550/160000] base_lr: 7.9149e-05 lr: 2.9263e-07 eta: 1 day, 10:50:20 time: 1.0006 data_time: 0.0043 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0081 decode.acc_seg: 99.4892 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loss: 0.0176 decode.loss_ce: 0.0084 decode.acc_seg: 99.7515 aux.loss_ce: 0.0092 aux.acc_seg: 99.3605 +04/17 18:12:54 - mmengine - INFO - Iter(train) [ 34800/160000] base_lr: 7.8991e-05 lr: 2.9205e-07 eta: 1 day, 10:46:11 time: 1.0012 data_time: 0.0046 memory: 8462 loss: 0.0191 decode.loss_ce: 0.0094 decode.acc_seg: 99.4804 aux.loss_ce: 0.0097 aux.acc_seg: 99.0023 +04/17 18:13:44 - mmengine - INFO - Iter(train) [ 34850/160000] base_lr: 7.8959e-05 lr: 2.9193e-07 eta: 1 day, 10:45:22 time: 1.0021 data_time: 0.0047 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0083 decode.acc_seg: 99.5070 aux.loss_ce: 0.0083 aux.acc_seg: 98.9429 +04/17 18:14:34 - mmengine - INFO - Iter(train) [ 34900/160000] base_lr: 7.8928e-05 lr: 2.9181e-07 eta: 1 day, 10:44:32 time: 1.0006 data_time: 0.0049 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7284 aux.loss_ce: 0.0076 aux.acc_seg: 99.1909 +04/17 18:15:24 - mmengine - INFO - Iter(train) [ 34950/160000] base_lr: 7.8896e-05 lr: 2.9170e-07 eta: 1 day, 10:43:42 time: 1.0003 data_time: 0.0044 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0081 decode.acc_seg: 99.5810 aux.loss_ce: 0.0089 aux.acc_seg: 99.0734 +04/17 18:16:14 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 18:16:14 - mmengine - INFO - Iter(train) [ 35000/160000] base_lr: 7.8865e-05 lr: 2.9158e-07 eta: 1 day, 10:42:52 time: 1.0006 data_time: 0.0050 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0074 decode.acc_seg: 99.7099 aux.loss_ce: 0.0085 aux.acc_seg: 99.0732 +04/17 18:17:05 - mmengine - INFO - Iter(train) [ 35050/160000] base_lr: 7.8833e-05 lr: 2.9146e-07 eta: 1 day, 10:42:03 time: 1.0005 data_time: 0.0044 memory: 8462 loss: 0.0179 decode.loss_ce: 0.0087 decode.acc_seg: 99.5447 aux.loss_ce: 0.0092 aux.acc_seg: 99.0398 +04/17 18:17:55 - mmengine - INFO - Iter(train) [ 35100/160000] base_lr: 7.8802e-05 lr: 2.9135e-07 eta: 1 day, 10:41:13 time: 1.0006 data_time: 0.0048 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.4404 aux.loss_ce: 0.0085 aux.acc_seg: 98.6664 +04/17 18:18:45 - mmengine - INFO - Iter(train) [ 35150/160000] base_lr: 7.8770e-05 lr: 2.9123e-07 eta: 1 day, 10:40:23 time: 1.0017 data_time: 0.0050 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.7715 aux.loss_ce: 0.0073 aux.acc_seg: 99.4728 +04/17 18:19:35 - mmengine - INFO - Iter(train) [ 35200/160000] base_lr: 7.8739e-05 lr: 2.9111e-07 eta: 1 day, 10:39:33 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0093 decode.acc_seg: 99.7454 aux.loss_ce: 0.0090 aux.acc_seg: 99.4465 +04/17 18:20:25 - mmengine - INFO - Iter(train) [ 35250/160000] base_lr: 7.8707e-05 lr: 2.9100e-07 eta: 1 day, 10:38:43 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0080 decode.acc_seg: 99.6777 aux.loss_ce: 0.0087 aux.acc_seg: 99.0917 +04/17 18:21:15 - mmengine - INFO - Iter(train) [ 35300/160000] base_lr: 7.8676e-05 lr: 2.9088e-07 eta: 1 day, 10:37:54 time: 1.0014 data_time: 0.0042 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0085 decode.acc_seg: 99.7107 aux.loss_ce: 0.0088 aux.acc_seg: 99.1545 +04/17 18:22:05 - mmengine - INFO - Iter(train) [ 35350/160000] base_lr: 7.8644e-05 lr: 2.9076e-07 eta: 1 day, 10:37:04 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0077 decode.acc_seg: 99.6267 aux.loss_ce: 0.0085 aux.acc_seg: 98.8953 +04/17 18:22:55 - mmengine - INFO - Iter(train) [ 35400/160000] base_lr: 7.8612e-05 lr: 2.9065e-07 eta: 1 day, 10:36:14 time: 0.9996 data_time: 0.0042 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0068 decode.acc_seg: 99.6824 aux.loss_ce: 0.0077 aux.acc_seg: 99.0627 +04/17 18:23:45 - mmengine - INFO - Iter(train) [ 35450/160000] base_lr: 7.8581e-05 lr: 2.9053e-07 eta: 1 day, 10:35:24 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0082 decode.acc_seg: 99.5533 aux.loss_ce: 0.0087 aux.acc_seg: 98.6879 +04/17 18:24:35 - mmengine - INFO - Iter(train) [ 35500/160000] base_lr: 7.8549e-05 lr: 2.9041e-07 eta: 1 day, 10:34:34 time: 1.0011 data_time: 0.0046 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0070 decode.acc_seg: 99.7019 aux.loss_ce: 0.0079 aux.acc_seg: 99.1934 +04/17 18:25:25 - mmengine - INFO - Iter(train) [ 35550/160000] base_lr: 7.8518e-05 lr: 2.9030e-07 eta: 1 day, 10:33:44 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0183 decode.loss_ce: 0.0092 decode.acc_seg: 99.6517 aux.loss_ce: 0.0091 aux.acc_seg: 99.1537 +04/17 18:26:15 - mmengine - INFO - Iter(train) [ 35600/160000] base_lr: 7.8486e-05 lr: 2.9018e-07 eta: 1 day, 10:32:55 time: 1.0007 data_time: 0.0047 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0083 decode.acc_seg: 99.6302 aux.loss_ce: 0.0092 aux.acc_seg: 99.0995 +04/17 18:27:05 - mmengine - INFO - Iter(train) [ 35650/160000] base_lr: 7.8455e-05 lr: 2.9006e-07 eta: 1 day, 10:32:05 time: 0.9998 data_time: 0.0045 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0086 decode.acc_seg: 99.5853 aux.loss_ce: 0.0087 aux.acc_seg: 99.2022 +04/17 18:27:55 - mmengine - INFO - Iter(train) [ 35700/160000] base_lr: 7.8423e-05 lr: 2.8995e-07 eta: 1 day, 10:31:15 time: 0.9993 data_time: 0.0046 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0081 decode.acc_seg: 99.5628 aux.loss_ce: 0.0090 aux.acc_seg: 98.5685 +04/17 18:28:45 - mmengine - INFO - Iter(train) [ 35750/160000] base_lr: 7.8392e-05 lr: 2.8983e-07 eta: 1 day, 10:30:25 time: 1.0009 data_time: 0.0052 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0075 decode.acc_seg: 99.7187 aux.loss_ce: 0.0080 aux.acc_seg: 99.4799 +04/17 18:29:35 - mmengine - INFO - Iter(train) [ 35800/160000] base_lr: 7.8360e-05 lr: 2.8971e-07 eta: 1 day, 10:29:35 time: 1.0014 data_time: 0.0046 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0076 decode.acc_seg: 99.6855 aux.loss_ce: 0.0087 aux.acc_seg: 99.1003 +04/17 18:30:25 - mmengine - INFO - Iter(train) [ 35850/160000] base_lr: 7.8329e-05 lr: 2.8960e-07 eta: 1 day, 10:28:45 time: 1.0012 data_time: 0.0047 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7375 aux.loss_ce: 0.0069 aux.acc_seg: 99.3561 +04/17 18:31:15 - mmengine - INFO - Iter(train) [ 35900/160000] base_lr: 7.8297e-05 lr: 2.8948e-07 eta: 1 day, 10:27:56 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0078 decode.acc_seg: 99.5060 aux.loss_ce: 0.0088 aux.acc_seg: 99.0116 +04/17 18:32:05 - mmengine - INFO - Iter(train) [ 35950/160000] base_lr: 7.8265e-05 lr: 2.8936e-07 eta: 1 day, 10:27:06 time: 1.0015 data_time: 0.0043 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0077 decode.acc_seg: 99.7074 aux.loss_ce: 0.0082 aux.acc_seg: 99.2037 +04/17 18:32:55 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 18:32:55 - mmengine - INFO - Iter(train) [ 36000/160000] base_lr: 7.8234e-05 lr: 2.8925e-07 eta: 1 day, 10:26:16 time: 1.0010 data_time: 0.0049 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0086 decode.acc_seg: 99.5695 aux.loss_ce: 0.0088 aux.acc_seg: 98.9298 +04/17 18:33:45 - mmengine - INFO - Iter(train) [ 36050/160000] base_lr: 7.8202e-05 lr: 2.8913e-07 eta: 1 day, 10:25:26 time: 1.0012 data_time: 0.0044 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0070 decode.acc_seg: 99.6040 aux.loss_ce: 0.0082 aux.acc_seg: 98.9573 +04/17 18:34:35 - mmengine - INFO - Iter(train) [ 36100/160000] base_lr: 7.8171e-05 lr: 2.8901e-07 eta: 1 day, 10:24:37 time: 1.0012 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.8489 aux.loss_ce: 0.0078 aux.acc_seg: 99.6672 +04/17 18:35:26 - mmengine - INFO - Iter(train) [ 36150/160000] base_lr: 7.8139e-05 lr: 2.8890e-07 eta: 1 day, 10:23:47 time: 1.0021 data_time: 0.0046 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0082 decode.acc_seg: 99.6416 aux.loss_ce: 0.0091 aux.acc_seg: 99.0370 +04/17 18:36:16 - mmengine - INFO - Iter(train) [ 36200/160000] base_lr: 7.8108e-05 lr: 2.8878e-07 eta: 1 day, 10:22:57 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0077 decode.acc_seg: 99.7070 aux.loss_ce: 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decode.loss_ce: 0.0078 decode.acc_seg: 99.6830 aux.loss_ce: 0.0077 aux.acc_seg: 99.3126 +04/17 18:40:26 - mmengine - INFO - Iter(train) [ 36450/160000] base_lr: 7.7950e-05 lr: 2.8820e-07 eta: 1 day, 10:18:48 time: 1.0016 data_time: 0.0048 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0082 decode.acc_seg: 99.6511 aux.loss_ce: 0.0093 aux.acc_seg: 98.9363 +04/17 18:41:16 - mmengine - INFO - Iter(train) [ 36500/160000] base_lr: 7.7918e-05 lr: 2.8808e-07 eta: 1 day, 10:17:58 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0085 decode.acc_seg: 99.7471 aux.loss_ce: 0.0093 aux.acc_seg: 99.2018 +04/17 18:42:06 - mmengine - INFO - Iter(train) [ 36550/160000] base_lr: 7.7887e-05 lr: 2.8796e-07 eta: 1 day, 10:17:08 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0073 decode.acc_seg: 99.5436 aux.loss_ce: 0.0085 aux.acc_seg: 98.8192 +04/17 18:42:56 - mmengine - INFO - Iter(train) [ 36600/160000] base_lr: 7.7855e-05 lr: 2.8785e-07 eta: 1 day, 10:16:18 time: 1.0000 data_time: 0.0043 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0078 decode.acc_seg: 99.7873 aux.loss_ce: 0.0083 aux.acc_seg: 99.2937 +04/17 18:43:46 - mmengine - INFO - Iter(train) [ 36650/160000] base_lr: 7.7824e-05 lr: 2.8773e-07 eta: 1 day, 10:15:28 time: 1.0014 data_time: 0.0049 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.7637 aux.loss_ce: 0.0077 aux.acc_seg: 99.2682 +04/17 18:44:36 - mmengine - INFO - Iter(train) [ 36700/160000] base_lr: 7.7792e-05 lr: 2.8761e-07 eta: 1 day, 10:14:39 time: 1.0011 data_time: 0.0043 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0090 decode.acc_seg: 99.5399 aux.loss_ce: 0.0090 aux.acc_seg: 99.2033 +04/17 18:45:26 - mmengine - INFO - Iter(train) [ 36750/160000] base_lr: 7.7761e-05 lr: 2.8750e-07 eta: 1 day, 10:13:49 time: 0.9999 data_time: 0.0047 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0084 decode.acc_seg: 99.6468 aux.loss_ce: 0.0090 aux.acc_seg: 98.8775 +04/17 18:46:16 - mmengine - INFO - Iter(train) [ 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aux.acc_seg: 99.1478 +04/17 18:49:36 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 18:49:36 - mmengine - INFO - Iter(train) [ 37000/160000] base_lr: 7.7603e-05 lr: 2.8691e-07 eta: 1 day, 10:09:40 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0078 decode.acc_seg: 99.7145 aux.loss_ce: 0.0085 aux.acc_seg: 98.6742 +04/17 18:50:26 - mmengine - INFO - Iter(train) [ 37050/160000] base_lr: 7.7571e-05 lr: 2.8680e-07 eta: 1 day, 10:08:50 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0074 decode.acc_seg: 99.6902 aux.loss_ce: 0.0082 aux.acc_seg: 99.0879 +04/17 18:51:16 - mmengine - INFO - Iter(train) [ 37100/160000] base_lr: 7.7540e-05 lr: 2.8668e-07 eta: 1 day, 10:08:00 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0070 decode.acc_seg: 99.7778 aux.loss_ce: 0.0084 aux.acc_seg: 99.3973 +04/17 18:52:06 - mmengine - INFO - Iter(train) [ 37150/160000] base_lr: 7.7508e-05 lr: 2.8656e-07 eta: 1 day, 10:07:10 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0075 decode.acc_seg: 99.6952 aux.loss_ce: 0.0079 aux.acc_seg: 99.2620 +04/17 18:52:56 - mmengine - INFO - Iter(train) [ 37200/160000] base_lr: 7.7477e-05 lr: 2.8645e-07 eta: 1 day, 10:06:20 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0071 decode.acc_seg: 99.7860 aux.loss_ce: 0.0082 aux.acc_seg: 99.2750 +04/17 18:53:46 - mmengine - INFO - Iter(train) [ 37250/160000] base_lr: 7.7445e-05 lr: 2.8633e-07 eta: 1 day, 10:05:30 time: 0.9995 data_time: 0.0043 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0087 decode.acc_seg: 99.5674 aux.loss_ce: 0.0091 aux.acc_seg: 98.4161 +04/17 18:54:36 - mmengine - INFO - Iter(train) [ 37300/160000] base_lr: 7.7414e-05 lr: 2.8621e-07 eta: 1 day, 10:04:40 time: 1.0016 data_time: 0.0048 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0076 decode.acc_seg: 99.6614 aux.loss_ce: 0.0085 aux.acc_seg: 98.8615 +04/17 18:55:26 - mmengine - INFO - Iter(train) [ 37350/160000] base_lr: 7.7382e-05 lr: 2.8610e-07 eta: 1 day, 10:03:51 time: 1.0001 data_time: 0.0045 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.6620 aux.loss_ce: 0.0073 aux.acc_seg: 99.1179 +04/17 18:56:17 - mmengine - INFO - Iter(train) [ 37400/160000] base_lr: 7.7351e-05 lr: 2.8598e-07 eta: 1 day, 10:03:01 time: 1.0004 data_time: 0.0047 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0077 decode.acc_seg: 99.6948 aux.loss_ce: 0.0087 aux.acc_seg: 99.0623 +04/17 18:57:07 - mmengine - INFO - Iter(train) [ 37450/160000] base_lr: 7.7319e-05 lr: 2.8586e-07 eta: 1 day, 10:02:11 time: 0.9997 data_time: 0.0044 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0080 decode.acc_seg: 99.6958 aux.loss_ce: 0.0087 aux.acc_seg: 99.1169 +04/17 18:57:57 - mmengine - INFO - Iter(train) [ 37500/160000] base_lr: 7.7288e-05 lr: 2.8575e-07 eta: 1 day, 10:01:21 time: 0.9990 data_time: 0.0042 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.7894 aux.loss_ce: 0.0079 aux.acc_seg: 99.3212 +04/17 18:58:47 - mmengine - INFO - Iter(train) [ 37550/160000] base_lr: 7.7256e-05 lr: 2.8563e-07 eta: 1 day, 10:00:31 time: 1.0024 data_time: 0.0059 memory: 8462 loss: 0.0196 decode.loss_ce: 0.0102 decode.acc_seg: 99.4537 aux.loss_ce: 0.0094 aux.acc_seg: 99.0900 +04/17 18:59:37 - mmengine - INFO - Iter(train) [ 37600/160000] base_lr: 7.7224e-05 lr: 2.8551e-07 eta: 1 day, 9:59:42 time: 1.0004 data_time: 0.0043 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0081 decode.acc_seg: 99.5842 aux.loss_ce: 0.0087 aux.acc_seg: 98.7751 +04/17 19:00:27 - mmengine - INFO - Iter(train) [ 37650/160000] base_lr: 7.7193e-05 lr: 2.8540e-07 eta: 1 day, 9:58:52 time: 1.0025 data_time: 0.0047 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.8184 aux.loss_ce: 0.0070 aux.acc_seg: 99.5562 +04/17 19:01:17 - mmengine - INFO - Iter(train) [ 37700/160000] base_lr: 7.7161e-05 lr: 2.8528e-07 eta: 1 day, 9:58:02 time: 1.0024 data_time: 0.0043 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.4846 aux.loss_ce: 0.0073 aux.acc_seg: 98.8129 +04/17 19:02:07 - mmengine - INFO - Iter(train) [ 37750/160000] base_lr: 7.7130e-05 lr: 2.8516e-07 eta: 1 day, 9:57:12 time: 1.0009 data_time: 0.0042 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0073 decode.acc_seg: 99.6445 aux.loss_ce: 0.0082 aux.acc_seg: 99.0534 +04/17 19:02:57 - mmengine - INFO - Iter(train) [ 37800/160000] base_lr: 7.7098e-05 lr: 2.8505e-07 eta: 1 day, 9:56:23 time: 1.0005 data_time: 0.0045 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.7906 aux.loss_ce: 0.0078 aux.acc_seg: 99.3464 +04/17 19:03:47 - mmengine - INFO - Iter(train) [ 37850/160000] base_lr: 7.7067e-05 lr: 2.8493e-07 eta: 1 day, 9:55:33 time: 1.0015 data_time: 0.0043 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0088 decode.acc_seg: 99.6288 aux.loss_ce: 0.0089 aux.acc_seg: 99.0606 +04/17 19:04:37 - mmengine - INFO - Iter(train) [ 37900/160000] base_lr: 7.7035e-05 lr: 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data_time: 0.0044 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0079 decode.acc_seg: 99.7562 aux.loss_ce: 0.0093 aux.acc_seg: 99.3286 +04/17 19:11:18 - mmengine - INFO - Iter(train) [ 38300/160000] base_lr: 7.6783e-05 lr: 2.8388e-07 eta: 1 day, 9:48:05 time: 1.0018 data_time: 0.0052 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0074 decode.acc_seg: 99.7210 aux.loss_ce: 0.0080 aux.acc_seg: 99.2395 +04/17 19:12:08 - mmengine - INFO - Iter(train) [ 38350/160000] base_lr: 7.6751e-05 lr: 2.8377e-07 eta: 1 day, 9:47:15 time: 1.0011 data_time: 0.0043 memory: 8462 loss: 0.0189 decode.loss_ce: 0.0093 decode.acc_seg: 99.5348 aux.loss_ce: 0.0096 aux.acc_seg: 98.8853 +04/17 19:12:58 - mmengine - INFO - Iter(train) [ 38400/160000] base_lr: 7.6720e-05 lr: 2.8365e-07 eta: 1 day, 9:46:25 time: 1.0017 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.7086 aux.loss_ce: 0.0080 aux.acc_seg: 99.3008 +04/17 19:13:48 - mmengine - INFO - Iter(train) [ 38450/160000] base_lr: 7.6688e-05 lr: 2.8353e-07 eta: 1 day, 9:45:36 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0084 decode.acc_seg: 99.4303 aux.loss_ce: 0.0089 aux.acc_seg: 98.4764 +04/17 19:14:38 - mmengine - INFO - Iter(train) [ 38500/160000] base_lr: 7.6657e-05 lr: 2.8342e-07 eta: 1 day, 9:44:46 time: 1.0006 data_time: 0.0044 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7578 aux.loss_ce: 0.0077 aux.acc_seg: 99.1474 +04/17 19:15:28 - mmengine - INFO - Iter(train) [ 38550/160000] base_lr: 7.6625e-05 lr: 2.8330e-07 eta: 1 day, 9:43:56 time: 1.0016 data_time: 0.0049 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7684 aux.loss_ce: 0.0071 aux.acc_seg: 99.2023 +04/17 19:16:18 - mmengine - INFO - Iter(train) [ 38600/160000] base_lr: 7.6594e-05 lr: 2.8318e-07 eta: 1 day, 9:43:06 time: 1.0022 data_time: 0.0044 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.2529 aux.loss_ce: 0.0075 aux.acc_seg: 98.7282 +04/17 19:17:08 - mmengine - INFO - Iter(train) [ 38650/160000] base_lr: 7.6562e-05 lr: 2.8307e-07 eta: 1 day, 9:42:16 time: 1.0006 data_time: 0.0046 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0077 decode.acc_seg: 99.5695 aux.loss_ce: 0.0087 aux.acc_seg: 98.8285 +04/17 19:17:58 - mmengine - INFO - Iter(train) [ 38700/160000] base_lr: 7.6530e-05 lr: 2.8295e-07 eta: 1 day, 9:41:27 time: 1.0010 data_time: 0.0044 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7311 aux.loss_ce: 0.0079 aux.acc_seg: 99.1293 +04/17 19:18:48 - mmengine - INFO - Iter(train) [ 38750/160000] base_lr: 7.6499e-05 lr: 2.8283e-07 eta: 1 day, 9:40:37 time: 1.0011 data_time: 0.0047 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.7602 aux.loss_ce: 0.0075 aux.acc_seg: 99.3006 +04/17 19:19:38 - mmengine - INFO - Iter(train) [ 38800/160000] base_lr: 7.6467e-05 lr: 2.8272e-07 eta: 1 day, 9:39:47 time: 0.9997 data_time: 0.0046 memory: 8462 loss: 0.0175 decode.loss_ce: 0.0089 decode.acc_seg: 99.7873 aux.loss_ce: 0.0086 aux.acc_seg: 99.3906 +04/17 19:20:28 - mmengine - INFO - Iter(train) [ 38850/160000] base_lr: 7.6436e-05 lr: 2.8260e-07 eta: 1 day, 9:38:57 time: 1.0017 data_time: 0.0048 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0081 decode.acc_seg: 99.6780 aux.loss_ce: 0.0087 aux.acc_seg: 99.0749 +04/17 19:21:18 - mmengine - INFO - Iter(train) [ 38900/160000] base_lr: 7.6404e-05 lr: 2.8248e-07 eta: 1 day, 9:38:07 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0187 decode.loss_ce: 0.0090 decode.acc_seg: 99.6393 aux.loss_ce: 0.0096 aux.acc_seg: 99.0166 +04/17 19:22:08 - mmengine - INFO - Iter(train) [ 38950/160000] base_lr: 7.6373e-05 lr: 2.8237e-07 eta: 1 day, 9:37:17 time: 1.0005 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0076 decode.acc_seg: 99.7993 aux.loss_ce: 0.0089 aux.acc_seg: 99.1705 +04/17 19:22:58 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 19:22:58 - mmengine - INFO - Iter(train) [ 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99.2432 +04/17 19:26:19 - mmengine - INFO - Iter(train) [ 39200/160000] base_lr: 7.6215e-05 lr: 2.8178e-07 eta: 1 day, 9:33:08 time: 1.0016 data_time: 0.0045 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0070 decode.acc_seg: 99.6433 aux.loss_ce: 0.0077 aux.acc_seg: 99.0658 +04/17 19:27:09 - mmengine - INFO - Iter(train) [ 39250/160000] base_lr: 7.6183e-05 lr: 2.8167e-07 eta: 1 day, 9:32:18 time: 1.0018 data_time: 0.0041 memory: 8462 loss: 0.0180 decode.loss_ce: 0.0090 decode.acc_seg: 99.6525 aux.loss_ce: 0.0090 aux.acc_seg: 99.0257 +04/17 19:27:59 - mmengine - INFO - Iter(train) [ 39300/160000] base_lr: 7.6152e-05 lr: 2.8155e-07 eta: 1 day, 9:31:28 time: 1.0014 data_time: 0.0045 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0074 decode.acc_seg: 99.8295 aux.loss_ce: 0.0080 aux.acc_seg: 99.4982 +04/17 19:28:49 - mmengine - INFO - Iter(train) [ 39350/160000] base_lr: 7.6120e-05 lr: 2.8143e-07 eta: 1 day, 9:30:38 time: 1.0003 data_time: 0.0044 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0078 decode.acc_seg: 99.5817 aux.loss_ce: 0.0094 aux.acc_seg: 98.6925 +04/17 19:29:39 - mmengine - INFO - Iter(train) [ 39400/160000] base_lr: 7.6089e-05 lr: 2.8132e-07 eta: 1 day, 9:29:49 time: 1.0016 data_time: 0.0041 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0078 decode.acc_seg: 99.7734 aux.loss_ce: 0.0085 aux.acc_seg: 99.3156 +04/17 19:30:29 - mmengine - INFO - Iter(train) [ 39450/160000] base_lr: 7.6057e-05 lr: 2.8120e-07 eta: 1 day, 9:28:59 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0088 decode.acc_seg: 99.7704 aux.loss_ce: 0.0089 aux.acc_seg: 99.4074 +04/17 19:31:19 - mmengine - INFO - Iter(train) [ 39500/160000] base_lr: 7.6026e-05 lr: 2.8108e-07 eta: 1 day, 9:28:09 time: 1.0006 data_time: 0.0045 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7780 aux.loss_ce: 0.0074 aux.acc_seg: 99.3631 +04/17 19:32:09 - mmengine - INFO - Iter(train) [ 39550/160000] base_lr: 7.5994e-05 lr: 2.8097e-07 eta: 1 day, 9:27:19 time: 1.0022 data_time: 0.0047 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7141 aux.loss_ce: 0.0077 aux.acc_seg: 99.1346 +04/17 19:32:59 - mmengine - INFO - Iter(train) [ 39600/160000] base_lr: 7.5963e-05 lr: 2.8085e-07 eta: 1 day, 9:26:29 time: 1.0008 data_time: 0.0051 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0074 decode.acc_seg: 99.7297 aux.loss_ce: 0.0081 aux.acc_seg: 99.2731 +04/17 19:33:49 - mmengine - INFO - Iter(train) [ 39650/160000] base_lr: 7.5931e-05 lr: 2.8073e-07 eta: 1 day, 9:25:39 time: 1.0010 data_time: 0.0046 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.4810 aux.loss_ce: 0.0071 aux.acc_seg: 98.8874 +04/17 19:34:39 - mmengine - INFO - Iter(train) [ 39700/160000] base_lr: 7.5900e-05 lr: 2.8062e-07 eta: 1 day, 9:24:50 time: 1.0007 data_time: 0.0043 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0078 decode.acc_seg: 99.7000 aux.loss_ce: 0.0090 aux.acc_seg: 99.2617 +04/17 19:35:29 - mmengine - INFO - Iter(train) [ 39750/160000] base_lr: 7.5868e-05 lr: 2.8050e-07 eta: 1 day, 9:24:00 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0078 decode.acc_seg: 99.6181 aux.loss_ce: 0.0084 aux.acc_seg: 98.8501 +04/17 19:36:19 - mmengine - INFO - Iter(train) [ 39800/160000] base_lr: 7.5836e-05 lr: 2.8038e-07 eta: 1 day, 9:23:10 time: 1.0013 data_time: 0.0043 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0077 decode.acc_seg: 99.7023 aux.loss_ce: 0.0088 aux.acc_seg: 99.0917 +04/17 19:37:09 - mmengine - INFO - Iter(train) [ 39850/160000] base_lr: 7.5805e-05 lr: 2.8027e-07 eta: 1 day, 9:22:20 time: 1.0010 data_time: 0.0049 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0079 decode.acc_seg: 99.6706 aux.loss_ce: 0.0086 aux.acc_seg: 98.9368 +04/17 19:37:59 - mmengine - INFO - Iter(train) [ 39900/160000] base_lr: 7.5773e-05 lr: 2.8015e-07 eta: 1 day, 9:21:30 time: 1.0016 data_time: 0.0052 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.4333 aux.loss_ce: 0.0079 aux.acc_seg: 98.9017 +04/17 19:38:49 - mmengine - INFO - Iter(train) [ 39950/160000] base_lr: 7.5742e-05 lr: 2.8003e-07 eta: 1 day, 9:20:41 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0186 decode.loss_ce: 0.0091 decode.acc_seg: 99.6988 aux.loss_ce: 0.0096 aux.acc_seg: 99.2237 +04/17 19:39:40 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 19:39:40 - mmengine - INFO - Iter(train) [ 40000/160000] base_lr: 7.5710e-05 lr: 2.7992e-07 eta: 1 day, 9:19:51 time: 1.0016 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0072 decode.acc_seg: 99.6504 aux.loss_ce: 0.0081 aux.acc_seg: 99.0877 +04/17 19:39:40 - mmengine - INFO - Saving checkpoint at 40000 iterations +04/17 19:39:49 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1158 data_time: 0.0016 memory: 4004 +04/17 19:39:55 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1157 data_time: 0.0015 memory: 4004 +04/17 19:40:01 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1156 data_time: 0.0014 memory: 4004 +04/17 19:40:07 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1155 data_time: 0.0012 memory: 4004 +04/17 19:40:07 - mmengine - INFO - per class results: +04/17 19:40:07 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.18 | 99.58 | 99.59 | 99.6 | 99.58 | +| contrast | 81.99 | 90.36 | 90.1 | 89.85 | 90.36 | ++------------+-------+-------+--------+-----------+--------+ +04/17 19:40:07 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2100 mIoU: 90.5800 mAcc: 94.9700 mFscore: 94.8500 mPrecision: 94.7200 mRecall: 94.9700 data_time: 0.0018 time: 0.1161 +04/17 19:40:57 - mmengine - INFO - Iter(train) [ 40050/160000] base_lr: 7.5679e-05 lr: 2.7980e-07 eta: 1 day, 9:19:02 time: 0.9999 data_time: 0.0051 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0085 decode.acc_seg: 99.7334 aux.loss_ce: 0.0089 aux.acc_seg: 99.3618 +04/17 19:41:47 - mmengine - INFO - Iter(train) [ 40100/160000] base_lr: 7.5647e-05 lr: 2.7968e-07 eta: 1 day, 9:18:12 time: 1.0000 data_time: 0.0047 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0078 decode.acc_seg: 99.6231 aux.loss_ce: 0.0079 aux.acc_seg: 99.2281 +04/17 19:42:37 - mmengine - INFO - Iter(train) [ 40150/160000] base_lr: 7.5616e-05 lr: 2.7957e-07 eta: 1 day, 9:17:22 time: 1.0004 data_time: 0.0049 memory: 8462 loss: 0.0197 decode.loss_ce: 0.0102 decode.acc_seg: 99.7002 aux.loss_ce: 0.0094 aux.acc_seg: 99.0236 +04/17 19:43:27 - mmengine - INFO - Iter(train) [ 40200/160000] base_lr: 7.5584e-05 lr: 2.7945e-07 eta: 1 day, 9:16:32 time: 1.0012 data_time: 0.0054 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0066 decode.acc_seg: 99.8011 aux.loss_ce: 0.0076 aux.acc_seg: 99.4436 +04/17 19:44:17 - mmengine - INFO - Iter(train) [ 40250/160000] base_lr: 7.5553e-05 lr: 2.7933e-07 eta: 1 day, 9:15:43 time: 1.0022 data_time: 0.0048 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6887 aux.loss_ce: 0.0077 aux.acc_seg: 99.2823 +04/17 19:45:07 - mmengine - INFO - Iter(train) [ 40300/160000] base_lr: 7.5521e-05 lr: 2.7922e-07 eta: 1 day, 9:14:53 time: 1.0010 data_time: 0.0048 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7808 aux.loss_ce: 0.0072 aux.acc_seg: 99.4278 +04/17 19:45:57 - mmengine - INFO - Iter(train) [ 40350/160000] base_lr: 7.5489e-05 lr: 2.7910e-07 eta: 1 day, 9:14:03 time: 1.0020 data_time: 0.0047 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7711 aux.loss_ce: 0.0072 aux.acc_seg: 99.3299 +04/17 19:46:48 - mmengine - INFO - Iter(train) [ 40400/160000] base_lr: 7.5458e-05 lr: 2.7898e-07 eta: 1 day, 9:13:13 time: 1.0018 data_time: 0.0045 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6933 aux.loss_ce: 0.0077 aux.acc_seg: 99.2861 +04/17 19:47:38 - mmengine - INFO - Iter(train) [ 40450/160000] base_lr: 7.5426e-05 lr: 2.7887e-07 eta: 1 day, 9:12:23 time: 1.0012 data_time: 0.0046 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0074 decode.acc_seg: 99.6700 aux.loss_ce: 0.0086 aux.acc_seg: 99.1879 +04/17 19:48:28 - mmengine - INFO - Iter(train) [ 40500/160000] base_lr: 7.5395e-05 lr: 2.7875e-07 eta: 1 day, 9:11:34 time: 1.0008 data_time: 0.0045 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0085 decode.acc_seg: 99.6632 aux.loss_ce: 0.0086 aux.acc_seg: 98.9334 +04/17 19:49:18 - mmengine - INFO - Iter(train) [ 40550/160000] base_lr: 7.5363e-05 lr: 2.7863e-07 eta: 1 day, 9:10:44 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.7723 aux.loss_ce: 0.0081 aux.acc_seg: 99.3284 +04/17 19:50:08 - mmengine - INFO - Iter(train) [ 40600/160000] base_lr: 7.5332e-05 lr: 2.7852e-07 eta: 1 day, 9:09:54 time: 1.0024 data_time: 0.0044 memory: 8462 loss: 0.0178 decode.loss_ce: 0.0085 decode.acc_seg: 99.6166 aux.loss_ce: 0.0094 aux.acc_seg: 98.9628 +04/17 19:50:58 - mmengine - INFO - Iter(train) [ 40650/160000] base_lr: 7.5300e-05 lr: 2.7840e-07 eta: 1 day, 9:09:04 time: 1.0010 data_time: 0.0042 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0081 decode.acc_seg: 99.6910 aux.loss_ce: 0.0086 aux.acc_seg: 99.0755 +04/17 19:51:48 - mmengine - INFO - Iter(train) [ 40700/160000] base_lr: 7.5269e-05 lr: 2.7828e-07 eta: 1 day, 9:08:14 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.5726 aux.loss_ce: 0.0078 aux.acc_seg: 98.9605 +04/17 19:52:38 - mmengine - INFO - Iter(train) [ 40750/160000] base_lr: 7.5237e-05 lr: 2.7817e-07 eta: 1 day, 9:07:25 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7034 aux.loss_ce: 0.0079 aux.acc_seg: 99.1114 +04/17 19:53:28 - mmengine - INFO - Iter(train) [ 40800/160000] base_lr: 7.5206e-05 lr: 2.7805e-07 eta: 1 day, 9:06:35 time: 1.0015 data_time: 0.0047 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0085 decode.acc_seg: 99.6832 aux.loss_ce: 0.0089 aux.acc_seg: 98.9595 +04/17 19:54:18 - mmengine - INFO - Iter(train) [ 40850/160000] base_lr: 7.5174e-05 lr: 2.7793e-07 eta: 1 day, 9:05:45 time: 1.0010 data_time: 0.0047 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0090 decode.acc_seg: 99.6548 aux.loss_ce: 0.0094 aux.acc_seg: 99.0057 +04/17 19:55:08 - mmengine - INFO - Iter(train) [ 40900/160000] base_lr: 7.5142e-05 lr: 2.7782e-07 eta: 1 day, 9:04:55 time: 1.0018 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0067 decode.acc_seg: 99.7723 aux.loss_ce: 0.0080 aux.acc_seg: 99.2199 +04/17 19:55:58 - mmengine - INFO - Iter(train) [ 40950/160000] base_lr: 7.5111e-05 lr: 2.7770e-07 eta: 1 day, 9:04:05 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.6653 aux.loss_ce: 0.0089 aux.acc_seg: 98.8714 +04/17 19:56:48 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 19:56:48 - mmengine - INFO - Iter(train) [ 41000/160000] base_lr: 7.5079e-05 lr: 2.7758e-07 eta: 1 day, 9:03:15 time: 1.0021 data_time: 0.0052 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0080 decode.acc_seg: 99.6866 aux.loss_ce: 0.0088 aux.acc_seg: 99.2544 +04/17 19:57:38 - mmengine - INFO - Iter(train) [ 41050/160000] base_lr: 7.5048e-05 lr: 2.7747e-07 eta: 1 day, 9:02:26 time: 1.0022 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7316 aux.loss_ce: 0.0075 aux.acc_seg: 99.3258 +04/17 19:58:28 - mmengine - INFO - Iter(train) [ 41100/160000] base_lr: 7.5016e-05 lr: 2.7735e-07 eta: 1 day, 9:01:36 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0184 decode.loss_ce: 0.0085 decode.acc_seg: 99.5567 aux.loss_ce: 0.0099 aux.acc_seg: 98.6467 +04/17 19:59:18 - mmengine - INFO - Iter(train) [ 41150/160000] base_lr: 7.4985e-05 lr: 2.7723e-07 eta: 1 day, 9:00:46 time: 1.0007 data_time: 0.0048 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.5911 aux.loss_ce: 0.0077 aux.acc_seg: 98.8766 +04/17 20:00:09 - mmengine - INFO - Iter(train) [ 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98.8382 +04/17 20:03:29 - mmengine - INFO - Iter(train) [ 41400/160000] base_lr: 7.4827e-05 lr: 2.7665e-07 eta: 1 day, 8:56:37 time: 1.0015 data_time: 0.0044 memory: 8462 loss: 0.0204 decode.loss_ce: 0.0106 decode.acc_seg: 99.4713 aux.loss_ce: 0.0098 aux.acc_seg: 98.8861 +04/17 20:04:19 - mmengine - INFO - Iter(train) [ 41450/160000] base_lr: 7.4795e-05 lr: 2.7653e-07 eta: 1 day, 8:55:47 time: 1.0011 data_time: 0.0045 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.6305 aux.loss_ce: 0.0077 aux.acc_seg: 99.0713 +04/17 20:05:09 - mmengine - INFO - Iter(train) [ 41500/160000] base_lr: 7.4764e-05 lr: 2.7642e-07 eta: 1 day, 8:54:57 time: 1.0014 data_time: 0.0042 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0065 decode.acc_seg: 99.6655 aux.loss_ce: 0.0078 aux.acc_seg: 98.9206 +04/17 20:05:59 - mmengine - INFO - Iter(train) [ 41550/160000] base_lr: 7.4732e-05 lr: 2.7630e-07 eta: 1 day, 8:54:07 time: 1.0006 data_time: 0.0044 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0073 decode.acc_seg: 99.5380 aux.loss_ce: 0.0084 aux.acc_seg: 98.7995 +04/17 20:06:49 - mmengine - INFO - Iter(train) [ 41600/160000] base_lr: 7.4701e-05 lr: 2.7618e-07 eta: 1 day, 8:53:17 time: 1.0016 data_time: 0.0048 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.6614 aux.loss_ce: 0.0076 aux.acc_seg: 98.7581 +04/17 20:07:39 - mmengine - INFO - Iter(train) [ 41650/160000] base_lr: 7.4669e-05 lr: 2.7607e-07 eta: 1 day, 8:52:28 time: 1.0018 data_time: 0.0048 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7755 aux.loss_ce: 0.0078 aux.acc_seg: 99.2527 +04/17 20:08:29 - mmengine - INFO - Iter(train) [ 41700/160000] base_lr: 7.4638e-05 lr: 2.7595e-07 eta: 1 day, 8:51:38 time: 1.0032 data_time: 0.0043 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6704 aux.loss_ce: 0.0079 aux.acc_seg: 98.9634 +04/17 20:09:19 - mmengine - INFO - Iter(train) [ 41750/160000] base_lr: 7.4606e-05 lr: 2.7583e-07 eta: 1 day, 8:50:48 time: 1.0015 data_time: 0.0041 memory: 8462 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.8398 aux.loss_ce: 0.0068 aux.acc_seg: 99.5270 +04/17 20:10:09 - mmengine - INFO - Iter(train) [ 41800/160000] base_lr: 7.4575e-05 lr: 2.7572e-07 eta: 1 day, 8:49:58 time: 1.0016 data_time: 0.0046 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0082 decode.acc_seg: 99.6449 aux.loss_ce: 0.0090 aux.acc_seg: 98.7869 +04/17 20:10:59 - mmengine - INFO - Iter(train) [ 41850/160000] base_lr: 7.4543e-05 lr: 2.7560e-07 eta: 1 day, 8:49:08 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.7562 aux.loss_ce: 0.0078 aux.acc_seg: 99.2947 +04/17 20:11:49 - mmengine - INFO - Iter(train) [ 41900/160000] base_lr: 7.4512e-05 lr: 2.7548e-07 eta: 1 day, 8:48:19 time: 1.0013 data_time: 0.0049 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7164 aux.loss_ce: 0.0075 aux.acc_seg: 99.2901 +04/17 20:12:39 - mmengine - INFO - Iter(train) [ 41950/160000] base_lr: 7.4480e-05 lr: 2.7537e-07 eta: 1 day, 8:47:29 time: 1.0010 data_time: 0.0047 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.6908 aux.loss_ce: 0.0084 aux.acc_seg: 99.1018 +04/17 20:13:30 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 20:13:30 - mmengine - INFO - Iter(train) [ 42000/160000] base_lr: 7.4448e-05 lr: 2.7525e-07 eta: 1 day, 8:46:39 time: 1.0012 data_time: 0.0046 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0083 decode.acc_seg: 99.6696 aux.loss_ce: 0.0085 aux.acc_seg: 99.3273 +04/17 20:14:20 - mmengine - INFO - Iter(train) [ 42050/160000] base_lr: 7.4417e-05 lr: 2.7513e-07 eta: 1 day, 8:45:49 time: 1.0016 data_time: 0.0045 memory: 8462 loss: 0.0181 decode.loss_ce: 0.0099 decode.acc_seg: 99.7437 aux.loss_ce: 0.0082 aux.acc_seg: 99.4400 +04/17 20:15:10 - mmengine - INFO - Iter(train) [ 42100/160000] base_lr: 7.4385e-05 lr: 2.7502e-07 eta: 1 day, 8:44:59 time: 1.0019 data_time: 0.0045 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0090 decode.acc_seg: 99.7183 aux.loss_ce: 0.0098 aux.acc_seg: 99.2462 +04/17 20:16:00 - mmengine - INFO - Iter(train) [ 42150/160000] base_lr: 7.4354e-05 lr: 2.7490e-07 eta: 1 day, 8:44:09 time: 1.0017 data_time: 0.0044 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0077 decode.acc_seg: 99.7400 aux.loss_ce: 0.0087 aux.acc_seg: 99.2044 +04/17 20:16:50 - mmengine - INFO - Iter(train) [ 42200/160000] base_lr: 7.4322e-05 lr: 2.7478e-07 eta: 1 day, 8:43:19 time: 1.0005 data_time: 0.0045 memory: 8462 loss: 0.0170 decode.loss_ce: 0.0080 decode.acc_seg: 99.5975 aux.loss_ce: 0.0090 aux.acc_seg: 99.0339 +04/17 20:17:40 - mmengine - INFO - Iter(train) [ 42250/160000] base_lr: 7.4291e-05 lr: 2.7467e-07 eta: 1 day, 8:42:30 time: 1.0018 data_time: 0.0054 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0081 decode.acc_seg: 99.6281 aux.loss_ce: 0.0090 aux.acc_seg: 98.9731 +04/17 20:18:30 - mmengine - INFO - Iter(train) [ 42300/160000] base_lr: 7.4259e-05 lr: 2.7455e-07 eta: 1 day, 8:41:40 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0188 decode.loss_ce: 0.0093 decode.acc_seg: 99.3917 aux.loss_ce: 0.0095 aux.acc_seg: 98.3717 +04/17 20:19:20 - mmengine - INFO - Iter(train) [ 42350/160000] base_lr: 7.4228e-05 lr: 2.7443e-07 eta: 1 day, 8:40:50 time: 1.0015 data_time: 0.0057 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0086 decode.acc_seg: 99.5617 aux.loss_ce: 0.0090 aux.acc_seg: 99.0385 +04/17 20:20:10 - mmengine - INFO - Iter(train) [ 42400/160000] base_lr: 7.4196e-05 lr: 2.7432e-07 eta: 1 day, 8:40:00 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.8081 aux.loss_ce: 0.0079 aux.acc_seg: 99.3773 +04/17 20:21:00 - mmengine - INFO - Iter(train) [ 42450/160000] base_lr: 7.4165e-05 lr: 2.7420e-07 eta: 1 day, 8:39:10 time: 0.9989 data_time: 0.0044 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0079 decode.acc_seg: 99.6317 aux.loss_ce: 0.0085 aux.acc_seg: 98.9735 +04/17 20:21:50 - mmengine - INFO - Iter(train) [ 42500/160000] base_lr: 7.4133e-05 lr: 2.7408e-07 eta: 1 day, 8:38:20 time: 1.0010 data_time: 0.0047 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0070 decode.acc_seg: 99.6326 aux.loss_ce: 0.0080 aux.acc_seg: 98.9738 +04/17 20:22:40 - mmengine - INFO - Iter(train) [ 42550/160000] base_lr: 7.4101e-05 lr: 2.7397e-07 eta: 1 day, 8:37:30 time: 1.0008 data_time: 0.0047 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.6315 aux.loss_ce: 0.0075 aux.acc_seg: 99.2037 +04/17 20:23:30 - mmengine - INFO - Iter(train) [ 42600/160000] base_lr: 7.4070e-05 lr: 2.7385e-07 eta: 1 day, 8:36:41 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7305 aux.loss_ce: 0.0075 aux.acc_seg: 99.2727 +04/17 20:24:20 - mmengine - INFO - Iter(train) [ 42650/160000] base_lr: 7.4038e-05 lr: 2.7373e-07 eta: 1 day, 8:35:51 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.6355 aux.loss_ce: 0.0079 aux.acc_seg: 98.8504 +04/17 20:25:10 - mmengine - INFO - Iter(train) [ 42700/160000] base_lr: 7.4007e-05 lr: 2.7362e-07 eta: 1 day, 8:35:01 time: 1.0007 data_time: 0.0051 memory: 8462 loss: 0.0162 decode.loss_ce: 0.0077 decode.acc_seg: 99.6918 aux.loss_ce: 0.0085 aux.acc_seg: 98.7600 +04/17 20:26:00 - mmengine - INFO - Iter(train) [ 42750/160000] base_lr: 7.3975e-05 lr: 2.7350e-07 eta: 1 day, 8:34:11 time: 1.0010 data_time: 0.0048 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0085 decode.acc_seg: 99.6593 aux.loss_ce: 0.0087 aux.acc_seg: 98.9767 +04/17 20:26:50 - mmengine - INFO - Iter(train) [ 42800/160000] base_lr: 7.3944e-05 lr: 2.7339e-07 eta: 1 day, 8:33:21 time: 1.0008 data_time: 0.0045 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6262 aux.loss_ce: 0.0078 aux.acc_seg: 99.1676 +04/17 20:27:40 - mmengine - INFO - Iter(train) [ 42850/160000] base_lr: 7.3912e-05 lr: 2.7327e-07 eta: 1 day, 8:32:31 time: 1.0015 data_time: 0.0049 memory: 8462 loss: 0.0182 decode.loss_ce: 0.0089 decode.acc_seg: 99.6262 aux.loss_ce: 0.0092 aux.acc_seg: 99.1257 +04/17 20:28:31 - mmengine - INFO - Iter(train) [ 42900/160000] base_lr: 7.3881e-05 lr: 2.7315e-07 eta: 1 day, 8:31:41 time: 1.0011 data_time: 0.0047 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0078 decode.acc_seg: 99.6033 aux.loss_ce: 0.0090 aux.acc_seg: 98.9317 +04/17 20:29:21 - mmengine - INFO - Iter(train) [ 42950/160000] base_lr: 7.3849e-05 lr: 2.7304e-07 eta: 1 day, 8:30:51 time: 1.0000 data_time: 0.0053 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0073 decode.acc_seg: 99.6120 aux.loss_ce: 0.0080 aux.acc_seg: 99.0517 +04/17 20:30:11 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 20:30:11 - mmengine - INFO - Iter(train) [ 43000/160000] base_lr: 7.3818e-05 lr: 2.7292e-07 eta: 1 day, 8:30:02 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0080 decode.acc_seg: 99.7398 aux.loss_ce: 0.0089 aux.acc_seg: 99.1747 +04/17 20:31:01 - mmengine - INFO - Iter(train) [ 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INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 20:46:52 - mmengine - INFO - Iter(train) [ 44000/160000] base_lr: 7.3187e-05 lr: 2.7059e-07 eta: 1 day, 8:13:24 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0079 decode.acc_seg: 99.6220 aux.loss_ce: 0.0082 aux.acc_seg: 98.9563 +04/17 20:47:42 - mmengine - INFO - Iter(train) [ 44050/160000] base_lr: 7.3155e-05 lr: 2.7047e-07 eta: 1 day, 8:12:34 time: 0.9998 data_time: 0.0048 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0075 decode.acc_seg: 99.6170 aux.loss_ce: 0.0083 aux.acc_seg: 99.1631 +04/17 20:48:32 - mmengine - INFO - Iter(train) [ 44100/160000] base_lr: 7.3123e-05 lr: 2.7035e-07 eta: 1 day, 8:11:45 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.6635 aux.loss_ce: 0.0077 aux.acc_seg: 99.0484 +04/17 20:49:22 - mmengine - INFO - Iter(train) [ 44150/160000] base_lr: 7.3092e-05 lr: 2.7024e-07 eta: 1 day, 8:10:55 time: 1.0011 data_time: 0.0049 memory: 8462 loss: 0.0172 decode.loss_ce: 0.0082 decode.acc_seg: 99.6815 aux.loss_ce: 0.0089 aux.acc_seg: 99.2743 +04/17 20:50:12 - mmengine - INFO - Iter(train) [ 44200/160000] base_lr: 7.3060e-05 lr: 2.7012e-07 eta: 1 day, 8:10:05 time: 1.0009 data_time: 0.0048 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0079 decode.acc_seg: 99.7707 aux.loss_ce: 0.0086 aux.acc_seg: 99.3401 +04/17 20:51:02 - mmengine - INFO - Iter(train) [ 44250/160000] base_lr: 7.3029e-05 lr: 2.7000e-07 eta: 1 day, 8:09:15 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.6984 aux.loss_ce: 0.0081 aux.acc_seg: 99.1928 +04/17 20:51:52 - mmengine - INFO - Iter(train) [ 44300/160000] base_lr: 7.2997e-05 lr: 2.6989e-07 eta: 1 day, 8:08:25 time: 1.0013 data_time: 0.0048 memory: 8462 loss: 0.0202 decode.loss_ce: 0.0098 decode.acc_seg: 99.5548 aux.loss_ce: 0.0105 aux.acc_seg: 98.5489 +04/17 20:52:42 - mmengine - INFO - Iter(train) [ 44350/160000] base_lr: 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mmengine - INFO - Iter(train) [ 44550/160000] base_lr: 7.2840e-05 lr: 2.6930e-07 eta: 1 day, 8:04:16 time: 1.0013 data_time: 0.0047 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.6046 aux.loss_ce: 0.0079 aux.acc_seg: 98.7156 +04/17 20:56:52 - mmengine - INFO - Iter(train) [ 44600/160000] base_lr: 7.2808e-05 lr: 2.6919e-07 eta: 1 day, 8:03:26 time: 1.0008 data_time: 0.0044 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.7654 aux.loss_ce: 0.0074 aux.acc_seg: 99.2189 +04/17 20:57:43 - mmengine - INFO - Iter(train) [ 44650/160000] base_lr: 7.2776e-05 lr: 2.6907e-07 eta: 1 day, 8:02:36 time: 1.0019 data_time: 0.0046 memory: 8462 loss: 0.0176 decode.loss_ce: 0.0085 decode.acc_seg: 99.7700 aux.loss_ce: 0.0092 aux.acc_seg: 99.3835 +04/17 20:58:33 - mmengine - INFO - Iter(train) [ 44700/160000] base_lr: 7.2745e-05 lr: 2.6895e-07 eta: 1 day, 8:01:46 time: 1.0019 data_time: 0.0052 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0080 decode.acc_seg: 99.6042 aux.loss_ce: 0.0088 aux.acc_seg: 99.1199 +04/17 20:59:23 - mmengine - INFO - Iter(train) [ 44750/160000] base_lr: 7.2713e-05 lr: 2.6884e-07 eta: 1 day, 8:00:56 time: 1.0007 data_time: 0.0044 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0080 decode.acc_seg: 99.7177 aux.loss_ce: 0.0087 aux.acc_seg: 99.2779 +04/17 21:00:13 - mmengine - INFO - Iter(train) [ 44800/160000] base_lr: 7.2682e-05 lr: 2.6872e-07 eta: 1 day, 8:00:06 time: 1.0030 data_time: 0.0047 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0071 decode.acc_seg: 99.7200 aux.loss_ce: 0.0086 aux.acc_seg: 99.2632 +04/17 21:01:03 - mmengine - INFO - Iter(train) [ 44850/160000] base_lr: 7.2650e-05 lr: 2.6860e-07 eta: 1 day, 7:59:16 time: 1.0022 data_time: 0.0044 memory: 8462 loss: 0.0171 decode.loss_ce: 0.0080 decode.acc_seg: 99.7496 aux.loss_ce: 0.0091 aux.acc_seg: 99.2411 +04/17 21:01:53 - mmengine - INFO - Iter(train) [ 44900/160000] base_lr: 7.2619e-05 lr: 2.6849e-07 eta: 1 day, 7:58:27 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0077 decode.acc_seg: 99.5272 aux.loss_ce: 0.0086 aux.acc_seg: 98.4587 +04/17 21:02:43 - mmengine - INFO - Iter(train) [ 44950/160000] base_lr: 7.2587e-05 lr: 2.6837e-07 eta: 1 day, 7:57:37 time: 1.0009 data_time: 0.0051 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0070 decode.acc_seg: 99.7227 aux.loss_ce: 0.0081 aux.acc_seg: 99.2695 +04/17 21:03:33 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 21:03:33 - mmengine - INFO - Iter(train) [ 45000/160000] base_lr: 7.2556e-05 lr: 2.6825e-07 eta: 1 day, 7:56:47 time: 1.0002 data_time: 0.0048 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.7528 aux.loss_ce: 0.0079 aux.acc_seg: 99.4102 +04/17 21:04:23 - mmengine - INFO - Iter(train) [ 45050/160000] base_lr: 7.2524e-05 lr: 2.6814e-07 eta: 1 day, 7:55:57 time: 1.0014 data_time: 0.0044 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0076 decode.acc_seg: 99.7311 aux.loss_ce: 0.0092 aux.acc_seg: 98.9716 +04/17 21:05:13 - mmengine - INFO - Iter(train) [ 45100/160000] base_lr: 7.2493e-05 lr: 2.6802e-07 eta: 1 day, 7:55:07 time: 1.0025 data_time: 0.0046 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7829 aux.loss_ce: 0.0076 aux.acc_seg: 99.1774 +04/17 21:06:03 - mmengine - INFO - Iter(train) [ 45150/160000] base_lr: 7.2461e-05 lr: 2.6790e-07 eta: 1 day, 7:54:17 time: 1.0001 data_time: 0.0042 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0075 decode.acc_seg: 99.6052 aux.loss_ce: 0.0083 aux.acc_seg: 98.9243 +04/17 21:06:53 - mmengine - INFO - Iter(train) [ 45200/160000] base_lr: 7.2429e-05 lr: 2.6779e-07 eta: 1 day, 7:53:27 time: 1.0000 data_time: 0.0050 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.7332 aux.loss_ce: 0.0078 aux.acc_seg: 99.3267 +04/17 21:07:43 - mmengine - INFO - Iter(train) [ 45250/160000] base_lr: 7.2398e-05 lr: 2.6767e-07 eta: 1 day, 7:52:37 time: 1.0008 data_time: 0.0051 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7673 aux.loss_ce: 0.0077 aux.acc_seg: 99.4791 +04/17 21:08:33 - mmengine - INFO - Iter(train) [ 45300/160000] base_lr: 7.2366e-05 lr: 2.6755e-07 eta: 1 day, 7:51:48 time: 1.0018 data_time: 0.0044 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7829 aux.loss_ce: 0.0077 aux.acc_seg: 99.3141 +04/17 21:09:23 - mmengine - INFO - Iter(train) [ 45350/160000] base_lr: 7.2335e-05 lr: 2.6744e-07 eta: 1 day, 7:50:58 time: 1.0025 data_time: 0.0053 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0071 decode.acc_seg: 99.6622 aux.loss_ce: 0.0079 aux.acc_seg: 99.2636 +04/17 21:10:13 - mmengine - INFO - Iter(train) [ 45400/160000] base_lr: 7.2303e-05 lr: 2.6732e-07 eta: 1 day, 7:50:08 time: 0.9997 data_time: 0.0048 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0081 decode.acc_seg: 99.6243 aux.loss_ce: 0.0083 aux.acc_seg: 99.2947 +04/17 21:11:03 - mmengine - INFO - Iter(train) [ 45450/160000] base_lr: 7.2272e-05 lr: 2.6720e-07 eta: 1 day, 7:49:18 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0079 decode.acc_seg: 99.6975 aux.loss_ce: 0.0088 aux.acc_seg: 99.0618 +04/17 21:11:54 - mmengine - INFO - Iter(train) [ 45500/160000] base_lr: 7.2240e-05 lr: 2.6709e-07 eta: 1 day, 7:48:28 time: 1.0007 data_time: 0.0045 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7581 aux.loss_ce: 0.0077 aux.acc_seg: 99.3103 +04/17 21:12:44 - mmengine - INFO - Iter(train) [ 45550/160000] base_lr: 7.2209e-05 lr: 2.6697e-07 eta: 1 day, 7:47:38 time: 1.0012 data_time: 0.0048 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0068 decode.acc_seg: 99.7274 aux.loss_ce: 0.0083 aux.acc_seg: 99.1192 +04/17 21:13:34 - mmengine - INFO - Iter(train) [ 45600/160000] base_lr: 7.2177e-05 lr: 2.6685e-07 eta: 1 day, 7:46:48 time: 1.0006 data_time: 0.0049 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.7150 aux.loss_ce: 0.0081 aux.acc_seg: 99.1716 +04/17 21:14:24 - mmengine - INFO - Iter(train) [ 45650/160000] base_lr: 7.2146e-05 lr: 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INFO - Iter(train) [ 45850/160000] base_lr: 7.2019e-05 lr: 2.6627e-07 eta: 1 day, 7:42:39 time: 1.0004 data_time: 0.0046 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0063 decode.acc_seg: 99.8165 aux.loss_ce: 0.0075 aux.acc_seg: 99.5226 +04/17 21:18:34 - mmengine - INFO - Iter(train) [ 45900/160000] base_lr: 7.1988e-05 lr: 2.6615e-07 eta: 1 day, 7:41:49 time: 1.0015 data_time: 0.0044 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.7004 aux.loss_ce: 0.0081 aux.acc_seg: 99.2018 +04/17 21:19:24 - mmengine - INFO - Iter(train) [ 45950/160000] base_lr: 7.1956e-05 lr: 2.6604e-07 eta: 1 day, 7:40:59 time: 1.0010 data_time: 0.0056 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0065 decode.acc_seg: 99.7581 aux.loss_ce: 0.0079 aux.acc_seg: 99.2363 +04/17 21:20:14 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 21:20:14 - mmengine - INFO - Iter(train) [ 46000/160000] base_lr: 7.1925e-05 lr: 2.6592e-07 eta: 1 day, 7:40:09 time: 1.0018 data_time: 0.0045 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0077 decode.acc_seg: 99.5554 aux.loss_ce: 0.0091 aux.acc_seg: 98.8052 +04/17 21:21:04 - mmengine - INFO - Iter(train) [ 46050/160000] base_lr: 7.1893e-05 lr: 2.6580e-07 eta: 1 day, 7:39:19 time: 1.0008 data_time: 0.0043 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0082 decode.acc_seg: 99.7744 aux.loss_ce: 0.0090 aux.acc_seg: 99.3090 +04/17 21:21:54 - mmengine - INFO - Iter(train) [ 46100/160000] base_lr: 7.1862e-05 lr: 2.6569e-07 eta: 1 day, 7:38:29 time: 1.0013 data_time: 0.0048 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.7307 aux.loss_ce: 0.0075 aux.acc_seg: 99.2559 +04/17 21:22:44 - mmengine - INFO - Iter(train) [ 46150/160000] base_lr: 7.1830e-05 lr: 2.6557e-07 eta: 1 day, 7:37:39 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0075 decode.acc_seg: 99.7231 aux.loss_ce: 0.0085 aux.acc_seg: 99.2443 +04/17 21:23:34 - mmengine - INFO - Iter(train) [ 46200/160000] base_lr: 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loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.7663 aux.loss_ce: 0.0078 aux.acc_seg: 99.2990 +04/17 21:33:35 - mmengine - INFO - Iter(train) [ 46800/160000] base_lr: 7.1420e-05 lr: 2.6405e-07 eta: 1 day, 7:26:51 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0071 decode.acc_seg: 99.8091 aux.loss_ce: 0.0085 aux.acc_seg: 99.3412 +04/17 21:34:25 - mmengine - INFO - Iter(train) [ 46850/160000] base_lr: 7.1388e-05 lr: 2.6394e-07 eta: 1 day, 7:26:01 time: 1.0011 data_time: 0.0044 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7366 aux.loss_ce: 0.0074 aux.acc_seg: 99.0967 +04/17 21:35:15 - mmengine - INFO - Iter(train) [ 46900/160000] base_lr: 7.1357e-05 lr: 2.6382e-07 eta: 1 day, 7:25:11 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.7536 aux.loss_ce: 0.0074 aux.acc_seg: 99.1590 +04/17 21:36:05 - mmengine - INFO - Iter(train) [ 46950/160000] base_lr: 7.1325e-05 lr: 2.6370e-07 eta: 1 day, 7:24:21 time: 0.9995 data_time: 0.0045 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0069 decode.acc_seg: 99.7025 aux.loss_ce: 0.0083 aux.acc_seg: 99.0747 +04/17 21:36:55 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 21:36:55 - mmengine - INFO - Iter(train) [ 47000/160000] base_lr: 7.1294e-05 lr: 2.6359e-07 eta: 1 day, 7:23:31 time: 1.0004 data_time: 0.0045 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6403 aux.loss_ce: 0.0079 aux.acc_seg: 99.0517 +04/17 21:37:45 - mmengine - INFO - Iter(train) [ 47050/160000] base_lr: 7.1262e-05 lr: 2.6347e-07 eta: 1 day, 7:22:41 time: 1.0009 data_time: 0.0050 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0073 decode.acc_seg: 99.7501 aux.loss_ce: 0.0085 aux.acc_seg: 99.1697 +04/17 21:38:35 - mmengine - INFO - Iter(train) [ 47100/160000] base_lr: 7.1231e-05 lr: 2.6335e-07 eta: 1 day, 7:21:51 time: 1.0017 data_time: 0.0044 memory: 8462 loss: 0.0165 decode.loss_ce: 0.0077 decode.acc_seg: 99.6986 aux.loss_ce: 0.0087 aux.acc_seg: 99.1751 +04/17 21:39:25 - mmengine - INFO - Iter(train) [ 47150/160000] base_lr: 7.1199e-05 lr: 2.6324e-07 eta: 1 day, 7:21:01 time: 1.0005 data_time: 0.0046 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7280 aux.loss_ce: 0.0075 aux.acc_seg: 99.3244 +04/17 21:40:15 - mmengine - INFO - Iter(train) [ 47200/160000] base_lr: 7.1168e-05 lr: 2.6312e-07 eta: 1 day, 7:20:12 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0074 decode.acc_seg: 99.7494 aux.loss_ce: 0.0084 aux.acc_seg: 99.3801 +04/17 21:41:05 - mmengine - INFO - Iter(train) [ 47250/160000] base_lr: 7.1136e-05 lr: 2.6300e-07 eta: 1 day, 7:19:22 time: 1.0014 data_time: 0.0047 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.7694 aux.loss_ce: 0.0081 aux.acc_seg: 99.4297 +04/17 21:41:55 - mmengine - INFO - Iter(train) [ 47300/160000] base_lr: 7.1105e-05 lr: 2.6289e-07 eta: 1 day, 7:18:32 time: 1.0004 data_time: 0.0044 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0067 decode.acc_seg: 99.7293 aux.loss_ce: 0.0078 aux.acc_seg: 99.2870 +04/17 21:42:45 - mmengine - INFO - Iter(train) [ 47350/160000] base_lr: 7.1073e-05 lr: 2.6277e-07 eta: 1 day, 7:17:42 time: 1.0014 data_time: 0.0047 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0074 decode.acc_seg: 99.7446 aux.loss_ce: 0.0084 aux.acc_seg: 99.1817 +04/17 21:43:35 - mmengine - INFO - Iter(train) [ 47400/160000] base_lr: 7.1041e-05 lr: 2.6265e-07 eta: 1 day, 7:16:52 time: 1.0012 data_time: 0.0048 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7017 aux.loss_ce: 0.0076 aux.acc_seg: 98.8943 +04/17 21:44:25 - mmengine - INFO - Iter(train) [ 47450/160000] base_lr: 7.1010e-05 lr: 2.6254e-07 eta: 1 day, 7:16:02 time: 1.0012 data_time: 0.0050 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0075 decode.acc_seg: 99.7063 aux.loss_ce: 0.0084 aux.acc_seg: 98.9916 +04/17 21:45:16 - mmengine - INFO - Iter(train) [ 47500/160000] base_lr: 7.0978e-05 lr: 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INFO - Iter(train) [ 47700/160000] base_lr: 7.0852e-05 lr: 2.6196e-07 eta: 1 day, 7:11:52 time: 1.0018 data_time: 0.0043 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0078 decode.acc_seg: 99.6267 aux.loss_ce: 0.0090 aux.acc_seg: 98.8432 +04/17 21:49:26 - mmengine - INFO - Iter(train) [ 47750/160000] base_lr: 7.0821e-05 lr: 2.6184e-07 eta: 1 day, 7:11:02 time: 1.0009 data_time: 0.0043 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0068 decode.acc_seg: 99.7084 aux.loss_ce: 0.0078 aux.acc_seg: 99.1014 +04/17 21:50:16 - mmengine - INFO - Iter(train) [ 47800/160000] base_lr: 7.0789e-05 lr: 2.6172e-07 eta: 1 day, 7:10:12 time: 1.0012 data_time: 0.0050 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.6197 aux.loss_ce: 0.0073 aux.acc_seg: 98.8844 +04/17 21:51:06 - mmengine - INFO - Iter(train) [ 47850/160000] base_lr: 7.0758e-05 lr: 2.6161e-07 eta: 1 day, 7:09:23 time: 1.0020 data_time: 0.0044 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0068 decode.acc_seg: 99.6948 aux.loss_ce: 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mmengine - INFO - Iter(train) [ 48250/160000] base_lr: 7.0505e-05 lr: 2.6067e-07 eta: 1 day, 7:02:44 time: 1.0001 data_time: 0.0048 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0070 decode.acc_seg: 99.6876 aux.loss_ce: 0.0080 aux.acc_seg: 98.9485 +04/17 21:58:36 - mmengine - INFO - Iter(train) [ 48300/160000] base_lr: 7.0474e-05 lr: 2.6056e-07 eta: 1 day, 7:01:54 time: 1.0016 data_time: 0.0050 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0073 decode.acc_seg: 99.6477 aux.loss_ce: 0.0083 aux.acc_seg: 98.9332 +04/17 21:59:27 - mmengine - INFO - Iter(train) [ 48350/160000] base_lr: 7.0442e-05 lr: 2.6044e-07 eta: 1 day, 7:01:04 time: 1.0017 data_time: 0.0044 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7740 aux.loss_ce: 0.0072 aux.acc_seg: 99.5386 +04/17 22:00:17 - mmengine - INFO - Iter(train) [ 48400/160000] base_lr: 7.0411e-05 lr: 2.6032e-07 eta: 1 day, 7:00:14 time: 1.0016 data_time: 0.0047 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0064 decode.acc_seg: 99.6397 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mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 22:10:18 - mmengine - INFO - Iter(train) [ 49000/160000] base_lr: 7.0032e-05 lr: 2.5892e-07 eta: 1 day, 6:50:16 time: 1.0002 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0070 decode.acc_seg: 99.7532 aux.loss_ce: 0.0077 aux.acc_seg: 99.3370 +04/17 22:11:08 - mmengine - INFO - Iter(train) [ 49050/160000] base_lr: 7.0000e-05 lr: 2.5881e-07 eta: 1 day, 6:49:26 time: 1.0013 data_time: 0.0046 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0074 decode.acc_seg: 99.5754 aux.loss_ce: 0.0087 aux.acc_seg: 98.7215 +04/17 22:11:58 - mmengine - INFO - Iter(train) [ 49100/160000] base_lr: 6.9969e-05 lr: 2.5869e-07 eta: 1 day, 6:48:36 time: 1.0005 data_time: 0.0044 memory: 8462 loss: 0.0174 decode.loss_ce: 0.0082 decode.acc_seg: 99.7231 aux.loss_ce: 0.0092 aux.acc_seg: 99.1968 +04/17 22:12:48 - mmengine - INFO - Iter(train) [ 49150/160000] base_lr: 6.9937e-05 lr: 2.5857e-07 eta: 1 day, 6:47:46 time: 1.0009 data_time: 0.0047 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0072 decode.acc_seg: 99.6813 aux.loss_ce: 0.0084 aux.acc_seg: 99.0835 +04/17 22:13:38 - mmengine - INFO - Iter(train) [ 49200/160000] base_lr: 6.9906e-05 lr: 2.5846e-07 eta: 1 day, 6:46:56 time: 1.0013 data_time: 0.0051 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0067 decode.acc_seg: 99.7419 aux.loss_ce: 0.0084 aux.acc_seg: 98.9325 +04/17 22:14:28 - mmengine - INFO - Iter(train) [ 49250/160000] base_lr: 6.9874e-05 lr: 2.5834e-07 eta: 1 day, 6:46:06 time: 1.0021 data_time: 0.0046 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.6363 aux.loss_ce: 0.0075 aux.acc_seg: 99.0494 +04/17 22:15:18 - mmengine - INFO - Iter(train) [ 49300/160000] base_lr: 6.9843e-05 lr: 2.5822e-07 eta: 1 day, 6:45:16 time: 1.0011 data_time: 0.0044 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0073 decode.acc_seg: 99.6635 aux.loss_ce: 0.0081 aux.acc_seg: 99.0404 +04/17 22:16:08 - mmengine - INFO - Iter(train) [ 49350/160000] base_lr: 6.9811e-05 lr: 2.5811e-07 eta: 1 day, 6:44:27 time: 1.0020 data_time: 0.0049 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0064 decode.acc_seg: 99.7753 aux.loss_ce: 0.0083 aux.acc_seg: 99.3837 +04/17 22:16:58 - mmengine - INFO - Iter(train) [ 49400/160000] base_lr: 6.9780e-05 lr: 2.5799e-07 eta: 1 day, 6:43:37 time: 0.9983 data_time: 0.0047 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7105 aux.loss_ce: 0.0070 aux.acc_seg: 99.2142 +04/17 22:17:48 - mmengine - INFO - Iter(train) [ 49450/160000] base_lr: 6.9748e-05 lr: 2.5787e-07 eta: 1 day, 6:42:47 time: 1.0017 data_time: 0.0047 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0079 decode.acc_seg: 99.7072 aux.loss_ce: 0.0089 aux.acc_seg: 99.4307 +04/17 22:18:38 - mmengine - INFO - Iter(train) [ 49500/160000] base_lr: 6.9717e-05 lr: 2.5776e-07 eta: 1 day, 6:41:57 time: 1.0010 data_time: 0.0049 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.6824 aux.loss_ce: 0.0076 aux.acc_seg: 98.8033 +04/17 22:19:28 - mmengine - INFO - Iter(train) [ 49550/160000] base_lr: 6.9685e-05 lr: 2.5764e-07 eta: 1 day, 6:41:07 time: 1.0011 data_time: 0.0048 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0066 decode.acc_seg: 99.7353 aux.loss_ce: 0.0082 aux.acc_seg: 99.1476 +04/17 22:20:18 - mmengine - INFO - Iter(train) [ 49600/160000] base_lr: 6.9653e-05 lr: 2.5752e-07 eta: 1 day, 6:40:17 time: 1.0010 data_time: 0.0046 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0075 decode.acc_seg: 99.7128 aux.loss_ce: 0.0082 aux.acc_seg: 99.0692 +04/17 22:21:08 - mmengine - INFO - Iter(train) [ 49650/160000] base_lr: 6.9622e-05 lr: 2.5741e-07 eta: 1 day, 6:39:27 time: 1.0017 data_time: 0.0049 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0072 decode.acc_seg: 99.7137 aux.loss_ce: 0.0080 aux.acc_seg: 99.2208 +04/17 22:21:58 - mmengine - INFO - Iter(train) [ 49700/160000] base_lr: 6.9590e-05 lr: 2.5729e-07 eta: 1 day, 6:38:37 time: 1.0017 data_time: 0.0046 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0063 decode.acc_seg: 99.6155 aux.loss_ce: 0.0081 aux.acc_seg: 98.8371 +04/17 22:22:49 - mmengine - INFO - Iter(train) [ 49750/160000] base_lr: 6.9559e-05 lr: 2.5717e-07 eta: 1 day, 6:37:47 time: 1.0002 data_time: 0.0045 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.7698 aux.loss_ce: 0.0076 aux.acc_seg: 99.4476 +04/17 22:23:39 - mmengine - INFO - Iter(train) [ 49800/160000] base_lr: 6.9527e-05 lr: 2.5706e-07 eta: 1 day, 6:36:57 time: 1.0008 data_time: 0.0046 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0068 decode.acc_seg: 99.6786 aux.loss_ce: 0.0081 aux.acc_seg: 99.0566 +04/17 22:24:29 - mmengine - INFO - Iter(train) [ 49850/160000] base_lr: 6.9496e-05 lr: 2.5694e-07 eta: 1 day, 6:36:08 time: 1.0015 data_time: 0.0048 memory: 8462 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7276 aux.loss_ce: 0.0068 aux.acc_seg: 99.3731 +04/17 22:25:19 - mmengine - INFO - Iter(train) [ 49900/160000] base_lr: 6.9464e-05 lr: 2.5682e-07 eta: 1 day, 6:35:18 time: 1.0006 data_time: 0.0047 memory: 8462 loss: 0.0164 decode.loss_ce: 0.0078 decode.acc_seg: 99.7438 aux.loss_ce: 0.0086 aux.acc_seg: 99.4097 +04/17 22:26:09 - mmengine - INFO - Iter(train) [ 49950/160000] base_lr: 6.9433e-05 lr: 2.5671e-07 eta: 1 day, 6:34:28 time: 1.0024 data_time: 0.0047 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.7375 aux.loss_ce: 0.0075 aux.acc_seg: 99.2811 +04/17 22:26:59 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 22:26:59 - mmengine - INFO - Iter(train) [ 50000/160000] base_lr: 6.9401e-05 lr: 2.5659e-07 eta: 1 day, 6:33:38 time: 1.0030 data_time: 0.0045 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.6641 aux.loss_ce: 0.0074 aux.acc_seg: 99.1444 +04/17 22:26:59 - mmengine - INFO - Saving checkpoint at 50000 iterations +04/17 22:27:09 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1159 data_time: 0.0014 memory: 4004 +04/17 22:27:15 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1158 data_time: 0.0014 memory: 4004 +04/17 22:27:20 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1160 data_time: 0.0015 memory: 4004 +04/17 22:27:26 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1161 data_time: 0.0016 memory: 4004 +04/17 22:27:26 - mmengine - INFO - per class results: +04/17 22:27:26 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.17 | 99.59 | 99.58 | 99.58 | 99.59 | +| contrast | 81.84 | 89.95 | 90.01 | 90.08 | 89.95 | ++------------+-------+-------+--------+-----------+--------+ +04/17 22:27:26 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2000 mIoU: 90.5100 mAcc: 94.7700 mFscore: 94.8000 mPrecision: 94.8300 mRecall: 94.7700 data_time: 0.0016 time: 0.1162 +04/17 22:28:17 - mmengine - INFO - Iter(train) [ 50050/160000] base_lr: 6.9370e-05 lr: 2.5647e-07 eta: 1 day, 6:32:49 time: 1.0014 data_time: 0.0044 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0072 decode.acc_seg: 99.6246 aux.loss_ce: 0.0081 aux.acc_seg: 99.0986 +04/17 22:29:07 - mmengine - INFO - Iter(train) [ 50100/160000] base_lr: 6.9338e-05 lr: 2.5636e-07 eta: 1 day, 6:31:59 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.6515 aux.loss_ce: 0.0077 aux.acc_seg: 98.9935 +04/17 22:29:57 - mmengine - INFO - Iter(train) [ 50150/160000] base_lr: 6.9306e-05 lr: 2.5624e-07 eta: 1 day, 6:31:09 time: 1.0027 data_time: 0.0048 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0060 decode.acc_seg: 99.7379 aux.loss_ce: 0.0070 aux.acc_seg: 98.9954 +04/17 22:30:47 - mmengine - INFO - Iter(train) [ 50200/160000] base_lr: 6.9275e-05 lr: 2.5612e-07 eta: 1 day, 6:30:19 time: 1.0015 data_time: 0.0045 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0076 decode.acc_seg: 99.6439 aux.loss_ce: 0.0093 aux.acc_seg: 99.0238 +04/17 22:31:37 - mmengine - INFO - Iter(train) [ 50250/160000] base_lr: 6.9243e-05 lr: 2.5601e-07 eta: 1 day, 6:29:29 time: 1.0022 data_time: 0.0044 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.6662 aux.loss_ce: 0.0077 aux.acc_seg: 99.1777 +04/17 22:32:27 - mmengine - INFO - Iter(train) [ 50300/160000] base_lr: 6.9212e-05 lr: 2.5589e-07 eta: 1 day, 6:28:39 time: 1.0010 data_time: 0.0045 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0060 decode.acc_seg: 99.7833 aux.loss_ce: 0.0076 aux.acc_seg: 99.2540 +04/17 22:33:17 - mmengine - INFO - Iter(train) [ 50350/160000] base_lr: 6.9180e-05 lr: 2.5577e-07 eta: 1 day, 6:27:50 time: 1.0016 data_time: 0.0046 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7509 aux.loss_ce: 0.0070 aux.acc_seg: 99.2617 +04/17 22:34:07 - mmengine - INFO - Iter(train) [ 50400/160000] base_lr: 6.9149e-05 lr: 2.5566e-07 eta: 1 day, 6:27:00 time: 1.0009 data_time: 0.0046 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.6490 aux.loss_ce: 0.0071 aux.acc_seg: 98.7896 +04/17 22:34:57 - mmengine - INFO - Iter(train) [ 50450/160000] base_lr: 6.9117e-05 lr: 2.5554e-07 eta: 1 day, 6:26:10 time: 1.0003 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0071 decode.acc_seg: 99.5760 aux.loss_ce: 0.0083 aux.acc_seg: 99.0036 +04/17 22:35:47 - mmengine - INFO - Iter(train) [ 50500/160000] base_lr: 6.9086e-05 lr: 2.5542e-07 eta: 1 day, 6:25:20 time: 1.0015 data_time: 0.0043 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0062 decode.acc_seg: 99.7562 aux.loss_ce: 0.0074 aux.acc_seg: 99.2680 +04/17 22:36:37 - mmengine - INFO - Iter(train) [ 50550/160000] base_lr: 6.9054e-05 lr: 2.5531e-07 eta: 1 day, 6:24:30 time: 1.0002 data_time: 0.0044 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.6731 aux.loss_ce: 0.0070 aux.acc_seg: 99.0002 +04/17 22:37:27 - mmengine - INFO - Iter(train) [ 50600/160000] base_lr: 6.9023e-05 lr: 2.5519e-07 eta: 1 day, 6:23:40 time: 1.0015 data_time: 0.0046 memory: 8462 loss: 0.0118 decode.loss_ce: 0.0055 decode.acc_seg: 99.8014 aux.loss_ce: 0.0062 aux.acc_seg: 99.4436 +04/17 22:38:17 - mmengine - INFO - Iter(train) [ 50650/160000] base_lr: 6.8991e-05 lr: 2.5507e-07 eta: 1 day, 6:22:50 time: 1.0010 data_time: 0.0048 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7143 aux.loss_ce: 0.0074 aux.acc_seg: 99.1955 +04/17 22:39:07 - mmengine - INFO - Iter(train) [ 50700/160000] base_lr: 6.8959e-05 lr: 2.5496e-07 eta: 1 day, 6:22:00 time: 1.0021 data_time: 0.0047 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.7755 aux.loss_ce: 0.0067 aux.acc_seg: 99.3412 +04/17 22:39:57 - mmengine - INFO - Iter(train) [ 50750/160000] base_lr: 6.8928e-05 lr: 2.5484e-07 eta: 1 day, 6:21:10 time: 1.0021 data_time: 0.0046 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0069 decode.acc_seg: 99.7391 aux.loss_ce: 0.0085 aux.acc_seg: 99.3994 +04/17 22:40:47 - mmengine - INFO - Iter(train) [ 50800/160000] base_lr: 6.8896e-05 lr: 2.5472e-07 eta: 1 day, 6:20:20 time: 1.0007 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0073 decode.acc_seg: 99.6870 aux.loss_ce: 0.0080 aux.acc_seg: 99.1617 +04/17 22:41:37 - mmengine - INFO - Iter(train) [ 50850/160000] base_lr: 6.8865e-05 lr: 2.5461e-07 eta: 1 day, 6:19:30 time: 1.0007 data_time: 0.0047 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0078 decode.acc_seg: 99.5440 aux.loss_ce: 0.0089 aux.acc_seg: 98.8024 +04/17 22:42:27 - mmengine - INFO - Iter(train) [ 50900/160000] base_lr: 6.8833e-05 lr: 2.5449e-07 eta: 1 day, 6:18:40 time: 0.9998 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0072 decode.acc_seg: 99.7692 aux.loss_ce: 0.0081 aux.acc_seg: 99.2157 +04/17 22:43:18 - mmengine - INFO - Iter(train) [ 50950/160000] base_lr: 6.8802e-05 lr: 2.5437e-07 eta: 1 day, 6:17:50 time: 1.0002 data_time: 0.0047 memory: 8462 loss: 0.0169 decode.loss_ce: 0.0080 decode.acc_seg: 99.6532 aux.loss_ce: 0.0088 aux.acc_seg: 98.7093 +04/17 22:44:08 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 22:44:08 - mmengine - INFO - Iter(train) [ 51000/160000] base_lr: 6.8770e-05 lr: 2.5426e-07 eta: 1 day, 6:17:00 time: 1.0009 data_time: 0.0049 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.6750 aux.loss_ce: 0.0079 aux.acc_seg: 98.8974 +04/17 22:44:58 - mmengine - INFO - Iter(train) [ 51050/160000] base_lr: 6.8739e-05 lr: 2.5414e-07 eta: 1 day, 6:16:10 time: 1.0019 data_time: 0.0048 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0065 decode.acc_seg: 99.6536 aux.loss_ce: 0.0078 aux.acc_seg: 99.1037 +04/17 22:45:48 - mmengine - INFO - Iter(train) [ 51100/160000] base_lr: 6.8707e-05 lr: 2.5402e-07 eta: 1 day, 6:15:20 time: 1.0011 data_time: 0.0044 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0068 decode.acc_seg: 99.7547 aux.loss_ce: 0.0089 aux.acc_seg: 99.1705 +04/17 22:46:38 - mmengine - INFO - Iter(train) [ 51150/160000] base_lr: 6.8676e-05 lr: 2.5391e-07 eta: 1 day, 6:14:30 time: 1.0000 data_time: 0.0048 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0072 decode.acc_seg: 99.5274 aux.loss_ce: 0.0080 aux.acc_seg: 99.0046 +04/17 22:47:28 - mmengine - INFO - Iter(train) [ 51200/160000] base_lr: 6.8644e-05 lr: 2.5379e-07 eta: 1 day, 6:13:40 time: 0.9996 data_time: 0.0048 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0071 decode.acc_seg: 99.5813 aux.loss_ce: 0.0081 aux.acc_seg: 99.1209 +04/17 22:48:18 - mmengine - INFO - Iter(train) [ 51250/160000] base_lr: 6.8612e-05 lr: 2.5367e-07 eta: 1 day, 6:12:50 time: 0.9982 data_time: 0.0046 memory: 8462 loss: 0.0126 decode.loss_ce: 0.0056 decode.acc_seg: 99.7341 aux.loss_ce: 0.0070 aux.acc_seg: 99.1972 +04/17 22:49:08 - mmengine - INFO - Iter(train) [ 51300/160000] base_lr: 6.8581e-05 lr: 2.5356e-07 eta: 1 day, 6:12:00 time: 0.9989 data_time: 0.0048 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.6454 aux.loss_ce: 0.0079 aux.acc_seg: 99.1423 +04/17 22:49:58 - mmengine - INFO - Iter(train) [ 51350/160000] base_lr: 6.8549e-05 lr: 2.5344e-07 eta: 1 day, 6:11:10 time: 0.9982 data_time: 0.0052 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0063 decode.acc_seg: 99.7147 aux.loss_ce: 0.0083 aux.acc_seg: 98.9279 +04/17 22:50:47 - mmengine - INFO - Iter(train) [ 51400/160000] base_lr: 6.8518e-05 lr: 2.5332e-07 eta: 1 day, 6:10:20 time: 0.9981 data_time: 0.0043 memory: 8462 loss: 0.0168 decode.loss_ce: 0.0076 decode.acc_seg: 99.6479 aux.loss_ce: 0.0092 aux.acc_seg: 98.9697 +04/17 22:51:37 - mmengine - INFO - Iter(train) [ 51450/160000] base_lr: 6.8486e-05 lr: 2.5321e-07 eta: 1 day, 6:09:30 time: 0.9994 data_time: 0.0053 memory: 8462 loss: 0.0194 decode.loss_ce: 0.0108 decode.acc_seg: 99.7135 aux.loss_ce: 0.0086 aux.acc_seg: 99.3416 +04/17 22:52:27 - mmengine - INFO - Iter(train) [ 51500/160000] base_lr: 6.8455e-05 lr: 2.5309e-07 eta: 1 day, 6:08:39 time: 0.9988 data_time: 0.0044 memory: 8462 loss: 0.0124 decode.loss_ce: 0.0060 decode.acc_seg: 99.7625 aux.loss_ce: 0.0064 aux.acc_seg: 99.3465 +04/17 22:53:17 - mmengine - INFO - Iter(train) [ 51550/160000] base_lr: 6.8423e-05 lr: 2.5297e-07 eta: 1 day, 6:07:49 time: 0.9995 data_time: 0.0046 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.6857 aux.loss_ce: 0.0076 aux.acc_seg: 98.9225 +04/17 22:54:07 - mmengine - INFO - Iter(train) [ 51600/160000] base_lr: 6.8392e-05 lr: 2.5286e-07 eta: 1 day, 6:06:59 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0074 decode.acc_seg: 99.5653 aux.loss_ce: 0.0085 aux.acc_seg: 98.5556 +04/17 22:54:57 - mmengine - INFO - Iter(train) [ 51650/160000] base_lr: 6.8360e-05 lr: 2.5274e-07 eta: 1 day, 6:06:09 time: 0.9984 data_time: 0.0052 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.7093 aux.loss_ce: 0.0076 aux.acc_seg: 99.0751 +04/17 22:55:47 - mmengine - INFO - Iter(train) [ 51700/160000] base_lr: 6.8329e-05 lr: 2.5262e-07 eta: 1 day, 6:05:19 time: 0.9996 data_time: 0.0050 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.7099 aux.loss_ce: 0.0070 aux.acc_seg: 99.1999 +04/17 22:56:37 - mmengine - INFO - Iter(train) [ 51750/160000] base_lr: 6.8297e-05 lr: 2.5251e-07 eta: 1 day, 6:04:29 time: 0.9982 data_time: 0.0051 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7078 aux.loss_ce: 0.0076 aux.acc_seg: 99.0191 +04/17 22:57:27 - mmengine - INFO - Iter(train) [ 51800/160000] base_lr: 6.8265e-05 lr: 2.5239e-07 eta: 1 day, 6:03:38 time: 0.9976 data_time: 0.0046 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.7412 aux.loss_ce: 0.0076 aux.acc_seg: 99.3313 +04/17 22:58:17 - mmengine - INFO - Iter(train) [ 51850/160000] base_lr: 6.8234e-05 lr: 2.5227e-07 eta: 1 day, 6:02:48 time: 0.9991 data_time: 0.0050 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.7326 aux.loss_ce: 0.0068 aux.acc_seg: 99.1741 +04/17 22:59:07 - mmengine - INFO - Iter(train) [ 51900/160000] base_lr: 6.8202e-05 lr: 2.5216e-07 eta: 1 day, 6:01:58 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0067 decode.acc_seg: 99.6784 aux.loss_ce: 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6.8076e-05 lr: 2.5169e-07 eta: 1 day, 5:58:37 time: 0.9978 data_time: 0.0051 memory: 8462 loss: 0.0159 decode.loss_ce: 0.0069 decode.acc_seg: 99.6073 aux.loss_ce: 0.0089 aux.acc_seg: 98.6242 +04/17 23:03:16 - mmengine - INFO - Iter(train) [ 52150/160000] base_lr: 6.8045e-05 lr: 2.5157e-07 eta: 1 day, 5:57:47 time: 0.9961 data_time: 0.0046 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0068 decode.acc_seg: 99.7011 aux.loss_ce: 0.0083 aux.acc_seg: 98.9805 +04/17 23:04:06 - mmengine - INFO - Iter(train) [ 52200/160000] base_lr: 6.8013e-05 lr: 2.5146e-07 eta: 1 day, 5:56:56 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0063 decode.acc_seg: 99.8224 aux.loss_ce: 0.0077 aux.acc_seg: 99.3618 +04/17 23:04:56 - mmengine - INFO - Iter(train) [ 52250/160000] base_lr: 6.7982e-05 lr: 2.5134e-07 eta: 1 day, 5:56:06 time: 0.9972 data_time: 0.0046 memory: 8462 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.7196 aux.loss_ce: 0.0066 aux.acc_seg: 99.0690 +04/17 23:05:46 - mmengine - INFO - Iter(train) [ 52300/160000] base_lr: 6.7950e-05 lr: 2.5122e-07 eta: 1 day, 5:55:16 time: 0.9983 data_time: 0.0044 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0070 decode.acc_seg: 99.6262 aux.loss_ce: 0.0084 aux.acc_seg: 99.0067 +04/17 23:06:36 - mmengine - INFO - Iter(train) [ 52350/160000] base_lr: 6.7918e-05 lr: 2.5111e-07 eta: 1 day, 5:54:25 time: 0.9967 data_time: 0.0046 memory: 8462 loss: 0.0167 decode.loss_ce: 0.0079 decode.acc_seg: 99.6027 aux.loss_ce: 0.0088 aux.acc_seg: 99.0757 +04/17 23:07:26 - mmengine - INFO - Iter(train) [ 52400/160000] base_lr: 6.7887e-05 lr: 2.5099e-07 eta: 1 day, 5:53:35 time: 0.9973 data_time: 0.0043 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.7801 aux.loss_ce: 0.0081 aux.acc_seg: 99.2386 +04/17 23:08:16 - mmengine - INFO - Iter(train) [ 52450/160000] base_lr: 6.7855e-05 lr: 2.5088e-07 eta: 1 day, 5:52:45 time: 0.9972 data_time: 0.0045 memory: 8462 loss: 0.0166 decode.loss_ce: 0.0083 decode.acc_seg: 99.4164 aux.loss_ce: 0.0083 aux.acc_seg: 98.9697 +04/17 23:09:05 - mmengine - INFO - Iter(train) [ 52500/160000] base_lr: 6.7824e-05 lr: 2.5076e-07 eta: 1 day, 5:51:55 time: 0.9969 data_time: 0.0047 memory: 8462 loss: 0.0177 decode.loss_ce: 0.0083 decode.acc_seg: 99.6887 aux.loss_ce: 0.0095 aux.acc_seg: 98.9767 +04/17 23:09:55 - mmengine - INFO - Iter(train) [ 52550/160000] base_lr: 6.7792e-05 lr: 2.5064e-07 eta: 1 day, 5:51:04 time: 0.9958 data_time: 0.0049 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.8032 aux.loss_ce: 0.0078 aux.acc_seg: 99.3263 +04/17 23:10:45 - mmengine - INFO - Iter(train) [ 52600/160000] base_lr: 6.7761e-05 lr: 2.5053e-07 eta: 1 day, 5:50:14 time: 0.9976 data_time: 0.0046 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.6906 aux.loss_ce: 0.0077 aux.acc_seg: 99.2094 +04/17 23:11:35 - mmengine - INFO - Iter(train) [ 52650/160000] base_lr: 6.7729e-05 lr: 2.5041e-07 eta: 1 day, 5:49:24 time: 0.9961 data_time: 0.0051 memory: 8462 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decode.acc_seg: 99.8381 aux.loss_ce: 0.0072 aux.acc_seg: 99.4926 +04/17 23:18:14 - mmengine - INFO - Iter(train) [ 53050/160000] base_lr: 6.7477e-05 lr: 2.4948e-07 eta: 1 day, 5:42:41 time: 0.9959 data_time: 0.0048 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7179 aux.loss_ce: 0.0068 aux.acc_seg: 99.3046 +04/17 23:19:04 - mmengine - INFO - Iter(train) [ 53100/160000] base_lr: 6.7445e-05 lr: 2.4936e-07 eta: 1 day, 5:41:50 time: 0.9959 data_time: 0.0046 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0073 decode.acc_seg: 99.5453 aux.loss_ce: 0.0085 aux.acc_seg: 98.7598 +04/17 23:19:53 - mmengine - INFO - Iter(train) [ 53150/160000] base_lr: 6.7414e-05 lr: 2.4924e-07 eta: 1 day, 5:41:00 time: 0.9960 data_time: 0.0047 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0064 decode.acc_seg: 99.7801 aux.loss_ce: 0.0076 aux.acc_seg: 99.2554 +04/17 23:20:43 - mmengine - INFO - Iter(train) [ 53200/160000] base_lr: 6.7382e-05 lr: 2.4913e-07 eta: 1 day, 5:40:10 time: 0.9960 data_time: 0.0049 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0074 decode.acc_seg: 99.6466 aux.loss_ce: 0.0082 aux.acc_seg: 99.1354 +04/17 23:21:33 - mmengine - INFO - Iter(train) [ 53250/160000] base_lr: 6.7351e-05 lr: 2.4901e-07 eta: 1 day, 5:39:19 time: 0.9958 data_time: 0.0050 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0064 decode.acc_seg: 99.7814 aux.loss_ce: 0.0078 aux.acc_seg: 99.4997 +04/17 23:22:23 - mmengine - INFO - Iter(train) [ 53300/160000] base_lr: 6.7319e-05 lr: 2.4889e-07 eta: 1 day, 5:38:29 time: 0.9975 data_time: 0.0049 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0070 decode.acc_seg: 99.6582 aux.loss_ce: 0.0080 aux.acc_seg: 98.8876 +04/17 23:23:13 - mmengine - INFO - Iter(train) [ 53350/160000] base_lr: 6.7287e-05 lr: 2.4878e-07 eta: 1 day, 5:37:39 time: 0.9974 data_time: 0.0055 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0074 decode.acc_seg: 99.5537 aux.loss_ce: 0.0083 aux.acc_seg: 98.9580 +04/17 23:24:02 - mmengine - INFO - Iter(train) [ 53400/160000] base_lr: 6.7256e-05 lr: 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INFO - Iter(train) [ 53600/160000] base_lr: 6.7130e-05 lr: 2.4819e-07 eta: 1 day, 5:33:27 time: 0.9958 data_time: 0.0044 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0073 decode.acc_seg: 99.5892 aux.loss_ce: 0.0083 aux.acc_seg: 98.8543 +04/17 23:28:11 - mmengine - INFO - Iter(train) [ 53650/160000] base_lr: 6.7098e-05 lr: 2.4808e-07 eta: 1 day, 5:32:36 time: 0.9959 data_time: 0.0046 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0064 decode.acc_seg: 99.7015 aux.loss_ce: 0.0081 aux.acc_seg: 99.0965 +04/17 23:29:01 - mmengine - INFO - Iter(train) [ 53700/160000] base_lr: 6.7067e-05 lr: 2.4796e-07 eta: 1 day, 5:31:46 time: 0.9967 data_time: 0.0048 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0073 decode.acc_seg: 99.7025 aux.loss_ce: 0.0083 aux.acc_seg: 99.1486 +04/17 23:29:51 - mmengine - INFO - Iter(train) [ 53750/160000] base_lr: 6.7035e-05 lr: 2.4784e-07 eta: 1 day, 5:30:55 time: 0.9963 data_time: 0.0048 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.5285 aux.loss_ce: 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decode.loss_ce: 0.0060 decode.acc_seg: 99.6927 aux.loss_ce: 0.0071 aux.acc_seg: 98.9550 +04/17 23:34:00 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/17 23:34:00 - mmengine - INFO - Iter(train) [ 54000/160000] base_lr: 6.6877e-05 lr: 2.4726e-07 eta: 1 day, 5:26:44 time: 0.9964 data_time: 0.0045 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0070 decode.acc_seg: 99.8268 aux.loss_ce: 0.0079 aux.acc_seg: 99.4104 +04/17 23:34:50 - mmengine - INFO - Iter(train) [ 54050/160000] base_lr: 6.6846e-05 lr: 2.4714e-07 eta: 1 day, 5:25:53 time: 0.9960 data_time: 0.0047 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7950 aux.loss_ce: 0.0075 aux.acc_seg: 99.4024 +04/17 23:35:40 - mmengine - INFO - Iter(train) [ 54100/160000] base_lr: 6.6814e-05 lr: 2.4703e-07 eta: 1 day, 5:25:03 time: 0.9975 data_time: 0.0050 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0066 decode.acc_seg: 99.6904 aux.loss_ce: 0.0078 aux.acc_seg: 99.1680 +04/17 23:36:30 - mmengine - INFO - Iter(train) [ 54150/160000] base_lr: 6.6783e-05 lr: 2.4691e-07 eta: 1 day, 5:24:12 time: 0.9958 data_time: 0.0046 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0060 decode.acc_seg: 99.6975 aux.loss_ce: 0.0073 aux.acc_seg: 98.7625 +04/17 23:37:20 - mmengine - INFO - Iter(train) [ 54200/160000] base_lr: 6.6751e-05 lr: 2.4679e-07 eta: 1 day, 5:23:22 time: 0.9977 data_time: 0.0044 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.7915 aux.loss_ce: 0.0076 aux.acc_seg: 99.3511 +04/17 23:38:09 - mmengine - INFO - Iter(train) [ 54250/160000] base_lr: 6.6720e-05 lr: 2.4668e-07 eta: 1 day, 5:22:32 time: 0.9960 data_time: 0.0049 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0067 decode.acc_seg: 99.7839 aux.loss_ce: 0.0079 aux.acc_seg: 99.3517 +04/17 23:38:59 - mmengine - INFO - Iter(train) [ 54300/160000] base_lr: 6.6688e-05 lr: 2.4656e-07 eta: 1 day, 5:21:41 time: 0.9978 data_time: 0.0054 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.6439 aux.loss_ce: 0.0077 aux.acc_seg: 99.0408 +04/17 23:39:49 - mmengine - INFO - Iter(train) [ 54350/160000] base_lr: 6.6657e-05 lr: 2.4644e-07 eta: 1 day, 5:20:51 time: 0.9975 data_time: 0.0049 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0072 decode.acc_seg: 99.8219 aux.loss_ce: 0.0085 aux.acc_seg: 99.3736 +04/17 23:40:39 - mmengine - INFO - Iter(train) [ 54400/160000] base_lr: 6.6625e-05 lr: 2.4633e-07 eta: 1 day, 5:20:01 time: 0.9955 data_time: 0.0046 memory: 8462 loss: 0.0118 decode.loss_ce: 0.0055 decode.acc_seg: 99.8287 aux.loss_ce: 0.0063 aux.acc_seg: 99.6265 +04/17 23:41:29 - mmengine - INFO - Iter(train) [ 54450/160000] base_lr: 6.6593e-05 lr: 2.4621e-07 eta: 1 day, 5:19:10 time: 0.9959 data_time: 0.0048 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.7332 aux.loss_ce: 0.0076 aux.acc_seg: 99.1829 +04/17 23:42:18 - mmengine - INFO - Iter(train) [ 54500/160000] base_lr: 6.6562e-05 lr: 2.4609e-07 eta: 1 day, 5:18:20 time: 0.9965 data_time: 0.0050 memory: 8462 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memory: 8462 loss: 0.0159 decode.loss_ce: 0.0071 decode.acc_seg: 99.8264 aux.loss_ce: 0.0088 aux.acc_seg: 99.1026 +04/17 23:52:16 - mmengine - INFO - Iter(train) [ 55100/160000] base_lr: 6.6183e-05 lr: 2.4469e-07 eta: 1 day, 5:08:16 time: 0.9961 data_time: 0.0047 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0058 decode.acc_seg: 99.7845 aux.loss_ce: 0.0070 aux.acc_seg: 99.3715 +04/17 23:53:06 - mmengine - INFO - Iter(train) [ 55150/160000] base_lr: 6.6152e-05 lr: 2.4458e-07 eta: 1 day, 5:07:26 time: 0.9962 data_time: 0.0046 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0061 decode.acc_seg: 99.7978 aux.loss_ce: 0.0071 aux.acc_seg: 99.4160 +04/17 23:53:56 - mmengine - INFO - Iter(train) [ 55200/160000] base_lr: 6.6120e-05 lr: 2.4446e-07 eta: 1 day, 5:06:35 time: 0.9975 data_time: 0.0052 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6943 aux.loss_ce: 0.0079 aux.acc_seg: 99.1989 +04/17 23:54:46 - mmengine - INFO - Iter(train) [ 55250/160000] base_lr: 6.6089e-05 lr: 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INFO - Iter(train) [ 55450/160000] base_lr: 6.5963e-05 lr: 2.4388e-07 eta: 1 day, 5:02:24 time: 0.9964 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0070 decode.acc_seg: 99.7166 aux.loss_ce: 0.0083 aux.acc_seg: 99.0023 +04/17 23:58:55 - mmengine - INFO - Iter(train) [ 55500/160000] base_lr: 6.5931e-05 lr: 2.4376e-07 eta: 1 day, 5:01:33 time: 0.9961 data_time: 0.0045 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7013 aux.loss_ce: 0.0074 aux.acc_seg: 99.1121 +04/17 23:59:45 - mmengine - INFO - Iter(train) [ 55550/160000] base_lr: 6.5899e-05 lr: 2.4364e-07 eta: 1 day, 5:00:43 time: 0.9968 data_time: 0.0048 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0063 decode.acc_seg: 99.6902 aux.loss_ce: 0.0083 aux.acc_seg: 98.8699 +04/18 00:00:35 - mmengine - INFO - Iter(train) [ 55600/160000] base_lr: 6.5868e-05 lr: 2.4353e-07 eta: 1 day, 4:59:53 time: 0.9970 data_time: 0.0051 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0067 decode.acc_seg: 99.6054 aux.loss_ce: 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decode.loss_ce: 0.0068 decode.acc_seg: 99.6552 aux.loss_ce: 0.0074 aux.acc_seg: 99.2012 +04/18 00:04:44 - mmengine - INFO - Iter(train) [ 55850/160000] base_lr: 6.5710e-05 lr: 2.4294e-07 eta: 1 day, 4:55:41 time: 0.9954 data_time: 0.0048 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7173 aux.loss_ce: 0.0074 aux.acc_seg: 98.9405 +04/18 00:05:34 - mmengine - INFO - Iter(train) [ 55900/160000] base_lr: 6.5679e-05 lr: 2.4283e-07 eta: 1 day, 4:54:51 time: 0.9964 data_time: 0.0045 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0072 decode.acc_seg: 99.7589 aux.loss_ce: 0.0082 aux.acc_seg: 99.3073 +04/18 00:06:23 - mmengine - INFO - Iter(train) [ 55950/160000] base_lr: 6.5647e-05 lr: 2.4271e-07 eta: 1 day, 4:54:00 time: 0.9960 data_time: 0.0048 memory: 8462 loss: 0.0122 decode.loss_ce: 0.0056 decode.acc_seg: 99.7185 aux.loss_ce: 0.0067 aux.acc_seg: 99.1304 +04/18 00:07:13 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 00:07:13 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time: 0.9973 data_time: 0.0051 memory: 8462 loss: 0.0126 decode.loss_ce: 0.0057 decode.acc_seg: 99.7337 aux.loss_ce: 0.0069 aux.acc_seg: 99.1795 +04/18 00:38:48 - mmengine - INFO - Iter(train) [ 57900/160000] base_lr: 6.4417e-05 lr: 2.3816e-07 eta: 1 day, 4:21:20 time: 0.9978 data_time: 0.0049 memory: 8462 loss: 0.0120 decode.loss_ce: 0.0058 decode.acc_seg: 99.7416 aux.loss_ce: 0.0062 aux.acc_seg: 99.4034 +04/18 00:39:37 - mmengine - INFO - Iter(train) [ 57950/160000] base_lr: 6.4385e-05 lr: 2.3805e-07 eta: 1 day, 4:20:30 time: 0.9971 data_time: 0.0048 memory: 8462 loss: 0.0160 decode.loss_ce: 0.0074 decode.acc_seg: 99.5874 aux.loss_ce: 0.0086 aux.acc_seg: 98.7101 +04/18 00:40:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 00:40:27 - mmengine - INFO - Iter(train) [ 58000/160000] base_lr: 6.4354e-05 lr: 2.3793e-07 eta: 1 day, 4:19:40 time: 0.9966 data_time: 0.0047 memory: 8462 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.6943 aux.loss_ce: 0.0072 aux.acc_seg: 99.1861 +04/18 00:41:17 - mmengine - INFO - Iter(train) [ 58050/160000] base_lr: 6.4322e-05 lr: 2.3781e-07 eta: 1 day, 4:18:49 time: 0.9964 data_time: 0.0048 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.6979 aux.loss_ce: 0.0074 aux.acc_seg: 98.9344 +04/18 00:42:07 - mmengine - INFO - Iter(train) [ 58100/160000] base_lr: 6.4291e-05 lr: 2.3770e-07 eta: 1 day, 4:17:59 time: 0.9974 data_time: 0.0054 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0068 decode.acc_seg: 99.7322 aux.loss_ce: 0.0078 aux.acc_seg: 99.4568 +04/18 00:42:57 - mmengine - INFO - Iter(train) [ 58150/160000] base_lr: 6.4259e-05 lr: 2.3758e-07 eta: 1 day, 4:17:09 time: 0.9968 data_time: 0.0051 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0064 decode.acc_seg: 99.7648 aux.loss_ce: 0.0076 aux.acc_seg: 99.4530 +04/18 00:43:47 - mmengine - INFO - Iter(train) [ 58200/160000] base_lr: 6.4228e-05 lr: 2.3746e-07 eta: 1 day, 4:16:18 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0064 decode.acc_seg: 99.7704 aux.loss_ce: 0.0083 aux.acc_seg: 99.3893 +04/18 00:44:36 - mmengine - INFO - Iter(train) [ 58250/160000] base_lr: 6.4196e-05 lr: 2.3735e-07 eta: 1 day, 4:15:28 time: 0.9988 data_time: 0.0049 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.6920 aux.loss_ce: 0.0075 aux.acc_seg: 99.1848 +04/18 00:45:26 - mmengine - INFO - Iter(train) [ 58300/160000] base_lr: 6.4164e-05 lr: 2.3723e-07 eta: 1 day, 4:14:38 time: 0.9967 data_time: 0.0046 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.8030 aux.loss_ce: 0.0074 aux.acc_seg: 99.2216 +04/18 00:46:16 - mmengine - INFO - Iter(train) [ 58350/160000] base_lr: 6.4133e-05 lr: 2.3711e-07 eta: 1 day, 4:13:48 time: 0.9956 data_time: 0.0046 memory: 8462 loss: 0.0154 decode.loss_ce: 0.0070 decode.acc_seg: 99.6845 aux.loss_ce: 0.0085 aux.acc_seg: 99.1455 +04/18 00:47:06 - mmengine - INFO - Iter(train) [ 58400/160000] base_lr: 6.4101e-05 lr: 2.3700e-07 eta: 1 day, 4:12:58 time: 0.9963 data_time: 0.0046 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0063 decode.acc_seg: 99.7093 aux.loss_ce: 0.0077 aux.acc_seg: 99.0812 +04/18 00:47:56 - mmengine - INFO - Iter(train) [ 58450/160000] base_lr: 6.4070e-05 lr: 2.3688e-07 eta: 1 day, 4:12:07 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.6807 aux.loss_ce: 0.0076 aux.acc_seg: 98.7923 +04/18 00:48:46 - mmengine - INFO - Iter(train) [ 58500/160000] base_lr: 6.4038e-05 lr: 2.3676e-07 eta: 1 day, 4:11:17 time: 0.9952 data_time: 0.0047 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0068 decode.acc_seg: 99.6386 aux.loss_ce: 0.0079 aux.acc_seg: 99.0294 +04/18 00:49:36 - mmengine - INFO - Iter(train) [ 58550/160000] base_lr: 6.4007e-05 lr: 2.3665e-07 eta: 1 day, 4:10:27 time: 0.9960 data_time: 0.0053 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0059 decode.acc_seg: 99.6319 aux.loss_ce: 0.0069 aux.acc_seg: 99.0234 +04/18 00:50:25 - mmengine - INFO - Iter(train) [ 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99.0902 +04/18 00:53:45 - mmengine - INFO - Iter(train) [ 58800/160000] base_lr: 6.3849e-05 lr: 2.3606e-07 eta: 1 day, 4:06:16 time: 0.9973 data_time: 0.0050 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0063 decode.acc_seg: 99.7572 aux.loss_ce: 0.0080 aux.acc_seg: 99.1959 +04/18 00:54:35 - mmengine - INFO - Iter(train) [ 58850/160000] base_lr: 6.3817e-05 lr: 2.3595e-07 eta: 1 day, 4:05:25 time: 0.9970 data_time: 0.0052 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0069 decode.acc_seg: 99.7452 aux.loss_ce: 0.0083 aux.acc_seg: 98.9761 +04/18 00:55:25 - mmengine - INFO - Iter(train) [ 58900/160000] base_lr: 6.3786e-05 lr: 2.3583e-07 eta: 1 day, 4:04:35 time: 0.9975 data_time: 0.0047 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0065 decode.acc_seg: 99.7129 aux.loss_ce: 0.0075 aux.acc_seg: 99.3151 +04/18 00:56:14 - mmengine - INFO - Iter(train) [ 58950/160000] base_lr: 6.3754e-05 lr: 2.3571e-07 eta: 1 day, 4:03:45 time: 0.9973 data_time: 0.0049 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0064 decode.acc_seg: 99.7519 aux.loss_ce: 0.0076 aux.acc_seg: 99.1943 +04/18 00:57:04 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 00:57:04 - mmengine - INFO - Iter(train) [ 59000/160000] base_lr: 6.3723e-05 lr: 2.3560e-07 eta: 1 day, 4:02:55 time: 0.9972 data_time: 0.0049 memory: 8462 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.8060 aux.loss_ce: 0.0066 aux.acc_seg: 99.4778 +04/18 00:57:54 - mmengine - INFO - Iter(train) [ 59050/160000] base_lr: 6.3691e-05 lr: 2.3548e-07 eta: 1 day, 4:02:05 time: 0.9980 data_time: 0.0049 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0076 decode.acc_seg: 99.6397 aux.loss_ce: 0.0081 aux.acc_seg: 98.9975 +04/18 00:58:44 - mmengine - INFO - Iter(train) [ 59100/160000] base_lr: 6.3660e-05 lr: 2.3536e-07 eta: 1 day, 4:01:14 time: 0.9956 data_time: 0.0049 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0064 decode.acc_seg: 99.6559 aux.loss_ce: 0.0077 aux.acc_seg: 99.0963 +04/18 00:59:34 - mmengine - INFO - Iter(train) [ 59150/160000] base_lr: 6.3628e-05 lr: 2.3525e-07 eta: 1 day, 4:00:24 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.6416 aux.loss_ce: 0.0075 aux.acc_seg: 99.0419 +04/18 01:00:24 - mmengine - INFO - Iter(train) [ 59200/160000] base_lr: 6.3597e-05 lr: 2.3513e-07 eta: 1 day, 3:59:34 time: 0.9968 data_time: 0.0047 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0058 decode.acc_seg: 99.7854 aux.loss_ce: 0.0070 aux.acc_seg: 99.3044 +04/18 01:01:13 - mmengine - INFO - Iter(train) [ 59250/160000] base_lr: 6.3565e-05 lr: 2.3501e-07 eta: 1 day, 3:58:44 time: 0.9961 data_time: 0.0047 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.6817 aux.loss_ce: 0.0077 aux.acc_seg: 99.2182 +04/18 01:02:03 - mmengine - INFO - Iter(train) [ 59300/160000] base_lr: 6.3534e-05 lr: 2.3490e-07 eta: 1 day, 3:57:53 time: 0.9978 data_time: 0.0051 memory: 8462 loss: 0.0131 decode.loss_ce: 0.0059 decode.acc_seg: 99.7379 aux.loss_ce: 0.0072 aux.acc_seg: 99.3759 +04/18 01:02:53 - mmengine - INFO - Iter(train) [ 59350/160000] base_lr: 6.3502e-05 lr: 2.3478e-07 eta: 1 day, 3:57:03 time: 0.9962 data_time: 0.0050 memory: 8462 loss: 0.0156 decode.loss_ce: 0.0074 decode.acc_seg: 99.6422 aux.loss_ce: 0.0082 aux.acc_seg: 99.2203 +04/18 01:03:43 - mmengine - INFO - Iter(train) [ 59400/160000] base_lr: 6.3470e-05 lr: 2.3466e-07 eta: 1 day, 3:56:13 time: 0.9984 data_time: 0.0052 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0060 decode.acc_seg: 99.7980 aux.loss_ce: 0.0069 aux.acc_seg: 99.4974 +04/18 01:04:33 - mmengine - INFO - Iter(train) [ 59450/160000] base_lr: 6.3439e-05 lr: 2.3455e-07 eta: 1 day, 3:55:23 time: 0.9968 data_time: 0.0049 memory: 8462 loss: 0.0163 decode.loss_ce: 0.0075 decode.acc_seg: 99.6365 aux.loss_ce: 0.0089 aux.acc_seg: 98.9510 +04/18 01:05:23 - mmengine - INFO - Iter(train) [ 59500/160000] base_lr: 6.3407e-05 lr: 2.3443e-07 eta: 1 day, 3:54:32 time: 0.9970 data_time: 0.0046 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7969 aux.loss_ce: 0.0070 aux.acc_seg: 99.3740 +04/18 01:06:13 - mmengine - INFO - Iter(train) [ 59550/160000] base_lr: 6.3376e-05 lr: 2.3431e-07 eta: 1 day, 3:53:42 time: 0.9966 data_time: 0.0047 memory: 8462 loss: 0.0134 decode.loss_ce: 0.0061 decode.acc_seg: 99.7887 aux.loss_ce: 0.0072 aux.acc_seg: 99.5459 +04/18 01:07:02 - mmengine - INFO - Iter(train) [ 59600/160000] base_lr: 6.3344e-05 lr: 2.3420e-07 eta: 1 day, 3:52:52 time: 0.9981 data_time: 0.0051 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.6906 aux.loss_ce: 0.0076 aux.acc_seg: 99.1089 +04/18 01:07:52 - mmengine - INFO - Iter(train) [ 59650/160000] base_lr: 6.3313e-05 lr: 2.3408e-07 eta: 1 day, 3:52:02 time: 0.9966 data_time: 0.0046 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0070 decode.acc_seg: 99.7128 aux.loss_ce: 0.0083 aux.acc_seg: 99.0110 +04/18 01:08:42 - mmengine - INFO - Iter(train) [ 59700/160000] base_lr: 6.3281e-05 lr: 2.3396e-07 eta: 1 day, 3:51:11 time: 0.9968 data_time: 0.0045 memory: 8462 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7534 aux.loss_ce: 0.0067 aux.acc_seg: 99.2552 +04/18 01:09:32 - mmengine - INFO - Iter(train) [ 59750/160000] base_lr: 6.3250e-05 lr: 2.3385e-07 eta: 1 day, 3:50:21 time: 0.9968 data_time: 0.0047 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0062 decode.acc_seg: 99.6611 aux.loss_ce: 0.0074 aux.acc_seg: 99.0505 +04/18 01:10:22 - mmengine - INFO - Iter(train) [ 59800/160000] base_lr: 6.3218e-05 lr: 2.3373e-07 eta: 1 day, 3:49:31 time: 0.9975 data_time: 0.0050 memory: 8462 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.4905 aux.loss_ce: 0.0075 aux.acc_seg: 99.0028 +04/18 01:11:12 - mmengine - INFO - Iter(train) [ 59850/160000] base_lr: 6.3187e-05 lr: 2.3361e-07 eta: 1 day, 3:48:41 time: 0.9974 data_time: 0.0049 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0066 decode.acc_seg: 99.7030 aux.loss_ce: 0.0082 aux.acc_seg: 99.0513 +04/18 01:12:01 - mmengine - INFO - Iter(train) [ 59900/160000] base_lr: 6.3155e-05 lr: 2.3350e-07 eta: 1 day, 3:47:51 time: 0.9963 data_time: 0.0049 memory: 8462 loss: 0.0153 decode.loss_ce: 0.0071 decode.acc_seg: 99.7084 aux.loss_ce: 0.0082 aux.acc_seg: 99.0517 +04/18 01:12:51 - mmengine - INFO - Iter(train) [ 59950/160000] base_lr: 6.3123e-05 lr: 2.3338e-07 eta: 1 day, 3:47:00 time: 0.9974 data_time: 0.0048 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0061 decode.acc_seg: 99.7242 aux.loss_ce: 0.0070 aux.acc_seg: 99.3149 +04/18 01:13:41 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 01:13:41 - mmengine - INFO - Iter(train) [ 60000/160000] base_lr: 6.3092e-05 lr: 2.3326e-07 eta: 1 day, 3:46:10 time: 0.9965 data_time: 0.0052 memory: 8462 loss: 0.0152 decode.loss_ce: 0.0069 decode.acc_seg: 99.7009 aux.loss_ce: 0.0083 aux.acc_seg: 99.0499 +04/18 01:13:41 - mmengine - INFO - Saving checkpoint at 60000 iterations +04/18 01:13:51 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1160 data_time: 0.0016 memory: 4004 +04/18 01:13:57 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1156 data_time: 0.0015 memory: 4004 +04/18 01:14:03 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1155 data_time: 0.0015 memory: 4004 +04/18 01:14:08 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1156 data_time: 0.0015 memory: 4004 +04/18 01:14:09 - mmengine - INFO - per class results: +04/18 01:14:09 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.17 | 99.59 | 99.59 | 99.58 | 99.59 | +| contrast | 81.83 | 89.83 | 90.01 | 90.18 | 89.83 | ++------------+-------+-------+--------+-----------+--------+ +04/18 01:14:09 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2000 mIoU: 90.5000 mAcc: 94.7100 mFscore: 94.8000 mPrecision: 94.8800 mRecall: 94.7100 data_time: 0.0019 time: 0.1161 +04/18 01:14:58 - mmengine - INFO - Iter(train) [ 60050/160000] base_lr: 6.3060e-05 lr: 2.3315e-07 eta: 1 day, 3:45:20 time: 0.9959 data_time: 0.0046 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7782 aux.loss_ce: 0.0074 aux.acc_seg: 99.3649 +04/18 01:15:48 - mmengine - INFO - Iter(train) [ 60100/160000] base_lr: 6.3029e-05 lr: 2.3303e-07 eta: 1 day, 3:44:30 time: 0.9967 data_time: 0.0047 memory: 8462 loss: 0.0173 decode.loss_ce: 0.0083 decode.acc_seg: 99.3910 aux.loss_ce: 0.0090 aux.acc_seg: 98.7228 +04/18 01:16:38 - mmengine - INFO - Iter(train) [ 60150/160000] base_lr: 6.2997e-05 lr: 2.3291e-07 eta: 1 day, 3:43:40 time: 0.9972 data_time: 0.0052 memory: 8462 loss: 0.0124 decode.loss_ce: 0.0055 decode.acc_seg: 99.7696 aux.loss_ce: 0.0069 aux.acc_seg: 99.1947 +04/18 01:17:28 - mmengine - INFO - Iter(train) [ 60200/160000] base_lr: 6.2966e-05 lr: 2.3280e-07 eta: 1 day, 3:42:49 time: 0.9970 data_time: 0.0050 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0062 decode.acc_seg: 99.7618 aux.loss_ce: 0.0085 aux.acc_seg: 99.2031 +04/18 01:18:18 - mmengine - INFO - Iter(train) [ 60250/160000] base_lr: 6.2934e-05 lr: 2.3268e-07 eta: 1 day, 3:41:59 time: 0.9972 data_time: 0.0050 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0065 decode.acc_seg: 99.6517 aux.loss_ce: 0.0079 aux.acc_seg: 99.0055 +04/18 01:19:08 - mmengine - INFO - Iter(train) [ 60300/160000] base_lr: 6.2903e-05 lr: 2.3256e-07 eta: 1 day, 3:41:09 time: 0.9966 data_time: 0.0046 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0066 decode.acc_seg: 99.7400 aux.loss_ce: 0.0082 aux.acc_seg: 99.1520 +04/18 01:19:57 - mmengine - INFO - Iter(train) [ 60350/160000] base_lr: 6.2871e-05 lr: 2.3245e-07 eta: 1 day, 3:40:19 time: 0.9976 data_time: 0.0044 memory: 8462 loss: 0.0122 decode.loss_ce: 0.0057 decode.acc_seg: 99.7942 aux.loss_ce: 0.0066 aux.acc_seg: 99.4230 +04/18 01:20:47 - mmengine - INFO - Iter(train) [ 60400/160000] base_lr: 6.2840e-05 lr: 2.3233e-07 eta: 1 day, 3:39:28 time: 0.9969 data_time: 0.0048 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0068 decode.acc_seg: 99.7206 aux.loss_ce: 0.0083 aux.acc_seg: 98.9014 +04/18 01:21:37 - mmengine - INFO - Iter(train) [ 60450/160000] base_lr: 6.2808e-05 lr: 2.3221e-07 eta: 1 day, 3:38:38 time: 0.9959 data_time: 0.0046 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0065 decode.acc_seg: 99.7553 aux.loss_ce: 0.0082 aux.acc_seg: 99.3738 +04/18 01:22:27 - mmengine - INFO - Iter(train) [ 60500/160000] base_lr: 6.2776e-05 lr: 2.3210e-07 eta: 1 day, 3:37:48 time: 0.9971 data_time: 0.0046 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0064 decode.acc_seg: 99.7364 aux.loss_ce: 0.0079 aux.acc_seg: 99.1091 +04/18 01:23:17 - mmengine - INFO - Iter(train) [ 60550/160000] base_lr: 6.2745e-05 lr: 2.3198e-07 eta: 1 day, 3:36:58 time: 0.9960 data_time: 0.0045 memory: 8462 loss: 0.0150 decode.loss_ce: 0.0068 decode.acc_seg: 99.7866 aux.loss_ce: 0.0082 aux.acc_seg: 99.3393 +04/18 01:24:07 - mmengine - INFO - Iter(train) [ 60600/160000] base_lr: 6.2713e-05 lr: 2.3186e-07 eta: 1 day, 3:36:08 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0062 decode.acc_seg: 99.7169 aux.loss_ce: 0.0073 aux.acc_seg: 99.0591 +04/18 01:24:56 - mmengine - INFO - Iter(train) [ 60650/160000] base_lr: 6.2682e-05 lr: 2.3175e-07 eta: 1 day, 3:35:17 time: 0.9964 data_time: 0.0049 memory: 8462 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7566 aux.loss_ce: 0.0073 aux.acc_seg: 99.3406 +04/18 01:25:46 - mmengine - INFO - Iter(train) [ 60700/160000] base_lr: 6.2650e-05 lr: 2.3163e-07 eta: 1 day, 3:34:27 time: 0.9955 data_time: 0.0045 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.7856 aux.loss_ce: 0.0075 aux.acc_seg: 99.3752 +04/18 01:26:36 - mmengine - INFO - Iter(train) [ 60750/160000] base_lr: 6.2619e-05 lr: 2.3151e-07 eta: 1 day, 3:33:37 time: 0.9950 data_time: 0.0046 memory: 8462 loss: 0.0146 decode.loss_ce: 0.0067 decode.acc_seg: 99.7543 aux.loss_ce: 0.0079 aux.acc_seg: 99.1589 +04/18 01:27:26 - mmengine - INFO - Iter(train) [ 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98.7904 +04/18 01:30:45 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 01:30:45 - mmengine - INFO - Iter(train) [ 61000/160000] base_lr: 6.2461e-05 lr: 2.3093e-07 eta: 1 day, 3:29:26 time: 0.9974 data_time: 0.0051 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0067 decode.acc_seg: 99.8030 aux.loss_ce: 0.0078 aux.acc_seg: 99.4591 +04/18 01:31:35 - mmengine - INFO - Iter(train) [ 61050/160000] base_lr: 6.2429e-05 lr: 2.3081e-07 eta: 1 day, 3:28:36 time: 0.9978 data_time: 0.0047 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.8186 aux.loss_ce: 0.0070 aux.acc_seg: 99.3097 +04/18 01:32:25 - mmengine - INFO - Iter(train) [ 61100/160000] base_lr: 6.2398e-05 lr: 2.3070e-07 eta: 1 day, 3:27:46 time: 0.9975 data_time: 0.0047 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0063 decode.acc_seg: 99.6538 aux.loss_ce: 0.0077 aux.acc_seg: 98.9876 +04/18 01:33:15 - mmengine - INFO - Iter(train) [ 61150/160000] base_lr: 6.2366e-05 lr: 2.3058e-07 eta: 1 day, 3:26:55 time: 0.9967 data_time: 0.0051 memory: 8462 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.7581 aux.loss_ce: 0.0072 aux.acc_seg: 99.3671 +04/18 01:34:05 - mmengine - INFO - Iter(train) [ 61200/160000] base_lr: 6.2335e-05 lr: 2.3046e-07 eta: 1 day, 3:26:05 time: 0.9977 data_time: 0.0049 memory: 8462 loss: 0.0158 decode.loss_ce: 0.0068 decode.acc_seg: 99.7314 aux.loss_ce: 0.0090 aux.acc_seg: 99.1180 +04/18 01:34:55 - mmengine - INFO - Iter(train) [ 61250/160000] base_lr: 6.2303e-05 lr: 2.3035e-07 eta: 1 day, 3:25:15 time: 0.9984 data_time: 0.0047 memory: 8462 loss: 0.0143 decode.loss_ce: 0.0065 decode.acc_seg: 99.7868 aux.loss_ce: 0.0078 aux.acc_seg: 99.3105 +04/18 01:35:45 - mmengine - INFO - Iter(train) [ 61300/160000] base_lr: 6.2272e-05 lr: 2.3023e-07 eta: 1 day, 3:24:25 time: 0.9972 data_time: 0.0048 memory: 8462 loss: 0.0161 decode.loss_ce: 0.0076 decode.acc_seg: 99.7469 aux.loss_ce: 0.0085 aux.acc_seg: 99.2950 +04/18 01:36:34 - mmengine - INFO - Iter(train) [ 61350/160000] base_lr: 6.2240e-05 lr: 2.3011e-07 eta: 1 day, 3:23:35 time: 0.9977 data_time: 0.0048 memory: 8462 loss: 0.0133 decode.loss_ce: 0.0062 decode.acc_seg: 99.6969 aux.loss_ce: 0.0070 aux.acc_seg: 99.0492 +04/18 01:37:24 - mmengine - INFO - Iter(train) [ 61400/160000] base_lr: 6.2209e-05 lr: 2.3000e-07 eta: 1 day, 3:22:44 time: 0.9963 data_time: 0.0048 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.6357 aux.loss_ce: 0.0073 aux.acc_seg: 98.8478 +04/18 01:38:14 - mmengine - INFO - Iter(train) [ 61450/160000] base_lr: 6.2177e-05 lr: 2.2988e-07 eta: 1 day, 3:21:54 time: 0.9967 data_time: 0.0048 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0067 decode.acc_seg: 99.7492 aux.loss_ce: 0.0080 aux.acc_seg: 99.3401 +04/18 01:39:04 - mmengine - INFO - Iter(train) [ 61500/160000] base_lr: 6.2146e-05 lr: 2.2976e-07 eta: 1 day, 3:21:04 time: 0.9971 data_time: 0.0048 memory: 8462 loss: 0.0116 decode.loss_ce: 0.0053 decode.acc_seg: 99.7766 aux.loss_ce: 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decode.loss_ce: 0.0060 decode.acc_seg: 99.7002 aux.loss_ce: 0.0075 aux.acc_seg: 99.2342 +04/18 01:43:13 - mmengine - INFO - Iter(train) [ 61750/160000] base_lr: 6.1988e-05 lr: 2.2918e-07 eta: 1 day, 3:16:53 time: 0.9980 data_time: 0.0053 memory: 8462 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7612 aux.loss_ce: 0.0075 aux.acc_seg: 99.1423 +04/18 01:44:03 - mmengine - INFO - Iter(train) [ 61800/160000] base_lr: 6.1956e-05 lr: 2.2906e-07 eta: 1 day, 3:16:03 time: 0.9973 data_time: 0.0052 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.6469 aux.loss_ce: 0.0075 aux.acc_seg: 98.9435 +04/18 01:44:53 - mmengine - INFO - Iter(train) [ 61850/160000] base_lr: 6.1925e-05 lr: 2.2895e-07 eta: 1 day, 3:15:13 time: 0.9978 data_time: 0.0045 memory: 8462 loss: 0.0123 decode.loss_ce: 0.0058 decode.acc_seg: 99.7133 aux.loss_ce: 0.0065 aux.acc_seg: 99.1039 +04/18 01:45:43 - mmengine - INFO - Iter(train) [ 61900/160000] base_lr: 6.1893e-05 lr: 2.2883e-07 eta: 1 day, 3:14:23 time: 0.9968 data_time: 0.0050 memory: 8462 loss: 0.0149 decode.loss_ce: 0.0069 decode.acc_seg: 99.7814 aux.loss_ce: 0.0079 aux.acc_seg: 99.4324 +04/18 01:46:33 - mmengine - INFO - Iter(train) [ 61950/160000] base_lr: 6.1862e-05 lr: 2.2872e-07 eta: 1 day, 3:13:33 time: 0.9974 data_time: 0.0047 memory: 8462 loss: 0.0142 decode.loss_ce: 0.0065 decode.acc_seg: 99.6693 aux.loss_ce: 0.0077 aux.acc_seg: 99.1661 +04/18 01:47:23 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 01:47:23 - mmengine - INFO - Iter(train) [ 62000/160000] base_lr: 6.1830e-05 lr: 2.2860e-07 eta: 1 day, 3:12:42 time: 0.9955 data_time: 0.0049 memory: 8462 loss: 0.0130 decode.loss_ce: 0.0055 decode.acc_seg: 99.8205 aux.loss_ce: 0.0075 aux.acc_seg: 99.5090 +04/18 01:48:13 - mmengine - INFO - Iter(train) [ 62050/160000] base_lr: 6.1798e-05 lr: 2.2848e-07 eta: 1 day, 3:11:52 time: 0.9960 data_time: 0.0050 memory: 8462 loss: 0.0140 decode.loss_ce: 0.0064 decode.acc_seg: 99.7391 aux.loss_ce: 0.0076 aux.acc_seg: 99.2418 +04/18 01:49:02 - mmengine - INFO - Iter(train) [ 62100/160000] base_lr: 6.1767e-05 lr: 2.2837e-07 eta: 1 day, 3:11:02 time: 0.9969 data_time: 0.0045 memory: 8462 loss: 0.0145 decode.loss_ce: 0.0068 decode.acc_seg: 99.7988 aux.loss_ce: 0.0077 aux.acc_seg: 99.5373 +04/18 01:49:52 - mmengine - INFO - Iter(train) [ 62150/160000] base_lr: 6.1735e-05 lr: 2.2825e-07 eta: 1 day, 3:10:12 time: 0.9966 data_time: 0.0047 memory: 8462 loss: 0.0122 decode.loss_ce: 0.0056 decode.acc_seg: 99.6365 aux.loss_ce: 0.0066 aux.acc_seg: 99.0641 +04/18 01:50:42 - mmengine - INFO - Iter(train) [ 62200/160000] base_lr: 6.1704e-05 lr: 2.2813e-07 eta: 1 day, 3:09:22 time: 0.9986 data_time: 0.0054 memory: 8462 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.7803 aux.loss_ce: 0.0077 aux.acc_seg: 99.3336 +04/18 01:51:32 - mmengine - INFO - Iter(train) [ 62250/160000] base_lr: 6.1672e-05 lr: 2.2802e-07 eta: 1 day, 3:08:31 time: 0.9963 data_time: 0.0048 memory: 8462 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99.1310 +04/18 02:01:30 - mmengine - INFO - Iter(train) [ 62850/160000] base_lr: 6.1294e-05 lr: 2.2662e-07 eta: 1 day, 2:58:29 time: 0.9970 data_time: 0.0047 memory: 8462 loss: 0.0147 decode.loss_ce: 0.0068 decode.acc_seg: 99.6639 aux.loss_ce: 0.0079 aux.acc_seg: 99.0673 +04/18 02:02:20 - mmengine - INFO - Iter(train) [ 62900/160000] base_lr: 6.1262e-05 lr: 2.2650e-07 eta: 1 day, 2:57:39 time: 0.9967 data_time: 0.0046 memory: 8462 loss: 0.0126 decode.loss_ce: 0.0059 decode.acc_seg: 99.7194 aux.loss_ce: 0.0068 aux.acc_seg: 99.1749 +04/18 02:03:10 - mmengine - INFO - Iter(train) [ 62950/160000] base_lr: 6.1231e-05 lr: 2.2638e-07 eta: 1 day, 2:56:49 time: 0.9971 data_time: 0.0049 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0061 decode.acc_seg: 99.6910 aux.loss_ce: 0.0071 aux.acc_seg: 99.0683 +04/18 02:04:00 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240417_083130 +04/18 02:04:00 - mmengine - INFO - Iter(train) [ 63000/160000] base_lr: 6.1199e-05 lr: 2.2627e-07 eta: 1 day, 2:55:59 time: 0.9970 data_time: 0.0051 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0059 decode.acc_seg: 99.7391 aux.loss_ce: 0.0073 aux.acc_seg: 99.4577 +04/18 02:04:50 - mmengine - INFO - Iter(train) [ 63050/160000] base_lr: 6.1168e-05 lr: 2.2615e-07 eta: 1 day, 2:55:09 time: 0.9969 data_time: 0.0047 memory: 8462 loss: 0.0121 decode.loss_ce: 0.0053 decode.acc_seg: 99.7906 aux.loss_ce: 0.0067 aux.acc_seg: 99.2176 +04/18 02:05:40 - mmengine - INFO - Iter(train) [ 63100/160000] base_lr: 6.1136e-05 lr: 2.2603e-07 eta: 1 day, 2:54:19 time: 0.9977 data_time: 0.0049 memory: 8462 loss: 0.0127 decode.loss_ce: 0.0059 decode.acc_seg: 99.7208 aux.loss_ce: 0.0068 aux.acc_seg: 99.0906 +04/18 02:06:30 - mmengine - INFO - Iter(train) [ 63150/160000] base_lr: 6.1104e-05 lr: 2.2592e-07 eta: 1 day, 2:53:29 time: 0.9964 data_time: 0.0047 memory: 8462 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.7169 aux.loss_ce: 0.0070 aux.acc_seg: 99.2624 +04/18 02:07:20 - mmengine - INFO - Iter(train) [ 63200/160000] base_lr: 6.1073e-05 lr: 2.2580e-07 eta: 1 day, 2:52:38 time: 0.9967 data_time: 0.0047 memory: 8462 loss: 0.0148 decode.loss_ce: 0.0068 decode.acc_seg: 99.7906 aux.loss_ce: 0.0080 aux.acc_seg: 99.3170 +04/18 02:08:09 - mmengine - INFO - Iter(train) [ 63250/160000] base_lr: 6.1041e-05 lr: 2.2568e-07 eta: 1 day, 2:51:48 time: 0.9979 data_time: 0.0050 memory: 8462 loss: 0.0128 decode.loss_ce: 0.0057 decode.acc_seg: 99.7709 aux.loss_ce: 0.0072 aux.acc_seg: 99.2205 +04/18 02:08:59 - mmengine - INFO - Iter(train) [ 63300/160000] base_lr: 6.1010e-05 lr: 2.2557e-07 eta: 1 day, 2:50:58 time: 0.9969 data_time: 0.0047 memory: 8462 loss: 0.0132 decode.loss_ce: 0.0057 decode.acc_seg: 99.6817 aux.loss_ce: 0.0075 aux.acc_seg: 99.1745 +04/18 02:09:49 - mmengine - INFO - Iter(train) [ 63350/160000] base_lr: 6.0978e-05 lr: 2.2545e-07 eta: 1 day, 2:50:08 time: 0.9983 data_time: 0.0056 memory: 8462 loss: 0.0157 decode.loss_ce: 0.0069 decode.acc_seg: 99.7620 aux.loss_ce: 0.0089 aux.acc_seg: 99.2807 +04/18 02:10:39 - mmengine - INFO - Iter(train) [ 63400/160000] base_lr: 6.0947e-05 lr: 2.2533e-07 eta: 1 day, 2:49:18 time: 0.9960 data_time: 0.0049 memory: 8462 loss: 0.0151 decode.loss_ce: 0.0068 decode.acc_seg: 99.7185 aux.loss_ce: 0.0083 aux.acc_seg: 99.2039 +04/18 02:11:29 - mmengine - INFO - Iter(train) [ 63450/160000] base_lr: 6.0915e-05 lr: 2.2522e-07 eta: 1 day, 2:48:28 time: 0.9980 data_time: 0.0053 memory: 8462 loss: 0.0141 decode.loss_ce: 0.0064 decode.acc_seg: 99.8201 aux.loss_ce: 0.0077 aux.acc_seg: 99.3877 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174801 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174802 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174803 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174804 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174801 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174802 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174803 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 174804 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 174770 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 174770 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 174770 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/18 02:20:10 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1925317996 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1925317996 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/18 02:20:11 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=101, + dilations=( + 1, + 1, + 1, + 1, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dropout_ratio=0.1, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + pretrained='open-mmlab://resnet101_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/R101_4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/18 02:20:13 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +04/18 02:20:14 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +04/18 02:20:14 - mmengine - INFO - load model from: open-mmlab://resnet101_v1c +04/18 02:20:14 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet101_v1c +04/18 02:20:14 - mmengine - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: fc.weight, fc.bias + +04/18 02:20:14 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/18 02:20:14 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/18 02:20:14 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/R101_4000. +04/18 02:20:46 - mmengine - INFO - Iter(train) [ 50/160000] lr: 9.9973e-03 eta: 1 day, 4:42:15 time: 0.5461 data_time: 0.0064 memory: 7635 loss: 0.1394 decode.loss_ce: 0.0903 decode.acc_seg: 96.5318 aux.loss_ce: 0.0491 aux.acc_seg: 96.5318 +04/18 02:21:14 - mmengine - INFO - Iter(train) [ 100/160000] lr: 9.9945e-03 eta: 1 day, 2:30:49 time: 0.5490 data_time: 0.0057 memory: 7635 loss: 0.0858 decode.loss_ce: 0.0528 decode.acc_seg: 97.9885 aux.loss_ce: 0.0330 aux.acc_seg: 97.1264 +04/18 02:21:41 - mmengine - INFO - Iter(train) [ 150/160000] lr: 9.9917e-03 eta: 1 day, 1:47:10 time: 0.5487 data_time: 0.0061 memory: 7635 loss: 0.0797 decode.loss_ce: 0.0480 decode.acc_seg: 98.1838 aux.loss_ce: 0.0317 aux.acc_seg: 96.6573 +Traceback (most recent call last): +Traceback (most recent call last): +Traceback (most recent call last): +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers + main()main()main()main() +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 592936 closing signal SIGINT + + + + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 592937 closing signal SIGINT + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 592938 closing signal SIGINT +runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 592939 closing signal SIGINT + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + model = self.train_loop.run() # type: ignoreoutputs = self.runner.model.train_step( + +model = self.train_loop.run() # type: ignoremodel = self.train_loop.run() # type: ignore File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 201, in update_params + self.run_iter(data_batch)self.run_iter(data_batch) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter +self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + self.step(**step_kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/scheduler/param_scheduler.py", line 115, in wrapper + outputs = self.runner.model.train_step(outputs = self.runner.model.train_step( + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + return wrapped(*args, **kwargs)outputs = self.runner.model.train_step( + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 253, in step + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss)optim_wrapper.update_params(parsed_loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 201, in update_params + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 201, in update_params + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 201, in update_params +self.optimizer.step(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper + self.step(**step_kwargs) +self.step(**step_kwargs) File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/scheduler/param_scheduler.py", line 115, in wrapper + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/scheduler/param_scheduler.py", line 115, in wrapper + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context +self.step(**step_kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/scheduler/param_scheduler.py", line 115, in wrapper +return wrapped(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 253, in step +return wrapped(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 253, in step + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/sgd.py", line 144, in step + return wrapped(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 253, in step + self.optimizer.step(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper + self.optimizer.step(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper + return func(*args, **kwargs) +self.optimizer.step(**kwargs) File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + + File "/opt/conda/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/sgd.py", line 144, in step + return func(*args, **kwargs)return func(*args, **kwargs) + + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + File "/opt/conda/lib/python3.8/site-packages/torch/optim/sgd.py", line 144, in step + return func(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/optim/sgd.py", line 144, in step + F.sgd(params_with_grad,F.sgd(params_with_grad,F.sgd(params_with_grad,F.sgd(params_with_grad, + + + + File "/opt/conda/lib/python3.8/site-packages/torch/optim/_functional.py", line 186, in sgd + File "/opt/conda/lib/python3.8/site-packages/torch/optim/_functional.py", line 194, in sgd + File "/opt/conda/lib/python3.8/site-packages/torch/optim/_functional.py", line 177, in sgd + File "/opt/conda/lib/python3.8/site-packages/torch/optim/_functional.py", line 194, in sgd + param.add_(d_p, alpha=alpha) buf.mul_(momentum).add_(d_p, alpha=1 - dampening)d_p = d_p.add(param, alpha=weight_decay) +param.add_(d_p, alpha=alpha) +KeyboardInterrupt + + +KeyboardInterruptKeyboardInterruptKeyboardInterrupt + + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 592901 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/18 02:22:14 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1770541458 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1770541458 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/18 02:22:14 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=101, + dilations=( + 1, + 1, + 1, + 1, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dropout_ratio=0.1, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + pretrained='open-mmlab://resnet101_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/R101_4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py:120: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert `seg_logits` into a predictionapplying a threshold + warnings.warn('For binary segmentation, we suggest using' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/18 02:22:16 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +04/18 02:22:17 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +04/18 02:22:18 - mmengine - INFO - load model from: open-mmlab://resnet101_v1c +04/18 02:22:18 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet101_v1c +04/18 02:22:18 - mmengine - WARNING - The model and loaded state dict do not match exactly + +unexpected key in source state_dict: fc.weight, fc.bias + +04/18 02:22:18 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/18 02:22:18 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/18 02:22:18 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/R101_4000. +04/18 02:22:50 - mmengine - INFO - Iter(train) [ 50/160000] lr: 9.9973e-03 eta: 1 day, 4:43:49 time: 0.5456 data_time: 0.0068 memory: 7635 loss: 0.1351 decode.loss_ce: 0.0875 decode.acc_seg: 97.6007 aux.loss_ce: 0.0476 aux.acc_seg: 97.6006 +04/18 02:23:17 - mmengine - INFO - Iter(train) [ 100/160000] lr: 9.9945e-03 eta: 1 day, 2:29:41 time: 0.5467 data_time: 0.0057 memory: 7635 loss: 0.0936 decode.loss_ce: 0.0562 decode.acc_seg: 97.6175 aux.loss_ce: 0.0374 aux.acc_seg: 96.2901 +04/18 02:23:45 - mmengine - INFO - Iter(train) [ 150/160000] lr: 9.9917e-03 eta: 1 day, 1:44:59 time: 0.5467 data_time: 0.0065 memory: 7635 loss: 0.0822 decode.loss_ce: 0.0503 decode.acc_seg: 97.9509 aux.loss_ce: 0.0319 aux.acc_seg: 97.1464 +04/18 02:24:12 - mmengine - INFO - Iter(train) [ 200/160000] lr: 9.9889e-03 eta: 1 day, 1:22:56 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0688 decode.loss_ce: 0.0416 decode.acc_seg: 98.1911 aux.loss_ce: 0.0272 aux.acc_seg: 96.7990 +04/18 02:24:40 - mmengine - INFO - Iter(train) [ 250/160000] lr: 9.9861e-03 eta: 1 day, 1:09:55 time: 0.5487 data_time: 0.0058 memory: 7635 loss: 0.0830 decode.loss_ce: 0.0518 decode.acc_seg: 98.1723 aux.loss_ce: 0.0313 aux.acc_seg: 96.9854 +04/18 02:25:07 - mmengine - INFO - Iter(train) [ 300/160000] lr: 9.9833e-03 eta: 1 day, 1:00:47 time: 0.5491 data_time: 0.0063 memory: 7635 loss: 0.0735 decode.loss_ce: 0.0464 decode.acc_seg: 98.2391 aux.loss_ce: 0.0270 aux.acc_seg: 97.6198 +04/18 02:25:34 - mmengine - INFO - Iter(train) [ 350/160000] lr: 9.9806e-03 eta: 1 day, 0:54:07 time: 0.5471 data_time: 0.0063 memory: 7635 loss: 0.0653 decode.loss_ce: 0.0413 decode.acc_seg: 98.3918 aux.loss_ce: 0.0241 aux.acc_seg: 97.8115 +04/18 02:26:02 - mmengine - INFO - Iter(train) [ 400/160000] lr: 9.9778e-03 eta: 1 day, 0:49:12 time: 0.5473 data_time: 0.0061 memory: 7635 loss: 0.0583 decode.loss_ce: 0.0371 decode.acc_seg: 98.5509 aux.loss_ce: 0.0212 aux.acc_seg: 98.1630 +04/18 02:26:29 - mmengine - INFO - Iter(train) [ 450/160000] lr: 9.9750e-03 eta: 1 day, 0:45:04 time: 0.5478 data_time: 0.0058 memory: 7635 loss: 0.0662 decode.loss_ce: 0.0430 decode.acc_seg: 98.4915 aux.loss_ce: 0.0232 aux.acc_seg: 98.0277 +04/18 02:26:57 - mmengine - INFO - Iter(train) [ 500/160000] lr: 9.9722e-03 eta: 1 day, 0:43:03 time: 0.5475 data_time: 0.0059 memory: 7635 loss: 0.0593 decode.loss_ce: 0.0378 decode.acc_seg: 98.2600 aux.loss_ce: 0.0215 aux.acc_seg: 97.6308 +04/18 02:27:24 - mmengine - INFO - Iter(train) [ 550/160000] lr: 9.9694e-03 eta: 1 day, 0:40:14 time: 0.5483 data_time: 0.0059 memory: 7635 loss: 0.0571 decode.loss_ce: 0.0364 decode.acc_seg: 97.7746 aux.loss_ce: 0.0207 aux.acc_seg: 96.9963 +04/18 02:27:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:27:52 - mmengine - INFO - Iter(train) [ 600/160000] lr: 9.9666e-03 eta: 1 day, 0:37:48 time: 0.5475 data_time: 0.0061 memory: 7635 loss: 0.0485 decode.loss_ce: 0.0307 decode.acc_seg: 99.0183 aux.loss_ce: 0.0178 aux.acc_seg: 98.3242 +04/18 02:28:19 - mmengine - INFO - Iter(train) [ 650/160000] lr: 9.9639e-03 eta: 1 day, 0:35:39 time: 0.5482 data_time: 0.0057 memory: 7635 loss: 0.0573 decode.loss_ce: 0.0367 decode.acc_seg: 99.0075 aux.loss_ce: 0.0206 aux.acc_seg: 98.4173 +04/18 02:28:46 - mmengine - INFO - Iter(train) [ 700/160000] lr: 9.9611e-03 eta: 1 day, 0:33:52 time: 0.5494 data_time: 0.0071 memory: 7635 loss: 0.0468 decode.loss_ce: 0.0293 decode.acc_seg: 98.9671 aux.loss_ce: 0.0175 aux.acc_seg: 98.4134 +04/18 02:29:14 - mmengine - INFO - Iter(train) [ 750/160000] lr: 9.9583e-03 eta: 1 day, 0:32:04 time: 0.5473 data_time: 0.0054 memory: 7635 loss: 0.0509 decode.loss_ce: 0.0322 decode.acc_seg: 98.5582 aux.loss_ce: 0.0187 aux.acc_seg: 97.6395 +04/18 02:29:41 - mmengine - INFO - Iter(train) [ 800/160000] lr: 9.9555e-03 eta: 1 day, 0:30:34 time: 0.5486 data_time: 0.0062 memory: 7635 loss: 0.0491 decode.loss_ce: 0.0310 decode.acc_seg: 99.2507 aux.loss_ce: 0.0181 aux.acc_seg: 98.8086 +04/18 02:30:09 - mmengine - INFO - Iter(train) [ 850/160000] lr: 9.9527e-03 eta: 1 day, 0:29:11 time: 0.5487 data_time: 0.0062 memory: 7635 loss: 0.0514 decode.loss_ce: 0.0329 decode.acc_seg: 98.4890 aux.loss_ce: 0.0185 aux.acc_seg: 97.9950 +04/18 02:30:36 - mmengine - INFO - Iter(train) [ 900/160000] lr: 9.9499e-03 eta: 1 day, 0:27:56 time: 0.5475 data_time: 0.0056 memory: 7635 loss: 0.0542 decode.loss_ce: 0.0350 decode.acc_seg: 98.8522 aux.loss_ce: 0.0192 aux.acc_seg: 98.3143 +04/18 02:31:03 - mmengine - INFO - Iter(train) [ 950/160000] lr: 9.9471e-03 eta: 1 day, 0:26:46 time: 0.5476 data_time: 0.0065 memory: 7635 loss: 0.0471 decode.loss_ce: 0.0298 decode.acc_seg: 98.2690 aux.loss_ce: 0.0173 aux.acc_seg: 97.9364 +04/18 02:31:31 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:31:31 - mmengine - INFO - Iter(train) [ 1000/160000] lr: 9.9444e-03 eta: 1 day, 0:25:43 time: 0.5505 data_time: 0.0067 memory: 7635 loss: 0.0458 decode.loss_ce: 0.0291 decode.acc_seg: 98.5917 aux.loss_ce: 0.0167 aux.acc_seg: 98.0520 +04/18 02:31:58 - mmengine - INFO - Iter(train) [ 1050/160000] lr: 9.9416e-03 eta: 1 day, 0:24:38 time: 0.5486 data_time: 0.0060 memory: 7635 loss: 0.0461 decode.loss_ce: 0.0289 decode.acc_seg: 99.0119 aux.loss_ce: 0.0172 aux.acc_seg: 98.5758 +04/18 02:32:26 - mmengine - INFO - Iter(train) [ 1100/160000] lr: 9.9388e-03 eta: 1 day, 0:23:44 time: 0.5486 data_time: 0.0055 memory: 7635 loss: 0.0462 decode.loss_ce: 0.0294 decode.acc_seg: 98.2671 aux.loss_ce: 0.0168 aux.acc_seg: 97.6591 +04/18 02:32:53 - mmengine - INFO - Iter(train) [ 1150/160000] lr: 9.9360e-03 eta: 1 day, 0:22:49 time: 0.5476 data_time: 0.0059 memory: 7635 loss: 0.0401 decode.loss_ce: 0.0249 decode.acc_seg: 98.8012 aux.loss_ce: 0.0152 aux.acc_seg: 98.5517 +04/18 02:33:21 - mmengine - INFO - Iter(train) [ 1200/160000] lr: 9.9332e-03 eta: 1 day, 0:21:56 time: 0.5482 data_time: 0.0059 memory: 7635 loss: 0.0461 decode.loss_ce: 0.0291 decode.acc_seg: 98.8809 aux.loss_ce: 0.0170 aux.acc_seg: 98.1678 +04/18 02:33:48 - mmengine - INFO - Iter(train) [ 1250/160000] lr: 9.9304e-03 eta: 1 day, 0:21:04 time: 0.5486 data_time: 0.0064 memory: 7635 loss: 0.0391 decode.loss_ce: 0.0240 decode.acc_seg: 98.9322 aux.loss_ce: 0.0151 aux.acc_seg: 98.2064 +04/18 02:34:16 - mmengine - INFO - Iter(train) [ 1300/160000] lr: 9.9276e-03 eta: 1 day, 0:20:12 time: 0.5475 data_time: 0.0066 memory: 7635 loss: 0.0476 decode.loss_ce: 0.0303 decode.acc_seg: 98.8741 aux.loss_ce: 0.0173 aux.acc_seg: 98.2177 +04/18 02:34:43 - mmengine - INFO - Iter(train) [ 1350/160000] lr: 9.9248e-03 eta: 1 day, 0:19:26 time: 0.5486 data_time: 0.0058 memory: 7635 loss: 0.0481 decode.loss_ce: 0.0312 decode.acc_seg: 98.3006 aux.loss_ce: 0.0170 aux.acc_seg: 97.7006 +04/18 02:35:10 - mmengine - INFO - Iter(train) [ 1400/160000] lr: 9.9221e-03 eta: 1 day, 0:18:39 time: 0.5489 data_time: 0.0063 memory: 7635 loss: 0.0418 decode.loss_ce: 0.0259 decode.acc_seg: 99.1931 aux.loss_ce: 0.0158 aux.acc_seg: 98.7233 +04/18 02:35:38 - mmengine - INFO - Iter(train) [ 1450/160000] lr: 9.9193e-03 eta: 1 day, 0:17:54 time: 0.5478 data_time: 0.0061 memory: 7635 loss: 0.0464 decode.loss_ce: 0.0294 decode.acc_seg: 98.9993 aux.loss_ce: 0.0170 aux.acc_seg: 98.6215 +04/18 02:36:05 - mmengine - INFO - Iter(train) [ 1500/160000] lr: 9.9165e-03 eta: 1 day, 0:17:09 time: 0.5479 data_time: 0.0060 memory: 7635 loss: 0.0441 decode.loss_ce: 0.0276 decode.acc_seg: 98.9414 aux.loss_ce: 0.0164 aux.acc_seg: 98.5452 +04/18 02:36:33 - mmengine - INFO - Iter(train) [ 1550/160000] lr: 9.9137e-03 eta: 1 day, 0:16:37 time: 0.5576 data_time: 0.0061 memory: 7635 loss: 0.0364 decode.loss_ce: 0.0221 decode.acc_seg: 99.5236 aux.loss_ce: 0.0143 aux.acc_seg: 99.0460 +04/18 02:37:00 - mmengine - INFO - Iter(train) [ 1600/160000] lr: 9.9109e-03 eta: 1 day, 0:16:04 time: 0.5485 data_time: 0.0061 memory: 7635 loss: 0.0416 decode.loss_ce: 0.0258 decode.acc_seg: 99.0429 aux.loss_ce: 0.0157 aux.acc_seg: 98.5963 +04/18 02:37:28 - mmengine - INFO - Iter(train) [ 1650/160000] lr: 9.9081e-03 eta: 1 day, 0:15:23 time: 0.5487 data_time: 0.0064 memory: 7635 loss: 0.0422 decode.loss_ce: 0.0264 decode.acc_seg: 99.1405 aux.loss_ce: 0.0158 aux.acc_seg: 98.7473 +04/18 02:37:55 - mmengine - INFO - Iter(train) [ 1700/160000] lr: 9.9053e-03 eta: 1 day, 0:14:43 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0418 decode.loss_ce: 0.0261 decode.acc_seg: 99.0495 aux.loss_ce: 0.0156 aux.acc_seg: 98.5112 +04/18 02:38:23 - mmengine - INFO - Iter(train) [ 1750/160000] lr: 9.9025e-03 eta: 1 day, 0:14:03 time: 0.5489 data_time: 0.0058 memory: 7635 loss: 0.0423 decode.loss_ce: 0.0264 decode.acc_seg: 98.7845 aux.loss_ce: 0.0159 aux.acc_seg: 98.3470 +04/18 02:38:50 - mmengine - INFO - Iter(train) [ 1800/160000] lr: 9.8998e-03 eta: 1 day, 0:13:24 time: 0.5480 data_time: 0.0059 memory: 7635 loss: 0.0401 decode.loss_ce: 0.0251 decode.acc_seg: 98.5924 aux.loss_ce: 0.0149 aux.acc_seg: 98.2556 +04/18 02:39:17 - mmengine - INFO - Iter(train) [ 1850/160000] lr: 9.8970e-03 eta: 1 day, 0:12:45 time: 0.5498 data_time: 0.0057 memory: 7635 loss: 0.0381 decode.loss_ce: 0.0236 decode.acc_seg: 99.0651 aux.loss_ce: 0.0144 aux.acc_seg: 98.5510 +04/18 02:39:45 - mmengine - INFO - Iter(train) [ 1900/160000] lr: 9.8942e-03 eta: 1 day, 0:12:09 time: 0.5500 data_time: 0.0064 memory: 7635 loss: 0.0368 decode.loss_ce: 0.0227 decode.acc_seg: 98.8979 aux.loss_ce: 0.0140 aux.acc_seg: 98.2182 +04/18 02:40:12 - mmengine - INFO - Iter(train) [ 1950/160000] lr: 9.8914e-03 eta: 1 day, 0:11:31 time: 0.5484 data_time: 0.0064 memory: 7635 loss: 0.0408 decode.loss_ce: 0.0254 decode.acc_seg: 98.6368 aux.loss_ce: 0.0154 aux.acc_seg: 98.1456 +04/18 02:40:40 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:40:40 - mmengine - INFO - Iter(train) [ 2000/160000] lr: 9.8886e-03 eta: 1 day, 0:10:54 time: 0.5498 data_time: 0.0057 memory: 7635 loss: 0.0379 decode.loss_ce: 0.0240 decode.acc_seg: 99.2053 aux.loss_ce: 0.0139 aux.acc_seg: 98.7584 +04/18 02:41:07 - mmengine - INFO - Iter(train) [ 2050/160000] lr: 9.8858e-03 eta: 1 day, 0:10:20 time: 0.5499 data_time: 0.0070 memory: 7635 loss: 0.0347 decode.loss_ce: 0.0211 decode.acc_seg: 99.1835 aux.loss_ce: 0.0135 aux.acc_seg: 98.8031 +04/18 02:41:35 - mmengine - INFO - Iter(train) [ 2100/160000] lr: 9.8830e-03 eta: 1 day, 0:09:45 time: 0.5491 data_time: 0.0069 memory: 7635 loss: 0.0420 decode.loss_ce: 0.0262 decode.acc_seg: 98.9922 aux.loss_ce: 0.0159 aux.acc_seg: 98.4855 +04/18 02:42:02 - mmengine - INFO - Iter(train) [ 2150/160000] lr: 9.8802e-03 eta: 1 day, 0:09:10 time: 0.5495 data_time: 0.0058 memory: 7635 loss: 0.0358 decode.loss_ce: 0.0222 decode.acc_seg: 99.1411 aux.loss_ce: 0.0136 aux.acc_seg: 98.6343 +04/18 02:42:30 - mmengine - INFO - Iter(train) [ 2200/160000] lr: 9.8775e-03 eta: 1 day, 0:08:32 time: 0.5481 data_time: 0.0061 memory: 7635 loss: 0.0408 decode.loss_ce: 0.0260 decode.acc_seg: 99.0074 aux.loss_ce: 0.0148 aux.acc_seg: 98.6520 +04/18 02:42:57 - mmengine - INFO - Iter(train) [ 2250/160000] lr: 9.8747e-03 eta: 1 day, 0:07:57 time: 0.5498 data_time: 0.0075 memory: 7635 loss: 0.0407 decode.loss_ce: 0.0253 decode.acc_seg: 98.9144 aux.loss_ce: 0.0154 aux.acc_seg: 98.1933 +04/18 02:43:24 - mmengine - INFO - Iter(train) [ 2300/160000] lr: 9.8719e-03 eta: 1 day, 0:07:21 time: 0.5472 data_time: 0.0059 memory: 7635 loss: 0.0358 decode.loss_ce: 0.0220 decode.acc_seg: 99.2257 aux.loss_ce: 0.0138 aux.acc_seg: 98.7956 +04/18 02:43:52 - mmengine - INFO - Iter(train) [ 2350/160000] lr: 9.8691e-03 eta: 1 day, 0:06:49 time: 0.5498 data_time: 0.0065 memory: 7635 loss: 0.0411 decode.loss_ce: 0.0260 decode.acc_seg: 98.4962 aux.loss_ce: 0.0151 aux.acc_seg: 98.0080 +04/18 02:44:19 - mmengine - INFO - Iter(train) [ 2400/160000] lr: 9.8663e-03 eta: 1 day, 0:06:16 time: 0.5484 data_time: 0.0061 memory: 7635 loss: 0.0372 decode.loss_ce: 0.0232 decode.acc_seg: 99.0525 aux.loss_ce: 0.0140 aux.acc_seg: 98.3671 +04/18 02:44:47 - mmengine - INFO - Iter(train) [ 2450/160000] lr: 9.8635e-03 eta: 1 day, 0:05:42 time: 0.5489 data_time: 0.0065 memory: 7635 loss: 0.0434 decode.loss_ce: 0.0275 decode.acc_seg: 98.9281 aux.loss_ce: 0.0159 aux.acc_seg: 98.5132 +04/18 02:45:14 - mmengine - INFO - Iter(train) [ 2500/160000] lr: 9.8607e-03 eta: 1 day, 0:05:09 time: 0.5488 data_time: 0.0062 memory: 7635 loss: 0.0360 decode.loss_ce: 0.0225 decode.acc_seg: 98.5716 aux.loss_ce: 0.0135 aux.acc_seg: 98.1297 +04/18 02:45:42 - mmengine - INFO - Iter(train) [ 2550/160000] lr: 9.8579e-03 eta: 1 day, 0:04:37 time: 0.5486 data_time: 0.0056 memory: 7635 loss: 0.0358 decode.loss_ce: 0.0223 decode.acc_seg: 99.2933 aux.loss_ce: 0.0136 aux.acc_seg: 98.6950 +04/18 02:46:09 - mmengine - INFO - Iter(train) [ 2600/160000] lr: 9.8551e-03 eta: 1 day, 0:04:06 time: 0.5507 data_time: 0.0060 memory: 7635 loss: 0.0354 decode.loss_ce: 0.0215 decode.acc_seg: 99.2549 aux.loss_ce: 0.0139 aux.acc_seg: 98.7345 +04/18 02:46:37 - mmengine - INFO - Iter(train) [ 2650/160000] lr: 9.8524e-03 eta: 1 day, 0:03:45 time: 0.5579 data_time: 0.0060 memory: 7635 loss: 0.0389 decode.loss_ce: 0.0242 decode.acc_seg: 99.0269 aux.loss_ce: 0.0147 aux.acc_seg: 98.4343 +04/18 02:47:04 - mmengine - INFO - Iter(train) [ 2700/160000] lr: 9.8496e-03 eta: 1 day, 0:03:14 time: 0.5500 data_time: 0.0064 memory: 7635 loss: 0.0323 decode.loss_ce: 0.0193 decode.acc_seg: 99.0790 aux.loss_ce: 0.0130 aux.acc_seg: 98.3951 +04/18 02:47:32 - mmengine - INFO - Iter(train) [ 2750/160000] lr: 9.8468e-03 eta: 1 day, 0:02:42 time: 0.5483 data_time: 0.0061 memory: 7635 loss: 0.0368 decode.loss_ce: 0.0225 decode.acc_seg: 99.2489 aux.loss_ce: 0.0142 aux.acc_seg: 98.7843 +04/18 02:47:59 - mmengine - INFO - Iter(train) [ 2800/160000] lr: 9.8440e-03 eta: 1 day, 0:02:10 time: 0.5489 data_time: 0.0057 memory: 7635 loss: 0.0415 decode.loss_ce: 0.0263 decode.acc_seg: 99.0236 aux.loss_ce: 0.0152 aux.acc_seg: 98.5974 +04/18 02:48:27 - mmengine - INFO - Iter(train) [ 2850/160000] lr: 9.8412e-03 eta: 1 day, 0:01:39 time: 0.5501 data_time: 0.0062 memory: 7635 loss: 0.0386 decode.loss_ce: 0.0239 decode.acc_seg: 99.1429 aux.loss_ce: 0.0147 aux.acc_seg: 98.5730 +04/18 02:48:54 - mmengine - INFO - Iter(train) [ 2900/160000] lr: 9.8384e-03 eta: 1 day, 0:01:06 time: 0.5494 data_time: 0.0059 memory: 7635 loss: 0.0350 decode.loss_ce: 0.0218 decode.acc_seg: 98.8841 aux.loss_ce: 0.0132 aux.acc_seg: 98.5057 +04/18 02:49:21 - mmengine - INFO - Iter(train) [ 2950/160000] lr: 9.8356e-03 eta: 1 day, 0:00:34 time: 0.5494 data_time: 0.0061 memory: 7635 loss: 0.0334 decode.loss_ce: 0.0203 decode.acc_seg: 99.3465 aux.loss_ce: 0.0130 aux.acc_seg: 98.6574 +04/18 02:49:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:49:49 - mmengine - INFO - Iter(train) [ 3000/160000] lr: 9.8328e-03 eta: 1 day, 0:00:02 time: 0.5490 data_time: 0.0057 memory: 7635 loss: 0.0348 decode.loss_ce: 0.0213 decode.acc_seg: 98.5440 aux.loss_ce: 0.0136 aux.acc_seg: 98.1440 +04/18 02:50:16 - mmengine - INFO - Iter(train) [ 3050/160000] lr: 9.8300e-03 eta: 23:59:30 time: 0.5473 data_time: 0.0061 memory: 7635 loss: 0.0345 decode.loss_ce: 0.0212 decode.acc_seg: 99.1501 aux.loss_ce: 0.0133 aux.acc_seg: 98.5846 +04/18 02:50:44 - mmengine - INFO - Iter(train) [ 3100/160000] lr: 9.8273e-03 eta: 23:58:59 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0375 decode.loss_ce: 0.0233 decode.acc_seg: 98.6369 aux.loss_ce: 0.0142 aux.acc_seg: 98.0972 +04/18 02:51:11 - mmengine - INFO - Iter(train) [ 3150/160000] lr: 9.8245e-03 eta: 23:58:28 time: 0.5484 data_time: 0.0059 memory: 7635 loss: 0.0327 decode.loss_ce: 0.0198 decode.acc_seg: 99.2376 aux.loss_ce: 0.0129 aux.acc_seg: 98.8457 +04/18 02:51:39 - mmengine - INFO - Iter(train) [ 3200/160000] lr: 9.8217e-03 eta: 23:57:55 time: 0.5483 data_time: 0.0059 memory: 7635 loss: 0.0337 decode.loss_ce: 0.0206 decode.acc_seg: 99.3318 aux.loss_ce: 0.0131 aux.acc_seg: 98.8579 +04/18 02:52:06 - mmengine - INFO - Iter(train) [ 3250/160000] lr: 9.8189e-03 eta: 23:57:24 time: 0.5481 data_time: 0.0056 memory: 7635 loss: 0.0344 decode.loss_ce: 0.0214 decode.acc_seg: 99.1192 aux.loss_ce: 0.0130 aux.acc_seg: 98.8572 +04/18 02:52:33 - mmengine - INFO - Iter(train) [ 3300/160000] lr: 9.8161e-03 eta: 23:56:51 time: 0.5476 data_time: 0.0056 memory: 7635 loss: 0.0364 decode.loss_ce: 0.0222 decode.acc_seg: 99.0077 aux.loss_ce: 0.0142 aux.acc_seg: 98.1403 +04/18 02:53:01 - mmengine - INFO - Iter(train) [ 3350/160000] lr: 9.8133e-03 eta: 23:56:17 time: 0.5477 data_time: 0.0063 memory: 7635 loss: 0.0344 decode.loss_ce: 0.0212 decode.acc_seg: 98.9128 aux.loss_ce: 0.0132 aux.acc_seg: 98.4770 +04/18 02:53:28 - mmengine - INFO - Iter(train) [ 3400/160000] lr: 9.8105e-03 eta: 23:55:46 time: 0.5483 data_time: 0.0056 memory: 7635 loss: 0.0381 decode.loss_ce: 0.0239 decode.acc_seg: 98.9027 aux.loss_ce: 0.0143 aux.acc_seg: 98.3586 +04/18 02:53:56 - mmengine - INFO - Iter(train) [ 3450/160000] lr: 9.8077e-03 eta: 23:55:15 time: 0.5497 data_time: 0.0059 memory: 7635 loss: 0.0315 decode.loss_ce: 0.0190 decode.acc_seg: 99.1760 aux.loss_ce: 0.0126 aux.acc_seg: 98.7520 +04/18 02:54:23 - mmengine - INFO - Iter(train) [ 3500/160000] lr: 9.8049e-03 eta: 23:54:45 time: 0.5499 data_time: 0.0064 memory: 7635 loss: 0.0307 decode.loss_ce: 0.0185 decode.acc_seg: 99.1366 aux.loss_ce: 0.0122 aux.acc_seg: 98.8712 +04/18 02:54:51 - mmengine - INFO - Iter(train) [ 3550/160000] lr: 9.8021e-03 eta: 23:54:15 time: 0.5485 data_time: 0.0060 memory: 7635 loss: 0.0334 decode.loss_ce: 0.0206 decode.acc_seg: 98.8063 aux.loss_ce: 0.0128 aux.acc_seg: 98.5059 +04/18 02:55:18 - mmengine - INFO - Iter(train) [ 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0.5487 data_time: 0.0059 memory: 7635 loss: 0.0302 decode.loss_ce: 0.0181 decode.acc_seg: 99.4102 aux.loss_ce: 0.0121 aux.acc_seg: 98.9926 +04/18 02:57:35 - mmengine - INFO - Iter(train) [ 3850/160000] lr: 9.7854e-03 eta: 23:51:27 time: 0.5496 data_time: 0.0064 memory: 7635 loss: 0.0317 decode.loss_ce: 0.0197 decode.acc_seg: 99.2857 aux.loss_ce: 0.0120 aux.acc_seg: 98.7676 +04/18 02:58:03 - mmengine - INFO - Iter(train) [ 3900/160000] lr: 9.7826e-03 eta: 23:50:56 time: 0.5485 data_time: 0.0058 memory: 7635 loss: 0.0323 decode.loss_ce: 0.0196 decode.acc_seg: 98.9332 aux.loss_ce: 0.0127 aux.acc_seg: 98.2819 +04/18 02:58:30 - mmengine - INFO - Iter(train) [ 3950/160000] lr: 9.7798e-03 eta: 23:50:26 time: 0.5494 data_time: 0.0066 memory: 7635 loss: 0.0311 decode.loss_ce: 0.0189 decode.acc_seg: 99.3972 aux.loss_ce: 0.0122 aux.acc_seg: 98.9279 +04/18 02:58:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 02:58:58 - mmengine - INFO - Iter(train) [ 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0.5488 data_time: 0.0059 memory: 7635 loss: 0.0281 decode.loss_ce: 0.0168 decode.acc_seg: 99.5010 aux.loss_ce: 0.0114 aux.acc_seg: 99.1861 +04/18 03:01:15 - mmengine - INFO - Iter(train) [ 4250/160000] lr: 9.7631e-03 eta: 23:47:31 time: 0.5507 data_time: 0.0066 memory: 7635 loss: 0.0352 decode.loss_ce: 0.0211 decode.acc_seg: 99.2168 aux.loss_ce: 0.0141 aux.acc_seg: 98.4035 +04/18 03:01:42 - mmengine - INFO - Iter(train) [ 4300/160000] lr: 9.7603e-03 eta: 23:47:01 time: 0.5481 data_time: 0.0060 memory: 7635 loss: 0.0297 decode.loss_ce: 0.0178 decode.acc_seg: 99.3208 aux.loss_ce: 0.0119 aux.acc_seg: 98.8874 +04/18 03:02:10 - mmengine - INFO - Iter(train) [ 4350/160000] lr: 9.7575e-03 eta: 23:46:31 time: 0.5472 data_time: 0.0059 memory: 7635 loss: 0.0298 decode.loss_ce: 0.0177 decode.acc_seg: 99.3001 aux.loss_ce: 0.0120 aux.acc_seg: 98.7158 +04/18 03:02:37 - mmengine - INFO - Iter(train) [ 4400/160000] lr: 9.7547e-03 eta: 23:46:02 time: 0.5486 data_time: 0.0063 memory: 7635 loss: 0.0314 decode.loss_ce: 0.0191 decode.acc_seg: 99.0993 aux.loss_ce: 0.0123 aux.acc_seg: 98.6669 +04/18 03:03:05 - mmengine - INFO - Iter(train) [ 4450/160000] lr: 9.7519e-03 eta: 23:45:33 time: 0.5493 data_time: 0.0058 memory: 7635 loss: 0.0312 decode.loss_ce: 0.0191 decode.acc_seg: 99.4136 aux.loss_ce: 0.0121 aux.acc_seg: 98.9943 +04/18 03:03:32 - mmengine - INFO - Iter(train) [ 4500/160000] lr: 9.7491e-03 eta: 23:45:04 time: 0.5473 data_time: 0.0061 memory: 7635 loss: 0.0307 decode.loss_ce: 0.0186 decode.acc_seg: 98.9962 aux.loss_ce: 0.0121 aux.acc_seg: 98.5571 +04/18 03:04:00 - mmengine - INFO - Iter(train) [ 4550/160000] lr: 9.7463e-03 eta: 23:44:33 time: 0.5480 data_time: 0.0068 memory: 7635 loss: 0.0311 decode.loss_ce: 0.0189 decode.acc_seg: 99.1083 aux.loss_ce: 0.0122 aux.acc_seg: 98.6803 +04/18 03:04:27 - mmengine - INFO - Iter(train) [ 4600/160000] lr: 9.7435e-03 eta: 23:44:04 time: 0.5486 data_time: 0.0063 memory: 7635 loss: 0.0330 decode.loss_ce: 0.0208 decode.acc_seg: 99.1116 aux.loss_ce: 0.0123 aux.acc_seg: 98.6354 +04/18 03:04:55 - mmengine - INFO - Iter(train) [ 4650/160000] lr: 9.7407e-03 eta: 23:43:34 time: 0.5480 data_time: 0.0057 memory: 7635 loss: 0.0337 decode.loss_ce: 0.0208 decode.acc_seg: 99.4368 aux.loss_ce: 0.0128 aux.acc_seg: 99.0678 +04/18 03:05:22 - mmengine - INFO - Iter(train) [ 4700/160000] lr: 9.7379e-03 eta: 23:43:04 time: 0.5483 data_time: 0.0067 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0175 decode.acc_seg: 99.5893 aux.loss_ce: 0.0119 aux.acc_seg: 99.0988 +04/18 03:05:49 - mmengine - INFO - Iter(train) [ 4750/160000] lr: 9.7351e-03 eta: 23:42:35 time: 0.5488 data_time: 0.0060 memory: 7635 loss: 0.0282 decode.loss_ce: 0.0171 decode.acc_seg: 99.1003 aux.loss_ce: 0.0111 aux.acc_seg: 98.5092 +04/18 03:06:17 - mmengine - INFO - Iter(train) [ 4800/160000] lr: 9.7323e-03 eta: 23:42:12 time: 0.5575 data_time: 0.0061 memory: 7635 loss: 0.0288 decode.loss_ce: 0.0173 decode.acc_seg: 99.3198 aux.loss_ce: 0.0115 aux.acc_seg: 98.9315 +04/18 03:06:44 - mmengine - INFO - Iter(train) [ 4850/160000] lr: 9.7296e-03 eta: 23:41:43 time: 0.5492 data_time: 0.0058 memory: 7635 loss: 0.0287 decode.loss_ce: 0.0169 decode.acc_seg: 99.5131 aux.loss_ce: 0.0118 aux.acc_seg: 98.9789 +04/18 03:07:12 - mmengine - INFO - Iter(train) [ 4900/160000] lr: 9.7268e-03 eta: 23:41:13 time: 0.5480 data_time: 0.0066 memory: 7635 loss: 0.0279 decode.loss_ce: 0.0165 decode.acc_seg: 99.4431 aux.loss_ce: 0.0114 aux.acc_seg: 98.9892 +04/18 03:07:39 - mmengine - INFO - Iter(train) [ 4950/160000] lr: 9.7240e-03 eta: 23:40:44 time: 0.5482 data_time: 0.0063 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0177 decode.acc_seg: 99.3356 aux.loss_ce: 0.0117 aux.acc_seg: 98.8717 +04/18 03:08:07 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:08:07 - mmengine - INFO - Iter(train) [ 5000/160000] lr: 9.7212e-03 eta: 23:40:14 time: 0.5493 data_time: 0.0061 memory: 7635 loss: 0.0278 decode.loss_ce: 0.0166 decode.acc_seg: 99.2800 aux.loss_ce: 0.0113 aux.acc_seg: 98.8735 +04/18 03:08:34 - mmengine - INFO - Iter(train) [ 5050/160000] lr: 9.7184e-03 eta: 23:39:44 time: 0.5496 data_time: 0.0063 memory: 7635 loss: 0.0299 decode.loss_ce: 0.0178 decode.acc_seg: 99.3108 aux.loss_ce: 0.0121 aux.acc_seg: 98.7395 +04/18 03:09:02 - mmengine - INFO - Iter(train) [ 5100/160000] lr: 9.7156e-03 eta: 23:39:15 time: 0.5485 data_time: 0.0059 memory: 7635 loss: 0.0287 decode.loss_ce: 0.0170 decode.acc_seg: 99.3157 aux.loss_ce: 0.0117 aux.acc_seg: 98.9011 +04/18 03:09:29 - mmengine - INFO - Iter(train) [ 5150/160000] lr: 9.7128e-03 eta: 23:38:46 time: 0.5492 data_time: 0.0058 memory: 7635 loss: 0.0331 decode.loss_ce: 0.0203 decode.acc_seg: 98.6803 aux.loss_ce: 0.0128 aux.acc_seg: 98.2692 +04/18 03:09:56 - mmengine - INFO - Iter(train) [ 5200/160000] lr: 9.7100e-03 eta: 23:38:18 time: 0.5486 data_time: 0.0059 memory: 7635 loss: 0.0281 decode.loss_ce: 0.0166 decode.acc_seg: 99.4009 aux.loss_ce: 0.0115 aux.acc_seg: 98.8865 +04/18 03:10:24 - mmengine - INFO - Iter(train) [ 5250/160000] lr: 9.7072e-03 eta: 23:37:48 time: 0.5481 data_time: 0.0061 memory: 7635 loss: 0.0294 decode.loss_ce: 0.0179 decode.acc_seg: 99.3403 aux.loss_ce: 0.0116 aux.acc_seg: 98.8310 +04/18 03:10:51 - mmengine - INFO - Iter(train) [ 5300/160000] lr: 9.7044e-03 eta: 23:37:19 time: 0.5487 data_time: 0.0071 memory: 7635 loss: 0.0315 decode.loss_ce: 0.0190 decode.acc_seg: 99.2280 aux.loss_ce: 0.0125 aux.acc_seg: 98.6730 +04/18 03:11:19 - mmengine - INFO - Iter(train) [ 5350/160000] lr: 9.7016e-03 eta: 23:36:50 time: 0.5479 data_time: 0.0060 memory: 7635 loss: 0.0302 decode.loss_ce: 0.0182 decode.acc_seg: 99.4350 aux.loss_ce: 0.0120 aux.acc_seg: 98.9041 +04/18 03:11:46 - mmengine - INFO - Iter(train) [ 5400/160000] lr: 9.6988e-03 eta: 23:36:20 time: 0.5480 data_time: 0.0066 memory: 7635 loss: 0.0296 decode.loss_ce: 0.0179 decode.acc_seg: 99.2143 aux.loss_ce: 0.0117 aux.acc_seg: 98.7977 +04/18 03:12:14 - mmengine - INFO - Iter(train) [ 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0.5488 data_time: 0.0062 memory: 7635 loss: 0.0275 decode.loss_ce: 0.0164 decode.acc_seg: 99.4640 aux.loss_ce: 0.0112 aux.acc_seg: 99.0702 +04/18 03:14:31 - mmengine - INFO - Iter(train) [ 5700/160000] lr: 9.6821e-03 eta: 23:33:24 time: 0.5480 data_time: 0.0063 memory: 7635 loss: 0.0277 decode.loss_ce: 0.0167 decode.acc_seg: 99.5169 aux.loss_ce: 0.0110 aux.acc_seg: 99.0870 +04/18 03:14:58 - mmengine - INFO - Iter(train) [ 5750/160000] lr: 9.6793e-03 eta: 23:32:55 time: 0.5487 data_time: 0.0065 memory: 7635 loss: 0.0268 decode.loss_ce: 0.0159 decode.acc_seg: 99.3650 aux.loss_ce: 0.0109 aux.acc_seg: 98.9162 +04/18 03:15:26 - mmengine - INFO - Iter(train) [ 5800/160000] lr: 9.6765e-03 eta: 23:32:26 time: 0.5484 data_time: 0.0058 memory: 7635 loss: 0.0273 decode.loss_ce: 0.0165 decode.acc_seg: 99.4540 aux.loss_ce: 0.0109 aux.acc_seg: 99.1590 +04/18 03:15:53 - mmengine - INFO - Iter(train) [ 5850/160000] lr: 9.6737e-03 eta: 23:31:57 time: 0.5490 data_time: 0.0069 memory: 7635 loss: 0.0270 decode.loss_ce: 0.0156 decode.acc_seg: 99.2741 aux.loss_ce: 0.0114 aux.acc_seg: 98.6178 +04/18 03:16:21 - mmengine - INFO - Iter(train) [ 5900/160000] lr: 9.6709e-03 eta: 23:31:34 time: 0.5472 data_time: 0.0066 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0144 decode.acc_seg: 99.6537 aux.loss_ce: 0.0102 aux.acc_seg: 99.2810 +04/18 03:16:48 - mmengine - INFO - Iter(train) [ 5950/160000] lr: 9.6681e-03 eta: 23:31:03 time: 0.5462 data_time: 0.0064 memory: 7635 loss: 0.0311 decode.loss_ce: 0.0186 decode.acc_seg: 99.5471 aux.loss_ce: 0.0125 aux.acc_seg: 99.0972 +04/18 03:17:15 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:17:15 - mmengine - INFO - Iter(train) [ 6000/160000] lr: 9.6653e-03 eta: 23:30:34 time: 0.5482 data_time: 0.0069 memory: 7635 loss: 0.0296 decode.loss_ce: 0.0179 decode.acc_seg: 99.0438 aux.loss_ce: 0.0117 aux.acc_seg: 98.4300 +04/18 03:17:43 - mmengine - INFO - Iter(train) [ 6050/160000] lr: 9.6625e-03 eta: 23:30:05 time: 0.5487 data_time: 0.0067 memory: 7635 loss: 0.0271 decode.loss_ce: 0.0160 decode.acc_seg: 99.4235 aux.loss_ce: 0.0111 aux.acc_seg: 98.7734 +04/18 03:18:10 - mmengine - INFO - Iter(train) [ 6100/160000] lr: 9.6597e-03 eta: 23:29:36 time: 0.5486 data_time: 0.0073 memory: 7635 loss: 0.0248 decode.loss_ce: 0.0142 decode.acc_seg: 99.3794 aux.loss_ce: 0.0106 aux.acc_seg: 98.8412 +04/18 03:18:38 - mmengine - INFO - Iter(train) [ 6150/160000] lr: 9.6569e-03 eta: 23:29:07 time: 0.5479 data_time: 0.0065 memory: 7635 loss: 0.0274 decode.loss_ce: 0.0160 decode.acc_seg: 99.5241 aux.loss_ce: 0.0115 aux.acc_seg: 98.9821 +04/18 03:19:05 - mmengine - INFO - Iter(train) [ 6200/160000] lr: 9.6541e-03 eta: 23:28:39 time: 0.5495 data_time: 0.0062 memory: 7635 loss: 0.0243 decode.loss_ce: 0.0142 decode.acc_seg: 99.4569 aux.loss_ce: 0.0101 aux.acc_seg: 99.0397 +04/18 03:19:32 - mmengine - INFO - Iter(train) [ 6250/160000] lr: 9.6513e-03 eta: 23:28:10 time: 0.5487 data_time: 0.0070 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0156 decode.acc_seg: 99.1268 aux.loss_ce: 0.0105 aux.acc_seg: 98.6498 +04/18 03:20:00 - mmengine - INFO - Iter(train) [ 6300/160000] lr: 9.6485e-03 eta: 23:27:41 time: 0.5485 data_time: 0.0056 memory: 7635 loss: 0.0288 decode.loss_ce: 0.0171 decode.acc_seg: 99.1766 aux.loss_ce: 0.0117 aux.acc_seg: 98.7195 +04/18 03:20:27 - mmengine - INFO - Iter(train) [ 6350/160000] lr: 9.6457e-03 eta: 23:27:11 time: 0.5473 data_time: 0.0059 memory: 7635 loss: 0.0277 decode.loss_ce: 0.0166 decode.acc_seg: 99.3714 aux.loss_ce: 0.0111 aux.acc_seg: 99.0213 +04/18 03:20:55 - mmengine - INFO - Iter(train) [ 6400/160000] lr: 9.6429e-03 eta: 23:26:42 time: 0.5484 data_time: 0.0065 memory: 7635 loss: 0.0238 decode.loss_ce: 0.0140 decode.acc_seg: 99.4395 aux.loss_ce: 0.0098 aux.acc_seg: 98.9776 +04/18 03:21:22 - mmengine - INFO - Iter(train) [ 6450/160000] lr: 9.6401e-03 eta: 23:26:13 time: 0.5465 data_time: 0.0062 memory: 7635 loss: 0.0280 decode.loss_ce: 0.0163 decode.acc_seg: 99.2767 aux.loss_ce: 0.0118 aux.acc_seg: 98.8447 +04/18 03:21:49 - mmengine - INFO - Iter(train) [ 6500/160000] lr: 9.6373e-03 eta: 23:25:43 time: 0.5475 data_time: 0.0065 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0153 decode.acc_seg: 98.8082 aux.loss_ce: 0.0106 aux.acc_seg: 98.3924 +04/18 03:22:17 - mmengine - INFO - Iter(train) [ 6550/160000] lr: 9.6345e-03 eta: 23:25:14 time: 0.5489 data_time: 0.0065 memory: 7635 loss: 0.0227 decode.loss_ce: 0.0132 decode.acc_seg: 99.4503 aux.loss_ce: 0.0095 aux.acc_seg: 99.0750 +04/18 03:22:44 - mmengine - INFO - Iter(train) [ 6600/160000] lr: 9.6317e-03 eta: 23:24:44 time: 0.5475 data_time: 0.0064 memory: 7635 loss: 0.0270 decode.loss_ce: 0.0161 decode.acc_seg: 98.9626 aux.loss_ce: 0.0109 aux.acc_seg: 98.5077 +04/18 03:23:12 - mmengine - INFO - Iter(train) [ 6650/160000] lr: 9.6290e-03 eta: 23:24:15 time: 0.5468 data_time: 0.0059 memory: 7635 loss: 0.0272 decode.loss_ce: 0.0159 decode.acc_seg: 99.0431 aux.loss_ce: 0.0113 aux.acc_seg: 98.3189 +04/18 03:23:39 - mmengine - INFO - Iter(train) [ 6700/160000] lr: 9.6262e-03 eta: 23:23:46 time: 0.5487 data_time: 0.0060 memory: 7635 loss: 0.0306 decode.loss_ce: 0.0186 decode.acc_seg: 99.2869 aux.loss_ce: 0.0120 aux.acc_seg: 98.6516 +04/18 03:24:06 - mmengine - INFO - Iter(train) [ 6750/160000] lr: 9.6234e-03 eta: 23:23:17 time: 0.5487 data_time: 0.0064 memory: 7635 loss: 0.0287 decode.loss_ce: 0.0174 decode.acc_seg: 99.5493 aux.loss_ce: 0.0113 aux.acc_seg: 99.0694 +04/18 03:24:34 - mmengine - INFO - Iter(train) [ 6800/160000] lr: 9.6206e-03 eta: 23:22:49 time: 0.5477 data_time: 0.0062 memory: 7635 loss: 0.0279 decode.loss_ce: 0.0163 decode.acc_seg: 99.2764 aux.loss_ce: 0.0116 aux.acc_seg: 98.6606 +04/18 03:25:01 - mmengine - INFO - Iter(train) [ 6850/160000] lr: 9.6178e-03 eta: 23:22:20 time: 0.5486 data_time: 0.0058 memory: 7635 loss: 0.0270 decode.loss_ce: 0.0162 decode.acc_seg: 99.4166 aux.loss_ce: 0.0108 aux.acc_seg: 98.8656 +04/18 03:25:29 - mmengine - INFO - Iter(train) [ 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+04/18 03:27:19 - mmengine - INFO - Iter(train) [ 7100/160000] lr: 9.6038e-03 eta: 23:20:00 time: 0.5488 data_time: 0.0067 memory: 7635 loss: 0.0264 decode.loss_ce: 0.0153 decode.acc_seg: 99.6102 aux.loss_ce: 0.0111 aux.acc_seg: 99.2232 +04/18 03:27:46 - mmengine - INFO - Iter(train) [ 7150/160000] lr: 9.6010e-03 eta: 23:19:31 time: 0.5486 data_time: 0.0063 memory: 7635 loss: 0.0264 decode.loss_ce: 0.0157 decode.acc_seg: 99.2992 aux.loss_ce: 0.0107 aux.acc_seg: 98.8308 +04/18 03:28:13 - mmengine - INFO - Iter(train) [ 7200/160000] lr: 9.5982e-03 eta: 23:19:02 time: 0.5487 data_time: 0.0066 memory: 7635 loss: 0.0250 decode.loss_ce: 0.0145 decode.acc_seg: 99.5803 aux.loss_ce: 0.0104 aux.acc_seg: 99.1144 +04/18 03:28:41 - mmengine - INFO - Iter(train) [ 7250/160000] lr: 9.5954e-03 eta: 23:18:33 time: 0.5475 data_time: 0.0060 memory: 7635 loss: 0.0232 decode.loss_ce: 0.0135 decode.acc_seg: 99.2552 aux.loss_ce: 0.0097 aux.acc_seg: 98.7329 +04/18 03:29:08 - mmengine - INFO - Iter(train) [ 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0.5492 data_time: 0.0056 memory: 7635 loss: 0.0243 decode.loss_ce: 0.0143 decode.acc_seg: 99.3121 aux.loss_ce: 0.0100 aux.acc_seg: 98.9280 +04/18 03:31:25 - mmengine - INFO - Iter(train) [ 7550/160000] lr: 9.5786e-03 eta: 23:15:40 time: 0.5472 data_time: 0.0057 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0126 decode.acc_seg: 99.5523 aux.loss_ce: 0.0093 aux.acc_seg: 99.1000 +04/18 03:31:53 - mmengine - INFO - Iter(train) [ 7600/160000] lr: 9.5758e-03 eta: 23:15:12 time: 0.5488 data_time: 0.0066 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0144 decode.acc_seg: 99.3397 aux.loss_ce: 0.0102 aux.acc_seg: 98.9182 +04/18 03:32:20 - mmengine - INFO - Iter(train) [ 7650/160000] lr: 9.5730e-03 eta: 23:14:43 time: 0.5485 data_time: 0.0061 memory: 7635 loss: 0.0265 decode.loss_ce: 0.0158 decode.acc_seg: 99.3401 aux.loss_ce: 0.0107 aux.acc_seg: 98.8952 +04/18 03:32:47 - mmengine - INFO - Iter(train) [ 7700/160000] lr: 9.5702e-03 eta: 23:14:16 time: 0.5497 data_time: 0.0062 memory: 7635 loss: 0.0237 decode.loss_ce: 0.0136 decode.acc_seg: 99.4659 aux.loss_ce: 0.0101 aux.acc_seg: 98.9071 +04/18 03:33:15 - mmengine - INFO - Iter(train) [ 7750/160000] lr: 9.5674e-03 eta: 23:13:47 time: 0.5481 data_time: 0.0063 memory: 7635 loss: 0.0251 decode.loss_ce: 0.0149 decode.acc_seg: 99.3489 aux.loss_ce: 0.0101 aux.acc_seg: 98.9504 +04/18 03:33:42 - mmengine - INFO - Iter(train) [ 7800/160000] lr: 9.5646e-03 eta: 23:13:18 time: 0.5488 data_time: 0.0061 memory: 7635 loss: 0.0258 decode.loss_ce: 0.0152 decode.acc_seg: 99.0079 aux.loss_ce: 0.0105 aux.acc_seg: 98.6009 +04/18 03:34:10 - mmengine - INFO - Iter(train) [ 7850/160000] lr: 9.5618e-03 eta: 23:12:49 time: 0.5474 data_time: 0.0061 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0120 decode.acc_seg: 99.4060 aux.loss_ce: 0.0092 aux.acc_seg: 98.8746 +04/18 03:34:37 - mmengine - INFO - Iter(train) [ 7900/160000] lr: 9.5590e-03 eta: 23:12:20 time: 0.5460 data_time: 0.0062 memory: 7635 loss: 0.0242 decode.loss_ce: 0.0140 decode.acc_seg: 99.5603 aux.loss_ce: 0.0102 aux.acc_seg: 99.1700 +04/18 03:35:04 - mmengine - INFO - Iter(train) [ 7950/160000] lr: 9.5562e-03 eta: 23:11:52 time: 0.5482 data_time: 0.0066 memory: 7635 loss: 0.0298 decode.loss_ce: 0.0182 decode.acc_seg: 99.1676 aux.loss_ce: 0.0115 aux.acc_seg: 98.7913 +04/18 03:35:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:35:32 - mmengine - INFO - Iter(train) [ 8000/160000] lr: 9.5534e-03 eta: 23:11:25 time: 0.5589 data_time: 0.0057 memory: 7635 loss: 0.0228 decode.loss_ce: 0.0131 decode.acc_seg: 99.5768 aux.loss_ce: 0.0096 aux.acc_seg: 99.0025 +04/18 03:35:59 - mmengine - INFO - Iter(train) [ 8050/160000] lr: 9.5506e-03 eta: 23:10:59 time: 0.5485 data_time: 0.0062 memory: 7635 loss: 0.0239 decode.loss_ce: 0.0138 decode.acc_seg: 99.4815 aux.loss_ce: 0.0101 aux.acc_seg: 99.1381 +04/18 03:36:27 - mmengine - INFO - Iter(train) [ 8100/160000] lr: 9.5478e-03 eta: 23:10:30 time: 0.5485 data_time: 0.0061 memory: 7635 loss: 0.0244 decode.loss_ce: 0.0143 decode.acc_seg: 99.3567 aux.loss_ce: 0.0101 aux.acc_seg: 98.9232 +04/18 03:36:54 - mmengine - INFO - Iter(train) [ 8150/160000] lr: 9.5450e-03 eta: 23:10:03 time: 0.5492 data_time: 0.0059 memory: 7635 loss: 0.0249 decode.loss_ce: 0.0142 decode.acc_seg: 99.4530 aux.loss_ce: 0.0107 aux.acc_seg: 98.9367 +04/18 03:37:22 - mmengine - INFO - Iter(train) [ 8200/160000] lr: 9.5422e-03 eta: 23:09:35 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0140 decode.acc_seg: 99.4133 aux.loss_ce: 0.0106 aux.acc_seg: 98.7640 +04/18 03:37:49 - mmengine - INFO - Iter(train) [ 8250/160000] lr: 9.5394e-03 eta: 23:09:06 time: 0.5481 data_time: 0.0059 memory: 7635 loss: 0.0251 decode.loss_ce: 0.0143 decode.acc_seg: 99.2445 aux.loss_ce: 0.0109 aux.acc_seg: 98.7236 +04/18 03:38:17 - mmengine - INFO - Iter(train) [ 8300/160000] lr: 9.5366e-03 eta: 23:08:37 time: 0.5480 data_time: 0.0059 memory: 7635 loss: 0.0239 decode.loss_ce: 0.0136 decode.acc_seg: 99.4275 aux.loss_ce: 0.0103 aux.acc_seg: 98.7861 +04/18 03:38:44 - mmengine - INFO - Iter(train) [ 8350/160000] lr: 9.5338e-03 eta: 23:08:08 time: 0.5482 data_time: 0.0070 memory: 7635 loss: 0.0264 decode.loss_ce: 0.0158 decode.acc_seg: 99.3280 aux.loss_ce: 0.0106 aux.acc_seg: 99.0131 +04/18 03:39:11 - mmengine - INFO - Iter(train) [ 8400/160000] lr: 9.5310e-03 eta: 23:07:40 time: 0.5491 data_time: 0.0057 memory: 7635 loss: 0.0277 decode.loss_ce: 0.0164 decode.acc_seg: 99.3407 aux.loss_ce: 0.0113 aux.acc_seg: 98.7556 +04/18 03:39:39 - mmengine - INFO - Iter(train) [ 8450/160000] lr: 9.5282e-03 eta: 23:07:11 time: 0.5479 data_time: 0.0067 memory: 7635 loss: 0.0247 decode.loss_ce: 0.0143 decode.acc_seg: 99.2147 aux.loss_ce: 0.0104 aux.acc_seg: 98.7143 +04/18 03:40:06 - mmengine - INFO - Iter(train) [ 8500/160000] lr: 9.5254e-03 eta: 23:06:43 time: 0.5479 data_time: 0.0066 memory: 7635 loss: 0.0243 decode.loss_ce: 0.0140 decode.acc_seg: 99.5707 aux.loss_ce: 0.0103 aux.acc_seg: 99.1184 +04/18 03:40:34 - mmengine - INFO - Iter(train) [ 8550/160000] lr: 9.5226e-03 eta: 23:06:14 time: 0.5482 data_time: 0.0063 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0145 decode.acc_seg: 99.4205 aux.loss_ce: 0.0101 aux.acc_seg: 99.0105 +04/18 03:41:01 - mmengine - INFO - Iter(train) [ 8600/160000] lr: 9.5198e-03 eta: 23:05:46 time: 0.5482 data_time: 0.0064 memory: 7635 loss: 0.0248 decode.loss_ce: 0.0146 decode.acc_seg: 99.4130 aux.loss_ce: 0.0102 aux.acc_seg: 98.9951 +04/18 03:41:28 - mmengine - INFO - Iter(train) [ 8650/160000] lr: 9.5170e-03 eta: 23:05:17 time: 0.5483 data_time: 0.0064 memory: 7635 loss: 0.0276 decode.loss_ce: 0.0165 decode.acc_seg: 99.3017 aux.loss_ce: 0.0111 aux.acc_seg: 98.7202 +04/18 03:41:56 - mmengine - INFO - Iter(train) [ 8700/160000] lr: 9.5142e-03 eta: 23:04:49 time: 0.5479 data_time: 0.0062 memory: 7635 loss: 0.0253 decode.loss_ce: 0.0152 decode.acc_seg: 99.3296 aux.loss_ce: 0.0101 aux.acc_seg: 98.8963 +04/18 03:42:23 - mmengine - INFO - Iter(train) [ 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0.5496 data_time: 0.0064 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0126 decode.acc_seg: 99.5750 aux.loss_ce: 0.0095 aux.acc_seg: 99.3172 +04/18 03:44:40 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:44:40 - mmengine - INFO - Iter(train) [ 9000/160000] lr: 9.4974e-03 eta: 23:02:00 time: 0.5492 data_time: 0.0061 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0130 decode.acc_seg: 99.4808 aux.loss_ce: 0.0094 aux.acc_seg: 98.8997 +04/18 03:45:08 - mmengine - INFO - Iter(train) [ 9050/160000] lr: 9.4946e-03 eta: 23:01:32 time: 0.5477 data_time: 0.0061 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0129 decode.acc_seg: 99.4164 aux.loss_ce: 0.0093 aux.acc_seg: 98.9769 +04/18 03:45:35 - mmengine - INFO - Iter(train) [ 9100/160000] lr: 9.4918e-03 eta: 23:01:07 time: 0.5567 data_time: 0.0059 memory: 7635 loss: 0.0238 decode.loss_ce: 0.0135 decode.acc_seg: 99.5447 aux.loss_ce: 0.0102 aux.acc_seg: 99.0881 +04/18 03:46:03 - mmengine - INFO - Iter(train) [ 9150/160000] lr: 9.4890e-03 eta: 23:00:38 time: 0.5485 data_time: 0.0065 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0141 decode.acc_seg: 99.6255 aux.loss_ce: 0.0104 aux.acc_seg: 99.1031 +04/18 03:46:30 - mmengine - INFO - Iter(train) [ 9200/160000] lr: 9.4862e-03 eta: 23:00:10 time: 0.5483 data_time: 0.0057 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0131 decode.acc_seg: 99.3305 aux.loss_ce: 0.0099 aux.acc_seg: 98.7550 +04/18 03:46:57 - mmengine - INFO - Iter(train) [ 9250/160000] lr: 9.4834e-03 eta: 22:59:42 time: 0.5492 data_time: 0.0058 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0125 decode.acc_seg: 99.6259 aux.loss_ce: 0.0097 aux.acc_seg: 99.1253 +04/18 03:47:25 - mmengine - INFO - Iter(train) [ 9300/160000] lr: 9.4806e-03 eta: 22:59:14 time: 0.5475 data_time: 0.0067 memory: 7635 loss: 0.0248 decode.loss_ce: 0.0144 decode.acc_seg: 99.5245 aux.loss_ce: 0.0104 aux.acc_seg: 98.9099 +04/18 03:47:52 - mmengine - INFO - Iter(train) [ 9350/160000] lr: 9.4778e-03 eta: 22:58:46 time: 0.5485 data_time: 0.0058 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0121 decode.acc_seg: 99.5957 aux.loss_ce: 0.0094 aux.acc_seg: 99.1341 +04/18 03:48:20 - mmengine - INFO - Iter(train) [ 9400/160000] lr: 9.4750e-03 eta: 22:58:18 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0239 decode.loss_ce: 0.0140 decode.acc_seg: 99.3546 aux.loss_ce: 0.0099 aux.acc_seg: 98.8052 +04/18 03:48:47 - mmengine - INFO - Iter(train) [ 9450/160000] lr: 9.4722e-03 eta: 22:57:50 time: 0.5483 data_time: 0.0065 memory: 7635 loss: 0.0260 decode.loss_ce: 0.0153 decode.acc_seg: 99.1859 aux.loss_ce: 0.0108 aux.acc_seg: 98.5625 +04/18 03:49:15 - mmengine - INFO - Iter(train) [ 9500/160000] lr: 9.4694e-03 eta: 22:57:22 time: 0.5482 data_time: 0.0062 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0127 decode.acc_seg: 99.4252 aux.loss_ce: 0.0092 aux.acc_seg: 98.7907 +04/18 03:49:42 - mmengine - INFO - Iter(train) [ 9550/160000] lr: 9.4666e-03 eta: 22:56:54 time: 0.5474 data_time: 0.0068 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0130 decode.acc_seg: 99.5365 aux.loss_ce: 0.0100 aux.acc_seg: 98.8636 +04/18 03:50:09 - mmengine - INFO - Iter(train) [ 9600/160000] lr: 9.4638e-03 eta: 22:56:26 time: 0.5483 data_time: 0.0063 memory: 7635 loss: 0.0244 decode.loss_ce: 0.0140 decode.acc_seg: 99.4267 aux.loss_ce: 0.0104 aux.acc_seg: 98.9110 +04/18 03:50:37 - mmengine - INFO - Iter(train) [ 9650/160000] lr: 9.4610e-03 eta: 22:55:58 time: 0.5474 data_time: 0.0058 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0118 decode.acc_seg: 99.5340 aux.loss_ce: 0.0089 aux.acc_seg: 99.1682 +04/18 03:51:04 - mmengine - INFO - Iter(train) [ 9700/160000] lr: 9.4582e-03 eta: 22:55:31 time: 0.5489 data_time: 0.0068 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0123 decode.acc_seg: 99.4224 aux.loss_ce: 0.0093 aux.acc_seg: 98.6237 +04/18 03:51:32 - mmengine - INFO - Iter(train) [ 9750/160000] lr: 9.4554e-03 eta: 22:55:03 time: 0.5488 data_time: 0.0058 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0120 decode.acc_seg: 99.6173 aux.loss_ce: 0.0091 aux.acc_seg: 99.2518 +04/18 03:51:59 - mmengine - INFO - Iter(train) [ 9800/160000] lr: 9.4526e-03 eta: 22:54:34 time: 0.5483 data_time: 0.0066 memory: 7635 loss: 0.0229 decode.loss_ce: 0.0133 decode.acc_seg: 99.6406 aux.loss_ce: 0.0096 aux.acc_seg: 99.3035 +04/18 03:52:27 - mmengine - INFO - Iter(train) [ 9850/160000] lr: 9.4498e-03 eta: 22:54:06 time: 0.5480 data_time: 0.0064 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0125 decode.acc_seg: 99.4288 aux.loss_ce: 0.0094 aux.acc_seg: 98.8976 +04/18 03:52:54 - mmengine - INFO - Iter(train) [ 9900/160000] lr: 9.4470e-03 eta: 22:53:38 time: 0.5480 data_time: 0.0068 memory: 7635 loss: 0.0232 decode.loss_ce: 0.0132 decode.acc_seg: 99.6505 aux.loss_ce: 0.0099 aux.acc_seg: 99.2436 +04/18 03:53:21 - mmengine - INFO - Iter(train) [ 9950/160000] lr: 9.4442e-03 eta: 22:53:10 time: 0.5498 data_time: 0.0057 memory: 7635 loss: 0.0249 decode.loss_ce: 0.0142 decode.acc_seg: 99.4144 aux.loss_ce: 0.0106 aux.acc_seg: 98.5002 +04/18 03:53:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 03:53:49 - mmengine - INFO - Iter(train) [ 10000/160000] lr: 9.4414e-03 eta: 22:52:42 time: 0.5481 data_time: 0.0058 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0132 decode.acc_seg: 99.3968 aux.loss_ce: 0.0098 aux.acc_seg: 99.0562 +04/18 03:53:49 - mmengine - INFO - Saving checkpoint at 10000 iterations +04/18 03:53:54 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:10 time: 0.0475 data_time: 0.0015 memory: 5542 +04/18 03:53:56 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:05 time: 0.0473 data_time: 0.0014 memory: 1657 +04/18 03:53:59 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0462 data_time: 0.0015 memory: 1657 +04/18 03:54:01 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0450 data_time: 0.0012 memory: 1657 +04/18 03:54:01 - mmengine - INFO - per class results: +04/18 03:54:01 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 98.95 | 99.35 | 99.47 | 99.6 | 99.35 | +| contrast | 78.15 | 90.4 | 87.74 | 85.23 | 90.4 | ++------------+-------+-------+--------+-----------+--------+ +04/18 03:54:01 - mmengine - INFO - Iter(val) [200/200] aAcc: 98.9900 mIoU: 88.5500 mAcc: 94.8700 mFscore: 93.6100 mPrecision: 92.4200 mRecall: 94.8700 data_time: 0.0023 time: 0.0518 +04/18 03:54:29 - mmengine - INFO - Iter(train) [ 10050/160000] lr: 9.4386e-03 eta: 22:52:17 time: 0.5487 data_time: 0.0069 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0122 decode.acc_seg: 99.3901 aux.loss_ce: 0.0089 aux.acc_seg: 98.9715 +04/18 03:54:56 - mmengine - INFO - Iter(train) [ 10100/160000] lr: 9.4358e-03 eta: 22:51:49 time: 0.5489 data_time: 0.0062 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.5948 aux.loss_ce: 0.0090 aux.acc_seg: 99.1281 +04/18 03:55:24 - mmengine - INFO - Iter(train) [ 10150/160000] lr: 9.4330e-03 eta: 22:51:24 time: 0.5693 data_time: 0.0059 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0120 decode.acc_seg: 99.5793 aux.loss_ce: 0.0094 aux.acc_seg: 99.0387 +04/18 03:55:51 - mmengine - INFO - Iter(train) [ 10200/160000] lr: 9.4302e-03 eta: 22:50:55 time: 0.5478 data_time: 0.0067 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0122 decode.acc_seg: 99.4923 aux.loss_ce: 0.0092 aux.acc_seg: 98.9777 +04/18 03:56:18 - mmengine - INFO - Iter(train) [ 10250/160000] lr: 9.4274e-03 eta: 22:50:27 time: 0.5491 data_time: 0.0055 memory: 7635 loss: 0.0257 decode.loss_ce: 0.0153 decode.acc_seg: 98.8754 aux.loss_ce: 0.0104 aux.acc_seg: 98.3413 +04/18 03:56:46 - mmengine - INFO - Iter(train) [ 10300/160000] lr: 9.4246e-03 eta: 22:50:00 time: 0.5485 data_time: 0.0058 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0124 decode.acc_seg: 99.2995 aux.loss_ce: 0.0091 aux.acc_seg: 98.8486 +04/18 03:57:13 - mmengine - INFO - Iter(train) [ 10350/160000] lr: 9.4218e-03 eta: 22:49:31 time: 0.5486 data_time: 0.0059 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0121 decode.acc_seg: 99.4930 aux.loss_ce: 0.0089 aux.acc_seg: 98.9901 +04/18 03:57:41 - mmengine - INFO - Iter(train) [ 10400/160000] lr: 9.4190e-03 eta: 22:49:03 time: 0.5489 data_time: 0.0062 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0127 decode.acc_seg: 99.5553 aux.loss_ce: 0.0098 aux.acc_seg: 99.2027 +04/18 03:58:08 - mmengine - INFO - Iter(train) [ 10450/160000] lr: 9.4162e-03 eta: 22:48:35 time: 0.5489 data_time: 0.0060 memory: 7635 loss: 0.0230 decode.loss_ce: 0.0133 decode.acc_seg: 99.4981 aux.loss_ce: 0.0098 aux.acc_seg: 99.0229 +04/18 03:58:36 - mmengine - INFO - Iter(train) [ 10500/160000] lr: 9.4134e-03 eta: 22:48:09 time: 0.5479 data_time: 0.0055 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0122 decode.acc_seg: 99.5740 aux.loss_ce: 0.0094 aux.acc_seg: 99.2808 +04/18 03:59:03 - mmengine - INFO - Iter(train) [ 10550/160000] lr: 9.4106e-03 eta: 22:47:41 time: 0.5484 data_time: 0.0060 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0124 decode.acc_seg: 99.5886 aux.loss_ce: 0.0091 aux.acc_seg: 99.0820 +04/18 03:59:30 - mmengine - INFO - Iter(train) [ 10600/160000] lr: 9.4078e-03 eta: 22:47:12 time: 0.5491 data_time: 0.0067 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0129 decode.acc_seg: 99.5251 aux.loss_ce: 0.0094 aux.acc_seg: 99.0472 +04/18 03:59:58 - mmengine - INFO - Iter(train) [ 10650/160000] lr: 9.4050e-03 eta: 22:46:45 time: 0.5491 data_time: 0.0063 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0110 decode.acc_seg: 99.5851 aux.loss_ce: 0.0085 aux.acc_seg: 99.1895 +04/18 04:00:25 - mmengine - INFO - Iter(train) [ 10700/160000] lr: 9.4022e-03 eta: 22:46:17 time: 0.5487 data_time: 0.0068 memory: 7635 loss: 0.0242 decode.loss_ce: 0.0137 decode.acc_seg: 99.5319 aux.loss_ce: 0.0105 aux.acc_seg: 98.8897 +04/18 04:00:53 - mmengine - INFO - Iter(train) [ 10750/160000] lr: 9.3993e-03 eta: 22:45:49 time: 0.5480 data_time: 0.0059 memory: 7635 loss: 0.0221 decode.loss_ce: 0.0127 decode.acc_seg: 99.5176 aux.loss_ce: 0.0094 aux.acc_seg: 99.0525 +04/18 04:01:20 - mmengine - INFO - Iter(train) [ 10800/160000] lr: 9.3965e-03 eta: 22:45:21 time: 0.5488 data_time: 0.0066 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0124 decode.acc_seg: 99.5129 aux.loss_ce: 0.0096 aux.acc_seg: 98.9785 +04/18 04:01:48 - mmengine - INFO - Iter(train) [ 10850/160000] lr: 9.3937e-03 eta: 22:44:54 time: 0.5498 data_time: 0.0065 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0131 decode.acc_seg: 99.6201 aux.loss_ce: 0.0095 aux.acc_seg: 99.2350 +04/18 04:02:15 - mmengine - INFO - Iter(train) [ 10900/160000] lr: 9.3909e-03 eta: 22:44:26 time: 0.5482 data_time: 0.0068 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0119 decode.acc_seg: 99.4088 aux.loss_ce: 0.0092 aux.acc_seg: 99.0273 +04/18 04:02:42 - mmengine - INFO - Iter(train) [ 10950/160000] lr: 9.3881e-03 eta: 22:43:58 time: 0.5486 data_time: 0.0064 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0112 decode.acc_seg: 99.6139 aux.loss_ce: 0.0089 aux.acc_seg: 99.1864 +04/18 04:03:10 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:03:10 - mmengine - INFO - Iter(train) [ 11000/160000] lr: 9.3853e-03 eta: 22:43:30 time: 0.5479 data_time: 0.0064 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0134 decode.acc_seg: 99.5029 aux.loss_ce: 0.0101 aux.acc_seg: 98.9841 +04/18 04:03:37 - mmengine - INFO - Iter(train) [ 11050/160000] lr: 9.3825e-03 eta: 22:43:03 time: 0.5497 data_time: 0.0067 memory: 7635 loss: 0.0235 decode.loss_ce: 0.0134 decode.acc_seg: 99.4128 aux.loss_ce: 0.0101 aux.acc_seg: 99.0168 +04/18 04:04:05 - mmengine - INFO - Iter(train) [ 11100/160000] lr: 9.3797e-03 eta: 22:42:35 time: 0.5491 data_time: 0.0060 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0129 decode.acc_seg: 99.6453 aux.loss_ce: 0.0096 aux.acc_seg: 99.2537 +04/18 04:04:32 - mmengine - INFO - Iter(train) [ 11150/160000] lr: 9.3769e-03 eta: 22:42:07 time: 0.5487 data_time: 0.0060 memory: 7635 loss: 0.0259 decode.loss_ce: 0.0149 decode.acc_seg: 99.3362 aux.loss_ce: 0.0110 aux.acc_seg: 98.9176 +04/18 04:05:00 - mmengine - INFO - Iter(train) [ 11200/160000] lr: 9.3741e-03 eta: 22:41:40 time: 0.5480 data_time: 0.0065 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0128 decode.acc_seg: 99.4145 aux.loss_ce: 0.0098 aux.acc_seg: 98.8956 +04/18 04:05:27 - mmengine - INFO - Iter(train) [ 11250/160000] lr: 9.3713e-03 eta: 22:41:14 time: 0.5502 data_time: 0.0061 memory: 7635 loss: 0.0227 decode.loss_ce: 0.0132 decode.acc_seg: 99.4885 aux.loss_ce: 0.0095 aux.acc_seg: 99.0182 +04/18 04:05:55 - mmengine - INFO - Iter(train) [ 11300/160000] lr: 9.3685e-03 eta: 22:40:47 time: 0.5487 data_time: 0.0064 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0111 decode.acc_seg: 99.3999 aux.loss_ce: 0.0089 aux.acc_seg: 98.7281 +04/18 04:06:22 - mmengine - INFO - Iter(train) [ 11350/160000] lr: 9.3657e-03 eta: 22:40:19 time: 0.5476 data_time: 0.0072 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.4430 aux.loss_ce: 0.0089 aux.acc_seg: 98.7743 +04/18 04:06:49 - mmengine - INFO - Iter(train) [ 11400/160000] lr: 9.3629e-03 eta: 22:39:51 time: 0.5485 data_time: 0.0065 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0120 decode.acc_seg: 99.4951 aux.loss_ce: 0.0091 aux.acc_seg: 99.0124 +04/18 04:07:17 - mmengine - INFO - Iter(train) [ 11450/160000] lr: 9.3601e-03 eta: 22:39:23 time: 0.5487 data_time: 0.0058 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0110 decode.acc_seg: 99.5588 aux.loss_ce: 0.0088 aux.acc_seg: 99.0472 +04/18 04:07:44 - mmengine - INFO - Iter(train) [ 11500/160000] lr: 9.3573e-03 eta: 22:38:55 time: 0.5485 data_time: 0.0064 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0119 decode.acc_seg: 99.4657 aux.loss_ce: 0.0093 aux.acc_seg: 98.9974 +04/18 04:08:12 - mmengine - INFO - Iter(train) [ 11550/160000] lr: 9.3545e-03 eta: 22:38:27 time: 0.5477 data_time: 0.0062 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0121 decode.acc_seg: 99.6387 aux.loss_ce: 0.0088 aux.acc_seg: 99.1696 +04/18 04:08:39 - mmengine - INFO - Iter(train) [ 11600/160000] lr: 9.3517e-03 eta: 22:38:00 time: 0.5493 data_time: 0.0060 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0113 decode.acc_seg: 99.5864 aux.loss_ce: 0.0087 aux.acc_seg: 99.1900 +04/18 04:09:07 - mmengine - INFO - Iter(train) [ 11650/160000] lr: 9.3489e-03 eta: 22:37:32 time: 0.5483 data_time: 0.0061 memory: 7635 loss: 0.0250 decode.loss_ce: 0.0142 decode.acc_seg: 99.3481 aux.loss_ce: 0.0108 aux.acc_seg: 98.6688 +04/18 04:09:34 - mmengine - INFO - Iter(train) [ 11700/160000] lr: 9.3461e-03 eta: 22:37:04 time: 0.5491 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0107 decode.acc_seg: 99.5366 aux.loss_ce: 0.0085 aux.acc_seg: 99.1977 +04/18 04:10:01 - mmengine - INFO - Iter(train) [ 11750/160000] lr: 9.3433e-03 eta: 22:36:36 time: 0.5486 data_time: 0.0058 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0115 decode.acc_seg: 99.5639 aux.loss_ce: 0.0085 aux.acc_seg: 99.1105 +04/18 04:10:29 - mmengine - INFO - Iter(train) [ 11800/160000] lr: 9.3404e-03 eta: 22:36:08 time: 0.5485 data_time: 0.0070 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0115 decode.acc_seg: 99.4719 aux.loss_ce: 0.0091 aux.acc_seg: 99.0666 +04/18 04:10:56 - mmengine - INFO - Iter(train) [ 11850/160000] lr: 9.3376e-03 eta: 22:35:40 time: 0.5481 data_time: 0.0062 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0122 decode.acc_seg: 99.5543 aux.loss_ce: 0.0092 aux.acc_seg: 99.1239 +04/18 04:11:24 - mmengine - INFO - Iter(train) [ 11900/160000] lr: 9.3348e-03 eta: 22:35:12 time: 0.5481 data_time: 0.0058 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0126 decode.acc_seg: 99.5976 aux.loss_ce: 0.0096 aux.acc_seg: 98.9672 +04/18 04:11:51 - mmengine - INFO - Iter(train) [ 11950/160000] lr: 9.3320e-03 eta: 22:34:44 time: 0.5485 data_time: 0.0063 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0131 decode.acc_seg: 99.4691 aux.loss_ce: 0.0095 aux.acc_seg: 98.9560 +04/18 04:12:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:12:18 - mmengine - INFO - Iter(train) [ 12000/160000] lr: 9.3292e-03 eta: 22:34:16 time: 0.5470 data_time: 0.0058 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0122 decode.acc_seg: 99.4463 aux.loss_ce: 0.0089 aux.acc_seg: 98.8301 +04/18 04:12:46 - mmengine - INFO - Iter(train) [ 12050/160000] lr: 9.3264e-03 eta: 22:33:48 time: 0.5477 data_time: 0.0060 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0122 decode.acc_seg: 99.6193 aux.loss_ce: 0.0094 aux.acc_seg: 99.1158 +04/18 04:13:13 - mmengine - INFO - Iter(train) [ 12100/160000] lr: 9.3236e-03 eta: 22:33:20 time: 0.5478 data_time: 0.0063 memory: 7635 loss: 0.0225 decode.loss_ce: 0.0131 decode.acc_seg: 99.3755 aux.loss_ce: 0.0094 aux.acc_seg: 98.8880 +04/18 04:13:41 - mmengine - INFO - Iter(train) [ 12150/160000] lr: 9.3208e-03 eta: 22:32:52 time: 0.5485 data_time: 0.0058 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0120 decode.acc_seg: 99.4139 aux.loss_ce: 0.0094 aux.acc_seg: 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Iter(train) [ 12400/160000] lr: 9.3068e-03 eta: 22:30:37 time: 0.5491 data_time: 0.0061 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.5073 aux.loss_ce: 0.0086 aux.acc_seg: 98.8900 +04/18 04:16:26 - mmengine - INFO - Iter(train) [ 12450/160000] lr: 9.3040e-03 eta: 22:30:09 time: 0.5496 data_time: 0.0067 memory: 7635 loss: 0.0222 decode.loss_ce: 0.0127 decode.acc_seg: 99.5250 aux.loss_ce: 0.0096 aux.acc_seg: 98.9526 +04/18 04:16:53 - mmengine - INFO - Iter(train) [ 12500/160000] lr: 9.3012e-03 eta: 22:29:42 time: 0.5496 data_time: 0.0061 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0120 decode.acc_seg: 99.2399 aux.loss_ce: 0.0095 aux.acc_seg: 98.6911 +04/18 04:17:20 - mmengine - INFO - Iter(train) [ 12550/160000] lr: 9.2984e-03 eta: 22:29:14 time: 0.5482 data_time: 0.0061 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0126 decode.acc_seg: 99.5748 aux.loss_ce: 0.0093 aux.acc_seg: 99.1762 +04/18 04:17:48 - mmengine - INFO - Iter(train) [ 12600/160000] lr: 9.2955e-03 eta: 22:28:46 time: 0.5485 data_time: 0.0060 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0115 decode.acc_seg: 99.2990 aux.loss_ce: 0.0089 aux.acc_seg: 98.8092 +04/18 04:18:15 - mmengine - INFO - Iter(train) [ 12650/160000] lr: 9.2927e-03 eta: 22:28:19 time: 0.5488 data_time: 0.0057 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0107 decode.acc_seg: 99.5843 aux.loss_ce: 0.0084 aux.acc_seg: 99.1313 +04/18 04:18:43 - mmengine - INFO - Iter(train) [ 12700/160000] lr: 9.2899e-03 eta: 22:27:53 time: 0.5496 data_time: 0.0061 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0107 decode.acc_seg: 99.6871 aux.loss_ce: 0.0087 aux.acc_seg: 99.2489 +04/18 04:19:10 - mmengine - INFO - Iter(train) [ 12750/160000] lr: 9.2871e-03 eta: 22:27:25 time: 0.5492 data_time: 0.0060 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0118 decode.acc_seg: 99.5813 aux.loss_ce: 0.0091 aux.acc_seg: 99.1307 +04/18 04:19:38 - mmengine - INFO - Iter(train) [ 12800/160000] lr: 9.2843e-03 eta: 22:26:57 time: 0.5488 data_time: 0.0054 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0111 decode.acc_seg: 99.3687 aux.loss_ce: 0.0090 aux.acc_seg: 98.7031 +04/18 04:20:05 - mmengine - INFO - Iter(train) [ 12850/160000] lr: 9.2815e-03 eta: 22:26:30 time: 0.5492 data_time: 0.0058 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0116 decode.acc_seg: 99.6304 aux.loss_ce: 0.0089 aux.acc_seg: 99.1039 +04/18 04:20:33 - mmengine - INFO - Iter(train) [ 12900/160000] lr: 9.2787e-03 eta: 22:26:02 time: 0.5487 data_time: 0.0063 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0121 decode.acc_seg: 99.5192 aux.loss_ce: 0.0094 aux.acc_seg: 99.0376 +04/18 04:21:00 - mmengine - INFO - Iter(train) [ 12950/160000] lr: 9.2759e-03 eta: 22:25:34 time: 0.5485 data_time: 0.0060 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0113 decode.acc_seg: 99.5872 aux.loss_ce: 0.0090 aux.acc_seg: 99.1456 +04/18 04:21:27 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:21:27 - mmengine - INFO - Iter(train) [ 13000/160000] lr: 9.2731e-03 eta: 22:25:07 time: 0.5489 data_time: 0.0058 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.6518 aux.loss_ce: 0.0085 aux.acc_seg: 99.3849 +04/18 04:21:55 - mmengine - INFO - Iter(train) [ 13050/160000] lr: 9.2703e-03 eta: 22:24:39 time: 0.5494 data_time: 0.0070 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0116 decode.acc_seg: 99.5750 aux.loss_ce: 0.0086 aux.acc_seg: 99.0207 +04/18 04:22:22 - mmengine - INFO - Iter(train) [ 13100/160000] lr: 9.2675e-03 eta: 22:24:11 time: 0.5494 data_time: 0.0060 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0106 decode.acc_seg: 99.4333 aux.loss_ce: 0.0081 aux.acc_seg: 99.0598 +04/18 04:22:50 - mmengine - INFO - Iter(train) [ 13150/160000] lr: 9.2647e-03 eta: 22:23:44 time: 0.5481 data_time: 0.0058 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0111 decode.acc_seg: 99.7065 aux.loss_ce: 0.0089 aux.acc_seg: 99.2218 +04/18 04:23:17 - mmengine - INFO - Iter(train) [ 13200/160000] lr: 9.2618e-03 eta: 22:23:16 time: 0.5474 data_time: 0.0059 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0115 decode.acc_seg: 99.3992 aux.loss_ce: 0.0089 aux.acc_seg: 98.8688 +04/18 04:23:45 - mmengine - INFO - Iter(train) [ 13250/160000] lr: 9.2590e-03 eta: 22:22:49 time: 0.5493 data_time: 0.0065 memory: 7635 loss: 0.0231 decode.loss_ce: 0.0133 decode.acc_seg: 99.5006 aux.loss_ce: 0.0099 aux.acc_seg: 98.9125 +04/18 04:24:12 - mmengine - INFO - Iter(train) [ 13300/160000] lr: 9.2562e-03 eta: 22:22:21 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0117 decode.acc_seg: 99.5731 aux.loss_ce: 0.0089 aux.acc_seg: 99.1207 +04/18 04:24:40 - mmengine - INFO - Iter(train) [ 13350/160000] lr: 9.2534e-03 eta: 22:21:54 time: 0.5500 data_time: 0.0068 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.5807 aux.loss_ce: 0.0085 aux.acc_seg: 99.0040 +04/18 04:25:07 - mmengine - INFO - Iter(train) [ 13400/160000] lr: 9.2506e-03 eta: 22:21:28 time: 0.5474 data_time: 0.0060 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0107 decode.acc_seg: 99.5396 aux.loss_ce: 0.0087 aux.acc_seg: 98.9564 +04/18 04:25:35 - mmengine - INFO - Iter(train) [ 13450/160000] lr: 9.2478e-03 eta: 22:21:01 time: 0.5490 data_time: 0.0060 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.6347 aux.loss_ce: 0.0089 aux.acc_seg: 99.2900 +04/18 04:26:02 - mmengine - INFO - Iter(train) [ 13500/160000] lr: 9.2450e-03 eta: 22:20:33 time: 0.5492 data_time: 0.0061 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0120 decode.acc_seg: 99.7210 aux.loss_ce: 0.0093 aux.acc_seg: 99.3200 +04/18 04:26:30 - mmengine - INFO - Iter(train) [ 13550/160000] lr: 9.2422e-03 eta: 22:20:06 time: 0.5489 data_time: 0.0063 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0118 decode.acc_seg: 99.6255 aux.loss_ce: 0.0093 aux.acc_seg: 99.3737 +04/18 04:26:57 - mmengine - INFO - Iter(train) [ 13600/160000] lr: 9.2394e-03 eta: 22:19:39 time: 0.5493 data_time: 0.0055 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0114 decode.acc_seg: 99.3721 aux.loss_ce: 0.0093 aux.acc_seg: 98.8181 +04/18 04:27:25 - mmengine - INFO - Iter(train) [ 13650/160000] lr: 9.2366e-03 eta: 22:19:11 time: 0.5491 data_time: 0.0064 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0116 decode.acc_seg: 99.5900 aux.loss_ce: 0.0088 aux.acc_seg: 99.2604 +04/18 04:27:52 - mmengine - INFO - Iter(train) [ 13700/160000] lr: 9.2338e-03 eta: 22:18:44 time: 0.5496 data_time: 0.0060 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0117 decode.acc_seg: 99.6017 aux.loss_ce: 0.0092 aux.acc_seg: 99.2126 +04/18 04:28:20 - mmengine - INFO - Iter(train) [ 13750/160000] lr: 9.2309e-03 eta: 22:18:18 time: 0.5484 data_time: 0.0058 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0120 decode.acc_seg: 99.3927 aux.loss_ce: 0.0092 aux.acc_seg: 98.9446 +04/18 04:28:47 - mmengine - INFO - Iter(train) [ 13800/160000] lr: 9.2281e-03 eta: 22:17:50 time: 0.5484 data_time: 0.0060 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0118 decode.acc_seg: 99.4184 aux.loss_ce: 0.0091 aux.acc_seg: 98.8906 +04/18 04:29:14 - mmengine 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aux.acc_seg: 99.0235 +04/18 04:31:04 - mmengine - INFO - Iter(train) [ 14050/160000] lr: 9.2141e-03 eta: 22:15:33 time: 0.5495 data_time: 0.0069 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0120 decode.acc_seg: 99.5310 aux.loss_ce: 0.0090 aux.acc_seg: 99.1996 +04/18 04:31:32 - mmengine - INFO - Iter(train) [ 14100/160000] lr: 9.2113e-03 eta: 22:15:05 time: 0.5483 data_time: 0.0058 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0109 decode.acc_seg: 99.5172 aux.loss_ce: 0.0090 aux.acc_seg: 99.0437 +04/18 04:31:59 - mmengine - INFO - Iter(train) [ 14150/160000] lr: 9.2085e-03 eta: 22:14:38 time: 0.5491 data_time: 0.0062 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0122 decode.acc_seg: 99.6517 aux.loss_ce: 0.0096 aux.acc_seg: 99.2456 +04/18 04:32:27 - mmengine - INFO - Iter(train) [ 14200/160000] lr: 9.2057e-03 eta: 22:14:10 time: 0.5494 data_time: 0.0060 memory: 7635 loss: 0.0220 decode.loss_ce: 0.0123 decode.acc_seg: 99.5095 aux.loss_ce: 0.0097 aux.acc_seg: 99.1067 +04/18 04:32:54 - mmengine 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9.1916e-03 eta: 22:11:56 time: 0.5595 data_time: 0.0067 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0112 decode.acc_seg: 99.6026 aux.loss_ce: 0.0089 aux.acc_seg: 99.0494 +04/18 04:35:12 - mmengine - INFO - Iter(train) [ 14500/160000] lr: 9.1888e-03 eta: 22:11:29 time: 0.5491 data_time: 0.0066 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0117 decode.acc_seg: 99.3559 aux.loss_ce: 0.0089 aux.acc_seg: 98.6353 +04/18 04:35:39 - mmengine - INFO - Iter(train) [ 14550/160000] lr: 9.1860e-03 eta: 22:11:02 time: 0.5500 data_time: 0.0059 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0115 decode.acc_seg: 99.5761 aux.loss_ce: 0.0089 aux.acc_seg: 99.1471 +04/18 04:36:07 - mmengine - INFO - Iter(train) [ 14600/160000] lr: 9.1832e-03 eta: 22:10:34 time: 0.5500 data_time: 0.0060 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.6475 aux.loss_ce: 0.0082 aux.acc_seg: 99.0929 +04/18 04:36:34 - mmengine - INFO - Iter(train) [ 14650/160000] lr: 9.1804e-03 eta: 22:10:07 time: 0.5498 data_time: 0.0065 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0113 decode.acc_seg: 99.6323 aux.loss_ce: 0.0092 aux.acc_seg: 99.2687 +04/18 04:37:02 - mmengine - INFO - Iter(train) [ 14700/160000] lr: 9.1776e-03 eta: 22:09:40 time: 0.5487 data_time: 0.0062 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0104 decode.acc_seg: 99.6775 aux.loss_ce: 0.0083 aux.acc_seg: 99.3504 +04/18 04:37:29 - mmengine - INFO - Iter(train) [ 14750/160000] lr: 9.1747e-03 eta: 22:09:13 time: 0.5499 data_time: 0.0065 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.5389 aux.loss_ce: 0.0087 aux.acc_seg: 98.9362 +04/18 04:37:57 - mmengine - INFO - Iter(train) [ 14800/160000] lr: 9.1719e-03 eta: 22:08:46 time: 0.5510 data_time: 0.0068 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0116 decode.acc_seg: 99.5256 aux.loss_ce: 0.0090 aux.acc_seg: 99.0493 +04/18 04:38:24 - mmengine - INFO - Iter(train) [ 14850/160000] lr: 9.1691e-03 eta: 22:08:20 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0115 decode.acc_seg: 99.7200 aux.loss_ce: 0.0090 aux.acc_seg: 99.3581 +04/18 04:38:52 - mmengine - INFO - Iter(train) [ 14900/160000] lr: 9.1663e-03 eta: 22:07:52 time: 0.5493 data_time: 0.0065 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0107 decode.acc_seg: 99.6327 aux.loss_ce: 0.0082 aux.acc_seg: 99.1156 +04/18 04:39:19 - mmengine - INFO - Iter(train) [ 14950/160000] lr: 9.1635e-03 eta: 22:07:25 time: 0.5489 data_time: 0.0061 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0106 decode.acc_seg: 99.5702 aux.loss_ce: 0.0090 aux.acc_seg: 98.9697 +04/18 04:39:47 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:39:47 - mmengine - INFO - Iter(train) [ 15000/160000] lr: 9.1607e-03 eta: 22:06:58 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0106 decode.acc_seg: 99.6482 aux.loss_ce: 0.0084 aux.acc_seg: 99.2591 +04/18 04:40:14 - mmengine - INFO - Iter(train) [ 15050/160000] lr: 9.1579e-03 eta: 22:06:30 time: 0.5496 data_time: 0.0066 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0111 decode.acc_seg: 99.5513 aux.loss_ce: 0.0087 aux.acc_seg: 99.0436 +04/18 04:40:41 - mmengine - INFO - Iter(train) [ 15100/160000] lr: 9.1551e-03 eta: 22:06:03 time: 0.5490 data_time: 0.0060 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0122 decode.acc_seg: 99.5356 aux.loss_ce: 0.0095 aux.acc_seg: 99.0978 +04/18 04:41:09 - mmengine - INFO - Iter(train) [ 15150/160000] lr: 9.1522e-03 eta: 22:05:36 time: 0.5500 data_time: 0.0062 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0115 decode.acc_seg: 99.5110 aux.loss_ce: 0.0092 aux.acc_seg: 98.9477 +04/18 04:41:36 - mmengine - INFO - Iter(train) [ 15200/160000] lr: 9.1494e-03 eta: 22:05:09 time: 0.5495 data_time: 0.0066 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0112 decode.acc_seg: 99.5566 aux.loss_ce: 0.0088 aux.acc_seg: 99.2162 +04/18 04:42:04 - mmengine - INFO - Iter(train) [ 15250/160000] lr: 9.1466e-03 eta: 22:04:41 time: 0.5492 data_time: 0.0065 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0110 decode.acc_seg: 99.4129 aux.loss_ce: 0.0091 aux.acc_seg: 98.9281 +04/18 04:42:31 - mmengine - INFO - Iter(train) [ 15300/160000] lr: 9.1438e-03 eta: 22:04:14 time: 0.5493 data_time: 0.0063 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0107 decode.acc_seg: 99.5400 aux.loss_ce: 0.0089 aux.acc_seg: 98.9194 +04/18 04:42:59 - mmengine - INFO - Iter(train) [ 15350/160000] lr: 9.1410e-03 eta: 22:03:47 time: 0.5485 data_time: 0.0063 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0107 decode.acc_seg: 99.3506 aux.loss_ce: 0.0088 aux.acc_seg: 98.7174 +04/18 04:43:26 - mmengine - INFO - Iter(train) [ 15400/160000] lr: 9.1382e-03 eta: 22:03:19 time: 0.5493 data_time: 0.0060 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0114 decode.acc_seg: 99.4677 aux.loss_ce: 0.0089 aux.acc_seg: 98.9253 +04/18 04:43:54 - mmengine - INFO - Iter(train) [ 15450/160000] lr: 9.1354e-03 eta: 22:02:52 time: 0.5500 data_time: 0.0068 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0117 decode.acc_seg: 99.5312 aux.loss_ce: 0.0093 aux.acc_seg: 99.0074 +04/18 04:44:21 - mmengine - INFO - Iter(train) [ 15500/160000] lr: 9.1326e-03 eta: 22:02:25 time: 0.5504 data_time: 0.0069 memory: 7635 loss: 0.0212 decode.loss_ce: 0.0116 decode.acc_seg: 99.4061 aux.loss_ce: 0.0095 aux.acc_seg: 98.9144 +04/18 04:44:49 - mmengine - INFO - Iter(train) [ 15550/160000] lr: 9.1297e-03 eta: 22:02:00 time: 0.5478 data_time: 0.0062 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0109 decode.acc_seg: 99.6532 aux.loss_ce: 0.0086 aux.acc_seg: 99.2592 +04/18 04:45:16 - mmengine - INFO - Iter(train) [ 15600/160000] lr: 9.1269e-03 eta: 22:01:33 time: 0.5504 data_time: 0.0062 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0106 decode.acc_seg: 99.5452 aux.loss_ce: 0.0092 aux.acc_seg: 98.8504 +04/18 04:45:44 - mmengine - INFO - Iter(train) [ 15650/160000] lr: 9.1241e-03 eta: 22:01:05 time: 0.5498 data_time: 0.0061 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0116 decode.acc_seg: 99.4576 aux.loss_ce: 0.0093 aux.acc_seg: 98.7809 +04/18 04:46:11 - mmengine - INFO - Iter(train) [ 15700/160000] lr: 9.1213e-03 eta: 22:00:38 time: 0.5502 data_time: 0.0057 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0141 decode.acc_seg: 99.4235 aux.loss_ce: 0.0105 aux.acc_seg: 98.8319 +04/18 04:46:39 - mmengine - INFO - Iter(train) [ 15750/160000] lr: 9.1185e-03 eta: 22:00:11 time: 0.5491 data_time: 0.0069 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0113 decode.acc_seg: 99.6474 aux.loss_ce: 0.0093 aux.acc_seg: 99.1494 +04/18 04:47:06 - mmengine - INFO - Iter(train) [ 15800/160000] lr: 9.1157e-03 eta: 21:59:44 time: 0.5513 data_time: 0.0058 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.6280 aux.loss_ce: 0.0086 aux.acc_seg: 99.1859 +04/18 04:47:34 - mmengine - INFO - Iter(train) [ 15850/160000] lr: 9.1129e-03 eta: 21:59:17 time: 0.5495 data_time: 0.0060 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0105 decode.acc_seg: 99.6848 aux.loss_ce: 0.0088 aux.acc_seg: 99.1300 +04/18 04:48:02 - mmengine - INFO - Iter(train) [ 15900/160000] lr: 9.1101e-03 eta: 21:58:51 time: 0.5592 data_time: 0.0056 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0116 decode.acc_seg: 99.5984 aux.loss_ce: 0.0092 aux.acc_seg: 99.1928 +04/18 04:48:29 - mmengine - INFO - Iter(train) [ 15950/160000] lr: 9.1072e-03 eta: 21:58:24 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0117 decode.acc_seg: 99.6102 aux.loss_ce: 0.0090 aux.acc_seg: 98.9958 +04/18 04:48:57 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:48:57 - mmengine - INFO - Iter(train) [ 16000/160000] lr: 9.1044e-03 eta: 21:57:57 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0098 decode.acc_seg: 99.6333 aux.loss_ce: 0.0085 aux.acc_seg: 99.1935 +04/18 04:49:24 - mmengine - INFO - Iter(train) [ 16050/160000] lr: 9.1016e-03 eta: 21:57:30 time: 0.5509 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0092 decode.acc_seg: 99.6289 aux.loss_ce: 0.0075 aux.acc_seg: 99.4611 +04/18 04:49:52 - mmengine - INFO - Iter(train) [ 16100/160000] lr: 9.0988e-03 eta: 21:57:03 time: 0.5509 data_time: 0.0058 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0095 decode.acc_seg: 99.5643 aux.loss_ce: 0.0079 aux.acc_seg: 99.2123 +04/18 04:50:19 - mmengine - INFO - Iter(train) [ 16150/160000] lr: 9.0960e-03 eta: 21:56:37 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0106 decode.acc_seg: 99.3545 aux.loss_ce: 0.0086 aux.acc_seg: 98.8721 +04/18 04:50:47 - mmengine - INFO - Iter(train) [ 16200/160000] lr: 9.0932e-03 eta: 21:56:10 time: 0.5496 data_time: 0.0062 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0108 decode.acc_seg: 99.6251 aux.loss_ce: 0.0088 aux.acc_seg: 99.1516 +04/18 04:51:14 - mmengine - INFO - Iter(train) [ 16250/160000] lr: 9.0904e-03 eta: 21:55:43 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.5217 aux.loss_ce: 0.0087 aux.acc_seg: 99.0500 +04/18 04:51:42 - mmengine - INFO - Iter(train) [ 16300/160000] lr: 9.0875e-03 eta: 21:55:16 time: 0.5493 data_time: 0.0063 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0108 decode.acc_seg: 99.4192 aux.loss_ce: 0.0084 aux.acc_seg: 99.1014 +04/18 04:52:09 - mmengine - INFO - Iter(train) [ 16350/160000] lr: 9.0847e-03 eta: 21:54:49 time: 0.5497 data_time: 0.0057 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0105 decode.acc_seg: 99.6097 aux.loss_ce: 0.0089 aux.acc_seg: 99.0688 +04/18 04:52:37 - mmengine - INFO - Iter(train) [ 16400/160000] lr: 9.0819e-03 eta: 21:54:22 time: 0.5507 data_time: 0.0061 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0126 decode.acc_seg: 99.4523 aux.loss_ce: 0.0091 aux.acc_seg: 99.0592 +04/18 04:53:04 - mmengine - INFO - Iter(train) [ 16450/160000] lr: 9.0791e-03 eta: 21:53:55 time: 0.5515 data_time: 0.0068 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0097 decode.acc_seg: 99.6587 aux.loss_ce: 0.0084 aux.acc_seg: 99.2566 +04/18 04:53:32 - mmengine - INFO - Iter(train) [ 16500/160000] lr: 9.0763e-03 eta: 21:53:27 time: 0.5503 data_time: 0.0057 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0093 decode.acc_seg: 99.5905 aux.loss_ce: 0.0077 aux.acc_seg: 99.2222 +04/18 04:53:59 - mmengine - INFO - Iter(train) [ 16550/160000] lr: 9.0735e-03 eta: 21:53:01 time: 0.5504 data_time: 0.0068 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.6407 aux.loss_ce: 0.0085 aux.acc_seg: 99.3345 +04/18 04:54:27 - mmengine - INFO - Iter(train) [ 16600/160000] lr: 9.0706e-03 eta: 21:52:36 time: 0.5699 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0100 decode.acc_seg: 99.7334 aux.loss_ce: 0.0082 aux.acc_seg: 99.4706 +04/18 04:54:55 - mmengine - INFO - Iter(train) [ 16650/160000] lr: 9.0678e-03 eta: 21:52:08 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0112 decode.acc_seg: 99.5617 aux.loss_ce: 0.0088 aux.acc_seg: 99.1335 +04/18 04:55:22 - mmengine - INFO - Iter(train) [ 16700/160000] lr: 9.0650e-03 eta: 21:51:41 time: 0.5510 data_time: 0.0058 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.6317 aux.loss_ce: 0.0084 aux.acc_seg: 99.2899 +04/18 04:55:50 - mmengine - INFO - Iter(train) [ 16750/160000] lr: 9.0622e-03 eta: 21:51:14 time: 0.5502 data_time: 0.0069 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0111 decode.acc_seg: 99.6498 aux.loss_ce: 0.0084 aux.acc_seg: 99.2376 +04/18 04:56:17 - mmengine - INFO - Iter(train) [ 16800/160000] lr: 9.0594e-03 eta: 21:50:48 time: 0.5512 data_time: 0.0062 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0117 decode.acc_seg: 99.6216 aux.loss_ce: 0.0091 aux.acc_seg: 99.2575 +04/18 04:56:45 - mmengine - INFO - Iter(train) [ 16850/160000] lr: 9.0566e-03 eta: 21:50:20 time: 0.5495 data_time: 0.0060 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0107 decode.acc_seg: 99.6372 aux.loss_ce: 0.0089 aux.acc_seg: 99.2227 +04/18 04:57:12 - mmengine - INFO - Iter(train) [ 16900/160000] lr: 9.0538e-03 eta: 21:49:53 time: 0.5494 data_time: 0.0058 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0101 decode.acc_seg: 99.3711 aux.loss_ce: 0.0080 aux.acc_seg: 98.9654 +04/18 04:57:40 - mmengine - INFO - Iter(train) [ 16950/160000] lr: 9.0509e-03 eta: 21:49:27 time: 0.5595 data_time: 0.0060 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0112 decode.acc_seg: 99.5635 aux.loss_ce: 0.0090 aux.acc_seg: 98.9095 +04/18 04:58:07 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 04:58:07 - mmengine - INFO - Iter(train) [ 17000/160000] lr: 9.0481e-03 eta: 21:49:00 time: 0.5507 data_time: 0.0058 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0129 decode.acc_seg: 99.0251 aux.loss_ce: 0.0094 aux.acc_seg: 98.6746 +04/18 04:58:35 - mmengine - INFO - Iter(train) [ 17050/160000] lr: 9.0453e-03 eta: 21:48:33 time: 0.5500 data_time: 0.0062 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.5245 aux.loss_ce: 0.0084 aux.acc_seg: 99.0348 +04/18 04:59:02 - mmengine - INFO - Iter(train) [ 17100/160000] lr: 9.0425e-03 eta: 21:48:06 time: 0.5504 data_time: 0.0054 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0116 decode.acc_seg: 99.5734 aux.loss_ce: 0.0091 aux.acc_seg: 99.1453 +04/18 04:59:30 - mmengine - INFO - Iter(train) [ 17150/160000] lr: 9.0397e-03 eta: 21:47:40 time: 0.5507 data_time: 0.0059 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0115 decode.acc_seg: 99.5340 aux.loss_ce: 0.0089 aux.acc_seg: 99.0964 +04/18 04:59:57 - mmengine - INFO - Iter(train) [ 17200/160000] lr: 9.0369e-03 eta: 21:47:13 time: 0.5511 data_time: 0.0070 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5158 aux.loss_ce: 0.0086 aux.acc_seg: 99.1407 +04/18 05:00:25 - mmengine - INFO - Iter(train) [ 17250/160000] lr: 9.0340e-03 eta: 21:46:46 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0104 decode.acc_seg: 99.5226 aux.loss_ce: 0.0081 aux.acc_seg: 99.0396 +04/18 05:00:52 - mmengine - INFO - Iter(train) [ 17300/160000] lr: 9.0312e-03 eta: 21:46:19 time: 0.5496 data_time: 0.0058 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0104 decode.acc_seg: 99.5439 aux.loss_ce: 0.0092 aux.acc_seg: 99.0997 +04/18 05:01:20 - mmengine - INFO - Iter(train) [ 17350/160000] lr: 9.0284e-03 eta: 21:45:52 time: 0.5498 data_time: 0.0068 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0107 decode.acc_seg: 99.7268 aux.loss_ce: 0.0089 aux.acc_seg: 99.3399 +04/18 05:01:48 - mmengine - INFO - Iter(train) [ 17400/160000] lr: 9.0256e-03 eta: 21:45:25 time: 0.5511 data_time: 0.0071 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0105 decode.acc_seg: 99.5956 aux.loss_ce: 0.0089 aux.acc_seg: 99.0271 +04/18 05:02:15 - mmengine - INFO - Iter(train) [ 17450/160000] lr: 9.0228e-03 eta: 21:44:58 time: 0.5498 data_time: 0.0058 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0103 decode.acc_seg: 99.6334 aux.loss_ce: 0.0080 aux.acc_seg: 99.0758 +04/18 05:02:43 - mmengine - INFO - Iter(train) [ 17500/160000] lr: 9.0200e-03 eta: 21:44:31 time: 0.5496 data_time: 0.0059 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0116 decode.acc_seg: 99.4657 aux.loss_ce: 0.0094 aux.acc_seg: 99.0963 +04/18 05:03:10 - mmengine - INFO - Iter(train) [ 17550/160000] lr: 9.0171e-03 eta: 21:44:04 time: 0.5508 data_time: 0.0062 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0100 decode.acc_seg: 99.6763 aux.loss_ce: 0.0085 aux.acc_seg: 99.2926 +04/18 05:03:38 - mmengine - INFO - Iter(train) [ 17600/160000] lr: 9.0143e-03 eta: 21:43:37 time: 0.5513 data_time: 0.0066 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0101 decode.acc_seg: 99.6868 aux.loss_ce: 0.0084 aux.acc_seg: 99.3676 +04/18 05:04:05 - mmengine - INFO - Iter(train) [ 17650/160000] lr: 9.0115e-03 eta: 21:43:10 time: 0.5506 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0099 decode.acc_seg: 99.6650 aux.loss_ce: 0.0083 aux.acc_seg: 99.2867 +04/18 05:04:33 - mmengine - INFO - Iter(train) [ 17700/160000] lr: 9.0087e-03 eta: 21:42:45 time: 0.5503 data_time: 0.0065 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.4845 aux.loss_ce: 0.0086 aux.acc_seg: 98.9722 +04/18 05:05:00 - mmengine - INFO - Iter(train) [ 17750/160000] lr: 9.0059e-03 eta: 21:42:18 time: 0.5516 data_time: 0.0066 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.7449 aux.loss_ce: 0.0084 aux.acc_seg: 99.3885 +04/18 05:05:28 - mmengine - INFO - Iter(train) [ 17800/160000] lr: 9.0031e-03 eta: 21:41:51 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0113 decode.acc_seg: 99.6144 aux.loss_ce: 0.0085 aux.acc_seg: 99.2468 +04/18 05:05:55 - mmengine - INFO - Iter(train) [ 17850/160000] lr: 9.0002e-03 eta: 21:41:24 time: 0.5495 data_time: 0.0061 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0111 decode.acc_seg: 99.5973 aux.loss_ce: 0.0092 aux.acc_seg: 98.9994 +04/18 05:06:23 - mmengine - INFO - Iter(train) [ 17900/160000] lr: 8.9974e-03 eta: 21:40:57 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0103 decode.acc_seg: 99.5664 aux.loss_ce: 0.0085 aux.acc_seg: 99.0761 +04/18 05:06:51 - mmengine - INFO - Iter(train) [ 17950/160000] lr: 8.9946e-03 eta: 21:40:30 time: 0.5504 data_time: 0.0056 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5418 aux.loss_ce: 0.0086 aux.acc_seg: 98.7377 +04/18 05:07:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:07:18 - mmengine - INFO - Iter(train) [ 18000/160000] lr: 8.9918e-03 eta: 21:40:03 time: 0.5504 data_time: 0.0069 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.4904 aux.loss_ce: 0.0085 aux.acc_seg: 98.9013 +04/18 05:07:46 - mmengine - INFO - Iter(train) [ 18050/160000] lr: 8.9890e-03 eta: 21:39:37 time: 0.5611 data_time: 0.0070 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.6426 aux.loss_ce: 0.0080 aux.acc_seg: 99.0942 +04/18 05:08:13 - mmengine - INFO - Iter(train) [ 18100/160000] lr: 8.9862e-03 eta: 21:39:10 time: 0.5509 data_time: 0.0060 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0110 decode.acc_seg: 99.6600 aux.loss_ce: 0.0088 aux.acc_seg: 99.1778 +04/18 05:08:41 - mmengine - INFO - Iter(train) [ 18150/160000] lr: 8.9833e-03 eta: 21:38:43 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5716 aux.loss_ce: 0.0085 aux.acc_seg: 99.0636 +04/18 05:09:08 - mmengine - INFO - Iter(train) [ 18200/160000] lr: 8.9805e-03 eta: 21:38:16 time: 0.5493 data_time: 0.0061 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0098 decode.acc_seg: 99.6521 aux.loss_ce: 0.0087 aux.acc_seg: 99.1826 +04/18 05:09:36 - mmengine - INFO - Iter(train) [ 18250/160000] lr: 8.9777e-03 eta: 21:37:49 time: 0.5515 data_time: 0.0057 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.6871 aux.loss_ce: 0.0086 aux.acc_seg: 99.2124 +04/18 05:10:03 - mmengine - INFO - Iter(train) [ 18300/160000] lr: 8.9749e-03 eta: 21:37:22 time: 0.5509 data_time: 0.0063 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.6368 aux.loss_ce: 0.0083 aux.acc_seg: 99.2934 +04/18 05:10:31 - mmengine - INFO - Iter(train) [ 18350/160000] lr: 8.9721e-03 eta: 21:36:55 time: 0.5511 data_time: 0.0058 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0109 decode.acc_seg: 99.6661 aux.loss_ce: 0.0091 aux.acc_seg: 99.1489 +04/18 05:10:58 - mmengine - INFO - Iter(train) [ 18400/160000] lr: 8.9692e-03 eta: 21:36:28 time: 0.5509 data_time: 0.0062 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.6330 aux.loss_ce: 0.0082 aux.acc_seg: 99.2747 +04/18 05:11:26 - mmengine - INFO - Iter(train) [ 18450/160000] lr: 8.9664e-03 eta: 21:36:01 time: 0.5518 data_time: 0.0064 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.6861 aux.loss_ce: 0.0084 aux.acc_seg: 99.2772 +04/18 05:11:54 - mmengine - INFO - Iter(train) [ 18500/160000] lr: 8.9636e-03 eta: 21:35:35 time: 0.5521 data_time: 0.0065 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0103 decode.acc_seg: 99.5523 aux.loss_ce: 0.0089 aux.acc_seg: 98.8003 +04/18 05:12:21 - mmengine - INFO - Iter(train) [ 18550/160000] lr: 8.9608e-03 eta: 21:35:08 time: 0.5509 data_time: 0.0065 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.6337 aux.loss_ce: 0.0088 aux.acc_seg: 99.2847 +04/18 05:12:49 - mmengine - INFO - Iter(train) [ 18600/160000] lr: 8.9580e-03 eta: 21:34:41 time: 0.5507 data_time: 0.0067 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.6078 aux.loss_ce: 0.0083 aux.acc_seg: 99.1495 +04/18 05:13:16 - mmengine - INFO - Iter(train) [ 18650/160000] lr: 8.9551e-03 eta: 21:34:14 time: 0.5496 data_time: 0.0058 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0102 decode.acc_seg: 99.6562 aux.loss_ce: 0.0084 aux.acc_seg: 99.2815 +04/18 05:13:44 - mmengine - INFO - Iter(train) [ 18700/160000] lr: 8.9523e-03 eta: 21:33:46 time: 0.5494 data_time: 0.0060 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0100 decode.acc_seg: 99.6137 aux.loss_ce: 0.0088 aux.acc_seg: 99.2285 +04/18 05:14:11 - mmengine - INFO - Iter(train) [ 18750/160000] lr: 8.9495e-03 eta: 21:33:21 time: 0.5596 data_time: 0.0066 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0113 decode.acc_seg: 99.4499 aux.loss_ce: 0.0090 aux.acc_seg: 98.6691 +04/18 05:14:39 - mmengine - INFO - Iter(train) [ 18800/160000] lr: 8.9467e-03 eta: 21:32:54 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.5760 aux.loss_ce: 0.0088 aux.acc_seg: 99.1145 +04/18 05:15:07 - mmengine - INFO - Iter(train) [ 18850/160000] lr: 8.9439e-03 eta: 21:32:28 time: 0.5528 data_time: 0.0071 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.6401 aux.loss_ce: 0.0088 aux.acc_seg: 99.1317 +04/18 05:15:34 - mmengine - INFO - Iter(train) [ 18900/160000] lr: 8.9411e-03 eta: 21:32:01 time: 0.5518 data_time: 0.0059 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0102 decode.acc_seg: 99.5771 aux.loss_ce: 0.0085 aux.acc_seg: 98.9853 +04/18 05:16:02 - mmengine - INFO - Iter(train) [ 18950/160000] lr: 8.9382e-03 eta: 21:31:34 time: 0.5523 data_time: 0.0060 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.5743 aux.loss_ce: 0.0086 aux.acc_seg: 99.2046 +04/18 05:16:29 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:16:29 - mmengine - INFO - Iter(train) [ 19000/160000] lr: 8.9354e-03 eta: 21:31:07 time: 0.5514 data_time: 0.0054 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0105 decode.acc_seg: 99.5222 aux.loss_ce: 0.0089 aux.acc_seg: 99.1184 +04/18 05:16:57 - mmengine - INFO - Iter(train) [ 19050/160000] lr: 8.9326e-03 eta: 21:30:40 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0096 decode.acc_seg: 99.6870 aux.loss_ce: 0.0078 aux.acc_seg: 99.2786 +04/18 05:17:24 - mmengine - INFO - Iter(train) [ 19100/160000] lr: 8.9298e-03 eta: 21:30:13 time: 0.5521 data_time: 0.0072 memory: 7635 loss: 0.0204 decode.loss_ce: 0.0112 decode.acc_seg: 99.5139 aux.loss_ce: 0.0092 aux.acc_seg: 98.9241 +04/18 05:17:52 - mmengine - INFO - Iter(train) [ 19150/160000] lr: 8.9270e-03 eta: 21:29:47 time: 0.5511 data_time: 0.0065 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0102 decode.acc_seg: 99.5030 aux.loss_ce: 0.0086 aux.acc_seg: 98.9733 +04/18 05:18:20 - mmengine - INFO - Iter(train) [ 19200/160000] lr: 8.9241e-03 eta: 21:29:21 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.6538 aux.loss_ce: 0.0087 aux.acc_seg: 99.2344 +04/18 05:18:47 - mmengine - INFO - Iter(train) [ 19250/160000] lr: 8.9213e-03 eta: 21:28:54 time: 0.5512 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.4306 aux.loss_ce: 0.0087 aux.acc_seg: 98.9574 +04/18 05:19:15 - mmengine - INFO - Iter(train) [ 19300/160000] lr: 8.9185e-03 eta: 21:28:27 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0109 decode.acc_seg: 99.5718 aux.loss_ce: 0.0093 aux.acc_seg: 98.9916 +04/18 05:19:42 - mmengine - INFO - Iter(train) [ 19350/160000] lr: 8.9157e-03 eta: 21:28:01 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0106 decode.acc_seg: 99.3697 aux.loss_ce: 0.0088 aux.acc_seg: 98.7476 +04/18 05:20:10 - mmengine - INFO - Iter(train) [ 19400/160000] lr: 8.9129e-03 eta: 21:27:34 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0106 decode.acc_seg: 99.7349 aux.loss_ce: 0.0084 aux.acc_seg: 99.3744 +04/18 05:20:37 - mmengine - INFO - Iter(train) [ 19450/160000] lr: 8.9100e-03 eta: 21:27:07 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.6332 aux.loss_ce: 0.0085 aux.acc_seg: 99.2365 +04/18 05:21:05 - mmengine - INFO - Iter(train) [ 19500/160000] lr: 8.9072e-03 eta: 21:26:40 time: 0.5513 data_time: 0.0065 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0102 decode.acc_seg: 99.6510 aux.loss_ce: 0.0085 aux.acc_seg: 99.2619 +04/18 05:21:33 - mmengine - INFO - Iter(train) [ 19550/160000] lr: 8.9044e-03 eta: 21:26:13 time: 0.5508 data_time: 0.0071 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0100 decode.acc_seg: 99.7253 aux.loss_ce: 0.0087 aux.acc_seg: 99.3011 +04/18 05:22:00 - mmengine - INFO - Iter(train) [ 19600/160000] lr: 8.9016e-03 eta: 21:25:47 time: 0.5517 data_time: 0.0061 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0107 decode.acc_seg: 99.5919 aux.loss_ce: 0.0083 aux.acc_seg: 99.2030 +04/18 05:22:28 - mmengine - INFO - Iter(train) [ 19650/160000] lr: 8.8987e-03 eta: 21:25:20 time: 0.5516 data_time: 0.0066 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.6017 aux.loss_ce: 0.0079 aux.acc_seg: 99.0779 +04/18 05:22:55 - mmengine - INFO - Iter(train) [ 19700/160000] lr: 8.8959e-03 eta: 21:24:53 time: 0.5512 data_time: 0.0063 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.6183 aux.loss_ce: 0.0083 aux.acc_seg: 99.2425 +04/18 05:23:23 - mmengine - INFO - Iter(train) [ 19750/160000] lr: 8.8931e-03 eta: 21:24:26 time: 0.5523 data_time: 0.0059 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0105 decode.acc_seg: 99.6702 aux.loss_ce: 0.0084 aux.acc_seg: 99.3751 +04/18 05:23:50 - mmengine - INFO - Iter(train) [ 19800/160000] lr: 8.8903e-03 eta: 21:24:00 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0119 decode.acc_seg: 99.6434 aux.loss_ce: 0.0091 aux.acc_seg: 99.2486 +04/18 05:24:18 - mmengine - INFO - Iter(train) [ 19850/160000] lr: 8.8875e-03 eta: 21:23:34 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0103 decode.acc_seg: 99.5049 aux.loss_ce: 0.0083 aux.acc_seg: 99.0289 +04/18 05:24:46 - mmengine - INFO - Iter(train) [ 19900/160000] lr: 8.8846e-03 eta: 21:23:07 time: 0.5499 data_time: 0.0060 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.6104 aux.loss_ce: 0.0085 aux.acc_seg: 99.0854 +04/18 05:25:13 - mmengine - INFO - Iter(train) [ 19950/160000] lr: 8.8818e-03 eta: 21:22:41 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0104 decode.acc_seg: 99.5382 aux.loss_ce: 0.0088 aux.acc_seg: 98.8459 +04/18 05:25:41 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:25:41 - mmengine - INFO - Iter(train) [ 20000/160000] lr: 8.8790e-03 eta: 21:22:14 time: 0.5526 data_time: 0.0070 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.4759 aux.loss_ce: 0.0081 aux.acc_seg: 98.9945 +04/18 05:25:41 - mmengine - INFO - Saving checkpoint at 20000 iterations +04/18 05:25:45 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0464 data_time: 0.0013 memory: 1657 +04/18 05:25:47 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0463 data_time: 0.0013 memory: 1657 +04/18 05:25:50 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0468 data_time: 0.0015 memory: 1657 +04/18 05:25:52 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0458 data_time: 0.0014 memory: 1657 +04/18 05:25:52 - mmengine - INFO - per class results: +04/18 05:25:52 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.05 | 99.56 | 99.52 | 99.49 | 99.56 | +| contrast | 79.18 | 87.62 | 88.38 | 89.15 | 87.62 | ++------------+-------+-------+--------+-----------+--------+ +04/18 05:25:52 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0800 mIoU: 89.1100 mAcc: 93.5900 mFscore: 93.9500 mPrecision: 94.3200 mRecall: 93.5900 data_time: 0.0014 time: 0.0465 +04/18 05:26:20 - mmengine - INFO - Iter(train) [ 20050/160000] lr: 8.8762e-03 eta: 21:21:48 time: 0.5514 data_time: 0.0057 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0120 decode.acc_seg: 99.6273 aux.loss_ce: 0.0090 aux.acc_seg: 99.1736 +04/18 05:26:47 - mmengine - INFO - Iter(train) [ 20100/160000] lr: 8.8734e-03 eta: 21:21:21 time: 0.5521 data_time: 0.0061 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.5410 aux.loss_ce: 0.0083 aux.acc_seg: 99.0590 +04/18 05:27:15 - mmengine - INFO - Iter(train) [ 20150/160000] lr: 8.8705e-03 eta: 21:20:54 time: 0.5500 data_time: 0.0060 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.5425 aux.loss_ce: 0.0086 aux.acc_seg: 98.8522 +04/18 05:27:42 - mmengine - INFO - Iter(train) [ 20200/160000] lr: 8.8677e-03 eta: 21:20:27 time: 0.5511 data_time: 0.0059 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.7218 aux.loss_ce: 0.0082 aux.acc_seg: 99.2821 +04/18 05:28:10 - mmengine - INFO - Iter(train) [ 20250/160000] lr: 8.8649e-03 eta: 21:20:00 time: 0.5521 data_time: 0.0061 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0102 decode.acc_seg: 99.5185 aux.loss_ce: 0.0085 aux.acc_seg: 99.1319 +04/18 05:28:38 - mmengine - INFO - Iter(train) [ 20300/160000] lr: 8.8621e-03 eta: 21:19:34 time: 0.5510 data_time: 0.0057 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.6010 aux.loss_ce: 0.0085 aux.acc_seg: 98.9961 +04/18 05:29:05 - mmengine - INFO - Iter(train) [ 20350/160000] lr: 8.8592e-03 eta: 21:19:07 time: 0.5508 data_time: 0.0064 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.6302 aux.loss_ce: 0.0087 aux.acc_seg: 99.1408 +04/18 05:29:33 - mmengine - INFO - Iter(train) [ 20400/160000] lr: 8.8564e-03 eta: 21:18:40 time: 0.5528 data_time: 0.0057 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0106 decode.acc_seg: 99.6640 aux.loss_ce: 0.0089 aux.acc_seg: 99.3505 +04/18 05:30:00 - mmengine - INFO - Iter(train) [ 20450/160000] lr: 8.8536e-03 eta: 21:18:14 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.5840 aux.loss_ce: 0.0082 aux.acc_seg: 99.1034 +04/18 05:30:28 - mmengine - INFO - Iter(train) [ 20500/160000] lr: 8.8508e-03 eta: 21:17:48 time: 0.5521 data_time: 0.0067 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0098 decode.acc_seg: 99.6780 aux.loss_ce: 0.0083 aux.acc_seg: 99.3616 +04/18 05:30:56 - mmengine - INFO - Iter(train) [ 20550/160000] lr: 8.8480e-03 eta: 21:17:21 time: 0.5506 data_time: 0.0061 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0117 decode.acc_seg: 99.4849 aux.loss_ce: 0.0092 aux.acc_seg: 98.8701 +04/18 05:31:23 - mmengine - INFO - Iter(train) [ 20600/160000] lr: 8.8451e-03 eta: 21:16:54 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0103 decode.acc_seg: 99.5954 aux.loss_ce: 0.0089 aux.acc_seg: 99.0963 +04/18 05:31:51 - mmengine - INFO - Iter(train) [ 20650/160000] lr: 8.8423e-03 eta: 21:16:27 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.7395 aux.loss_ce: 0.0083 aux.acc_seg: 99.3160 +04/18 05:32:18 - mmengine - INFO - Iter(train) [ 20700/160000] lr: 8.8395e-03 eta: 21:16:01 time: 0.5512 data_time: 0.0057 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0122 decode.acc_seg: 99.4198 aux.loss_ce: 0.0093 aux.acc_seg: 98.7784 +04/18 05:32:46 - mmengine - INFO - Iter(train) [ 20750/160000] lr: 8.8367e-03 eta: 21:15:34 time: 0.5497 data_time: 0.0060 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0101 decode.acc_seg: 99.6177 aux.loss_ce: 0.0085 aux.acc_seg: 99.2023 +04/18 05:33:14 - mmengine - INFO - Iter(train) [ 20800/160000] lr: 8.8338e-03 eta: 21:15:07 time: 0.5523 data_time: 0.0072 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0106 decode.acc_seg: 99.5582 aux.loss_ce: 0.0089 aux.acc_seg: 99.0932 +04/18 05:33:41 - mmengine - INFO - Iter(train) [ 20850/160000] lr: 8.8310e-03 eta: 21:14:41 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0095 decode.acc_seg: 99.5618 aux.loss_ce: 0.0078 aux.acc_seg: 99.1218 +04/18 05:34:09 - mmengine - INFO - Iter(train) [ 20900/160000] lr: 8.8282e-03 eta: 21:14:15 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0105 decode.acc_seg: 99.6472 aux.loss_ce: 0.0087 aux.acc_seg: 99.1203 +04/18 05:34:36 - mmengine - INFO - Iter(train) [ 20950/160000] lr: 8.8254e-03 eta: 21:13:48 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0110 decode.acc_seg: 99.6499 aux.loss_ce: 0.0090 aux.acc_seg: 99.1760 +04/18 05:35:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:35:04 - mmengine - INFO - Iter(train) [ 21000/160000] lr: 8.8225e-03 eta: 21:13:21 time: 0.5513 data_time: 0.0058 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0097 decode.acc_seg: 99.5724 aux.loss_ce: 0.0079 aux.acc_seg: 99.1061 +04/18 05:35:32 - mmengine - INFO - Iter(train) [ 21050/160000] lr: 8.8197e-03 eta: 21:12:54 time: 0.5525 data_time: 0.0056 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.5926 aux.loss_ce: 0.0084 aux.acc_seg: 99.2459 +04/18 05:35:59 - mmengine - INFO - Iter(train) [ 21100/160000] lr: 8.8169e-03 eta: 21:12:28 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.5698 aux.loss_ce: 0.0083 aux.acc_seg: 99.0493 +04/18 05:36:27 - mmengine - INFO - Iter(train) [ 21150/160000] lr: 8.8141e-03 eta: 21:12:01 time: 0.5513 data_time: 0.0059 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.5793 aux.loss_ce: 0.0082 aux.acc_seg: 99.1617 +04/18 05:36:55 - mmengine - INFO - Iter(train) [ 21200/160000] lr: 8.8112e-03 eta: 21:11:34 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0090 decode.acc_seg: 99.5918 aux.loss_ce: 0.0078 aux.acc_seg: 99.0768 +04/18 05:37:22 - mmengine - INFO - Iter(train) [ 21250/160000] lr: 8.8084e-03 eta: 21:11:08 time: 0.5513 data_time: 0.0067 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0095 decode.acc_seg: 99.5050 aux.loss_ce: 0.0086 aux.acc_seg: 98.8276 +04/18 05:37:50 - mmengine - INFO - Iter(train) [ 21300/160000] lr: 8.8056e-03 eta: 21:10:41 time: 0.5502 data_time: 0.0057 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.6953 aux.loss_ce: 0.0081 aux.acc_seg: 99.3996 +04/18 05:38:17 - mmengine - INFO - Iter(train) [ 21350/160000] lr: 8.8028e-03 eta: 21:10:14 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.4920 aux.loss_ce: 0.0080 aux.acc_seg: 98.9112 +04/18 05:38:45 - mmengine - INFO - Iter(train) [ 21400/160000] lr: 8.7999e-03 eta: 21:09:47 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0110 decode.acc_seg: 99.5637 aux.loss_ce: 0.0084 aux.acc_seg: 99.2282 +04/18 05:39:13 - mmengine - INFO - Iter(train) [ 21450/160000] lr: 8.7971e-03 eta: 21:09:21 time: 0.5530 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0095 decode.acc_seg: 99.6919 aux.loss_ce: 0.0078 aux.acc_seg: 99.2809 +04/18 05:39:40 - mmengine - INFO - Iter(train) [ 21500/160000] lr: 8.7943e-03 eta: 21:08:54 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0224 decode.loss_ce: 0.0130 decode.acc_seg: 99.5067 aux.loss_ce: 0.0094 aux.acc_seg: 99.1303 +04/18 05:40:08 - mmengine - INFO - Iter(train) [ 21550/160000] lr: 8.7915e-03 eta: 21:08:28 time: 0.5595 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.6182 aux.loss_ce: 0.0080 aux.acc_seg: 99.1472 +04/18 05:40:35 - mmengine - INFO - Iter(train) [ 21600/160000] lr: 8.7886e-03 eta: 21:08:01 time: 0.5515 data_time: 0.0058 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.6891 aux.loss_ce: 0.0086 aux.acc_seg: 99.2013 +04/18 05:41:03 - mmengine - INFO - Iter(train) [ 21650/160000] lr: 8.7858e-03 eta: 21:07:34 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0100 decode.acc_seg: 99.5457 aux.loss_ce: 0.0083 aux.acc_seg: 98.9417 +04/18 05:41:31 - mmengine - INFO - Iter(train) [ 21700/160000] lr: 8.7830e-03 eta: 21:07:08 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.7236 aux.loss_ce: 0.0082 aux.acc_seg: 99.4304 +04/18 05:41:58 - mmengine - INFO - Iter(train) [ 21750/160000] lr: 8.7802e-03 eta: 21:06:42 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0118 decode.acc_seg: 99.7230 aux.loss_ce: 0.0090 aux.acc_seg: 99.4003 +04/18 05:42:26 - mmengine - INFO - Iter(train) [ 21800/160000] lr: 8.7773e-03 eta: 21:06:15 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.6181 aux.loss_ce: 0.0086 aux.acc_seg: 99.2075 +04/18 05:42:54 - mmengine - INFO - Iter(train) [ 21850/160000] lr: 8.7745e-03 eta: 21:05:49 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.5230 aux.loss_ce: 0.0082 aux.acc_seg: 98.9681 +04/18 05:43:22 - mmengine - INFO - Iter(train) [ 21900/160000] lr: 8.7717e-03 eta: 21:05:23 time: 0.5621 data_time: 0.0071 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.5763 aux.loss_ce: 0.0084 aux.acc_seg: 99.0456 +04/18 05:43:49 - mmengine - INFO - Iter(train) [ 21950/160000] lr: 8.7689e-03 eta: 21:04:57 time: 0.5519 data_time: 0.0071 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0089 decode.acc_seg: 99.6490 aux.loss_ce: 0.0077 aux.acc_seg: 99.1749 +04/18 05:44:17 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:44:17 - mmengine - INFO - Iter(train) [ 22000/160000] lr: 8.7660e-03 eta: 21:04:30 time: 0.5518 data_time: 0.0057 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.6772 aux.loss_ce: 0.0085 aux.acc_seg: 99.2457 +04/18 05:44:45 - mmengine - INFO - Iter(train) [ 22050/160000] lr: 8.7632e-03 eta: 21:04:04 time: 0.5551 data_time: 0.0065 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.6129 aux.loss_ce: 0.0085 aux.acc_seg: 99.1654 +04/18 05:45:12 - mmengine - INFO - Iter(train) [ 22100/160000] lr: 8.7604e-03 eta: 21:03:37 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0099 decode.acc_seg: 99.5454 aux.loss_ce: 0.0083 aux.acc_seg: 99.1231 +04/18 05:45:40 - mmengine - INFO - Iter(train) [ 22150/160000] lr: 8.7576e-03 eta: 21:03:11 time: 0.5527 data_time: 0.0056 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0098 decode.acc_seg: 99.6480 aux.loss_ce: 0.0087 aux.acc_seg: 99.1308 +04/18 05:46:07 - mmengine - INFO - Iter(train) [ 22200/160000] lr: 8.7547e-03 eta: 21:02:45 time: 0.5541 data_time: 0.0061 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5435 aux.loss_ce: 0.0085 aux.acc_seg: 98.9551 +04/18 05:46:35 - mmengine - INFO - Iter(train) [ 22250/160000] lr: 8.7519e-03 eta: 21:02:18 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0098 decode.acc_seg: 99.6762 aux.loss_ce: 0.0080 aux.acc_seg: 99.3376 +04/18 05:47:03 - mmengine - INFO - Iter(train) [ 22300/160000] lr: 8.7491e-03 eta: 21:01:52 time: 0.5527 data_time: 0.0060 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0101 decode.acc_seg: 99.4088 aux.loss_ce: 0.0087 aux.acc_seg: 98.6343 +04/18 05:47:30 - mmengine - INFO - Iter(train) [ 22350/160000] lr: 8.7463e-03 eta: 21:01:25 time: 0.5531 data_time: 0.0059 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.5303 aux.loss_ce: 0.0086 aux.acc_seg: 98.9007 +04/18 05:47:58 - mmengine - INFO - Iter(train) [ 22400/160000] lr: 8.7434e-03 eta: 21:00:59 time: 0.5521 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.7247 aux.loss_ce: 0.0082 aux.acc_seg: 99.3110 +04/18 05:48:26 - mmengine - INFO - Iter(train) [ 22450/160000] lr: 8.7406e-03 eta: 21:00:32 time: 0.5526 data_time: 0.0067 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0103 decode.acc_seg: 99.5754 aux.loss_ce: 0.0089 aux.acc_seg: 98.9209 +04/18 05:48:53 - mmengine - INFO - Iter(train) [ 22500/160000] lr: 8.7378e-03 eta: 21:00:06 time: 0.5520 data_time: 0.0054 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.6193 aux.loss_ce: 0.0084 aux.acc_seg: 99.2662 +04/18 05:49:21 - mmengine - INFO - Iter(train) [ 22550/160000] lr: 8.7350e-03 eta: 20:59:39 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0100 decode.acc_seg: 99.6494 aux.loss_ce: 0.0085 aux.acc_seg: 99.2477 +04/18 05:49:49 - mmengine - INFO - Iter(train) [ 22600/160000] lr: 8.7321e-03 eta: 20:59:13 time: 0.5533 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.4904 aux.loss_ce: 0.0080 aux.acc_seg: 98.8156 +04/18 05:50:16 - mmengine - INFO - Iter(train) [ 22650/160000] lr: 8.7293e-03 eta: 20:58:47 time: 0.5545 data_time: 0.0056 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0102 decode.acc_seg: 99.6898 aux.loss_ce: 0.0089 aux.acc_seg: 99.2451 +04/18 05:50:44 - mmengine - INFO - Iter(train) [ 22700/160000] lr: 8.7265e-03 eta: 20:58:20 time: 0.5535 data_time: 0.0063 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0098 decode.acc_seg: 99.6656 aux.loss_ce: 0.0085 aux.acc_seg: 99.1815 +04/18 05:51:12 - mmengine - INFO - Iter(train) [ 22750/160000] lr: 8.7236e-03 eta: 20:57:53 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0102 decode.acc_seg: 99.6284 aux.loss_ce: 0.0084 aux.acc_seg: 99.1419 +04/18 05:51:39 - mmengine - INFO - Iter(train) [ 22800/160000] lr: 8.7208e-03 eta: 20:57:27 time: 0.5536 data_time: 0.0070 memory: 7635 loss: 0.0218 decode.loss_ce: 0.0122 decode.acc_seg: 99.6552 aux.loss_ce: 0.0096 aux.acc_seg: 99.1392 +04/18 05:52:07 - mmengine - INFO - Iter(train) [ 22850/160000] lr: 8.7180e-03 eta: 20:57:00 time: 0.5536 data_time: 0.0061 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0107 decode.acc_seg: 99.5021 aux.loss_ce: 0.0085 aux.acc_seg: 99.2125 +04/18 05:52:35 - mmengine - INFO - Iter(train) [ 22900/160000] lr: 8.7152e-03 eta: 20:56:33 time: 0.5529 data_time: 0.0061 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.6344 aux.loss_ce: 0.0081 aux.acc_seg: 99.2249 +04/18 05:53:02 - mmengine - INFO - Iter(train) [ 22950/160000] lr: 8.7123e-03 eta: 20:56:07 time: 0.5539 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.5120 aux.loss_ce: 0.0080 aux.acc_seg: 99.1488 +04/18 05:53:30 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 05:53:30 - mmengine - INFO - Iter(train) [ 23000/160000] lr: 8.7095e-03 eta: 20:55:41 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.5515 aux.loss_ce: 0.0081 aux.acc_seg: 99.0876 +04/18 05:53:58 - mmengine - INFO - Iter(train) [ 23050/160000] lr: 8.7067e-03 eta: 20:55:15 time: 0.5530 data_time: 0.0063 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0098 decode.acc_seg: 99.6166 aux.loss_ce: 0.0083 aux.acc_seg: 99.1988 +04/18 05:54:25 - mmengine - INFO - Iter(train) [ 23100/160000] lr: 8.7038e-03 eta: 20:54:48 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.7205 aux.loss_ce: 0.0082 aux.acc_seg: 99.4087 +04/18 05:54:53 - mmengine - INFO - Iter(train) [ 23150/160000] lr: 8.7010e-03 eta: 20:54:22 time: 0.5532 data_time: 0.0058 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.7180 aux.loss_ce: 0.0082 aux.acc_seg: 99.4081 +04/18 05:55:21 - mmengine - INFO - Iter(train) [ 23200/160000] lr: 8.6982e-03 eta: 20:53:55 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.6326 aux.loss_ce: 0.0084 aux.acc_seg: 99.2283 +04/18 05:55:48 - mmengine 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8.6840e-03 eta: 20:51:42 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0091 decode.acc_seg: 99.6366 aux.loss_ce: 0.0086 aux.acc_seg: 98.9037 +04/18 05:58:07 - mmengine - INFO - Iter(train) [ 23500/160000] lr: 8.6812e-03 eta: 20:51:15 time: 0.5540 data_time: 0.0056 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0102 decode.acc_seg: 99.5785 aux.loss_ce: 0.0087 aux.acc_seg: 99.1714 +04/18 05:58:34 - mmengine - INFO - Iter(train) [ 23550/160000] lr: 8.6784e-03 eta: 20:50:49 time: 0.5528 data_time: 0.0071 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.6135 aux.loss_ce: 0.0080 aux.acc_seg: 99.1868 +04/18 05:59:02 - mmengine - INFO - Iter(train) [ 23600/160000] lr: 8.6756e-03 eta: 20:50:22 time: 0.5525 data_time: 0.0059 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0090 decode.acc_seg: 99.6444 aux.loss_ce: 0.0077 aux.acc_seg: 99.1253 +04/18 05:59:30 - mmengine - INFO - Iter(train) [ 23650/160000] lr: 8.6727e-03 eta: 20:49:56 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0088 decode.acc_seg: 99.6012 aux.loss_ce: 0.0077 aux.acc_seg: 99.2838 +04/18 05:59:58 - mmengine - INFO - Iter(train) [ 23700/160000] lr: 8.6699e-03 eta: 20:49:30 time: 0.5640 data_time: 0.0056 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0102 decode.acc_seg: 99.4806 aux.loss_ce: 0.0090 aux.acc_seg: 99.0386 +04/18 06:00:25 - mmengine - INFO - Iter(train) [ 23750/160000] lr: 8.6671e-03 eta: 20:49:04 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6102 aux.loss_ce: 0.0080 aux.acc_seg: 99.1569 +04/18 06:00:53 - mmengine - INFO - Iter(train) [ 23800/160000] lr: 8.6642e-03 eta: 20:48:37 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0103 decode.acc_seg: 99.6158 aux.loss_ce: 0.0086 aux.acc_seg: 99.3257 +04/18 06:01:21 - mmengine - INFO - Iter(train) [ 23850/160000] lr: 8.6614e-03 eta: 20:48:10 time: 0.5531 data_time: 0.0060 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0105 decode.acc_seg: 99.5465 aux.loss_ce: 0.0086 aux.acc_seg: 99.0709 +04/18 06:01:48 - mmengine - INFO - Iter(train) [ 23900/160000] lr: 8.6586e-03 eta: 20:47:44 time: 0.5531 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.5972 aux.loss_ce: 0.0088 aux.acc_seg: 99.0875 +04/18 06:02:16 - mmengine - INFO - Iter(train) [ 23950/160000] lr: 8.6558e-03 eta: 20:47:17 time: 0.5530 data_time: 0.0058 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0101 decode.acc_seg: 99.3958 aux.loss_ce: 0.0082 aux.acc_seg: 98.8238 +04/18 06:02:44 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:02:44 - mmengine - INFO - Iter(train) [ 24000/160000] lr: 8.6529e-03 eta: 20:46:50 time: 0.5530 data_time: 0.0057 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.6724 aux.loss_ce: 0.0083 aux.acc_seg: 98.9887 +04/18 06:03:11 - mmengine - INFO - Iter(train) [ 24050/160000] lr: 8.6501e-03 eta: 20:46:24 time: 0.5532 data_time: 0.0067 memory: 7635 loss: 0.0223 decode.loss_ce: 0.0130 decode.acc_seg: 99.6555 aux.loss_ce: 0.0093 aux.acc_seg: 99.1264 +04/18 06:03:39 - mmengine - INFO - Iter(train) [ 24100/160000] lr: 8.6473e-03 eta: 20:45:58 time: 0.5545 data_time: 0.0057 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0105 decode.acc_seg: 99.4915 aux.loss_ce: 0.0085 aux.acc_seg: 98.9536 +04/18 06:04:07 - mmengine - INFO - Iter(train) [ 24150/160000] lr: 8.6444e-03 eta: 20:45:32 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0092 decode.acc_seg: 99.6381 aux.loss_ce: 0.0078 aux.acc_seg: 99.0611 +04/18 06:04:34 - mmengine - INFO - Iter(train) [ 24200/160000] lr: 8.6416e-03 eta: 20:45:05 time: 0.5539 data_time: 0.0060 memory: 7635 loss: 0.0209 decode.loss_ce: 0.0119 decode.acc_seg: 99.5026 aux.loss_ce: 0.0090 aux.acc_seg: 98.9678 +04/18 06:05:02 - mmengine - INFO - Iter(train) [ 24250/160000] lr: 8.6388e-03 eta: 20:44:38 time: 0.5525 data_time: 0.0059 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.5783 aux.loss_ce: 0.0078 aux.acc_seg: 99.0633 +04/18 06:05:30 - mmengine - INFO - Iter(train) [ 24300/160000] lr: 8.6359e-03 eta: 20:44:11 time: 0.5530 data_time: 0.0063 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0104 decode.acc_seg: 99.5662 aux.loss_ce: 0.0089 aux.acc_seg: 99.2458 +04/18 06:05:57 - mmengine - INFO - Iter(train) [ 24350/160000] lr: 8.6331e-03 eta: 20:43:45 time: 0.5536 data_time: 0.0068 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0108 decode.acc_seg: 99.5570 aux.loss_ce: 0.0090 aux.acc_seg: 99.0466 +04/18 06:06:25 - mmengine - INFO - Iter(train) [ 24400/160000] lr: 8.6303e-03 eta: 20:43:18 time: 0.5525 data_time: 0.0070 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0083 decode.acc_seg: 99.6946 aux.loss_ce: 0.0073 aux.acc_seg: 99.3561 +04/18 06:06:53 - mmengine - INFO - Iter(train) [ 24450/160000] lr: 8.6275e-03 eta: 20:42:51 time: 0.5517 data_time: 0.0069 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0085 decode.acc_seg: 99.6692 aux.loss_ce: 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mmengine - INFO - Iter(train) [ 24700/160000] lr: 8.6133e-03 eta: 20:40:38 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.6115 aux.loss_ce: 0.0082 aux.acc_seg: 99.1424 +04/18 06:09:38 - mmengine - INFO - Iter(train) [ 24750/160000] lr: 8.6105e-03 eta: 20:40:11 time: 0.5536 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.5449 aux.loss_ce: 0.0078 aux.acc_seg: 99.0828 +04/18 06:10:06 - mmengine - INFO - Iter(train) [ 24800/160000] lr: 8.6076e-03 eta: 20:39:45 time: 0.5536 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0091 decode.acc_seg: 99.7059 aux.loss_ce: 0.0076 aux.acc_seg: 99.2213 +04/18 06:10:34 - mmengine - INFO - Iter(train) [ 24850/160000] lr: 8.6048e-03 eta: 20:39:18 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6322 aux.loss_ce: 0.0079 aux.acc_seg: 99.3381 +04/18 06:11:01 - mmengine - INFO - Iter(train) [ 24900/160000] lr: 8.6020e-03 eta: 20:38:51 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6635 aux.loss_ce: 0.0084 aux.acc_seg: 98.9992 +04/18 06:11:29 - mmengine - INFO - Iter(train) [ 24950/160000] lr: 8.5991e-03 eta: 20:38:24 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.5434 aux.loss_ce: 0.0083 aux.acc_seg: 98.9474 +04/18 06:11:57 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:11:57 - mmengine - INFO - Iter(train) [ 25000/160000] lr: 8.5963e-03 eta: 20:37:57 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.5576 aux.loss_ce: 0.0081 aux.acc_seg: 99.0066 +04/18 06:12:24 - mmengine - INFO - Iter(train) [ 25050/160000] lr: 8.5935e-03 eta: 20:37:30 time: 0.5531 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.6518 aux.loss_ce: 0.0082 aux.acc_seg: 99.0782 +04/18 06:12:52 - mmengine - INFO - Iter(train) [ 25100/160000] lr: 8.5906e-03 eta: 20:37:04 time: 0.5525 data_time: 0.0060 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.6090 aux.loss_ce: 0.0083 aux.acc_seg: 99.0969 +04/18 06:13:20 - mmengine - INFO - Iter(train) [ 25150/160000] lr: 8.5878e-03 eta: 20:36:38 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0234 decode.loss_ce: 0.0139 decode.acc_seg: 99.2514 aux.loss_ce: 0.0096 aux.acc_seg: 98.7681 +04/18 06:13:48 - mmengine - INFO - Iter(train) [ 25200/160000] lr: 8.5850e-03 eta: 20:36:11 time: 0.5537 data_time: 0.0062 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0105 decode.acc_seg: 99.5507 aux.loss_ce: 0.0087 aux.acc_seg: 99.0808 +04/18 06:14:15 - mmengine - INFO - Iter(train) [ 25250/160000] lr: 8.5821e-03 eta: 20:35:45 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0100 decode.acc_seg: 99.5605 aux.loss_ce: 0.0089 aux.acc_seg: 98.8799 +04/18 06:14:43 - mmengine - INFO - Iter(train) [ 25300/160000] lr: 8.5793e-03 eta: 20:35:18 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0119 decode.acc_seg: 99.6689 aux.loss_ce: 0.0096 aux.acc_seg: 99.2425 +04/18 06:15:11 - mmengine - INFO - Iter(train) [ 25350/160000] lr: 8.5765e-03 eta: 20:34:52 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0091 decode.acc_seg: 99.7281 aux.loss_ce: 0.0076 aux.acc_seg: 99.4073 +04/18 06:15:38 - mmengine - INFO - Iter(train) [ 25400/160000] lr: 8.5736e-03 eta: 20:34:25 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.6294 aux.loss_ce: 0.0087 aux.acc_seg: 99.0431 +04/18 06:16:06 - mmengine - INFO - Iter(train) [ 25450/160000] lr: 8.5708e-03 eta: 20:33:58 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0102 decode.acc_seg: 99.6443 aux.loss_ce: 0.0087 aux.acc_seg: 99.1878 +04/18 06:16:34 - mmengine - INFO - Iter(train) [ 25500/160000] lr: 8.5680e-03 eta: 20:33:32 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0103 decode.acc_seg: 99.6306 aux.loss_ce: 0.0089 aux.acc_seg: 99.1586 +04/18 06:17:01 - mmengine - INFO - Iter(train) [ 25550/160000] lr: 8.5651e-03 eta: 20:33:05 time: 0.5539 data_time: 0.0062 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.6760 aux.loss_ce: 0.0085 aux.acc_seg: 99.2848 +04/18 06:17:29 - mmengine - INFO - Iter(train) [ 25600/160000] lr: 8.5623e-03 eta: 20:32:38 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0098 decode.acc_seg: 99.6841 aux.loss_ce: 0.0084 aux.acc_seg: 99.1635 +04/18 06:17:57 - mmengine - INFO - Iter(train) [ 25650/160000] lr: 8.5595e-03 eta: 20:32:12 time: 0.5549 data_time: 0.0061 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0107 decode.acc_seg: 99.5690 aux.loss_ce: 0.0086 aux.acc_seg: 98.9656 +04/18 06:18:24 - mmengine - INFO - Iter(train) [ 25700/160000] lr: 8.5566e-03 eta: 20:31:45 time: 0.5554 data_time: 0.0069 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0107 decode.acc_seg: 99.6641 aux.loss_ce: 0.0086 aux.acc_seg: 99.2069 +04/18 06:18:52 - mmengine - INFO - Iter(train) [ 25750/160000] lr: 8.5538e-03 eta: 20:31:19 time: 0.5546 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.5224 aux.loss_ce: 0.0083 aux.acc_seg: 98.9250 +04/18 06:19:20 - mmengine - INFO - Iter(train) [ 25800/160000] lr: 8.5510e-03 eta: 20:30:52 time: 0.5558 data_time: 0.0058 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.5760 aux.loss_ce: 0.0084 aux.acc_seg: 98.9920 +04/18 06:19:48 - mmengine - INFO - Iter(train) [ 25850/160000] lr: 8.5481e-03 eta: 20:30:26 time: 0.5557 data_time: 0.0065 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0100 decode.acc_seg: 99.6197 aux.loss_ce: 0.0085 aux.acc_seg: 99.2267 +04/18 06:20:15 - mmengine - INFO - Iter(train) [ 25900/160000] lr: 8.5453e-03 eta: 20:30:00 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.7183 aux.loss_ce: 0.0083 aux.acc_seg: 99.2774 +04/18 06:20:43 - mmengine - INFO - Iter(train) [ 25950/160000] lr: 8.5425e-03 eta: 20:29:33 time: 0.5543 data_time: 0.0056 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0098 decode.acc_seg: 99.6219 aux.loss_ce: 0.0090 aux.acc_seg: 99.0978 +04/18 06:21:11 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:21:11 - mmengine - INFO - Iter(train) [ 26000/160000] lr: 8.5396e-03 eta: 20:29:07 time: 0.5536 data_time: 0.0067 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0102 decode.acc_seg: 99.6169 aux.loss_ce: 0.0086 aux.acc_seg: 99.1028 +04/18 06:21:39 - mmengine - INFO - Iter(train) [ 26050/160000] lr: 8.5368e-03 eta: 20:28:40 time: 0.5528 data_time: 0.0073 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0100 decode.acc_seg: 99.6387 aux.loss_ce: 0.0083 aux.acc_seg: 99.1585 +04/18 06:22:06 - mmengine - INFO - Iter(train) [ 26100/160000] lr: 8.5340e-03 eta: 20:28:13 time: 0.5531 data_time: 0.0060 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.6833 aux.loss_ce: 0.0084 aux.acc_seg: 99.2471 +04/18 06:22:34 - mmengine - INFO - Iter(train) [ 26150/160000] lr: 8.5311e-03 eta: 20:27:47 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0096 decode.acc_seg: 99.6755 aux.loss_ce: 0.0092 aux.acc_seg: 98.9544 +04/18 06:23:02 - mmengine - INFO - Iter(train) [ 26200/160000] lr: 8.5283e-03 eta: 20:27:20 time: 0.5529 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6167 aux.loss_ce: 0.0081 aux.acc_seg: 99.0394 +04/18 06:23:29 - mmengine - INFO - Iter(train) [ 26250/160000] lr: 8.5255e-03 eta: 20:26:54 time: 0.5622 data_time: 0.0070 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.5710 aux.loss_ce: 0.0085 aux.acc_seg: 98.9657 +04/18 06:23:57 - mmengine - INFO - Iter(train) [ 26300/160000] lr: 8.5226e-03 eta: 20:26:28 time: 0.5558 data_time: 0.0070 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0097 decode.acc_seg: 99.5872 aux.loss_ce: 0.0086 aux.acc_seg: 99.1512 +04/18 06:24:25 - mmengine - INFO - Iter(train) [ 26350/160000] lr: 8.5198e-03 eta: 20:26:01 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0091 decode.acc_seg: 99.5209 aux.loss_ce: 0.0078 aux.acc_seg: 99.1055 +04/18 06:24:53 - mmengine - INFO - Iter(train) [ 26400/160000] lr: 8.5170e-03 eta: 20:25:35 time: 0.5538 data_time: 0.0059 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0089 decode.acc_seg: 99.6354 aux.loss_ce: 0.0077 aux.acc_seg: 99.2619 +04/18 06:25:20 - mmengine - INFO - Iter(train) [ 26450/160000] lr: 8.5141e-03 eta: 20:25:08 time: 0.5545 data_time: 0.0063 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0082 decode.acc_seg: 99.6066 aux.loss_ce: 0.0073 aux.acc_seg: 99.0895 +04/18 06:25:48 - mmengine - INFO - Iter(train) [ 26500/160000] lr: 8.5113e-03 eta: 20:24:42 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6694 aux.loss_ce: 0.0080 aux.acc_seg: 99.1294 +04/18 06:26:16 - mmengine - INFO - Iter(train) [ 26550/160000] lr: 8.5085e-03 eta: 20:24:15 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0089 decode.acc_seg: 99.6081 aux.loss_ce: 0.0078 aux.acc_seg: 99.1234 +04/18 06:26:44 - mmengine - INFO - Iter(train) [ 26600/160000] lr: 8.5056e-03 eta: 20:23:49 time: 0.5548 data_time: 0.0067 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.5845 aux.loss_ce: 0.0081 aux.acc_seg: 99.1042 +04/18 06:27:11 - mmengine - INFO - Iter(train) [ 26650/160000] lr: 8.5028e-03 eta: 20:23:23 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.5620 aux.loss_ce: 0.0082 aux.acc_seg: 99.1332 +04/18 06:27:39 - mmengine - INFO - Iter(train) [ 26700/160000] lr: 8.5000e-03 eta: 20:22:56 time: 0.5545 data_time: 0.0066 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0105 decode.acc_seg: 99.4620 aux.loss_ce: 0.0089 aux.acc_seg: 98.9699 +04/18 06:28:07 - mmengine - INFO - Iter(train) 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time: 0.5555 data_time: 0.0068 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0095 decode.acc_seg: 99.6460 aux.loss_ce: 0.0086 aux.acc_seg: 99.1088 +04/18 06:30:26 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:30:26 - mmengine - INFO - Iter(train) [ 27000/160000] lr: 8.4829e-03 eta: 20:20:18 time: 0.5542 data_time: 0.0074 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.6244 aux.loss_ce: 0.0086 aux.acc_seg: 99.0749 +04/18 06:30:53 - mmengine - INFO - Iter(train) [ 27050/160000] lr: 8.4801e-03 eta: 20:19:51 time: 0.5542 data_time: 0.0070 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6664 aux.loss_ce: 0.0082 aux.acc_seg: 99.1160 +04/18 06:31:21 - mmengine - INFO - Iter(train) [ 27100/160000] lr: 8.4773e-03 eta: 20:19:25 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0099 decode.acc_seg: 99.4372 aux.loss_ce: 0.0082 aux.acc_seg: 98.8480 +04/18 06:31:49 - mmengine - INFO - Iter(train) [ 27150/160000] lr: 8.4744e-03 eta: 20:18:58 time: 0.5541 data_time: 0.0056 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.7565 aux.loss_ce: 0.0076 aux.acc_seg: 99.4385 +04/18 06:32:17 - mmengine - INFO - Iter(train) [ 27200/160000] lr: 8.4716e-03 eta: 20:18:32 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.5640 aux.loss_ce: 0.0081 aux.acc_seg: 98.9797 +04/18 06:32:44 - mmengine - INFO - Iter(train) [ 27250/160000] lr: 8.4688e-03 eta: 20:18:05 time: 0.5553 data_time: 0.0062 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0101 decode.acc_seg: 99.5849 aux.loss_ce: 0.0082 aux.acc_seg: 99.2076 +04/18 06:33:12 - mmengine - INFO - Iter(train) [ 27300/160000] lr: 8.4659e-03 eta: 20:17:39 time: 0.5558 data_time: 0.0065 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.4665 aux.loss_ce: 0.0082 aux.acc_seg: 99.2051 +04/18 06:33:40 - mmengine - INFO - Iter(train) [ 27350/160000] lr: 8.4631e-03 eta: 20:17:13 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.5988 aux.loss_ce: 0.0082 aux.acc_seg: 98.9725 +04/18 06:34:08 - mmengine - INFO - Iter(train) [ 27400/160000] lr: 8.4602e-03 eta: 20:16:47 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0094 decode.acc_seg: 99.6114 aux.loss_ce: 0.0078 aux.acc_seg: 99.2443 +04/18 06:34:35 - mmengine - INFO - Iter(train) [ 27450/160000] lr: 8.4574e-03 eta: 20:16:20 time: 0.5562 data_time: 0.0062 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0091 decode.acc_seg: 99.7229 aux.loss_ce: 0.0081 aux.acc_seg: 99.2241 +04/18 06:35:03 - mmengine - INFO - Iter(train) [ 27500/160000] lr: 8.4546e-03 eta: 20:15:54 time: 0.5559 data_time: 0.0057 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.5677 aux.loss_ce: 0.0083 aux.acc_seg: 99.1057 +04/18 06:35:31 - mmengine - INFO - Iter(train) [ 27550/160000] lr: 8.4517e-03 eta: 20:15:27 time: 0.5555 data_time: 0.0067 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.7557 aux.loss_ce: 0.0083 aux.acc_seg: 99.3876 +04/18 06:35:59 - mmengine - INFO - Iter(train) [ 27600/160000] lr: 8.4489e-03 eta: 20:15:01 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0094 decode.acc_seg: 99.7061 aux.loss_ce: 0.0083 aux.acc_seg: 99.2312 +04/18 06:36:26 - mmengine - INFO - Iter(train) [ 27650/160000] lr: 8.4461e-03 eta: 20:14:34 time: 0.5547 data_time: 0.0068 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0101 decode.acc_seg: 99.4464 aux.loss_ce: 0.0090 aux.acc_seg: 98.5444 +04/18 06:36:54 - mmengine - INFO - Iter(train) [ 27700/160000] lr: 8.4432e-03 eta: 20:14:08 time: 0.5551 data_time: 0.0062 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.6277 aux.loss_ce: 0.0081 aux.acc_seg: 99.2190 +04/18 06:37:22 - mmengine - INFO - Iter(train) [ 27750/160000] lr: 8.4404e-03 eta: 20:13:41 time: 0.5538 data_time: 0.0061 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.5312 aux.loss_ce: 0.0086 aux.acc_seg: 98.8728 +04/18 06:37:50 - mmengine - INFO - Iter(train) [ 27800/160000] lr: 8.4375e-03 eta: 20:13:14 time: 0.5547 data_time: 0.0061 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0098 decode.acc_seg: 99.6878 aux.loss_ce: 0.0086 aux.acc_seg: 99.2142 +04/18 06:38:17 - mmengine - INFO - Iter(train) [ 27850/160000] lr: 8.4347e-03 eta: 20:12:48 time: 0.5538 data_time: 0.0063 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0124 decode.acc_seg: 99.5290 aux.loss_ce: 0.0093 aux.acc_seg: 99.2845 +04/18 06:38:45 - mmengine - INFO - Iter(train) [ 27900/160000] lr: 8.4319e-03 eta: 20:12:21 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0087 decode.acc_seg: 99.6517 aux.loss_ce: 0.0077 aux.acc_seg: 99.2413 +04/18 06:39:13 - mmengine - INFO - Iter(train) [ 27950/160000] lr: 8.4290e-03 eta: 20:11:54 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.5343 aux.loss_ce: 0.0086 aux.acc_seg: 99.1739 +04/18 06:39:40 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:39:40 - mmengine - INFO - Iter(train) [ 28000/160000] lr: 8.4262e-03 eta: 20:11:27 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.5786 aux.loss_ce: 0.0079 aux.acc_seg: 99.1202 +04/18 06:40:08 - mmengine - INFO - Iter(train) [ 28050/160000] lr: 8.4233e-03 eta: 20:11:01 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.7616 aux.loss_ce: 0.0082 aux.acc_seg: 99.3387 +04/18 06:40:36 - mmengine - INFO - Iter(train) [ 28100/160000] lr: 8.4205e-03 eta: 20:10:34 time: 0.5538 data_time: 0.0060 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0098 decode.acc_seg: 99.4782 aux.loss_ce: 0.0085 aux.acc_seg: 99.0201 +04/18 06:41:03 - mmengine - INFO - Iter(train) [ 28150/160000] lr: 8.4177e-03 eta: 20:10:07 time: 0.5535 data_time: 0.0063 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0097 decode.acc_seg: 99.6655 aux.loss_ce: 0.0083 aux.acc_seg: 99.2508 +04/18 06:41:31 - mmengine - INFO - Iter(train) [ 28200/160000] lr: 8.4148e-03 eta: 20:09:40 time: 0.5526 data_time: 0.0061 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0101 decode.acc_seg: 99.4870 aux.loss_ce: 0.0088 aux.acc_seg: 99.1462 +04/18 06:41:59 - mmengine - INFO - Iter(train) [ 28250/160000] lr: 8.4120e-03 eta: 20:09:13 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0094 decode.acc_seg: 99.5909 aux.loss_ce: 0.0082 aux.acc_seg: 99.0626 +04/18 06:42:27 - mmengine - INFO - Iter(train) [ 28300/160000] lr: 8.4092e-03 eta: 20:08:47 time: 0.5556 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0093 decode.acc_seg: 99.6944 aux.loss_ce: 0.0080 aux.acc_seg: 99.3399 +04/18 06:42:54 - mmengine - INFO - Iter(train) [ 28350/160000] lr: 8.4063e-03 eta: 20:08:20 time: 0.5536 data_time: 0.0059 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0093 decode.acc_seg: 99.7280 aux.loss_ce: 0.0083 aux.acc_seg: 99.4580 +04/18 06:43:22 - mmengine - INFO - Iter(train) [ 28400/160000] lr: 8.4035e-03 eta: 20:07:54 time: 0.5551 data_time: 0.0069 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0089 decode.acc_seg: 99.6412 aux.loss_ce: 0.0078 aux.acc_seg: 99.1178 +04/18 06:43:50 - mmengine - INFO - Iter(train) [ 28450/160000] lr: 8.4006e-03 eta: 20:07:28 time: 0.5532 data_time: 0.0059 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.5481 aux.loss_ce: 0.0085 aux.acc_seg: 99.0436 +04/18 06:44:18 - mmengine - INFO - Iter(train) [ 28500/160000] lr: 8.3978e-03 eta: 20:07:02 time: 0.5532 data_time: 0.0066 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0099 decode.acc_seg: 99.5734 aux.loss_ce: 0.0088 aux.acc_seg: 99.0263 +04/18 06:44:46 - mmengine - INFO - Iter(train) [ 28550/160000] lr: 8.3950e-03 eta: 20:06:35 time: 0.5546 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0091 decode.acc_seg: 99.6369 aux.loss_ce: 0.0081 aux.acc_seg: 99.3268 +04/18 06:45:13 - mmengine - INFO - Iter(train) [ 28600/160000] lr: 8.3921e-03 eta: 20:06:08 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0095 decode.acc_seg: 99.5734 aux.loss_ce: 0.0081 aux.acc_seg: 98.9872 +04/18 06:45:41 - mmengine - INFO - Iter(train) [ 28650/160000] lr: 8.3893e-03 eta: 20:05:42 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.7258 aux.loss_ce: 0.0082 aux.acc_seg: 99.3052 +04/18 06:46:09 - mmengine - INFO - Iter(train) [ 28700/160000] lr: 8.3864e-03 eta: 20:05:15 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0080 decode.acc_seg: 99.5783 aux.loss_ce: 0.0076 aux.acc_seg: 99.2734 +04/18 06:46:36 - mmengine - INFO - Iter(train) [ 28750/160000] lr: 8.3836e-03 eta: 20:04:48 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0103 decode.acc_seg: 99.5633 aux.loss_ce: 0.0090 aux.acc_seg: 99.0564 +04/18 06:47:04 - mmengine - INFO - Iter(train) [ 28800/160000] lr: 8.3808e-03 eta: 20:04:21 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0109 decode.acc_seg: 99.5342 aux.loss_ce: 0.0088 aux.acc_seg: 99.1306 +04/18 06:47:32 - mmengine - INFO - Iter(train) [ 28850/160000] lr: 8.3779e-03 eta: 20:03:54 time: 0.5549 data_time: 0.0061 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.6271 aux.loss_ce: 0.0084 aux.acc_seg: 99.1467 +04/18 06:48:00 - mmengine - INFO - Iter(train) [ 28900/160000] lr: 8.3751e-03 eta: 20:03:27 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0097 decode.acc_seg: 99.5158 aux.loss_ce: 0.0085 aux.acc_seg: 98.9241 +04/18 06:48:27 - mmengine - INFO - Iter(train) [ 28950/160000] lr: 8.3722e-03 eta: 20:03:01 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0080 decode.acc_seg: 99.6894 aux.loss_ce: 0.0070 aux.acc_seg: 99.1712 +04/18 06:48:55 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:48:55 - mmengine - INFO - Iter(train) [ 29000/160000] lr: 8.3694e-03 eta: 20:02:34 time: 0.5523 data_time: 0.0068 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.5784 aux.loss_ce: 0.0083 aux.acc_seg: 99.1201 +04/18 06:49:23 - mmengine - INFO - Iter(train) [ 29050/160000] lr: 8.3666e-03 eta: 20:02:07 time: 0.5536 data_time: 0.0062 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.6448 aux.loss_ce: 0.0083 aux.acc_seg: 99.1606 +04/18 06:49:50 - mmengine - INFO - Iter(train) [ 29100/160000] lr: 8.3637e-03 eta: 20:01:40 time: 0.5520 data_time: 0.0066 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.5769 aux.loss_ce: 0.0080 aux.acc_seg: 99.1119 +04/18 06:50:18 - mmengine - INFO - Iter(train) [ 29150/160000] lr: 8.3609e-03 eta: 20:01:13 time: 0.5529 data_time: 0.0066 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6526 aux.loss_ce: 0.0078 aux.acc_seg: 99.1117 +04/18 06:50:46 - mmengine - INFO - Iter(train) [ 29200/160000] lr: 8.3580e-03 eta: 20:00:46 time: 0.5541 data_time: 0.0058 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.6968 aux.loss_ce: 0.0073 aux.acc_seg: 99.3466 +04/18 06:51:13 - mmengine - INFO - Iter(train) [ 29250/160000] lr: 8.3552e-03 eta: 20:00:20 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0095 decode.acc_seg: 99.6399 aux.loss_ce: 0.0080 aux.acc_seg: 99.0940 +04/18 06:51:41 - mmengine - INFO - Iter(train) [ 29300/160000] lr: 8.3524e-03 eta: 19:59:53 time: 0.5537 data_time: 0.0070 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6080 aux.loss_ce: 0.0080 aux.acc_seg: 98.9253 +04/18 06:52:09 - mmengine - INFO - Iter(train) [ 29350/160000] lr: 8.3495e-03 eta: 19:59:26 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0095 decode.acc_seg: 99.6967 aux.loss_ce: 0.0081 aux.acc_seg: 99.2575 +04/18 06:52:36 - mmengine - INFO - Iter(train) [ 29400/160000] lr: 8.3467e-03 eta: 19:58:58 time: 0.5512 data_time: 0.0062 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.6092 aux.loss_ce: 0.0085 aux.acc_seg: 99.0195 +04/18 06:53:04 - mmengine - INFO - Iter(train) [ 29450/160000] lr: 8.3438e-03 eta: 19:58:32 time: 0.5548 data_time: 0.0058 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0088 decode.acc_seg: 99.6880 aux.loss_ce: 0.0078 aux.acc_seg: 99.3057 +04/18 06:53:32 - mmengine - INFO - Iter(train) [ 29500/160000] lr: 8.3410e-03 eta: 19:58:05 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.7135 aux.loss_ce: 0.0081 aux.acc_seg: 99.4377 +04/18 06:54:00 - mmengine - INFO - Iter(train) [ 29550/160000] lr: 8.3381e-03 eta: 19:57:38 time: 0.5548 data_time: 0.0067 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.5240 aux.loss_ce: 0.0082 aux.acc_seg: 99.0030 +04/18 06:54:27 - mmengine - INFO - Iter(train) [ 29600/160000] lr: 8.3353e-03 eta: 19:57:11 time: 0.5544 data_time: 0.0068 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0097 decode.acc_seg: 99.7782 aux.loss_ce: 0.0088 aux.acc_seg: 99.4354 +04/18 06:54:55 - mmengine - INFO - Iter(train) [ 29650/160000] lr: 8.3325e-03 eta: 19:56:45 time: 0.5536 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.7258 aux.loss_ce: 0.0082 aux.acc_seg: 99.3110 +04/18 06:55:23 - mmengine - INFO - Iter(train) [ 29700/160000] lr: 8.3296e-03 eta: 19:56:18 time: 0.5535 data_time: 0.0059 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0100 decode.acc_seg: 99.3491 aux.loss_ce: 0.0084 aux.acc_seg: 98.8539 +04/18 06:55:50 - mmengine - INFO - Iter(train) [ 29750/160000] lr: 8.3268e-03 eta: 19:55:51 time: 0.5531 data_time: 0.0061 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0089 decode.acc_seg: 99.5722 aux.loss_ce: 0.0078 aux.acc_seg: 99.1220 +04/18 06:56:18 - mmengine - INFO - Iter(train) [ 29800/160000] lr: 8.3239e-03 eta: 19:55:24 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0103 decode.acc_seg: 99.6004 aux.loss_ce: 0.0080 aux.acc_seg: 99.2584 +04/18 06:56:46 - mmengine - INFO - Iter(train) [ 29850/160000] lr: 8.3211e-03 eta: 19:54:57 time: 0.5534 data_time: 0.0060 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0108 decode.acc_seg: 99.6187 aux.loss_ce: 0.0089 aux.acc_seg: 99.1012 +04/18 06:57:13 - mmengine - INFO - Iter(train) [ 29900/160000] lr: 8.3182e-03 eta: 19:54:30 time: 0.5540 data_time: 0.0059 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6822 aux.loss_ce: 0.0084 aux.acc_seg: 99.1999 +04/18 06:57:41 - mmengine - INFO - Iter(train) [ 29950/160000] lr: 8.3154e-03 eta: 19:54:03 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0084 decode.acc_seg: 99.6488 aux.loss_ce: 0.0076 aux.acc_seg: 99.1222 +04/18 06:58:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 06:58:09 - mmengine - INFO - Iter(train) [ 30000/160000] lr: 8.3126e-03 eta: 19:53:37 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0098 decode.acc_seg: 99.6754 aux.loss_ce: 0.0083 aux.acc_seg: 99.3052 +04/18 06:58:09 - mmengine - INFO - Saving checkpoint at 30000 iterations +04/18 06:58:13 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:06 time: 0.0462 data_time: 0.0014 memory: 1657 +04/18 06:58:15 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0475 data_time: 0.0014 memory: 1657 +04/18 06:58:18 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0463 data_time: 0.0014 memory: 1657 +04/18 06:58:20 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0457 data_time: 0.0013 memory: 1657 +04/18 06:58:20 - mmengine - INFO - per class results: +04/18 06:58:20 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.47 | 99.53 | 99.58 | 99.47 | +| contrast | 79.8 | 89.93 | 88.77 | 87.63 | 89.93 | ++------------+-------+-------+--------+-----------+--------+ +04/18 06:58:20 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0900 mIoU: 89.4300 mAcc: 94.7000 mFscore: 94.1500 mPrecision: 93.6000 mRecall: 94.7000 data_time: 0.0015 time: 0.0464 +04/18 06:58:48 - mmengine - INFO - Iter(train) [ 30050/160000] lr: 8.3097e-03 eta: 19:53:11 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.6932 aux.loss_ce: 0.0085 aux.acc_seg: 99.2504 +04/18 06:59:16 - mmengine - INFO - Iter(train) [ 30100/160000] lr: 8.3069e-03 eta: 19:52:44 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.6229 aux.loss_ce: 0.0082 aux.acc_seg: 99.0599 +04/18 06:59:43 - mmengine - INFO - Iter(train) [ 30150/160000] lr: 8.3040e-03 eta: 19:52:17 time: 0.5537 data_time: 0.0066 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0097 decode.acc_seg: 99.7074 aux.loss_ce: 0.0081 aux.acc_seg: 99.2696 +04/18 07:00:11 - mmengine - INFO - Iter(train) [ 30200/160000] lr: 8.3012e-03 eta: 19:51:50 time: 0.5526 data_time: 0.0061 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0101 decode.acc_seg: 99.6636 aux.loss_ce: 0.0083 aux.acc_seg: 99.2550 +04/18 07:00:39 - mmengine - INFO - Iter(train) [ 30250/160000] lr: 8.2983e-03 eta: 19:51:24 time: 0.5532 data_time: 0.0063 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0098 decode.acc_seg: 99.6634 aux.loss_ce: 0.0083 aux.acc_seg: 99.2562 +04/18 07:01:06 - mmengine - INFO - Iter(train) [ 30300/160000] lr: 8.2955e-03 eta: 19:50:57 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.6403 aux.loss_ce: 0.0079 aux.acc_seg: 99.2498 +04/18 07:01:34 - mmengine - INFO - Iter(train) [ 30350/160000] lr: 8.2927e-03 eta: 19:50:30 time: 0.5537 data_time: 0.0066 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0094 decode.acc_seg: 99.6887 aux.loss_ce: 0.0086 aux.acc_seg: 99.3160 +04/18 07:02:02 - mmengine - INFO - Iter(train) [ 30400/160000] lr: 8.2898e-03 eta: 19:50:03 time: 0.5544 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.6344 aux.loss_ce: 0.0077 aux.acc_seg: 99.1701 +04/18 07:02:29 - mmengine - INFO - Iter(train) [ 30450/160000] lr: 8.2870e-03 eta: 19:49:36 time: 0.5528 data_time: 0.0057 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.6485 aux.loss_ce: 0.0084 aux.acc_seg: 99.3006 +04/18 07:02:57 - mmengine - INFO - Iter(train) [ 30500/160000] lr: 8.2841e-03 eta: 19:49:10 time: 0.5638 data_time: 0.0068 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.6327 aux.loss_ce: 0.0081 aux.acc_seg: 99.2662 +04/18 07:03:25 - mmengine - INFO - Iter(train) [ 30550/160000] lr: 8.2813e-03 eta: 19:48:43 time: 0.5538 data_time: 0.0063 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0099 decode.acc_seg: 99.6390 aux.loss_ce: 0.0081 aux.acc_seg: 99.1124 +04/18 07:03:53 - mmengine - INFO - Iter(train) [ 30600/160000] lr: 8.2784e-03 eta: 19:48:16 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0093 decode.acc_seg: 99.6712 aux.loss_ce: 0.0087 aux.acc_seg: 99.2329 +04/18 07:04:20 - mmengine - INFO - Iter(train) [ 30650/160000] lr: 8.2756e-03 eta: 19:47:49 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0106 decode.acc_seg: 99.4262 aux.loss_ce: 0.0093 aux.acc_seg: 99.0763 +04/18 07:04:48 - mmengine - INFO - Iter(train) [ 30700/160000] lr: 8.2728e-03 eta: 19:47:22 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0103 decode.acc_seg: 99.4432 aux.loss_ce: 0.0094 aux.acc_seg: 98.5764 +04/18 07:05:16 - mmengine - INFO - Iter(train) [ 30750/160000] lr: 8.2699e-03 eta: 19:46:55 time: 0.5530 data_time: 0.0077 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0096 decode.acc_seg: 99.5905 aux.loss_ce: 0.0081 aux.acc_seg: 99.1282 +04/18 07:05:43 - mmengine - INFO - Iter(train) [ 30800/160000] lr: 8.2671e-03 eta: 19:46:28 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6326 aux.loss_ce: 0.0080 aux.acc_seg: 99.1488 +04/18 07:06:11 - mmengine - INFO - Iter(train) [ 30850/160000] lr: 8.2642e-03 eta: 19:46:01 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0082 decode.acc_seg: 99.5945 aux.loss_ce: 0.0074 aux.acc_seg: 99.1685 +04/18 07:06:39 - mmengine - INFO - Iter(train) [ 30900/160000] lr: 8.2614e-03 eta: 19:45:34 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0095 decode.acc_seg: 99.7173 aux.loss_ce: 0.0081 aux.acc_seg: 99.3325 +04/18 07:07:07 - mmengine - INFO - Iter(train) [ 30950/160000] lr: 8.2585e-03 eta: 19:45:07 time: 0.5541 data_time: 0.0059 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.6530 aux.loss_ce: 0.0080 aux.acc_seg: 99.1598 +04/18 07:07:34 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:07:34 - mmengine - INFO - Iter(train) [ 31000/160000] lr: 8.2557e-03 eta: 19:44:40 time: 0.5538 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.6911 aux.loss_ce: 0.0084 aux.acc_seg: 99.1821 +04/18 07:08:02 - mmengine - INFO - Iter(train) [ 31050/160000] lr: 8.2528e-03 eta: 19:44:14 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0102 decode.acc_seg: 99.5356 aux.loss_ce: 0.0087 aux.acc_seg: 99.1998 +04/18 07:08:30 - mmengine - INFO - Iter(train) [ 31100/160000] lr: 8.2500e-03 eta: 19:43:47 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0089 decode.acc_seg: 99.5303 aux.loss_ce: 0.0076 aux.acc_seg: 99.0437 +04/18 07:08:58 - mmengine - INFO - Iter(train) [ 31150/160000] lr: 8.2471e-03 eta: 19:43:20 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.7702 aux.loss_ce: 0.0083 aux.acc_seg: 99.3576 +04/18 07:09:25 - mmengine - INFO - Iter(train) [ 31200/160000] lr: 8.2443e-03 eta: 19:42:53 time: 0.5550 data_time: 0.0062 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0094 decode.acc_seg: 99.6806 aux.loss_ce: 0.0087 aux.acc_seg: 99.3296 +04/18 07:09:53 - mmengine - INFO - Iter(train) [ 31250/160000] lr: 8.2415e-03 eta: 19:42:26 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.5939 aux.loss_ce: 0.0082 aux.acc_seg: 99.2475 +04/18 07:10:21 - mmengine - INFO - Iter(train) [ 31300/160000] lr: 8.2386e-03 eta: 19:42:00 time: 0.5541 data_time: 0.0060 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0090 decode.acc_seg: 99.6605 aux.loss_ce: 0.0077 aux.acc_seg: 99.1757 +04/18 07:10:48 - mmengine - INFO - Iter(train) [ 31350/160000] lr: 8.2358e-03 eta: 19:41:33 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6571 aux.loss_ce: 0.0082 aux.acc_seg: 99.0974 +04/18 07:11:16 - mmengine - INFO - Iter(train) [ 31400/160000] lr: 8.2329e-03 eta: 19:41:06 time: 0.5538 data_time: 0.0069 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6479 aux.loss_ce: 0.0081 aux.acc_seg: 98.9635 +04/18 07:11:44 - mmengine - INFO - Iter(train) [ 31450/160000] lr: 8.2301e-03 eta: 19:40:39 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6761 aux.loss_ce: 0.0079 aux.acc_seg: 99.3599 +04/18 07:12:12 - mmengine - INFO - Iter(train) [ 31500/160000] lr: 8.2272e-03 eta: 19:40:12 time: 0.5548 data_time: 0.0062 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.7297 aux.loss_ce: 0.0081 aux.acc_seg: 99.3115 +04/18 07:12:39 - mmengine - INFO - Iter(train) [ 31550/160000] lr: 8.2244e-03 eta: 19:39:45 time: 0.5535 data_time: 0.0068 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.6945 aux.loss_ce: 0.0082 aux.acc_seg: 99.3441 +04/18 07:13:07 - mmengine - INFO - Iter(train) [ 31600/160000] lr: 8.2215e-03 eta: 19:39:19 time: 0.5717 data_time: 0.0058 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.5731 aux.loss_ce: 0.0080 aux.acc_seg: 99.0817 +04/18 07:13:35 - mmengine - INFO - Iter(train) [ 31650/160000] lr: 8.2187e-03 eta: 19:38:52 time: 0.5542 data_time: 0.0068 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0095 decode.acc_seg: 99.6840 aux.loss_ce: 0.0085 aux.acc_seg: 99.1050 +04/18 07:14:03 - mmengine - INFO - Iter(train) [ 31700/160000] lr: 8.2158e-03 eta: 19:38:25 time: 0.5544 data_time: 0.0059 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6806 aux.loss_ce: 0.0077 aux.acc_seg: 99.2702 +04/18 07:14:30 - mmengine - INFO - Iter(train) [ 31750/160000] lr: 8.2130e-03 eta: 19:37:58 time: 0.5534 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.6800 aux.loss_ce: 0.0078 aux.acc_seg: 99.0911 +04/18 07:14:58 - mmengine - INFO - Iter(train) [ 31800/160000] lr: 8.2101e-03 eta: 19:37:31 time: 0.5537 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.6931 aux.loss_ce: 0.0073 aux.acc_seg: 99.2317 +04/18 07:15:26 - mmengine - INFO - Iter(train) [ 31850/160000] lr: 8.2073e-03 eta: 19:37:04 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0108 decode.acc_seg: 99.6500 aux.loss_ce: 0.0087 aux.acc_seg: 99.3303 +04/18 07:15:53 - mmengine - INFO - Iter(train) [ 31900/160000] lr: 8.2045e-03 eta: 19:36:37 time: 0.5531 data_time: 0.0062 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.6323 aux.loss_ce: 0.0084 aux.acc_seg: 99.2227 +04/18 07:16:21 - mmengine - INFO - Iter(train) [ 31950/160000] lr: 8.2016e-03 eta: 19:36:10 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0088 decode.acc_seg: 99.6806 aux.loss_ce: 0.0079 aux.acc_seg: 99.1612 +04/18 07:16:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:16:49 - mmengine - INFO - Iter(train) [ 32000/160000] lr: 8.1988e-03 eta: 19:35:43 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.6737 aux.loss_ce: 0.0075 aux.acc_seg: 99.1329 +04/18 07:17:16 - mmengine - INFO - Iter(train) [ 32050/160000] lr: 8.1959e-03 eta: 19:35:16 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6179 aux.loss_ce: 0.0078 aux.acc_seg: 99.2297 +04/18 07:17:44 - mmengine - INFO - Iter(train) [ 32100/160000] lr: 8.1931e-03 eta: 19:34:49 time: 0.5544 data_time: 0.0057 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0085 decode.acc_seg: 99.6239 aux.loss_ce: 0.0071 aux.acc_seg: 99.1700 +04/18 07:18:12 - mmengine - INFO - Iter(train) [ 32150/160000] lr: 8.1902e-03 eta: 19:34:22 time: 0.5551 data_time: 0.0058 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0103 decode.acc_seg: 99.5864 aux.loss_ce: 0.0083 aux.acc_seg: 99.2222 +04/18 07:18:40 - mmengine - INFO - Iter(train) [ 32200/160000] lr: 8.1874e-03 eta: 19:33:55 time: 0.5546 data_time: 0.0065 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0108 decode.acc_seg: 99.4581 aux.loss_ce: 0.0091 aux.acc_seg: 98.7620 +04/18 07:19:07 - mmengine - INFO - Iter(train) [ 32250/160000] lr: 8.1845e-03 eta: 19:33:28 time: 0.5551 data_time: 0.0069 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0093 decode.acc_seg: 99.5605 aux.loss_ce: 0.0079 aux.acc_seg: 99.0670 +04/18 07:19:35 - mmengine - INFO - Iter(train) [ 32300/160000] lr: 8.1817e-03 eta: 19:33:02 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0103 decode.acc_seg: 99.6199 aux.loss_ce: 0.0088 aux.acc_seg: 99.1172 +04/18 07:20:03 - mmengine - INFO - Iter(train) [ 32350/160000] lr: 8.1788e-03 eta: 19:32:35 time: 0.5535 data_time: 0.0066 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6283 aux.loss_ce: 0.0080 aux.acc_seg: 99.1041 +04/18 07:20:31 - mmengine - INFO - Iter(train) [ 32400/160000] lr: 8.1760e-03 eta: 19:32:08 time: 0.5545 data_time: 0.0071 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0117 decode.acc_seg: 99.4681 aux.loss_ce: 0.0096 aux.acc_seg: 98.9940 +04/18 07:20:58 - mmengine - INFO - Iter(train) [ 32450/160000] lr: 8.1731e-03 eta: 19:31:41 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.6467 aux.loss_ce: 0.0077 aux.acc_seg: 99.0788 +04/18 07:21:26 - mmengine - INFO - Iter(train) [ 32500/160000] lr: 8.1703e-03 eta: 19:31:14 time: 0.5550 data_time: 0.0063 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0120 decode.acc_seg: 99.3916 aux.loss_ce: 0.0087 aux.acc_seg: 99.2522 +04/18 07:21:54 - mmengine - INFO - Iter(train) [ 32550/160000] lr: 8.1674e-03 eta: 19:30:47 time: 0.5538 data_time: 0.0069 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.6855 aux.loss_ce: 0.0083 aux.acc_seg: 99.2477 +04/18 07:22:21 - mmengine - INFO - Iter(train) [ 32600/160000] lr: 8.1646e-03 eta: 19:30:20 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0085 decode.acc_seg: 99.6750 aux.loss_ce: 0.0075 aux.acc_seg: 99.2866 +04/18 07:22:49 - mmengine - INFO - Iter(train) [ 32650/160000] lr: 8.1617e-03 eta: 19:29:53 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0100 decode.acc_seg: 99.4937 aux.loss_ce: 0.0086 aux.acc_seg: 98.9815 +04/18 07:23:17 - mmengine - INFO - Iter(train) [ 32700/160000] lr: 8.1589e-03 eta: 19:29:27 time: 0.5529 data_time: 0.0061 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.6044 aux.loss_ce: 0.0082 aux.acc_seg: 99.1128 +04/18 07:23:45 - mmengine - INFO - Iter(train) [ 32750/160000] lr: 8.1560e-03 eta: 19:29:00 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6833 aux.loss_ce: 0.0080 aux.acc_seg: 99.1595 +04/18 07:24:13 - mmengine - INFO - Iter(train) [ 32800/160000] lr: 8.1532e-03 eta: 19:28:33 time: 0.5539 data_time: 0.0059 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0085 decode.acc_seg: 99.6199 aux.loss_ce: 0.0076 aux.acc_seg: 99.2431 +04/18 07:24:40 - mmengine - INFO - Iter(train) [ 32850/160000] lr: 8.1503e-03 eta: 19:28:06 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.6441 aux.loss_ce: 0.0082 aux.acc_seg: 99.1406 +04/18 07:25:08 - mmengine - INFO - Iter(train) [ 32900/160000] lr: 8.1475e-03 eta: 19:27:39 time: 0.5537 data_time: 0.0060 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0099 decode.acc_seg: 99.4864 aux.loss_ce: 0.0086 aux.acc_seg: 99.0148 +04/18 07:25:36 - mmengine - INFO - Iter(train) [ 32950/160000] lr: 8.1446e-03 eta: 19:27:12 time: 0.5534 data_time: 0.0058 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0082 decode.acc_seg: 99.5735 aux.loss_ce: 0.0071 aux.acc_seg: 99.2791 +04/18 07:26:03 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:26:03 - mmengine - INFO - Iter(train) [ 33000/160000] lr: 8.1418e-03 eta: 19:26:45 time: 0.5538 data_time: 0.0066 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0082 decode.acc_seg: 99.6367 aux.loss_ce: 0.0076 aux.acc_seg: 99.0615 +04/18 07:26:31 - mmengine - INFO - Iter(train) [ 33050/160000] lr: 8.1389e-03 eta: 19:26:17 time: 0.5534 data_time: 0.0056 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0083 decode.acc_seg: 99.4673 aux.loss_ce: 0.0074 aux.acc_seg: 99.0739 +04/18 07:26:59 - mmengine - INFO - Iter(train) [ 33100/160000] lr: 8.1361e-03 eta: 19:25:50 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0089 decode.acc_seg: 99.6405 aux.loss_ce: 0.0085 aux.acc_seg: 99.1338 +04/18 07:27:26 - mmengine - INFO - Iter(train) [ 33150/160000] lr: 8.1332e-03 eta: 19:25:23 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.5017 aux.loss_ce: 0.0087 aux.acc_seg: 99.0799 +04/18 07:27:54 - mmengine - INFO - Iter(train) [ 33200/160000] lr: 8.1304e-03 eta: 19:24:56 time: 0.5534 data_time: 0.0071 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.5747 aux.loss_ce: 0.0081 aux.acc_seg: 99.1100 +04/18 07:28:22 - mmengine - INFO - Iter(train) [ 33250/160000] lr: 8.1275e-03 eta: 19:24:29 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0085 decode.acc_seg: 99.7563 aux.loss_ce: 0.0076 aux.acc_seg: 99.3136 +04/18 07:28:49 - mmengine - INFO - Iter(train) [ 33300/160000] lr: 8.1247e-03 eta: 19:24:02 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0083 decode.acc_seg: 99.7503 aux.loss_ce: 0.0079 aux.acc_seg: 99.3759 +04/18 07:29:17 - mmengine - INFO - Iter(train) [ 33350/160000] lr: 8.1218e-03 eta: 19:23:35 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6707 aux.loss_ce: 0.0079 aux.acc_seg: 99.0377 +04/18 07:29:45 - mmengine - INFO - Iter(train) [ 33400/160000] lr: 8.1190e-03 eta: 19:23:08 time: 0.5534 data_time: 0.0069 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.5971 aux.loss_ce: 0.0075 aux.acc_seg: 98.8136 +04/18 07:30:12 - mmengine - INFO - Iter(train) [ 33450/160000] lr: 8.1161e-03 eta: 19:22:41 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6467 aux.loss_ce: 0.0084 aux.acc_seg: 99.2325 +04/18 07:30:40 - mmengine - INFO - Iter(train) [ 33500/160000] lr: 8.1133e-03 eta: 19:22:14 time: 0.5527 data_time: 0.0063 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0085 decode.acc_seg: 99.6486 aux.loss_ce: 0.0077 aux.acc_seg: 99.1936 +04/18 07:31:08 - mmengine - INFO - Iter(train) [ 33550/160000] lr: 8.1104e-03 eta: 19:21:46 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0087 decode.acc_seg: 99.6886 aux.loss_ce: 0.0075 aux.acc_seg: 99.3878 +04/18 07:31:35 - mmengine - INFO - Iter(train) [ 33600/160000] lr: 8.1076e-03 eta: 19:21:19 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.6814 aux.loss_ce: 0.0073 aux.acc_seg: 99.2887 +04/18 07:32:03 - mmengine - INFO - Iter(train) [ 33650/160000] lr: 8.1047e-03 eta: 19:20:52 time: 0.5537 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6370 aux.loss_ce: 0.0081 aux.acc_seg: 99.2607 +04/18 07:32:31 - mmengine - INFO - Iter(train) [ 33700/160000] lr: 8.1019e-03 eta: 19:20:25 time: 0.5624 data_time: 0.0066 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.6526 aux.loss_ce: 0.0081 aux.acc_seg: 99.2915 +04/18 07:32:59 - mmengine - INFO - Iter(train) [ 33750/160000] lr: 8.0990e-03 eta: 19:19:58 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0102 decode.acc_seg: 99.6324 aux.loss_ce: 0.0085 aux.acc_seg: 99.3615 +04/18 07:33:26 - mmengine - INFO - Iter(train) [ 33800/160000] lr: 8.0962e-03 eta: 19:19:31 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0093 decode.acc_seg: 99.6343 aux.loss_ce: 0.0080 aux.acc_seg: 99.2024 +04/18 07:33:54 - mmengine - INFO - Iter(train) [ 33850/160000] lr: 8.0933e-03 eta: 19:19:04 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0102 decode.acc_seg: 99.7551 aux.loss_ce: 0.0084 aux.acc_seg: 99.4288 +04/18 07:34:22 - mmengine - INFO - Iter(train) [ 33900/160000] lr: 8.0905e-03 eta: 19:18:37 time: 0.5521 data_time: 0.0061 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.5685 aux.loss_ce: 0.0079 aux.acc_seg: 99.0632 +04/18 07:34:49 - mmengine - INFO - Iter(train) [ 33950/160000] lr: 8.0876e-03 eta: 19:18:09 time: 0.5523 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.6301 aux.loss_ce: 0.0083 aux.acc_seg: 99.0987 +04/18 07:35:17 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:35:17 - mmengine - INFO - Iter(train) [ 34000/160000] lr: 8.0848e-03 eta: 19:17:42 time: 0.5528 data_time: 0.0066 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0098 decode.acc_seg: 99.6192 aux.loss_ce: 0.0085 aux.acc_seg: 98.8934 +04/18 07:35:45 - mmengine - INFO - Iter(train) [ 34050/160000] lr: 8.0819e-03 eta: 19:17:15 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0127 decode.acc_seg: 99.1635 aux.loss_ce: 0.0090 aux.acc_seg: 98.8476 +04/18 07:36:12 - mmengine - INFO - Iter(train) [ 34100/160000] lr: 8.0791e-03 eta: 19:16:47 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0216 decode.loss_ce: 0.0124 decode.acc_seg: 99.4928 aux.loss_ce: 0.0092 aux.acc_seg: 99.1305 +04/18 07:36:40 - mmengine - INFO - Iter(train) [ 34150/160000] lr: 8.0762e-03 eta: 19:16:20 time: 0.5552 data_time: 0.0062 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.6648 aux.loss_ce: 0.0082 aux.acc_seg: 99.2626 +04/18 07:37:08 - mmengine - INFO - Iter(train) [ 34200/160000] lr: 8.0734e-03 eta: 19:15:53 time: 0.5536 data_time: 0.0062 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.6975 aux.loss_ce: 0.0083 aux.acc_seg: 99.3648 +04/18 07:37:35 - mmengine - INFO - Iter(train) [ 34250/160000] lr: 8.0705e-03 eta: 19:15:26 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.6301 aux.loss_ce: 0.0086 aux.acc_seg: 99.1169 +04/18 07:38:03 - mmengine - INFO - Iter(train) [ 34300/160000] lr: 8.0677e-03 eta: 19:14:59 time: 0.5546 data_time: 0.0063 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0108 decode.acc_seg: 99.7034 aux.loss_ce: 0.0093 aux.acc_seg: 99.1738 +04/18 07:38:31 - mmengine - INFO - Iter(train) [ 34350/160000] lr: 8.0648e-03 eta: 19:14:33 time: 0.5553 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.6557 aux.loss_ce: 0.0080 aux.acc_seg: 99.2526 +04/18 07:38:59 - mmengine - INFO - Iter(train) [ 34400/160000] lr: 8.0620e-03 eta: 19:14:06 time: 0.5548 data_time: 0.0067 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0100 decode.acc_seg: 99.4610 aux.loss_ce: 0.0086 aux.acc_seg: 98.9637 +04/18 07:39:26 - mmengine - INFO - Iter(train) [ 34450/160000] lr: 8.0591e-03 eta: 19:13:39 time: 0.5554 data_time: 0.0057 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0098 decode.acc_seg: 99.6735 aux.loss_ce: 0.0088 aux.acc_seg: 99.1503 +04/18 07:39:54 - mmengine - INFO - Iter(train) [ 34500/160000] lr: 8.0563e-03 eta: 19:13:12 time: 0.5548 data_time: 0.0063 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0098 decode.acc_seg: 99.5838 aux.loss_ce: 0.0085 aux.acc_seg: 99.2102 +04/18 07:40:22 - mmengine - INFO - Iter(train) [ 34550/160000] lr: 8.0534e-03 eta: 19:12:45 time: 0.5552 data_time: 0.0060 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.5566 aux.loss_ce: 0.0075 aux.acc_seg: 99.0582 +04/18 07:40:50 - mmengine - INFO - Iter(train) [ 34600/160000] lr: 8.0506e-03 eta: 19:12:19 time: 0.5556 data_time: 0.0070 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6641 aux.loss_ce: 0.0078 aux.acc_seg: 99.1263 +04/18 07:41:18 - mmengine - INFO - Iter(train) [ 34650/160000] lr: 8.0477e-03 eta: 19:11:52 time: 0.5545 data_time: 0.0063 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0100 decode.acc_seg: 99.5957 aux.loss_ce: 0.0086 aux.acc_seg: 99.1197 +04/18 07:41:45 - mmengine - INFO - Iter(train) [ 34700/160000] lr: 8.0448e-03 eta: 19:11:25 time: 0.5540 data_time: 0.0060 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.5585 aux.loss_ce: 0.0078 aux.acc_seg: 99.0187 +04/18 07:42:13 - mmengine - INFO - Iter(train) [ 34750/160000] lr: 8.0420e-03 eta: 19:10:58 time: 0.5547 data_time: 0.0058 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.6587 aux.loss_ce: 0.0081 aux.acc_seg: 99.2609 +04/18 07:42:41 - mmengine - INFO - Iter(train) [ 34800/160000] lr: 8.0391e-03 eta: 19:10:32 time: 0.5561 data_time: 0.0071 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0086 decode.acc_seg: 99.6828 aux.loss_ce: 0.0076 aux.acc_seg: 99.2761 +04/18 07:43:09 - mmengine - INFO - Iter(train) [ 34850/160000] lr: 8.0363e-03 eta: 19:10:05 time: 0.5544 data_time: 0.0061 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0100 decode.acc_seg: 99.6472 aux.loss_ce: 0.0093 aux.acc_seg: 99.2065 +04/18 07:43:37 - mmengine - INFO - Iter(train) [ 34900/160000] lr: 8.0334e-03 eta: 19:09:38 time: 0.5544 data_time: 0.0059 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.7450 aux.loss_ce: 0.0079 aux.acc_seg: 99.3112 +04/18 07:44:04 - mmengine - INFO - Iter(train) [ 34950/160000] lr: 8.0306e-03 eta: 19:09:11 time: 0.5557 data_time: 0.0063 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.6366 aux.loss_ce: 0.0074 aux.acc_seg: 99.2044 +04/18 07:44:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:44:32 - mmengine - INFO - Iter(train) [ 35000/160000] lr: 8.0277e-03 eta: 19:08:45 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6536 aux.loss_ce: 0.0081 aux.acc_seg: 98.9813 +04/18 07:45:00 - mmengine - INFO - Iter(train) [ 35050/160000] lr: 8.0249e-03 eta: 19:08:18 time: 0.5559 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.5843 aux.loss_ce: 0.0075 aux.acc_seg: 99.2393 +04/18 07:45:28 - mmengine - INFO - Iter(train) [ 35100/160000] lr: 8.0220e-03 eta: 19:07:51 time: 0.5569 data_time: 0.0060 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6353 aux.loss_ce: 0.0078 aux.acc_seg: 99.0710 +04/18 07:45:56 - mmengine - INFO - Iter(train) [ 35150/160000] lr: 8.0192e-03 eta: 19:07:24 time: 0.5550 data_time: 0.0060 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0102 decode.acc_seg: 99.5936 aux.loss_ce: 0.0088 aux.acc_seg: 98.9582 +04/18 07:46:23 - mmengine - INFO - Iter(train) [ 35200/160000] lr: 8.0163e-03 eta: 19:06:57 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.5790 aux.loss_ce: 0.0083 aux.acc_seg: 99.0090 +04/18 07:46:51 - mmengine - INFO - Iter(train) [ 35250/160000] lr: 8.0135e-03 eta: 19:06:30 time: 0.5546 data_time: 0.0064 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0109 decode.acc_seg: 99.6765 aux.loss_ce: 0.0088 aux.acc_seg: 99.2990 +04/18 07:47:19 - mmengine - INFO - Iter(train) [ 35300/160000] lr: 8.0106e-03 eta: 19:06:04 time: 0.5561 data_time: 0.0062 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6948 aux.loss_ce: 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mmengine - INFO - Iter(train) [ 35550/160000] lr: 7.9963e-03 eta: 19:03:49 time: 0.5559 data_time: 0.0073 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0096 decode.acc_seg: 99.7468 aux.loss_ce: 0.0082 aux.acc_seg: 99.4647 +04/18 07:50:06 - mmengine - INFO - Iter(train) [ 35600/160000] lr: 7.9935e-03 eta: 19:03:22 time: 0.5548 data_time: 0.0062 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0093 decode.acc_seg: 99.5835 aux.loss_ce: 0.0089 aux.acc_seg: 99.1254 +04/18 07:50:33 - mmengine - INFO - Iter(train) [ 35650/160000] lr: 7.9906e-03 eta: 19:02:55 time: 0.5532 data_time: 0.0058 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6332 aux.loss_ce: 0.0079 aux.acc_seg: 99.2542 +04/18 07:51:01 - mmengine - INFO - Iter(train) [ 35700/160000] lr: 7.9878e-03 eta: 19:02:29 time: 0.5561 data_time: 0.0058 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0093 decode.acc_seg: 99.5657 aux.loss_ce: 0.0077 aux.acc_seg: 99.2030 +04/18 07:51:29 - mmengine - INFO - Iter(train) [ 35750/160000] lr: 7.9849e-03 eta: 19:02:02 time: 0.5547 data_time: 0.0067 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0099 decode.acc_seg: 99.7052 aux.loss_ce: 0.0087 aux.acc_seg: 99.2721 +04/18 07:51:57 - mmengine - INFO - Iter(train) [ 35800/160000] lr: 7.9820e-03 eta: 19:01:35 time: 0.5581 data_time: 0.0071 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.5608 aux.loss_ce: 0.0080 aux.acc_seg: 99.0271 +04/18 07:52:25 - mmengine - INFO - Iter(train) [ 35850/160000] lr: 7.9792e-03 eta: 19:01:08 time: 0.5644 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0095 decode.acc_seg: 99.5785 aux.loss_ce: 0.0086 aux.acc_seg: 98.9521 +04/18 07:52:52 - mmengine - INFO - Iter(train) [ 35900/160000] lr: 7.9763e-03 eta: 19:00:42 time: 0.5556 data_time: 0.0064 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0097 decode.acc_seg: 99.5304 aux.loss_ce: 0.0083 aux.acc_seg: 99.1608 +04/18 07:53:20 - mmengine - INFO - Iter(train) [ 35950/160000] lr: 7.9735e-03 eta: 19:00:15 time: 0.5558 data_time: 0.0069 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0095 decode.acc_seg: 99.6070 aux.loss_ce: 0.0082 aux.acc_seg: 99.2854 +04/18 07:53:48 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 07:53:48 - mmengine - INFO - Iter(train) [ 36000/160000] lr: 7.9706e-03 eta: 18:59:49 time: 0.5550 data_time: 0.0065 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0098 decode.acc_seg: 99.7172 aux.loss_ce: 0.0082 aux.acc_seg: 99.4210 +04/18 07:54:16 - mmengine - INFO - Iter(train) [ 36050/160000] lr: 7.9678e-03 eta: 18:59:22 time: 0.5554 data_time: 0.0066 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.4981 aux.loss_ce: 0.0089 aux.acc_seg: 98.9285 +04/18 07:54:44 - mmengine - INFO - Iter(train) [ 36100/160000] lr: 7.9649e-03 eta: 18:58:55 time: 0.5553 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0088 decode.acc_seg: 99.6424 aux.loss_ce: 0.0079 aux.acc_seg: 99.1971 +04/18 07:55:12 - mmengine - INFO - Iter(train) [ 36150/160000] lr: 7.9621e-03 eta: 18:58:28 time: 0.5571 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.6611 aux.loss_ce: 0.0080 aux.acc_seg: 99.1581 +04/18 07:55:39 - mmengine - INFO - Iter(train) [ 36200/160000] lr: 7.9592e-03 eta: 18:58:01 time: 0.5560 data_time: 0.0066 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.6945 aux.loss_ce: 0.0079 aux.acc_seg: 99.3343 +04/18 07:56:07 - mmengine - INFO - Iter(train) [ 36250/160000] lr: 7.9563e-03 eta: 18:57:34 time: 0.5548 data_time: 0.0062 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.5932 aux.loss_ce: 0.0080 aux.acc_seg: 99.1081 +04/18 07:56:35 - mmengine - INFO - Iter(train) [ 36300/160000] lr: 7.9535e-03 eta: 18:57:08 time: 0.5556 data_time: 0.0068 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0091 decode.acc_seg: 99.7787 aux.loss_ce: 0.0080 aux.acc_seg: 99.4762 +04/18 07:57:03 - mmengine - INFO - Iter(train) [ 36350/160000] lr: 7.9506e-03 eta: 18:56:41 time: 0.5554 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6775 aux.loss_ce: 0.0079 aux.acc_seg: 99.3327 +04/18 07:57:31 - mmengine - INFO - Iter(train) [ 36400/160000] lr: 7.9478e-03 eta: 18:56:14 time: 0.5567 data_time: 0.0060 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0089 decode.acc_seg: 99.5826 aux.loss_ce: 0.0078 aux.acc_seg: 99.0872 +04/18 07:57:58 - mmengine - INFO - Iter(train) [ 36450/160000] lr: 7.9449e-03 eta: 18:55:47 time: 0.5553 data_time: 0.0065 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6811 aux.loss_ce: 0.0082 aux.acc_seg: 99.2513 +04/18 07:58:26 - mmengine - INFO - Iter(train) [ 36500/160000] lr: 7.9421e-03 eta: 18:55:20 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.7375 aux.loss_ce: 0.0079 aux.acc_seg: 99.3763 +04/18 07:58:54 - mmengine - INFO - Iter(train) [ 36550/160000] lr: 7.9392e-03 eta: 18:54:53 time: 0.5528 data_time: 0.0059 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0084 decode.acc_seg: 99.7747 aux.loss_ce: 0.0076 aux.acc_seg: 99.4019 +04/18 07:59:22 - mmengine - INFO - Iter(train) [ 36600/160000] lr: 7.9363e-03 eta: 18:54:26 time: 0.5534 data_time: 0.0068 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6769 aux.loss_ce: 0.0080 aux.acc_seg: 99.1664 +04/18 07:59:49 - mmengine - INFO - Iter(train) [ 36650/160000] lr: 7.9335e-03 eta: 18:53:59 time: 0.5552 data_time: 0.0063 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.5338 aux.loss_ce: 0.0084 aux.acc_seg: 99.1665 +04/18 08:00:17 - mmengine - INFO - Iter(train) [ 36700/160000] lr: 7.9306e-03 eta: 18:53:32 time: 0.5555 data_time: 0.0063 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0109 decode.acc_seg: 99.6617 aux.loss_ce: 0.0095 aux.acc_seg: 99.2718 +04/18 08:00:45 - mmengine - INFO - Iter(train) [ 36750/160000] lr: 7.9278e-03 eta: 18:53:05 time: 0.5557 data_time: 0.0060 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.6224 aux.loss_ce: 0.0086 aux.acc_seg: 99.1976 +04/18 08:01:13 - mmengine - INFO - Iter(train) [ 36800/160000] lr: 7.9249e-03 eta: 18:52:38 time: 0.5542 data_time: 0.0060 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.7107 aux.loss_ce: 0.0086 aux.acc_seg: 99.1987 +04/18 08:01:40 - mmengine - INFO - Iter(train) [ 36850/160000] lr: 7.9221e-03 eta: 18:52:11 time: 0.5554 data_time: 0.0064 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0084 decode.acc_seg: 99.7684 aux.loss_ce: 0.0073 aux.acc_seg: 99.3653 +04/18 08:02:08 - mmengine - INFO - Iter(train) [ 36900/160000] lr: 7.9192e-03 eta: 18:51:44 time: 0.5554 data_time: 0.0061 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0097 decode.acc_seg: 99.6262 aux.loss_ce: 0.0084 aux.acc_seg: 99.1335 +04/18 08:02:36 - mmengine - INFO - Iter(train) [ 36950/160000] lr: 7.9163e-03 eta: 18:51:17 time: 0.5564 data_time: 0.0059 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6028 aux.loss_ce: 0.0081 aux.acc_seg: 99.0002 +04/18 08:03:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:03:04 - mmengine - INFO - Iter(train) [ 37000/160000] lr: 7.9135e-03 eta: 18:50:51 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0089 decode.acc_seg: 99.7021 aux.loss_ce: 0.0077 aux.acc_seg: 99.1801 +04/18 08:03:32 - mmengine - INFO - Iter(train) [ 37050/160000] lr: 7.9106e-03 eta: 18:50:24 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0106 decode.acc_seg: 99.6867 aux.loss_ce: 0.0082 aux.acc_seg: 99.2112 +04/18 08:03:59 - mmengine - INFO - Iter(train) [ 37100/160000] lr: 7.9078e-03 eta: 18:49:57 time: 0.5555 data_time: 0.0062 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.6346 aux.loss_ce: 0.0084 aux.acc_seg: 99.0377 +04/18 08:04:27 - mmengine - INFO - Iter(train) [ 37150/160000] lr: 7.9049e-03 eta: 18:49:30 time: 0.5554 data_time: 0.0061 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.5802 aux.loss_ce: 0.0084 aux.acc_seg: 99.2209 +04/18 08:04:55 - mmengine - INFO - Iter(train) [ 37200/160000] lr: 7.9020e-03 eta: 18:49:03 time: 0.5567 data_time: 0.0067 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0093 decode.acc_seg: 99.6939 aux.loss_ce: 0.0080 aux.acc_seg: 99.2533 +04/18 08:05:23 - mmengine - INFO - Iter(train) [ 37250/160000] lr: 7.8992e-03 eta: 18:48:36 time: 0.5557 data_time: 0.0063 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.6069 aux.loss_ce: 0.0086 aux.acc_seg: 98.9867 +04/18 08:05:50 - mmengine - INFO - Iter(train) [ 37300/160000] lr: 7.8963e-03 eta: 18:48:09 time: 0.5555 data_time: 0.0070 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.4745 aux.loss_ce: 0.0082 aux.acc_seg: 99.0009 +04/18 08:06:18 - mmengine - INFO - Iter(train) [ 37350/160000] lr: 7.8935e-03 eta: 18:47:42 time: 0.5573 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.7446 aux.loss_ce: 0.0077 aux.acc_seg: 99.3535 +04/18 08:06:46 - mmengine - INFO - Iter(train) [ 37400/160000] lr: 7.8906e-03 eta: 18:47:15 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0084 decode.acc_seg: 99.6960 aux.loss_ce: 0.0073 aux.acc_seg: 99.2872 +04/18 08:07:14 - mmengine - INFO - Iter(train) [ 37450/160000] lr: 7.8877e-03 eta: 18:46:48 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6699 aux.loss_ce: 0.0084 aux.acc_seg: 99.2398 +04/18 08:07:41 - mmengine - INFO - Iter(train) [ 37500/160000] lr: 7.8849e-03 eta: 18:46:20 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.6231 aux.loss_ce: 0.0084 aux.acc_seg: 99.0577 +04/18 08:08:09 - mmengine - INFO - Iter(train) [ 37550/160000] lr: 7.8820e-03 eta: 18:45:53 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.7350 aux.loss_ce: 0.0084 aux.acc_seg: 99.3443 +04/18 08:08:37 - mmengine - INFO - Iter(train) [ 37600/160000] lr: 7.8792e-03 eta: 18:45:26 time: 0.5532 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0079 decode.acc_seg: 99.6883 aux.loss_ce: 0.0071 aux.acc_seg: 99.3621 +04/18 08:09:05 - mmengine - INFO - Iter(train) [ 37650/160000] lr: 7.8763e-03 eta: 18:44:59 time: 0.5553 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.6845 aux.loss_ce: 0.0074 aux.acc_seg: 98.9697 +04/18 08:09:32 - mmengine - INFO - Iter(train) [ 37700/160000] lr: 7.8734e-03 eta: 18:44:32 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0112 decode.acc_seg: 99.4838 aux.loss_ce: 0.0088 aux.acc_seg: 99.1152 +04/18 08:10:00 - mmengine - INFO - Iter(train) [ 37750/160000] lr: 7.8706e-03 eta: 18:44:05 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0108 decode.acc_seg: 99.6160 aux.loss_ce: 0.0098 aux.acc_seg: 99.0363 +04/18 08:10:28 - mmengine - INFO - Iter(train) [ 37800/160000] lr: 7.8677e-03 eta: 18:43:38 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0105 decode.acc_seg: 99.2393 aux.loss_ce: 0.0085 aux.acc_seg: 98.8298 +04/18 08:10:56 - mmengine - INFO - Iter(train) [ 37850/160000] lr: 7.8649e-03 eta: 18:43:11 time: 0.5554 data_time: 0.0060 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0090 decode.acc_seg: 99.6859 aux.loss_ce: 0.0080 aux.acc_seg: 99.2764 +04/18 08:11:23 - mmengine - INFO - Iter(train) [ 37900/160000] lr: 7.8620e-03 eta: 18:42:44 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0108 decode.acc_seg: 99.5868 aux.loss_ce: 0.0087 aux.acc_seg: 99.1085 +04/18 08:11:51 - mmengine - INFO - Iter(train) [ 37950/160000] lr: 7.8591e-03 eta: 18:42:16 time: 0.5546 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0101 decode.acc_seg: 99.6949 aux.loss_ce: 0.0086 aux.acc_seg: 99.2368 +04/18 08:12:19 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:12:19 - mmengine - INFO - Iter(train) [ 38000/160000] lr: 7.8563e-03 eta: 18:41:49 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6777 aux.loss_ce: 0.0079 aux.acc_seg: 99.1671 +04/18 08:12:47 - mmengine - INFO - Iter(train) [ 38050/160000] lr: 7.8534e-03 eta: 18:41:23 time: 0.5631 data_time: 0.0062 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0073 decode.acc_seg: 99.5980 aux.loss_ce: 0.0070 aux.acc_seg: 99.1495 +04/18 08:13:14 - mmengine - INFO - Iter(train) [ 38100/160000] lr: 7.8506e-03 eta: 18:40:56 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.6484 aux.loss_ce: 0.0083 aux.acc_seg: 98.9731 +04/18 08:13:42 - mmengine - INFO - Iter(train) [ 38150/160000] lr: 7.8477e-03 eta: 18:40:28 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6856 aux.loss_ce: 0.0077 aux.acc_seg: 99.0777 +04/18 08:14:10 - mmengine - INFO - Iter(train) [ 38200/160000] lr: 7.8448e-03 eta: 18:40:01 time: 0.5559 data_time: 0.0057 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6652 aux.loss_ce: 0.0077 aux.acc_seg: 99.2684 +04/18 08:14:38 - mmengine - INFO - Iter(train) [ 38250/160000] lr: 7.8420e-03 eta: 18:39:34 time: 0.5537 data_time: 0.0071 memory: 7635 loss: 0.0199 decode.loss_ce: 0.0107 decode.acc_seg: 99.4609 aux.loss_ce: 0.0092 aux.acc_seg: 98.8550 +04/18 08:15:05 - mmengine - INFO - Iter(train) [ 38300/160000] lr: 7.8391e-03 eta: 18:39:07 time: 0.5519 data_time: 0.0060 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.5614 aux.loss_ce: 0.0081 aux.acc_seg: 99.1699 +04/18 08:15:33 - mmengine - INFO - Iter(train) [ 38350/160000] lr: 7.8363e-03 eta: 18:38:40 time: 0.5548 data_time: 0.0061 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.5927 aux.loss_ce: 0.0080 aux.acc_seg: 99.0862 +04/18 08:16:01 - mmengine - INFO - Iter(train) [ 38400/160000] lr: 7.8334e-03 eta: 18:38:13 time: 0.5544 data_time: 0.0064 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.7965 aux.loss_ce: 0.0082 aux.acc_seg: 99.5151 +04/18 08:16:28 - mmengine - INFO - Iter(train) [ 38450/160000] lr: 7.8305e-03 eta: 18:37:45 time: 0.5540 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0083 decode.acc_seg: 99.6346 aux.loss_ce: 0.0073 aux.acc_seg: 99.1762 +04/18 08:16:56 - mmengine - INFO - Iter(train) [ 38500/160000] lr: 7.8277e-03 eta: 18:37:18 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0096 decode.acc_seg: 99.5115 aux.loss_ce: 0.0090 aux.acc_seg: 98.9328 +04/18 08:17:24 - mmengine - INFO - Iter(train) [ 38550/160000] lr: 7.8248e-03 eta: 18:36:51 time: 0.5544 data_time: 0.0064 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.6286 aux.loss_ce: 0.0083 aux.acc_seg: 99.1783 +04/18 08:17:52 - mmengine - INFO - Iter(train) [ 38600/160000] lr: 7.8219e-03 eta: 18:36:24 time: 0.5539 data_time: 0.0059 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6795 aux.loss_ce: 0.0081 aux.acc_seg: 99.2602 +04/18 08:18:19 - mmengine - INFO - Iter(train) [ 38650/160000] lr: 7.8191e-03 eta: 18:35:57 time: 0.5525 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.5373 aux.loss_ce: 0.0087 aux.acc_seg: 98.7154 +04/18 08:18:47 - mmengine - INFO - Iter(train) [ 38700/160000] lr: 7.8162e-03 eta: 18:35:29 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0084 decode.acc_seg: 99.6734 aux.loss_ce: 0.0082 aux.acc_seg: 99.0753 +04/18 08:19:15 - mmengine - INFO - Iter(train) [ 38750/160000] lr: 7.8134e-03 eta: 18:35:02 time: 0.5524 data_time: 0.0062 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6527 aux.loss_ce: 0.0077 aux.acc_seg: 99.2320 +04/18 08:19:42 - mmengine - INFO - Iter(train) [ 38800/160000] lr: 7.8105e-03 eta: 18:34:35 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.5894 aux.loss_ce: 0.0083 aux.acc_seg: 99.0193 +04/18 08:20:10 - mmengine - INFO - Iter(train) [ 38850/160000] lr: 7.8076e-03 eta: 18:34:07 time: 0.5535 data_time: 0.0067 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0089 decode.acc_seg: 99.6009 aux.loss_ce: 0.0087 aux.acc_seg: 98.7836 +04/18 08:20:38 - mmengine - INFO - Iter(train) [ 38900/160000] lr: 7.8048e-03 eta: 18:33:40 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0094 decode.acc_seg: 99.6873 aux.loss_ce: 0.0080 aux.acc_seg: 99.2844 +04/18 08:21:05 - mmengine - INFO - Iter(train) [ 38950/160000] lr: 7.8019e-03 eta: 18:33:13 time: 0.5547 data_time: 0.0062 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0102 decode.acc_seg: 99.6688 aux.loss_ce: 0.0087 aux.acc_seg: 99.1081 +04/18 08:21:33 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:21:33 - mmengine - INFO - Iter(train) [ 39000/160000] lr: 7.7990e-03 eta: 18:32:45 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0090 decode.acc_seg: 99.6576 aux.loss_ce: 0.0080 aux.acc_seg: 99.2667 +04/18 08:22:01 - mmengine - INFO - Iter(train) [ 39050/160000] lr: 7.7962e-03 eta: 18:32:18 time: 0.5548 data_time: 0.0071 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.6274 aux.loss_ce: 0.0081 aux.acc_seg: 99.0817 +04/18 08:22:29 - mmengine - INFO - Iter(train) [ 39100/160000] lr: 7.7933e-03 eta: 18:31:51 time: 0.5544 data_time: 0.0059 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0099 decode.acc_seg: 99.5752 aux.loss_ce: 0.0081 aux.acc_seg: 99.3050 +04/18 08:22:56 - mmengine - INFO - Iter(train) [ 39150/160000] lr: 7.7904e-03 eta: 18:31:24 time: 0.5536 data_time: 0.0059 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.5658 aux.loss_ce: 0.0083 aux.acc_seg: 99.1587 +04/18 08:23:24 - mmengine - INFO - Iter(train) [ 39200/160000] lr: 7.7876e-03 eta: 18:30:57 time: 0.5538 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.5652 aux.loss_ce: 0.0077 aux.acc_seg: 98.9863 +04/18 08:23:52 - mmengine - INFO - Iter(train) [ 39250/160000] lr: 7.7847e-03 eta: 18:30:30 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0098 decode.acc_seg: 99.7126 aux.loss_ce: 0.0081 aux.acc_seg: 99.4448 +04/18 08:24:19 - mmengine - INFO - Iter(train) [ 39300/160000] lr: 7.7819e-03 eta: 18:30:02 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0116 decode.acc_seg: 99.6580 aux.loss_ce: 0.0094 aux.acc_seg: 99.0866 +04/18 08:24:47 - mmengine - INFO - Iter(train) [ 39350/160000] lr: 7.7790e-03 eta: 18:29:35 time: 0.5529 data_time: 0.0069 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.7506 aux.loss_ce: 0.0077 aux.acc_seg: 99.2886 +04/18 08:25:15 - mmengine - INFO - Iter(train) [ 39400/160000] lr: 7.7761e-03 eta: 18:29:08 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.6969 aux.loss_ce: 0.0085 aux.acc_seg: 99.2848 +04/18 08:25:42 - mmengine - INFO - Iter(train) [ 39450/160000] lr: 7.7733e-03 eta: 18:28:40 time: 0.5536 data_time: 0.0070 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.5288 aux.loss_ce: 0.0082 aux.acc_seg: 99.0371 +04/18 08:26:10 - mmengine - INFO - Iter(train) [ 39500/160000] lr: 7.7704e-03 eta: 18:28:13 time: 0.5525 data_time: 0.0067 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0109 decode.acc_seg: 99.4141 aux.loss_ce: 0.0093 aux.acc_seg: 98.8440 +04/18 08:26:38 - mmengine - INFO - Iter(train) [ 39550/160000] lr: 7.7675e-03 eta: 18:27:45 time: 0.5533 data_time: 0.0057 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0092 decode.acc_seg: 99.7376 aux.loss_ce: 0.0085 aux.acc_seg: 99.2864 +04/18 08:27:05 - mmengine - INFO - Iter(train) [ 39600/160000] lr: 7.7647e-03 eta: 18:27:18 time: 0.5536 data_time: 0.0071 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0105 decode.acc_seg: 99.5536 aux.loss_ce: 0.0091 aux.acc_seg: 99.0755 +04/18 08:27:33 - mmengine - INFO - Iter(train) [ 39650/160000] lr: 7.7618e-03 eta: 18:26:51 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6278 aux.loss_ce: 0.0077 aux.acc_seg: 99.2082 +04/18 08:28:01 - mmengine - INFO - Iter(train) [ 39700/160000] lr: 7.7589e-03 eta: 18:26:23 time: 0.5530 data_time: 0.0059 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.4593 aux.loss_ce: 0.0086 aux.acc_seg: 98.7346 +04/18 08:28:28 - mmengine - INFO - Iter(train) [ 39750/160000] lr: 7.7561e-03 eta: 18:25:56 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.6967 aux.loss_ce: 0.0086 aux.acc_seg: 99.1289 +04/18 08:28:56 - mmengine - INFO - Iter(train) [ 39800/160000] lr: 7.7532e-03 eta: 18:25:29 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6865 aux.loss_ce: 0.0081 aux.acc_seg: 99.2378 +04/18 08:29:24 - mmengine - INFO - Iter(train) [ 39850/160000] lr: 7.7503e-03 eta: 18:25:01 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.6639 aux.loss_ce: 0.0083 aux.acc_seg: 99.2950 +04/18 08:29:51 - mmengine - INFO - Iter(train) [ 39900/160000] lr: 7.7475e-03 eta: 18:24:34 time: 0.5523 data_time: 0.0071 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.5162 aux.loss_ce: 0.0084 aux.acc_seg: 98.6649 +04/18 08:30:19 - mmengine - INFO - Iter(train) [ 39950/160000] lr: 7.7446e-03 eta: 18:24:06 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0080 decode.acc_seg: 99.7220 aux.loss_ce: 0.0071 aux.acc_seg: 99.3871 +04/18 08:30:47 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:30:47 - mmengine - INFO - Iter(train) [ 40000/160000] lr: 7.7417e-03 eta: 18:23:38 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6464 aux.loss_ce: 0.0077 aux.acc_seg: 99.1118 +04/18 08:30:47 - mmengine - INFO - Saving checkpoint at 40000 iterations +04/18 08:30:51 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:06 time: 0.0462 data_time: 0.0014 memory: 1657 +04/18 08:30:53 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0463 data_time: 0.0015 memory: 1657 +04/18 08:30:55 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0464 data_time: 0.0014 memory: 1657 +04/18 08:30:57 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0456 data_time: 0.0014 memory: 1657 +04/18 08:30:58 - mmengine - INFO - per class results: +04/18 08:30:58 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.47 | 99.53 | 99.58 | 99.47 | +| contrast | 79.93 | 90.03 | 88.85 | 87.69 | 90.03 | ++------------+-------+-------+--------+-----------+--------+ +04/18 08:30:58 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1000 mIoU: 89.5000 mAcc: 94.7500 mFscore: 94.1900 mPrecision: 93.6400 mRecall: 94.7500 data_time: 0.0015 time: 0.0463 +04/18 08:31:25 - mmengine - INFO - Iter(train) [ 40050/160000] lr: 7.7389e-03 eta: 18:23:11 time: 0.5502 data_time: 0.0066 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6489 aux.loss_ce: 0.0078 aux.acc_seg: 99.2826 +04/18 08:31:53 - mmengine - INFO - Iter(train) [ 40100/160000] lr: 7.7360e-03 eta: 18:22:43 time: 0.5498 data_time: 0.0061 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.6363 aux.loss_ce: 0.0073 aux.acc_seg: 99.0268 +04/18 08:32:20 - mmengine - INFO - Iter(train) [ 40150/160000] lr: 7.7332e-03 eta: 18:22:16 time: 0.5515 data_time: 0.0062 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0084 decode.acc_seg: 99.6901 aux.loss_ce: 0.0076 aux.acc_seg: 99.2650 +04/18 08:32:48 - mmengine - INFO - Iter(train) [ 40200/160000] lr: 7.7303e-03 eta: 18:21:48 time: 0.5500 data_time: 0.0062 memory: 7635 loss: 0.0214 decode.loss_ce: 0.0116 decode.acc_seg: 99.6566 aux.loss_ce: 0.0098 aux.acc_seg: 99.1296 +04/18 08:33:15 - mmengine - INFO - Iter(train) [ 40250/160000] lr: 7.7274e-03 eta: 18:21:20 time: 0.5496 data_time: 0.0062 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.5221 aux.loss_ce: 0.0085 aux.acc_seg: 99.0771 +04/18 08:33:43 - mmengine - INFO - Iter(train) [ 40300/160000] lr: 7.7246e-03 eta: 18:20:53 time: 0.5524 data_time: 0.0067 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.6153 aux.loss_ce: 0.0079 aux.acc_seg: 99.1534 +04/18 08:34:10 - mmengine - INFO - Iter(train) [ 40350/160000] lr: 7.7217e-03 eta: 18:20:25 time: 0.5520 data_time: 0.0069 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0086 decode.acc_seg: 99.7101 aux.loss_ce: 0.0074 aux.acc_seg: 99.4150 +04/18 08:34:38 - mmengine - INFO - Iter(train) [ 40400/160000] lr: 7.7188e-03 eta: 18:19:57 time: 0.5489 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6420 aux.loss_ce: 0.0079 aux.acc_seg: 99.0813 +04/18 08:35:06 - mmengine - INFO - Iter(train) [ 40450/160000] lr: 7.7160e-03 eta: 18:19:29 time: 0.5513 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.6397 aux.loss_ce: 0.0074 aux.acc_seg: 99.1655 +04/18 08:35:33 - mmengine - INFO - Iter(train) [ 40500/160000] lr: 7.7131e-03 eta: 18:19:02 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0090 decode.acc_seg: 99.6161 aux.loss_ce: 0.0078 aux.acc_seg: 99.1043 +04/18 08:36:01 - mmengine - INFO - Iter(train) [ 40550/160000] lr: 7.7102e-03 eta: 18:18:34 time: 0.5513 data_time: 0.0067 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.5161 aux.loss_ce: 0.0080 aux.acc_seg: 98.9918 +04/18 08:36:28 - mmengine - INFO - Iter(train) [ 40600/160000] lr: 7.7074e-03 eta: 18:18:06 time: 0.5497 data_time: 0.0069 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.6079 aux.loss_ce: 0.0080 aux.acc_seg: 99.0249 +04/18 08:36:56 - mmengine - INFO - Iter(train) [ 40650/160000] lr: 7.7045e-03 eta: 18:17:39 time: 0.5511 data_time: 0.0061 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.5480 aux.loss_ce: 0.0082 aux.acc_seg: 99.1893 +04/18 08:37:23 - mmengine - INFO - Iter(train) [ 40700/160000] lr: 7.7016e-03 eta: 18:17:11 time: 0.5499 data_time: 0.0060 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0106 decode.acc_seg: 99.5810 aux.loss_ce: 0.0095 aux.acc_seg: 99.2526 +04/18 08:37:51 - mmengine - INFO - Iter(train) [ 40750/160000] lr: 7.6988e-03 eta: 18:16:43 time: 0.5503 data_time: 0.0067 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6409 aux.loss_ce: 0.0081 aux.acc_seg: 99.0313 +04/18 08:38:18 - mmengine - INFO - Iter(train) [ 40800/160000] lr: 7.6959e-03 eta: 18:16:15 time: 0.5496 data_time: 0.0067 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0108 decode.acc_seg: 99.6670 aux.loss_ce: 0.0088 aux.acc_seg: 99.2678 +04/18 08:38:46 - mmengine - INFO - Iter(train) [ 40850/160000] lr: 7.6930e-03 eta: 18:15:48 time: 0.5506 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0087 decode.acc_seg: 99.5914 aux.loss_ce: 0.0077 aux.acc_seg: 99.2438 +04/18 08:39:13 - mmengine - INFO - Iter(train) [ 40900/160000] lr: 7.6901e-03 eta: 18:15:20 time: 0.5510 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0087 decode.acc_seg: 99.6388 aux.loss_ce: 0.0077 aux.acc_seg: 99.3010 +04/18 08:39:41 - mmengine - INFO - Iter(train) [ 40950/160000] lr: 7.6873e-03 eta: 18:14:52 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.7011 aux.loss_ce: 0.0078 aux.acc_seg: 99.4434 +04/18 08:40:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:40:09 - mmengine - INFO - Iter(train) [ 41000/160000] lr: 7.6844e-03 eta: 18:14:25 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0093 decode.acc_seg: 99.6187 aux.loss_ce: 0.0085 aux.acc_seg: 98.9475 +04/18 08:40:36 - mmengine - INFO - Iter(train) [ 41050/160000] lr: 7.6815e-03 eta: 18:13:57 time: 0.5507 data_time: 0.0066 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6560 aux.loss_ce: 0.0081 aux.acc_seg: 99.3476 +04/18 08:41:04 - mmengine - INFO - Iter(train) [ 41100/160000] lr: 7.6787e-03 eta: 18:13:29 time: 0.5513 data_time: 0.0060 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6568 aux.loss_ce: 0.0077 aux.acc_seg: 99.0934 +04/18 08:41:31 - mmengine - INFO - Iter(train) [ 41150/160000] lr: 7.6758e-03 eta: 18:13:01 time: 0.5521 data_time: 0.0067 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0087 decode.acc_seg: 99.7006 aux.loss_ce: 0.0076 aux.acc_seg: 99.2484 +04/18 08:41:59 - mmengine - INFO - Iter(train) [ 41200/160000] lr: 7.6729e-03 eta: 18:12:34 time: 0.5503 data_time: 0.0064 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0097 decode.acc_seg: 99.5850 aux.loss_ce: 0.0087 aux.acc_seg: 99.0829 +04/18 08:42:26 - mmengine - INFO - Iter(train) [ 41250/160000] lr: 7.6701e-03 eta: 18:12:06 time: 0.5498 data_time: 0.0062 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.6686 aux.loss_ce: 0.0086 aux.acc_seg: 98.9568 +04/18 08:42:54 - mmengine - INFO - Iter(train) [ 41300/160000] lr: 7.6672e-03 eta: 18:11:39 time: 0.5518 data_time: 0.0070 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6409 aux.loss_ce: 0.0080 aux.acc_seg: 99.1268 +04/18 08:43:22 - mmengine - INFO - Iter(train) [ 41350/160000] lr: 7.6643e-03 eta: 18:11:11 time: 0.5498 data_time: 0.0061 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0081 decode.acc_seg: 99.6580 aux.loss_ce: 0.0075 aux.acc_seg: 99.0657 +04/18 08:43:49 - mmengine - INFO - Iter(train) [ 41400/160000] lr: 7.6615e-03 eta: 18:10:43 time: 0.5502 data_time: 0.0059 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0086 decode.acc_seg: 99.6943 aux.loss_ce: 0.0076 aux.acc_seg: 99.2686 +04/18 08:44:17 - mmengine - INFO - Iter(train) [ 41450/160000] lr: 7.6586e-03 eta: 18:10:15 time: 0.5504 data_time: 0.0062 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.5800 aux.loss_ce: 0.0084 aux.acc_seg: 99.1014 +04/18 08:44:44 - mmengine - INFO - Iter(train) [ 41500/160000] lr: 7.6557e-03 eta: 18:09:48 time: 0.5502 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0081 decode.acc_seg: 99.6276 aux.loss_ce: 0.0077 aux.acc_seg: 99.1901 +04/18 08:45:12 - mmengine - INFO - Iter(train) [ 41550/160000] lr: 7.6529e-03 eta: 18:09:20 time: 0.5602 data_time: 0.0078 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0080 decode.acc_seg: 99.6048 aux.loss_ce: 0.0076 aux.acc_seg: 99.1132 +04/18 08:45:39 - mmengine - INFO - Iter(train) [ 41600/160000] lr: 7.6500e-03 eta: 18:08:53 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.6599 aux.loss_ce: 0.0085 aux.acc_seg: 99.1570 +04/18 08:46:07 - mmengine - INFO - Iter(train) [ 41650/160000] lr: 7.6471e-03 eta: 18:08:25 time: 0.5505 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0083 decode.acc_seg: 99.6052 aux.loss_ce: 0.0079 aux.acc_seg: 99.1905 +04/18 08:46:35 - mmengine - INFO - Iter(train) [ 41700/160000] lr: 7.6442e-03 eta: 18:07:57 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.6144 aux.loss_ce: 0.0083 aux.acc_seg: 99.2826 +04/18 08:47:02 - mmengine - INFO - Iter(train) [ 41750/160000] lr: 7.6414e-03 eta: 18:07:29 time: 0.5497 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6094 aux.loss_ce: 0.0080 aux.acc_seg: 99.2477 +04/18 08:47:30 - mmengine - INFO - Iter(train) [ 41800/160000] lr: 7.6385e-03 eta: 18:07:02 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0111 decode.acc_seg: 99.6124 aux.loss_ce: 0.0092 aux.acc_seg: 99.1284 +04/18 08:47:57 - mmengine - INFO - Iter(train) [ 41850/160000] lr: 7.6356e-03 eta: 18:06:34 time: 0.5502 data_time: 0.0059 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.5851 aux.loss_ce: 0.0079 aux.acc_seg: 98.9687 +04/18 08:48:25 - mmengine - INFO - Iter(train) [ 41900/160000] lr: 7.6328e-03 eta: 18:06:07 time: 0.5513 data_time: 0.0059 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0113 decode.acc_seg: 99.1785 aux.loss_ce: 0.0088 aux.acc_seg: 98.7809 +04/18 08:48:52 - mmengine - INFO - Iter(train) [ 41950/160000] lr: 7.6299e-03 eta: 18:05:39 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.7153 aux.loss_ce: 0.0086 aux.acc_seg: 99.2450 +04/18 08:49:20 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:49:20 - mmengine - INFO - Iter(train) [ 42000/160000] lr: 7.6270e-03 eta: 18:05:11 time: 0.5518 data_time: 0.0068 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.7354 aux.loss_ce: 0.0086 aux.acc_seg: 99.2950 +04/18 08:49:48 - mmengine - INFO - Iter(train) [ 42050/160000] lr: 7.6242e-03 eta: 18:04:44 time: 0.5514 data_time: 0.0069 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0093 decode.acc_seg: 99.6522 aux.loss_ce: 0.0082 aux.acc_seg: 99.2332 +04/18 08:50:15 - mmengine - INFO - Iter(train) [ 42100/160000] lr: 7.6213e-03 eta: 18:04:16 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.6117 aux.loss_ce: 0.0082 aux.acc_seg: 98.8979 +04/18 08:50:43 - mmengine - INFO - Iter(train) [ 42150/160000] lr: 7.6184e-03 eta: 18:03:48 time: 0.5510 data_time: 0.0067 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.4720 aux.loss_ce: 0.0081 aux.acc_seg: 99.0637 +04/18 08:51:10 - mmengine - INFO - Iter(train) [ 42200/160000] lr: 7.6155e-03 eta: 18:03:21 time: 0.5513 data_time: 0.0068 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.6223 aux.loss_ce: 0.0085 aux.acc_seg: 99.1876 +04/18 08:51:38 - mmengine - INFO - Iter(train) [ 42250/160000] lr: 7.6127e-03 eta: 18:02:53 time: 0.5508 data_time: 0.0059 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0098 decode.acc_seg: 99.6596 aux.loss_ce: 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mmengine - INFO - Iter(train) [ 42500/160000] lr: 7.5983e-03 eta: 18:00:36 time: 0.5513 data_time: 0.0067 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.7454 aux.loss_ce: 0.0076 aux.acc_seg: 99.2842 +04/18 08:54:24 - mmengine - INFO - Iter(train) [ 42550/160000] lr: 7.5954e-03 eta: 18:00:08 time: 0.5515 data_time: 0.0068 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.5263 aux.loss_ce: 0.0079 aux.acc_seg: 99.0045 +04/18 08:54:51 - mmengine - INFO - Iter(train) [ 42600/160000] lr: 7.5926e-03 eta: 17:59:40 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.5923 aux.loss_ce: 0.0079 aux.acc_seg: 98.9909 +04/18 08:55:19 - mmengine - INFO - Iter(train) [ 42650/160000] lr: 7.5897e-03 eta: 17:59:13 time: 0.5536 data_time: 0.0074 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0099 decode.acc_seg: 99.6208 aux.loss_ce: 0.0082 aux.acc_seg: 99.2052 +04/18 08:55:47 - mmengine - INFO - Iter(train) [ 42700/160000] lr: 7.5868e-03 eta: 17:58:46 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0100 decode.acc_seg: 99.7200 aux.loss_ce: 0.0082 aux.acc_seg: 99.3009 +04/18 08:56:14 - mmengine - INFO - Iter(train) [ 42750/160000] lr: 7.5840e-03 eta: 17:58:18 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6605 aux.loss_ce: 0.0084 aux.acc_seg: 99.1475 +04/18 08:56:42 - mmengine - INFO - Iter(train) [ 42800/160000] lr: 7.5811e-03 eta: 17:57:50 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0073 decode.acc_seg: 99.7220 aux.loss_ce: 0.0070 aux.acc_seg: 99.3004 +04/18 08:57:09 - mmengine - INFO - Iter(train) [ 42850/160000] lr: 7.5782e-03 eta: 17:57:23 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.7429 aux.loss_ce: 0.0077 aux.acc_seg: 99.2773 +04/18 08:57:37 - mmengine - INFO - Iter(train) [ 42900/160000] lr: 7.5753e-03 eta: 17:56:55 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0122 decode.acc_seg: 99.5868 aux.loss_ce: 0.0097 aux.acc_seg: 99.0719 +04/18 08:58:04 - mmengine - INFO - Iter(train) [ 42950/160000] lr: 7.5725e-03 eta: 17:56:27 time: 0.5522 data_time: 0.0069 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6851 aux.loss_ce: 0.0079 aux.acc_seg: 99.1350 +04/18 08:58:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 08:58:32 - mmengine - INFO - Iter(train) [ 43000/160000] lr: 7.5696e-03 eta: 17:56:00 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.5961 aux.loss_ce: 0.0088 aux.acc_seg: 99.0003 +04/18 08:59:00 - mmengine - INFO - Iter(train) [ 43050/160000] lr: 7.5667e-03 eta: 17:55:32 time: 0.5530 data_time: 0.0069 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0099 decode.acc_seg: 99.4495 aux.loss_ce: 0.0085 aux.acc_seg: 98.6427 +04/18 08:59:27 - mmengine - INFO - Iter(train) [ 43100/160000] lr: 7.5638e-03 eta: 17:55:05 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0097 decode.acc_seg: 99.7053 aux.loss_ce: 0.0085 aux.acc_seg: 99.2823 +04/18 08:59:55 - mmengine - INFO - Iter(train) [ 43150/160000] lr: 7.5610e-03 eta: 17:54:37 time: 0.5523 data_time: 0.0061 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0102 decode.acc_seg: 99.6934 aux.loss_ce: 0.0092 aux.acc_seg: 99.2806 +04/18 09:00:22 - mmengine - INFO - Iter(train) [ 43200/160000] lr: 7.5581e-03 eta: 17:54:09 time: 0.5502 data_time: 0.0060 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0086 decode.acc_seg: 99.6169 aux.loss_ce: 0.0084 aux.acc_seg: 99.1678 +04/18 09:00:50 - mmengine - INFO - Iter(train) [ 43250/160000] lr: 7.5552e-03 eta: 17:53:42 time: 0.5511 data_time: 0.0070 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.8030 aux.loss_ce: 0.0081 aux.acc_seg: 99.2701 +04/18 09:01:18 - mmengine - INFO - Iter(train) [ 43300/160000] lr: 7.5524e-03 eta: 17:53:14 time: 0.5518 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.7141 aux.loss_ce: 0.0076 aux.acc_seg: 99.3835 +04/18 09:01:45 - mmengine - INFO - Iter(train) [ 43350/160000] lr: 7.5495e-03 eta: 17:52:47 time: 0.5523 data_time: 0.0068 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0087 decode.acc_seg: 99.6002 aux.loss_ce: 0.0079 aux.acc_seg: 99.1570 +04/18 09:02:13 - mmengine - INFO - Iter(train) [ 43400/160000] lr: 7.5466e-03 eta: 17:52:19 time: 0.5535 data_time: 0.0069 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0096 decode.acc_seg: 99.7343 aux.loss_ce: 0.0085 aux.acc_seg: 99.3286 +04/18 09:02:41 - mmengine - INFO - Iter(train) [ 43450/160000] lr: 7.5437e-03 eta: 17:51:52 time: 0.5505 data_time: 0.0058 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.6410 aux.loss_ce: 0.0082 aux.acc_seg: 99.2811 +04/18 09:03:08 - mmengine - INFO - Iter(train) [ 43500/160000] lr: 7.5409e-03 eta: 17:51:24 time: 0.5511 data_time: 0.0071 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6983 aux.loss_ce: 0.0078 aux.acc_seg: 99.2352 +04/18 09:03:36 - mmengine - INFO - Iter(train) [ 43550/160000] lr: 7.5380e-03 eta: 17:50:57 time: 0.5500 data_time: 0.0059 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0091 decode.acc_seg: 99.5979 aux.loss_ce: 0.0083 aux.acc_seg: 99.1764 +04/18 09:04:03 - mmengine - INFO - Iter(train) [ 43600/160000] lr: 7.5351e-03 eta: 17:50:29 time: 0.5506 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.7025 aux.loss_ce: 0.0082 aux.acc_seg: 99.3903 +04/18 09:04:31 - mmengine - INFO - Iter(train) [ 43650/160000] lr: 7.5322e-03 eta: 17:50:02 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0104 decode.acc_seg: 99.5419 aux.loss_ce: 0.0085 aux.acc_seg: 99.0887 +04/18 09:04:59 - mmengine - INFO - Iter(train) [ 43700/160000] lr: 7.5294e-03 eta: 17:49:34 time: 0.5517 data_time: 0.0058 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0092 decode.acc_seg: 99.7018 aux.loss_ce: 0.0086 aux.acc_seg: 99.1895 +04/18 09:05:26 - mmengine - INFO - Iter(train) [ 43750/160000] lr: 7.5265e-03 eta: 17:49:07 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.6678 aux.loss_ce: 0.0084 aux.acc_seg: 99.2646 +04/18 09:05:54 - mmengine - INFO - Iter(train) [ 43800/160000] lr: 7.5236e-03 eta: 17:48:39 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.6360 aux.loss_ce: 0.0082 aux.acc_seg: 98.9284 +04/18 09:06:21 - mmengine - INFO - Iter(train) [ 43850/160000] lr: 7.5207e-03 eta: 17:48:11 time: 0.5518 data_time: 0.0061 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0087 decode.acc_seg: 99.7040 aux.loss_ce: 0.0078 aux.acc_seg: 99.3532 +04/18 09:06:49 - mmengine - INFO - Iter(train) [ 43900/160000] lr: 7.5179e-03 eta: 17:47:44 time: 0.5518 data_time: 0.0067 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6299 aux.loss_ce: 0.0081 aux.acc_seg: 99.2554 +04/18 09:07:17 - mmengine - INFO - Iter(train) [ 43950/160000] lr: 7.5150e-03 eta: 17:47:16 time: 0.5502 data_time: 0.0063 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0093 decode.acc_seg: 99.5738 aux.loss_ce: 0.0082 aux.acc_seg: 99.0312 +04/18 09:07:44 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:07:44 - mmengine - INFO - Iter(train) [ 44000/160000] lr: 7.5121e-03 eta: 17:46:49 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.5694 aux.loss_ce: 0.0082 aux.acc_seg: 99.0113 +04/18 09:08:12 - mmengine - INFO - Iter(train) [ 44050/160000] lr: 7.5092e-03 eta: 17:46:21 time: 0.5525 data_time: 0.0067 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.7020 aux.loss_ce: 0.0083 aux.acc_seg: 99.2927 +04/18 09:08:39 - mmengine - INFO - Iter(train) [ 44100/160000] lr: 7.5064e-03 eta: 17:45:54 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.7297 aux.loss_ce: 0.0081 aux.acc_seg: 99.3670 +04/18 09:09:07 - mmengine - INFO - Iter(train) [ 44150/160000] lr: 7.5035e-03 eta: 17:45:26 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0095 decode.acc_seg: 99.6385 aux.loss_ce: 0.0090 aux.acc_seg: 99.0378 +04/18 09:09:35 - mmengine - INFO - Iter(train) [ 44200/160000] lr: 7.5006e-03 eta: 17:44:59 time: 0.5520 data_time: 0.0074 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.6935 aux.loss_ce: 0.0085 aux.acc_seg: 99.0993 +04/18 09:10:02 - mmengine - INFO - Iter(train) [ 44250/160000] lr: 7.4977e-03 eta: 17:44:31 time: 0.5524 data_time: 0.0062 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0103 decode.acc_seg: 99.6043 aux.loss_ce: 0.0093 aux.acc_seg: 98.8139 +04/18 09:10:30 - mmengine - INFO - Iter(train) [ 44300/160000] lr: 7.4949e-03 eta: 17:44:03 time: 0.5511 data_time: 0.0058 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0107 decode.acc_seg: 99.6945 aux.loss_ce: 0.0090 aux.acc_seg: 99.2631 +04/18 09:10:57 - mmengine - INFO - Iter(train) [ 44350/160000] lr: 7.4920e-03 eta: 17:43:36 time: 0.5506 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6607 aux.loss_ce: 0.0079 aux.acc_seg: 99.0323 +04/18 09:11:25 - mmengine - INFO - Iter(train) [ 44400/160000] lr: 7.4891e-03 eta: 17:43:08 time: 0.5532 data_time: 0.0067 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.6331 aux.loss_ce: 0.0085 aux.acc_seg: 99.1103 +04/18 09:11:53 - mmengine - INFO - Iter(train) [ 44450/160000] lr: 7.4862e-03 eta: 17:42:41 time: 0.5526 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.5473 aux.loss_ce: 0.0082 aux.acc_seg: 99.2693 +04/18 09:12:20 - mmengine - INFO - Iter(train) [ 44500/160000] lr: 7.4833e-03 eta: 17:42:14 time: 0.5713 data_time: 0.0069 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.7069 aux.loss_ce: 0.0089 aux.acc_seg: 99.3418 +04/18 09:12:48 - mmengine - INFO - Iter(train) [ 44550/160000] lr: 7.4805e-03 eta: 17:41:46 time: 0.5519 data_time: 0.0060 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.6710 aux.loss_ce: 0.0083 aux.acc_seg: 99.2764 +04/18 09:13:16 - mmengine - INFO - Iter(train) [ 44600/160000] lr: 7.4776e-03 eta: 17:41:19 time: 0.5518 data_time: 0.0060 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0100 decode.acc_seg: 99.6036 aux.loss_ce: 0.0087 aux.acc_seg: 99.1800 +04/18 09:13:43 - mmengine - INFO - Iter(train) [ 44650/160000] lr: 7.4747e-03 eta: 17:40:51 time: 0.5531 data_time: 0.0062 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0108 decode.acc_seg: 99.5868 aux.loss_ce: 0.0093 aux.acc_seg: 99.1148 +04/18 09:14:11 - mmengine - INFO - Iter(train) [ 44700/160000] lr: 7.4718e-03 eta: 17:40:23 time: 0.5532 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.7260 aux.loss_ce: 0.0076 aux.acc_seg: 99.3047 +04/18 09:14:39 - mmengine - INFO - Iter(train) [ 44750/160000] lr: 7.4690e-03 eta: 17:39:56 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0094 decode.acc_seg: 99.6604 aux.loss_ce: 0.0094 aux.acc_seg: 99.2559 +04/18 09:15:06 - mmengine - INFO - Iter(train) [ 44800/160000] lr: 7.4661e-03 eta: 17:39:29 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.7090 aux.loss_ce: 0.0075 aux.acc_seg: 99.2836 +04/18 09:15:34 - mmengine - INFO - Iter(train) [ 44850/160000] lr: 7.4632e-03 eta: 17:39:01 time: 0.5527 data_time: 0.0063 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0093 decode.acc_seg: 99.7059 aux.loss_ce: 0.0083 aux.acc_seg: 99.1607 +04/18 09:16:01 - mmengine - INFO - Iter(train) [ 44900/160000] lr: 7.4603e-03 eta: 17:38:33 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0088 decode.acc_seg: 99.6642 aux.loss_ce: 0.0080 aux.acc_seg: 99.3046 +04/18 09:16:29 - mmengine - INFO - Iter(train) [ 44950/160000] lr: 7.4575e-03 eta: 17:38:06 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0095 decode.acc_seg: 99.5405 aux.loss_ce: 0.0080 aux.acc_seg: 99.0601 +04/18 09:16:57 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:16:57 - mmengine - INFO - Iter(train) [ 45000/160000] lr: 7.4546e-03 eta: 17:37:38 time: 0.5516 data_time: 0.0058 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.6920 aux.loss_ce: 0.0082 aux.acc_seg: 99.1603 +04/18 09:17:24 - mmengine - INFO - Iter(train) [ 45050/160000] lr: 7.4517e-03 eta: 17:37:11 time: 0.5528 data_time: 0.0059 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.7298 aux.loss_ce: 0.0084 aux.acc_seg: 99.3738 +04/18 09:17:52 - mmengine - INFO - Iter(train) [ 45100/160000] lr: 7.4488e-03 eta: 17:36:43 time: 0.5535 data_time: 0.0062 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0104 decode.acc_seg: 99.5881 aux.loss_ce: 0.0088 aux.acc_seg: 99.1511 +04/18 09:18:20 - mmengine - INFO - Iter(train) [ 45150/160000] lr: 7.4459e-03 eta: 17:36:16 time: 0.5501 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6174 aux.loss_ce: 0.0080 aux.acc_seg: 99.2241 +04/18 09:18:47 - mmengine - INFO - Iter(train) [ 45200/160000] lr: 7.4431e-03 eta: 17:35:48 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0106 decode.acc_seg: 99.2593 aux.loss_ce: 0.0086 aux.acc_seg: 98.8551 +04/18 09:19:15 - mmengine - INFO - Iter(train) [ 45250/160000] lr: 7.4402e-03 eta: 17:35:21 time: 0.5512 data_time: 0.0066 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6578 aux.loss_ce: 0.0082 aux.acc_seg: 99.1809 +04/18 09:19:42 - mmengine - INFO - Iter(train) [ 45300/160000] lr: 7.4373e-03 eta: 17:34:53 time: 0.5518 data_time: 0.0065 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6301 aux.loss_ce: 0.0082 aux.acc_seg: 99.1590 +04/18 09:20:10 - mmengine - INFO - Iter(train) [ 45350/160000] lr: 7.4344e-03 eta: 17:34:26 time: 0.5525 data_time: 0.0058 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0096 decode.acc_seg: 99.2689 aux.loss_ce: 0.0088 aux.acc_seg: 98.5504 +04/18 09:20:38 - mmengine - INFO - Iter(train) [ 45400/160000] lr: 7.4316e-03 eta: 17:33:58 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0092 decode.acc_seg: 99.6595 aux.loss_ce: 0.0085 aux.acc_seg: 99.1810 +04/18 09:21:05 - mmengine - INFO - Iter(train) [ 45450/160000] lr: 7.4287e-03 eta: 17:33:31 time: 0.5519 data_time: 0.0073 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0084 decode.acc_seg: 99.5793 aux.loss_ce: 0.0075 aux.acc_seg: 99.0155 +04/18 09:21:33 - mmengine - INFO - Iter(train) [ 45500/160000] lr: 7.4258e-03 eta: 17:33:03 time: 0.5536 data_time: 0.0067 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.6611 aux.loss_ce: 0.0080 aux.acc_seg: 99.2641 +04/18 09:22:00 - mmengine - INFO - Iter(train) [ 45550/160000] lr: 7.4229e-03 eta: 17:32:36 time: 0.5537 data_time: 0.0072 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0082 decode.acc_seg: 99.6822 aux.loss_ce: 0.0075 aux.acc_seg: 99.3224 +04/18 09:22:28 - mmengine - INFO - Iter(train) [ 45600/160000] lr: 7.4200e-03 eta: 17:32:08 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.6726 aux.loss_ce: 0.0076 aux.acc_seg: 99.3276 +04/18 09:22:56 - mmengine - INFO - Iter(train) [ 45650/160000] lr: 7.4172e-03 eta: 17:31:41 time: 0.5516 data_time: 0.0068 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.6734 aux.loss_ce: 0.0073 aux.acc_seg: 99.1992 +04/18 09:23:23 - mmengine - INFO - Iter(train) [ 45700/160000] lr: 7.4143e-03 eta: 17:31:13 time: 0.5521 data_time: 0.0059 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.6832 aux.loss_ce: 0.0076 aux.acc_seg: 99.1064 +04/18 09:23:51 - mmengine - INFO - Iter(train) [ 45750/160000] lr: 7.4114e-03 eta: 17:30:46 time: 0.5528 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.6417 aux.loss_ce: 0.0082 aux.acc_seg: 98.9037 +04/18 09:24:19 - mmengine - INFO - Iter(train) [ 45800/160000] lr: 7.4085e-03 eta: 17:30:18 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.7640 aux.loss_ce: 0.0077 aux.acc_seg: 99.3542 +04/18 09:24:46 - mmengine - INFO - Iter(train) [ 45850/160000] lr: 7.4056e-03 eta: 17:29:51 time: 0.5615 data_time: 0.0066 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0091 decode.acc_seg: 99.5231 aux.loss_ce: 0.0086 aux.acc_seg: 99.2666 +04/18 09:25:14 - mmengine - INFO - Iter(train) [ 45900/160000] lr: 7.4028e-03 eta: 17:29:23 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.6744 aux.loss_ce: 0.0079 aux.acc_seg: 99.1490 +04/18 09:25:42 - mmengine - INFO - Iter(train) [ 45950/160000] lr: 7.3999e-03 eta: 17:28:56 time: 0.5509 data_time: 0.0065 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.7808 aux.loss_ce: 0.0070 aux.acc_seg: 99.5138 +04/18 09:26:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:26:09 - mmengine - INFO - Iter(train) [ 46000/160000] lr: 7.3970e-03 eta: 17:28:28 time: 0.5508 data_time: 0.0061 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7713 aux.loss_ce: 0.0076 aux.acc_seg: 99.3100 +04/18 09:26:37 - mmengine - INFO - Iter(train) [ 46050/160000] lr: 7.3941e-03 eta: 17:28:01 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0086 decode.acc_seg: 99.6272 aux.loss_ce: 0.0085 aux.acc_seg: 99.0365 +04/18 09:27:05 - mmengine - INFO - Iter(train) [ 46100/160000] lr: 7.3912e-03 eta: 17:27:33 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0081 decode.acc_seg: 99.7292 aux.loss_ce: 0.0076 aux.acc_seg: 99.2253 +04/18 09:27:32 - mmengine - INFO - Iter(train) [ 46150/160000] lr: 7.3884e-03 eta: 17:27:06 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0082 decode.acc_seg: 99.7085 aux.loss_ce: 0.0071 aux.acc_seg: 99.2450 +04/18 09:28:00 - mmengine - INFO - Iter(train) [ 46200/160000] lr: 7.3855e-03 eta: 17:26:38 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.7254 aux.loss_ce: 0.0084 aux.acc_seg: 99.3106 +04/18 09:28:27 - mmengine - INFO - Iter(train) [ 46250/160000] lr: 7.3826e-03 eta: 17:26:11 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.6780 aux.loss_ce: 0.0080 aux.acc_seg: 98.9218 +04/18 09:28:55 - mmengine - INFO - Iter(train) [ 46300/160000] lr: 7.3797e-03 eta: 17:25:43 time: 0.5525 data_time: 0.0059 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0095 decode.acc_seg: 99.6510 aux.loss_ce: 0.0083 aux.acc_seg: 99.0337 +04/18 09:29:23 - mmengine - INFO - Iter(train) [ 46350/160000] lr: 7.3768e-03 eta: 17:25:16 time: 0.5534 data_time: 0.0070 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0101 decode.acc_seg: 99.5838 aux.loss_ce: 0.0086 aux.acc_seg: 99.1583 +04/18 09:29:50 - mmengine - INFO - Iter(train) [ 46400/160000] lr: 7.3739e-03 eta: 17:24:48 time: 0.5523 data_time: 0.0058 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6799 aux.loss_ce: 0.0078 aux.acc_seg: 99.2291 +04/18 09:30:18 - mmengine - INFO - Iter(train) [ 46450/160000] lr: 7.3711e-03 eta: 17:24:21 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0082 decode.acc_seg: 99.6817 aux.loss_ce: 0.0080 aux.acc_seg: 99.2268 +04/18 09:30:46 - mmengine - INFO - Iter(train) [ 46500/160000] lr: 7.3682e-03 eta: 17:23:53 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.3092 aux.loss_ce: 0.0085 aux.acc_seg: 98.7722 +04/18 09:31:13 - mmengine - INFO - Iter(train) [ 46550/160000] lr: 7.3653e-03 eta: 17:23:26 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6584 aux.loss_ce: 0.0081 aux.acc_seg: 99.2076 +04/18 09:31:41 - mmengine - INFO - Iter(train) [ 46600/160000] lr: 7.3624e-03 eta: 17:22:58 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0090 decode.acc_seg: 99.6082 aux.loss_ce: 0.0083 aux.acc_seg: 98.9571 +04/18 09:32:08 - mmengine - INFO - Iter(train) [ 46650/160000] lr: 7.3595e-03 eta: 17:22:31 time: 0.5520 data_time: 0.0071 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.7278 aux.loss_ce: 0.0086 aux.acc_seg: 99.2349 +04/18 09:32:36 - mmengine - INFO - Iter(train) [ 46700/160000] lr: 7.3567e-03 eta: 17:22:03 time: 0.5520 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6774 aux.loss_ce: 0.0080 aux.acc_seg: 99.1701 +04/18 09:33:04 - mmengine - INFO - Iter(train) [ 46750/160000] lr: 7.3538e-03 eta: 17:21:36 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.6366 aux.loss_ce: 0.0080 aux.acc_seg: 99.0657 +04/18 09:33:31 - mmengine - INFO - Iter(train) [ 46800/160000] lr: 7.3509e-03 eta: 17:21:08 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.6181 aux.loss_ce: 0.0075 aux.acc_seg: 99.1819 +04/18 09:33:59 - mmengine - INFO - Iter(train) [ 46850/160000] lr: 7.3480e-03 eta: 17:20:41 time: 0.5534 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.6229 aux.loss_ce: 0.0079 aux.acc_seg: 98.9841 +04/18 09:34:27 - mmengine - INFO - Iter(train) [ 46900/160000] lr: 7.3451e-03 eta: 17:20:13 time: 0.5522 data_time: 0.0069 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0074 decode.acc_seg: 99.7402 aux.loss_ce: 0.0069 aux.acc_seg: 99.2221 +04/18 09:34:54 - mmengine - INFO - Iter(train) [ 46950/160000] lr: 7.3422e-03 eta: 17:19:46 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0103 decode.acc_seg: 99.5117 aux.loss_ce: 0.0085 aux.acc_seg: 98.9362 +04/18 09:35:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:35:22 - mmengine - INFO - Iter(train) [ 47000/160000] lr: 7.3394e-03 eta: 17:19:18 time: 0.5526 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.6680 aux.loss_ce: 0.0085 aux.acc_seg: 99.1042 +04/18 09:35:50 - mmengine - INFO - Iter(train) [ 47050/160000] lr: 7.3365e-03 eta: 17:18:51 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0205 decode.loss_ce: 0.0115 decode.acc_seg: 99.3422 aux.loss_ce: 0.0090 aux.acc_seg: 98.6979 +04/18 09:36:17 - mmengine - INFO - Iter(train) [ 47100/160000] lr: 7.3336e-03 eta: 17:18:23 time: 0.5527 data_time: 0.0071 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0103 decode.acc_seg: 99.6514 aux.loss_ce: 0.0087 aux.acc_seg: 99.1013 +04/18 09:36:45 - mmengine - INFO - Iter(train) [ 47150/160000] lr: 7.3307e-03 eta: 17:17:56 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.7099 aux.loss_ce: 0.0078 aux.acc_seg: 99.3170 +04/18 09:37:12 - mmengine - INFO - Iter(train) [ 47200/160000] lr: 7.3278e-03 eta: 17:17:28 time: 0.5493 data_time: 0.0062 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0094 decode.acc_seg: 99.6957 aux.loss_ce: 0.0086 aux.acc_seg: 99.2879 +04/18 09:37:40 - mmengine - INFO - Iter(train) [ 47250/160000] lr: 7.3249e-03 eta: 17:17:00 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0095 decode.acc_seg: 99.5607 aux.loss_ce: 0.0082 aux.acc_seg: 99.0628 +04/18 09:38:08 - mmengine - INFO - Iter(train) [ 47300/160000] lr: 7.3221e-03 eta: 17:16:33 time: 0.5520 data_time: 0.0059 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0091 decode.acc_seg: 99.6764 aux.loss_ce: 0.0082 aux.acc_seg: 99.3887 +04/18 09:38:35 - mmengine - INFO - Iter(train) [ 47350/160000] lr: 7.3192e-03 eta: 17:16:05 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6683 aux.loss_ce: 0.0081 aux.acc_seg: 99.2015 +04/18 09:39:03 - mmengine - INFO - Iter(train) [ 47400/160000] lr: 7.3163e-03 eta: 17:15:38 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.6044 aux.loss_ce: 0.0075 aux.acc_seg: 99.1012 +04/18 09:39:30 - mmengine - INFO - Iter(train) [ 47450/160000] lr: 7.3134e-03 eta: 17:15:10 time: 0.5514 data_time: 0.0067 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6548 aux.loss_ce: 0.0084 aux.acc_seg: 99.2237 +04/18 09:39:58 - mmengine - INFO - Iter(train) [ 47500/160000] lr: 7.3105e-03 eta: 17:14:42 time: 0.5513 data_time: 0.0066 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.6684 aux.loss_ce: 0.0085 aux.acc_seg: 99.2130 +04/18 09:40:26 - mmengine - INFO - Iter(train) [ 47550/160000] lr: 7.3076e-03 eta: 17:14:15 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0226 decode.loss_ce: 0.0126 decode.acc_seg: 99.5839 aux.loss_ce: 0.0100 aux.acc_seg: 98.9649 +04/18 09:40:53 - mmengine - INFO - Iter(train) [ 47600/160000] lr: 7.3048e-03 eta: 17:13:47 time: 0.5520 data_time: 0.0068 memory: 7635 loss: 0.0236 decode.loss_ce: 0.0134 decode.acc_seg: 99.2194 aux.loss_ce: 0.0102 aux.acc_seg: 98.6823 +04/18 09:41:21 - mmengine - INFO - Iter(train) [ 47650/160000] lr: 7.3019e-03 eta: 17:13:20 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0108 decode.acc_seg: 99.6519 aux.loss_ce: 0.0089 aux.acc_seg: 99.2672 +04/18 09:41:48 - mmengine - INFO - Iter(train) [ 47700/160000] lr: 7.2990e-03 eta: 17:12:52 time: 0.5513 data_time: 0.0060 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.4692 aux.loss_ce: 0.0086 aux.acc_seg: 99.1116 +04/18 09:42:16 - mmengine - INFO - Iter(train) [ 47750/160000] lr: 7.2961e-03 eta: 17:12:25 time: 0.5505 data_time: 0.0066 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6764 aux.loss_ce: 0.0079 aux.acc_seg: 99.1522 +04/18 09:42:44 - mmengine - INFO - Iter(train) [ 47800/160000] lr: 7.2932e-03 eta: 17:11:57 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.6787 aux.loss_ce: 0.0080 aux.acc_seg: 99.2792 +04/18 09:43:11 - mmengine - INFO - Iter(train) [ 47850/160000] lr: 7.2903e-03 eta: 17:11:30 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.7006 aux.loss_ce: 0.0079 aux.acc_seg: 99.2769 +04/18 09:43:39 - mmengine - INFO - Iter(train) [ 47900/160000] lr: 7.2874e-03 eta: 17:11:02 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.6428 aux.loss_ce: 0.0083 aux.acc_seg: 98.9472 +04/18 09:44:07 - mmengine - INFO - Iter(train) [ 47950/160000] lr: 7.2846e-03 eta: 17:10:35 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6430 aux.loss_ce: 0.0082 aux.acc_seg: 99.1258 +04/18 09:44:34 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:44:34 - mmengine - INFO - Iter(train) [ 48000/160000] lr: 7.2817e-03 eta: 17:10:07 time: 0.5605 data_time: 0.0063 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.5721 aux.loss_ce: 0.0083 aux.acc_seg: 99.0805 +04/18 09:45:02 - mmengine - INFO - Iter(train) [ 48050/160000] lr: 7.2788e-03 eta: 17:09:40 time: 0.5507 data_time: 0.0067 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.7004 aux.loss_ce: 0.0074 aux.acc_seg: 99.3549 +04/18 09:45:29 - mmengine - INFO - Iter(train) [ 48100/160000] lr: 7.2759e-03 eta: 17:09:12 time: 0.5531 data_time: 0.0066 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0096 decode.acc_seg: 99.6485 aux.loss_ce: 0.0086 aux.acc_seg: 99.1604 +04/18 09:45:57 - mmengine - INFO - Iter(train) [ 48150/160000] lr: 7.2730e-03 eta: 17:08:44 time: 0.5517 data_time: 0.0062 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.6278 aux.loss_ce: 0.0080 aux.acc_seg: 99.2423 +04/18 09:46:25 - mmengine - INFO - Iter(train) [ 48200/160000] lr: 7.2701e-03 eta: 17:08:17 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0088 decode.acc_seg: 99.5866 aux.loss_ce: 0.0079 aux.acc_seg: 99.2974 +04/18 09:46:52 - mmengine - INFO - Iter(train) [ 48250/160000] lr: 7.2672e-03 eta: 17:07:49 time: 0.5526 data_time: 0.0067 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.5598 aux.loss_ce: 0.0084 aux.acc_seg: 98.9615 +04/18 09:47:20 - mmengine - INFO - Iter(train) [ 48300/160000] lr: 7.2644e-03 eta: 17:07:22 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0123 decode.acc_seg: 99.5892 aux.loss_ce: 0.0096 aux.acc_seg: 98.7660 +04/18 09:47:48 - mmengine - INFO - Iter(train) [ 48350/160000] lr: 7.2615e-03 eta: 17:06:54 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0085 decode.acc_seg: 99.6997 aux.loss_ce: 0.0076 aux.acc_seg: 99.2211 +04/18 09:48:15 - mmengine - INFO - Iter(train) [ 48400/160000] lr: 7.2586e-03 eta: 17:06:27 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0094 decode.acc_seg: 99.6227 aux.loss_ce: 0.0078 aux.acc_seg: 99.1485 +04/18 09:48:43 - mmengine - INFO - Iter(train) [ 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0.5520 data_time: 0.0060 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0097 decode.acc_seg: 99.3075 aux.loss_ce: 0.0088 aux.acc_seg: 98.7808 +04/18 09:51:01 - mmengine - INFO - Iter(train) [ 48700/160000] lr: 7.2413e-03 eta: 17:03:41 time: 0.5515 data_time: 0.0069 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.7275 aux.loss_ce: 0.0077 aux.acc_seg: 99.4738 +04/18 09:51:28 - mmengine - INFO - Iter(train) [ 48750/160000] lr: 7.2384e-03 eta: 17:03:14 time: 0.5525 data_time: 0.0068 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.5996 aux.loss_ce: 0.0078 aux.acc_seg: 99.1944 +04/18 09:51:56 - mmengine - INFO - Iter(train) [ 48800/160000] lr: 7.2355e-03 eta: 17:02:46 time: 0.5519 data_time: 0.0060 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.7039 aux.loss_ce: 0.0080 aux.acc_seg: 99.2879 +04/18 09:52:24 - mmengine - INFO - Iter(train) [ 48850/160000] lr: 7.2326e-03 eta: 17:02:19 time: 0.5526 data_time: 0.0064 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.6964 aux.loss_ce: 0.0085 aux.acc_seg: 99.1631 +04/18 09:52:51 - mmengine - INFO - Iter(train) [ 48900/160000] lr: 7.2297e-03 eta: 17:01:51 time: 0.5520 data_time: 0.0063 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6101 aux.loss_ce: 0.0082 aux.acc_seg: 99.1882 +04/18 09:53:19 - mmengine - INFO - Iter(train) [ 48950/160000] lr: 7.2268e-03 eta: 17:01:24 time: 0.5513 data_time: 0.0065 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.7277 aux.loss_ce: 0.0077 aux.acc_seg: 99.3268 +04/18 09:53:47 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 09:53:47 - mmengine - INFO - Iter(train) [ 49000/160000] lr: 7.2239e-03 eta: 17:00:56 time: 0.5522 data_time: 0.0072 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0099 decode.acc_seg: 99.6124 aux.loss_ce: 0.0084 aux.acc_seg: 99.0455 +04/18 09:54:14 - mmengine - INFO - Iter(train) [ 49050/160000] lr: 7.2211e-03 eta: 17:00:29 time: 0.5514 data_time: 0.0068 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.6682 aux.loss_ce: 0.0085 aux.acc_seg: 99.1941 +04/18 09:54:42 - mmengine - INFO - Iter(train) [ 49100/160000] lr: 7.2182e-03 eta: 17:00:01 time: 0.5519 data_time: 0.0071 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6275 aux.loss_ce: 0.0077 aux.acc_seg: 99.1561 +04/18 09:55:10 - mmengine - INFO - Iter(train) [ 49150/160000] lr: 7.2153e-03 eta: 16:59:34 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.5423 aux.loss_ce: 0.0080 aux.acc_seg: 99.0732 +04/18 09:55:37 - mmengine - INFO - Iter(train) [ 49200/160000] lr: 7.2124e-03 eta: 16:59:06 time: 0.5534 data_time: 0.0067 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.7293 aux.loss_ce: 0.0073 aux.acc_seg: 99.3897 +04/18 09:56:05 - mmengine - INFO - Iter(train) [ 49250/160000] lr: 7.2095e-03 eta: 16:58:39 time: 0.5522 data_time: 0.0060 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.5699 aux.loss_ce: 0.0076 aux.acc_seg: 99.1997 +04/18 09:56:32 - mmengine - INFO - Iter(train) [ 49300/160000] lr: 7.2066e-03 eta: 16:58:11 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.5998 aux.loss_ce: 0.0077 aux.acc_seg: 99.1120 +04/18 09:57:00 - mmengine - INFO - Iter(train) [ 49350/160000] lr: 7.2037e-03 eta: 16:57:43 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.6954 aux.loss_ce: 0.0076 aux.acc_seg: 99.2864 +04/18 09:57:27 - mmengine - INFO - Iter(train) [ 49400/160000] lr: 7.2008e-03 eta: 16:57:16 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6588 aux.loss_ce: 0.0076 aux.acc_seg: 99.0170 +04/18 09:57:55 - mmengine - INFO - Iter(train) [ 49450/160000] lr: 7.1979e-03 eta: 16:56:48 time: 0.5498 data_time: 0.0057 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7878 aux.loss_ce: 0.0080 aux.acc_seg: 99.3705 +04/18 09:58:23 - mmengine - INFO - Iter(train) [ 49500/160000] lr: 7.1951e-03 eta: 16:56:20 time: 0.5513 data_time: 0.0066 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0091 decode.acc_seg: 99.6921 aux.loss_ce: 0.0077 aux.acc_seg: 99.1593 +04/18 09:58:50 - mmengine - INFO - Iter(train) [ 49550/160000] lr: 7.1922e-03 eta: 16:55:53 time: 0.5501 data_time: 0.0060 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0090 decode.acc_seg: 99.6333 aux.loss_ce: 0.0084 aux.acc_seg: 99.1590 +04/18 09:59:18 - mmengine - INFO - Iter(train) [ 49600/160000] lr: 7.1893e-03 eta: 16:55:25 time: 0.5501 data_time: 0.0060 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.5110 aux.loss_ce: 0.0079 aux.acc_seg: 98.7459 +04/18 09:59:45 - mmengine - INFO - Iter(train) [ 49650/160000] lr: 7.1864e-03 eta: 16:54:58 time: 0.5508 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.6520 aux.loss_ce: 0.0082 aux.acc_seg: 99.2351 +04/18 10:00:13 - mmengine - INFO - Iter(train) [ 49700/160000] lr: 7.1835e-03 eta: 16:54:30 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6666 aux.loss_ce: 0.0073 aux.acc_seg: 99.3447 +04/18 10:00:41 - mmengine - INFO - Iter(train) [ 49750/160000] lr: 7.1806e-03 eta: 16:54:02 time: 0.5512 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6932 aux.loss_ce: 0.0072 aux.acc_seg: 99.3723 +04/18 10:01:08 - mmengine - INFO - Iter(train) [ 49800/160000] lr: 7.1777e-03 eta: 16:53:35 time: 0.5522 data_time: 0.0077 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.7171 aux.loss_ce: 0.0083 aux.acc_seg: 99.3166 +04/18 10:01:36 - mmengine - INFO - Iter(train) [ 49850/160000] lr: 7.1748e-03 eta: 16:53:07 time: 0.5522 data_time: 0.0061 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.7813 aux.loss_ce: 0.0082 aux.acc_seg: 99.4865 +04/18 10:02:04 - mmengine - INFO - Iter(train) [ 49900/160000] lr: 7.1719e-03 eta: 16:52:40 time: 0.5525 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7205 aux.loss_ce: 0.0077 aux.acc_seg: 99.1055 +04/18 10:02:31 - mmengine - INFO - Iter(train) [ 49950/160000] lr: 7.1690e-03 eta: 16:52:12 time: 0.5514 data_time: 0.0073 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.7235 aux.loss_ce: 0.0074 aux.acc_seg: 99.3393 +04/18 10:02:59 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:02:59 - mmengine - INFO - Iter(train) [ 50000/160000] lr: 7.1662e-03 eta: 16:51:45 time: 0.5518 data_time: 0.0062 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0086 decode.acc_seg: 99.6929 aux.loss_ce: 0.0083 aux.acc_seg: 99.2558 +04/18 10:02:59 - mmengine - INFO - Saving checkpoint at 50000 iterations +04/18 10:03:03 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0466 data_time: 0.0015 memory: 1657 +04/18 10:03:05 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0468 data_time: 0.0013 memory: 1657 +04/18 10:03:07 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0470 data_time: 0.0016 memory: 1657 +04/18 10:03:10 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0460 data_time: 0.0013 memory: 1657 +04/18 10:03:10 - mmengine - INFO - per class results: +04/18 10:03:10 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.46 | 99.53 | 99.6 | 99.46 | +| contrast | 79.91 | 90.32 | 88.83 | 87.39 | 90.32 | ++------------+-------+-------+--------+-----------+--------+ +04/18 10:03:10 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0900 mIoU: 89.4800 mAcc: 94.8900 mFscore: 94.1800 mPrecision: 93.4900 mRecall: 94.8900 data_time: 0.0015 time: 0.0467 +04/18 10:03:37 - mmengine - INFO - Iter(train) [ 50050/160000] lr: 7.1633e-03 eta: 16:51:17 time: 0.5494 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.6303 aux.loss_ce: 0.0078 aux.acc_seg: 99.1614 +04/18 10:04:05 - mmengine - INFO - Iter(train) [ 50100/160000] lr: 7.1604e-03 eta: 16:50:50 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.7437 aux.loss_ce: 0.0078 aux.acc_seg: 99.2997 +04/18 10:04:33 - mmengine - INFO - Iter(train) [ 50150/160000] lr: 7.1575e-03 eta: 16:50:22 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.7126 aux.loss_ce: 0.0075 aux.acc_seg: 99.1648 +04/18 10:05:00 - mmengine - INFO - Iter(train) [ 50200/160000] lr: 7.1546e-03 eta: 16:49:55 time: 0.5508 data_time: 0.0068 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0087 decode.acc_seg: 99.7084 aux.loss_ce: 0.0076 aux.acc_seg: 99.4479 +04/18 10:05:28 - mmengine - INFO - Iter(train) [ 50250/160000] lr: 7.1517e-03 eta: 16:49:27 time: 0.5500 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6487 aux.loss_ce: 0.0082 aux.acc_seg: 99.1045 +04/18 10:05:55 - mmengine - INFO - Iter(train) [ 50300/160000] lr: 7.1488e-03 eta: 16:48:59 time: 0.5500 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6172 aux.loss_ce: 0.0081 aux.acc_seg: 98.9217 +04/18 10:06:23 - mmengine - INFO - Iter(train) [ 50350/160000] lr: 7.1459e-03 eta: 16:48:32 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0084 decode.acc_seg: 99.4938 aux.loss_ce: 0.0076 aux.acc_seg: 99.0082 +04/18 10:06:51 - mmengine - INFO - Iter(train) [ 50400/160000] lr: 7.1430e-03 eta: 16:48:04 time: 0.5517 data_time: 0.0069 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0091 decode.acc_seg: 99.6501 aux.loss_ce: 0.0088 aux.acc_seg: 99.2349 +04/18 10:07:18 - mmengine - INFO - Iter(train) [ 50450/160000] lr: 7.1401e-03 eta: 16:47:37 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.5913 aux.loss_ce: 0.0083 aux.acc_seg: 99.2395 +04/18 10:07:46 - mmengine - INFO - Iter(train) [ 50500/160000] lr: 7.1372e-03 eta: 16:47:09 time: 0.5531 data_time: 0.0066 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0091 decode.acc_seg: 99.7045 aux.loss_ce: 0.0085 aux.acc_seg: 99.1563 +04/18 10:08:14 - mmengine - INFO - Iter(train) [ 50550/160000] lr: 7.1343e-03 eta: 16:46:42 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0103 decode.acc_seg: 99.6216 aux.loss_ce: 0.0083 aux.acc_seg: 99.1870 +04/18 10:08:41 - mmengine - INFO - Iter(train) [ 50600/160000] lr: 7.1315e-03 eta: 16:46:14 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6038 aux.loss_ce: 0.0079 aux.acc_seg: 99.2425 +04/18 10:09:09 - mmengine - INFO - Iter(train) [ 50650/160000] lr: 7.1286e-03 eta: 16:45:46 time: 0.5527 data_time: 0.0073 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0091 decode.acc_seg: 99.6921 aux.loss_ce: 0.0085 aux.acc_seg: 99.2167 +04/18 10:09:36 - mmengine - INFO - Iter(train) [ 50700/160000] lr: 7.1257e-03 eta: 16:45:19 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.6984 aux.loss_ce: 0.0082 aux.acc_seg: 99.3204 +04/18 10:10:04 - mmengine - INFO - Iter(train) [ 50750/160000] lr: 7.1228e-03 eta: 16:44:51 time: 0.5508 data_time: 0.0060 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7176 aux.loss_ce: 0.0072 aux.acc_seg: 99.1421 +04/18 10:10:31 - mmengine - INFO - Iter(train) [ 50800/160000] lr: 7.1199e-03 eta: 16:44:23 time: 0.5517 data_time: 0.0061 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0094 decode.acc_seg: 99.6450 aux.loss_ce: 0.0081 aux.acc_seg: 99.1481 +04/18 10:10:59 - mmengine - INFO - Iter(train) [ 50850/160000] lr: 7.1170e-03 eta: 16:43:56 time: 0.5520 data_time: 0.0070 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6152 aux.loss_ce: 0.0084 aux.acc_seg: 99.1262 +04/18 10:11:27 - mmengine - INFO - Iter(train) [ 50900/160000] lr: 7.1141e-03 eta: 16:43:28 time: 0.5511 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.7204 aux.loss_ce: 0.0077 aux.acc_seg: 99.4347 +04/18 10:11:54 - mmengine - INFO - Iter(train) [ 50950/160000] lr: 7.1112e-03 eta: 16:43:01 time: 0.5504 data_time: 0.0060 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.7308 aux.loss_ce: 0.0077 aux.acc_seg: 99.2590 +04/18 10:12:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:12:22 - mmengine - INFO - Iter(train) [ 51000/160000] lr: 7.1083e-03 eta: 16:42:33 time: 0.5508 data_time: 0.0061 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.7273 aux.loss_ce: 0.0080 aux.acc_seg: 99.3155 +04/18 10:12:49 - mmengine - INFO - Iter(train) [ 51050/160000] lr: 7.1054e-03 eta: 16:42:06 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6755 aux.loss_ce: 0.0080 aux.acc_seg: 99.0463 +04/18 10:13:17 - mmengine - INFO - Iter(train) [ 51100/160000] lr: 7.1025e-03 eta: 16:41:38 time: 0.5522 data_time: 0.0068 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6352 aux.loss_ce: 0.0081 aux.acc_seg: 99.0726 +04/18 10:13:45 - mmengine - INFO - Iter(train) [ 51150/160000] lr: 7.0996e-03 eta: 16:41:10 time: 0.5523 data_time: 0.0060 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.6531 aux.loss_ce: 0.0072 aux.acc_seg: 99.2215 +04/18 10:14:12 - mmengine - INFO - Iter(train) [ 51200/160000] lr: 7.0967e-03 eta: 16:40:43 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.5941 aux.loss_ce: 0.0082 aux.acc_seg: 98.9841 +04/18 10:14:40 - mmengine - INFO - Iter(train) [ 51250/160000] lr: 7.0938e-03 eta: 16:40:15 time: 0.5519 data_time: 0.0072 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0083 decode.acc_seg: 99.7270 aux.loss_ce: 0.0075 aux.acc_seg: 99.2428 +04/18 10:15:07 - mmengine - INFO - Iter(train) [ 51300/160000] lr: 7.0910e-03 eta: 16:39:48 time: 0.5517 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.4575 aux.loss_ce: 0.0082 aux.acc_seg: 99.0960 +04/18 10:15:35 - mmengine - INFO - Iter(train) [ 51350/160000] lr: 7.0881e-03 eta: 16:39:20 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.7530 aux.loss_ce: 0.0074 aux.acc_seg: 99.3126 +04/18 10:16:03 - mmengine - INFO - Iter(train) [ 51400/160000] lr: 7.0852e-03 eta: 16:38:52 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.7535 aux.loss_ce: 0.0078 aux.acc_seg: 99.3334 +04/18 10:16:30 - mmengine - INFO - Iter(train) [ 51450/160000] lr: 7.0823e-03 eta: 16:38:25 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0098 decode.acc_seg: 99.6828 aux.loss_ce: 0.0083 aux.acc_seg: 99.2489 +04/18 10:16:58 - mmengine - INFO - Iter(train) [ 51500/160000] lr: 7.0794e-03 eta: 16:37:57 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.6384 aux.loss_ce: 0.0081 aux.acc_seg: 99.1660 +04/18 10:17:25 - mmengine - INFO - Iter(train) [ 51550/160000] lr: 7.0765e-03 eta: 16:37:30 time: 0.5522 data_time: 0.0066 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.7622 aux.loss_ce: 0.0075 aux.acc_seg: 99.3407 +04/18 10:17:53 - mmengine - INFO - Iter(train) [ 51600/160000] lr: 7.0736e-03 eta: 16:37:02 time: 0.5505 data_time: 0.0065 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.7211 aux.loss_ce: 0.0079 aux.acc_seg: 99.2453 +04/18 10:18:21 - mmengine - INFO - Iter(train) [ 51650/160000] lr: 7.0707e-03 eta: 16:36:34 time: 0.5527 data_time: 0.0068 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.4807 aux.loss_ce: 0.0081 aux.acc_seg: 99.0744 +04/18 10:18:48 - mmengine - INFO - Iter(train) [ 51700/160000] lr: 7.0678e-03 eta: 16:36:07 time: 0.5525 data_time: 0.0075 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.7381 aux.loss_ce: 0.0081 aux.acc_seg: 99.2805 +04/18 10:19:16 - mmengine - INFO - Iter(train) [ 51750/160000] lr: 7.0649e-03 eta: 16:35:39 time: 0.5515 data_time: 0.0064 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.6535 aux.loss_ce: 0.0082 aux.acc_seg: 99.1571 +04/18 10:19:43 - mmengine - INFO - Iter(train) [ 51800/160000] lr: 7.0620e-03 eta: 16:35:12 time: 0.5509 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.7637 aux.loss_ce: 0.0073 aux.acc_seg: 99.2836 +04/18 10:20:11 - mmengine - INFO - Iter(train) [ 51850/160000] lr: 7.0591e-03 eta: 16:34:44 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0098 decode.acc_seg: 99.6417 aux.loss_ce: 0.0085 aux.acc_seg: 99.1693 +04/18 10:20:39 - mmengine - INFO - Iter(train) [ 51900/160000] lr: 7.0562e-03 eta: 16:34:16 time: 0.5524 data_time: 0.0062 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0091 decode.acc_seg: 99.6486 aux.loss_ce: 0.0082 aux.acc_seg: 99.2789 +04/18 10:21:06 - mmengine - INFO - Iter(train) [ 51950/160000] lr: 7.0533e-03 eta: 16:33:49 time: 0.5602 data_time: 0.0062 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.6376 aux.loss_ce: 0.0082 aux.acc_seg: 99.2391 +04/18 10:21:34 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:21:34 - mmengine - INFO - Iter(train) [ 52000/160000] lr: 7.0504e-03 eta: 16:33:21 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0085 decode.acc_seg: 99.6520 aux.loss_ce: 0.0078 aux.acc_seg: 99.3771 +04/18 10:22:02 - mmengine - INFO - Iter(train) [ 52050/160000] lr: 7.0475e-03 eta: 16:32:54 time: 0.5515 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6364 aux.loss_ce: 0.0075 aux.acc_seg: 99.0461 +04/18 10:22:29 - mmengine - INFO - Iter(train) [ 52100/160000] lr: 7.0446e-03 eta: 16:32:26 time: 0.5516 data_time: 0.0070 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.6502 aux.loss_ce: 0.0087 aux.acc_seg: 99.2046 +04/18 10:22:57 - mmengine - INFO - Iter(train) [ 52150/160000] lr: 7.0417e-03 eta: 16:31:59 time: 0.5526 data_time: 0.0069 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6889 aux.loss_ce: 0.0079 aux.acc_seg: 99.3339 +04/18 10:23:24 - mmengine - INFO - Iter(train) [ 52200/160000] lr: 7.0388e-03 eta: 16:31:31 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0075 decode.acc_seg: 99.6093 aux.loss_ce: 0.0070 aux.acc_seg: 99.0957 +04/18 10:23:52 - mmengine - INFO - Iter(train) [ 52250/160000] lr: 7.0359e-03 eta: 16:31:03 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0092 decode.acc_seg: 99.6466 aux.loss_ce: 0.0088 aux.acc_seg: 98.8050 +04/18 10:24:19 - mmengine - INFO - Iter(train) [ 52300/160000] lr: 7.0330e-03 eta: 16:30:36 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0086 decode.acc_seg: 99.6104 aux.loss_ce: 0.0081 aux.acc_seg: 99.2305 +04/18 10:24:47 - mmengine - INFO - Iter(train) [ 52350/160000] lr: 7.0301e-03 eta: 16:30:08 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.7419 aux.loss_ce: 0.0085 aux.acc_seg: 99.3154 +04/18 10:25:15 - mmengine - INFO - Iter(train) [ 52400/160000] lr: 7.0272e-03 eta: 16:29:41 time: 0.5508 data_time: 0.0066 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0088 decode.acc_seg: 99.7513 aux.loss_ce: 0.0086 aux.acc_seg: 99.3443 +04/18 10:25:42 - mmengine - INFO - Iter(train) [ 52450/160000] lr: 7.0244e-03 eta: 16:29:13 time: 0.5508 data_time: 0.0060 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6203 aux.loss_ce: 0.0074 aux.acc_seg: 99.1570 +04/18 10:26:10 - mmengine - INFO - Iter(train) [ 52500/160000] lr: 7.0215e-03 eta: 16:28:45 time: 0.5515 data_time: 0.0059 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.7168 aux.loss_ce: 0.0074 aux.acc_seg: 99.2850 +04/18 10:26:37 - mmengine - INFO - Iter(train) [ 52550/160000] lr: 7.0186e-03 eta: 16:28:18 time: 0.5516 data_time: 0.0064 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6927 aux.loss_ce: 0.0074 aux.acc_seg: 99.2032 +04/18 10:27:05 - mmengine - INFO - Iter(train) [ 52600/160000] lr: 7.0157e-03 eta: 16:27:50 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6718 aux.loss_ce: 0.0081 aux.acc_seg: 99.2196 +04/18 10:27:33 - mmengine - INFO - Iter(train) [ 52650/160000] lr: 7.0128e-03 eta: 16:27:23 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0076 decode.acc_seg: 99.7131 aux.loss_ce: 0.0072 aux.acc_seg: 99.2583 +04/18 10:28:00 - mmengine - INFO - Iter(train) [ 52700/160000] lr: 7.0099e-03 eta: 16:26:55 time: 0.5527 data_time: 0.0070 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0102 decode.acc_seg: 99.6510 aux.loss_ce: 0.0081 aux.acc_seg: 99.2446 +04/18 10:28:28 - mmengine - INFO - Iter(train) [ 52750/160000] lr: 7.0070e-03 eta: 16:26:27 time: 0.5522 data_time: 0.0068 memory: 7635 loss: 0.0210 decode.loss_ce: 0.0114 decode.acc_seg: 99.5508 aux.loss_ce: 0.0095 aux.acc_seg: 99.0664 +04/18 10:28:55 - mmengine - INFO - Iter(train) [ 52800/160000] lr: 7.0041e-03 eta: 16:26:00 time: 0.5517 data_time: 0.0071 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0091 decode.acc_seg: 99.5591 aux.loss_ce: 0.0077 aux.acc_seg: 98.9380 +04/18 10:29:23 - mmengine - INFO - Iter(train) [ 52850/160000] lr: 7.0012e-03 eta: 16:25:32 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.6599 aux.loss_ce: 0.0083 aux.acc_seg: 99.0653 +04/18 10:29:51 - mmengine - INFO - Iter(train) [ 52900/160000] lr: 6.9983e-03 eta: 16:25:05 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0097 decode.acc_seg: 99.7003 aux.loss_ce: 0.0088 aux.acc_seg: 99.2614 +04/18 10:30:18 - mmengine - INFO - Iter(train) [ 52950/160000] lr: 6.9954e-03 eta: 16:24:37 time: 0.5528 data_time: 0.0063 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.5527 aux.loss_ce: 0.0083 aux.acc_seg: 99.1763 +04/18 10:30:46 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:30:46 - mmengine - INFO - Iter(train) [ 53000/160000] lr: 6.9925e-03 eta: 16:24:09 time: 0.5511 data_time: 0.0055 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0100 decode.acc_seg: 99.7108 aux.loss_ce: 0.0090 aux.acc_seg: 99.2771 +04/18 10:31:14 - mmengine - INFO - Iter(train) [ 53050/160000] lr: 6.9896e-03 eta: 16:23:42 time: 0.5529 data_time: 0.0059 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0101 decode.acc_seg: 99.6829 aux.loss_ce: 0.0085 aux.acc_seg: 99.2652 +04/18 10:31:41 - mmengine - INFO - Iter(train) [ 53100/160000] lr: 6.9867e-03 eta: 16:23:15 time: 0.5501 data_time: 0.0058 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0098 decode.acc_seg: 99.5369 aux.loss_ce: 0.0088 aux.acc_seg: 98.8965 +04/18 10:32:09 - mmengine - INFO - Iter(train) [ 53150/160000] lr: 6.9838e-03 eta: 16:22:47 time: 0.5526 data_time: 0.0064 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.6242 aux.loss_ce: 0.0081 aux.acc_seg: 99.2612 +04/18 10:32:36 - mmengine - INFO - Iter(train) [ 53200/160000] lr: 6.9809e-03 eta: 16:22:19 time: 0.5519 data_time: 0.0068 memory: 7635 loss: 0.0189 decode.loss_ce: 0.0103 decode.acc_seg: 99.5462 aux.loss_ce: 0.0086 aux.acc_seg: 98.9160 +04/18 10:33:04 - mmengine - INFO - Iter(train) [ 53250/160000] lr: 6.9780e-03 eta: 16:21:52 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0099 decode.acc_seg: 99.6872 aux.loss_ce: 0.0086 aux.acc_seg: 99.2883 +04/18 10:33:32 - mmengine - INFO - Iter(train) [ 53300/160000] lr: 6.9751e-03 eta: 16:21:24 time: 0.5513 data_time: 0.0061 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.6056 aux.loss_ce: 0.0082 aux.acc_seg: 99.2646 +04/18 10:33:59 - mmengine - INFO - Iter(train) [ 53350/160000] lr: 6.9722e-03 eta: 16:20:57 time: 0.5496 data_time: 0.0068 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.7437 aux.loss_ce: 0.0078 aux.acc_seg: 99.2231 +04/18 10:34:27 - mmengine - INFO - Iter(train) [ 53400/160000] lr: 6.9693e-03 eta: 16:20:29 time: 0.5525 data_time: 0.0071 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0100 decode.acc_seg: 99.6415 aux.loss_ce: 0.0082 aux.acc_seg: 99.1047 +04/18 10:34:54 - mmengine - INFO - Iter(train) [ 53450/160000] lr: 6.9664e-03 eta: 16:20:01 time: 0.5512 data_time: 0.0071 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.6616 aux.loss_ce: 0.0076 aux.acc_seg: 99.1767 +04/18 10:35:22 - mmengine - INFO - Iter(train) [ 53500/160000] lr: 6.9635e-03 eta: 16:19:34 time: 0.5510 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6323 aux.loss_ce: 0.0079 aux.acc_seg: 99.0246 +04/18 10:35:50 - mmengine - INFO - Iter(train) [ 53550/160000] lr: 6.9606e-03 eta: 16:19:06 time: 0.5522 data_time: 0.0071 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.6636 aux.loss_ce: 0.0084 aux.acc_seg: 99.0689 +04/18 10:36:17 - mmengine - INFO - Iter(train) [ 53600/160000] lr: 6.9577e-03 eta: 16:18:39 time: 0.5532 data_time: 0.0071 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6490 aux.loss_ce: 0.0080 aux.acc_seg: 99.1546 +04/18 10:36:45 - mmengine - INFO - Iter(train) [ 53650/160000] lr: 6.9548e-03 eta: 16:18:11 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.7125 aux.loss_ce: 0.0083 aux.acc_seg: 99.2896 +04/18 10:37:12 - mmengine - INFO - Iter(train) [ 53700/160000] lr: 6.9519e-03 eta: 16:17:44 time: 0.5513 data_time: 0.0063 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.7014 aux.loss_ce: 0.0083 aux.acc_seg: 99.2769 +04/18 10:37:40 - mmengine - INFO - Iter(train) [ 53750/160000] lr: 6.9490e-03 eta: 16:17:16 time: 0.5508 data_time: 0.0071 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.6697 aux.loss_ce: 0.0080 aux.acc_seg: 99.1458 +04/18 10:38:08 - mmengine - INFO - Iter(train) [ 53800/160000] lr: 6.9461e-03 eta: 16:16:48 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.7752 aux.loss_ce: 0.0074 aux.acc_seg: 99.4241 +04/18 10:38:35 - mmengine - INFO - Iter(train) [ 53850/160000] lr: 6.9432e-03 eta: 16:16:21 time: 0.5525 data_time: 0.0059 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.6603 aux.loss_ce: 0.0076 aux.acc_seg: 99.3434 +04/18 10:39:03 - mmengine - INFO - Iter(train) [ 53900/160000] lr: 6.9403e-03 eta: 16:15:53 time: 0.5512 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.6707 aux.loss_ce: 0.0079 aux.acc_seg: 99.0522 +04/18 10:39:30 - mmengine - INFO - Iter(train) [ 53950/160000] lr: 6.9374e-03 eta: 16:15:26 time: 0.5522 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.6368 aux.loss_ce: 0.0078 aux.acc_seg: 99.3043 +04/18 10:39:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:39:58 - mmengine - INFO - Iter(train) [ 54000/160000] lr: 6.9345e-03 eta: 16:14:58 time: 0.5502 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.7044 aux.loss_ce: 0.0079 aux.acc_seg: 99.3671 +04/18 10:40:26 - mmengine - INFO - Iter(train) [ 54050/160000] lr: 6.9316e-03 eta: 16:14:30 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.6016 aux.loss_ce: 0.0081 aux.acc_seg: 99.1320 +04/18 10:40:53 - mmengine - INFO - Iter(train) [ 54100/160000] lr: 6.9287e-03 eta: 16:14:03 time: 0.5505 data_time: 0.0068 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0091 decode.acc_seg: 99.6835 aux.loss_ce: 0.0078 aux.acc_seg: 99.2524 +04/18 10:41:21 - mmengine - INFO - Iter(train) [ 54150/160000] lr: 6.9258e-03 eta: 16:13:35 time: 0.5519 data_time: 0.0065 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.6569 aux.loss_ce: 0.0078 aux.acc_seg: 99.2126 +04/18 10:41:49 - mmengine - INFO - Iter(train) [ 54200/160000] lr: 6.9229e-03 eta: 16:13:08 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.7202 aux.loss_ce: 0.0074 aux.acc_seg: 99.4156 +04/18 10:42:16 - mmengine - INFO - Iter(train) [ 54250/160000] lr: 6.9200e-03 eta: 16:12:40 time: 0.5512 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0084 decode.acc_seg: 99.5613 aux.loss_ce: 0.0075 aux.acc_seg: 99.0467 +04/18 10:42:44 - mmengine - INFO - Iter(train) [ 54300/160000] lr: 6.9171e-03 eta: 16:12:13 time: 0.5514 data_time: 0.0062 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7068 aux.loss_ce: 0.0077 aux.acc_seg: 99.3682 +04/18 10:43:11 - mmengine - INFO - Iter(train) [ 54350/160000] lr: 6.9142e-03 eta: 16:11:45 time: 0.5512 data_time: 0.0071 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0084 decode.acc_seg: 99.6916 aux.loss_ce: 0.0076 aux.acc_seg: 99.2680 +04/18 10:43:39 - mmengine - INFO - Iter(train) [ 54400/160000] lr: 6.9113e-03 eta: 16:11:17 time: 0.5515 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.6791 aux.loss_ce: 0.0084 aux.acc_seg: 99.2279 +04/18 10:44:07 - mmengine - INFO - Iter(train) [ 54450/160000] lr: 6.9084e-03 eta: 16:10:50 time: 0.5521 data_time: 0.0067 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0096 decode.acc_seg: 99.6384 aux.loss_ce: 0.0084 aux.acc_seg: 99.0820 +04/18 10:44:34 - mmengine - INFO - Iter(train) [ 54500/160000] lr: 6.9055e-03 eta: 16:10:22 time: 0.5512 data_time: 0.0068 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0093 decode.acc_seg: 99.5789 aux.loss_ce: 0.0085 aux.acc_seg: 99.2597 +04/18 10:45:02 - mmengine - INFO - Iter(train) [ 54550/160000] lr: 6.9025e-03 eta: 16:09:55 time: 0.5517 data_time: 0.0070 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.6681 aux.loss_ce: 0.0079 aux.acc_seg: 99.1036 +04/18 10:45:29 - mmengine - INFO - Iter(train) [ 54600/160000] lr: 6.8996e-03 eta: 16:09:27 time: 0.5519 data_time: 0.0058 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7135 aux.loss_ce: 0.0080 aux.acc_seg: 99.3480 +04/18 10:45:57 - mmengine - INFO - Iter(train) [ 54650/160000] lr: 6.8967e-03 eta: 16:08:59 time: 0.5526 data_time: 0.0073 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0085 decode.acc_seg: 99.6518 aux.loss_ce: 0.0078 aux.acc_seg: 99.1810 +04/18 10:46:25 - mmengine - INFO - Iter(train) [ 54700/160000] lr: 6.8938e-03 eta: 16:08:32 time: 0.5516 data_time: 0.0067 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0093 decode.acc_seg: 99.6024 aux.loss_ce: 0.0091 aux.acc_seg: 98.9346 +04/18 10:46:52 - mmengine - INFO - Iter(train) [ 54750/160000] lr: 6.8909e-03 eta: 16:08:04 time: 0.5523 data_time: 0.0064 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7321 aux.loss_ce: 0.0079 aux.acc_seg: 99.2522 +04/18 10:47:20 - mmengine - INFO - Iter(train) [ 54800/160000] lr: 6.8880e-03 eta: 16:07:37 time: 0.5510 data_time: 0.0070 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.6336 aux.loss_ce: 0.0080 aux.acc_seg: 99.1231 +04/18 10:47:47 - mmengine - INFO - Iter(train) [ 54850/160000] lr: 6.8851e-03 eta: 16:07:09 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6531 aux.loss_ce: 0.0082 aux.acc_seg: 99.1193 +04/18 10:48:15 - mmengine - INFO - Iter(train) [ 54900/160000] lr: 6.8822e-03 eta: 16:06:42 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0206 decode.loss_ce: 0.0119 decode.acc_seg: 99.6003 aux.loss_ce: 0.0087 aux.acc_seg: 99.1993 +04/18 10:48:43 - mmengine - INFO - Iter(train) [ 54950/160000] lr: 6.8793e-03 eta: 16:06:14 time: 0.5521 data_time: 0.0066 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.6529 aux.loss_ce: 0.0087 aux.acc_seg: 99.1139 +04/18 10:49:10 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:49:10 - mmengine - INFO - Iter(train) [ 55000/160000] lr: 6.8764e-03 eta: 16:05:46 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0100 decode.acc_seg: 99.3012 aux.loss_ce: 0.0084 aux.acc_seg: 98.9002 +04/18 10:49:38 - mmengine - INFO - Iter(train) [ 55050/160000] lr: 6.8735e-03 eta: 16:05:19 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0095 decode.acc_seg: 99.6247 aux.loss_ce: 0.0085 aux.acc_seg: 99.1119 +04/18 10:50:05 - mmengine - INFO - Iter(train) [ 55100/160000] lr: 6.8706e-03 eta: 16:04:51 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0094 decode.acc_seg: 99.6411 aux.loss_ce: 0.0089 aux.acc_seg: 99.0154 +04/18 10:50:33 - mmengine - INFO - Iter(train) [ 55150/160000] lr: 6.8677e-03 eta: 16:04:24 time: 0.5511 data_time: 0.0063 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6661 aux.loss_ce: 0.0077 aux.acc_seg: 99.2380 +04/18 10:51:01 - mmengine - INFO - Iter(train) [ 55200/160000] lr: 6.8648e-03 eta: 16:03:56 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6775 aux.loss_ce: 0.0080 aux.acc_seg: 99.2329 +04/18 10:51:28 - mmengine - INFO - Iter(train) [ 55250/160000] lr: 6.8619e-03 eta: 16:03:29 time: 0.5520 data_time: 0.0069 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.5274 aux.loss_ce: 0.0079 aux.acc_seg: 98.9709 +04/18 10:51:56 - mmengine - INFO - Iter(train) [ 55300/160000] lr: 6.8590e-03 eta: 16:03:01 time: 0.5508 data_time: 0.0067 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0093 decode.acc_seg: 99.6504 aux.loss_ce: 0.0082 aux.acc_seg: 99.0874 +04/18 10:52:24 - mmengine - INFO - Iter(train) [ 55350/160000] lr: 6.8561e-03 eta: 16:02:34 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6454 aux.loss_ce: 0.0077 aux.acc_seg: 99.1481 +04/18 10:52:51 - mmengine - INFO - Iter(train) [ 55400/160000] lr: 6.8532e-03 eta: 16:02:06 time: 0.5520 data_time: 0.0076 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0104 decode.acc_seg: 99.7279 aux.loss_ce: 0.0088 aux.acc_seg: 99.1906 +04/18 10:53:19 - mmengine - INFO - Iter(train) [ 55450/160000] lr: 6.8503e-03 eta: 16:01:38 time: 0.5512 data_time: 0.0067 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0121 decode.acc_seg: 99.5101 aux.loss_ce: 0.0096 aux.acc_seg: 98.7386 +04/18 10:53:46 - mmengine - INFO - Iter(train) [ 55500/160000] lr: 6.8474e-03 eta: 16:01:11 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6796 aux.loss_ce: 0.0077 aux.acc_seg: 99.2816 +04/18 10:54:14 - mmengine - INFO - Iter(train) [ 55550/160000] lr: 6.8445e-03 eta: 16:00:43 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0096 decode.acc_seg: 99.6401 aux.loss_ce: 0.0089 aux.acc_seg: 99.1049 +04/18 10:54:42 - mmengine - INFO - Iter(train) [ 55600/160000] lr: 6.8416e-03 eta: 16:00:16 time: 0.5524 data_time: 0.0070 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.4155 aux.loss_ce: 0.0081 aux.acc_seg: 99.0135 +04/18 10:55:09 - mmengine - INFO - Iter(train) [ 55650/160000] lr: 6.8386e-03 eta: 15:59:48 time: 0.5518 data_time: 0.0062 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6309 aux.loss_ce: 0.0079 aux.acc_seg: 99.1927 +04/18 10:55:37 - mmengine - INFO - Iter(train) [ 55700/160000] lr: 6.8357e-03 eta: 15:59:20 time: 0.5519 data_time: 0.0062 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7025 aux.loss_ce: 0.0079 aux.acc_seg: 99.2126 +04/18 10:56:04 - mmengine - INFO - Iter(train) [ 55750/160000] lr: 6.8328e-03 eta: 15:58:53 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.5903 aux.loss_ce: 0.0079 aux.acc_seg: 99.2051 +04/18 10:56:32 - mmengine - INFO - Iter(train) [ 55800/160000] lr: 6.8299e-03 eta: 15:58:25 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.7313 aux.loss_ce: 0.0076 aux.acc_seg: 99.3532 +04/18 10:57:00 - mmengine - INFO - Iter(train) [ 55850/160000] lr: 6.8270e-03 eta: 15:57:58 time: 0.5504 data_time: 0.0061 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0079 decode.acc_seg: 99.6006 aux.loss_ce: 0.0074 aux.acc_seg: 99.1119 +04/18 10:57:27 - mmengine - INFO - Iter(train) [ 55900/160000] lr: 6.8241e-03 eta: 15:57:30 time: 0.5519 data_time: 0.0066 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6524 aux.loss_ce: 0.0075 aux.acc_seg: 99.1038 +04/18 10:57:55 - mmengine - INFO - Iter(train) [ 55950/160000] lr: 6.8212e-03 eta: 15:57:03 time: 0.5494 data_time: 0.0055 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.6177 aux.loss_ce: 0.0081 aux.acc_seg: 99.1803 +04/18 10:58:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 10:58:22 - mmengine - INFO - Iter(train) [ 56000/160000] lr: 6.8183e-03 eta: 15:56:35 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6518 aux.loss_ce: 0.0076 aux.acc_seg: 99.2232 +04/18 10:58:50 - mmengine - INFO - Iter(train) [ 56050/160000] lr: 6.8154e-03 eta: 15:56:07 time: 0.5505 data_time: 0.0064 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0084 decode.acc_seg: 99.6863 aux.loss_ce: 0.0075 aux.acc_seg: 99.2343 +04/18 10:59:18 - mmengine - INFO - Iter(train) [ 56100/160000] lr: 6.8125e-03 eta: 15:55:40 time: 0.5526 data_time: 0.0062 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.7315 aux.loss_ce: 0.0081 aux.acc_seg: 99.4270 +04/18 10:59:45 - mmengine - INFO - Iter(train) [ 56150/160000] lr: 6.8096e-03 eta: 15:55:12 time: 0.5525 data_time: 0.0072 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.7353 aux.loss_ce: 0.0077 aux.acc_seg: 99.2964 +04/18 11:00:13 - mmengine - INFO - Iter(train) [ 56200/160000] lr: 6.8067e-03 eta: 15:54:44 time: 0.5529 data_time: 0.0066 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.7451 aux.loss_ce: 0.0085 aux.acc_seg: 99.1852 +04/18 11:00:40 - mmengine - INFO - Iter(train) [ 56250/160000] lr: 6.8038e-03 eta: 15:54:17 time: 0.5504 data_time: 0.0064 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.5820 aux.loss_ce: 0.0084 aux.acc_seg: 99.1267 +04/18 11:01:08 - mmengine - INFO - Iter(train) [ 56300/160000] lr: 6.8009e-03 eta: 15:53:50 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.5940 aux.loss_ce: 0.0077 aux.acc_seg: 99.0654 +04/18 11:01:36 - mmengine - INFO - Iter(train) [ 56350/160000] lr: 6.7980e-03 eta: 15:53:22 time: 0.5511 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.6688 aux.loss_ce: 0.0083 aux.acc_seg: 99.1933 +04/18 11:02:03 - mmengine - INFO - Iter(train) [ 56400/160000] lr: 6.7950e-03 eta: 15:52:54 time: 0.5526 data_time: 0.0062 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.5991 aux.loss_ce: 0.0084 aux.acc_seg: 99.0885 +04/18 11:02:31 - mmengine - INFO - Iter(train) [ 56450/160000] lr: 6.7921e-03 eta: 15:52:27 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6034 aux.loss_ce: 0.0082 aux.acc_seg: 99.0778 +04/18 11:02:58 - mmengine - INFO - Iter(train) [ 56500/160000] lr: 6.7892e-03 eta: 15:51:59 time: 0.5517 data_time: 0.0071 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.5968 aux.loss_ce: 0.0084 aux.acc_seg: 99.0685 +04/18 11:03:26 - mmengine - INFO - Iter(train) [ 56550/160000] lr: 6.7863e-03 eta: 15:51:31 time: 0.5508 data_time: 0.0065 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0087 decode.acc_seg: 99.6239 aux.loss_ce: 0.0085 aux.acc_seg: 99.1067 +04/18 11:03:54 - mmengine - INFO - Iter(train) [ 56600/160000] lr: 6.7834e-03 eta: 15:51:04 time: 0.5506 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7949 aux.loss_ce: 0.0077 aux.acc_seg: 99.3800 +04/18 11:04:21 - mmengine - INFO - Iter(train) [ 56650/160000] lr: 6.7805e-03 eta: 15:50:36 time: 0.5518 data_time: 0.0073 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.6842 aux.loss_ce: 0.0079 aux.acc_seg: 98.9669 +04/18 11:04:49 - mmengine - INFO - Iter(train) [ 56700/160000] lr: 6.7776e-03 eta: 15:50:08 time: 0.5525 data_time: 0.0064 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7064 aux.loss_ce: 0.0075 aux.acc_seg: 99.3327 +04/18 11:05:16 - mmengine - INFO - Iter(train) [ 56750/160000] lr: 6.7747e-03 eta: 15:49:41 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0088 decode.acc_seg: 99.5516 aux.loss_ce: 0.0080 aux.acc_seg: 99.0128 +04/18 11:05:44 - mmengine - INFO - Iter(train) [ 56800/160000] lr: 6.7718e-03 eta: 15:49:13 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0084 decode.acc_seg: 99.6427 aux.loss_ce: 0.0076 aux.acc_seg: 98.9308 +04/18 11:06:11 - mmengine - INFO - Iter(train) [ 56850/160000] lr: 6.7689e-03 eta: 15:48:46 time: 0.5513 data_time: 0.0060 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7216 aux.loss_ce: 0.0076 aux.acc_seg: 99.3689 +04/18 11:06:39 - mmengine - INFO - Iter(train) [ 56900/160000] lr: 6.7660e-03 eta: 15:48:18 time: 0.5510 data_time: 0.0059 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0091 decode.acc_seg: 99.6036 aux.loss_ce: 0.0088 aux.acc_seg: 98.8121 +04/18 11:07:07 - mmengine - INFO - Iter(train) [ 56950/160000] lr: 6.7630e-03 eta: 15:47:50 time: 0.5505 data_time: 0.0067 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6259 aux.loss_ce: 0.0082 aux.acc_seg: 99.0293 +04/18 11:07:34 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:07:34 - mmengine - INFO - Iter(train) [ 57000/160000] lr: 6.7601e-03 eta: 15:47:23 time: 0.5509 data_time: 0.0060 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.6693 aux.loss_ce: 0.0081 aux.acc_seg: 99.0869 +04/18 11:08:02 - mmengine - INFO - Iter(train) [ 57050/160000] lr: 6.7572e-03 eta: 15:46:55 time: 0.5509 data_time: 0.0060 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6544 aux.loss_ce: 0.0081 aux.acc_seg: 99.0747 +04/18 11:08:29 - mmengine - INFO - Iter(train) [ 57100/160000] lr: 6.7543e-03 eta: 15:46:27 time: 0.5516 data_time: 0.0067 memory: 7635 loss: 0.0192 decode.loss_ce: 0.0102 decode.acc_seg: 99.7098 aux.loss_ce: 0.0091 aux.acc_seg: 99.2833 +04/18 11:08:57 - mmengine - INFO - Iter(train) [ 57150/160000] lr: 6.7514e-03 eta: 15:46:00 time: 0.5514 data_time: 0.0069 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0104 decode.acc_seg: 99.5404 aux.loss_ce: 0.0084 aux.acc_seg: 99.0448 +04/18 11:09:25 - mmengine - INFO - Iter(train) [ 57200/160000] lr: 6.7485e-03 eta: 15:45:32 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0092 decode.acc_seg: 99.7238 aux.loss_ce: 0.0083 aux.acc_seg: 99.3373 +04/18 11:09:52 - mmengine - INFO - Iter(train) [ 57250/160000] lr: 6.7456e-03 eta: 15:45:05 time: 0.5520 data_time: 0.0066 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.6117 aux.loss_ce: 0.0077 aux.acc_seg: 99.0710 +04/18 11:10:20 - mmengine - INFO - Iter(train) [ 57300/160000] lr: 6.7427e-03 eta: 15:44:37 time: 0.5508 data_time: 0.0064 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.5892 aux.loss_ce: 0.0080 aux.acc_seg: 99.1467 +04/18 11:10:47 - mmengine - INFO - Iter(train) [ 57350/160000] lr: 6.7398e-03 eta: 15:44:09 time: 0.5499 data_time: 0.0062 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.6417 aux.loss_ce: 0.0086 aux.acc_seg: 99.1454 +04/18 11:11:15 - mmengine - INFO - Iter(train) [ 57400/160000] lr: 6.7369e-03 eta: 15:43:42 time: 0.5508 data_time: 0.0066 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0086 decode.acc_seg: 99.8440 aux.loss_ce: 0.0083 aux.acc_seg: 99.5678 +04/18 11:11:43 - mmengine - INFO - Iter(train) [ 57450/160000] lr: 6.7339e-03 eta: 15:43:14 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.7033 aux.loss_ce: 0.0080 aux.acc_seg: 99.3217 +04/18 11:12:10 - mmengine - INFO - Iter(train) [ 57500/160000] lr: 6.7310e-03 eta: 15:42:47 time: 0.5516 data_time: 0.0073 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0094 decode.acc_seg: 99.6173 aux.loss_ce: 0.0085 aux.acc_seg: 99.0421 +04/18 11:12:38 - mmengine - INFO - Iter(train) [ 57550/160000] lr: 6.7281e-03 eta: 15:42:19 time: 0.5508 data_time: 0.0067 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7353 aux.loss_ce: 0.0077 aux.acc_seg: 99.3336 +04/18 11:13:05 - mmengine - INFO - Iter(train) [ 57600/160000] lr: 6.7252e-03 eta: 15:41:51 time: 0.5518 data_time: 0.0068 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0093 decode.acc_seg: 99.4840 aux.loss_ce: 0.0082 aux.acc_seg: 99.0304 +04/18 11:13:33 - mmengine - INFO - Iter(train) [ 57650/160000] lr: 6.7223e-03 eta: 15:41:24 time: 0.5531 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0087 decode.acc_seg: 99.6527 aux.loss_ce: 0.0078 aux.acc_seg: 99.0683 +04/18 11:14:00 - mmengine - INFO - Iter(train) [ 57700/160000] lr: 6.7194e-03 eta: 15:40:56 time: 0.5517 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6759 aux.loss_ce: 0.0078 aux.acc_seg: 99.2591 +04/18 11:14:28 - mmengine - INFO - Iter(train) [ 57750/160000] lr: 6.7165e-03 eta: 15:40:28 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.7263 aux.loss_ce: 0.0084 aux.acc_seg: 99.3308 +04/18 11:14:56 - mmengine - INFO - Iter(train) [ 57800/160000] lr: 6.7136e-03 eta: 15:40:01 time: 0.5523 data_time: 0.0061 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.7467 aux.loss_ce: 0.0074 aux.acc_seg: 99.4081 +04/18 11:15:23 - mmengine - INFO - Iter(train) [ 57850/160000] lr: 6.7107e-03 eta: 15:39:33 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.6671 aux.loss_ce: 0.0079 aux.acc_seg: 99.3294 +04/18 11:15:51 - mmengine - INFO - Iter(train) [ 57900/160000] lr: 6.7077e-03 eta: 15:39:05 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0082 decode.acc_seg: 99.6827 aux.loss_ce: 0.0085 aux.acc_seg: 99.1842 +04/18 11:16:18 - mmengine - INFO - Iter(train) [ 57950/160000] lr: 6.7048e-03 eta: 15:38:38 time: 0.5503 data_time: 0.0060 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.6693 aux.loss_ce: 0.0072 aux.acc_seg: 99.3571 +04/18 11:16:46 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:16:46 - mmengine - INFO - Iter(train) [ 58000/160000] lr: 6.7019e-03 eta: 15:38:10 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7209 aux.loss_ce: 0.0077 aux.acc_seg: 99.3996 +04/18 11:17:13 - mmengine - INFO - Iter(train) [ 58050/160000] lr: 6.6990e-03 eta: 15:37:42 time: 0.5505 data_time: 0.0068 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.7552 aux.loss_ce: 0.0080 aux.acc_seg: 99.3280 +04/18 11:17:41 - mmengine - INFO - Iter(train) [ 58100/160000] lr: 6.6961e-03 eta: 15:37:15 time: 0.5514 data_time: 0.0067 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0089 decode.acc_seg: 99.6637 aux.loss_ce: 0.0080 aux.acc_seg: 99.1197 +04/18 11:18:08 - mmengine - INFO - Iter(train) [ 58150/160000] lr: 6.6932e-03 eta: 15:36:47 time: 0.5507 data_time: 0.0069 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0107 decode.acc_seg: 99.4823 aux.loss_ce: 0.0088 aux.acc_seg: 98.7581 +04/18 11:18:36 - mmengine - INFO - Iter(train) [ 58200/160000] lr: 6.6903e-03 eta: 15:36:19 time: 0.5510 data_time: 0.0069 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0093 decode.acc_seg: 99.5644 aux.loss_ce: 0.0087 aux.acc_seg: 99.1653 +04/18 11:19:04 - mmengine - INFO - Iter(train) [ 58250/160000] lr: 6.6873e-03 eta: 15:35:52 time: 0.5503 data_time: 0.0065 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.5499 aux.loss_ce: 0.0084 aux.acc_seg: 99.2001 +04/18 11:19:31 - mmengine - INFO - Iter(train) [ 58300/160000] lr: 6.6844e-03 eta: 15:35:24 time: 0.5503 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0081 decode.acc_seg: 99.6187 aux.loss_ce: 0.0076 aux.acc_seg: 99.1308 +04/18 11:19:59 - mmengine - INFO - Iter(train) [ 58350/160000] lr: 6.6815e-03 eta: 15:34:56 time: 0.5507 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0085 decode.acc_seg: 99.6239 aux.loss_ce: 0.0082 aux.acc_seg: 99.1610 +04/18 11:20:26 - mmengine - INFO - Iter(train) [ 58400/160000] lr: 6.6786e-03 eta: 15:34:29 time: 0.5600 data_time: 0.0066 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0072 decode.acc_seg: 99.6520 aux.loss_ce: 0.0067 aux.acc_seg: 99.0705 +04/18 11:20:54 - mmengine - INFO - Iter(train) [ 58450/160000] lr: 6.6757e-03 eta: 15:34:01 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6987 aux.loss_ce: 0.0075 aux.acc_seg: 99.2962 +04/18 11:21:22 - mmengine - INFO - Iter(train) [ 58500/160000] lr: 6.6728e-03 eta: 15:33:34 time: 0.5530 data_time: 0.0070 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7561 aux.loss_ce: 0.0079 aux.acc_seg: 99.3450 +04/18 11:21:49 - mmengine - INFO - Iter(train) [ 58550/160000] lr: 6.6699e-03 eta: 15:33:06 time: 0.5514 data_time: 0.0062 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6677 aux.loss_ce: 0.0077 aux.acc_seg: 99.0487 +04/18 11:22:17 - mmengine - INFO - Iter(train) [ 58600/160000] lr: 6.6670e-03 eta: 15:32:39 time: 0.5516 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6812 aux.loss_ce: 0.0077 aux.acc_seg: 99.1712 +04/18 11:22:44 - mmengine - INFO - Iter(train) [ 58650/160000] lr: 6.6640e-03 eta: 15:32:11 time: 0.5511 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.5533 aux.loss_ce: 0.0079 aux.acc_seg: 98.9416 +04/18 11:23:12 - mmengine - INFO - Iter(train) [ 58700/160000] lr: 6.6611e-03 eta: 15:31:43 time: 0.5511 data_time: 0.0062 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.7086 aux.loss_ce: 0.0072 aux.acc_seg: 99.3958 +04/18 11:23:40 - mmengine - INFO - Iter(train) [ 58750/160000] lr: 6.6582e-03 eta: 15:31:16 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.5885 aux.loss_ce: 0.0086 aux.acc_seg: 98.8952 +04/18 11:24:07 - mmengine - INFO - Iter(train) [ 58800/160000] lr: 6.6553e-03 eta: 15:30:48 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6488 aux.loss_ce: 0.0078 aux.acc_seg: 98.9652 +04/18 11:24:35 - mmengine - INFO - Iter(train) [ 58850/160000] lr: 6.6524e-03 eta: 15:30:20 time: 0.5511 data_time: 0.0060 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.5907 aux.loss_ce: 0.0076 aux.acc_seg: 98.9631 +04/18 11:25:02 - mmengine - INFO - Iter(train) [ 58900/160000] lr: 6.6495e-03 eta: 15:29:53 time: 0.5522 data_time: 0.0067 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6446 aux.loss_ce: 0.0081 aux.acc_seg: 99.4057 +04/18 11:25:30 - mmengine - INFO - Iter(train) [ 58950/160000] lr: 6.6465e-03 eta: 15:29:25 time: 0.5521 data_time: 0.0066 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6372 aux.loss_ce: 0.0079 aux.acc_seg: 99.0109 +04/18 11:25:57 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:25:57 - mmengine - INFO - Iter(train) [ 59000/160000] lr: 6.6436e-03 eta: 15:28:58 time: 0.5523 data_time: 0.0066 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.5790 aux.loss_ce: 0.0079 aux.acc_seg: 99.1814 +04/18 11:26:25 - mmengine - INFO - Iter(train) [ 59050/160000] lr: 6.6407e-03 eta: 15:28:30 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6516 aux.loss_ce: 0.0078 aux.acc_seg: 99.1557 +04/18 11:26:53 - mmengine - INFO - Iter(train) [ 59100/160000] lr: 6.6378e-03 eta: 15:28:02 time: 0.5521 data_time: 0.0071 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.6309 aux.loss_ce: 0.0079 aux.acc_seg: 99.1999 +04/18 11:27:20 - mmengine - INFO - Iter(train) [ 59150/160000] lr: 6.6349e-03 eta: 15:27:35 time: 0.5497 data_time: 0.0068 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.6934 aux.loss_ce: 0.0073 aux.acc_seg: 99.2998 +04/18 11:27:48 - mmengine - INFO - Iter(train) [ 59200/160000] lr: 6.6320e-03 eta: 15:27:07 time: 0.5516 data_time: 0.0062 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.7056 aux.loss_ce: 0.0074 aux.acc_seg: 99.2040 +04/18 11:28:15 - mmengine - INFO - Iter(train) [ 59250/160000] lr: 6.6291e-03 eta: 15:26:39 time: 0.5504 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.5614 aux.loss_ce: 0.0074 aux.acc_seg: 98.7409 +04/18 11:28:43 - mmengine - INFO - Iter(train) [ 59300/160000] lr: 6.6261e-03 eta: 15:26:12 time: 0.5523 data_time: 0.0071 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6745 aux.loss_ce: 0.0078 aux.acc_seg: 99.0722 +04/18 11:29:10 - mmengine - INFO - Iter(train) [ 59350/160000] lr: 6.6232e-03 eta: 15:25:44 time: 0.5518 data_time: 0.0069 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7550 aux.loss_ce: 0.0073 aux.acc_seg: 99.2683 +04/18 11:29:38 - mmengine - INFO - Iter(train) [ 59400/160000] lr: 6.6203e-03 eta: 15:25:17 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7341 aux.loss_ce: 0.0074 aux.acc_seg: 99.1190 +04/18 11:30:06 - mmengine - INFO - Iter(train) [ 59450/160000] lr: 6.6174e-03 eta: 15:24:49 time: 0.5511 data_time: 0.0068 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6515 aux.loss_ce: 0.0078 aux.acc_seg: 99.1786 +04/18 11:30:33 - mmengine - INFO - Iter(train) [ 59500/160000] lr: 6.6145e-03 eta: 15:24:21 time: 0.5499 data_time: 0.0061 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.7619 aux.loss_ce: 0.0075 aux.acc_seg: 99.3440 +04/18 11:31:01 - mmengine - INFO - Iter(train) [ 59550/160000] lr: 6.6116e-03 eta: 15:23:54 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0089 decode.acc_seg: 99.5975 aux.loss_ce: 0.0085 aux.acc_seg: 98.8824 +04/18 11:31:28 - mmengine - INFO - Iter(train) [ 59600/160000] lr: 6.6086e-03 eta: 15:23:26 time: 0.5508 data_time: 0.0063 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6656 aux.loss_ce: 0.0081 aux.acc_seg: 99.1330 +04/18 11:31:56 - mmengine - INFO - Iter(train) [ 59650/160000] lr: 6.6057e-03 eta: 15:22:58 time: 0.5508 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.6439 aux.loss_ce: 0.0081 aux.acc_seg: 99.0078 +04/18 11:32:24 - mmengine - INFO - Iter(train) [ 59700/160000] lr: 6.6028e-03 eta: 15:22:31 time: 0.5511 data_time: 0.0068 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0085 decode.acc_seg: 99.6826 aux.loss_ce: 0.0083 aux.acc_seg: 99.2955 +04/18 11:32:51 - mmengine - INFO - Iter(train) [ 59750/160000] lr: 6.5999e-03 eta: 15:22:03 time: 0.5512 data_time: 0.0072 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0091 decode.acc_seg: 99.6603 aux.loss_ce: 0.0080 aux.acc_seg: 99.3613 +04/18 11:33:19 - mmengine - INFO - Iter(train) [ 59800/160000] lr: 6.5970e-03 eta: 15:21:35 time: 0.5516 data_time: 0.0069 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.7003 aux.loss_ce: 0.0079 aux.acc_seg: 99.1389 +04/18 11:33:46 - mmengine - INFO - Iter(train) [ 59850/160000] lr: 6.5940e-03 eta: 15:21:08 time: 0.5512 data_time: 0.0073 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0086 decode.acc_seg: 99.6959 aux.loss_ce: 0.0081 aux.acc_seg: 99.3964 +04/18 11:34:14 - mmengine - INFO - Iter(train) [ 59900/160000] lr: 6.5911e-03 eta: 15:20:40 time: 0.5511 data_time: 0.0063 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.4831 aux.loss_ce: 0.0087 aux.acc_seg: 98.9328 +04/18 11:34:41 - mmengine - INFO - Iter(train) [ 59950/160000] lr: 6.5882e-03 eta: 15:20:13 time: 0.5516 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.7497 aux.loss_ce: 0.0073 aux.acc_seg: 99.2151 +04/18 11:35:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:35:09 - mmengine - INFO - Iter(train) [ 60000/160000] lr: 6.5853e-03 eta: 15:19:45 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6785 aux.loss_ce: 0.0081 aux.acc_seg: 99.2693 +04/18 11:35:09 - mmengine - INFO - Saving checkpoint at 60000 iterations +04/18 11:35:13 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0462 data_time: 0.0013 memory: 1657 +04/18 11:35:15 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0462 data_time: 0.0014 memory: 1657 +04/18 11:35:18 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0462 data_time: 0.0013 memory: 1657 +04/18 11:35:20 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0454 data_time: 0.0013 memory: 1657 +04/18 11:35:20 - mmengine - INFO - per class results: +04/18 11:35:20 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.58 | 99.53 | 99.48 | 99.58 | +| contrast | 79.36 | 87.46 | 88.49 | 89.55 | 87.46 | ++------------+-------+-------+--------+-----------+--------+ +04/18 11:35:20 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0900 mIoU: 89.2100 mAcc: 93.5200 mFscore: 94.0100 mPrecision: 94.5200 mRecall: 93.5200 data_time: 0.0015 time: 0.0465 +04/18 11:35:48 - mmengine - INFO - Iter(train) [ 60050/160000] lr: 6.5824e-03 eta: 15:19:18 time: 0.5519 data_time: 0.0062 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.7064 aux.loss_ce: 0.0078 aux.acc_seg: 99.3352 +04/18 11:36:15 - mmengine - INFO - Iter(train) [ 60100/160000] lr: 6.5795e-03 eta: 15:18:50 time: 0.5499 data_time: 0.0063 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.4396 aux.loss_ce: 0.0081 aux.acc_seg: 98.9150 +04/18 11:36:43 - mmengine - INFO - Iter(train) [ 60150/160000] lr: 6.5765e-03 eta: 15:18:22 time: 0.5512 data_time: 0.0060 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0109 decode.acc_seg: 99.6948 aux.loss_ce: 0.0086 aux.acc_seg: 99.2352 +04/18 11:37:10 - mmengine - INFO - Iter(train) [ 60200/160000] lr: 6.5736e-03 eta: 15:17:54 time: 0.5514 data_time: 0.0066 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.6398 aux.loss_ce: 0.0081 aux.acc_seg: 99.0908 +04/18 11:37:38 - mmengine - INFO - Iter(train) [ 60250/160000] lr: 6.5707e-03 eta: 15:17:27 time: 0.5507 data_time: 0.0063 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.7389 aux.loss_ce: 0.0083 aux.acc_seg: 99.3309 +04/18 11:38:05 - mmengine - INFO - Iter(train) [ 60300/160000] lr: 6.5678e-03 eta: 15:16:59 time: 0.5512 data_time: 0.0060 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0088 decode.acc_seg: 99.5622 aux.loss_ce: 0.0078 aux.acc_seg: 99.2293 +04/18 11:38:33 - mmengine - INFO - Iter(train) [ 60350/160000] lr: 6.5649e-03 eta: 15:16:32 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.7232 aux.loss_ce: 0.0070 aux.acc_seg: 99.2214 +04/18 11:39:01 - mmengine - INFO - Iter(train) [ 60400/160000] lr: 6.5619e-03 eta: 15:16:04 time: 0.5524 data_time: 0.0070 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0078 decode.acc_seg: 99.7540 aux.loss_ce: 0.0081 aux.acc_seg: 99.2719 +04/18 11:39:28 - mmengine - INFO - Iter(train) [ 60450/160000] lr: 6.5590e-03 eta: 15:15:36 time: 0.5500 data_time: 0.0063 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0083 decode.acc_seg: 99.5952 aux.loss_ce: 0.0077 aux.acc_seg: 99.0477 +04/18 11:39:56 - mmengine - INFO - Iter(train) [ 60500/160000] lr: 6.5561e-03 eta: 15:15:09 time: 0.5512 data_time: 0.0061 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0083 decode.acc_seg: 99.7226 aux.loss_ce: 0.0082 aux.acc_seg: 99.1763 +04/18 11:40:23 - mmengine - INFO - Iter(train) [ 60550/160000] lr: 6.5532e-03 eta: 15:14:41 time: 0.5591 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7407 aux.loss_ce: 0.0075 aux.acc_seg: 99.0932 +04/18 11:40:51 - mmengine - INFO - Iter(train) [ 60600/160000] lr: 6.5503e-03 eta: 15:14:14 time: 0.5582 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7237 aux.loss_ce: 0.0074 aux.acc_seg: 99.2720 +04/18 11:41:19 - mmengine - INFO - Iter(train) [ 60650/160000] lr: 6.5473e-03 eta: 15:13:46 time: 0.5513 data_time: 0.0058 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.7244 aux.loss_ce: 0.0080 aux.acc_seg: 99.3115 +04/18 11:41:46 - mmengine - INFO - Iter(train) [ 60700/160000] lr: 6.5444e-03 eta: 15:13:18 time: 0.5509 data_time: 0.0062 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.6064 aux.loss_ce: 0.0075 aux.acc_seg: 99.2377 +04/18 11:42:14 - mmengine - INFO - Iter(train) [ 60750/160000] lr: 6.5415e-03 eta: 15:12:51 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6486 aux.loss_ce: 0.0072 aux.acc_seg: 99.1142 +04/18 11:42:41 - mmengine - INFO - Iter(train) [ 60800/160000] lr: 6.5386e-03 eta: 15:12:23 time: 0.5524 data_time: 0.0068 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6813 aux.loss_ce: 0.0077 aux.acc_seg: 99.2957 +04/18 11:43:09 - mmengine - INFO - Iter(train) [ 60850/160000] lr: 6.5357e-03 eta: 15:11:55 time: 0.5508 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.7236 aux.loss_ce: 0.0072 aux.acc_seg: 99.3913 +04/18 11:43:36 - mmengine - INFO - Iter(train) [ 60900/160000] lr: 6.5327e-03 eta: 15:11:28 time: 0.5526 data_time: 0.0075 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.6771 aux.loss_ce: 0.0076 aux.acc_seg: 99.3098 +04/18 11:44:04 - mmengine - INFO - Iter(train) [ 60950/160000] lr: 6.5298e-03 eta: 15:11:00 time: 0.5517 data_time: 0.0059 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6767 aux.loss_ce: 0.0075 aux.acc_seg: 99.2650 +04/18 11:44:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:44:32 - mmengine - INFO - Iter(train) [ 61000/160000] lr: 6.5269e-03 eta: 15:10:32 time: 0.5503 data_time: 0.0064 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.6779 aux.loss_ce: 0.0075 aux.acc_seg: 99.0499 +04/18 11:44:59 - mmengine - INFO - Iter(train) [ 61050/160000] lr: 6.5240e-03 eta: 15:10:05 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7244 aux.loss_ce: 0.0076 aux.acc_seg: 99.3732 +04/18 11:45:27 - mmengine - INFO - Iter(train) [ 61100/160000] lr: 6.5211e-03 eta: 15:09:37 time: 0.5503 data_time: 0.0061 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0072 decode.acc_seg: 99.7668 aux.loss_ce: 0.0067 aux.acc_seg: 99.5061 +04/18 11:45:54 - mmengine - INFO - Iter(train) [ 61150/160000] lr: 6.5181e-03 eta: 15:09:10 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.7355 aux.loss_ce: 0.0079 aux.acc_seg: 99.3558 +04/18 11:46:22 - mmengine - INFO - Iter(train) [ 61200/160000] lr: 6.5152e-03 eta: 15:08:42 time: 0.5528 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.7243 aux.loss_ce: 0.0079 aux.acc_seg: 99.3180 +04/18 11:46:49 - mmengine - INFO - Iter(train) [ 61250/160000] lr: 6.5123e-03 eta: 15:08:14 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6181 aux.loss_ce: 0.0081 aux.acc_seg: 98.9738 +04/18 11:47:17 - mmengine - INFO - Iter(train) [ 61300/160000] lr: 6.5094e-03 eta: 15:07:47 time: 0.5513 data_time: 0.0072 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6649 aux.loss_ce: 0.0074 aux.acc_seg: 99.2748 +04/18 11:47:45 - mmengine - INFO - Iter(train) [ 61350/160000] lr: 6.5064e-03 eta: 15:07:19 time: 0.5510 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0071 decode.acc_seg: 99.6904 aux.loss_ce: 0.0070 aux.acc_seg: 99.2872 +04/18 11:48:12 - mmengine - INFO - Iter(train) [ 61400/160000] lr: 6.5035e-03 eta: 15:06:51 time: 0.5499 data_time: 0.0060 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.7398 aux.loss_ce: 0.0079 aux.acc_seg: 99.1993 +04/18 11:48:40 - mmengine - INFO - Iter(train) [ 61450/160000] lr: 6.5006e-03 eta: 15:06:24 time: 0.5509 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6499 aux.loss_ce: 0.0073 aux.acc_seg: 99.1858 +04/18 11:49:07 - mmengine - INFO - Iter(train) [ 61500/160000] lr: 6.4977e-03 eta: 15:05:56 time: 0.5512 data_time: 0.0066 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.6922 aux.loss_ce: 0.0080 aux.acc_seg: 99.2148 +04/18 11:49:35 - mmengine - INFO - Iter(train) [ 61550/160000] lr: 6.4948e-03 eta: 15:05:29 time: 0.5604 data_time: 0.0062 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6339 aux.loss_ce: 0.0078 aux.acc_seg: 99.1366 +04/18 11:50:02 - mmengine - INFO - Iter(train) [ 61600/160000] lr: 6.4918e-03 eta: 15:05:01 time: 0.5510 data_time: 0.0071 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.7356 aux.loss_ce: 0.0076 aux.acc_seg: 99.3462 +04/18 11:50:30 - mmengine - INFO - Iter(train) [ 61650/160000] lr: 6.4889e-03 eta: 15:04:34 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7243 aux.loss_ce: 0.0072 aux.acc_seg: 99.2740 +04/18 11:50:58 - mmengine - INFO - Iter(train) [ 61700/160000] lr: 6.4860e-03 eta: 15:04:06 time: 0.5504 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.7153 aux.loss_ce: 0.0074 aux.acc_seg: 99.3555 +04/18 11:51:25 - mmengine - INFO - Iter(train) [ 61750/160000] lr: 6.4831e-03 eta: 15:03:38 time: 0.5530 data_time: 0.0072 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7630 aux.loss_ce: 0.0078 aux.acc_seg: 99.3668 +04/18 11:51:53 - mmengine - INFO - Iter(train) [ 61800/160000] lr: 6.4801e-03 eta: 15:03:11 time: 0.5507 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.6725 aux.loss_ce: 0.0074 aux.acc_seg: 99.3766 +04/18 11:52:20 - mmengine - INFO - Iter(train) [ 61850/160000] lr: 6.4772e-03 eta: 15:02:43 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6899 aux.loss_ce: 0.0077 aux.acc_seg: 99.1867 +04/18 11:52:48 - mmengine - INFO - Iter(train) [ 61900/160000] lr: 6.4743e-03 eta: 15:02:15 time: 0.5504 data_time: 0.0065 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7381 aux.loss_ce: 0.0077 aux.acc_seg: 99.1766 +04/18 11:53:16 - mmengine - INFO - Iter(train) [ 61950/160000] lr: 6.4714e-03 eta: 15:01:48 time: 0.5504 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7315 aux.loss_ce: 0.0076 aux.acc_seg: 99.3332 +04/18 11:53:43 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 11:53:43 - mmengine - INFO - Iter(train) [ 62000/160000] lr: 6.4684e-03 eta: 15:01:20 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7339 aux.loss_ce: 0.0075 aux.acc_seg: 99.2385 +04/18 11:54:11 - mmengine - INFO - Iter(train) [ 62050/160000] lr: 6.4655e-03 eta: 15:00:52 time: 0.5513 data_time: 0.0068 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.7033 aux.loss_ce: 0.0076 aux.acc_seg: 98.9886 +04/18 11:54:38 - mmengine - INFO - Iter(train) [ 62100/160000] lr: 6.4626e-03 eta: 15:00:25 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6926 aux.loss_ce: 0.0080 aux.acc_seg: 99.2916 +04/18 11:55:06 - mmengine - INFO - Iter(train) [ 62150/160000] lr: 6.4597e-03 eta: 14:59:57 time: 0.5506 data_time: 0.0072 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7195 aux.loss_ce: 0.0069 aux.acc_seg: 99.2236 +04/18 11:55:33 - mmengine - INFO - Iter(train) [ 62200/160000] lr: 6.4567e-03 eta: 14:59:30 time: 0.5500 data_time: 0.0061 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.5876 aux.loss_ce: 0.0076 aux.acc_seg: 99.0355 +04/18 11:56:01 - mmengine - INFO - Iter(train) [ 62250/160000] lr: 6.4538e-03 eta: 14:59:02 time: 0.5513 data_time: 0.0061 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0088 decode.acc_seg: 99.6231 aux.loss_ce: 0.0077 aux.acc_seg: 99.2679 +04/18 11:56:29 - mmengine - INFO - Iter(train) [ 62300/160000] lr: 6.4509e-03 eta: 14:58:34 time: 0.5528 data_time: 0.0058 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6235 aux.loss_ce: 0.0084 aux.acc_seg: 99.0924 +04/18 11:56:56 - mmengine - INFO - Iter(train) [ 62350/160000] lr: 6.4480e-03 eta: 14:58:07 time: 0.5515 data_time: 0.0059 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.6250 aux.loss_ce: 0.0080 aux.acc_seg: 99.3446 +04/18 11:57:24 - mmengine - INFO - Iter(train) [ 62400/160000] lr: 6.4450e-03 eta: 14:57:39 time: 0.5521 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7289 aux.loss_ce: 0.0074 aux.acc_seg: 99.4224 +04/18 11:57:51 - mmengine - INFO - Iter(train) [ 62450/160000] lr: 6.4421e-03 eta: 14:57:11 time: 0.5504 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.7499 aux.loss_ce: 0.0076 aux.acc_seg: 99.4639 +04/18 11:58:19 - mmengine - INFO - Iter(train) [ 62500/160000] lr: 6.4392e-03 eta: 14:56:44 time: 0.5516 data_time: 0.0067 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0085 decode.acc_seg: 99.6426 aux.loss_ce: 0.0079 aux.acc_seg: 99.1708 +04/18 11:58:46 - mmengine - INFO - Iter(train) [ 62550/160000] lr: 6.4363e-03 eta: 14:56:16 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0197 decode.loss_ce: 0.0105 decode.acc_seg: 99.4515 aux.loss_ce: 0.0091 aux.acc_seg: 98.9024 +04/18 11:59:14 - mmengine - INFO - Iter(train) [ 62600/160000] lr: 6.4333e-03 eta: 14:55:48 time: 0.5528 data_time: 0.0059 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0091 decode.acc_seg: 99.5965 aux.loss_ce: 0.0083 aux.acc_seg: 99.0153 +04/18 11:59:42 - mmengine - INFO - Iter(train) [ 62650/160000] lr: 6.4304e-03 eta: 14:55:21 time: 0.5504 data_time: 0.0068 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0095 decode.acc_seg: 99.6124 aux.loss_ce: 0.0084 aux.acc_seg: 99.0512 +04/18 12:00:09 - mmengine - INFO - Iter(train) [ 62700/160000] lr: 6.4275e-03 eta: 14:54:53 time: 0.5606 data_time: 0.0062 memory: 7635 loss: 0.0266 decode.loss_ce: 0.0156 decode.acc_seg: 99.2701 aux.loss_ce: 0.0110 aux.acc_seg: 98.7589 +04/18 12:00:37 - mmengine - INFO - Iter(train) [ 62750/160000] lr: 6.4246e-03 eta: 14:54:26 time: 0.5517 data_time: 0.0062 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0124 decode.acc_seg: 99.6608 aux.loss_ce: 0.0095 aux.acc_seg: 99.0810 +04/18 12:01:05 - mmengine - INFO - Iter(train) [ 62800/160000] lr: 6.4216e-03 eta: 14:53:58 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0095 decode.acc_seg: 99.4710 aux.loss_ce: 0.0087 aux.acc_seg: 98.8008 +04/18 12:01:32 - mmengine - INFO - Iter(train) [ 62850/160000] lr: 6.4187e-03 eta: 14:53:31 time: 0.5511 data_time: 0.0070 memory: 7635 loss: 0.0202 decode.loss_ce: 0.0111 decode.acc_seg: 99.6174 aux.loss_ce: 0.0090 aux.acc_seg: 99.0654 +04/18 12:02:00 - mmengine - INFO - Iter(train) [ 62900/160000] lr: 6.4158e-03 eta: 14:53:03 time: 0.5524 data_time: 0.0069 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0085 decode.acc_seg: 99.6057 aux.loss_ce: 0.0076 aux.acc_seg: 99.3256 +04/18 12:02:27 - mmengine - INFO - Iter(train) [ 62950/160000] lr: 6.4129e-03 eta: 14:52:35 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0080 decode.acc_seg: 99.6496 aux.loss_ce: 0.0073 aux.acc_seg: 99.2011 +04/18 12:02:55 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:02:55 - mmengine - INFO - Iter(train) [ 63000/160000] lr: 6.4099e-03 eta: 14:52:08 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.3012 aux.loss_ce: 0.0087 aux.acc_seg: 98.7721 +04/18 12:03:22 - mmengine - INFO - Iter(train) [ 63050/160000] lr: 6.4070e-03 eta: 14:51:40 time: 0.5515 data_time: 0.0058 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0083 decode.acc_seg: 99.7809 aux.loss_ce: 0.0079 aux.acc_seg: 99.3269 +04/18 12:03:50 - mmengine - INFO - Iter(train) [ 63100/160000] lr: 6.4041e-03 eta: 14:51:13 time: 0.5504 data_time: 0.0068 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0105 decode.acc_seg: 99.6111 aux.loss_ce: 0.0090 aux.acc_seg: 99.1572 +04/18 12:04:18 - mmengine - INFO - Iter(train) [ 63150/160000] lr: 6.4011e-03 eta: 14:50:45 time: 0.5515 data_time: 0.0062 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.7617 aux.loss_ce: 0.0079 aux.acc_seg: 99.3432 +04/18 12:04:45 - mmengine - INFO - Iter(train) [ 63200/160000] lr: 6.3982e-03 eta: 14:50:17 time: 0.5522 data_time: 0.0072 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.6676 aux.loss_ce: 0.0078 aux.acc_seg: 99.3055 +04/18 12:05:13 - mmengine - INFO - Iter(train) [ 63250/160000] lr: 6.3953e-03 eta: 14:49:50 time: 0.5516 data_time: 0.0068 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.5938 aux.loss_ce: 0.0084 aux.acc_seg: 99.3444 +04/18 12:05:40 - mmengine - INFO - Iter(train) [ 63300/160000] lr: 6.3924e-03 eta: 14:49:22 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0233 decode.loss_ce: 0.0133 decode.acc_seg: 99.6986 aux.loss_ce: 0.0101 aux.acc_seg: 99.2738 +04/18 12:06:08 - mmengine - INFO - Iter(train) [ 63350/160000] lr: 6.3894e-03 eta: 14:48:55 time: 0.5510 data_time: 0.0069 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.7083 aux.loss_ce: 0.0086 aux.acc_seg: 99.1220 +04/18 12:06:36 - mmengine - INFO - Iter(train) [ 63400/160000] lr: 6.3865e-03 eta: 14:48:27 time: 0.5513 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.7265 aux.loss_ce: 0.0080 aux.acc_seg: 99.3199 +04/18 12:07:03 - mmengine - INFO - Iter(train) [ 63450/160000] lr: 6.3836e-03 eta: 14:47:59 time: 0.5518 data_time: 0.0062 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0085 decode.acc_seg: 99.7110 aux.loss_ce: 0.0073 aux.acc_seg: 99.2965 +04/18 12:07:31 - mmengine - INFO - Iter(train) [ 63500/160000] lr: 6.3806e-03 eta: 14:47:32 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6314 aux.loss_ce: 0.0081 aux.acc_seg: 99.1665 +04/18 12:07:58 - mmengine - INFO - Iter(train) [ 63550/160000] lr: 6.3777e-03 eta: 14:47:04 time: 0.5510 data_time: 0.0071 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.6786 aux.loss_ce: 0.0080 aux.acc_seg: 99.1355 +04/18 12:08:26 - mmengine - INFO - Iter(train) [ 63600/160000] lr: 6.3748e-03 eta: 14:46:36 time: 0.5505 data_time: 0.0071 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6197 aux.loss_ce: 0.0082 aux.acc_seg: 99.1724 +04/18 12:08:53 - mmengine - INFO - Iter(train) [ 63650/160000] lr: 6.3719e-03 eta: 14:46:09 time: 0.5515 data_time: 0.0068 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0091 decode.acc_seg: 99.6713 aux.loss_ce: 0.0087 aux.acc_seg: 98.9584 +04/18 12:09:21 - mmengine - INFO - Iter(train) [ 63700/160000] lr: 6.3689e-03 eta: 14:45:41 time: 0.5517 data_time: 0.0060 memory: 7635 loss: 0.0217 decode.loss_ce: 0.0119 decode.acc_seg: 99.4674 aux.loss_ce: 0.0098 aux.acc_seg: 98.8377 +04/18 12:09:49 - mmengine - INFO - Iter(train) [ 63750/160000] lr: 6.3660e-03 eta: 14:45:14 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.5516 aux.loss_ce: 0.0085 aux.acc_seg: 99.1558 +04/18 12:10:16 - mmengine - INFO - Iter(train) 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+04/18 12:12:07 - mmengine - INFO - Iter(train) [ 64000/160000] lr: 6.3513e-03 eta: 14:42:56 time: 0.5514 data_time: 0.0070 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0083 decode.acc_seg: 99.6565 aux.loss_ce: 0.0076 aux.acc_seg: 99.2448 +04/18 12:12:34 - mmengine - INFO - Iter(train) [ 64050/160000] lr: 6.3484e-03 eta: 14:42:28 time: 0.5506 data_time: 0.0063 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.6697 aux.loss_ce: 0.0071 aux.acc_seg: 99.0233 +04/18 12:13:02 - mmengine - INFO - Iter(train) [ 64100/160000] lr: 6.3455e-03 eta: 14:42:01 time: 0.5509 data_time: 0.0069 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0088 decode.acc_seg: 99.6822 aux.loss_ce: 0.0085 aux.acc_seg: 99.2085 +04/18 12:13:29 - mmengine - INFO - Iter(train) [ 64150/160000] lr: 6.3426e-03 eta: 14:41:33 time: 0.5498 data_time: 0.0063 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.5502 aux.loss_ce: 0.0081 aux.acc_seg: 98.8747 +04/18 12:13:57 - mmengine - INFO - Iter(train) 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time: 0.5505 data_time: 0.0061 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.6955 aux.loss_ce: 0.0080 aux.acc_seg: 99.3121 +04/18 12:16:15 - mmengine - INFO - Iter(train) [ 64450/160000] lr: 6.3250e-03 eta: 14:38:47 time: 0.5507 data_time: 0.0063 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.6198 aux.loss_ce: 0.0082 aux.acc_seg: 99.0871 +04/18 12:16:42 - mmengine - INFO - Iter(train) [ 64500/160000] lr: 6.3220e-03 eta: 14:38:19 time: 0.5518 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.5845 aux.loss_ce: 0.0082 aux.acc_seg: 99.1028 +04/18 12:17:10 - mmengine - INFO - Iter(train) [ 64550/160000] lr: 6.3191e-03 eta: 14:37:52 time: 0.5518 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.6962 aux.loss_ce: 0.0076 aux.acc_seg: 99.2999 +04/18 12:17:38 - mmengine - INFO - Iter(train) [ 64600/160000] lr: 6.3162e-03 eta: 14:37:24 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6766 aux.loss_ce: 0.0077 aux.acc_seg: 99.3655 +04/18 12:18:05 - mmengine - INFO - Iter(train) [ 64650/160000] lr: 6.3132e-03 eta: 14:36:57 time: 0.5502 data_time: 0.0060 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.5746 aux.loss_ce: 0.0085 aux.acc_seg: 99.2217 +04/18 12:18:33 - mmengine - INFO - Iter(train) [ 64700/160000] lr: 6.3103e-03 eta: 14:36:29 time: 0.5505 data_time: 0.0072 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0092 decode.acc_seg: 99.6564 aux.loss_ce: 0.0079 aux.acc_seg: 99.1640 +04/18 12:19:00 - mmengine - INFO - Iter(train) [ 64750/160000] lr: 6.3074e-03 eta: 14:36:01 time: 0.5514 data_time: 0.0070 memory: 7635 loss: 0.0200 decode.loss_ce: 0.0112 decode.acc_seg: 99.6309 aux.loss_ce: 0.0088 aux.acc_seg: 99.1497 +04/18 12:19:28 - mmengine - INFO - Iter(train) [ 64800/160000] lr: 6.3044e-03 eta: 14:35:34 time: 0.5519 data_time: 0.0070 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0095 decode.acc_seg: 99.6595 aux.loss_ce: 0.0085 aux.acc_seg: 99.1340 +04/18 12:19:56 - mmengine - INFO - Iter(train) [ 64850/160000] lr: 6.3015e-03 eta: 14:35:06 time: 0.5502 data_time: 0.0066 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0092 decode.acc_seg: 99.6979 aux.loss_ce: 0.0080 aux.acc_seg: 99.3130 +04/18 12:20:23 - mmengine - INFO - Iter(train) [ 64900/160000] lr: 6.2986e-03 eta: 14:34:39 time: 0.5522 data_time: 0.0070 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6875 aux.loss_ce: 0.0076 aux.acc_seg: 99.2385 +04/18 12:20:51 - mmengine - INFO - Iter(train) [ 64950/160000] lr: 6.2956e-03 eta: 14:34:11 time: 0.5515 data_time: 0.0067 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7477 aux.loss_ce: 0.0079 aux.acc_seg: 99.1247 +04/18 12:21:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:21:18 - mmengine - INFO - Iter(train) [ 65000/160000] lr: 6.2927e-03 eta: 14:33:44 time: 0.5523 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7310 aux.loss_ce: 0.0074 aux.acc_seg: 99.1592 +04/18 12:21:46 - mmengine - INFO - Iter(train) [ 65050/160000] lr: 6.2898e-03 eta: 14:33:16 time: 0.5518 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.7298 aux.loss_ce: 0.0075 aux.acc_seg: 99.3653 +04/18 12:22:14 - mmengine - INFO - Iter(train) [ 65100/160000] lr: 6.2868e-03 eta: 14:32:48 time: 0.5513 data_time: 0.0059 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7375 aux.loss_ce: 0.0079 aux.acc_seg: 99.2416 +04/18 12:22:41 - mmengine - INFO - Iter(train) [ 65150/160000] lr: 6.2839e-03 eta: 14:32:21 time: 0.5517 data_time: 0.0061 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0100 decode.acc_seg: 99.6408 aux.loss_ce: 0.0090 aux.acc_seg: 99.1256 +04/18 12:23:09 - mmengine - INFO - Iter(train) [ 65200/160000] lr: 6.2810e-03 eta: 14:31:53 time: 0.5528 data_time: 0.0072 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0099 decode.acc_seg: 99.6358 aux.loss_ce: 0.0087 aux.acc_seg: 99.0165 +04/18 12:23:36 - mmengine - INFO - Iter(train) [ 65250/160000] lr: 6.2780e-03 eta: 14:31:25 time: 0.5512 data_time: 0.0059 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.6083 aux.loss_ce: 0.0078 aux.acc_seg: 99.0692 +04/18 12:24:04 - mmengine - INFO - Iter(train) [ 65300/160000] lr: 6.2751e-03 eta: 14:30:58 time: 0.5505 data_time: 0.0066 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.7094 aux.loss_ce: 0.0080 aux.acc_seg: 99.0721 +04/18 12:24:31 - mmengine - INFO - Iter(train) [ 65350/160000] lr: 6.2722e-03 eta: 14:30:30 time: 0.5510 data_time: 0.0068 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6728 aux.loss_ce: 0.0081 aux.acc_seg: 99.2450 +04/18 12:24:59 - mmengine - INFO - Iter(train) [ 65400/160000] lr: 6.2692e-03 eta: 14:30:02 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0086 decode.acc_seg: 99.6277 aux.loss_ce: 0.0084 aux.acc_seg: 98.9611 +04/18 12:25:27 - mmengine - INFO - Iter(train) [ 65450/160000] lr: 6.2663e-03 eta: 14:29:35 time: 0.5513 data_time: 0.0062 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6113 aux.loss_ce: 0.0079 aux.acc_seg: 99.1420 +04/18 12:25:54 - mmengine - INFO - Iter(train) [ 65500/160000] lr: 6.2634e-03 eta: 14:29:07 time: 0.5505 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0077 decode.acc_seg: 99.6889 aux.loss_ce: 0.0072 aux.acc_seg: 99.1988 +04/18 12:26:22 - mmengine - INFO - Iter(train) [ 65550/160000] lr: 6.2604e-03 eta: 14:28:40 time: 0.5523 data_time: 0.0074 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0092 decode.acc_seg: 99.7055 aux.loss_ce: 0.0087 aux.acc_seg: 99.0686 +04/18 12:26:49 - mmengine - INFO - Iter(train) [ 65600/160000] lr: 6.2575e-03 eta: 14:28:12 time: 0.5511 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6802 aux.loss_ce: 0.0076 aux.acc_seg: 99.2186 +04/18 12:27:17 - mmengine 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6.2428e-03 eta: 14:25:54 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0080 decode.acc_seg: 99.7318 aux.loss_ce: 0.0073 aux.acc_seg: 99.1998 +04/18 12:29:35 - mmengine - INFO - Iter(train) [ 65900/160000] lr: 6.2399e-03 eta: 14:25:26 time: 0.5510 data_time: 0.0058 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0091 decode.acc_seg: 99.7058 aux.loss_ce: 0.0079 aux.acc_seg: 99.3279 +04/18 12:30:03 - mmengine - INFO - Iter(train) [ 65950/160000] lr: 6.2369e-03 eta: 14:24:59 time: 0.5516 data_time: 0.0063 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0092 decode.acc_seg: 99.6469 aux.loss_ce: 0.0085 aux.acc_seg: 98.9905 +04/18 12:30:30 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:30:30 - mmengine - INFO - Iter(train) [ 66000/160000] lr: 6.2340e-03 eta: 14:24:32 time: 0.5514 data_time: 0.0080 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.7491 aux.loss_ce: 0.0081 aux.acc_seg: 99.3939 +04/18 12:30:58 - mmengine - INFO - Iter(train) [ 66050/160000] lr: 6.2311e-03 eta: 14:24:04 time: 0.5508 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7186 aux.loss_ce: 0.0075 aux.acc_seg: 99.1418 +04/18 12:31:25 - mmengine - INFO - Iter(train) [ 66100/160000] lr: 6.2281e-03 eta: 14:23:36 time: 0.5509 data_time: 0.0061 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0079 decode.acc_seg: 99.6423 aux.loss_ce: 0.0074 aux.acc_seg: 99.1426 +04/18 12:31:53 - mmengine - INFO - Iter(train) [ 66150/160000] lr: 6.2252e-03 eta: 14:23:09 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.7011 aux.loss_ce: 0.0078 aux.acc_seg: 99.2793 +04/18 12:32:21 - mmengine - INFO - Iter(train) [ 66200/160000] lr: 6.2223e-03 eta: 14:22:41 time: 0.5513 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.7555 aux.loss_ce: 0.0079 aux.acc_seg: 99.2439 +04/18 12:32:48 - mmengine - INFO - Iter(train) [ 66250/160000] lr: 6.2193e-03 eta: 14:22:13 time: 0.5502 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0069 decode.acc_seg: 99.6030 aux.loss_ce: 0.0068 aux.acc_seg: 99.2440 +04/18 12:33:16 - mmengine - INFO - Iter(train) [ 66300/160000] lr: 6.2164e-03 eta: 14:21:46 time: 0.5508 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7398 aux.loss_ce: 0.0076 aux.acc_seg: 99.4539 +04/18 12:33:43 - mmengine - INFO - Iter(train) [ 66350/160000] lr: 6.2135e-03 eta: 14:21:18 time: 0.5521 data_time: 0.0068 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.6774 aux.loss_ce: 0.0072 aux.acc_seg: 99.1939 +04/18 12:34:11 - mmengine - INFO - Iter(train) [ 66400/160000] lr: 6.2105e-03 eta: 14:20:50 time: 0.5514 data_time: 0.0061 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.6902 aux.loss_ce: 0.0073 aux.acc_seg: 99.3798 +04/18 12:34:38 - mmengine - INFO - Iter(train) [ 66450/160000] lr: 6.2076e-03 eta: 14:20:23 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.7005 aux.loss_ce: 0.0078 aux.acc_seg: 99.1853 +04/18 12:35:06 - mmengine - INFO - Iter(train) [ 66500/160000] lr: 6.2046e-03 eta: 14:19:55 time: 0.5516 data_time: 0.0062 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.8232 aux.loss_ce: 0.0075 aux.acc_seg: 99.3772 +04/18 12:35:33 - mmengine - INFO - Iter(train) [ 66550/160000] lr: 6.2017e-03 eta: 14:19:28 time: 0.5500 data_time: 0.0082 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6459 aux.loss_ce: 0.0077 aux.acc_seg: 99.1036 +04/18 12:36:01 - mmengine - INFO - Iter(train) [ 66600/160000] lr: 6.1988e-03 eta: 14:19:00 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6597 aux.loss_ce: 0.0074 aux.acc_seg: 99.0806 +04/18 12:36:29 - mmengine - INFO - Iter(train) [ 66650/160000] lr: 6.1958e-03 eta: 14:18:32 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6656 aux.loss_ce: 0.0074 aux.acc_seg: 99.1944 +04/18 12:36:56 - mmengine - INFO - Iter(train) [ 66700/160000] lr: 6.1929e-03 eta: 14:18:05 time: 0.5526 data_time: 0.0072 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.6866 aux.loss_ce: 0.0073 aux.acc_seg: 99.2916 +04/18 12:37:24 - mmengine - INFO - Iter(train) [ 66750/160000] lr: 6.1899e-03 eta: 14:17:37 time: 0.5517 data_time: 0.0062 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.7363 aux.loss_ce: 0.0079 aux.acc_seg: 99.0746 +04/18 12:37:51 - mmengine - INFO - Iter(train) [ 66800/160000] lr: 6.1870e-03 eta: 14:17:09 time: 0.5501 data_time: 0.0062 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.5464 aux.loss_ce: 0.0076 aux.acc_seg: 98.8960 +04/18 12:38:19 - mmengine - INFO - Iter(train) [ 66850/160000] lr: 6.1841e-03 eta: 14:16:42 time: 0.5516 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6633 aux.loss_ce: 0.0078 aux.acc_seg: 99.1499 +04/18 12:38:46 - mmengine - INFO - Iter(train) [ 66900/160000] lr: 6.1811e-03 eta: 14:16:14 time: 0.5518 data_time: 0.0062 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6593 aux.loss_ce: 0.0074 aux.acc_seg: 99.3145 +04/18 12:39:14 - mmengine - INFO - Iter(train) [ 66950/160000] lr: 6.1782e-03 eta: 14:15:47 time: 0.5515 data_time: 0.0066 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.6510 aux.loss_ce: 0.0080 aux.acc_seg: 99.1521 +04/18 12:39:42 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:39:42 - mmengine - INFO - Iter(train) [ 67000/160000] lr: 6.1753e-03 eta: 14:15:19 time: 0.5611 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6480 aux.loss_ce: 0.0075 aux.acc_seg: 99.1254 +04/18 12:40:09 - mmengine - INFO - Iter(train) [ 67050/160000] lr: 6.1723e-03 eta: 14:14:51 time: 0.5510 data_time: 0.0059 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6650 aux.loss_ce: 0.0078 aux.acc_seg: 99.1677 +04/18 12:40:37 - mmengine - INFO - Iter(train) [ 67100/160000] lr: 6.1694e-03 eta: 14:14:24 time: 0.5521 data_time: 0.0060 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.6387 aux.loss_ce: 0.0073 aux.acc_seg: 99.0540 +04/18 12:41:05 - mmengine - INFO - Iter(train) [ 67150/160000] lr: 6.1664e-03 eta: 14:13:56 time: 0.5517 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7343 aux.loss_ce: 0.0073 aux.acc_seg: 99.3545 +04/18 12:41:32 - mmengine - INFO - Iter(train) [ 67200/160000] lr: 6.1635e-03 eta: 14:13:29 time: 0.5520 data_time: 0.0076 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.6905 aux.loss_ce: 0.0075 aux.acc_seg: 99.4012 +04/18 12:42:00 - mmengine - INFO - Iter(train) [ 67250/160000] lr: 6.1606e-03 eta: 14:13:01 time: 0.5515 data_time: 0.0071 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.7496 aux.loss_ce: 0.0073 aux.acc_seg: 99.2722 +04/18 12:42:27 - mmengine - INFO - Iter(train) [ 67300/160000] lr: 6.1576e-03 eta: 14:12:33 time: 0.5520 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7684 aux.loss_ce: 0.0069 aux.acc_seg: 99.2039 +04/18 12:42:55 - mmengine - INFO - Iter(train) [ 67350/160000] lr: 6.1547e-03 eta: 14:12:06 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7545 aux.loss_ce: 0.0080 aux.acc_seg: 99.4624 +04/18 12:43:22 - mmengine - INFO - Iter(train) [ 67400/160000] lr: 6.1517e-03 eta: 14:11:38 time: 0.5511 data_time: 0.0068 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0090 decode.acc_seg: 99.6899 aux.loss_ce: 0.0077 aux.acc_seg: 99.2165 +04/18 12:43:50 - mmengine - INFO - Iter(train) [ 67450/160000] lr: 6.1488e-03 eta: 14:11:10 time: 0.5508 data_time: 0.0064 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0099 decode.acc_seg: 99.5389 aux.loss_ce: 0.0087 aux.acc_seg: 98.9109 +04/18 12:44:17 - mmengine - INFO - Iter(train) [ 67500/160000] lr: 6.1458e-03 eta: 14:10:43 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0196 decode.loss_ce: 0.0107 decode.acc_seg: 99.4827 aux.loss_ce: 0.0089 aux.acc_seg: 99.0738 +04/18 12:44:45 - mmengine - INFO - Iter(train) [ 67550/160000] lr: 6.1429e-03 eta: 14:10:15 time: 0.5509 data_time: 0.0062 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0087 decode.acc_seg: 99.7127 aux.loss_ce: 0.0083 aux.acc_seg: 99.0096 +04/18 12:45:13 - mmengine - INFO - Iter(train) [ 67600/160000] lr: 6.1400e-03 eta: 14:09:47 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0104 decode.acc_seg: 99.5878 aux.loss_ce: 0.0087 aux.acc_seg: 99.0252 +04/18 12:45:40 - mmengine - INFO - Iter(train) [ 67650/160000] lr: 6.1370e-03 eta: 14:09:20 time: 0.5486 data_time: 0.0066 memory: 7635 loss: 0.0181 decode.loss_ce: 0.0095 decode.acc_seg: 99.6152 aux.loss_ce: 0.0086 aux.acc_seg: 99.1368 +04/18 12:46:08 - mmengine - INFO - Iter(train) 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time: 0.5512 data_time: 0.0072 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.7409 aux.loss_ce: 0.0075 aux.acc_seg: 99.3134 +04/18 12:48:25 - mmengine - INFO - Iter(train) [ 67950/160000] lr: 6.1194e-03 eta: 14:06:34 time: 0.5510 data_time: 0.0067 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.6959 aux.loss_ce: 0.0076 aux.acc_seg: 99.3150 +04/18 12:48:53 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:48:53 - mmengine - INFO - Iter(train) [ 68000/160000] lr: 6.1164e-03 eta: 14:06:06 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0099 decode.acc_seg: 99.6530 aux.loss_ce: 0.0086 aux.acc_seg: 99.1934 +04/18 12:49:21 - mmengine - INFO - Iter(train) [ 68050/160000] lr: 6.1135e-03 eta: 14:05:39 time: 0.5508 data_time: 0.0067 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6723 aux.loss_ce: 0.0085 aux.acc_seg: 99.1469 +04/18 12:49:48 - mmengine - INFO - Iter(train) [ 68100/160000] lr: 6.1105e-03 eta: 14:05:11 time: 0.5508 data_time: 0.0060 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.6007 aux.loss_ce: 0.0081 aux.acc_seg: 98.8534 +04/18 12:50:16 - mmengine - INFO - Iter(train) [ 68150/160000] lr: 6.1076e-03 eta: 14:04:44 time: 0.5515 data_time: 0.0067 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.7796 aux.loss_ce: 0.0079 aux.acc_seg: 99.3534 +04/18 12:50:44 - mmengine - INFO - Iter(train) [ 68200/160000] lr: 6.1047e-03 eta: 14:04:16 time: 0.5523 data_time: 0.0074 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.7065 aux.loss_ce: 0.0078 aux.acc_seg: 99.1721 +04/18 12:51:11 - mmengine - INFO - Iter(train) [ 68250/160000] lr: 6.1017e-03 eta: 14:03:49 time: 0.5510 data_time: 0.0066 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.4453 aux.loss_ce: 0.0081 aux.acc_seg: 99.0548 +04/18 12:51:39 - mmengine - INFO - Iter(train) [ 68300/160000] lr: 6.0988e-03 eta: 14:03:21 time: 0.5502 data_time: 0.0070 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.5903 aux.loss_ce: 0.0077 aux.acc_seg: 98.9627 +04/18 12:52:06 - mmengine - INFO - Iter(train) [ 68350/160000] lr: 6.0958e-03 eta: 14:02:53 time: 0.5509 data_time: 0.0068 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0080 decode.acc_seg: 99.6009 aux.loss_ce: 0.0075 aux.acc_seg: 99.1240 +04/18 12:52:34 - mmengine - INFO - Iter(train) [ 68400/160000] lr: 6.0929e-03 eta: 14:02:26 time: 0.5503 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.6278 aux.loss_ce: 0.0075 aux.acc_seg: 99.0184 +04/18 12:53:01 - mmengine - INFO - Iter(train) [ 68450/160000] lr: 6.0899e-03 eta: 14:01:58 time: 0.5499 data_time: 0.0060 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.7849 aux.loss_ce: 0.0076 aux.acc_seg: 99.3752 +04/18 12:53:29 - mmengine - INFO - Iter(train) [ 68500/160000] lr: 6.0870e-03 eta: 14:01:30 time: 0.5518 data_time: 0.0061 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.6494 aux.loss_ce: 0.0074 aux.acc_seg: 99.0561 +04/18 12:53:57 - mmengine - INFO - Iter(train) [ 68550/160000] lr: 6.0840e-03 eta: 14:01:03 time: 0.5506 data_time: 0.0069 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.7048 aux.loss_ce: 0.0077 aux.acc_seg: 99.2647 +04/18 12:54:24 - mmengine - INFO - Iter(train) [ 68600/160000] lr: 6.0811e-03 eta: 14:00:35 time: 0.5503 data_time: 0.0062 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6004 aux.loss_ce: 0.0073 aux.acc_seg: 99.1581 +04/18 12:54:52 - mmengine - INFO - Iter(train) [ 68650/160000] lr: 6.0782e-03 eta: 14:00:07 time: 0.5499 data_time: 0.0058 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.6217 aux.loss_ce: 0.0080 aux.acc_seg: 99.0794 +04/18 12:55:19 - mmengine - INFO - Iter(train) [ 68700/160000] lr: 6.0752e-03 eta: 13:59:40 time: 0.5519 data_time: 0.0070 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7984 aux.loss_ce: 0.0076 aux.acc_seg: 99.4544 +04/18 12:55:47 - mmengine - INFO - Iter(train) [ 68750/160000] lr: 6.0723e-03 eta: 13:59:12 time: 0.5500 data_time: 0.0058 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6758 aux.loss_ce: 0.0077 aux.acc_seg: 99.0948 +04/18 12:56:14 - mmengine - INFO - Iter(train) [ 68800/160000] lr: 6.0693e-03 eta: 13:58:44 time: 0.5506 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6945 aux.loss_ce: 0.0074 aux.acc_seg: 99.2737 +04/18 12:56:42 - mmengine - INFO - Iter(train) [ 68850/160000] lr: 6.0664e-03 eta: 13:58:17 time: 0.5505 data_time: 0.0061 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.7359 aux.loss_ce: 0.0078 aux.acc_seg: 99.2151 +04/18 12:57:09 - mmengine - INFO - Iter(train) [ 68900/160000] lr: 6.0634e-03 eta: 13:57:49 time: 0.5498 data_time: 0.0059 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7167 aux.loss_ce: 0.0072 aux.acc_seg: 99.2746 +04/18 12:57:37 - mmengine - INFO - Iter(train) [ 68950/160000] lr: 6.0605e-03 eta: 13:57:21 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6468 aux.loss_ce: 0.0074 aux.acc_seg: 99.1611 +04/18 12:58:05 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 12:58:05 - mmengine - INFO - Iter(train) [ 69000/160000] lr: 6.0575e-03 eta: 13:56:54 time: 0.5516 data_time: 0.0062 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6240 aux.loss_ce: 0.0073 aux.acc_seg: 99.1300 +04/18 12:58:32 - mmengine - INFO - Iter(train) [ 69050/160000] lr: 6.0546e-03 eta: 13:56:26 time: 0.5504 data_time: 0.0061 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6925 aux.loss_ce: 0.0071 aux.acc_seg: 99.2249 +04/18 12:59:00 - mmengine - INFO - Iter(train) [ 69100/160000] lr: 6.0516e-03 eta: 13:55:59 time: 0.5507 data_time: 0.0063 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.6224 aux.loss_ce: 0.0081 aux.acc_seg: 99.2496 +04/18 12:59:27 - mmengine - INFO - Iter(train) [ 69150/160000] lr: 6.0487e-03 eta: 13:55:31 time: 0.5623 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.5975 aux.loss_ce: 0.0076 aux.acc_seg: 99.0157 +04/18 12:59:55 - mmengine - INFO - Iter(train) [ 69200/160000] lr: 6.0458e-03 eta: 13:55:04 time: 0.5602 data_time: 0.0068 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.7068 aux.loss_ce: 0.0080 aux.acc_seg: 99.0401 +04/18 13:00:23 - mmengine - INFO - Iter(train) [ 69250/160000] lr: 6.0428e-03 eta: 13:54:36 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.7402 aux.loss_ce: 0.0078 aux.acc_seg: 99.2205 +04/18 13:00:50 - mmengine - INFO - Iter(train) [ 69300/160000] lr: 6.0399e-03 eta: 13:54:09 time: 0.5508 data_time: 0.0065 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.7506 aux.loss_ce: 0.0077 aux.acc_seg: 99.2965 +04/18 13:01:18 - mmengine - INFO - Iter(train) [ 69350/160000] lr: 6.0369e-03 eta: 13:53:41 time: 0.5513 data_time: 0.0069 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0079 decode.acc_seg: 99.7013 aux.loss_ce: 0.0074 aux.acc_seg: 99.3340 +04/18 13:01:45 - mmengine - INFO - Iter(train) [ 69400/160000] lr: 6.0340e-03 eta: 13:53:13 time: 0.5512 data_time: 0.0065 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6690 aux.loss_ce: 0.0073 aux.acc_seg: 99.2468 +04/18 13:02:13 - mmengine - INFO - Iter(train) [ 69450/160000] lr: 6.0310e-03 eta: 13:52:46 time: 0.5509 data_time: 0.0064 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0105 decode.acc_seg: 99.6007 aux.loss_ce: 0.0089 aux.acc_seg: 99.1673 +04/18 13:02:41 - mmengine - INFO - Iter(train) [ 69500/160000] lr: 6.0281e-03 eta: 13:52:18 time: 0.5520 data_time: 0.0061 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0078 decode.acc_seg: 99.7236 aux.loss_ce: 0.0073 aux.acc_seg: 99.3158 +04/18 13:03:08 - mmengine - INFO - Iter(train) [ 69550/160000] lr: 6.0251e-03 eta: 13:51:50 time: 0.5511 data_time: 0.0061 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.6690 aux.loss_ce: 0.0074 aux.acc_seg: 99.1807 +04/18 13:03:36 - mmengine - INFO - Iter(train) [ 69600/160000] lr: 6.0222e-03 eta: 13:51:23 time: 0.5529 data_time: 0.0069 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0096 decode.acc_seg: 99.6646 aux.loss_ce: 0.0086 aux.acc_seg: 99.1276 +04/18 13:04:03 - mmengine - INFO - Iter(train) [ 69650/160000] lr: 6.0192e-03 eta: 13:50:55 time: 0.5506 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6944 aux.loss_ce: 0.0079 aux.acc_seg: 99.4189 +04/18 13:04:31 - mmengine - INFO - Iter(train) [ 69700/160000] lr: 6.0163e-03 eta: 13:50:28 time: 0.5500 data_time: 0.0070 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0093 decode.acc_seg: 99.6805 aux.loss_ce: 0.0084 aux.acc_seg: 99.1603 +04/18 13:04:58 - mmengine - INFO - Iter(train) [ 69750/160000] lr: 6.0133e-03 eta: 13:50:00 time: 0.5520 data_time: 0.0065 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0111 decode.acc_seg: 99.6716 aux.loss_ce: 0.0092 aux.acc_seg: 99.1631 +04/18 13:05:26 - mmengine - INFO - Iter(train) [ 69800/160000] lr: 6.0104e-03 eta: 13:49:32 time: 0.5505 data_time: 0.0066 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0096 decode.acc_seg: 99.5812 aux.loss_ce: 0.0087 aux.acc_seg: 99.0966 +04/18 13:05:54 - mmengine - INFO - Iter(train) [ 69850/160000] lr: 6.0074e-03 eta: 13:49:05 time: 0.5523 data_time: 0.0062 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0094 decode.acc_seg: 99.7193 aux.loss_ce: 0.0083 aux.acc_seg: 99.1334 +04/18 13:06:21 - mmengine - INFO - Iter(train) [ 69900/160000] lr: 6.0045e-03 eta: 13:48:37 time: 0.5517 data_time: 0.0065 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0082 decode.acc_seg: 99.6904 aux.loss_ce: 0.0080 aux.acc_seg: 99.2169 +04/18 13:06:49 - mmengine - INFO - Iter(train) [ 69950/160000] lr: 6.0015e-03 eta: 13:48:09 time: 0.5511 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.6339 aux.loss_ce: 0.0076 aux.acc_seg: 99.2690 +04/18 13:07:16 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:07:16 - mmengine - INFO - Iter(train) [ 70000/160000] lr: 5.9986e-03 eta: 13:47:42 time: 0.5509 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7218 aux.loss_ce: 0.0071 aux.acc_seg: 99.2356 +04/18 13:07:16 - mmengine - INFO - Saving checkpoint at 70000 iterations +04/18 13:07:20 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0468 data_time: 0.0015 memory: 1657 +04/18 13:07:23 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0466 data_time: 0.0014 memory: 1657 +04/18 13:07:25 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0466 data_time: 0.0013 memory: 1657 +04/18 13:07:27 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0458 data_time: 0.0012 memory: 1657 +04/18 13:07:28 - mmengine - INFO - per class results: +04/18 13:07:28 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.06 | 99.44 | 99.53 | 99.62 | 99.44 | +| contrast | 80.05 | 90.89 | 88.92 | 87.04 | 90.89 | ++------------+-------+-------+--------+-----------+--------+ +04/18 13:07:28 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1000 mIoU: 89.5600 mAcc: 95.1600 mFscore: 94.2200 mPrecision: 93.3300 mRecall: 95.1600 data_time: 0.0015 time: 0.0467 +04/18 13:07:55 - mmengine - INFO - Iter(train) [ 70050/160000] lr: 5.9956e-03 eta: 13:47:14 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.7017 aux.loss_ce: 0.0077 aux.acc_seg: 99.0495 +04/18 13:08:23 - mmengine - INFO - Iter(train) [ 70100/160000] lr: 5.9927e-03 eta: 13:46:47 time: 0.5523 data_time: 0.0077 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.7711 aux.loss_ce: 0.0075 aux.acc_seg: 99.3266 +04/18 13:08:50 - mmengine - INFO - Iter(train) [ 70150/160000] lr: 5.9897e-03 eta: 13:46:19 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6188 aux.loss_ce: 0.0082 aux.acc_seg: 98.9286 +04/18 13:09:18 - mmengine - INFO - Iter(train) [ 70200/160000] lr: 5.9868e-03 eta: 13:45:52 time: 0.5494 data_time: 0.0061 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6087 aux.loss_ce: 0.0078 aux.acc_seg: 99.0937 +04/18 13:09:45 - mmengine - INFO - Iter(train) [ 70250/160000] lr: 5.9838e-03 eta: 13:45:24 time: 0.5502 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0085 decode.acc_seg: 99.7354 aux.loss_ce: 0.0076 aux.acc_seg: 99.2040 +04/18 13:10:13 - mmengine - INFO - Iter(train) [ 70300/160000] lr: 5.9809e-03 eta: 13:44:57 time: 0.5513 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6964 aux.loss_ce: 0.0078 aux.acc_seg: 99.2430 +04/18 13:10:41 - mmengine - INFO - Iter(train) [ 70350/160000] lr: 5.9779e-03 eta: 13:44:29 time: 0.5516 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6499 aux.loss_ce: 0.0075 aux.acc_seg: 99.1297 +04/18 13:11:08 - mmengine - INFO - Iter(train) [ 70400/160000] lr: 5.9750e-03 eta: 13:44:01 time: 0.5504 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.6212 aux.loss_ce: 0.0075 aux.acc_seg: 99.3167 +04/18 13:11:36 - mmengine - INFO - Iter(train) [ 70450/160000] lr: 5.9720e-03 eta: 13:43:34 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0078 decode.acc_seg: 99.6600 aux.loss_ce: 0.0080 aux.acc_seg: 99.1551 +04/18 13:12:04 - mmengine - INFO - Iter(train) [ 70500/160000] lr: 5.9691e-03 eta: 13:43:06 time: 0.5520 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.7507 aux.loss_ce: 0.0075 aux.acc_seg: 99.5346 +04/18 13:12:31 - mmengine - INFO - Iter(train) [ 70550/160000] lr: 5.9661e-03 eta: 13:42:39 time: 0.5515 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.7468 aux.loss_ce: 0.0080 aux.acc_seg: 99.0608 +04/18 13:12:59 - mmengine - INFO - Iter(train) [ 70600/160000] lr: 5.9632e-03 eta: 13:42:11 time: 0.5515 data_time: 0.0069 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6384 aux.loss_ce: 0.0078 aux.acc_seg: 98.9050 +04/18 13:13:26 - mmengine - INFO - Iter(train) [ 70650/160000] lr: 5.9602e-03 eta: 13:41:44 time: 0.5515 data_time: 0.0068 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0087 decode.acc_seg: 99.7053 aux.loss_ce: 0.0082 aux.acc_seg: 99.2291 +04/18 13:13:54 - mmengine - INFO - Iter(train) [ 70700/160000] lr: 5.9573e-03 eta: 13:41:16 time: 0.5520 data_time: 0.0071 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7040 aux.loss_ce: 0.0077 aux.acc_seg: 99.1316 +04/18 13:14:22 - mmengine - INFO - Iter(train) [ 70750/160000] lr: 5.9543e-03 eta: 13:40:48 time: 0.5527 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.5953 aux.loss_ce: 0.0078 aux.acc_seg: 98.9807 +04/18 13:14:49 - mmengine - INFO - Iter(train) [ 70800/160000] lr: 5.9514e-03 eta: 13:40:21 time: 0.5515 data_time: 0.0060 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6740 aux.loss_ce: 0.0073 aux.acc_seg: 99.1641 +04/18 13:15:17 - mmengine - INFO - Iter(train) [ 70850/160000] lr: 5.9484e-03 eta: 13:39:53 time: 0.5507 data_time: 0.0065 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0081 decode.acc_seg: 99.6813 aux.loss_ce: 0.0082 aux.acc_seg: 99.0903 +04/18 13:15:44 - mmengine - INFO - Iter(train) [ 70900/160000] lr: 5.9455e-03 eta: 13:39:26 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.5258 aux.loss_ce: 0.0084 aux.acc_seg: 98.8321 +04/18 13:16:12 - mmengine - INFO - Iter(train) [ 70950/160000] lr: 5.9425e-03 eta: 13:38:58 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.6739 aux.loss_ce: 0.0082 aux.acc_seg: 98.9123 +04/18 13:16:40 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:16:40 - mmengine - INFO - Iter(train) [ 71000/160000] lr: 5.9396e-03 eta: 13:38:30 time: 0.5512 data_time: 0.0060 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.5271 aux.loss_ce: 0.0075 aux.acc_seg: 99.0655 +04/18 13:17:07 - mmengine - INFO - Iter(train) [ 71050/160000] lr: 5.9366e-03 eta: 13:38:03 time: 0.5532 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7773 aux.loss_ce: 0.0075 aux.acc_seg: 99.3194 +04/18 13:17:35 - mmengine - INFO - Iter(train) [ 71100/160000] lr: 5.9337e-03 eta: 13:37:35 time: 0.5505 data_time: 0.0062 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.6755 aux.loss_ce: 0.0078 aux.acc_seg: 99.1154 +04/18 13:18:02 - mmengine - INFO - Iter(train) [ 71150/160000] lr: 5.9307e-03 eta: 13:37:08 time: 0.5518 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.6461 aux.loss_ce: 0.0072 aux.acc_seg: 99.0748 +04/18 13:18:30 - mmengine - INFO - Iter(train) [ 71200/160000] lr: 5.9278e-03 eta: 13:36:40 time: 0.5517 data_time: 0.0068 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7775 aux.loss_ce: 0.0081 aux.acc_seg: 99.2649 +04/18 13:18:58 - mmengine - INFO - Iter(train) [ 71250/160000] lr: 5.9248e-03 eta: 13:36:12 time: 0.5516 data_time: 0.0071 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.5831 aux.loss_ce: 0.0072 aux.acc_seg: 98.8583 +04/18 13:19:25 - mmengine - INFO - Iter(train) [ 71300/160000] lr: 5.9218e-03 eta: 13:35:45 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6793 aux.loss_ce: 0.0074 aux.acc_seg: 99.3607 +04/18 13:19:53 - mmengine - INFO - Iter(train) [ 71350/160000] lr: 5.9189e-03 eta: 13:35:18 time: 0.5516 data_time: 0.0062 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.7224 aux.loss_ce: 0.0078 aux.acc_seg: 99.3318 +04/18 13:20:21 - mmengine - INFO - Iter(train) [ 71400/160000] lr: 5.9159e-03 eta: 13:34:50 time: 0.5523 data_time: 0.0062 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6738 aux.loss_ce: 0.0081 aux.acc_seg: 99.1942 +04/18 13:20:48 - mmengine - INFO - Iter(train) [ 71450/160000] lr: 5.9130e-03 eta: 13:34:22 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0084 decode.acc_seg: 99.6629 aux.loss_ce: 0.0080 aux.acc_seg: 99.0391 +04/18 13:21:16 - mmengine - INFO - Iter(train) [ 71500/160000] lr: 5.9100e-03 eta: 13:33:55 time: 0.5531 data_time: 0.0060 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7650 aux.loss_ce: 0.0074 aux.acc_seg: 99.3747 +04/18 13:21:44 - mmengine - INFO - Iter(train) [ 71550/160000] lr: 5.9071e-03 eta: 13:33:27 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7768 aux.loss_ce: 0.0076 aux.acc_seg: 99.4062 +04/18 13:22:11 - mmengine - INFO - Iter(train) [ 71600/160000] lr: 5.9041e-03 eta: 13:33:00 time: 0.5510 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7050 aux.loss_ce: 0.0072 aux.acc_seg: 99.2457 +04/18 13:22:39 - mmengine - INFO - Iter(train) [ 71650/160000] lr: 5.9012e-03 eta: 13:32:32 time: 0.5524 data_time: 0.0075 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0074 decode.acc_seg: 99.7500 aux.loss_ce: 0.0080 aux.acc_seg: 99.1837 +04/18 13:23:06 - mmengine - INFO - Iter(train) [ 71700/160000] lr: 5.8982e-03 eta: 13:32:05 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0083 decode.acc_seg: 99.7140 aux.loss_ce: 0.0079 aux.acc_seg: 99.2368 +04/18 13:23:34 - mmengine - INFO - Iter(train) [ 71750/160000] lr: 5.8953e-03 eta: 13:31:37 time: 0.5511 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.6075 aux.loss_ce: 0.0073 aux.acc_seg: 99.2095 +04/18 13:24:02 - mmengine - INFO - Iter(train) [ 71800/160000] lr: 5.8923e-03 eta: 13:31:10 time: 0.5530 data_time: 0.0067 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0082 decode.acc_seg: 99.6995 aux.loss_ce: 0.0074 aux.acc_seg: 99.3881 +04/18 13:24:29 - mmengine - INFO - Iter(train) [ 71850/160000] lr: 5.8893e-03 eta: 13:30:42 time: 0.5511 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.7274 aux.loss_ce: 0.0074 aux.acc_seg: 99.4112 +04/18 13:24:57 - mmengine - INFO - Iter(train) [ 71900/160000] lr: 5.8864e-03 eta: 13:30:14 time: 0.5520 data_time: 0.0061 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.5666 aux.loss_ce: 0.0079 aux.acc_seg: 98.9028 +04/18 13:25:25 - mmengine - INFO - Iter(train) [ 71950/160000] lr: 5.8834e-03 eta: 13:29:47 time: 0.5530 data_time: 0.0067 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.7374 aux.loss_ce: 0.0077 aux.acc_seg: 99.2367 +04/18 13:25:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:25:52 - mmengine - INFO - Iter(train) [ 72000/160000] lr: 5.8805e-03 eta: 13:29:19 time: 0.5529 data_time: 0.0064 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0085 decode.acc_seg: 99.7155 aux.loss_ce: 0.0083 aux.acc_seg: 99.0468 +04/18 13:26:20 - mmengine - INFO - Iter(train) [ 72050/160000] lr: 5.8775e-03 eta: 13:28:52 time: 0.5527 data_time: 0.0061 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0086 decode.acc_seg: 99.6279 aux.loss_ce: 0.0080 aux.acc_seg: 99.0722 +04/18 13:26:47 - mmengine - INFO - Iter(train) [ 72100/160000] lr: 5.8746e-03 eta: 13:28:24 time: 0.5514 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.6089 aux.loss_ce: 0.0074 aux.acc_seg: 99.1879 +04/18 13:27:15 - mmengine - INFO - Iter(train) [ 72150/160000] lr: 5.8716e-03 eta: 13:27:57 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.7412 aux.loss_ce: 0.0080 aux.acc_seg: 99.2281 +04/18 13:27:43 - mmengine - INFO - Iter(train) [ 72200/160000] lr: 5.8687e-03 eta: 13:27:29 time: 0.5529 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.5743 aux.loss_ce: 0.0078 aux.acc_seg: 99.1511 +04/18 13:28:10 - mmengine - INFO - Iter(train) [ 72250/160000] lr: 5.8657e-03 eta: 13:27:02 time: 0.5520 data_time: 0.0064 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6676 aux.loss_ce: 0.0072 aux.acc_seg: 99.2157 +04/18 13:28:38 - mmengine - INFO - Iter(train) [ 72300/160000] lr: 5.8627e-03 eta: 13:26:34 time: 0.5533 data_time: 0.0060 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0079 decode.acc_seg: 99.6785 aux.loss_ce: 0.0072 aux.acc_seg: 99.3123 +04/18 13:29:06 - mmengine - INFO - Iter(train) [ 72350/160000] lr: 5.8598e-03 eta: 13:26:06 time: 0.5529 data_time: 0.0070 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6458 aux.loss_ce: 0.0081 aux.acc_seg: 99.1272 +04/18 13:29:33 - mmengine - INFO - Iter(train) [ 72400/160000] lr: 5.8568e-03 eta: 13:25:39 time: 0.5621 data_time: 0.0071 memory: 7635 loss: 0.0186 decode.loss_ce: 0.0102 decode.acc_seg: 99.5074 aux.loss_ce: 0.0084 aux.acc_seg: 99.0326 +04/18 13:30:01 - mmengine - INFO - Iter(train) [ 72450/160000] lr: 5.8539e-03 eta: 13:25:12 time: 0.5519 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.6523 aux.loss_ce: 0.0077 aux.acc_seg: 99.0721 +04/18 13:30:29 - mmengine - INFO - Iter(train) [ 72500/160000] lr: 5.8509e-03 eta: 13:24:44 time: 0.5510 data_time: 0.0060 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6679 aux.loss_ce: 0.0078 aux.acc_seg: 99.3586 +04/18 13:30:56 - mmengine - INFO - Iter(train) [ 72550/160000] lr: 5.8480e-03 eta: 13:24:16 time: 0.5515 data_time: 0.0061 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7038 aux.loss_ce: 0.0071 aux.acc_seg: 99.1717 +04/18 13:31:24 - mmengine - INFO - Iter(train) [ 72600/160000] lr: 5.8450e-03 eta: 13:23:49 time: 0.5533 data_time: 0.0059 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.6857 aux.loss_ce: 0.0074 aux.acc_seg: 98.9161 +04/18 13:31:52 - mmengine - INFO - Iter(train) [ 72650/160000] lr: 5.8420e-03 eta: 13:23:21 time: 0.5540 data_time: 0.0071 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.7041 aux.loss_ce: 0.0080 aux.acc_seg: 99.2405 +04/18 13:32:19 - mmengine - INFO - Iter(train) [ 72700/160000] lr: 5.8391e-03 eta: 13:22:54 time: 0.5524 data_time: 0.0066 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0081 decode.acc_seg: 99.6450 aux.loss_ce: 0.0079 aux.acc_seg: 98.9261 +04/18 13:32:47 - mmengine - INFO - Iter(train) [ 72750/160000] lr: 5.8361e-03 eta: 13:22:26 time: 0.5525 data_time: 0.0069 memory: 7635 loss: 0.0207 decode.loss_ce: 0.0118 decode.acc_seg: 99.3946 aux.loss_ce: 0.0090 aux.acc_seg: 99.1690 +04/18 13:33:14 - mmengine - INFO - Iter(train) [ 72800/160000] lr: 5.8332e-03 eta: 13:21:59 time: 0.5519 data_time: 0.0058 memory: 7635 loss: 0.0190 decode.loss_ce: 0.0103 decode.acc_seg: 99.4988 aux.loss_ce: 0.0088 aux.acc_seg: 98.8207 +04/18 13:33:42 - mmengine - INFO - Iter(train) [ 72850/160000] lr: 5.8302e-03 eta: 13:21:31 time: 0.5530 data_time: 0.0068 memory: 7635 loss: 0.0201 decode.loss_ce: 0.0111 decode.acc_seg: 99.6811 aux.loss_ce: 0.0090 aux.acc_seg: 99.2533 +04/18 13:34:10 - mmengine - INFO - Iter(train) [ 72900/160000] lr: 5.8272e-03 eta: 13:21:04 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0194 decode.loss_ce: 0.0108 decode.acc_seg: 99.4739 aux.loss_ce: 0.0086 aux.acc_seg: 99.0827 +04/18 13:34:37 - mmengine - INFO - Iter(train) [ 72950/160000] lr: 5.8243e-03 eta: 13:20:36 time: 0.5532 data_time: 0.0072 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0092 decode.acc_seg: 99.6920 aux.loss_ce: 0.0086 aux.acc_seg: 99.1225 +04/18 13:35:05 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:35:05 - mmengine - INFO - Iter(train) [ 73000/160000] lr: 5.8213e-03 eta: 13:20:08 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.6268 aux.loss_ce: 0.0081 aux.acc_seg: 99.2789 +04/18 13:35:33 - mmengine - INFO - Iter(train) [ 73050/160000] lr: 5.8184e-03 eta: 13:19:41 time: 0.5532 data_time: 0.0064 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.6858 aux.loss_ce: 0.0081 aux.acc_seg: 99.2652 +04/18 13:36:00 - mmengine - INFO - Iter(train) [ 73100/160000] lr: 5.8154e-03 eta: 13:19:13 time: 0.5517 data_time: 0.0071 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0081 decode.acc_seg: 99.6793 aux.loss_ce: 0.0078 aux.acc_seg: 99.1027 +04/18 13:36:28 - mmengine - INFO - Iter(train) [ 73150/160000] lr: 5.8125e-03 eta: 13:18:46 time: 0.5520 data_time: 0.0069 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.5923 aux.loss_ce: 0.0077 aux.acc_seg: 99.0482 +04/18 13:36:55 - mmengine - INFO - Iter(train) [ 73200/160000] lr: 5.8095e-03 eta: 13:18:18 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.6848 aux.loss_ce: 0.0082 aux.acc_seg: 99.1988 +04/18 13:37:23 - mmengine - INFO - Iter(train) [ 73250/160000] lr: 5.8065e-03 eta: 13:17:51 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.7367 aux.loss_ce: 0.0079 aux.acc_seg: 99.3934 +04/18 13:37:51 - mmengine - INFO - Iter(train) [ 73300/160000] lr: 5.8036e-03 eta: 13:17:23 time: 0.5531 data_time: 0.0061 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.7201 aux.loss_ce: 0.0078 aux.acc_seg: 99.3034 +04/18 13:38:18 - mmengine - INFO - Iter(train) [ 73350/160000] lr: 5.8006e-03 eta: 13:16:56 time: 0.5530 data_time: 0.0072 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.7479 aux.loss_ce: 0.0075 aux.acc_seg: 99.3849 +04/18 13:38:46 - mmengine - INFO - Iter(train) [ 73400/160000] lr: 5.7976e-03 eta: 13:16:28 time: 0.5514 data_time: 0.0061 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0100 decode.acc_seg: 99.3410 aux.loss_ce: 0.0089 aux.acc_seg: 98.5963 +04/18 13:39:14 - mmengine - INFO - Iter(train) [ 73450/160000] lr: 5.7947e-03 eta: 13:16:01 time: 0.5611 data_time: 0.0071 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.7078 aux.loss_ce: 0.0079 aux.acc_seg: 99.0885 +04/18 13:39:41 - mmengine - INFO - Iter(train) [ 73500/160000] lr: 5.7917e-03 eta: 13:15:33 time: 0.5535 data_time: 0.0065 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6415 aux.loss_ce: 0.0082 aux.acc_seg: 99.0867 +04/18 13:40:09 - mmengine - INFO - Iter(train) [ 73550/160000] lr: 5.7888e-03 eta: 13:15:05 time: 0.5513 data_time: 0.0058 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.6291 aux.loss_ce: 0.0073 aux.acc_seg: 99.2481 +04/18 13:40:37 - mmengine - INFO - Iter(train) [ 73600/160000] lr: 5.7858e-03 eta: 13:14:38 time: 0.5520 data_time: 0.0071 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0083 decode.acc_seg: 99.6764 aux.loss_ce: 0.0082 aux.acc_seg: 99.1766 +04/18 13:41:04 - mmengine - INFO - Iter(train) [ 73650/160000] lr: 5.7828e-03 eta: 13:14:10 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.6997 aux.loss_ce: 0.0077 aux.acc_seg: 99.1902 +04/18 13:41:32 - mmengine - INFO - Iter(train) [ 73700/160000] lr: 5.7799e-03 eta: 13:13:43 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0089 decode.acc_seg: 99.5741 aux.loss_ce: 0.0083 aux.acc_seg: 99.1199 +04/18 13:42:00 - mmengine - INFO - Iter(train) [ 73750/160000] lr: 5.7769e-03 eta: 13:13:15 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.6207 aux.loss_ce: 0.0078 aux.acc_seg: 98.9251 +04/18 13:42:27 - mmengine - INFO - Iter(train) [ 73800/160000] lr: 5.7740e-03 eta: 13:12:48 time: 0.5527 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7057 aux.loss_ce: 0.0073 aux.acc_seg: 99.1655 +04/18 13:42:55 - mmengine - INFO - Iter(train) [ 73850/160000] lr: 5.7710e-03 eta: 13:12:20 time: 0.5541 data_time: 0.0060 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.6880 aux.loss_ce: 0.0073 aux.acc_seg: 99.2225 +04/18 13:43:22 - mmengine - INFO - Iter(train) [ 73900/160000] lr: 5.7680e-03 eta: 13:11:53 time: 0.5520 data_time: 0.0063 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0091 decode.acc_seg: 99.7266 aux.loss_ce: 0.0077 aux.acc_seg: 99.3498 +04/18 13:43:50 - mmengine - INFO - Iter(train) [ 73950/160000] lr: 5.7651e-03 eta: 13:11:25 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6967 aux.loss_ce: 0.0075 aux.acc_seg: 99.1593 +04/18 13:44:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:44:18 - mmengine - INFO - Iter(train) [ 74000/160000] lr: 5.7621e-03 eta: 13:10:58 time: 0.5507 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.7504 aux.loss_ce: 0.0075 aux.acc_seg: 99.2293 +04/18 13:44:45 - mmengine - INFO - Iter(train) [ 74050/160000] lr: 5.7591e-03 eta: 13:10:30 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7253 aux.loss_ce: 0.0074 aux.acc_seg: 99.3150 +04/18 13:45:13 - mmengine - INFO - Iter(train) [ 74100/160000] lr: 5.7562e-03 eta: 13:10:02 time: 0.5513 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7438 aux.loss_ce: 0.0073 aux.acc_seg: 99.2490 +04/18 13:45:41 - mmengine - INFO - Iter(train) [ 74150/160000] lr: 5.7532e-03 eta: 13:09:35 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6561 aux.loss_ce: 0.0072 aux.acc_seg: 99.1373 +04/18 13:46:08 - mmengine - INFO - Iter(train) [ 74200/160000] lr: 5.7503e-03 eta: 13:09:07 time: 0.5527 data_time: 0.0068 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.6842 aux.loss_ce: 0.0073 aux.acc_seg: 99.4061 +04/18 13:46:36 - mmengine - INFO - Iter(train) [ 74250/160000] lr: 5.7473e-03 eta: 13:08:40 time: 0.5513 data_time: 0.0061 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7683 aux.loss_ce: 0.0074 aux.acc_seg: 99.3338 +04/18 13:47:04 - mmengine - INFO - Iter(train) [ 74300/160000] lr: 5.7443e-03 eta: 13:08:12 time: 0.5521 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7014 aux.loss_ce: 0.0073 aux.acc_seg: 99.2202 +04/18 13:47:31 - mmengine - INFO - Iter(train) [ 74350/160000] lr: 5.7414e-03 eta: 13:07:45 time: 0.5522 data_time: 0.0071 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7216 aux.loss_ce: 0.0076 aux.acc_seg: 99.2443 +04/18 13:47:59 - mmengine - INFO - Iter(train) [ 74400/160000] lr: 5.7384e-03 eta: 13:07:17 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7744 aux.loss_ce: 0.0072 aux.acc_seg: 99.3647 +04/18 13:48:27 - mmengine - INFO - Iter(train) [ 74450/160000] lr: 5.7354e-03 eta: 13:06:50 time: 0.5514 data_time: 0.0063 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6039 aux.loss_ce: 0.0078 aux.acc_seg: 99.1303 +04/18 13:48:54 - mmengine - INFO - Iter(train) [ 74500/160000] lr: 5.7325e-03 eta: 13:06:22 time: 0.5527 data_time: 0.0060 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6873 aux.loss_ce: 0.0075 aux.acc_seg: 99.2661 +04/18 13:49:22 - mmengine - INFO - Iter(train) [ 74550/160000] lr: 5.7295e-03 eta: 13:05:55 time: 0.5601 data_time: 0.0067 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7502 aux.loss_ce: 0.0072 aux.acc_seg: 99.4673 +04/18 13:49:50 - mmengine - INFO - Iter(train) [ 74600/160000] lr: 5.7265e-03 eta: 13:05:27 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6539 aux.loss_ce: 0.0074 aux.acc_seg: 99.1780 +04/18 13:50:17 - mmengine - INFO - Iter(train) [ 74650/160000] lr: 5.7236e-03 eta: 13:05:00 time: 0.5522 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6793 aux.loss_ce: 0.0076 aux.acc_seg: 99.0724 +04/18 13:50:45 - mmengine - INFO - Iter(train) [ 74700/160000] lr: 5.7206e-03 eta: 13:04:32 time: 0.5520 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.7586 aux.loss_ce: 0.0072 aux.acc_seg: 99.3429 +04/18 13:51:13 - mmengine - INFO - Iter(train) [ 74750/160000] lr: 5.7176e-03 eta: 13:04:05 time: 0.5539 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7895 aux.loss_ce: 0.0070 aux.acc_seg: 99.3579 +04/18 13:51:40 - mmengine - INFO - Iter(train) [ 74800/160000] lr: 5.7147e-03 eta: 13:03:37 time: 0.5555 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0072 decode.acc_seg: 99.6549 aux.loss_ce: 0.0069 aux.acc_seg: 99.0606 +04/18 13:52:08 - mmengine - INFO - Iter(train) [ 74850/160000] lr: 5.7117e-03 eta: 13:03:10 time: 0.5523 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.6537 aux.loss_ce: 0.0075 aux.acc_seg: 99.2857 +04/18 13:52:36 - mmengine - INFO - Iter(train) [ 74900/160000] lr: 5.7088e-03 eta: 13:02:42 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.6949 aux.loss_ce: 0.0080 aux.acc_seg: 99.3430 +04/18 13:53:03 - mmengine - INFO - Iter(train) [ 74950/160000] lr: 5.7058e-03 eta: 13:02:15 time: 0.5525 data_time: 0.0067 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6485 aux.loss_ce: 0.0076 aux.acc_seg: 99.1668 +04/18 13:53:31 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 13:53:31 - mmengine - INFO - Iter(train) [ 75000/160000] lr: 5.7028e-03 eta: 13:01:47 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0070 decode.acc_seg: 99.7306 aux.loss_ce: 0.0069 aux.acc_seg: 99.3453 +04/18 13:53:59 - mmengine - INFO - Iter(train) [ 75050/160000] lr: 5.6999e-03 eta: 13:01:20 time: 0.5542 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.6854 aux.loss_ce: 0.0077 aux.acc_seg: 99.3029 +04/18 13:54:26 - mmengine - INFO - Iter(train) [ 75100/160000] lr: 5.6969e-03 eta: 13:00:52 time: 0.5522 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.6496 aux.loss_ce: 0.0079 aux.acc_seg: 99.2293 +04/18 13:54:54 - mmengine - INFO - Iter(train) [ 75150/160000] lr: 5.6939e-03 eta: 13:00:24 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0086 decode.acc_seg: 99.7335 aux.loss_ce: 0.0076 aux.acc_seg: 99.2230 +04/18 13:55:22 - mmengine - INFO - Iter(train) [ 75200/160000] lr: 5.6910e-03 eta: 12:59:57 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.6819 aux.loss_ce: 0.0083 aux.acc_seg: 99.0951 +04/18 13:55:49 - mmengine - INFO - Iter(train) [ 75250/160000] lr: 5.6880e-03 eta: 12:59:29 time: 0.5526 data_time: 0.0069 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6603 aux.loss_ce: 0.0081 aux.acc_seg: 99.1907 +04/18 13:56:17 - mmengine - INFO - Iter(train) [ 75300/160000] lr: 5.6850e-03 eta: 12:59:02 time: 0.5514 data_time: 0.0062 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0082 decode.acc_seg: 99.6013 aux.loss_ce: 0.0076 aux.acc_seg: 99.2129 +04/18 13:56:44 - mmengine - INFO - Iter(train) [ 75350/160000] lr: 5.6821e-03 eta: 12:58:34 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0087 decode.acc_seg: 99.7513 aux.loss_ce: 0.0085 aux.acc_seg: 99.2340 +04/18 13:57:12 - mmengine - INFO - Iter(train) [ 75400/160000] lr: 5.6791e-03 eta: 12:58:07 time: 0.5529 data_time: 0.0069 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.7286 aux.loss_ce: 0.0081 aux.acc_seg: 99.3635 +04/18 13:57:40 - mmengine - INFO - Iter(train) [ 75450/160000] lr: 5.6761e-03 eta: 12:57:39 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7062 aux.loss_ce: 0.0076 aux.acc_seg: 99.2773 +04/18 13:58:07 - mmengine - INFO - Iter(train) [ 75500/160000] lr: 5.6731e-03 eta: 12:57:12 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6880 aux.loss_ce: 0.0079 aux.acc_seg: 99.1724 +04/18 13:58:35 - mmengine - INFO - Iter(train) [ 75550/160000] lr: 5.6702e-03 eta: 12:56:44 time: 0.5509 data_time: 0.0061 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.5934 aux.loss_ce: 0.0082 aux.acc_seg: 99.0724 +04/18 13:59:03 - mmengine - INFO - Iter(train) [ 75600/160000] lr: 5.6672e-03 eta: 12:56:17 time: 0.5612 data_time: 0.0064 memory: 7635 loss: 0.0187 decode.loss_ce: 0.0098 decode.acc_seg: 99.6265 aux.loss_ce: 0.0088 aux.acc_seg: 99.2264 +04/18 13:59:31 - mmengine - INFO - Iter(train) [ 75650/160000] lr: 5.6642e-03 eta: 12:55:49 time: 0.5617 data_time: 0.0067 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0086 decode.acc_seg: 99.6624 aux.loss_ce: 0.0076 aux.acc_seg: 99.1029 +04/18 13:59:58 - mmengine - INFO - Iter(train) [ 75700/160000] lr: 5.6613e-03 eta: 12:55:22 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6754 aux.loss_ce: 0.0077 aux.acc_seg: 99.2651 +04/18 14:00:26 - mmengine - INFO - Iter(train) [ 75750/160000] lr: 5.6583e-03 eta: 12:54:54 time: 0.5508 data_time: 0.0063 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0076 decode.acc_seg: 99.6983 aux.loss_ce: 0.0072 aux.acc_seg: 99.2643 +04/18 14:00:54 - mmengine - INFO - Iter(train) [ 75800/160000] lr: 5.6553e-03 eta: 12:54:27 time: 0.5529 data_time: 0.0073 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6488 aux.loss_ce: 0.0078 aux.acc_seg: 99.1465 +04/18 14:01:21 - mmengine - INFO - Iter(train) [ 75850/160000] lr: 5.6524e-03 eta: 12:53:59 time: 0.5518 data_time: 0.0066 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0082 decode.acc_seg: 99.5838 aux.loss_ce: 0.0080 aux.acc_seg: 99.0612 +04/18 14:01:49 - mmengine - INFO - Iter(train) [ 75900/160000] lr: 5.6494e-03 eta: 12:53:32 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7929 aux.loss_ce: 0.0072 aux.acc_seg: 99.5135 +04/18 14:02:17 - mmengine - INFO - Iter(train) [ 75950/160000] lr: 5.6464e-03 eta: 12:53:04 time: 0.5544 data_time: 0.0070 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.6447 aux.loss_ce: 0.0080 aux.acc_seg: 99.0114 +04/18 14:02:44 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:02:44 - mmengine - INFO - Iter(train) [ 76000/160000] lr: 5.6435e-03 eta: 12:52:37 time: 0.5505 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6880 aux.loss_ce: 0.0079 aux.acc_seg: 99.0818 +04/18 14:03:12 - mmengine - INFO - Iter(train) [ 76050/160000] lr: 5.6405e-03 eta: 12:52:09 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0078 decode.acc_seg: 99.7631 aux.loss_ce: 0.0082 aux.acc_seg: 99.4489 +04/18 14:03:39 - mmengine - INFO - Iter(train) [ 76100/160000] lr: 5.6375e-03 eta: 12:51:41 time: 0.5519 data_time: 0.0066 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6859 aux.loss_ce: 0.0072 aux.acc_seg: 99.2662 +04/18 14:04:07 - mmengine - INFO - Iter(train) [ 76150/160000] lr: 5.6346e-03 eta: 12:51:14 time: 0.5537 data_time: 0.0062 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7334 aux.loss_ce: 0.0077 aux.acc_seg: 99.4172 +04/18 14:04:35 - mmengine - INFO - Iter(train) [ 76200/160000] lr: 5.6316e-03 eta: 12:50:46 time: 0.5527 data_time: 0.0066 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7077 aux.loss_ce: 0.0075 aux.acc_seg: 99.2603 +04/18 14:05:02 - mmengine - INFO - Iter(train) [ 76250/160000] lr: 5.6286e-03 eta: 12:50:19 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6560 aux.loss_ce: 0.0073 aux.acc_seg: 99.2674 +04/18 14:05:30 - mmengine - INFO - Iter(train) [ 76300/160000] lr: 5.6256e-03 eta: 12:49:51 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6660 aux.loss_ce: 0.0073 aux.acc_seg: 98.9780 +04/18 14:05:58 - mmengine - INFO - Iter(train) [ 76350/160000] lr: 5.6227e-03 eta: 12:49:24 time: 0.5534 data_time: 0.0067 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0103 decode.acc_seg: 99.7115 aux.loss_ce: 0.0088 aux.acc_seg: 99.1650 +04/18 14:06:25 - mmengine - INFO - Iter(train) [ 76400/160000] lr: 5.6197e-03 eta: 12:48:56 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0084 decode.acc_seg: 99.6535 aux.loss_ce: 0.0085 aux.acc_seg: 99.1377 +04/18 14:06:53 - mmengine - INFO - Iter(train) [ 76450/160000] lr: 5.6167e-03 eta: 12:48:29 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.6810 aux.loss_ce: 0.0085 aux.acc_seg: 99.1442 +04/18 14:07:21 - mmengine - INFO - Iter(train) [ 76500/160000] lr: 5.6138e-03 eta: 12:48:01 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.6929 aux.loss_ce: 0.0078 aux.acc_seg: 99.1059 +04/18 14:07:48 - mmengine - INFO - Iter(train) [ 76550/160000] lr: 5.6108e-03 eta: 12:47:34 time: 0.5533 data_time: 0.0074 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.6104 aux.loss_ce: 0.0074 aux.acc_seg: 99.3080 +04/18 14:08:16 - mmengine - INFO - Iter(train) [ 76600/160000] lr: 5.6078e-03 eta: 12:47:06 time: 0.5530 data_time: 0.0072 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6520 aux.loss_ce: 0.0079 aux.acc_seg: 99.2299 +04/18 14:08:44 - mmengine - INFO - Iter(train) [ 76650/160000] lr: 5.6048e-03 eta: 12:46:39 time: 0.5530 data_time: 0.0071 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7184 aux.loss_ce: 0.0078 aux.acc_seg: 99.2104 +04/18 14:09:11 - mmengine - INFO - Iter(train) [ 76700/160000] lr: 5.6019e-03 eta: 12:46:11 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7515 aux.loss_ce: 0.0073 aux.acc_seg: 99.2829 +04/18 14:09:39 - mmengine - INFO - Iter(train) [ 76750/160000] lr: 5.5989e-03 eta: 12:45:44 time: 0.5530 data_time: 0.0067 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7278 aux.loss_ce: 0.0082 aux.acc_seg: 99.1805 +04/18 14:10:07 - mmengine - INFO - Iter(train) [ 76800/160000] lr: 5.5959e-03 eta: 12:45:16 time: 0.5519 data_time: 0.0059 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0086 decode.acc_seg: 99.6566 aux.loss_ce: 0.0082 aux.acc_seg: 99.1994 +04/18 14:10:34 - mmengine - INFO - Iter(train) [ 76850/160000] lr: 5.5930e-03 eta: 12:44:49 time: 0.5539 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.7783 aux.loss_ce: 0.0076 aux.acc_seg: 99.3851 +04/18 14:11:02 - mmengine - INFO - Iter(train) [ 76900/160000] lr: 5.5900e-03 eta: 12:44:21 time: 0.5537 data_time: 0.0069 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.5828 aux.loss_ce: 0.0083 aux.acc_seg: 99.1943 +04/18 14:11:30 - mmengine - INFO - Iter(train) [ 76950/160000] lr: 5.5870e-03 eta: 12:43:54 time: 0.5526 data_time: 0.0068 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.7047 aux.loss_ce: 0.0079 aux.acc_seg: 99.2355 +04/18 14:11:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:11:58 - mmengine - INFO - Iter(train) [ 77000/160000] lr: 5.5840e-03 eta: 12:43:26 time: 0.5531 data_time: 0.0062 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0094 decode.acc_seg: 99.5891 aux.loss_ce: 0.0079 aux.acc_seg: 99.2018 +04/18 14:12:25 - mmengine - INFO - Iter(train) [ 77050/160000] lr: 5.5811e-03 eta: 12:42:59 time: 0.5541 data_time: 0.0073 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0097 decode.acc_seg: 99.5165 aux.loss_ce: 0.0083 aux.acc_seg: 98.8960 +04/18 14:12:53 - mmengine - INFO - Iter(train) [ 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0.5532 data_time: 0.0073 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0077 decode.acc_seg: 99.6530 aux.loss_ce: 0.0072 aux.acc_seg: 99.3362 +04/18 14:15:11 - mmengine - INFO - Iter(train) [ 77350/160000] lr: 5.5632e-03 eta: 12:40:14 time: 0.5533 data_time: 0.0073 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.7839 aux.loss_ce: 0.0075 aux.acc_seg: 99.4560 +04/18 14:15:39 - mmengine - INFO - Iter(train) [ 77400/160000] lr: 5.5602e-03 eta: 12:39:46 time: 0.5534 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.5231 aux.loss_ce: 0.0074 aux.acc_seg: 99.0528 +04/18 14:16:07 - mmengine - INFO - Iter(train) [ 77450/160000] lr: 5.5573e-03 eta: 12:39:18 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6844 aux.loss_ce: 0.0076 aux.acc_seg: 99.3047 +04/18 14:16:34 - mmengine - INFO - Iter(train) [ 77500/160000] lr: 5.5543e-03 eta: 12:38:51 time: 0.5543 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.8195 aux.loss_ce: 0.0074 aux.acc_seg: 99.4947 +04/18 14:17:02 - mmengine - INFO - Iter(train) [ 77550/160000] lr: 5.5513e-03 eta: 12:38:23 time: 0.5514 data_time: 0.0060 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.6561 aux.loss_ce: 0.0077 aux.acc_seg: 99.2005 +04/18 14:17:30 - mmengine - INFO - Iter(train) [ 77600/160000] lr: 5.5483e-03 eta: 12:37:56 time: 0.5542 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.6532 aux.loss_ce: 0.0081 aux.acc_seg: 99.0906 +04/18 14:17:57 - mmengine - INFO - Iter(train) [ 77650/160000] lr: 5.5454e-03 eta: 12:37:28 time: 0.5527 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.6670 aux.loss_ce: 0.0079 aux.acc_seg: 99.1526 +04/18 14:18:25 - mmengine - INFO - Iter(train) [ 77700/160000] lr: 5.5424e-03 eta: 12:37:01 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.6713 aux.loss_ce: 0.0078 aux.acc_seg: 99.3465 +04/18 14:18:53 - mmengine - INFO - Iter(train) [ 77750/160000] lr: 5.5394e-03 eta: 12:36:34 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6013 aux.loss_ce: 0.0079 aux.acc_seg: 99.0311 +04/18 14:19:21 - mmengine - INFO - Iter(train) [ 77800/160000] lr: 5.5364e-03 eta: 12:36:06 time: 0.5526 data_time: 0.0057 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7191 aux.loss_ce: 0.0076 aux.acc_seg: 99.1973 +04/18 14:19:48 - mmengine - INFO - Iter(train) [ 77850/160000] lr: 5.5335e-03 eta: 12:35:39 time: 0.5515 data_time: 0.0063 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6448 aux.loss_ce: 0.0081 aux.acc_seg: 99.0735 +04/18 14:20:16 - mmengine - INFO - Iter(train) [ 77900/160000] lr: 5.5305e-03 eta: 12:35:11 time: 0.5545 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.7859 aux.loss_ce: 0.0075 aux.acc_seg: 99.3713 +04/18 14:20:44 - mmengine - INFO - Iter(train) [ 77950/160000] lr: 5.5275e-03 eta: 12:34:44 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0077 decode.acc_seg: 99.7121 aux.loss_ce: 0.0080 aux.acc_seg: 99.0719 +04/18 14:21:11 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:21:11 - mmengine - INFO - Iter(train) [ 78000/160000] lr: 5.5245e-03 eta: 12:34:16 time: 0.5513 data_time: 0.0062 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6497 aux.loss_ce: 0.0077 aux.acc_seg: 99.0651 +04/18 14:21:39 - mmengine - INFO - Iter(train) [ 78050/160000] lr: 5.5216e-03 eta: 12:33:49 time: 0.5553 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6351 aux.loss_ce: 0.0076 aux.acc_seg: 99.0310 +04/18 14:22:07 - mmengine - INFO - Iter(train) [ 78100/160000] lr: 5.5186e-03 eta: 12:33:21 time: 0.5536 data_time: 0.0069 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.7007 aux.loss_ce: 0.0081 aux.acc_seg: 99.2872 +04/18 14:22:34 - mmengine - INFO - Iter(train) [ 78150/160000] lr: 5.5156e-03 eta: 12:32:53 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6365 aux.loss_ce: 0.0076 aux.acc_seg: 99.1067 +04/18 14:23:02 - mmengine - INFO - Iter(train) [ 78200/160000] lr: 5.5126e-03 eta: 12:32:26 time: 0.5523 data_time: 0.0067 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0080 decode.acc_seg: 99.7033 aux.loss_ce: 0.0085 aux.acc_seg: 99.1899 +04/18 14:23:30 - mmengine - INFO - Iter(train) [ 78250/160000] lr: 5.5096e-03 eta: 12:31:58 time: 0.5516 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7140 aux.loss_ce: 0.0072 aux.acc_seg: 99.2377 +04/18 14:23:57 - mmengine - INFO - Iter(train) [ 78300/160000] lr: 5.5067e-03 eta: 12:31:31 time: 0.5529 data_time: 0.0060 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6404 aux.loss_ce: 0.0074 aux.acc_seg: 99.1546 +04/18 14:24:25 - mmengine - INFO - Iter(train) [ 78350/160000] lr: 5.5037e-03 eta: 12:31:03 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7976 aux.loss_ce: 0.0071 aux.acc_seg: 99.3376 +04/18 14:24:53 - mmengine - INFO - Iter(train) [ 78400/160000] lr: 5.5007e-03 eta: 12:30:36 time: 0.5523 data_time: 0.0074 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.7155 aux.loss_ce: 0.0077 aux.acc_seg: 99.3938 +04/18 14:25:20 - mmengine - INFO - Iter(train) [ 78450/160000] lr: 5.4977e-03 eta: 12:30:08 time: 0.5541 data_time: 0.0061 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6658 aux.loss_ce: 0.0080 aux.acc_seg: 99.1904 +04/18 14:25:48 - mmengine - INFO - Iter(train) [ 78500/160000] lr: 5.4948e-03 eta: 12:29:41 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0082 decode.acc_seg: 99.7759 aux.loss_ce: 0.0082 aux.acc_seg: 99.3614 +04/18 14:26:16 - mmengine - INFO - Iter(train) [ 78550/160000] lr: 5.4918e-03 eta: 12:29:13 time: 0.5521 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6882 aux.loss_ce: 0.0074 aux.acc_seg: 99.4184 +04/18 14:26:43 - mmengine - INFO - Iter(train) [ 78600/160000] lr: 5.4888e-03 eta: 12:28:46 time: 0.5546 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7941 aux.loss_ce: 0.0077 aux.acc_seg: 99.3733 +04/18 14:27:11 - mmengine - INFO - Iter(train) [ 78650/160000] lr: 5.4858e-03 eta: 12:28:18 time: 0.5536 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.6781 aux.loss_ce: 0.0075 aux.acc_seg: 99.1870 +04/18 14:27:39 - mmengine - INFO - Iter(train) [ 78700/160000] lr: 5.4828e-03 eta: 12:27:51 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0073 decode.acc_seg: 99.7382 aux.loss_ce: 0.0078 aux.acc_seg: 99.1807 +04/18 14:28:06 - mmengine - INFO - Iter(train) [ 78750/160000] lr: 5.4799e-03 eta: 12:27:23 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7094 aux.loss_ce: 0.0078 aux.acc_seg: 99.2322 +04/18 14:28:34 - mmengine - INFO - Iter(train) [ 78800/160000] lr: 5.4769e-03 eta: 12:26:56 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6613 aux.loss_ce: 0.0074 aux.acc_seg: 99.0807 +04/18 14:29:02 - mmengine - INFO - Iter(train) [ 78850/160000] lr: 5.4739e-03 eta: 12:26:28 time: 0.5516 data_time: 0.0064 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.7021 aux.loss_ce: 0.0078 aux.acc_seg: 99.1442 +04/18 14:29:30 - mmengine - INFO - Iter(train) [ 78900/160000] lr: 5.4709e-03 eta: 12:26:01 time: 0.5537 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.6992 aux.loss_ce: 0.0075 aux.acc_seg: 99.3137 +04/18 14:29:57 - mmengine - INFO - Iter(train) [ 78950/160000] lr: 5.4679e-03 eta: 12:25:33 time: 0.5531 data_time: 0.0074 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.6041 aux.loss_ce: 0.0077 aux.acc_seg: 99.0901 +04/18 14:30:25 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:30:25 - mmengine - INFO - Iter(train) [ 79000/160000] lr: 5.4650e-03 eta: 12:25:06 time: 0.5531 data_time: 0.0072 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0074 decode.acc_seg: 99.7106 aux.loss_ce: 0.0071 aux.acc_seg: 99.2305 +04/18 14:30:53 - mmengine - INFO - Iter(train) [ 79050/160000] lr: 5.4620e-03 eta: 12:24:38 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7114 aux.loss_ce: 0.0074 aux.acc_seg: 99.4676 +04/18 14:31:21 - mmengine - INFO - Iter(train) [ 79100/160000] lr: 5.4590e-03 eta: 12:24:11 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6846 aux.loss_ce: 0.0078 aux.acc_seg: 99.1245 +04/18 14:31:48 - mmengine - INFO - Iter(train) [ 79150/160000] lr: 5.4560e-03 eta: 12:23:43 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6811 aux.loss_ce: 0.0079 aux.acc_seg: 99.1734 +04/18 14:32:16 - mmengine - INFO - Iter(train) [ 79200/160000] lr: 5.4530e-03 eta: 12:23:16 time: 0.5547 data_time: 0.0075 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6526 aux.loss_ce: 0.0074 aux.acc_seg: 99.3362 +04/18 14:32:44 - mmengine - INFO - Iter(train) [ 79250/160000] lr: 5.4501e-03 eta: 12:22:48 time: 0.5550 data_time: 0.0072 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7242 aux.loss_ce: 0.0074 aux.acc_seg: 99.3617 +04/18 14:33:11 - mmengine - INFO - Iter(train) [ 79300/160000] lr: 5.4471e-03 eta: 12:22:21 time: 0.5529 data_time: 0.0063 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7284 aux.loss_ce: 0.0073 aux.acc_seg: 99.1774 +04/18 14:33:39 - mmengine - INFO - Iter(train) [ 79350/160000] lr: 5.4441e-03 eta: 12:21:53 time: 0.5535 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.7021 aux.loss_ce: 0.0072 aux.acc_seg: 99.0726 +04/18 14:34:07 - mmengine - INFO - Iter(train) [ 79400/160000] lr: 5.4411e-03 eta: 12:21:26 time: 0.5528 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0073 decode.acc_seg: 99.7086 aux.loss_ce: 0.0081 aux.acc_seg: 99.2033 +04/18 14:34:34 - mmengine - INFO - Iter(train) [ 79450/160000] lr: 5.4381e-03 eta: 12:20:58 time: 0.5532 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6589 aux.loss_ce: 0.0071 aux.acc_seg: 99.1291 +04/18 14:35:02 - mmengine - INFO - Iter(train) [ 79500/160000] lr: 5.4351e-03 eta: 12:20:31 time: 0.5532 data_time: 0.0066 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6635 aux.loss_ce: 0.0079 aux.acc_seg: 99.1943 +04/18 14:35:30 - mmengine - INFO - Iter(train) [ 79550/160000] lr: 5.4322e-03 eta: 12:20:03 time: 0.5551 data_time: 0.0075 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.7507 aux.loss_ce: 0.0078 aux.acc_seg: 99.2860 +04/18 14:35:57 - mmengine - INFO - Iter(train) [ 79600/160000] lr: 5.4292e-03 eta: 12:19:36 time: 0.5517 data_time: 0.0069 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.7174 aux.loss_ce: 0.0080 aux.acc_seg: 99.2744 +04/18 14:36:25 - mmengine - INFO - Iter(train) [ 79650/160000] lr: 5.4262e-03 eta: 12:19:08 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6916 aux.loss_ce: 0.0082 aux.acc_seg: 99.2051 +04/18 14:36:53 - mmengine - INFO - Iter(train) [ 79700/160000] lr: 5.4232e-03 eta: 12:18:41 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0208 decode.loss_ce: 0.0115 decode.acc_seg: 99.3102 aux.loss_ce: 0.0094 aux.acc_seg: 98.8945 +04/18 14:37:20 - mmengine - INFO - Iter(train) [ 79750/160000] lr: 5.4202e-03 eta: 12:18:13 time: 0.5535 data_time: 0.0061 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.5147 aux.loss_ce: 0.0083 aux.acc_seg: 99.0007 +04/18 14:37:48 - mmengine - INFO - Iter(train) [ 79800/160000] lr: 5.4172e-03 eta: 12:17:46 time: 0.5529 data_time: 0.0071 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0106 decode.acc_seg: 99.5624 aux.loss_ce: 0.0085 aux.acc_seg: 99.2125 +04/18 14:38:16 - mmengine - INFO - Iter(train) [ 79850/160000] lr: 5.4143e-03 eta: 12:17:18 time: 0.5536 data_time: 0.0066 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6892 aux.loss_ce: 0.0078 aux.acc_seg: 99.3053 +04/18 14:38:44 - mmengine - INFO - Iter(train) [ 79900/160000] lr: 5.4113e-03 eta: 12:16:51 time: 0.5626 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7086 aux.loss_ce: 0.0075 aux.acc_seg: 99.1970 +04/18 14:39:11 - mmengine - INFO - Iter(train) [ 79950/160000] lr: 5.4083e-03 eta: 12:16:23 time: 0.5522 data_time: 0.0060 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.5859 aux.loss_ce: 0.0082 aux.acc_seg: 99.0210 +04/18 14:39:39 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:39:39 - mmengine - INFO - Iter(train) [ 80000/160000] lr: 5.4053e-03 eta: 12:15:56 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7456 aux.loss_ce: 0.0077 aux.acc_seg: 99.3921 +04/18 14:39:39 - mmengine - INFO - Saving checkpoint at 80000 iterations +04/18 14:39:43 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0461 data_time: 0.0014 memory: 1657 +04/18 14:39:45 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0463 data_time: 0.0015 memory: 1657 +04/18 14:39:48 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0466 data_time: 0.0013 memory: 1657 +04/18 14:39:50 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0457 data_time: 0.0015 memory: 1657 +04/18 14:39:50 - mmengine - INFO - per class results: +04/18 14:39:50 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.09 | 99.53 | 99.54 | 99.56 | 99.53 | +| contrast | 80.33 | 89.42 | 89.09 | 88.77 | 89.42 | ++------------+-------+-------+--------+-----------+--------+ +04/18 14:39:50 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1300 mIoU: 89.7100 mAcc: 94.4700 mFscore: 94.3200 mPrecision: 94.1700 mRecall: 94.4700 data_time: 0.0016 time: 0.0467 +04/18 14:40:18 - mmengine - INFO - Iter(train) [ 80050/160000] lr: 5.4023e-03 eta: 12:15:28 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.7221 aux.loss_ce: 0.0074 aux.acc_seg: 99.2517 +04/18 14:40:46 - mmengine - INFO - Iter(train) [ 80100/160000] lr: 5.3993e-03 eta: 12:15:01 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.7221 aux.loss_ce: 0.0081 aux.acc_seg: 99.3535 +04/18 14:41:13 - mmengine - INFO - Iter(train) [ 80150/160000] lr: 5.3964e-03 eta: 12:14:33 time: 0.5540 data_time: 0.0070 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0077 decode.acc_seg: 99.6485 aux.loss_ce: 0.0084 aux.acc_seg: 99.0163 +04/18 14:41:41 - mmengine - INFO - Iter(train) [ 80200/160000] lr: 5.3934e-03 eta: 12:14:06 time: 0.5531 data_time: 0.0058 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6730 aux.loss_ce: 0.0077 aux.acc_seg: 99.1230 +04/18 14:42:09 - mmengine - INFO - Iter(train) [ 80250/160000] lr: 5.3904e-03 eta: 12:13:38 time: 0.5530 data_time: 0.0062 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0078 decode.acc_seg: 99.7345 aux.loss_ce: 0.0071 aux.acc_seg: 99.4181 +04/18 14:42:36 - mmengine - INFO - Iter(train) [ 80300/160000] lr: 5.3874e-03 eta: 12:13:11 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.6847 aux.loss_ce: 0.0075 aux.acc_seg: 99.0840 +04/18 14:43:04 - mmengine - INFO - Iter(train) [ 80350/160000] lr: 5.3844e-03 eta: 12:12:43 time: 0.5529 data_time: 0.0065 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0083 decode.acc_seg: 99.7005 aux.loss_ce: 0.0080 aux.acc_seg: 99.3281 +04/18 14:43:32 - mmengine - INFO - Iter(train) [ 80400/160000] lr: 5.3814e-03 eta: 12:12:16 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.7069 aux.loss_ce: 0.0082 aux.acc_seg: 99.2474 +04/18 14:43:59 - mmengine - INFO - Iter(train) [ 80450/160000] lr: 5.3784e-03 eta: 12:11:48 time: 0.5531 data_time: 0.0070 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.6648 aux.loss_ce: 0.0075 aux.acc_seg: 99.0881 +04/18 14:44:27 - mmengine - INFO - Iter(train) [ 80500/160000] lr: 5.3755e-03 eta: 12:11:21 time: 0.5526 data_time: 0.0060 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6853 aux.loss_ce: 0.0077 aux.acc_seg: 99.1436 +04/18 14:44:55 - mmengine - INFO - Iter(train) [ 80550/160000] lr: 5.3725e-03 eta: 12:10:53 time: 0.5547 data_time: 0.0072 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0070 decode.acc_seg: 99.7228 aux.loss_ce: 0.0068 aux.acc_seg: 99.3650 +04/18 14:45:23 - mmengine - INFO - Iter(train) [ 80600/160000] lr: 5.3695e-03 eta: 12:10:26 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7419 aux.loss_ce: 0.0070 aux.acc_seg: 99.3039 +04/18 14:45:50 - mmengine - INFO - Iter(train) [ 80650/160000] lr: 5.3665e-03 eta: 12:09:58 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.6632 aux.loss_ce: 0.0075 aux.acc_seg: 99.2009 +04/18 14:46:18 - mmengine - INFO - Iter(train) [ 80700/160000] lr: 5.3635e-03 eta: 12:09:31 time: 0.5541 data_time: 0.0078 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.6882 aux.loss_ce: 0.0075 aux.acc_seg: 99.1914 +04/18 14:46:46 - mmengine - INFO - Iter(train) [ 80750/160000] lr: 5.3605e-03 eta: 12:09:03 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6524 aux.loss_ce: 0.0075 aux.acc_seg: 98.9561 +04/18 14:47:13 - mmengine - INFO - Iter(train) [ 80800/160000] lr: 5.3575e-03 eta: 12:08:36 time: 0.5532 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.7296 aux.loss_ce: 0.0071 aux.acc_seg: 99.1956 +04/18 14:47:41 - mmengine - INFO - Iter(train) [ 80850/160000] lr: 5.3545e-03 eta: 12:08:08 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.6763 aux.loss_ce: 0.0070 aux.acc_seg: 99.1087 +04/18 14:48:09 - mmengine - INFO - Iter(train) [ 80900/160000] lr: 5.3516e-03 eta: 12:07:41 time: 0.5536 data_time: 0.0073 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0080 decode.acc_seg: 99.7311 aux.loss_ce: 0.0085 aux.acc_seg: 99.1544 +04/18 14:48:36 - mmengine - INFO - Iter(train) [ 80950/160000] lr: 5.3486e-03 eta: 12:07:13 time: 0.5522 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0074 decode.acc_seg: 99.7464 aux.loss_ce: 0.0071 aux.acc_seg: 99.3919 +04/18 14:49:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:49:04 - mmengine - INFO - Iter(train) [ 81000/160000] lr: 5.3456e-03 eta: 12:06:46 time: 0.5528 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.5862 aux.loss_ce: 0.0077 aux.acc_seg: 99.0725 +04/18 14:49:32 - mmengine - INFO - Iter(train) [ 81050/160000] lr: 5.3426e-03 eta: 12:06:18 time: 0.5526 data_time: 0.0072 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7510 aux.loss_ce: 0.0070 aux.acc_seg: 99.4220 +04/18 14:50:00 - mmengine - INFO - Iter(train) [ 81100/160000] lr: 5.3396e-03 eta: 12:05:51 time: 0.5517 data_time: 0.0062 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6530 aux.loss_ce: 0.0078 aux.acc_seg: 99.1486 +04/18 14:50:27 - mmengine - INFO - Iter(train) [ 81150/160000] lr: 5.3366e-03 eta: 12:05:23 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0073 decode.acc_seg: 99.7410 aux.loss_ce: 0.0071 aux.acc_seg: 99.3360 +04/18 14:50:55 - mmengine - INFO - Iter(train) [ 81200/160000] lr: 5.3336e-03 eta: 12:04:56 time: 0.5545 data_time: 0.0079 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6817 aux.loss_ce: 0.0073 aux.acc_seg: 99.2269 +04/18 14:51:23 - mmengine - INFO - Iter(train) [ 81250/160000] lr: 5.3306e-03 eta: 12:04:28 time: 0.5537 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7697 aux.loss_ce: 0.0077 aux.acc_seg: 99.3053 +04/18 14:51:50 - mmengine - INFO - Iter(train) [ 81300/160000] lr: 5.3277e-03 eta: 12:04:01 time: 0.5535 data_time: 0.0071 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6440 aux.loss_ce: 0.0073 aux.acc_seg: 99.1206 +04/18 14:52:18 - mmengine - INFO - Iter(train) [ 81350/160000] lr: 5.3247e-03 eta: 12:03:33 time: 0.5528 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.7443 aux.loss_ce: 0.0074 aux.acc_seg: 99.0421 +04/18 14:52:46 - mmengine - INFO - Iter(train) [ 81400/160000] lr: 5.3217e-03 eta: 12:03:06 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7526 aux.loss_ce: 0.0074 aux.acc_seg: 99.3560 +04/18 14:53:13 - mmengine - INFO - Iter(train) [ 81450/160000] lr: 5.3187e-03 eta: 12:02:38 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.6834 aux.loss_ce: 0.0076 aux.acc_seg: 99.1532 +04/18 14:53:41 - mmengine - INFO - Iter(train) [ 81500/160000] lr: 5.3157e-03 eta: 12:02:10 time: 0.5549 data_time: 0.0073 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.7202 aux.loss_ce: 0.0072 aux.acc_seg: 99.2405 +04/18 14:54:09 - mmengine - INFO - Iter(train) [ 81550/160000] lr: 5.3127e-03 eta: 12:01:43 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0085 decode.acc_seg: 99.7066 aux.loss_ce: 0.0078 aux.acc_seg: 99.3187 +04/18 14:54:36 - mmengine - INFO - Iter(train) [ 81600/160000] lr: 5.3097e-03 eta: 12:01:15 time: 0.5529 data_time: 0.0069 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.6075 aux.loss_ce: 0.0081 aux.acc_seg: 99.0890 +04/18 14:55:04 - mmengine - INFO - Iter(train) [ 81650/160000] lr: 5.3067e-03 eta: 12:00:48 time: 0.5524 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.7453 aux.loss_ce: 0.0079 aux.acc_seg: 99.2027 +04/18 14:55:32 - mmengine - INFO - Iter(train) [ 81700/160000] lr: 5.3037e-03 eta: 12:00:20 time: 0.5525 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.7040 aux.loss_ce: 0.0075 aux.acc_seg: 99.0768 +04/18 14:55:59 - mmengine - INFO - Iter(train) [ 81750/160000] lr: 5.3007e-03 eta: 11:59:53 time: 0.5535 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.6970 aux.loss_ce: 0.0073 aux.acc_seg: 99.2434 +04/18 14:56:27 - mmengine - INFO - Iter(train) [ 81800/160000] lr: 5.2978e-03 eta: 11:59:25 time: 0.5534 data_time: 0.0068 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6740 aux.loss_ce: 0.0078 aux.acc_seg: 99.1517 +04/18 14:56:55 - mmengine - INFO - Iter(train) [ 81850/160000] lr: 5.2948e-03 eta: 11:58:58 time: 0.5533 data_time: 0.0060 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0069 decode.acc_seg: 99.7755 aux.loss_ce: 0.0067 aux.acc_seg: 99.4009 +04/18 14:57:23 - mmengine - INFO - Iter(train) [ 81900/160000] lr: 5.2918e-03 eta: 11:58:30 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.6841 aux.loss_ce: 0.0072 aux.acc_seg: 99.2474 +04/18 14:57:50 - mmengine - INFO - Iter(train) [ 81950/160000] lr: 5.2888e-03 eta: 11:58:03 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0065 decode.acc_seg: 99.8104 aux.loss_ce: 0.0065 aux.acc_seg: 99.4801 +04/18 14:58:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 14:58:18 - mmengine - INFO - Iter(train) [ 82000/160000] lr: 5.2858e-03 eta: 11:57:35 time: 0.5540 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.7541 aux.loss_ce: 0.0071 aux.acc_seg: 99.4308 +04/18 14:58:46 - mmengine - INFO - Iter(train) [ 82050/160000] lr: 5.2828e-03 eta: 11:57:08 time: 0.5534 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0078 decode.acc_seg: 99.6637 aux.loss_ce: 0.0082 aux.acc_seg: 99.0720 +04/18 14:59:14 - mmengine - INFO - Iter(train) [ 82100/160000] lr: 5.2798e-03 eta: 11:56:41 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6006 aux.loss_ce: 0.0078 aux.acc_seg: 99.0711 +04/18 14:59:41 - mmengine - INFO - Iter(train) [ 82150/160000] lr: 5.2768e-03 eta: 11:56:13 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.7355 aux.loss_ce: 0.0077 aux.acc_seg: 99.1730 +04/18 15:00:09 - mmengine - INFO - Iter(train) [ 82200/160000] lr: 5.2738e-03 eta: 11:55:45 time: 0.5544 data_time: 0.0062 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6276 aux.loss_ce: 0.0077 aux.acc_seg: 99.0049 +04/18 15:00:37 - mmengine - INFO - Iter(train) [ 82250/160000] lr: 5.2708e-03 eta: 11:55:18 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0086 decode.acc_seg: 99.6283 aux.loss_ce: 0.0075 aux.acc_seg: 99.3985 +04/18 15:01:04 - mmengine - INFO - Iter(train) [ 82300/160000] lr: 5.2678e-03 eta: 11:54:50 time: 0.5550 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6887 aux.loss_ce: 0.0075 aux.acc_seg: 99.0437 +04/18 15:01:32 - mmengine - INFO - Iter(train) [ 82350/160000] lr: 5.2648e-03 eta: 11:54:23 time: 0.5541 data_time: 0.0070 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.6777 aux.loss_ce: 0.0074 aux.acc_seg: 99.2339 +04/18 15:02:00 - mmengine - INFO - Iter(train) [ 82400/160000] lr: 5.2618e-03 eta: 11:53:55 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6851 aux.loss_ce: 0.0076 aux.acc_seg: 99.2001 +04/18 15:02:27 - mmengine - INFO - Iter(train) [ 82450/160000] lr: 5.2589e-03 eta: 11:53:28 time: 0.5537 data_time: 0.0069 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.7082 aux.loss_ce: 0.0083 aux.acc_seg: 99.3728 +04/18 15:02:55 - mmengine - INFO - Iter(train) [ 82500/160000] lr: 5.2559e-03 eta: 11:53:00 time: 0.5536 data_time: 0.0067 memory: 7635 loss: 0.0246 decode.loss_ce: 0.0147 decode.acc_seg: 99.1263 aux.loss_ce: 0.0099 aux.acc_seg: 98.6214 +04/18 15:03:23 - mmengine - INFO - Iter(train) [ 82550/160000] lr: 5.2529e-03 eta: 11:52:33 time: 0.5535 data_time: 0.0069 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0099 decode.acc_seg: 99.6870 aux.loss_ce: 0.0089 aux.acc_seg: 99.1781 +04/18 15:03:50 - mmengine - INFO - Iter(train) [ 82600/160000] lr: 5.2499e-03 eta: 11:52:05 time: 0.5532 data_time: 0.0060 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0088 decode.acc_seg: 99.6472 aux.loss_ce: 0.0081 aux.acc_seg: 99.2063 +04/18 15:04:18 - mmengine - INFO - Iter(train) [ 82650/160000] lr: 5.2469e-03 eta: 11:51:38 time: 0.5618 data_time: 0.0068 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0091 decode.acc_seg: 99.6465 aux.loss_ce: 0.0079 aux.acc_seg: 99.1975 +04/18 15:04:46 - mmengine - INFO - Iter(train) [ 82700/160000] lr: 5.2439e-03 eta: 11:51:10 time: 0.5523 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.7294 aux.loss_ce: 0.0074 aux.acc_seg: 99.3320 +04/18 15:05:13 - mmengine - INFO - Iter(train) [ 82750/160000] lr: 5.2409e-03 eta: 11:50:43 time: 0.5535 data_time: 0.0073 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6490 aux.loss_ce: 0.0080 aux.acc_seg: 99.2598 +04/18 15:05:41 - mmengine - INFO - Iter(train) [ 82800/160000] lr: 5.2379e-03 eta: 11:50:15 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0094 decode.acc_seg: 99.6695 aux.loss_ce: 0.0082 aux.acc_seg: 99.2054 +04/18 15:06:09 - mmengine - INFO - Iter(train) [ 82850/160000] lr: 5.2349e-03 eta: 11:49:47 time: 0.5548 data_time: 0.0071 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6936 aux.loss_ce: 0.0077 aux.acc_seg: 99.1436 +04/18 15:06:36 - mmengine - INFO - Iter(train) [ 82900/160000] lr: 5.2319e-03 eta: 11:49:20 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0079 decode.acc_seg: 99.6571 aux.loss_ce: 0.0077 aux.acc_seg: 99.1663 +04/18 15:07:04 - mmengine - INFO - Iter(train) [ 82950/160000] lr: 5.2289e-03 eta: 11:48:52 time: 0.5527 data_time: 0.0065 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6861 aux.loss_ce: 0.0074 aux.acc_seg: 99.4237 +04/18 15:07:32 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:07:32 - mmengine - INFO - Iter(train) [ 83000/160000] lr: 5.2259e-03 eta: 11:48:25 time: 0.5541 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6954 aux.loss_ce: 0.0074 aux.acc_seg: 99.1837 +04/18 15:07:59 - mmengine - INFO - Iter(train) [ 83050/160000] lr: 5.2229e-03 eta: 11:47:57 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6863 aux.loss_ce: 0.0078 aux.acc_seg: 99.1245 +04/18 15:08:27 - mmengine - INFO - Iter(train) [ 83100/160000] lr: 5.2199e-03 eta: 11:47:30 time: 0.5610 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7141 aux.loss_ce: 0.0070 aux.acc_seg: 99.1758 +04/18 15:08:55 - mmengine - INFO - Iter(train) [ 83150/160000] lr: 5.2169e-03 eta: 11:47:02 time: 0.5549 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7163 aux.loss_ce: 0.0073 aux.acc_seg: 99.2763 +04/18 15:09:23 - mmengine - INFO - Iter(train) [ 83200/160000] lr: 5.2139e-03 eta: 11:46:35 time: 0.5541 data_time: 0.0064 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6291 aux.loss_ce: 0.0076 aux.acc_seg: 98.9625 +04/18 15:09:50 - mmengine - INFO - Iter(train) [ 83250/160000] lr: 5.2109e-03 eta: 11:46:07 time: 0.5529 data_time: 0.0070 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7611 aux.loss_ce: 0.0070 aux.acc_seg: 99.2933 +04/18 15:10:18 - mmengine - INFO - Iter(train) [ 83300/160000] lr: 5.2079e-03 eta: 11:45:40 time: 0.5558 data_time: 0.0061 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7195 aux.loss_ce: 0.0073 aux.acc_seg: 99.1021 +04/18 15:10:46 - mmengine - INFO - Iter(train) [ 83350/160000] lr: 5.2049e-03 eta: 11:45:12 time: 0.5535 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.6616 aux.loss_ce: 0.0075 aux.acc_seg: 99.2410 +04/18 15:11:14 - mmengine - INFO - Iter(train) [ 83400/160000] lr: 5.2019e-03 eta: 11:44:45 time: 0.5545 data_time: 0.0073 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.7234 aux.loss_ce: 0.0079 aux.acc_seg: 99.1467 +04/18 15:11:41 - mmengine - INFO - Iter(train) 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time: 0.5526 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.6420 aux.loss_ce: 0.0077 aux.acc_seg: 99.1487 +04/18 15:14:00 - mmengine - INFO - Iter(train) [ 83700/160000] lr: 5.1840e-03 eta: 11:42:00 time: 0.5528 data_time: 0.0066 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.6781 aux.loss_ce: 0.0071 aux.acc_seg: 99.2856 +04/18 15:14:27 - mmengine - INFO - Iter(train) [ 83750/160000] lr: 5.1810e-03 eta: 11:41:32 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0087 decode.acc_seg: 99.6272 aux.loss_ce: 0.0083 aux.acc_seg: 99.1007 +04/18 15:14:55 - mmengine - INFO - Iter(train) [ 83800/160000] lr: 5.1780e-03 eta: 11:41:05 time: 0.5538 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6845 aux.loss_ce: 0.0074 aux.acc_seg: 99.2111 +04/18 15:15:23 - mmengine - INFO - Iter(train) [ 83850/160000] lr: 5.1750e-03 eta: 11:40:37 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.7717 aux.loss_ce: 0.0081 aux.acc_seg: 99.3909 +04/18 15:15:51 - mmengine - INFO - Iter(train) [ 83900/160000] lr: 5.1720e-03 eta: 11:40:10 time: 0.5549 data_time: 0.0060 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0081 decode.acc_seg: 99.7340 aux.loss_ce: 0.0073 aux.acc_seg: 99.3512 +04/18 15:16:18 - mmengine - INFO - Iter(train) [ 83950/160000] lr: 5.1690e-03 eta: 11:39:42 time: 0.5532 data_time: 0.0064 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0083 decode.acc_seg: 99.7579 aux.loss_ce: 0.0083 aux.acc_seg: 99.1828 +04/18 15:16:46 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:16:46 - mmengine - INFO - Iter(train) [ 84000/160000] lr: 5.1660e-03 eta: 11:39:15 time: 0.5530 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7683 aux.loss_ce: 0.0075 aux.acc_seg: 99.2795 +04/18 15:17:14 - mmengine - INFO - Iter(train) [ 84050/160000] lr: 5.1630e-03 eta: 11:38:47 time: 0.5528 data_time: 0.0063 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7567 aux.loss_ce: 0.0076 aux.acc_seg: 99.4097 +04/18 15:17:41 - mmengine - INFO - Iter(train) [ 84100/160000] lr: 5.1600e-03 eta: 11:38:19 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7196 aux.loss_ce: 0.0077 aux.acc_seg: 99.2937 +04/18 15:18:09 - mmengine - INFO - Iter(train) [ 84150/160000] lr: 5.1570e-03 eta: 11:37:52 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6591 aux.loss_ce: 0.0076 aux.acc_seg: 99.2619 +04/18 15:18:37 - mmengine - INFO - Iter(train) [ 84200/160000] lr: 5.1540e-03 eta: 11:37:25 time: 0.5541 data_time: 0.0062 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0075 decode.acc_seg: 99.7203 aux.loss_ce: 0.0069 aux.acc_seg: 99.3240 +04/18 15:19:05 - mmengine - INFO - Iter(train) [ 84250/160000] lr: 5.1510e-03 eta: 11:36:57 time: 0.5528 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.7210 aux.loss_ce: 0.0072 aux.acc_seg: 99.3785 +04/18 15:19:32 - mmengine - INFO - Iter(train) [ 84300/160000] lr: 5.1480e-03 eta: 11:36:30 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0097 decode.acc_seg: 99.6539 aux.loss_ce: 0.0085 aux.acc_seg: 99.0205 +04/18 15:20:00 - mmengine - INFO - Iter(train) [ 84350/160000] lr: 5.1450e-03 eta: 11:36:02 time: 0.5548 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.5545 aux.loss_ce: 0.0077 aux.acc_seg: 98.9246 +04/18 15:20:28 - mmengine - INFO - Iter(train) [ 84400/160000] lr: 5.1420e-03 eta: 11:35:35 time: 0.5524 data_time: 0.0067 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6613 aux.loss_ce: 0.0080 aux.acc_seg: 99.2380 +04/18 15:20:55 - mmengine - INFO - Iter(train) [ 84450/160000] lr: 5.1390e-03 eta: 11:35:07 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7334 aux.loss_ce: 0.0073 aux.acc_seg: 99.2209 +04/18 15:21:23 - mmengine - INFO - Iter(train) [ 84500/160000] lr: 5.1360e-03 eta: 11:34:39 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.7145 aux.loss_ce: 0.0071 aux.acc_seg: 99.2967 +04/18 15:21:51 - mmengine - INFO - Iter(train) [ 84550/160000] lr: 5.1330e-03 eta: 11:34:12 time: 0.5532 data_time: 0.0062 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.6774 aux.loss_ce: 0.0075 aux.acc_seg: 98.9341 +04/18 15:22:18 - mmengine - INFO - Iter(train) [ 84600/160000] lr: 5.1300e-03 eta: 11:33:44 time: 0.5535 data_time: 0.0069 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.6275 aux.loss_ce: 0.0082 aux.acc_seg: 99.0343 +04/18 15:22:46 - mmengine - INFO - Iter(train) [ 84650/160000] lr: 5.1270e-03 eta: 11:33:17 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.6758 aux.loss_ce: 0.0077 aux.acc_seg: 98.8980 +04/18 15:23:14 - mmengine - INFO - Iter(train) [ 84700/160000] lr: 5.1239e-03 eta: 11:32:49 time: 0.5528 data_time: 0.0061 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0101 decode.acc_seg: 99.6681 aux.loss_ce: 0.0084 aux.acc_seg: 99.2866 +04/18 15:23:42 - mmengine - INFO - Iter(train) [ 84750/160000] lr: 5.1209e-03 eta: 11:32:22 time: 0.5524 data_time: 0.0066 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6438 aux.loss_ce: 0.0078 aux.acc_seg: 99.1773 +04/18 15:24:09 - mmengine - INFO - Iter(train) [ 84800/160000] lr: 5.1179e-03 eta: 11:31:54 time: 0.5625 data_time: 0.0059 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7628 aux.loss_ce: 0.0077 aux.acc_seg: 99.3494 +04/18 15:24:37 - mmengine - INFO - Iter(train) [ 84850/160000] lr: 5.1149e-03 eta: 11:31:27 time: 0.5527 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0070 decode.acc_seg: 99.7654 aux.loss_ce: 0.0069 aux.acc_seg: 99.4826 +04/18 15:25:05 - mmengine - INFO - Iter(train) [ 84900/160000] lr: 5.1119e-03 eta: 11:30:59 time: 0.5560 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.6209 aux.loss_ce: 0.0080 aux.acc_seg: 98.9641 +04/18 15:25:32 - mmengine - INFO - Iter(train) [ 84950/160000] lr: 5.1089e-03 eta: 11:30:32 time: 0.5538 data_time: 0.0070 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7670 aux.loss_ce: 0.0071 aux.acc_seg: 99.3470 +04/18 15:26:00 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:26:00 - mmengine - INFO - Iter(train) [ 85000/160000] lr: 5.1059e-03 eta: 11:30:04 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.6502 aux.loss_ce: 0.0082 aux.acc_seg: 98.8712 +04/18 15:26:28 - mmengine - INFO - Iter(train) [ 85050/160000] lr: 5.1029e-03 eta: 11:29:37 time: 0.5550 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0077 decode.acc_seg: 99.5624 aux.loss_ce: 0.0072 aux.acc_seg: 99.3143 +04/18 15:26:56 - mmengine - INFO - Iter(train) [ 85100/160000] lr: 5.0999e-03 eta: 11:29:09 time: 0.5558 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0091 decode.acc_seg: 99.5568 aux.loss_ce: 0.0082 aux.acc_seg: 99.1658 +04/18 15:27:23 - mmengine - INFO - Iter(train) [ 85150/160000] lr: 5.0969e-03 eta: 11:28:42 time: 0.5537 data_time: 0.0072 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7728 aux.loss_ce: 0.0075 aux.acc_seg: 99.3503 +04/18 15:27:51 - mmengine - INFO - Iter(train) [ 85200/160000] lr: 5.0939e-03 eta: 11:28:14 time: 0.5544 data_time: 0.0072 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6938 aux.loss_ce: 0.0077 aux.acc_seg: 99.1798 +04/18 15:28:19 - mmengine - INFO - Iter(train) [ 85250/160000] lr: 5.0909e-03 eta: 11:27:47 time: 0.5535 data_time: 0.0063 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.6203 aux.loss_ce: 0.0081 aux.acc_seg: 99.1729 +04/18 15:28:47 - mmengine - INFO - Iter(train) [ 85300/160000] lr: 5.0879e-03 eta: 11:27:19 time: 0.5635 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7382 aux.loss_ce: 0.0076 aux.acc_seg: 99.4714 +04/18 15:29:14 - mmengine - INFO - Iter(train) [ 85350/160000] lr: 5.0849e-03 eta: 11:26:52 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6572 aux.loss_ce: 0.0077 aux.acc_seg: 99.1914 +04/18 15:29:42 - mmengine - INFO - Iter(train) [ 85400/160000] lr: 5.0819e-03 eta: 11:26:24 time: 0.5531 data_time: 0.0066 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.7693 aux.loss_ce: 0.0071 aux.acc_seg: 99.3942 +04/18 15:30:10 - mmengine - INFO - Iter(train) [ 85450/160000] lr: 5.0789e-03 eta: 11:25:57 time: 0.5522 data_time: 0.0068 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.6550 aux.loss_ce: 0.0071 aux.acc_seg: 99.2377 +04/18 15:30:37 - mmengine - INFO - Iter(train) [ 85500/160000] lr: 5.0759e-03 eta: 11:25:29 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7290 aux.loss_ce: 0.0076 aux.acc_seg: 99.3373 +04/18 15:31:05 - mmengine - INFO - Iter(train) [ 85550/160000] lr: 5.0729e-03 eta: 11:25:02 time: 0.5521 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6910 aux.loss_ce: 0.0077 aux.acc_seg: 99.3348 +04/18 15:31:33 - mmengine - INFO - Iter(train) [ 85600/160000] lr: 5.0699e-03 eta: 11:24:34 time: 0.5540 data_time: 0.0072 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.7341 aux.loss_ce: 0.0076 aux.acc_seg: 99.1769 +04/18 15:32:00 - mmengine - INFO - Iter(train) [ 85650/160000] lr: 5.0669e-03 eta: 11:24:07 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0082 decode.acc_seg: 99.5660 aux.loss_ce: 0.0080 aux.acc_seg: 99.0636 +04/18 15:32:28 - mmengine - INFO - Iter(train) [ 85700/160000] lr: 5.0639e-03 eta: 11:23:39 time: 0.5530 data_time: 0.0063 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7385 aux.loss_ce: 0.0073 aux.acc_seg: 99.3439 +04/18 15:32:56 - mmengine - INFO - Iter(train) [ 85750/160000] lr: 5.0609e-03 eta: 11:23:12 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.8116 aux.loss_ce: 0.0075 aux.acc_seg: 99.3443 +04/18 15:33:23 - mmengine - INFO - Iter(train) [ 85800/160000] lr: 5.0578e-03 eta: 11:22:44 time: 0.5526 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6855 aux.loss_ce: 0.0074 aux.acc_seg: 99.2805 +04/18 15:33:51 - mmengine - INFO - Iter(train) [ 85850/160000] lr: 5.0548e-03 eta: 11:22:16 time: 0.5528 data_time: 0.0073 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.7166 aux.loss_ce: 0.0078 aux.acc_seg: 99.2358 +04/18 15:34:19 - mmengine - INFO - Iter(train) [ 85900/160000] lr: 5.0518e-03 eta: 11:21:49 time: 0.5523 data_time: 0.0068 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.6441 aux.loss_ce: 0.0072 aux.acc_seg: 99.1346 +04/18 15:34:47 - mmengine - INFO - Iter(train) [ 85950/160000] lr: 5.0488e-03 eta: 11:21:21 time: 0.5537 data_time: 0.0073 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.6896 aux.loss_ce: 0.0077 aux.acc_seg: 99.3023 +04/18 15:35:14 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:35:14 - mmengine - INFO - Iter(train) [ 86000/160000] lr: 5.0458e-03 eta: 11:20:54 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7148 aux.loss_ce: 0.0075 aux.acc_seg: 99.0259 +04/18 15:35:42 - mmengine - INFO - Iter(train) [ 86050/160000] lr: 5.0428e-03 eta: 11:20:26 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7795 aux.loss_ce: 0.0073 aux.acc_seg: 99.4060 +04/18 15:36:10 - mmengine - INFO - Iter(train) [ 86100/160000] lr: 5.0398e-03 eta: 11:19:59 time: 0.5544 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.5991 aux.loss_ce: 0.0077 aux.acc_seg: 99.0725 +04/18 15:36:37 - mmengine - INFO - Iter(train) [ 86150/160000] lr: 5.0368e-03 eta: 11:19:31 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.8203 aux.loss_ce: 0.0070 aux.acc_seg: 99.3801 +04/18 15:37:05 - mmengine - INFO - Iter(train) [ 86200/160000] lr: 5.0338e-03 eta: 11:19:04 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7426 aux.loss_ce: 0.0073 aux.acc_seg: 99.3389 +04/18 15:37:33 - mmengine - INFO - Iter(train) [ 86250/160000] lr: 5.0308e-03 eta: 11:18:36 time: 0.5532 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.7746 aux.loss_ce: 0.0073 aux.acc_seg: 99.3625 +04/18 15:38:00 - mmengine - INFO - Iter(train) [ 86300/160000] lr: 5.0278e-03 eta: 11:18:09 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6654 aux.loss_ce: 0.0072 aux.acc_seg: 99.1960 +04/18 15:38:28 - mmengine - INFO - Iter(train) [ 86350/160000] lr: 5.0248e-03 eta: 11:17:41 time: 0.5529 data_time: 0.0068 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7266 aux.loss_ce: 0.0068 aux.acc_seg: 99.2655 +04/18 15:38:56 - mmengine - INFO - Iter(train) [ 86400/160000] lr: 5.0218e-03 eta: 11:17:14 time: 0.5542 data_time: 0.0075 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.6494 aux.loss_ce: 0.0077 aux.acc_seg: 98.9733 +04/18 15:39:24 - mmengine - INFO - Iter(train) [ 86450/160000] lr: 5.0187e-03 eta: 11:16:46 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7817 aux.loss_ce: 0.0074 aux.acc_seg: 99.4470 +04/18 15:39:52 - mmengine - INFO - Iter(train) [ 86500/160000] lr: 5.0157e-03 eta: 11:16:19 time: 0.5537 data_time: 0.0068 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0068 decode.acc_seg: 99.7622 aux.loss_ce: 0.0067 aux.acc_seg: 99.3397 +04/18 15:40:19 - mmengine - INFO - Iter(train) [ 86550/160000] lr: 5.0127e-03 eta: 11:15:51 time: 0.5546 data_time: 0.0074 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7285 aux.loss_ce: 0.0070 aux.acc_seg: 99.3051 +04/18 15:40:47 - mmengine - INFO - Iter(train) [ 86600/160000] lr: 5.0097e-03 eta: 11:15:24 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7585 aux.loss_ce: 0.0075 aux.acc_seg: 99.2744 +04/18 15:41:15 - mmengine - INFO - Iter(train) [ 86650/160000] lr: 5.0067e-03 eta: 11:14:56 time: 0.5522 data_time: 0.0063 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6712 aux.loss_ce: 0.0073 aux.acc_seg: 99.2598 +04/18 15:41:42 - mmengine - INFO - Iter(train) [ 86700/160000] lr: 5.0037e-03 eta: 11:14:29 time: 0.5550 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.7520 aux.loss_ce: 0.0075 aux.acc_seg: 99.2879 +04/18 15:42:10 - mmengine - INFO - Iter(train) [ 86750/160000] lr: 5.0007e-03 eta: 11:14:01 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7194 aux.loss_ce: 0.0071 aux.acc_seg: 99.3279 +04/18 15:42:38 - mmengine - INFO - Iter(train) [ 86800/160000] lr: 4.9977e-03 eta: 11:13:34 time: 0.5544 data_time: 0.0064 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.6115 aux.loss_ce: 0.0075 aux.acc_seg: 98.7851 +04/18 15:43:06 - mmengine - INFO - Iter(train) [ 86850/160000] lr: 4.9947e-03 eta: 11:13:06 time: 0.5544 data_time: 0.0061 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7292 aux.loss_ce: 0.0067 aux.acc_seg: 99.2359 +04/18 15:43:33 - mmengine - INFO - Iter(train) [ 86900/160000] lr: 4.9916e-03 eta: 11:12:39 time: 0.5528 data_time: 0.0062 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.6617 aux.loss_ce: 0.0074 aux.acc_seg: 99.1792 +04/18 15:44:01 - mmengine - INFO - Iter(train) [ 86950/160000] lr: 4.9886e-03 eta: 11:12:11 time: 0.5533 data_time: 0.0059 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7668 aux.loss_ce: 0.0077 aux.acc_seg: 99.3586 +04/18 15:44:29 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:44:29 - mmengine - INFO - Iter(train) [ 87000/160000] lr: 4.9856e-03 eta: 11:11:44 time: 0.5541 data_time: 0.0071 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7103 aux.loss_ce: 0.0068 aux.acc_seg: 99.3274 +04/18 15:44:56 - mmengine - INFO - Iter(train) [ 87050/160000] lr: 4.9826e-03 eta: 11:11:16 time: 0.5550 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.6572 aux.loss_ce: 0.0075 aux.acc_seg: 98.9265 +04/18 15:45:24 - mmengine - INFO - Iter(train) [ 87100/160000] lr: 4.9796e-03 eta: 11:10:49 time: 0.5545 data_time: 0.0067 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0080 decode.acc_seg: 99.6841 aux.loss_ce: 0.0076 aux.acc_seg: 99.2471 +04/18 15:45:52 - mmengine - INFO - Iter(train) [ 87150/160000] lr: 4.9766e-03 eta: 11:10:21 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0074 decode.acc_seg: 99.7620 aux.loss_ce: 0.0071 aux.acc_seg: 99.3556 +04/18 15:46:19 - mmengine - INFO - Iter(train) [ 87200/160000] lr: 4.9736e-03 eta: 11:09:53 time: 0.5535 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6668 aux.loss_ce: 0.0076 aux.acc_seg: 99.0356 +04/18 15:46:47 - mmengine - INFO - Iter(train) [ 87250/160000] lr: 4.9706e-03 eta: 11:09:26 time: 0.5544 data_time: 0.0066 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7335 aux.loss_ce: 0.0072 aux.acc_seg: 99.2883 +04/18 15:47:15 - mmengine - INFO - Iter(train) [ 87300/160000] lr: 4.9676e-03 eta: 11:08:58 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0219 decode.loss_ce: 0.0124 decode.acc_seg: 99.3621 aux.loss_ce: 0.0095 aux.acc_seg: 99.0041 +04/18 15:47:43 - mmengine - INFO - Iter(train) [ 87350/160000] lr: 4.9645e-03 eta: 11:08:31 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0231 decode.loss_ce: 0.0136 decode.acc_seg: 99.5957 aux.loss_ce: 0.0095 aux.acc_seg: 99.2460 +04/18 15:48:10 - mmengine - INFO - Iter(train) [ 87400/160000] lr: 4.9615e-03 eta: 11:08:03 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0102 decode.acc_seg: 99.7156 aux.loss_ce: 0.0086 aux.acc_seg: 99.2757 +04/18 15:48:38 - mmengine - INFO - Iter(train) [ 87450/160000] lr: 4.9585e-03 eta: 11:07:36 time: 0.5541 data_time: 0.0070 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0089 decode.acc_seg: 99.8028 aux.loss_ce: 0.0080 aux.acc_seg: 99.5077 +04/18 15:49:06 - mmengine - INFO - Iter(train) [ 87500/160000] lr: 4.9555e-03 eta: 11:07:08 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0087 decode.acc_seg: 99.6773 aux.loss_ce: 0.0080 aux.acc_seg: 99.0063 +04/18 15:49:34 - mmengine - INFO - Iter(train) [ 87550/160000] lr: 4.9525e-03 eta: 11:06:41 time: 0.5524 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0080 decode.acc_seg: 99.8137 aux.loss_ce: 0.0074 aux.acc_seg: 99.5594 +04/18 15:50:01 - mmengine - INFO - Iter(train) [ 87600/160000] lr: 4.9495e-03 eta: 11:06:13 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7032 aux.loss_ce: 0.0075 aux.acc_seg: 99.2753 +04/18 15:50:29 - mmengine - INFO - Iter(train) [ 87650/160000] lr: 4.9465e-03 eta: 11:05:46 time: 0.5525 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.7018 aux.loss_ce: 0.0078 aux.acc_seg: 99.2401 +04/18 15:50:57 - mmengine - INFO - Iter(train) [ 87700/160000] lr: 4.9434e-03 eta: 11:05:18 time: 0.5522 data_time: 0.0062 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.7213 aux.loss_ce: 0.0070 aux.acc_seg: 99.3948 +04/18 15:51:24 - mmengine - INFO - Iter(train) [ 87750/160000] lr: 4.9404e-03 eta: 11:04:51 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0083 decode.acc_seg: 99.6844 aux.loss_ce: 0.0074 aux.acc_seg: 99.4108 +04/18 15:51:52 - mmengine - INFO - Iter(train) [ 87800/160000] lr: 4.9374e-03 eta: 11:04:23 time: 0.5544 data_time: 0.0077 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0070 decode.acc_seg: 99.7691 aux.loss_ce: 0.0076 aux.acc_seg: 99.2150 +04/18 15:52:20 - mmengine - INFO - Iter(train) [ 87850/160000] lr: 4.9344e-03 eta: 11:03:56 time: 0.5553 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7274 aux.loss_ce: 0.0071 aux.acc_seg: 99.1962 +04/18 15:52:48 - mmengine - INFO - Iter(train) [ 87900/160000] lr: 4.9314e-03 eta: 11:03:28 time: 0.5538 data_time: 0.0070 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6800 aux.loss_ce: 0.0080 aux.acc_seg: 99.1891 +04/18 15:53:15 - mmengine - INFO - Iter(train) [ 87950/160000] lr: 4.9284e-03 eta: 11:03:01 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.7053 aux.loss_ce: 0.0070 aux.acc_seg: 99.3465 +04/18 15:53:43 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 15:53:43 - mmengine - INFO - Iter(train) [ 88000/160000] lr: 4.9254e-03 eta: 11:02:33 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7036 aux.loss_ce: 0.0072 aux.acc_seg: 99.0846 +04/18 15:54:11 - mmengine - INFO - Iter(train) [ 88050/160000] lr: 4.9223e-03 eta: 11:02:06 time: 0.5529 data_time: 0.0066 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.4520 aux.loss_ce: 0.0082 aux.acc_seg: 99.1279 +04/18 15:54:38 - mmengine - INFO - Iter(train) [ 88100/160000] lr: 4.9193e-03 eta: 11:01:38 time: 0.5519 data_time: 0.0064 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.6961 aux.loss_ce: 0.0071 aux.acc_seg: 99.1393 +04/18 15:55:06 - mmengine - INFO - Iter(train) [ 88150/160000] lr: 4.9163e-03 eta: 11:01:11 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7077 aux.loss_ce: 0.0074 aux.acc_seg: 99.4089 +04/18 15:55:34 - mmengine - INFO - Iter(train) [ 88200/160000] lr: 4.9133e-03 eta: 11:00:43 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7110 aux.loss_ce: 0.0077 aux.acc_seg: 99.1214 +04/18 15:56:02 - mmengine - INFO - Iter(train) [ 88250/160000] lr: 4.9103e-03 eta: 11:00:16 time: 0.5534 data_time: 0.0060 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6861 aux.loss_ce: 0.0077 aux.acc_seg: 99.0960 +04/18 15:56:29 - mmengine - INFO - Iter(train) [ 88300/160000] lr: 4.9073e-03 eta: 10:59:48 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0071 decode.acc_seg: 99.7285 aux.loss_ce: 0.0070 aux.acc_seg: 99.1835 +04/18 15:56:57 - mmengine - INFO - Iter(train) [ 88350/160000] lr: 4.9042e-03 eta: 10:59:20 time: 0.5532 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.7697 aux.loss_ce: 0.0070 aux.acc_seg: 99.2548 +04/18 15:57:25 - mmengine - INFO - Iter(train) [ 88400/160000] lr: 4.9012e-03 eta: 10:58:53 time: 0.5537 data_time: 0.0070 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.7449 aux.loss_ce: 0.0078 aux.acc_seg: 99.2819 +04/18 15:57:52 - mmengine - INFO - Iter(train) [ 88450/160000] lr: 4.8982e-03 eta: 10:58:25 time: 0.5517 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7245 aux.loss_ce: 0.0074 aux.acc_seg: 99.2621 +04/18 15:58:20 - mmengine - INFO - Iter(train) [ 88500/160000] lr: 4.8952e-03 eta: 10:57:58 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7017 aux.loss_ce: 0.0074 aux.acc_seg: 99.0706 +04/18 15:58:48 - mmengine - INFO - Iter(train) [ 88550/160000] lr: 4.8922e-03 eta: 10:57:31 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0086 decode.acc_seg: 99.5434 aux.loss_ce: 0.0077 aux.acc_seg: 99.1254 +04/18 15:59:16 - mmengine - INFO - Iter(train) [ 88600/160000] lr: 4.8891e-03 eta: 10:57:03 time: 0.5532 data_time: 0.0063 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7069 aux.loss_ce: 0.0082 aux.acc_seg: 98.9732 +04/18 15:59:44 - mmengine - INFO - Iter(train) [ 88650/160000] lr: 4.8861e-03 eta: 10:56:35 time: 0.5542 data_time: 0.0063 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7394 aux.loss_ce: 0.0076 aux.acc_seg: 99.3766 +04/18 16:00:11 - mmengine - INFO - Iter(train) [ 88700/160000] lr: 4.8831e-03 eta: 10:56:08 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6796 aux.loss_ce: 0.0073 aux.acc_seg: 99.2290 +04/18 16:00:39 - mmengine - INFO - Iter(train) [ 88750/160000] lr: 4.8801e-03 eta: 10:55:40 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7298 aux.loss_ce: 0.0069 aux.acc_seg: 99.1305 +04/18 16:01:07 - mmengine - INFO - Iter(train) [ 88800/160000] lr: 4.8771e-03 eta: 10:55:13 time: 0.5526 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.5977 aux.loss_ce: 0.0077 aux.acc_seg: 99.0127 +04/18 16:01:34 - mmengine - INFO - Iter(train) [ 88850/160000] lr: 4.8741e-03 eta: 10:54:45 time: 0.5544 data_time: 0.0066 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0093 decode.acc_seg: 99.5275 aux.loss_ce: 0.0084 aux.acc_seg: 99.0695 +04/18 16:02:02 - mmengine - INFO - Iter(train) [ 88900/160000] lr: 4.8710e-03 eta: 10:54:18 time: 0.5525 data_time: 0.0060 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0074 decode.acc_seg: 99.6779 aux.loss_ce: 0.0078 aux.acc_seg: 99.1474 +04/18 16:02:30 - mmengine - INFO - Iter(train) [ 88950/160000] lr: 4.8680e-03 eta: 10:53:50 time: 0.5552 data_time: 0.0072 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6627 aux.loss_ce: 0.0080 aux.acc_seg: 99.1783 +04/18 16:02:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:02:58 - mmengine - INFO - Iter(train) [ 89000/160000] lr: 4.8650e-03 eta: 10:53:23 time: 0.5524 data_time: 0.0067 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7494 aux.loss_ce: 0.0069 aux.acc_seg: 99.2952 +04/18 16:03:25 - mmengine - INFO - Iter(train) [ 89050/160000] lr: 4.8620e-03 eta: 10:52:55 time: 0.5539 data_time: 0.0073 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.6391 aux.loss_ce: 0.0079 aux.acc_seg: 99.3408 +04/18 16:03:53 - mmengine - INFO - Iter(train) [ 89100/160000] lr: 4.8590e-03 eta: 10:52:28 time: 0.5538 data_time: 0.0061 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0090 decode.acc_seg: 99.6519 aux.loss_ce: 0.0085 aux.acc_seg: 99.1333 +04/18 16:04:21 - mmengine - INFO - Iter(train) [ 89150/160000] lr: 4.8559e-03 eta: 10:52:00 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0080 decode.acc_seg: 99.6053 aux.loss_ce: 0.0078 aux.acc_seg: 99.1386 +04/18 16:04:48 - mmengine - INFO - Iter(train) [ 89200/160000] lr: 4.8529e-03 eta: 10:51:33 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.7516 aux.loss_ce: 0.0079 aux.acc_seg: 99.3876 +04/18 16:05:16 - mmengine - INFO - Iter(train) [ 89250/160000] lr: 4.8499e-03 eta: 10:51:05 time: 0.5555 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6994 aux.loss_ce: 0.0074 aux.acc_seg: 99.2689 +04/18 16:05:44 - mmengine - INFO - Iter(train) [ 89300/160000] lr: 4.8469e-03 eta: 10:50:38 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0076 decode.acc_seg: 99.6260 aux.loss_ce: 0.0074 aux.acc_seg: 99.0972 +04/18 16:06:12 - mmengine - INFO - Iter(train) [ 89350/160000] lr: 4.8438e-03 eta: 10:50:10 time: 0.5546 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6920 aux.loss_ce: 0.0075 aux.acc_seg: 99.0008 +04/18 16:06:39 - mmengine - INFO - Iter(train) [ 89400/160000] lr: 4.8408e-03 eta: 10:49:43 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.6706 aux.loss_ce: 0.0069 aux.acc_seg: 99.0152 +04/18 16:07:07 - mmengine - INFO - Iter(train) [ 89450/160000] lr: 4.8378e-03 eta: 10:49:15 time: 0.5539 data_time: 0.0074 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.7231 aux.loss_ce: 0.0075 aux.acc_seg: 99.2122 +04/18 16:07:35 - mmengine - INFO - Iter(train) [ 89500/160000] lr: 4.8348e-03 eta: 10:48:47 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0069 decode.acc_seg: 99.7463 aux.loss_ce: 0.0068 aux.acc_seg: 99.3187 +04/18 16:08:03 - mmengine - INFO - Iter(train) [ 89550/160000] lr: 4.8318e-03 eta: 10:48:20 time: 0.5644 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7202 aux.loss_ce: 0.0073 aux.acc_seg: 99.1396 +04/18 16:08:30 - mmengine - INFO - Iter(train) [ 89600/160000] lr: 4.8287e-03 eta: 10:47:53 time: 0.5543 data_time: 0.0071 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7922 aux.loss_ce: 0.0070 aux.acc_seg: 99.4041 +04/18 16:08:58 - mmengine - INFO - Iter(train) [ 89650/160000] lr: 4.8257e-03 eta: 10:47:25 time: 0.5531 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7718 aux.loss_ce: 0.0072 aux.acc_seg: 99.2868 +04/18 16:09:26 - mmengine - INFO - Iter(train) [ 89700/160000] lr: 4.8227e-03 eta: 10:46:58 time: 0.5551 data_time: 0.0080 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.6888 aux.loss_ce: 0.0069 aux.acc_seg: 99.3211 +04/18 16:09:54 - mmengine - INFO - Iter(train) [ 89750/160000] lr: 4.8197e-03 eta: 10:46:30 time: 0.5533 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.5870 aux.loss_ce: 0.0075 aux.acc_seg: 98.8684 +04/18 16:10:21 - mmengine - INFO - Iter(train) [ 89800/160000] lr: 4.8166e-03 eta: 10:46:02 time: 0.5538 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0081 decode.acc_seg: 99.8167 aux.loss_ce: 0.0072 aux.acc_seg: 99.5503 +04/18 16:10:49 - mmengine - INFO - Iter(train) [ 89850/160000] lr: 4.8136e-03 eta: 10:45:35 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0085 decode.acc_seg: 99.7402 aux.loss_ce: 0.0085 aux.acc_seg: 99.2199 +04/18 16:11:17 - mmengine - INFO - Iter(train) [ 89900/160000] lr: 4.8106e-03 eta: 10:45:07 time: 0.5541 data_time: 0.0062 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0090 decode.acc_seg: 99.6343 aux.loss_ce: 0.0075 aux.acc_seg: 99.0417 +04/18 16:11:44 - mmengine - INFO - Iter(train) [ 89950/160000] lr: 4.8076e-03 eta: 10:44:40 time: 0.5520 data_time: 0.0062 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0092 decode.acc_seg: 99.6977 aux.loss_ce: 0.0081 aux.acc_seg: 99.2770 +04/18 16:12:12 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:12:12 - mmengine - INFO - Iter(train) [ 90000/160000] lr: 4.8045e-03 eta: 10:44:12 time: 0.5534 data_time: 0.0065 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6110 aux.loss_ce: 0.0079 aux.acc_seg: 98.7445 +04/18 16:12:12 - mmengine - INFO - Saving checkpoint at 90000 iterations +04/18 16:12:16 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0469 data_time: 0.0016 memory: 1657 +04/18 16:12:18 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0467 data_time: 0.0013 memory: 1657 +04/18 16:12:21 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0468 data_time: 0.0014 memory: 1657 +04/18 16:12:23 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0451 data_time: 0.0012 memory: 1657 +04/18 16:12:23 - mmengine - INFO - per class results: +04/18 16:12:23 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 98.99 | 99.43 | 99.49 | 99.55 | 99.43 | +| contrast | 78.56 | 89.25 | 87.99 | 86.76 | 89.25 | ++------------+-------+-------+--------+-----------+--------+ +04/18 16:12:23 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.0300 mIoU: 88.7700 mAcc: 94.3400 mFscore: 93.7400 mPrecision: 93.1600 mRecall: 94.3400 data_time: 0.0016 time: 0.0468 +04/18 16:12:51 - mmengine - INFO - Iter(train) [ 90050/160000] lr: 4.8015e-03 eta: 10:43:45 time: 0.5541 data_time: 0.0073 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0088 decode.acc_seg: 99.7517 aux.loss_ce: 0.0079 aux.acc_seg: 99.4351 +04/18 16:13:19 - mmengine - INFO - Iter(train) [ 90100/160000] lr: 4.7985e-03 eta: 10:43:17 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0080 decode.acc_seg: 99.7671 aux.loss_ce: 0.0075 aux.acc_seg: 99.3597 +04/18 16:13:46 - mmengine - INFO - Iter(train) [ 90150/160000] lr: 4.7955e-03 eta: 10:42:50 time: 0.5537 data_time: 0.0068 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0089 decode.acc_seg: 99.6665 aux.loss_ce: 0.0081 aux.acc_seg: 99.1957 +04/18 16:14:14 - mmengine - INFO - Iter(train) [ 90200/160000] lr: 4.7924e-03 eta: 10:42:22 time: 0.5526 data_time: 0.0057 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.6493 aux.loss_ce: 0.0070 aux.acc_seg: 99.1049 +04/18 16:14:42 - mmengine - INFO - Iter(train) [ 90250/160000] lr: 4.7894e-03 eta: 10:41:55 time: 0.5531 data_time: 0.0066 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0081 decode.acc_seg: 99.6822 aux.loss_ce: 0.0083 aux.acc_seg: 99.0961 +04/18 16:15:10 - mmengine - INFO - Iter(train) [ 90300/160000] lr: 4.7864e-03 eta: 10:41:27 time: 0.5532 data_time: 0.0068 memory: 7635 loss: 0.0182 decode.loss_ce: 0.0098 decode.acc_seg: 99.6121 aux.loss_ce: 0.0084 aux.acc_seg: 99.2744 +04/18 16:15:37 - mmengine - INFO - Iter(train) [ 90350/160000] lr: 4.7834e-03 eta: 10:41:00 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0068 decode.acc_seg: 99.6592 aux.loss_ce: 0.0067 aux.acc_seg: 99.2595 +04/18 16:16:05 - mmengine - INFO - Iter(train) [ 90400/160000] lr: 4.7803e-03 eta: 10:40:32 time: 0.5538 data_time: 0.0071 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7413 aux.loss_ce: 0.0075 aux.acc_seg: 99.2717 +04/18 16:16:33 - mmengine - INFO - Iter(train) [ 90450/160000] lr: 4.7773e-03 eta: 10:40:04 time: 0.5532 data_time: 0.0066 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7602 aux.loss_ce: 0.0075 aux.acc_seg: 99.4248 +04/18 16:17:00 - mmengine - INFO - Iter(train) [ 90500/160000] lr: 4.7743e-03 eta: 10:39:37 time: 0.5643 data_time: 0.0076 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0075 decode.acc_seg: 99.6515 aux.loss_ce: 0.0080 aux.acc_seg: 99.1366 +04/18 16:17:28 - mmengine - INFO - Iter(train) [ 90550/160000] lr: 4.7713e-03 eta: 10:39:09 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.6815 aux.loss_ce: 0.0075 aux.acc_seg: 99.3804 +04/18 16:17:56 - mmengine - INFO - Iter(train) [ 90600/160000] lr: 4.7682e-03 eta: 10:38:42 time: 0.5634 data_time: 0.0065 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0077 decode.acc_seg: 99.7453 aux.loss_ce: 0.0082 aux.acc_seg: 99.2341 +04/18 16:18:24 - mmengine - INFO - Iter(train) [ 90650/160000] lr: 4.7652e-03 eta: 10:38:15 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7275 aux.loss_ce: 0.0076 aux.acc_seg: 99.4253 +04/18 16:18:52 - mmengine - INFO - Iter(train) [ 90700/160000] lr: 4.7622e-03 eta: 10:37:47 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6181 aux.loss_ce: 0.0077 aux.acc_seg: 99.0476 +04/18 16:19:19 - mmengine - INFO - Iter(train) [ 90750/160000] lr: 4.7592e-03 eta: 10:37:20 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0093 decode.acc_seg: 99.5783 aux.loss_ce: 0.0081 aux.acc_seg: 99.0585 +04/18 16:19:47 - mmengine - INFO - Iter(train) [ 90800/160000] lr: 4.7561e-03 eta: 10:36:52 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0185 decode.loss_ce: 0.0101 decode.acc_seg: 99.7410 aux.loss_ce: 0.0084 aux.acc_seg: 99.3400 +04/18 16:20:15 - mmengine - INFO - Iter(train) [ 90850/160000] lr: 4.7531e-03 eta: 10:36:24 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6765 aux.loss_ce: 0.0080 aux.acc_seg: 99.1732 +04/18 16:20:42 - mmengine - INFO - Iter(train) [ 90900/160000] lr: 4.7501e-03 eta: 10:35:57 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.6199 aux.loss_ce: 0.0074 aux.acc_seg: 99.3221 +04/18 16:21:10 - mmengine - INFO - Iter(train) [ 90950/160000] lr: 4.7470e-03 eta: 10:35:29 time: 0.5540 data_time: 0.0062 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.5703 aux.loss_ce: 0.0079 aux.acc_seg: 99.0297 +04/18 16:21:38 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:21:38 - mmengine - INFO - Iter(train) [ 91000/160000] lr: 4.7440e-03 eta: 10:35:02 time: 0.5558 data_time: 0.0069 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0085 decode.acc_seg: 99.5575 aux.loss_ce: 0.0081 aux.acc_seg: 99.2346 +04/18 16:22:05 - mmengine - INFO - Iter(train) [ 91050/160000] lr: 4.7410e-03 eta: 10:34:34 time: 0.5534 data_time: 0.0071 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0092 decode.acc_seg: 99.6915 aux.loss_ce: 0.0084 aux.acc_seg: 99.2305 +04/18 16:22:33 - mmengine - INFO - Iter(train) [ 91100/160000] lr: 4.7380e-03 eta: 10:34:07 time: 0.5547 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6708 aux.loss_ce: 0.0072 aux.acc_seg: 99.2778 +04/18 16:23:01 - mmengine - INFO - Iter(train) [ 91150/160000] lr: 4.7349e-03 eta: 10:33:39 time: 0.5542 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7328 aux.loss_ce: 0.0074 aux.acc_seg: 99.3443 +04/18 16:23:29 - mmengine - INFO - Iter(train) [ 91200/160000] lr: 4.7319e-03 eta: 10:33:12 time: 0.5562 data_time: 0.0079 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0068 decode.acc_seg: 99.7856 aux.loss_ce: 0.0068 aux.acc_seg: 99.3442 +04/18 16:23:56 - mmengine - INFO - Iter(train) [ 91250/160000] lr: 4.7289e-03 eta: 10:32:44 time: 0.5530 data_time: 0.0060 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7177 aux.loss_ce: 0.0073 aux.acc_seg: 99.3159 +04/18 16:24:24 - mmengine - INFO - Iter(train) [ 91300/160000] lr: 4.7258e-03 eta: 10:32:17 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0083 decode.acc_seg: 99.7583 aux.loss_ce: 0.0074 aux.acc_seg: 99.3390 +04/18 16:24:52 - mmengine - INFO - Iter(train) [ 91350/160000] lr: 4.7228e-03 eta: 10:31:49 time: 0.5560 data_time: 0.0063 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0088 decode.acc_seg: 99.7421 aux.loss_ce: 0.0084 aux.acc_seg: 99.2332 +04/18 16:25:19 - mmengine - INFO - Iter(train) [ 91400/160000] lr: 4.7198e-03 eta: 10:31:21 time: 0.5546 data_time: 0.0067 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7305 aux.loss_ce: 0.0076 aux.acc_seg: 99.3952 +04/18 16:25:47 - mmengine - INFO - Iter(train) [ 91450/160000] lr: 4.7168e-03 eta: 10:30:54 time: 0.5536 data_time: 0.0065 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.7017 aux.loss_ce: 0.0080 aux.acc_seg: 99.1828 +04/18 16:26:15 - mmengine - INFO - Iter(train) [ 91500/160000] lr: 4.7137e-03 eta: 10:30:26 time: 0.5532 data_time: 0.0070 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7032 aux.loss_ce: 0.0073 aux.acc_seg: 99.1995 +04/18 16:26:43 - mmengine - INFO - Iter(train) [ 91550/160000] lr: 4.7107e-03 eta: 10:29:59 time: 0.5549 data_time: 0.0060 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.7380 aux.loss_ce: 0.0075 aux.acc_seg: 99.1542 +04/18 16:27:10 - mmengine - INFO - Iter(train) [ 91600/160000] lr: 4.7077e-03 eta: 10:29:31 time: 0.5535 data_time: 0.0067 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6866 aux.loss_ce: 0.0074 aux.acc_seg: 99.2903 +04/18 16:27:38 - mmengine - INFO - Iter(train) [ 91650/160000] lr: 4.7046e-03 eta: 10:29:04 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0065 decode.acc_seg: 99.7046 aux.loss_ce: 0.0067 aux.acc_seg: 99.1618 +04/18 16:28:06 - mmengine - INFO - Iter(train) [ 91700/160000] lr: 4.7016e-03 eta: 10:28:36 time: 0.5545 data_time: 0.0062 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7621 aux.loss_ce: 0.0069 aux.acc_seg: 99.4017 +04/18 16:28:34 - mmengine - INFO - Iter(train) [ 91750/160000] lr: 4.6986e-03 eta: 10:28:09 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.6921 aux.loss_ce: 0.0076 aux.acc_seg: 99.2706 +04/18 16:29:02 - mmengine - INFO - Iter(train) [ 91800/160000] lr: 4.6955e-03 eta: 10:27:41 time: 0.5538 data_time: 0.0069 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7231 aux.loss_ce: 0.0073 aux.acc_seg: 99.1256 +04/18 16:29:29 - mmengine - INFO - Iter(train) [ 91850/160000] lr: 4.6925e-03 eta: 10:27:14 time: 0.5539 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7126 aux.loss_ce: 0.0073 aux.acc_seg: 99.1622 +04/18 16:29:57 - mmengine - INFO - Iter(train) [ 91900/160000] lr: 4.6895e-03 eta: 10:26:46 time: 0.5536 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0068 decode.acc_seg: 99.7128 aux.loss_ce: 0.0068 aux.acc_seg: 99.2221 +04/18 16:30:25 - mmengine - INFO - Iter(train) [ 91950/160000] lr: 4.6864e-03 eta: 10:26:19 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0081 decode.acc_seg: 99.5994 aux.loss_ce: 0.0077 aux.acc_seg: 99.2487 +04/18 16:30:53 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:30:53 - mmengine - INFO - Iter(train) [ 92000/160000] lr: 4.6834e-03 eta: 10:25:51 time: 0.5554 data_time: 0.0074 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.6864 aux.loss_ce: 0.0069 aux.acc_seg: 99.2065 +04/18 16:31:20 - mmengine - INFO - Iter(train) [ 92050/160000] lr: 4.6804e-03 eta: 10:25:24 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7125 aux.loss_ce: 0.0069 aux.acc_seg: 99.1871 +04/18 16:31:48 - mmengine - INFO - Iter(train) [ 92100/160000] lr: 4.6773e-03 eta: 10:24:56 time: 0.5545 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6840 aux.loss_ce: 0.0077 aux.acc_seg: 99.2039 +04/18 16:32:16 - mmengine - INFO - Iter(train) [ 92150/160000] lr: 4.6743e-03 eta: 10:24:29 time: 0.5534 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7873 aux.loss_ce: 0.0072 aux.acc_seg: 99.4144 +04/18 16:32:43 - mmengine - INFO - Iter(train) [ 92200/160000] lr: 4.6713e-03 eta: 10:24:01 time: 0.5551 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7953 aux.loss_ce: 0.0069 aux.acc_seg: 99.4600 +04/18 16:33:11 - mmengine - INFO - Iter(train) [ 92250/160000] lr: 4.6682e-03 eta: 10:23:34 time: 0.5552 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.6154 aux.loss_ce: 0.0075 aux.acc_seg: 99.2498 +04/18 16:33:39 - mmengine - INFO - Iter(train) [ 92300/160000] lr: 4.6652e-03 eta: 10:23:06 time: 0.5536 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7125 aux.loss_ce: 0.0073 aux.acc_seg: 99.1919 +04/18 16:34:07 - mmengine - INFO - Iter(train) [ 92350/160000] lr: 4.6622e-03 eta: 10:22:39 time: 0.5560 data_time: 0.0071 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7135 aux.loss_ce: 0.0074 aux.acc_seg: 99.3181 +04/18 16:34:34 - mmengine - INFO - Iter(train) [ 92400/160000] lr: 4.6591e-03 eta: 10:22:11 time: 0.5533 data_time: 0.0065 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.5176 aux.loss_ce: 0.0074 aux.acc_seg: 99.0997 +04/18 16:35:02 - mmengine - INFO - Iter(train) [ 92450/160000] lr: 4.6561e-03 eta: 10:21:44 time: 0.5544 data_time: 0.0068 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.6918 aux.loss_ce: 0.0076 aux.acc_seg: 99.3507 +04/18 16:35:30 - mmengine - INFO - Iter(train) [ 92500/160000] lr: 4.6531e-03 eta: 10:21:16 time: 0.5534 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7289 aux.loss_ce: 0.0069 aux.acc_seg: 99.3187 +04/18 16:35:58 - mmengine - INFO - Iter(train) [ 92550/160000] lr: 4.6500e-03 eta: 10:20:48 time: 0.5542 data_time: 0.0070 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.5818 aux.loss_ce: 0.0071 aux.acc_seg: 98.9644 +04/18 16:36:25 - mmengine - INFO - Iter(train) [ 92600/160000] lr: 4.6470e-03 eta: 10:20:21 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7667 aux.loss_ce: 0.0068 aux.acc_seg: 99.2359 +04/18 16:36:53 - mmengine - INFO - Iter(train) [ 92650/160000] lr: 4.6439e-03 eta: 10:19:53 time: 0.5530 data_time: 0.0065 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0089 decode.acc_seg: 99.7192 aux.loss_ce: 0.0080 aux.acc_seg: 99.1530 +04/18 16:37:21 - mmengine - INFO - Iter(train) [ 92700/160000] lr: 4.6409e-03 eta: 10:19:26 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6608 aux.loss_ce: 0.0073 aux.acc_seg: 99.2210 +04/18 16:37:48 - mmengine - INFO - Iter(train) [ 92750/160000] lr: 4.6379e-03 eta: 10:18:58 time: 0.5545 data_time: 0.0065 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6756 aux.loss_ce: 0.0074 aux.acc_seg: 99.1357 +04/18 16:38:16 - mmengine - INFO - Iter(train) [ 92800/160000] lr: 4.6348e-03 eta: 10:18:31 time: 0.5544 data_time: 0.0071 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6621 aux.loss_ce: 0.0077 aux.acc_seg: 99.0524 +04/18 16:38:44 - mmengine - INFO - Iter(train) [ 92850/160000] lr: 4.6318e-03 eta: 10:18:03 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0084 decode.acc_seg: 99.7317 aux.loss_ce: 0.0078 aux.acc_seg: 99.3055 +04/18 16:39:12 - mmengine - INFO - Iter(train) [ 92900/160000] lr: 4.6288e-03 eta: 10:17:36 time: 0.5529 data_time: 0.0072 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0089 decode.acc_seg: 99.7147 aux.loss_ce: 0.0083 aux.acc_seg: 99.0110 +04/18 16:39:40 - mmengine - INFO - Iter(train) [ 92950/160000] lr: 4.6257e-03 eta: 10:17:08 time: 0.5548 data_time: 0.0063 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6904 aux.loss_ce: 0.0073 aux.acc_seg: 99.1941 +04/18 16:40:07 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:40:07 - mmengine - INFO - Iter(train) [ 93000/160000] lr: 4.6227e-03 eta: 10:16:41 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.7231 aux.loss_ce: 0.0076 aux.acc_seg: 99.0689 +04/18 16:40:35 - mmengine - INFO - Iter(train) [ 93050/160000] lr: 4.6197e-03 eta: 10:16:13 time: 0.5521 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7027 aux.loss_ce: 0.0075 aux.acc_seg: 99.0228 +04/18 16:41:03 - mmengine - INFO - Iter(train) [ 93100/160000] lr: 4.6166e-03 eta: 10:15:46 time: 0.5525 data_time: 0.0069 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.6995 aux.loss_ce: 0.0076 aux.acc_seg: 99.1519 +04/18 16:41:30 - mmengine - INFO - Iter(train) [ 93150/160000] lr: 4.6136e-03 eta: 10:15:18 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0078 decode.acc_seg: 99.7832 aux.loss_ce: 0.0076 aux.acc_seg: 99.2928 +04/18 16:41:58 - mmengine - INFO - Iter(train) [ 93200/160000] lr: 4.6105e-03 eta: 10:14:51 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.6873 aux.loss_ce: 0.0068 aux.acc_seg: 99.1235 +04/18 16:42:26 - mmengine - INFO - Iter(train) [ 93250/160000] lr: 4.6075e-03 eta: 10:14:23 time: 0.5548 data_time: 0.0066 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6369 aux.loss_ce: 0.0071 aux.acc_seg: 99.0698 +04/18 16:42:54 - mmengine - INFO - Iter(train) [ 93300/160000] lr: 4.6045e-03 eta: 10:13:56 time: 0.5528 data_time: 0.0069 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7134 aux.loss_ce: 0.0072 aux.acc_seg: 99.1560 +04/18 16:43:21 - mmengine - INFO - Iter(train) [ 93350/160000] lr: 4.6014e-03 eta: 10:13:28 time: 0.5539 data_time: 0.0072 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6781 aux.loss_ce: 0.0074 aux.acc_seg: 99.1940 +04/18 16:43:49 - mmengine - INFO - Iter(train) [ 93400/160000] lr: 4.5984e-03 eta: 10:13:00 time: 0.5544 data_time: 0.0081 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.6734 aux.loss_ce: 0.0074 aux.acc_seg: 99.1181 +04/18 16:44:17 - mmengine - INFO - Iter(train) [ 93450/160000] lr: 4.5953e-03 eta: 10:12:33 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.7462 aux.loss_ce: 0.0075 aux.acc_seg: 99.3246 +04/18 16:44:45 - mmengine - INFO - Iter(train) [ 93500/160000] lr: 4.5923e-03 eta: 10:12:05 time: 0.5547 data_time: 0.0064 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7552 aux.loss_ce: 0.0070 aux.acc_seg: 99.3195 +04/18 16:45:12 - mmengine - INFO - Iter(train) [ 93550/160000] lr: 4.5893e-03 eta: 10:11:38 time: 0.5537 data_time: 0.0072 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7588 aux.loss_ce: 0.0072 aux.acc_seg: 99.3825 +04/18 16:45:40 - mmengine - INFO - Iter(train) [ 93600/160000] lr: 4.5862e-03 eta: 10:11:10 time: 0.5536 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.6302 aux.loss_ce: 0.0073 aux.acc_seg: 99.0357 +04/18 16:46:08 - mmengine - INFO - Iter(train) [ 93650/160000] lr: 4.5832e-03 eta: 10:10:43 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.7753 aux.loss_ce: 0.0079 aux.acc_seg: 99.3404 +04/18 16:46:35 - mmengine 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4.5680e-03 eta: 10:08:25 time: 0.5543 data_time: 0.0078 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6845 aux.loss_ce: 0.0073 aux.acc_seg: 99.1134 +04/18 16:48:54 - mmengine - INFO - Iter(train) [ 93950/160000] lr: 4.5649e-03 eta: 10:07:58 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6857 aux.loss_ce: 0.0073 aux.acc_seg: 99.0434 +04/18 16:49:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:49:22 - mmengine - INFO - Iter(train) [ 94000/160000] lr: 4.5619e-03 eta: 10:07:30 time: 0.5557 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7552 aux.loss_ce: 0.0068 aux.acc_seg: 99.4005 +04/18 16:49:50 - mmengine - INFO - Iter(train) [ 94050/160000] lr: 4.5589e-03 eta: 10:07:03 time: 0.5542 data_time: 0.0072 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0070 decode.acc_seg: 99.6277 aux.loss_ce: 0.0080 aux.acc_seg: 98.4986 +04/18 16:50:18 - mmengine - INFO - Iter(train) [ 94100/160000] lr: 4.5558e-03 eta: 10:06:35 time: 0.5549 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7393 aux.loss_ce: 0.0069 aux.acc_seg: 99.3925 +04/18 16:50:45 - mmengine - INFO - Iter(train) [ 94150/160000] lr: 4.5528e-03 eta: 10:06:08 time: 0.5538 data_time: 0.0062 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6656 aux.loss_ce: 0.0074 aux.acc_seg: 99.0781 +04/18 16:51:13 - mmengine - INFO - Iter(train) [ 94200/160000] lr: 4.5497e-03 eta: 10:05:40 time: 0.5537 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0068 decode.acc_seg: 99.6878 aux.loss_ce: 0.0068 aux.acc_seg: 99.2552 +04/18 16:51:41 - mmengine - INFO - Iter(train) [ 94250/160000] lr: 4.5467e-03 eta: 10:05:12 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6560 aux.loss_ce: 0.0072 aux.acc_seg: 99.1100 +04/18 16:52:08 - mmengine - INFO - Iter(train) [ 94300/160000] lr: 4.5436e-03 eta: 10:04:45 time: 0.5532 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7941 aux.loss_ce: 0.0076 aux.acc_seg: 99.3337 +04/18 16:52:36 - mmengine - INFO - Iter(train) [ 94350/160000] lr: 4.5406e-03 eta: 10:04:17 time: 0.5547 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.6806 aux.loss_ce: 0.0072 aux.acc_seg: 99.1579 +04/18 16:53:04 - mmengine - INFO - Iter(train) [ 94400/160000] lr: 4.5375e-03 eta: 10:03:50 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.6963 aux.loss_ce: 0.0072 aux.acc_seg: 99.0783 +04/18 16:53:32 - mmengine - INFO - Iter(train) [ 94450/160000] lr: 4.5345e-03 eta: 10:03:22 time: 0.5536 data_time: 0.0067 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7437 aux.loss_ce: 0.0072 aux.acc_seg: 99.2533 +04/18 16:53:59 - mmengine - INFO - Iter(train) [ 94500/160000] lr: 4.5315e-03 eta: 10:02:55 time: 0.5544 data_time: 0.0071 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.8111 aux.loss_ce: 0.0070 aux.acc_seg: 99.3376 +04/18 16:54:27 - mmengine - INFO - Iter(train) [ 94550/160000] lr: 4.5284e-03 eta: 10:02:27 time: 0.5529 data_time: 0.0068 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0066 decode.acc_seg: 99.7833 aux.loss_ce: 0.0067 aux.acc_seg: 99.3541 +04/18 16:54:55 - mmengine - INFO - Iter(train) [ 94600/160000] lr: 4.5254e-03 eta: 10:02:00 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7384 aux.loss_ce: 0.0076 aux.acc_seg: 99.1609 +04/18 16:55:23 - mmengine - INFO - Iter(train) [ 94650/160000] lr: 4.5223e-03 eta: 10:01:32 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7441 aux.loss_ce: 0.0076 aux.acc_seg: 99.2318 +04/18 16:55:50 - mmengine - INFO - Iter(train) [ 94700/160000] lr: 4.5193e-03 eta: 10:01:05 time: 0.5552 data_time: 0.0062 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.6926 aux.loss_ce: 0.0073 aux.acc_seg: 99.1946 +04/18 16:56:18 - mmengine - INFO - Iter(train) [ 94750/160000] lr: 4.5162e-03 eta: 10:00:37 time: 0.5530 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.6918 aux.loss_ce: 0.0069 aux.acc_seg: 99.3544 +04/18 16:56:46 - mmengine - INFO - Iter(train) [ 94800/160000] lr: 4.5132e-03 eta: 10:00:09 time: 0.5624 data_time: 0.0072 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.6819 aux.loss_ce: 0.0072 aux.acc_seg: 99.2666 +04/18 16:57:13 - mmengine - INFO - Iter(train) [ 94850/160000] lr: 4.5101e-03 eta: 9:59:42 time: 0.5553 data_time: 0.0059 memory: 7635 loss: 0.0124 decode.loss_ce: 0.0059 decode.acc_seg: 99.7990 aux.loss_ce: 0.0065 aux.acc_seg: 99.3868 +04/18 16:57:41 - mmengine - INFO - Iter(train) [ 94900/160000] lr: 4.5071e-03 eta: 9:59:14 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7571 aux.loss_ce: 0.0074 aux.acc_seg: 99.2595 +04/18 16:58:09 - mmengine - INFO - Iter(train) [ 94950/160000] lr: 4.5040e-03 eta: 9:58:47 time: 0.5638 data_time: 0.0060 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6574 aux.loss_ce: 0.0082 aux.acc_seg: 99.1121 +04/18 16:58:37 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 16:58:37 - mmengine - INFO - Iter(train) [ 95000/160000] lr: 4.5010e-03 eta: 9:58:19 time: 0.5526 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6932 aux.loss_ce: 0.0072 aux.acc_seg: 99.2917 +04/18 16:59:05 - mmengine - INFO - Iter(train) [ 95050/160000] lr: 4.4980e-03 eta: 9:57:52 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7259 aux.loss_ce: 0.0072 aux.acc_seg: 99.1473 +04/18 16:59:32 - mmengine - INFO - Iter(train) [ 95100/160000] lr: 4.4949e-03 eta: 9:57:24 time: 0.5531 data_time: 0.0067 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7579 aux.loss_ce: 0.0068 aux.acc_seg: 99.3444 +04/18 17:00:00 - mmengine - INFO - Iter(train) [ 95150/160000] lr: 4.4919e-03 eta: 9:56:57 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.6657 aux.loss_ce: 0.0081 aux.acc_seg: 99.2908 +04/18 17:00:28 - mmengine - INFO - Iter(train) [ 95200/160000] lr: 4.4888e-03 eta: 9:56:29 time: 0.5539 data_time: 0.0072 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.5678 aux.loss_ce: 0.0079 aux.acc_seg: 99.0845 +04/18 17:00:55 - mmengine - INFO - Iter(train) [ 95250/160000] lr: 4.4858e-03 eta: 9:56:02 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0183 decode.loss_ce: 0.0103 decode.acc_seg: 99.4879 aux.loss_ce: 0.0080 aux.acc_seg: 99.0593 +04/18 17:01:23 - mmengine - INFO - Iter(train) [ 95300/160000] lr: 4.4827e-03 eta: 9:55:34 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0198 decode.loss_ce: 0.0108 decode.acc_seg: 99.6470 aux.loss_ce: 0.0091 aux.acc_seg: 99.1091 +04/18 17:01:51 - mmengine - INFO - Iter(train) [ 95350/160000] lr: 4.4797e-03 eta: 9:55:07 time: 0.5540 data_time: 0.0064 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0100 decode.acc_seg: 99.6401 aux.loss_ce: 0.0079 aux.acc_seg: 99.2100 +04/18 17:02:19 - mmengine - INFO - Iter(train) [ 95400/160000] lr: 4.4766e-03 eta: 9:54:39 time: 0.5550 data_time: 0.0072 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0091 decode.acc_seg: 99.6162 aux.loss_ce: 0.0080 aux.acc_seg: 99.3341 +04/18 17:02:46 - mmengine - INFO - Iter(train) [ 95450/160000] lr: 4.4736e-03 eta: 9:54:11 time: 0.5549 data_time: 0.0068 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.6016 aux.loss_ce: 0.0079 aux.acc_seg: 99.1653 +04/18 17:03:14 - mmengine - INFO - Iter(train) [ 95500/160000] lr: 4.4705e-03 eta: 9:53:44 time: 0.5543 data_time: 0.0068 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.7846 aux.loss_ce: 0.0072 aux.acc_seg: 99.3245 +04/18 17:03:42 - mmengine - INFO - Iter(train) [ 95550/160000] lr: 4.4675e-03 eta: 9:53:16 time: 0.5537 data_time: 0.0065 memory: 7635 loss: 0.0195 decode.loss_ce: 0.0105 decode.acc_seg: 99.6505 aux.loss_ce: 0.0090 aux.acc_seg: 99.2621 +04/18 17:04:10 - mmengine - INFO - Iter(train) [ 95600/160000] lr: 4.4644e-03 eta: 9:52:49 time: 0.5546 data_time: 0.0077 memory: 7635 loss: 0.0193 decode.loss_ce: 0.0100 decode.acc_seg: 99.6714 aux.loss_ce: 0.0092 aux.acc_seg: 99.0884 +04/18 17:04:37 - mmengine - INFO - Iter(train) [ 95650/160000] lr: 4.4614e-03 eta: 9:52:21 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.6899 aux.loss_ce: 0.0075 aux.acc_seg: 99.3049 +04/18 17:05:05 - mmengine - INFO - Iter(train) [ 95700/160000] lr: 4.4583e-03 eta: 9:51:54 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.6909 aux.loss_ce: 0.0077 aux.acc_seg: 99.1796 +04/18 17:05:33 - mmengine - INFO - Iter(train) [ 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0.5551 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7738 aux.loss_ce: 0.0073 aux.acc_seg: 99.3678 +04/18 17:07:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:07:52 - mmengine - INFO - Iter(train) [ 96000/160000] lr: 4.4400e-03 eta: 9:49:09 time: 0.5632 data_time: 0.0064 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7273 aux.loss_ce: 0.0073 aux.acc_seg: 99.2279 +04/18 17:08:19 - mmengine - INFO - Iter(train) [ 96050/160000] lr: 4.4370e-03 eta: 9:48:41 time: 0.5545 data_time: 0.0074 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6448 aux.loss_ce: 0.0073 aux.acc_seg: 99.2266 +04/18 17:08:47 - mmengine - INFO - Iter(train) [ 96100/160000] lr: 4.4339e-03 eta: 9:48:13 time: 0.5546 data_time: 0.0064 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0082 decode.acc_seg: 99.6461 aux.loss_ce: 0.0075 aux.acc_seg: 99.1035 +04/18 17:09:15 - mmengine - INFO - Iter(train) [ 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0.5549 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.6655 aux.loss_ce: 0.0073 aux.acc_seg: 99.1374 +04/18 17:11:33 - mmengine - INFO - Iter(train) [ 96400/160000] lr: 4.4156e-03 eta: 9:45:28 time: 0.5550 data_time: 0.0072 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.7654 aux.loss_ce: 0.0065 aux.acc_seg: 99.3459 +04/18 17:12:01 - mmengine - INFO - Iter(train) [ 96450/160000] lr: 4.4125e-03 eta: 9:45:01 time: 0.5531 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6296 aux.loss_ce: 0.0077 aux.acc_seg: 98.8319 +04/18 17:12:29 - mmengine - INFO - Iter(train) [ 96500/160000] lr: 4.4095e-03 eta: 9:44:33 time: 0.5550 data_time: 0.0081 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7513 aux.loss_ce: 0.0069 aux.acc_seg: 99.2723 +04/18 17:12:57 - mmengine - INFO - Iter(train) [ 96550/160000] lr: 4.4064e-03 eta: 9:44:06 time: 0.5556 data_time: 0.0075 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7525 aux.loss_ce: 0.0077 aux.acc_seg: 99.3343 +04/18 17:13:24 - mmengine - INFO - Iter(train) [ 96600/160000] lr: 4.4034e-03 eta: 9:43:38 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7320 aux.loss_ce: 0.0073 aux.acc_seg: 99.3683 +04/18 17:13:52 - mmengine - INFO - Iter(train) [ 96650/160000] lr: 4.4003e-03 eta: 9:43:10 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7233 aux.loss_ce: 0.0073 aux.acc_seg: 99.2402 +04/18 17:14:20 - mmengine - INFO - Iter(train) [ 96700/160000] lr: 4.3973e-03 eta: 9:42:43 time: 0.5549 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7185 aux.loss_ce: 0.0069 aux.acc_seg: 99.1770 +04/18 17:14:48 - mmengine - INFO - Iter(train) [ 96750/160000] lr: 4.3942e-03 eta: 9:42:15 time: 0.5568 data_time: 0.0073 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.7624 aux.loss_ce: 0.0077 aux.acc_seg: 99.2085 +04/18 17:15:15 - mmengine - INFO - Iter(train) [ 96800/160000] lr: 4.3912e-03 eta: 9:41:48 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.5921 aux.loss_ce: 0.0077 aux.acc_seg: 98.8240 +04/18 17:15:43 - mmengine - INFO - Iter(train) [ 96850/160000] lr: 4.3881e-03 eta: 9:41:20 time: 0.5558 data_time: 0.0083 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7164 aux.loss_ce: 0.0069 aux.acc_seg: 99.2104 +04/18 17:16:11 - mmengine - INFO - Iter(train) [ 96900/160000] lr: 4.3851e-03 eta: 9:40:53 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0086 decode.acc_seg: 99.6477 aux.loss_ce: 0.0087 aux.acc_seg: 99.0242 +04/18 17:16:39 - mmengine - INFO - Iter(train) [ 96950/160000] lr: 4.3820e-03 eta: 9:40:25 time: 0.5621 data_time: 0.0067 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.6933 aux.loss_ce: 0.0077 aux.acc_seg: 99.1102 +04/18 17:17:06 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:17:06 - mmengine - INFO - Iter(train) [ 97000/160000] lr: 4.3789e-03 eta: 9:39:58 time: 0.5567 data_time: 0.0077 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.5578 aux.loss_ce: 0.0079 aux.acc_seg: 99.0413 +04/18 17:17:34 - mmengine - INFO - Iter(train) [ 97050/160000] lr: 4.3759e-03 eta: 9:39:30 time: 0.5549 data_time: 0.0069 memory: 7635 loss: 0.0179 decode.loss_ce: 0.0093 decode.acc_seg: 99.7400 aux.loss_ce: 0.0086 aux.acc_seg: 99.1406 +04/18 17:18:02 - mmengine - INFO - Iter(train) [ 97100/160000] lr: 4.3728e-03 eta: 9:39:03 time: 0.5626 data_time: 0.0069 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.6466 aux.loss_ce: 0.0074 aux.acc_seg: 99.1657 +04/18 17:18:30 - mmengine - INFO - Iter(train) [ 97150/160000] lr: 4.3698e-03 eta: 9:38:35 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7083 aux.loss_ce: 0.0070 aux.acc_seg: 99.2443 +04/18 17:18:58 - mmengine - INFO - Iter(train) [ 97200/160000] lr: 4.3667e-03 eta: 9:38:08 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7644 aux.loss_ce: 0.0073 aux.acc_seg: 99.2749 +04/18 17:19:25 - mmengine - INFO - Iter(train) [ 97250/160000] lr: 4.3637e-03 eta: 9:37:40 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7007 aux.loss_ce: 0.0067 aux.acc_seg: 99.1323 +04/18 17:19:53 - mmengine - INFO - Iter(train) [ 97300/160000] lr: 4.3606e-03 eta: 9:37:13 time: 0.5536 data_time: 0.0065 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7317 aux.loss_ce: 0.0074 aux.acc_seg: 99.1363 +04/18 17:20:21 - mmengine - INFO - Iter(train) [ 97350/160000] lr: 4.3575e-03 eta: 9:36:45 time: 0.5533 data_time: 0.0066 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.6843 aux.loss_ce: 0.0068 aux.acc_seg: 99.1193 +04/18 17:20:48 - mmengine - INFO - Iter(train) [ 97400/160000] lr: 4.3545e-03 eta: 9:36:17 time: 0.5542 data_time: 0.0070 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.6987 aux.loss_ce: 0.0078 aux.acc_seg: 99.2604 +04/18 17:21:16 - mmengine - INFO - Iter(train) [ 97450/160000] lr: 4.3514e-03 eta: 9:35:50 time: 0.5532 data_time: 0.0061 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6772 aux.loss_ce: 0.0074 aux.acc_seg: 99.1599 +04/18 17:21:44 - mmengine - INFO - Iter(train) [ 97500/160000] lr: 4.3484e-03 eta: 9:35:22 time: 0.5556 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7450 aux.loss_ce: 0.0070 aux.acc_seg: 99.1950 +04/18 17:22:12 - mmengine - INFO - Iter(train) [ 97550/160000] lr: 4.3453e-03 eta: 9:34:55 time: 0.5539 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.6667 aux.loss_ce: 0.0069 aux.acc_seg: 99.1509 +04/18 17:22:39 - mmengine - INFO - Iter(train) [ 97600/160000] lr: 4.3422e-03 eta: 9:34:27 time: 0.5554 data_time: 0.0073 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.6469 aux.loss_ce: 0.0076 aux.acc_seg: 99.0324 +04/18 17:23:07 - mmengine - INFO - Iter(train) [ 97650/160000] lr: 4.3392e-03 eta: 9:34:00 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7666 aux.loss_ce: 0.0075 aux.acc_seg: 99.3659 +04/18 17:23:35 - mmengine - INFO - Iter(train) [ 97700/160000] lr: 4.3361e-03 eta: 9:33:32 time: 0.5561 data_time: 0.0063 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6807 aux.loss_ce: 0.0075 aux.acc_seg: 99.2209 +04/18 17:24:03 - mmengine - INFO - Iter(train) [ 97750/160000] lr: 4.3331e-03 eta: 9:33:05 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7061 aux.loss_ce: 0.0076 aux.acc_seg: 99.2447 +04/18 17:24:30 - mmengine - INFO - Iter(train) [ 97800/160000] lr: 4.3300e-03 eta: 9:32:37 time: 0.5560 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.6998 aux.loss_ce: 0.0071 aux.acc_seg: 99.1749 +04/18 17:24:58 - mmengine - INFO - Iter(train) [ 97850/160000] lr: 4.3269e-03 eta: 9:32:10 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6622 aux.loss_ce: 0.0074 aux.acc_seg: 99.0404 +04/18 17:25:26 - mmengine - INFO - Iter(train) [ 97900/160000] lr: 4.3239e-03 eta: 9:31:42 time: 0.5540 data_time: 0.0070 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7380 aux.loss_ce: 0.0072 aux.acc_seg: 99.3701 +04/18 17:25:54 - mmengine - INFO - Iter(train) [ 97950/160000] lr: 4.3208e-03 eta: 9:31:14 time: 0.5563 data_time: 0.0064 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7115 aux.loss_ce: 0.0071 aux.acc_seg: 99.0584 +04/18 17:26:21 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:26:21 - mmengine - INFO - Iter(train) [ 98000/160000] lr: 4.3178e-03 eta: 9:30:47 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.7242 aux.loss_ce: 0.0078 aux.acc_seg: 99.2871 +04/18 17:26:49 - mmengine - INFO - Iter(train) [ 98050/160000] lr: 4.3147e-03 eta: 9:30:19 time: 0.5557 data_time: 0.0071 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.7675 aux.loss_ce: 0.0071 aux.acc_seg: 99.3314 +04/18 17:27:17 - mmengine - INFO - Iter(train) [ 98100/160000] lr: 4.3116e-03 eta: 9:29:52 time: 0.5544 data_time: 0.0080 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7635 aux.loss_ce: 0.0071 aux.acc_seg: 99.3004 +04/18 17:27:45 - mmengine - INFO - Iter(train) [ 98150/160000] lr: 4.3086e-03 eta: 9:29:24 time: 0.5616 data_time: 0.0064 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7717 aux.loss_ce: 0.0071 aux.acc_seg: 99.2148 +04/18 17:28:13 - mmengine - INFO - Iter(train) [ 98200/160000] lr: 4.3055e-03 eta: 9:28:57 time: 0.5636 data_time: 0.0074 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7410 aux.loss_ce: 0.0070 aux.acc_seg: 99.2487 +04/18 17:28:40 - mmengine - INFO - Iter(train) [ 98250/160000] lr: 4.3025e-03 eta: 9:28:29 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6316 aux.loss_ce: 0.0073 aux.acc_seg: 99.0919 +04/18 17:29:08 - mmengine - INFO - Iter(train) [ 98300/160000] lr: 4.2994e-03 eta: 9:28:02 time: 0.5546 data_time: 0.0067 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6526 aux.loss_ce: 0.0075 aux.acc_seg: 99.1125 +04/18 17:29:36 - mmengine - INFO - Iter(train) [ 98350/160000] lr: 4.2963e-03 eta: 9:27:34 time: 0.5553 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.6505 aux.loss_ce: 0.0071 aux.acc_seg: 99.1550 +04/18 17:30:04 - mmengine - INFO - Iter(train) [ 98400/160000] lr: 4.2933e-03 eta: 9:27:07 time: 0.5535 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.7371 aux.loss_ce: 0.0078 aux.acc_seg: 99.3375 +04/18 17:30:31 - mmengine - INFO - Iter(train) [ 98450/160000] lr: 4.2902e-03 eta: 9:26:39 time: 0.5554 data_time: 0.0062 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7246 aux.loss_ce: 0.0072 aux.acc_seg: 99.1135 +04/18 17:30:59 - mmengine - INFO - Iter(train) [ 98500/160000] lr: 4.2871e-03 eta: 9:26:12 time: 0.5536 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.6385 aux.loss_ce: 0.0071 aux.acc_seg: 99.0829 +04/18 17:31:27 - mmengine - INFO - Iter(train) [ 98550/160000] lr: 4.2841e-03 eta: 9:25:44 time: 0.5546 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.6452 aux.loss_ce: 0.0072 aux.acc_seg: 99.2522 +04/18 17:31:55 - mmengine - INFO - Iter(train) [ 98600/160000] lr: 4.2810e-03 eta: 9:25:17 time: 0.5544 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.6967 aux.loss_ce: 0.0077 aux.acc_seg: 99.3668 +04/18 17:32:22 - mmengine - INFO - Iter(train) [ 98650/160000] lr: 4.2779e-03 eta: 9:24:49 time: 0.5551 data_time: 0.0068 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.5887 aux.loss_ce: 0.0076 aux.acc_seg: 99.0882 +04/18 17:32:50 - mmengine - INFO - Iter(train) [ 98700/160000] lr: 4.2749e-03 eta: 9:24:21 time: 0.5542 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.7547 aux.loss_ce: 0.0074 aux.acc_seg: 99.2813 +04/18 17:33:18 - mmengine - INFO - Iter(train) [ 98750/160000] lr: 4.2718e-03 eta: 9:23:54 time: 0.5552 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7483 aux.loss_ce: 0.0069 aux.acc_seg: 99.3092 +04/18 17:33:46 - mmengine - INFO - Iter(train) [ 98800/160000] lr: 4.2688e-03 eta: 9:23:26 time: 0.5534 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.7491 aux.loss_ce: 0.0078 aux.acc_seg: 99.3162 +04/18 17:34:13 - mmengine - INFO - Iter(train) [ 98850/160000] lr: 4.2657e-03 eta: 9:22:59 time: 0.5554 data_time: 0.0060 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.6961 aux.loss_ce: 0.0080 aux.acc_seg: 99.2959 +04/18 17:34:41 - mmengine - INFO - Iter(train) [ 98900/160000] lr: 4.2626e-03 eta: 9:22:31 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0215 decode.loss_ce: 0.0117 decode.acc_seg: 99.6341 aux.loss_ce: 0.0098 aux.acc_seg: 99.1272 +04/18 17:35:09 - mmengine - INFO - Iter(train) [ 98950/160000] lr: 4.2596e-03 eta: 9:22:04 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0191 decode.loss_ce: 0.0104 decode.acc_seg: 99.6702 aux.loss_ce: 0.0086 aux.acc_seg: 99.2450 +04/18 17:35:36 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:35:36 - mmengine - INFO - Iter(train) [ 99000/160000] lr: 4.2565e-03 eta: 9:21:36 time: 0.5561 data_time: 0.0065 memory: 7635 loss: 0.0188 decode.loss_ce: 0.0100 decode.acc_seg: 99.7582 aux.loss_ce: 0.0089 aux.acc_seg: 99.3401 +04/18 17:36:04 - mmengine - INFO - Iter(train) [ 99050/160000] lr: 4.2534e-03 eta: 9:21:09 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0086 decode.acc_seg: 99.6223 aux.loss_ce: 0.0079 aux.acc_seg: 98.9206 +04/18 17:36:32 - mmengine - INFO - Iter(train) [ 99100/160000] lr: 4.2504e-03 eta: 9:20:41 time: 0.5555 data_time: 0.0065 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.5854 aux.loss_ce: 0.0076 aux.acc_seg: 99.0214 +04/18 17:37:00 - mmengine - INFO - Iter(train) [ 99150/160000] lr: 4.2473e-03 eta: 9:20:13 time: 0.5547 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6884 aux.loss_ce: 0.0075 aux.acc_seg: 99.1795 +04/18 17:37:28 - mmengine - INFO - Iter(train) [ 99200/160000] lr: 4.2442e-03 eta: 9:19:46 time: 0.5554 data_time: 0.0060 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.6729 aux.loss_ce: 0.0070 aux.acc_seg: 98.8970 +04/18 17:37:55 - mmengine - INFO - Iter(train) [ 99250/160000] lr: 4.2412e-03 eta: 9:19:18 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7354 aux.loss_ce: 0.0072 aux.acc_seg: 99.3119 +04/18 17:38:23 - mmengine - INFO - Iter(train) [ 99300/160000] lr: 4.2381e-03 eta: 9:18:51 time: 0.5542 data_time: 0.0064 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0080 decode.acc_seg: 99.6769 aux.loss_ce: 0.0081 aux.acc_seg: 99.1219 +04/18 17:38:51 - mmengine - INFO - Iter(train) [ 99350/160000] lr: 4.2350e-03 eta: 9:18:23 time: 0.5549 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7375 aux.loss_ce: 0.0077 aux.acc_seg: 99.3154 +04/18 17:39:19 - mmengine - INFO - Iter(train) [ 99400/160000] lr: 4.2319e-03 eta: 9:17:56 time: 0.5561 data_time: 0.0076 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0090 decode.acc_seg: 99.5961 aux.loss_ce: 0.0082 aux.acc_seg: 99.1252 +04/18 17:39:46 - mmengine - INFO - Iter(train) [ 99450/160000] lr: 4.2289e-03 eta: 9:17:28 time: 0.5535 data_time: 0.0064 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6661 aux.loss_ce: 0.0077 aux.acc_seg: 99.2162 +04/18 17:40:14 - mmengine - INFO - Iter(train) [ 99500/160000] lr: 4.2258e-03 eta: 9:17:01 time: 0.5546 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.5756 aux.loss_ce: 0.0077 aux.acc_seg: 99.0845 +04/18 17:40:42 - mmengine - INFO - Iter(train) [ 99550/160000] lr: 4.2227e-03 eta: 9:16:33 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.7802 aux.loss_ce: 0.0073 aux.acc_seg: 99.4399 +04/18 17:41:10 - mmengine - INFO - Iter(train) [ 99600/160000] lr: 4.2197e-03 eta: 9:16:06 time: 0.5573 data_time: 0.0072 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0075 decode.acc_seg: 99.6706 aux.loss_ce: 0.0079 aux.acc_seg: 99.1669 +04/18 17:41:37 - mmengine - INFO - Iter(train) [ 99650/160000] lr: 4.2166e-03 eta: 9:15:38 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7578 aux.loss_ce: 0.0074 aux.acc_seg: 99.3618 +04/18 17:42:05 - mmengine - INFO - Iter(train) [ 99700/160000] lr: 4.2135e-03 eta: 9:15:11 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7287 aux.loss_ce: 0.0073 aux.acc_seg: 99.1139 +04/18 17:42:33 - mmengine - INFO - Iter(train) [ 99750/160000] lr: 4.2105e-03 eta: 9:14:43 time: 0.5559 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6978 aux.loss_ce: 0.0074 aux.acc_seg: 99.2369 +04/18 17:43:01 - mmengine - INFO - Iter(train) [ 99800/160000] lr: 4.2074e-03 eta: 9:14:15 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.7423 aux.loss_ce: 0.0078 aux.acc_seg: 99.2477 +04/18 17:43:28 - mmengine - INFO - Iter(train) [ 99850/160000] lr: 4.2043e-03 eta: 9:13:48 time: 0.5541 data_time: 0.0062 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7376 aux.loss_ce: 0.0070 aux.acc_seg: 99.2662 +04/18 17:43:56 - mmengine - INFO - Iter(train) [ 99900/160000] lr: 4.2013e-03 eta: 9:13:20 time: 0.5547 data_time: 0.0075 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6868 aux.loss_ce: 0.0078 aux.acc_seg: 99.4017 +04/18 17:44:24 - mmengine - INFO - Iter(train) [ 99950/160000] lr: 4.1982e-03 eta: 9:12:53 time: 0.5559 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7042 aux.loss_ce: 0.0074 aux.acc_seg: 99.1630 +04/18 17:44:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:44:52 - mmengine - INFO - Iter(train) [100000/160000] lr: 4.1951e-03 eta: 9:12:25 time: 0.5547 data_time: 0.0063 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7372 aux.loss_ce: 0.0077 aux.acc_seg: 99.2908 +04/18 17:44:52 - mmengine - INFO - Saving checkpoint at 100000 iterations +04/18 17:44:56 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0462 data_time: 0.0015 memory: 1657 +04/18 17:44:58 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0465 data_time: 0.0015 memory: 1657 +04/18 17:45:00 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0472 data_time: 0.0015 memory: 1657 +04/18 17:45:03 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0458 data_time: 0.0014 memory: 1657 +04/18 17:45:03 - mmengine - INFO - per class results: +04/18 17:45:03 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.07 | 99.44 | 99.53 | 99.62 | 99.44 | +| contrast | 80.19 | 90.93 | 89.01 | 87.16 | 90.93 | ++------------+-------+-------+--------+-----------+--------+ +04/18 17:45:03 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1000 mIoU: 89.6300 mAcc: 95.1900 mFscore: 94.2700 mPrecision: 93.3900 mRecall: 95.1900 data_time: 0.0015 time: 0.0466 +04/18 17:45:31 - mmengine - INFO - Iter(train) [100050/160000] lr: 4.1920e-03 eta: 9:11:58 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.7095 aux.loss_ce: 0.0080 aux.acc_seg: 99.1308 +04/18 17:45:58 - mmengine - INFO - Iter(train) [100100/160000] lr: 4.1890e-03 eta: 9:11:30 time: 0.5565 data_time: 0.0071 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.6163 aux.loss_ce: 0.0075 aux.acc_seg: 99.1165 +04/18 17:46:26 - mmengine - INFO - Iter(train) [100150/160000] lr: 4.1859e-03 eta: 9:11:03 time: 0.5543 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.6577 aux.loss_ce: 0.0077 aux.acc_seg: 99.1933 +04/18 17:46:54 - mmengine - INFO - Iter(train) [100200/160000] lr: 4.1828e-03 eta: 9:10:35 time: 0.5547 data_time: 0.0067 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7790 aux.loss_ce: 0.0073 aux.acc_seg: 99.3054 +04/18 17:47:22 - mmengine - INFO - Iter(train) [100250/160000] lr: 4.1798e-03 eta: 9:10:08 time: 0.5552 data_time: 0.0063 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7389 aux.loss_ce: 0.0079 aux.acc_seg: 99.2993 +04/18 17:47:49 - mmengine - INFO - Iter(train) [100300/160000] lr: 4.1767e-03 eta: 9:09:40 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7589 aux.loss_ce: 0.0070 aux.acc_seg: 99.3131 +04/18 17:48:17 - mmengine - INFO - Iter(train) [100350/160000] lr: 4.1736e-03 eta: 9:09:13 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.7858 aux.loss_ce: 0.0077 aux.acc_seg: 99.3250 +04/18 17:48:45 - mmengine - INFO - Iter(train) [100400/160000] lr: 4.1705e-03 eta: 9:08:45 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7776 aux.loss_ce: 0.0074 aux.acc_seg: 99.4458 +04/18 17:49:13 - mmengine - INFO - Iter(train) [100450/160000] lr: 4.1675e-03 eta: 9:08:17 time: 0.5536 data_time: 0.0068 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.6532 aux.loss_ce: 0.0079 aux.acc_seg: 99.0548 +04/18 17:49:41 - mmengine - INFO - Iter(train) [100500/160000] lr: 4.1644e-03 eta: 9:07:50 time: 0.5540 data_time: 0.0069 memory: 7635 loss: 0.0169 decode.loss_ce: 0.0090 decode.acc_seg: 99.7375 aux.loss_ce: 0.0079 aux.acc_seg: 99.4618 +04/18 17:50:08 - mmengine - INFO - Iter(train) [100550/160000] lr: 4.1613e-03 eta: 9:07:22 time: 0.5534 data_time: 0.0062 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.7165 aux.loss_ce: 0.0078 aux.acc_seg: 99.1873 +04/18 17:50:36 - mmengine - INFO - Iter(train) [100600/160000] lr: 4.1582e-03 eta: 9:06:55 time: 0.5562 data_time: 0.0060 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0096 decode.acc_seg: 99.6803 aux.loss_ce: 0.0081 aux.acc_seg: 99.2851 +04/18 17:51:04 - mmengine - INFO - Iter(train) [100650/160000] lr: 4.1552e-03 eta: 9:06:27 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.6935 aux.loss_ce: 0.0077 aux.acc_seg: 99.1413 +04/18 17:51:31 - mmengine - INFO - Iter(train) [100700/160000] lr: 4.1521e-03 eta: 9:06:00 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0176 decode.loss_ce: 0.0091 decode.acc_seg: 99.5483 aux.loss_ce: 0.0086 aux.acc_seg: 98.9243 +04/18 17:51:59 - mmengine - INFO - Iter(train) [100750/160000] lr: 4.1490e-03 eta: 9:05:32 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0084 decode.acc_seg: 99.6213 aux.loss_ce: 0.0083 aux.acc_seg: 99.0323 +04/18 17:52:27 - mmengine - INFO - Iter(train) [100800/160000] lr: 4.1459e-03 eta: 9:05:05 time: 0.5547 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0083 decode.acc_seg: 99.6117 aux.loss_ce: 0.0078 aux.acc_seg: 99.1339 +04/18 17:52:55 - mmengine - INFO - Iter(train) [100850/160000] lr: 4.1429e-03 eta: 9:04:37 time: 0.5561 data_time: 0.0072 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.6839 aux.loss_ce: 0.0078 aux.acc_seg: 99.2059 +04/18 17:53:23 - mmengine - INFO - Iter(train) [100900/160000] lr: 4.1398e-03 eta: 9:04:09 time: 0.5554 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0075 decode.acc_seg: 99.6908 aux.loss_ce: 0.0073 aux.acc_seg: 99.3784 +04/18 17:53:50 - mmengine - INFO - Iter(train) [100950/160000] lr: 4.1367e-03 eta: 9:03:42 time: 0.5545 data_time: 0.0067 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.8081 aux.loss_ce: 0.0076 aux.acc_seg: 99.3566 +04/18 17:54:18 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 17:54:18 - mmengine - INFO - Iter(train) [101000/160000] lr: 4.1336e-03 eta: 9:03:14 time: 0.5547 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6223 aux.loss_ce: 0.0076 aux.acc_seg: 99.1516 +04/18 17:54:46 - mmengine - INFO - Iter(train) [101050/160000] lr: 4.1306e-03 eta: 9:02:47 time: 0.5568 data_time: 0.0066 memory: 7635 loss: 0.0171 decode.loss_ce: 0.0088 decode.acc_seg: 99.6987 aux.loss_ce: 0.0083 aux.acc_seg: 99.3050 +04/18 17:55:14 - mmengine - INFO - Iter(train) [101100/160000] lr: 4.1275e-03 eta: 9:02:19 time: 0.5555 data_time: 0.0062 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.7017 aux.loss_ce: 0.0076 aux.acc_seg: 99.1898 +04/18 17:55:41 - mmengine - INFO - Iter(train) [101150/160000] lr: 4.1244e-03 eta: 9:01:52 time: 0.5542 data_time: 0.0080 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0083 decode.acc_seg: 99.6719 aux.loss_ce: 0.0076 aux.acc_seg: 99.1834 +04/18 17:56:09 - mmengine - INFO - Iter(train) [101200/160000] lr: 4.1213e-03 eta: 9:01:24 time: 0.5546 data_time: 0.0064 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.6968 aux.loss_ce: 0.0071 aux.acc_seg: 99.3033 +04/18 17:56:37 - mmengine - INFO - Iter(train) [101250/160000] lr: 4.1182e-03 eta: 9:00:57 time: 0.5557 data_time: 0.0066 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0077 decode.acc_seg: 99.6788 aux.loss_ce: 0.0072 aux.acc_seg: 99.1690 +04/18 17:57:05 - mmengine - INFO - Iter(train) [101300/160000] lr: 4.1152e-03 eta: 9:00:29 time: 0.5537 data_time: 0.0073 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.7045 aux.loss_ce: 0.0077 aux.acc_seg: 99.1835 +04/18 17:57:32 - mmengine - INFO - Iter(train) [101350/160000] lr: 4.1121e-03 eta: 9:00:02 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7789 aux.loss_ce: 0.0076 aux.acc_seg: 99.2284 +04/18 17:58:00 - mmengine - INFO - Iter(train) [101400/160000] lr: 4.1090e-03 eta: 8:59:34 time: 0.5628 data_time: 0.0062 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0082 decode.acc_seg: 99.4895 aux.loss_ce: 0.0077 aux.acc_seg: 99.0331 +04/18 17:58:28 - mmengine - INFO - Iter(train) [101450/160000] lr: 4.1059e-03 eta: 8:59:06 time: 0.5548 data_time: 0.0067 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.6900 aux.loss_ce: 0.0082 aux.acc_seg: 99.1838 +04/18 17:58:56 - mmengine - INFO - Iter(train) [101500/160000] lr: 4.1029e-03 eta: 8:58:39 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.7442 aux.loss_ce: 0.0072 aux.acc_seg: 99.3762 +04/18 17:59:24 - mmengine - INFO - Iter(train) [101550/160000] lr: 4.0998e-03 eta: 8:58:11 time: 0.5647 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7145 aux.loss_ce: 0.0075 aux.acc_seg: 99.0725 +04/18 17:59:51 - mmengine - INFO - Iter(train) [101600/160000] lr: 4.0967e-03 eta: 8:57:44 time: 0.5545 data_time: 0.0075 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.7160 aux.loss_ce: 0.0073 aux.acc_seg: 99.2603 +04/18 18:00:19 - mmengine - INFO - Iter(train) [101650/160000] lr: 4.0936e-03 eta: 8:57:16 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.7094 aux.loss_ce: 0.0080 aux.acc_seg: 99.0880 +04/18 18:00:47 - mmengine - INFO - Iter(train) [101700/160000] lr: 4.0905e-03 eta: 8:56:49 time: 0.5558 data_time: 0.0068 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7360 aux.loss_ce: 0.0076 aux.acc_seg: 99.4184 +04/18 18:01:15 - mmengine - INFO - Iter(train) [101750/160000] lr: 4.0875e-03 eta: 8:56:21 time: 0.5538 data_time: 0.0063 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6214 aux.loss_ce: 0.0076 aux.acc_seg: 99.1718 +04/18 18:01:42 - mmengine - INFO - Iter(train) [101800/160000] lr: 4.0844e-03 eta: 8:55:54 time: 0.5538 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.6907 aux.loss_ce: 0.0074 aux.acc_seg: 99.0748 +04/18 18:02:10 - mmengine - INFO - Iter(train) [101850/160000] lr: 4.0813e-03 eta: 8:55:26 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.7403 aux.loss_ce: 0.0078 aux.acc_seg: 99.4285 +04/18 18:02:38 - mmengine - INFO - Iter(train) [101900/160000] lr: 4.0782e-03 eta: 8:54:58 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.7054 aux.loss_ce: 0.0075 aux.acc_seg: 99.3196 +04/18 18:03:06 - mmengine - INFO - Iter(train) [101950/160000] lr: 4.0751e-03 eta: 8:54:31 time: 0.5563 data_time: 0.0059 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0076 decode.acc_seg: 99.7217 aux.loss_ce: 0.0074 aux.acc_seg: 99.2308 +04/18 18:03:33 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:03:33 - mmengine - INFO - Iter(train) [102000/160000] lr: 4.0721e-03 eta: 8:54:03 time: 0.5546 data_time: 0.0056 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.6972 aux.loss_ce: 0.0078 aux.acc_seg: 99.2855 +04/18 18:04:01 - mmengine - INFO - Iter(train) [102050/160000] lr: 4.0690e-03 eta: 8:53:36 time: 0.5556 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7612 aux.loss_ce: 0.0075 aux.acc_seg: 99.3612 +04/18 18:04:29 - mmengine - INFO - Iter(train) [102100/160000] lr: 4.0659e-03 eta: 8:53:08 time: 0.5556 data_time: 0.0069 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7481 aux.loss_ce: 0.0072 aux.acc_seg: 99.3841 +04/18 18:04:57 - mmengine - INFO - Iter(train) [102150/160000] lr: 4.0628e-03 eta: 8:52:41 time: 0.5547 data_time: 0.0071 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7428 aux.loss_ce: 0.0074 aux.acc_seg: 99.4309 +04/18 18:05:25 - mmengine - INFO - Iter(train) [102200/160000] lr: 4.0597e-03 eta: 8:52:13 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.7394 aux.loss_ce: 0.0079 aux.acc_seg: 99.1361 +04/18 18:05:52 - mmengine - INFO - Iter(train) [102250/160000] lr: 4.0566e-03 eta: 8:51:46 time: 0.5551 data_time: 0.0073 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7008 aux.loss_ce: 0.0079 aux.acc_seg: 99.1044 +04/18 18:06:20 - mmengine - INFO - Iter(train) [102300/160000] lr: 4.0536e-03 eta: 8:51:18 time: 0.5558 data_time: 0.0067 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7474 aux.loss_ce: 0.0070 aux.acc_seg: 99.4012 +04/18 18:06:48 - mmengine - INFO - Iter(train) [102350/160000] lr: 4.0505e-03 eta: 8:50:51 time: 0.5554 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0071 decode.acc_seg: 99.6811 aux.loss_ce: 0.0070 aux.acc_seg: 99.3768 +04/18 18:07:16 - mmengine - INFO - Iter(train) [102400/160000] lr: 4.0474e-03 eta: 8:50:23 time: 0.5557 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0075 decode.acc_seg: 99.5892 aux.loss_ce: 0.0078 aux.acc_seg: 99.0476 +04/18 18:07:43 - mmengine - INFO - Iter(train) [102450/160000] lr: 4.0443e-03 eta: 8:49:56 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7593 aux.loss_ce: 0.0068 aux.acc_seg: 99.3846 +04/18 18:08:11 - mmengine - INFO - Iter(train) [102500/160000] lr: 4.0412e-03 eta: 8:49:28 time: 0.5534 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7579 aux.loss_ce: 0.0069 aux.acc_seg: 99.1947 +04/18 18:08:39 - mmengine - INFO - Iter(train) [102550/160000] lr: 4.0381e-03 eta: 8:49:01 time: 0.5557 data_time: 0.0073 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.7660 aux.loss_ce: 0.0077 aux.acc_seg: 99.3835 +04/18 18:09:07 - mmengine - INFO - Iter(train) [102600/160000] lr: 4.0351e-03 eta: 8:48:33 time: 0.5553 data_time: 0.0072 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7365 aux.loss_ce: 0.0069 aux.acc_seg: 99.1929 +04/18 18:09:35 - mmengine - INFO - Iter(train) [102650/160000] lr: 4.0320e-03 eta: 8:48:05 time: 0.5547 data_time: 0.0077 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.7366 aux.loss_ce: 0.0069 aux.acc_seg: 99.3207 +04/18 18:10:02 - mmengine - INFO - Iter(train) [102700/160000] lr: 4.0289e-03 eta: 8:47:38 time: 0.5551 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7978 aux.loss_ce: 0.0071 aux.acc_seg: 99.5609 +04/18 18:10:30 - mmengine - INFO - Iter(train) [102750/160000] lr: 4.0258e-03 eta: 8:47:10 time: 0.5563 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0073 decode.acc_seg: 99.6626 aux.loss_ce: 0.0074 aux.acc_seg: 99.0096 +04/18 18:10:58 - mmengine - INFO - Iter(train) [102800/160000] lr: 4.0227e-03 eta: 8:46:43 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7425 aux.loss_ce: 0.0072 aux.acc_seg: 99.2924 +04/18 18:11:26 - mmengine - INFO - Iter(train) [102850/160000] lr: 4.0196e-03 eta: 8:46:15 time: 0.5558 data_time: 0.0063 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6971 aux.loss_ce: 0.0072 aux.acc_seg: 99.1423 +04/18 18:11:54 - mmengine - INFO - Iter(train) [102900/160000] lr: 4.0165e-03 eta: 8:45:48 time: 0.5554 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7680 aux.loss_ce: 0.0075 aux.acc_seg: 99.3282 +04/18 18:12:21 - mmengine - INFO - Iter(train) [102950/160000] lr: 4.0134e-03 eta: 8:45:20 time: 0.5552 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7619 aux.loss_ce: 0.0073 aux.acc_seg: 99.3407 +04/18 18:12:49 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:12:49 - mmengine - INFO - Iter(train) [103000/160000] lr: 4.0104e-03 eta: 8:44:53 time: 0.5548 data_time: 0.0072 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.7692 aux.loss_ce: 0.0080 aux.acc_seg: 99.3644 +04/18 18:13:17 - mmengine - INFO - Iter(train) [103050/160000] lr: 4.0073e-03 eta: 8:44:25 time: 0.5536 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.7481 aux.loss_ce: 0.0071 aux.acc_seg: 99.2605 +04/18 18:13:44 - mmengine - INFO - Iter(train) [103100/160000] lr: 4.0042e-03 eta: 8:43:57 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7490 aux.loss_ce: 0.0071 aux.acc_seg: 99.2566 +04/18 18:14:12 - mmengine - INFO - Iter(train) [103150/160000] lr: 4.0011e-03 eta: 8:43:30 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.7028 aux.loss_ce: 0.0080 aux.acc_seg: 99.1325 +04/18 18:14:40 - mmengine - INFO - Iter(train) [103200/160000] lr: 3.9980e-03 eta: 8:43:02 time: 0.5540 data_time: 0.0063 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.7586 aux.loss_ce: 0.0076 aux.acc_seg: 99.3856 +04/18 18:15:08 - mmengine - INFO - Iter(train) [103250/160000] lr: 3.9949e-03 eta: 8:42:35 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0065 decode.acc_seg: 99.8170 aux.loss_ce: 0.0068 aux.acc_seg: 99.4820 +04/18 18:15:35 - mmengine - INFO - Iter(train) [103300/160000] lr: 3.9918e-03 eta: 8:42:07 time: 0.5553 data_time: 0.0075 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0077 decode.acc_seg: 99.6673 aux.loss_ce: 0.0083 aux.acc_seg: 99.0726 +04/18 18:16:03 - mmengine - INFO - Iter(train) [103350/160000] lr: 3.9887e-03 eta: 8:41:40 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0070 decode.acc_seg: 99.7534 aux.loss_ce: 0.0077 aux.acc_seg: 99.4044 +04/18 18:16:31 - mmengine - INFO - Iter(train) [103400/160000] lr: 3.9857e-03 eta: 8:41:12 time: 0.5556 data_time: 0.0077 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0074 decode.acc_seg: 99.6698 aux.loss_ce: 0.0081 aux.acc_seg: 98.9725 +04/18 18:16:59 - mmengine - INFO - Iter(train) [103450/160000] lr: 3.9826e-03 eta: 8:40:44 time: 0.5536 data_time: 0.0062 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7816 aux.loss_ce: 0.0073 aux.acc_seg: 99.2882 +04/18 18:17:26 - mmengine - INFO - Iter(train) [103500/160000] lr: 3.9795e-03 eta: 8:40:17 time: 0.5540 data_time: 0.0071 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0081 decode.acc_seg: 99.4746 aux.loss_ce: 0.0076 aux.acc_seg: 99.2682 +04/18 18:17:54 - mmengine - INFO - Iter(train) [103550/160000] lr: 3.9764e-03 eta: 8:39:49 time: 0.5635 data_time: 0.0063 memory: 7635 loss: 0.0166 decode.loss_ce: 0.0084 decode.acc_seg: 99.6692 aux.loss_ce: 0.0082 aux.acc_seg: 99.2133 +04/18 18:18:22 - mmengine - INFO - Iter(train) [103600/160000] lr: 3.9733e-03 eta: 8:39:22 time: 0.5538 data_time: 0.0069 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0077 decode.acc_seg: 99.7363 aux.loss_ce: 0.0079 aux.acc_seg: 99.2662 +04/18 18:18:50 - mmengine - INFO - Iter(train) [103650/160000] lr: 3.9702e-03 eta: 8:38:54 time: 0.5544 data_time: 0.0062 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0072 decode.acc_seg: 99.6899 aux.loss_ce: 0.0070 aux.acc_seg: 99.1610 +04/18 18:19:18 - mmengine - INFO - Iter(train) [103700/160000] lr: 3.9671e-03 eta: 8:38:27 time: 0.5572 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7503 aux.loss_ce: 0.0074 aux.acc_seg: 99.4403 +04/18 18:19:46 - mmengine - INFO - Iter(train) [103750/160000] lr: 3.9640e-03 eta: 8:37:59 time: 0.5565 data_time: 0.0071 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.6954 aux.loss_ce: 0.0079 aux.acc_seg: 99.1589 +04/18 18:20:13 - mmengine - INFO - Iter(train) [103800/160000] lr: 3.9609e-03 eta: 8:37:32 time: 0.5537 data_time: 0.0066 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6759 aux.loss_ce: 0.0080 aux.acc_seg: 99.1508 +04/18 18:20:41 - mmengine - INFO - Iter(train) [103850/160000] lr: 3.9578e-03 eta: 8:37:04 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0092 decode.acc_seg: 99.7488 aux.loss_ce: 0.0078 aux.acc_seg: 99.3685 +04/18 18:21:09 - mmengine - INFO - Iter(train) [103900/160000] lr: 3.9548e-03 eta: 8:36:37 time: 0.5563 data_time: 0.0065 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.8124 aux.loss_ce: 0.0073 aux.acc_seg: 99.4577 +04/18 18:21:37 - mmengine - INFO - Iter(train) [103950/160000] lr: 3.9517e-03 eta: 8:36:09 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.6668 aux.loss_ce: 0.0074 aux.acc_seg: 98.9861 +04/18 18:22:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:22:04 - mmengine - INFO - Iter(train) [104000/160000] lr: 3.9486e-03 eta: 8:35:41 time: 0.5547 data_time: 0.0072 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0076 decode.acc_seg: 99.7065 aux.loss_ce: 0.0077 aux.acc_seg: 99.3050 +04/18 18:22:32 - mmengine - INFO - Iter(train) [104050/160000] lr: 3.9455e-03 eta: 8:35:14 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6196 aux.loss_ce: 0.0073 aux.acc_seg: 99.0684 +04/18 18:23:00 - mmengine - INFO - Iter(train) [104100/160000] lr: 3.9424e-03 eta: 8:34:46 time: 0.5548 data_time: 0.0074 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.6983 aux.loss_ce: 0.0072 aux.acc_seg: 99.3368 +04/18 18:23:28 - mmengine - INFO - Iter(train) [104150/160000] lr: 3.9393e-03 eta: 8:34:19 time: 0.5542 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.8059 aux.loss_ce: 0.0074 aux.acc_seg: 99.3739 +04/18 18:23:55 - mmengine - INFO - Iter(train) [104200/160000] lr: 3.9362e-03 eta: 8:33:51 time: 0.5543 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7657 aux.loss_ce: 0.0069 aux.acc_seg: 99.5224 +04/18 18:24:23 - mmengine - INFO - Iter(train) [104250/160000] lr: 3.9331e-03 eta: 8:33:24 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.8130 aux.loss_ce: 0.0071 aux.acc_seg: 99.3957 +04/18 18:24:51 - mmengine - INFO - Iter(train) [104300/160000] lr: 3.9300e-03 eta: 8:32:56 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7690 aux.loss_ce: 0.0074 aux.acc_seg: 99.3905 +04/18 18:25:19 - mmengine - INFO - Iter(train) [104350/160000] lr: 3.9269e-03 eta: 8:32:28 time: 0.5543 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7915 aux.loss_ce: 0.0072 aux.acc_seg: 99.4453 +04/18 18:25:46 - mmengine - INFO - Iter(train) [104400/160000] lr: 3.9238e-03 eta: 8:32:01 time: 0.5546 data_time: 0.0070 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.6736 aux.loss_ce: 0.0076 aux.acc_seg: 99.2351 +04/18 18:26:14 - mmengine - INFO - Iter(train) [104450/160000] lr: 3.9207e-03 eta: 8:31:33 time: 0.5548 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7692 aux.loss_ce: 0.0071 aux.acc_seg: 99.3866 +04/18 18:26:42 - mmengine - INFO - Iter(train) [104500/160000] lr: 3.9176e-03 eta: 8:31:06 time: 0.5535 data_time: 0.0067 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0073 decode.acc_seg: 99.6496 aux.loss_ce: 0.0079 aux.acc_seg: 99.0063 +04/18 18:27:10 - mmengine - INFO - Iter(train) [104550/160000] lr: 3.9145e-03 eta: 8:30:38 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7830 aux.loss_ce: 0.0068 aux.acc_seg: 99.2777 +04/18 18:27:37 - mmengine - INFO - Iter(train) [104600/160000] lr: 3.9114e-03 eta: 8:30:11 time: 0.5547 data_time: 0.0067 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0078 decode.acc_seg: 99.5623 aux.loss_ce: 0.0078 aux.acc_seg: 98.9859 +04/18 18:28:05 - mmengine - INFO - Iter(train) [104650/160000] lr: 3.9083e-03 eta: 8:29:43 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0080 decode.acc_seg: 99.6039 aux.loss_ce: 0.0076 aux.acc_seg: 99.2819 +04/18 18:28:33 - mmengine - INFO - Iter(train) [104700/160000] lr: 3.9052e-03 eta: 8:29:16 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.7609 aux.loss_ce: 0.0075 aux.acc_seg: 99.2799 +04/18 18:29:01 - mmengine - INFO - Iter(train) [104750/160000] lr: 3.9021e-03 eta: 8:28:48 time: 0.5547 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.7188 aux.loss_ce: 0.0079 aux.acc_seg: 99.0936 +04/18 18:29:29 - mmengine - INFO - Iter(train) [104800/160000] lr: 3.8990e-03 eta: 8:28:21 time: 0.5535 data_time: 0.0069 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7534 aux.loss_ce: 0.0069 aux.acc_seg: 99.4607 +04/18 18:29:56 - mmengine - INFO - Iter(train) [104850/160000] lr: 3.8960e-03 eta: 8:27:53 time: 0.5539 data_time: 0.0065 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.7664 aux.loss_ce: 0.0078 aux.acc_seg: 99.3903 +04/18 18:30:24 - mmengine - INFO - Iter(train) [104900/160000] lr: 3.8929e-03 eta: 8:27:25 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.6962 aux.loss_ce: 0.0082 aux.acc_seg: 99.1224 +04/18 18:30:52 - mmengine - INFO - Iter(train) [104950/160000] lr: 3.8898e-03 eta: 8:26:58 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6739 aux.loss_ce: 0.0075 aux.acc_seg: 99.1560 +04/18 18:31:20 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:31:20 - mmengine - INFO - Iter(train) [105000/160000] lr: 3.8867e-03 eta: 8:26:30 time: 0.5538 data_time: 0.0060 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0092 decode.acc_seg: 99.6804 aux.loss_ce: 0.0081 aux.acc_seg: 99.2293 +04/18 18:31:47 - mmengine - INFO - Iter(train) [105050/160000] lr: 3.8836e-03 eta: 8:26:03 time: 0.5533 data_time: 0.0061 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7019 aux.loss_ce: 0.0076 aux.acc_seg: 99.3044 +04/18 18:32:15 - mmengine - INFO - Iter(train) [105100/160000] lr: 3.8805e-03 eta: 8:25:35 time: 0.5554 data_time: 0.0066 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7594 aux.loss_ce: 0.0068 aux.acc_seg: 99.5090 +04/18 18:32:43 - mmengine - INFO - Iter(train) [105150/160000] lr: 3.8774e-03 eta: 8:25:08 time: 0.5548 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7445 aux.loss_ce: 0.0070 aux.acc_seg: 99.3206 +04/18 18:33:11 - mmengine - INFO - Iter(train) [105200/160000] lr: 3.8743e-03 eta: 8:24:40 time: 0.5550 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7156 aux.loss_ce: 0.0074 aux.acc_seg: 99.3709 +04/18 18:33:38 - mmengine - INFO - Iter(train) [105250/160000] lr: 3.8712e-03 eta: 8:24:12 time: 0.5539 data_time: 0.0062 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0066 decode.acc_seg: 99.7749 aux.loss_ce: 0.0067 aux.acc_seg: 99.3821 +04/18 18:34:06 - mmengine - INFO - Iter(train) [105300/160000] lr: 3.8681e-03 eta: 8:23:45 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7286 aux.loss_ce: 0.0076 aux.acc_seg: 99.3585 +04/18 18:34:34 - mmengine - INFO - Iter(train) [105350/160000] lr: 3.8650e-03 eta: 8:23:17 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7470 aux.loss_ce: 0.0073 aux.acc_seg: 99.2317 +04/18 18:35:02 - mmengine - INFO - Iter(train) [105400/160000] lr: 3.8619e-03 eta: 8:22:50 time: 0.5548 data_time: 0.0070 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0091 decode.acc_seg: 99.6312 aux.loss_ce: 0.0079 aux.acc_seg: 99.1449 +04/18 18:35:29 - mmengine - INFO - Iter(train) [105450/160000] lr: 3.8588e-03 eta: 8:22:22 time: 0.5547 data_time: 0.0064 memory: 7635 loss: 0.0180 decode.loss_ce: 0.0096 decode.acc_seg: 99.5889 aux.loss_ce: 0.0084 aux.acc_seg: 99.1507 +04/18 18:35:57 - mmengine - INFO - Iter(train) [105500/160000] lr: 3.8557e-03 eta: 8:21:55 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.5057 aux.loss_ce: 0.0075 aux.acc_seg: 99.1350 +04/18 18:36:25 - mmengine - INFO - Iter(train) [105550/160000] lr: 3.8526e-03 eta: 8:21:27 time: 0.5560 data_time: 0.0069 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0079 decode.acc_seg: 99.6590 aux.loss_ce: 0.0080 aux.acc_seg: 98.8886 +04/18 18:36:53 - mmengine - INFO - Iter(train) [105600/160000] lr: 3.8495e-03 eta: 8:20:59 time: 0.5535 data_time: 0.0071 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0082 decode.acc_seg: 99.7232 aux.loss_ce: 0.0081 aux.acc_seg: 99.2006 +04/18 18:37:21 - mmengine - INFO - Iter(train) [105650/160000] lr: 3.8464e-03 eta: 8:20:32 time: 0.5552 data_time: 0.0070 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0077 decode.acc_seg: 99.6098 aux.loss_ce: 0.0080 aux.acc_seg: 98.9934 +04/18 18:37:48 - mmengine - INFO - Iter(train) [105700/160000] lr: 3.8433e-03 eta: 8:20:04 time: 0.5629 data_time: 0.0066 memory: 7635 loss: 0.0175 decode.loss_ce: 0.0091 decode.acc_seg: 99.6754 aux.loss_ce: 0.0084 aux.acc_seg: 99.1598 +04/18 18:38:16 - mmengine - INFO - Iter(train) [105750/160000] lr: 3.8402e-03 eta: 8:19:37 time: 0.5532 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0075 decode.acc_seg: 99.7292 aux.loss_ce: 0.0080 aux.acc_seg: 99.3091 +04/18 18:38:44 - mmengine - INFO - Iter(train) [105800/160000] lr: 3.8371e-03 eta: 8:19:09 time: 0.5552 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7055 aux.loss_ce: 0.0074 aux.acc_seg: 99.2315 +04/18 18:39:12 - mmengine - INFO - Iter(train) [105850/160000] lr: 3.8339e-03 eta: 8:18:42 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6668 aux.loss_ce: 0.0073 aux.acc_seg: 99.1922 +04/18 18:39:40 - mmengine - INFO - Iter(train) [105900/160000] lr: 3.8308e-03 eta: 8:18:14 time: 0.5559 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.8177 aux.loss_ce: 0.0070 aux.acc_seg: 99.5051 +04/18 18:40:07 - mmengine - INFO - Iter(train) [105950/160000] lr: 3.8277e-03 eta: 8:17:47 time: 0.5563 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.6799 aux.loss_ce: 0.0079 aux.acc_seg: 99.0003 +04/18 18:40:35 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:40:35 - mmengine - INFO - Iter(train) [106000/160000] lr: 3.8246e-03 eta: 8:17:19 time: 0.5556 data_time: 0.0063 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7479 aux.loss_ce: 0.0071 aux.acc_seg: 99.2925 +04/18 18:41:03 - mmengine - INFO - Iter(train) [106050/160000] lr: 3.8215e-03 eta: 8:16:52 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7302 aux.loss_ce: 0.0067 aux.acc_seg: 99.1390 +04/18 18:41:31 - mmengine - INFO - Iter(train) [106100/160000] lr: 3.8184e-03 eta: 8:16:24 time: 0.5549 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7599 aux.loss_ce: 0.0074 aux.acc_seg: 99.2908 +04/18 18:41:58 - mmengine - INFO - Iter(train) [106150/160000] lr: 3.8153e-03 eta: 8:15:56 time: 0.5550 data_time: 0.0063 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0086 decode.acc_seg: 99.6630 aux.loss_ce: 0.0078 aux.acc_seg: 99.1592 +04/18 18:42:26 - mmengine - INFO - Iter(train) [106200/160000] lr: 3.8122e-03 eta: 8:15:29 time: 0.5537 data_time: 0.0066 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0081 decode.acc_seg: 99.5579 aux.loss_ce: 0.0074 aux.acc_seg: 99.1849 +04/18 18:42:54 - mmengine - INFO - Iter(train) [106250/160000] lr: 3.8091e-03 eta: 8:15:01 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0077 decode.acc_seg: 99.6932 aux.loss_ce: 0.0078 aux.acc_seg: 99.1446 +04/18 18:43:22 - mmengine - INFO - Iter(train) [106300/160000] lr: 3.8060e-03 eta: 8:14:34 time: 0.5545 data_time: 0.0070 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6707 aux.loss_ce: 0.0074 aux.acc_seg: 99.2505 +04/18 18:43:49 - mmengine - INFO - Iter(train) [106350/160000] lr: 3.8029e-03 eta: 8:14:06 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0080 decode.acc_seg: 99.6784 aux.loss_ce: 0.0084 aux.acc_seg: 99.0918 +04/18 18:44:17 - mmengine - INFO - Iter(train) [106400/160000] lr: 3.7998e-03 eta: 8:13:38 time: 0.5547 data_time: 0.0072 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.5859 aux.loss_ce: 0.0075 aux.acc_seg: 98.9934 +04/18 18:44:45 - mmengine - INFO - Iter(train) [106450/160000] lr: 3.7967e-03 eta: 8:13:11 time: 0.5557 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.8007 aux.loss_ce: 0.0072 aux.acc_seg: 99.3562 +04/18 18:45:13 - mmengine - INFO - Iter(train) [106500/160000] lr: 3.7936e-03 eta: 8:12:43 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.6293 aux.loss_ce: 0.0076 aux.acc_seg: 99.0341 +04/18 18:45:40 - mmengine - INFO - Iter(train) [106550/160000] lr: 3.7905e-03 eta: 8:12:16 time: 0.5560 data_time: 0.0068 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0078 decode.acc_seg: 99.7142 aux.loss_ce: 0.0072 aux.acc_seg: 99.3312 +04/18 18:46:08 - mmengine - INFO - Iter(train) [106600/160000] lr: 3.7874e-03 eta: 8:11:48 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0088 decode.acc_seg: 99.6775 aux.loss_ce: 0.0080 aux.acc_seg: 99.2909 +04/18 18:46:36 - mmengine - INFO - Iter(train) [106650/160000] lr: 3.7843e-03 eta: 8:11:21 time: 0.5556 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.7286 aux.loss_ce: 0.0074 aux.acc_seg: 99.2894 +04/18 18:47:04 - mmengine - INFO - Iter(train) [106700/160000] lr: 3.7812e-03 eta: 8:10:53 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0079 decode.acc_seg: 99.7343 aux.loss_ce: 0.0080 aux.acc_seg: 99.1952 +04/18 18:47:32 - mmengine - INFO - Iter(train) [106750/160000] lr: 3.7780e-03 eta: 8:10:25 time: 0.5556 data_time: 0.0064 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0085 decode.acc_seg: 99.7152 aux.loss_ce: 0.0080 aux.acc_seg: 99.2158 +04/18 18:48:00 - mmengine - INFO - Iter(train) [106800/160000] lr: 3.7749e-03 eta: 8:09:58 time: 0.5551 data_time: 0.0060 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.6323 aux.loss_ce: 0.0079 aux.acc_seg: 99.2817 +04/18 18:48:27 - mmengine - INFO - Iter(train) [106850/160000] lr: 3.7718e-03 eta: 8:09:31 time: 0.5556 data_time: 0.0071 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.6947 aux.loss_ce: 0.0079 aux.acc_seg: 99.2083 +04/18 18:48:55 - mmengine - INFO - Iter(train) [106900/160000] lr: 3.7687e-03 eta: 8:09:03 time: 0.5559 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0074 decode.acc_seg: 99.6970 aux.loss_ce: 0.0075 aux.acc_seg: 99.2016 +04/18 18:49:23 - mmengine - INFO - Iter(train) [106950/160000] lr: 3.7656e-03 eta: 8:08:35 time: 0.5625 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7646 aux.loss_ce: 0.0072 aux.acc_seg: 99.2825 +04/18 18:49:51 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:49:51 - mmengine - INFO - Iter(train) [107000/160000] lr: 3.7625e-03 eta: 8:08:08 time: 0.5543 data_time: 0.0064 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.7506 aux.loss_ce: 0.0077 aux.acc_seg: 99.2103 +04/18 18:50:18 - mmengine - INFO - Iter(train) [107050/160000] lr: 3.7594e-03 eta: 8:07:40 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0079 decode.acc_seg: 99.7283 aux.loss_ce: 0.0076 aux.acc_seg: 99.3507 +04/18 18:50:46 - mmengine - INFO - Iter(train) [107100/160000] lr: 3.7563e-03 eta: 8:07:13 time: 0.5571 data_time: 0.0063 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.6904 aux.loss_ce: 0.0075 aux.acc_seg: 99.0345 +04/18 18:51:14 - mmengine - INFO - Iter(train) [107150/160000] lr: 3.7532e-03 eta: 8:06:45 time: 0.5549 data_time: 0.0069 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.7509 aux.loss_ce: 0.0077 aux.acc_seg: 99.4362 +04/18 18:51:42 - mmengine - INFO - Iter(train) [107200/160000] lr: 3.7501e-03 eta: 8:06:18 time: 0.5553 data_time: 0.0075 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.5761 aux.loss_ce: 0.0073 aux.acc_seg: 98.9529 +04/18 18:52:10 - mmengine - INFO - Iter(train) [107250/160000] lr: 3.7470e-03 eta: 8:05:50 time: 0.5565 data_time: 0.0061 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.6921 aux.loss_ce: 0.0075 aux.acc_seg: 99.1709 +04/18 18:52:37 - mmengine - INFO - Iter(train) [107300/160000] lr: 3.7438e-03 eta: 8:05:22 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7155 aux.loss_ce: 0.0074 aux.acc_seg: 99.2370 +04/18 18:53:05 - mmengine - INFO - Iter(train) [107350/160000] lr: 3.7407e-03 eta: 8:04:55 time: 0.5540 data_time: 0.0076 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.6866 aux.loss_ce: 0.0079 aux.acc_seg: 99.1728 +04/18 18:53:33 - mmengine - INFO - Iter(train) [107400/160000] lr: 3.7376e-03 eta: 8:04:27 time: 0.5555 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7092 aux.loss_ce: 0.0075 aux.acc_seg: 99.1659 +04/18 18:54:01 - mmengine - INFO - Iter(train) [107450/160000] lr: 3.7345e-03 eta: 8:04:00 time: 0.5558 data_time: 0.0068 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6946 aux.loss_ce: 0.0071 aux.acc_seg: 99.2869 +04/18 18:54:28 - mmengine - INFO - Iter(train) [107500/160000] lr: 3.7314e-03 eta: 8:03:32 time: 0.5542 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7839 aux.loss_ce: 0.0071 aux.acc_seg: 99.3629 +04/18 18:54:56 - mmengine - INFO - Iter(train) [107550/160000] lr: 3.7283e-03 eta: 8:03:05 time: 0.5542 data_time: 0.0071 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7341 aux.loss_ce: 0.0069 aux.acc_seg: 99.3241 +04/18 18:55:24 - mmengine - INFO - Iter(train) [107600/160000] lr: 3.7252e-03 eta: 8:02:37 time: 0.5525 data_time: 0.0068 memory: 7635 loss: 0.0124 decode.loss_ce: 0.0058 decode.acc_seg: 99.7838 aux.loss_ce: 0.0065 aux.acc_seg: 99.1969 +04/18 18:55:51 - mmengine - INFO - Iter(train) [107650/160000] lr: 3.7221e-03 eta: 8:02:09 time: 0.5545 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.6625 aux.loss_ce: 0.0074 aux.acc_seg: 99.0898 +04/18 18:56:19 - mmengine - INFO - Iter(train) [107700/160000] lr: 3.7189e-03 eta: 8:01:42 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7855 aux.loss_ce: 0.0071 aux.acc_seg: 99.3336 +04/18 18:56:47 - mmengine - INFO - Iter(train) [107750/160000] lr: 3.7158e-03 eta: 8:01:14 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6257 aux.loss_ce: 0.0072 aux.acc_seg: 99.2513 +04/18 18:57:15 - mmengine - INFO - Iter(train) [107800/160000] lr: 3.7127e-03 eta: 8:00:47 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7494 aux.loss_ce: 0.0077 aux.acc_seg: 99.2947 +04/18 18:57:43 - mmengine - INFO - Iter(train) [107850/160000] lr: 3.7096e-03 eta: 8:00:19 time: 0.5542 data_time: 0.0062 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7247 aux.loss_ce: 0.0067 aux.acc_seg: 99.3230 +04/18 18:58:11 - mmengine - INFO - Iter(train) [107900/160000] lr: 3.7065e-03 eta: 7:59:52 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.6934 aux.loss_ce: 0.0075 aux.acc_seg: 99.1262 +04/18 18:58:38 - mmengine - INFO - Iter(train) [107950/160000] lr: 3.7034e-03 eta: 7:59:24 time: 0.5556 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7154 aux.loss_ce: 0.0072 aux.acc_seg: 99.3361 +04/18 18:59:06 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 18:59:06 - mmengine - INFO - Iter(train) [108000/160000] lr: 3.7003e-03 eta: 7:58:56 time: 0.5529 data_time: 0.0066 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0072 decode.acc_seg: 99.5666 aux.loss_ce: 0.0079 aux.acc_seg: 98.5047 +04/18 18:59:34 - mmengine - INFO - Iter(train) [108050/160000] lr: 3.6971e-03 eta: 7:58:29 time: 0.5557 data_time: 0.0064 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7347 aux.loss_ce: 0.0075 aux.acc_seg: 99.1542 +04/18 19:00:02 - mmengine - INFO - Iter(train) [108100/160000] lr: 3.6940e-03 eta: 7:58:01 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7680 aux.loss_ce: 0.0069 aux.acc_seg: 99.2745 +04/18 19:00:29 - mmengine - INFO - Iter(train) [108150/160000] lr: 3.6909e-03 eta: 7:57:34 time: 0.5542 data_time: 0.0065 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.6890 aux.loss_ce: 0.0070 aux.acc_seg: 99.1372 +04/18 19:00:57 - mmengine - INFO - Iter(train) [108200/160000] lr: 3.6878e-03 eta: 7:57:06 time: 0.5549 data_time: 0.0074 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7164 aux.loss_ce: 0.0070 aux.acc_seg: 99.0399 +04/18 19:01:25 - mmengine - INFO - Iter(train) [108250/160000] lr: 3.6847e-03 eta: 7:56:39 time: 0.5550 data_time: 0.0064 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7497 aux.loss_ce: 0.0071 aux.acc_seg: 99.2955 +04/18 19:01:53 - mmengine - INFO - Iter(train) [108300/160000] lr: 3.6816e-03 eta: 7:56:11 time: 0.5561 data_time: 0.0063 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0066 decode.acc_seg: 99.7405 aux.loss_ce: 0.0068 aux.acc_seg: 99.2576 +04/18 19:02:20 - mmengine - INFO - Iter(train) [108350/160000] lr: 3.6784e-03 eta: 7:55:43 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7471 aux.loss_ce: 0.0071 aux.acc_seg: 99.3069 +04/18 19:02:48 - mmengine - INFO - Iter(train) [108400/160000] lr: 3.6753e-03 eta: 7:55:16 time: 0.5539 data_time: 0.0070 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7011 aux.loss_ce: 0.0071 aux.acc_seg: 99.3650 +04/18 19:03:16 - mmengine - INFO - Iter(train) [108450/160000] lr: 3.6722e-03 eta: 7:54:48 time: 0.5553 data_time: 0.0065 memory: 7635 loss: 0.0125 decode.loss_ce: 0.0061 decode.acc_seg: 99.7404 aux.loss_ce: 0.0064 aux.acc_seg: 99.4333 +04/18 19:03:44 - mmengine - INFO - Iter(train) [108500/160000] lr: 3.6691e-03 eta: 7:54:21 time: 0.5537 data_time: 0.0064 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7160 aux.loss_ce: 0.0071 aux.acc_seg: 99.0922 +04/18 19:04:12 - mmengine - INFO - Iter(train) [108550/160000] lr: 3.6660e-03 eta: 7:53:53 time: 0.5558 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.6211 aux.loss_ce: 0.0075 aux.acc_seg: 99.1413 +04/18 19:04:39 - mmengine - INFO - Iter(train) [108600/160000] lr: 3.6628e-03 eta: 7:53:26 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.6576 aux.loss_ce: 0.0068 aux.acc_seg: 99.2223 +04/18 19:05:07 - mmengine - INFO - Iter(train) [108650/160000] lr: 3.6597e-03 eta: 7:52:58 time: 0.5551 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.7253 aux.loss_ce: 0.0070 aux.acc_seg: 99.1011 +04/18 19:05:35 - mmengine - INFO - Iter(train) [108700/160000] lr: 3.6566e-03 eta: 7:52:30 time: 0.5552 data_time: 0.0061 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0069 decode.acc_seg: 99.6749 aux.loss_ce: 0.0068 aux.acc_seg: 99.3142 +04/18 19:06:03 - mmengine - INFO - Iter(train) [108750/160000] lr: 3.6535e-03 eta: 7:52:03 time: 0.5544 data_time: 0.0077 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.6973 aux.loss_ce: 0.0067 aux.acc_seg: 99.2664 +04/18 19:06:30 - mmengine - INFO - Iter(train) [108800/160000] lr: 3.6504e-03 eta: 7:51:35 time: 0.5554 data_time: 0.0075 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.8243 aux.loss_ce: 0.0076 aux.acc_seg: 99.4201 +04/18 19:06:58 - mmengine - INFO - Iter(train) [108850/160000] lr: 3.6472e-03 eta: 7:51:08 time: 0.5546 data_time: 0.0074 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6761 aux.loss_ce: 0.0073 aux.acc_seg: 99.2339 +04/18 19:07:26 - mmengine - INFO - Iter(train) [108900/160000] lr: 3.6441e-03 eta: 7:50:40 time: 0.5529 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.6829 aux.loss_ce: 0.0073 aux.acc_seg: 99.0892 +04/18 19:07:54 - mmengine - INFO - Iter(train) [108950/160000] lr: 3.6410e-03 eta: 7:50:13 time: 0.5547 data_time: 0.0073 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7084 aux.loss_ce: 0.0075 aux.acc_seg: 99.1124 +04/18 19:08:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:08:22 - mmengine - INFO - Iter(train) [109000/160000] lr: 3.6379e-03 eta: 7:49:45 time: 0.5546 data_time: 0.0062 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.8028 aux.loss_ce: 0.0067 aux.acc_seg: 99.3844 +04/18 19:08:49 - mmengine - INFO - Iter(train) [109050/160000] lr: 3.6348e-03 eta: 7:49:17 time: 0.5520 data_time: 0.0064 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7956 aux.loss_ce: 0.0071 aux.acc_seg: 99.3993 +04/18 19:09:17 - mmengine - INFO - Iter(train) [109100/160000] lr: 3.6316e-03 eta: 7:48:50 time: 0.5635 data_time: 0.0070 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0064 decode.acc_seg: 99.7819 aux.loss_ce: 0.0068 aux.acc_seg: 99.3765 +04/18 19:09:45 - mmengine - INFO - Iter(train) [109150/160000] lr: 3.6285e-03 eta: 7:48:22 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7561 aux.loss_ce: 0.0077 aux.acc_seg: 99.4558 +04/18 19:10:13 - mmengine - INFO - Iter(train) [109200/160000] lr: 3.6254e-03 eta: 7:47:55 time: 0.5543 data_time: 0.0063 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0071 decode.acc_seg: 99.6473 aux.loss_ce: 0.0070 aux.acc_seg: 99.2697 +04/18 19:10:41 - mmengine - INFO - Iter(train) [109250/160000] lr: 3.6223e-03 eta: 7:47:27 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7118 aux.loss_ce: 0.0068 aux.acc_seg: 99.1602 +04/18 19:11:08 - mmengine - INFO - Iter(train) [109300/160000] lr: 3.6191e-03 eta: 7:47:00 time: 0.5561 data_time: 0.0071 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7101 aux.loss_ce: 0.0072 aux.acc_seg: 99.0958 +04/18 19:11:36 - mmengine - INFO - Iter(train) [109350/160000] lr: 3.6160e-03 eta: 7:46:32 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0079 decode.acc_seg: 99.6615 aux.loss_ce: 0.0075 aux.acc_seg: 99.2090 +04/18 19:12:04 - mmengine - INFO - Iter(train) [109400/160000] lr: 3.6129e-03 eta: 7:46:04 time: 0.5555 data_time: 0.0069 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.6904 aux.loss_ce: 0.0077 aux.acc_seg: 99.2303 +04/18 19:12:32 - mmengine - INFO - Iter(train) [109450/160000] lr: 3.6098e-03 eta: 7:45:37 time: 0.5556 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0075 decode.acc_seg: 99.7541 aux.loss_ce: 0.0078 aux.acc_seg: 99.2632 +04/18 19:12:59 - mmengine - INFO - Iter(train) [109500/160000] lr: 3.6066e-03 eta: 7:45:09 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0082 decode.acc_seg: 99.7097 aux.loss_ce: 0.0075 aux.acc_seg: 99.2345 +04/18 19:13:27 - mmengine - INFO - Iter(train) [109550/160000] lr: 3.6035e-03 eta: 7:44:42 time: 0.5552 data_time: 0.0069 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0080 decode.acc_seg: 99.7431 aux.loss_ce: 0.0071 aux.acc_seg: 99.4443 +04/18 19:13:55 - mmengine - INFO - Iter(train) [109600/160000] lr: 3.6004e-03 eta: 7:44:14 time: 0.5556 data_time: 0.0078 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.6223 aux.loss_ce: 0.0073 aux.acc_seg: 99.1431 +04/18 19:14:23 - mmengine - INFO - Iter(train) [109650/160000] lr: 3.5973e-03 eta: 7:43:47 time: 0.5559 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0076 decode.acc_seg: 99.6944 aux.loss_ce: 0.0072 aux.acc_seg: 99.1576 +04/18 19:14:50 - mmengine - INFO - Iter(train) [109700/160000] lr: 3.5941e-03 eta: 7:43:19 time: 0.5559 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7169 aux.loss_ce: 0.0069 aux.acc_seg: 99.2173 +04/18 19:15:18 - mmengine - INFO - Iter(train) [109750/160000] lr: 3.5910e-03 eta: 7:42:51 time: 0.5562 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6673 aux.loss_ce: 0.0072 aux.acc_seg: 99.1226 +04/18 19:15:46 - mmengine - INFO - Iter(train) [109800/160000] lr: 3.5879e-03 eta: 7:42:24 time: 0.5551 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0068 decode.acc_seg: 99.7271 aux.loss_ce: 0.0067 aux.acc_seg: 99.3597 +04/18 19:16:14 - mmengine - INFO - Iter(train) [109850/160000] lr: 3.5848e-03 eta: 7:41:56 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6505 aux.loss_ce: 0.0075 aux.acc_seg: 99.0623 +04/18 19:16:41 - mmengine - INFO - Iter(train) [109900/160000] lr: 3.5816e-03 eta: 7:41:29 time: 0.5546 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.7892 aux.loss_ce: 0.0072 aux.acc_seg: 99.2321 +04/18 19:17:09 - mmengine - INFO - Iter(train) [109950/160000] lr: 3.5785e-03 eta: 7:41:01 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.6477 aux.loss_ce: 0.0079 aux.acc_seg: 99.0071 +04/18 19:17:37 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:17:37 - mmengine - INFO - Iter(train) [110000/160000] lr: 3.5754e-03 eta: 7:40:34 time: 0.5638 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7181 aux.loss_ce: 0.0071 aux.acc_seg: 99.2119 +04/18 19:17:37 - mmengine - INFO - Saving checkpoint at 110000 iterations +04/18 19:17:41 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0464 data_time: 0.0015 memory: 1657 +04/18 19:17:43 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0466 data_time: 0.0015 memory: 1657 +04/18 19:17:46 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0466 data_time: 0.0014 memory: 1657 +04/18 19:17:48 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0453 data_time: 0.0011 memory: 1657 +04/18 19:17:48 - mmengine - INFO - per class results: +04/18 19:17:48 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 98.93 | 99.5 | 99.46 | 99.42 | 99.5 | +| contrast | 76.86 | 86.05 | 86.91 | 87.8 | 86.05 | ++------------+-------+-------+--------+-----------+--------+ +04/18 19:17:48 - mmengine - INFO - Iter(val) [200/200] aAcc: 98.9700 mIoU: 87.8900 mAcc: 92.7800 mFscore: 93.1900 mPrecision: 93.6100 mRecall: 92.7800 data_time: 0.0015 time: 0.0464 +04/18 19:18:16 - mmengine - INFO - Iter(train) [110050/160000] lr: 3.5723e-03 eta: 7:40:06 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0080 decode.acc_seg: 99.7857 aux.loss_ce: 0.0078 aux.acc_seg: 99.4260 +04/18 19:18:44 - mmengine - INFO - Iter(train) [110100/160000] lr: 3.5691e-03 eta: 7:39:39 time: 0.5547 data_time: 0.0073 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0076 decode.acc_seg: 99.6800 aux.loss_ce: 0.0081 aux.acc_seg: 99.0413 +04/18 19:19:12 - mmengine - INFO - Iter(train) [110150/160000] lr: 3.5660e-03 eta: 7:39:11 time: 0.5560 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0077 decode.acc_seg: 99.6807 aux.loss_ce: 0.0074 aux.acc_seg: 99.0971 +04/18 19:19:39 - mmengine - INFO - Iter(train) [110200/160000] lr: 3.5629e-03 eta: 7:38:43 time: 0.5548 data_time: 0.0069 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0066 decode.acc_seg: 99.7349 aux.loss_ce: 0.0066 aux.acc_seg: 99.3257 +04/18 19:20:07 - mmengine - INFO - Iter(train) [110250/160000] lr: 3.5597e-03 eta: 7:38:16 time: 0.5544 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7406 aux.loss_ce: 0.0071 aux.acc_seg: 99.2209 +04/18 19:20:35 - mmengine - INFO - Iter(train) [110300/160000] lr: 3.5566e-03 eta: 7:37:48 time: 0.5554 data_time: 0.0068 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6781 aux.loss_ce: 0.0071 aux.acc_seg: 99.2702 +04/18 19:21:03 - mmengine - INFO - Iter(train) [110350/160000] lr: 3.5535e-03 eta: 7:37:21 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7687 aux.loss_ce: 0.0074 aux.acc_seg: 99.3010 +04/18 19:21:30 - mmengine - INFO - Iter(train) [110400/160000] lr: 3.5504e-03 eta: 7:36:53 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7117 aux.loss_ce: 0.0075 aux.acc_seg: 99.1607 +04/18 19:21:58 - mmengine - INFO - Iter(train) [110450/160000] lr: 3.5472e-03 eta: 7:36:25 time: 0.5547 data_time: 0.0061 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.6261 aux.loss_ce: 0.0072 aux.acc_seg: 99.0324 +04/18 19:22:26 - mmengine - INFO - Iter(train) [110500/160000] lr: 3.5441e-03 eta: 7:35:58 time: 0.5656 data_time: 0.0066 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7314 aux.loss_ce: 0.0073 aux.acc_seg: 99.1668 +04/18 19:22:54 - mmengine - INFO - Iter(train) [110550/160000] lr: 3.5410e-03 eta: 7:35:30 time: 0.5550 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.6418 aux.loss_ce: 0.0075 aux.acc_seg: 99.0856 +04/18 19:23:22 - mmengine - INFO - Iter(train) [110600/160000] lr: 3.5378e-03 eta: 7:35:03 time: 0.5529 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7288 aux.loss_ce: 0.0073 aux.acc_seg: 99.1767 +04/18 19:23:49 - mmengine - INFO - Iter(train) [110650/160000] lr: 3.5347e-03 eta: 7:34:35 time: 0.5557 data_time: 0.0070 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7032 aux.loss_ce: 0.0075 aux.acc_seg: 99.1522 +04/18 19:24:17 - mmengine - INFO - Iter(train) [110700/160000] lr: 3.5316e-03 eta: 7:34:08 time: 0.5549 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.7876 aux.loss_ce: 0.0070 aux.acc_seg: 99.5091 +04/18 19:24:45 - mmengine - INFO - Iter(train) [110750/160000] lr: 3.5284e-03 eta: 7:33:40 time: 0.5541 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7217 aux.loss_ce: 0.0071 aux.acc_seg: 99.5057 +04/18 19:25:13 - mmengine - INFO - Iter(train) [110800/160000] lr: 3.5253e-03 eta: 7:33:12 time: 0.5557 data_time: 0.0069 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.7141 aux.loss_ce: 0.0069 aux.acc_seg: 99.3195 +04/18 19:25:40 - mmengine - INFO - Iter(train) [110850/160000] lr: 3.5222e-03 eta: 7:32:45 time: 0.5560 data_time: 0.0073 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0068 decode.acc_seg: 99.7230 aux.loss_ce: 0.0077 aux.acc_seg: 99.1342 +04/18 19:26:08 - mmengine - INFO - Iter(train) [110900/160000] lr: 3.5190e-03 eta: 7:32:17 time: 0.5554 data_time: 0.0071 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.6499 aux.loss_ce: 0.0075 aux.acc_seg: 99.1427 +04/18 19:26:36 - mmengine - INFO - Iter(train) [110950/160000] lr: 3.5159e-03 eta: 7:31:50 time: 0.5551 data_time: 0.0066 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.7088 aux.loss_ce: 0.0068 aux.acc_seg: 99.2192 +04/18 19:27:04 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:27:04 - mmengine - INFO - Iter(train) [111000/160000] lr: 3.5128e-03 eta: 7:31:22 time: 0.5542 data_time: 0.0076 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7270 aux.loss_ce: 0.0072 aux.acc_seg: 99.3582 +04/18 19:27:32 - mmengine - INFO - Iter(train) [111050/160000] lr: 3.5096e-03 eta: 7:30:55 time: 0.5552 data_time: 0.0062 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.6961 aux.loss_ce: 0.0074 aux.acc_seg: 99.1317 +04/18 19:27:59 - mmengine - INFO - Iter(train) [111100/160000] lr: 3.5065e-03 eta: 7:30:27 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7753 aux.loss_ce: 0.0071 aux.acc_seg: 99.2970 +04/18 19:28:27 - mmengine - INFO - Iter(train) [111150/160000] lr: 3.5034e-03 eta: 7:29:59 time: 0.5545 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7406 aux.loss_ce: 0.0072 aux.acc_seg: 99.2530 +04/18 19:28:55 - mmengine - INFO - Iter(train) [111200/160000] lr: 3.5002e-03 eta: 7:29:32 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.5930 aux.loss_ce: 0.0080 aux.acc_seg: 99.2295 +04/18 19:29:23 - mmengine - INFO - Iter(train) [111250/160000] lr: 3.4971e-03 eta: 7:29:04 time: 0.5539 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.6919 aux.loss_ce: 0.0075 aux.acc_seg: 99.1432 +04/18 19:29:51 - mmengine - INFO - Iter(train) [111300/160000] lr: 3.4940e-03 eta: 7:28:37 time: 0.5560 data_time: 0.0067 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0082 decode.acc_seg: 99.5827 aux.loss_ce: 0.0078 aux.acc_seg: 99.0034 +04/18 19:30:18 - mmengine - INFO - Iter(train) [111350/160000] lr: 3.4908e-03 eta: 7:28:09 time: 0.5544 data_time: 0.0068 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0073 decode.acc_seg: 99.7358 aux.loss_ce: 0.0078 aux.acc_seg: 99.3553 +04/18 19:30:46 - mmengine - INFO - Iter(train) [111400/160000] lr: 3.4877e-03 eta: 7:27:42 time: 0.5548 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7693 aux.loss_ce: 0.0071 aux.acc_seg: 99.3146 +04/18 19:31:14 - mmengine - INFO - Iter(train) [111450/160000] lr: 3.4845e-03 eta: 7:27:14 time: 0.5535 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.7491 aux.loss_ce: 0.0070 aux.acc_seg: 99.2060 +04/18 19:31:42 - mmengine - INFO - Iter(train) [111500/160000] lr: 3.4814e-03 eta: 7:26:46 time: 0.5545 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6525 aux.loss_ce: 0.0073 aux.acc_seg: 99.1815 +04/18 19:32:09 - mmengine - INFO - Iter(train) [111550/160000] lr: 3.4783e-03 eta: 7:26:19 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0075 decode.acc_seg: 99.7223 aux.loss_ce: 0.0074 aux.acc_seg: 99.3914 +04/18 19:32:37 - mmengine - INFO - Iter(train) [111600/160000] lr: 3.4751e-03 eta: 7:25:51 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7206 aux.loss_ce: 0.0069 aux.acc_seg: 99.2196 +04/18 19:33:05 - mmengine - INFO - Iter(train) [111650/160000] lr: 3.4720e-03 eta: 7:25:24 time: 0.5526 data_time: 0.0067 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.6882 aux.loss_ce: 0.0068 aux.acc_seg: 99.2639 +04/18 19:33:33 - mmengine - INFO - Iter(train) [111700/160000] lr: 3.4689e-03 eta: 7:24:56 time: 0.5563 data_time: 0.0067 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.6841 aux.loss_ce: 0.0071 aux.acc_seg: 99.1851 +04/18 19:34:00 - mmengine - INFO - Iter(train) [111750/160000] lr: 3.4657e-03 eta: 7:24:28 time: 0.5552 data_time: 0.0063 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7263 aux.loss_ce: 0.0074 aux.acc_seg: 99.1424 +04/18 19:34:28 - mmengine - INFO - Iter(train) [111800/160000] lr: 3.4626e-03 eta: 7:24:01 time: 0.5550 data_time: 0.0061 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0066 decode.acc_seg: 99.7540 aux.loss_ce: 0.0068 aux.acc_seg: 99.3311 +04/18 19:34:56 - mmengine - INFO - Iter(train) [111850/160000] lr: 3.4594e-03 eta: 7:23:33 time: 0.5552 data_time: 0.0074 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0067 decode.acc_seg: 99.7018 aux.loss_ce: 0.0069 aux.acc_seg: 99.1357 +04/18 19:35:24 - mmengine - INFO - Iter(train) [111900/160000] lr: 3.4563e-03 eta: 7:23:06 time: 0.5551 data_time: 0.0069 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7384 aux.loss_ce: 0.0068 aux.acc_seg: 99.2832 +04/18 19:35:51 - mmengine - INFO - Iter(train) [111950/160000] lr: 3.4532e-03 eta: 7:22:38 time: 0.5549 data_time: 0.0073 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.6992 aux.loss_ce: 0.0069 aux.acc_seg: 99.2122 +04/18 19:36:19 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:36:19 - mmengine - INFO - Iter(train) [112000/160000] lr: 3.4500e-03 eta: 7:22:10 time: 0.5561 data_time: 0.0062 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7575 aux.loss_ce: 0.0069 aux.acc_seg: 99.3261 +04/18 19:36:47 - mmengine - INFO - Iter(train) [112050/160000] lr: 3.4469e-03 eta: 7:21:43 time: 0.5567 data_time: 0.0062 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.6931 aux.loss_ce: 0.0077 aux.acc_seg: 99.0832 +04/18 19:37:15 - mmengine - INFO - Iter(train) [112100/160000] lr: 3.4437e-03 eta: 7:21:15 time: 0.5619 data_time: 0.0068 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7355 aux.loss_ce: 0.0074 aux.acc_seg: 99.2790 +04/18 19:37:43 - mmengine - INFO - Iter(train) [112150/160000] lr: 3.4406e-03 eta: 7:20:48 time: 0.5642 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0074 decode.acc_seg: 99.6672 aux.loss_ce: 0.0072 aux.acc_seg: 99.2986 +04/18 19:38:11 - mmengine - INFO - Iter(train) [112200/160000] lr: 3.4374e-03 eta: 7:20:20 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.7649 aux.loss_ce: 0.0069 aux.acc_seg: 99.3267 +04/18 19:38:38 - mmengine - INFO - Iter(train) [112250/160000] lr: 3.4343e-03 eta: 7:19:53 time: 0.5560 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.6828 aux.loss_ce: 0.0074 aux.acc_seg: 99.1042 +04/18 19:39:06 - mmengine - INFO - Iter(train) [112300/160000] lr: 3.4312e-03 eta: 7:19:25 time: 0.5541 data_time: 0.0060 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.7375 aux.loss_ce: 0.0071 aux.acc_seg: 99.4389 +04/18 19:39:34 - mmengine - INFO - Iter(train) [112350/160000] lr: 3.4280e-03 eta: 7:18:58 time: 0.5552 data_time: 0.0074 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6963 aux.loss_ce: 0.0072 aux.acc_seg: 99.0375 +04/18 19:40:02 - mmengine - INFO - Iter(train) [112400/160000] lr: 3.4249e-03 eta: 7:18:30 time: 0.5565 data_time: 0.0073 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7343 aux.loss_ce: 0.0070 aux.acc_seg: 99.1905 +04/18 19:40:29 - mmengine - INFO - Iter(train) [112450/160000] lr: 3.4217e-03 eta: 7:18:02 time: 0.5547 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6407 aux.loss_ce: 0.0075 aux.acc_seg: 99.0631 +04/18 19:40:57 - mmengine - INFO - Iter(train) [112500/160000] lr: 3.4186e-03 eta: 7:17:35 time: 0.5556 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7557 aux.loss_ce: 0.0075 aux.acc_seg: 99.4360 +04/18 19:41:25 - mmengine - INFO - Iter(train) [112550/160000] lr: 3.4154e-03 eta: 7:17:07 time: 0.5533 data_time: 0.0064 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0085 decode.acc_seg: 99.6600 aux.loss_ce: 0.0079 aux.acc_seg: 99.1341 +04/18 19:41:53 - mmengine - INFO - Iter(train) [112600/160000] lr: 3.4123e-03 eta: 7:16:40 time: 0.5545 data_time: 0.0075 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0099 decode.acc_seg: 99.6852 aux.loss_ce: 0.0078 aux.acc_seg: 99.3675 +04/18 19:42:20 - mmengine - INFO - Iter(train) [112650/160000] lr: 3.4092e-03 eta: 7:16:12 time: 0.5636 data_time: 0.0067 memory: 7635 loss: 0.0178 decode.loss_ce: 0.0094 decode.acc_seg: 99.7015 aux.loss_ce: 0.0084 aux.acc_seg: 99.2482 +04/18 19:42:48 - mmengine - INFO - Iter(train) [112700/160000] lr: 3.4060e-03 eta: 7:15:44 time: 0.5542 data_time: 0.0061 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.6842 aux.loss_ce: 0.0070 aux.acc_seg: 99.1373 +04/18 19:43:16 - mmengine - INFO - Iter(train) [112750/160000] lr: 3.4029e-03 eta: 7:15:17 time: 0.5547 data_time: 0.0066 memory: 7635 loss: 0.0174 decode.loss_ce: 0.0089 decode.acc_seg: 99.6509 aux.loss_ce: 0.0085 aux.acc_seg: 99.0849 +04/18 19:43:44 - mmengine - INFO - Iter(train) [112800/160000] lr: 3.3997e-03 eta: 7:14:49 time: 0.5550 data_time: 0.0075 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.6431 aux.loss_ce: 0.0075 aux.acc_seg: 99.1863 +04/18 19:44:12 - mmengine - INFO - Iter(train) [112850/160000] lr: 3.3966e-03 eta: 7:14:22 time: 0.5558 data_time: 0.0062 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0078 decode.acc_seg: 99.6466 aux.loss_ce: 0.0079 aux.acc_seg: 99.2352 +04/18 19:44:39 - mmengine - INFO - Iter(train) [112900/160000] lr: 3.3934e-03 eta: 7:13:54 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.7649 aux.loss_ce: 0.0077 aux.acc_seg: 99.3188 +04/18 19:45:07 - mmengine - INFO - Iter(train) [112950/160000] lr: 3.3903e-03 eta: 7:13:26 time: 0.5555 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7283 aux.loss_ce: 0.0073 aux.acc_seg: 99.2187 +04/18 19:45:35 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:45:35 - mmengine - INFO - Iter(train) [113000/160000] lr: 3.3871e-03 eta: 7:12:59 time: 0.5547 data_time: 0.0071 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0079 decode.acc_seg: 99.6073 aux.loss_ce: 0.0083 aux.acc_seg: 98.9552 +04/18 19:46:03 - mmengine - INFO - Iter(train) [113050/160000] lr: 3.3840e-03 eta: 7:12:31 time: 0.5572 data_time: 0.0074 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7059 aux.loss_ce: 0.0074 aux.acc_seg: 99.1577 +04/18 19:46:30 - mmengine - INFO - Iter(train) [113100/160000] lr: 3.3808e-03 eta: 7:12:04 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.7546 aux.loss_ce: 0.0078 aux.acc_seg: 99.2054 +04/18 19:46:58 - mmengine - INFO - Iter(train) [113150/160000] lr: 3.3777e-03 eta: 7:11:36 time: 0.5567 data_time: 0.0069 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0075 decode.acc_seg: 99.6556 aux.loss_ce: 0.0077 aux.acc_seg: 99.0924 +04/18 19:47:26 - mmengine - INFO - Iter(train) [113200/160000] lr: 3.3745e-03 eta: 7:11:09 time: 0.5619 data_time: 0.0062 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.6857 aux.loss_ce: 0.0074 aux.acc_seg: 98.9956 +04/18 19:47:54 - mmengine - INFO - Iter(train) [113250/160000] lr: 3.3714e-03 eta: 7:10:41 time: 0.5630 data_time: 0.0066 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0070 decode.acc_seg: 99.6474 aux.loss_ce: 0.0072 aux.acc_seg: 99.1713 +04/18 19:48:22 - mmengine - INFO - Iter(train) [113300/160000] lr: 3.3682e-03 eta: 7:10:13 time: 0.5558 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7250 aux.loss_ce: 0.0073 aux.acc_seg: 99.3504 +04/18 19:48:49 - mmengine - INFO - Iter(train) [113350/160000] lr: 3.3651e-03 eta: 7:09:46 time: 0.5532 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7818 aux.loss_ce: 0.0071 aux.acc_seg: 99.4512 +04/18 19:49:17 - mmengine - INFO - Iter(train) [113400/160000] lr: 3.3619e-03 eta: 7:09:18 time: 0.5560 data_time: 0.0072 memory: 7635 loss: 0.0157 decode.loss_ce: 0.0078 decode.acc_seg: 99.7405 aux.loss_ce: 0.0079 aux.acc_seg: 99.1410 +04/18 19:49:45 - mmengine - INFO - Iter(train) [113450/160000] lr: 3.3588e-03 eta: 7:08:51 time: 0.5569 data_time: 0.0075 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.6954 aux.loss_ce: 0.0075 aux.acc_seg: 99.2711 +04/18 19:50:13 - mmengine - INFO - Iter(train) [113500/160000] lr: 3.3556e-03 eta: 7:08:23 time: 0.5547 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.6919 aux.loss_ce: 0.0072 aux.acc_seg: 99.0643 +04/18 19:50:40 - mmengine - INFO - Iter(train) [113550/160000] lr: 3.3525e-03 eta: 7:07:55 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7746 aux.loss_ce: 0.0073 aux.acc_seg: 99.2249 +04/18 19:51:08 - mmengine - INFO - Iter(train) [113600/160000] lr: 3.3493e-03 eta: 7:07:28 time: 0.5530 data_time: 0.0073 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0071 decode.acc_seg: 99.6819 aux.loss_ce: 0.0076 aux.acc_seg: 99.1260 +04/18 19:51:36 - mmengine - INFO - Iter(train) [113650/160000] lr: 3.3462e-03 eta: 7:07:00 time: 0.5534 data_time: 0.0077 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.7600 aux.loss_ce: 0.0068 aux.acc_seg: 99.3909 +04/18 19:52:04 - mmengine - INFO - Iter(train) [113700/160000] lr: 3.3430e-03 eta: 7:06:33 time: 0.5542 data_time: 0.0060 memory: 7635 loss: 0.0170 decode.loss_ce: 0.0088 decode.acc_seg: 99.6358 aux.loss_ce: 0.0082 aux.acc_seg: 99.0391 +04/18 19:52:32 - mmengine - INFO - Iter(train) [113750/160000] lr: 3.3399e-03 eta: 7:06:05 time: 0.5547 data_time: 0.0062 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.6324 aux.loss_ce: 0.0081 aux.acc_seg: 99.2223 +04/18 19:52:59 - mmengine - INFO - Iter(train) [113800/160000] lr: 3.3367e-03 eta: 7:05:37 time: 0.5555 data_time: 0.0073 memory: 7635 loss: 0.0203 decode.loss_ce: 0.0109 decode.acc_seg: 99.4678 aux.loss_ce: 0.0094 aux.acc_seg: 98.9538 +04/18 19:53:27 - mmengine - INFO - Iter(train) [113850/160000] lr: 3.3336e-03 eta: 7:05:10 time: 0.5554 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0072 decode.acc_seg: 99.6481 aux.loss_ce: 0.0069 aux.acc_seg: 99.2805 +04/18 19:53:55 - mmengine - INFO - Iter(train) [113900/160000] lr: 3.3304e-03 eta: 7:04:42 time: 0.5567 data_time: 0.0067 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0077 decode.acc_seg: 99.7268 aux.loss_ce: 0.0076 aux.acc_seg: 99.1993 +04/18 19:54:23 - mmengine - INFO - Iter(train) [113950/160000] lr: 3.3273e-03 eta: 7:04:15 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0078 decode.acc_seg: 99.7354 aux.loss_ce: 0.0074 aux.acc_seg: 99.4023 +04/18 19:54:50 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 19:54:50 - mmengine - INFO - Iter(train) [114000/160000] lr: 3.3241e-03 eta: 7:03:47 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.6449 aux.loss_ce: 0.0076 aux.acc_seg: 99.0225 +04/18 19:55:18 - mmengine - INFO - Iter(train) [114050/160000] lr: 3.3210e-03 eta: 7:03:19 time: 0.5533 data_time: 0.0062 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.6897 aux.loss_ce: 0.0076 aux.acc_seg: 99.1895 +04/18 19:55:46 - mmengine - INFO - Iter(train) [114100/160000] lr: 3.3178e-03 eta: 7:02:52 time: 0.5558 data_time: 0.0070 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.6418 aux.loss_ce: 0.0074 aux.acc_seg: 99.0686 +04/18 19:56:14 - mmengine - INFO - Iter(train) [114150/160000] lr: 3.3147e-03 eta: 7:02:24 time: 0.5549 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0077 decode.acc_seg: 99.7098 aux.loss_ce: 0.0077 aux.acc_seg: 99.3317 +04/18 19:56:41 - mmengine - INFO - Iter(train) [114200/160000] lr: 3.3115e-03 eta: 7:01:57 time: 0.5560 data_time: 0.0065 memory: 7635 loss: 0.0177 decode.loss_ce: 0.0095 decode.acc_seg: 99.6345 aux.loss_ce: 0.0081 aux.acc_seg: 98.8980 +04/18 19:57:09 - mmengine - INFO - Iter(train) [114250/160000] lr: 3.3083e-03 eta: 7:01:29 time: 0.5647 data_time: 0.0065 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.7429 aux.loss_ce: 0.0075 aux.acc_seg: 99.4370 +04/18 19:57:37 - mmengine - INFO - Iter(train) [114300/160000] lr: 3.3052e-03 eta: 7:01:02 time: 0.5725 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.6933 aux.loss_ce: 0.0079 aux.acc_seg: 99.0668 +04/18 19:58:05 - mmengine - INFO - Iter(train) [114350/160000] lr: 3.3020e-03 eta: 7:00:34 time: 0.5539 data_time: 0.0066 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7735 aux.loss_ce: 0.0069 aux.acc_seg: 99.4714 +04/18 19:58:33 - mmengine - INFO - Iter(train) [114400/160000] lr: 3.2989e-03 eta: 7:00:06 time: 0.5551 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7492 aux.loss_ce: 0.0071 aux.acc_seg: 99.3660 +04/18 19:59:00 - mmengine - INFO - Iter(train) [114450/160000] lr: 3.2957e-03 eta: 6:59:39 time: 0.5549 data_time: 0.0065 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7345 aux.loss_ce: 0.0073 aux.acc_seg: 99.1737 +04/18 19:59:28 - mmengine - INFO - Iter(train) [114500/160000] lr: 3.2926e-03 eta: 6:59:11 time: 0.5561 data_time: 0.0056 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7748 aux.loss_ce: 0.0071 aux.acc_seg: 99.1717 +04/18 19:59:56 - mmengine - INFO - Iter(train) [114550/160000] lr: 3.2894e-03 eta: 6:58:44 time: 0.5544 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7562 aux.loss_ce: 0.0073 aux.acc_seg: 99.1133 +04/18 20:00:24 - mmengine - INFO - Iter(train) [114600/160000] lr: 3.2863e-03 eta: 6:58:16 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.6882 aux.loss_ce: 0.0076 aux.acc_seg: 99.2651 +04/18 20:00:51 - mmengine - INFO - Iter(train) [114650/160000] lr: 3.2831e-03 eta: 6:57:48 time: 0.5543 data_time: 0.0062 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.8008 aux.loss_ce: 0.0068 aux.acc_seg: 99.3563 +04/18 20:01:19 - mmengine - INFO - Iter(train) [114700/160000] lr: 3.2799e-03 eta: 6:57:21 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7588 aux.loss_ce: 0.0070 aux.acc_seg: 99.4811 +04/18 20:01:47 - mmengine - INFO - Iter(train) [114750/160000] lr: 3.2768e-03 eta: 6:56:53 time: 0.5556 data_time: 0.0065 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7975 aux.loss_ce: 0.0071 aux.acc_seg: 99.3069 +04/18 20:02:15 - mmengine - INFO - Iter(train) [114800/160000] lr: 3.2736e-03 eta: 6:56:26 time: 0.5538 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7615 aux.loss_ce: 0.0073 aux.acc_seg: 99.4371 +04/18 20:02:43 - mmengine - INFO - Iter(train) [114850/160000] lr: 3.2705e-03 eta: 6:55:58 time: 0.5564 data_time: 0.0064 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0061 decode.acc_seg: 99.7408 aux.loss_ce: 0.0067 aux.acc_seg: 99.3417 +04/18 20:03:10 - mmengine - INFO - Iter(train) [114900/160000] lr: 3.2673e-03 eta: 6:55:30 time: 0.5568 data_time: 0.0072 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.6960 aux.loss_ce: 0.0075 aux.acc_seg: 99.2154 +04/18 20:03:38 - mmengine - INFO - Iter(train) [114950/160000] lr: 3.2641e-03 eta: 6:55:03 time: 0.5526 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0067 decode.acc_seg: 99.6946 aux.loss_ce: 0.0070 aux.acc_seg: 99.2776 +04/18 20:04:06 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:04:06 - mmengine - INFO - Iter(train) [115000/160000] lr: 3.2610e-03 eta: 6:54:35 time: 0.5558 data_time: 0.0075 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7103 aux.loss_ce: 0.0072 aux.acc_seg: 99.0646 +04/18 20:04:34 - mmengine - INFO - Iter(train) [115050/160000] lr: 3.2578e-03 eta: 6:54:08 time: 0.5538 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6477 aux.loss_ce: 0.0072 aux.acc_seg: 99.2191 +04/18 20:05:01 - mmengine - INFO - Iter(train) [115100/160000] lr: 3.2547e-03 eta: 6:53:40 time: 0.5549 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7582 aux.loss_ce: 0.0073 aux.acc_seg: 99.3569 +04/18 20:05:29 - mmengine - INFO - Iter(train) [115150/160000] lr: 3.2515e-03 eta: 6:53:13 time: 0.5551 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7608 aux.loss_ce: 0.0072 aux.acc_seg: 99.2516 +04/18 20:05:57 - mmengine - INFO - Iter(train) [115200/160000] lr: 3.2483e-03 eta: 6:52:45 time: 0.5542 data_time: 0.0071 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6777 aux.loss_ce: 0.0076 aux.acc_seg: 99.1147 +04/18 20:06:25 - mmengine - INFO - Iter(train) [115250/160000] lr: 3.2452e-03 eta: 6:52:17 time: 0.5561 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7757 aux.loss_ce: 0.0074 aux.acc_seg: 99.3195 +04/18 20:06:52 - mmengine - INFO - Iter(train) [115300/160000] lr: 3.2420e-03 eta: 6:51:50 time: 0.5568 data_time: 0.0077 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7368 aux.loss_ce: 0.0073 aux.acc_seg: 99.2317 +04/18 20:07:20 - mmengine - INFO - Iter(train) [115350/160000] lr: 3.2388e-03 eta: 6:51:22 time: 0.5553 data_time: 0.0068 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7233 aux.loss_ce: 0.0068 aux.acc_seg: 99.2118 +04/18 20:07:48 - mmengine - INFO - Iter(train) [115400/160000] lr: 3.2357e-03 eta: 6:50:55 time: 0.5643 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7232 aux.loss_ce: 0.0071 aux.acc_seg: 99.2002 +04/18 20:08:16 - mmengine - INFO - Iter(train) [115450/160000] lr: 3.2325e-03 eta: 6:50:27 time: 0.5546 data_time: 0.0077 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7201 aux.loss_ce: 0.0071 aux.acc_seg: 99.2654 +04/18 20:08:44 - mmengine - INFO - Iter(train) [115500/160000] lr: 3.2293e-03 eta: 6:49:59 time: 0.5553 data_time: 0.0068 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0064 decode.acc_seg: 99.7291 aux.loss_ce: 0.0067 aux.acc_seg: 99.1709 +04/18 20:09:12 - mmengine - INFO - Iter(train) [115550/160000] lr: 3.2262e-03 eta: 6:49:32 time: 0.5546 data_time: 0.0062 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0077 decode.acc_seg: 99.6208 aux.loss_ce: 0.0071 aux.acc_seg: 99.1677 +04/18 20:09:39 - mmengine - INFO - Iter(train) [115600/160000] lr: 3.2230e-03 eta: 6:49:04 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6173 aux.loss_ce: 0.0076 aux.acc_seg: 99.1159 +04/18 20:10:07 - mmengine - INFO - Iter(train) [115650/160000] lr: 3.2199e-03 eta: 6:48:37 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7155 aux.loss_ce: 0.0069 aux.acc_seg: 99.2580 +04/18 20:10:35 - mmengine - INFO - Iter(train) [115700/160000] lr: 3.2167e-03 eta: 6:48:09 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0065 decode.acc_seg: 99.7417 aux.loss_ce: 0.0069 aux.acc_seg: 99.3217 +04/18 20:11:03 - mmengine - INFO - Iter(train) [115750/160000] lr: 3.2135e-03 eta: 6:47:41 time: 0.5552 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7369 aux.loss_ce: 0.0072 aux.acc_seg: 99.1931 +04/18 20:11:30 - mmengine - INFO - Iter(train) [115800/160000] lr: 3.2104e-03 eta: 6:47:14 time: 0.5556 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7627 aux.loss_ce: 0.0070 aux.acc_seg: 99.3275 +04/18 20:11:58 - mmengine - INFO - Iter(train) [115850/160000] lr: 3.2072e-03 eta: 6:46:46 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7222 aux.loss_ce: 0.0071 aux.acc_seg: 99.2514 +04/18 20:12:26 - mmengine - INFO - Iter(train) [115900/160000] lr: 3.2040e-03 eta: 6:46:19 time: 0.5558 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7405 aux.loss_ce: 0.0072 aux.acc_seg: 99.2482 +04/18 20:12:54 - mmengine - INFO - Iter(train) [115950/160000] lr: 3.2009e-03 eta: 6:45:51 time: 0.5549 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7407 aux.loss_ce: 0.0076 aux.acc_seg: 99.2984 +04/18 20:13:22 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:13:22 - mmengine - INFO - Iter(train) [116000/160000] lr: 3.1977e-03 eta: 6:45:24 time: 0.5552 data_time: 0.0065 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6977 aux.loss_ce: 0.0072 aux.acc_seg: 99.2315 +04/18 20:13:49 - mmengine - INFO - Iter(train) [116050/160000] lr: 3.1945e-03 eta: 6:44:56 time: 0.5551 data_time: 0.0061 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.8324 aux.loss_ce: 0.0070 aux.acc_seg: 99.5077 +04/18 20:14:17 - mmengine - INFO - Iter(train) [116100/160000] lr: 3.1913e-03 eta: 6:44:28 time: 0.5574 data_time: 0.0070 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7281 aux.loss_ce: 0.0071 aux.acc_seg: 99.3584 +04/18 20:14:45 - mmengine - INFO - Iter(train) [116150/160000] lr: 3.1882e-03 eta: 6:44:01 time: 0.5553 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0072 decode.acc_seg: 99.7532 aux.loss_ce: 0.0073 aux.acc_seg: 99.2749 +04/18 20:15:13 - mmengine - INFO - Iter(train) [116200/160000] lr: 3.1850e-03 eta: 6:43:33 time: 0.5565 data_time: 0.0071 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7695 aux.loss_ce: 0.0070 aux.acc_seg: 99.1457 +04/18 20:15:41 - mmengine - INFO - Iter(train) [116250/160000] lr: 3.1818e-03 eta: 6:43:06 time: 0.5553 data_time: 0.0076 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7055 aux.loss_ce: 0.0077 aux.acc_seg: 99.0957 +04/18 20:16:08 - mmengine - INFO - Iter(train) [116300/160000] lr: 3.1787e-03 eta: 6:42:38 time: 0.5552 data_time: 0.0060 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.7753 aux.loss_ce: 0.0076 aux.acc_seg: 99.3449 +04/18 20:16:36 - mmengine - INFO - Iter(train) [116350/160000] lr: 3.1755e-03 eta: 6:42:10 time: 0.5556 data_time: 0.0073 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7267 aux.loss_ce: 0.0076 aux.acc_seg: 99.2901 +04/18 20:17:04 - mmengine - INFO - Iter(train) [116400/160000] lr: 3.1723e-03 eta: 6:41:43 time: 0.5553 data_time: 0.0069 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7842 aux.loss_ce: 0.0074 aux.acc_seg: 99.4181 +04/18 20:17:32 - mmengine - INFO - Iter(train) [116450/160000] lr: 3.1692e-03 eta: 6:41:15 time: 0.5569 data_time: 0.0067 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7127 aux.loss_ce: 0.0068 aux.acc_seg: 99.2112 +04/18 20:18:00 - mmengine - INFO - Iter(train) [116500/160000] lr: 3.1660e-03 eta: 6:40:48 time: 0.5567 data_time: 0.0070 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.7738 aux.loss_ce: 0.0070 aux.acc_seg: 99.2536 +04/18 20:18:28 - mmengine - INFO - Iter(train) [116550/160000] lr: 3.1628e-03 eta: 6:40:20 time: 0.5561 data_time: 0.0078 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7598 aux.loss_ce: 0.0074 aux.acc_seg: 99.3213 +04/18 20:18:55 - mmengine - INFO - Iter(train) [116600/160000] lr: 3.1596e-03 eta: 6:39:53 time: 0.5547 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7788 aux.loss_ce: 0.0071 aux.acc_seg: 99.2798 +04/18 20:19:23 - mmengine - INFO - Iter(train) [116650/160000] lr: 3.1565e-03 eta: 6:39:25 time: 0.5531 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7314 aux.loss_ce: 0.0073 aux.acc_seg: 99.2580 +04/18 20:19:51 - mmengine - INFO - Iter(train) [116700/160000] lr: 3.1533e-03 eta: 6:38:57 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7270 aux.loss_ce: 0.0072 aux.acc_seg: 99.2430 +04/18 20:20:19 - mmengine - INFO - Iter(train) [116750/160000] lr: 3.1501e-03 eta: 6:38:30 time: 0.5544 data_time: 0.0074 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0071 decode.acc_seg: 99.7726 aux.loss_ce: 0.0073 aux.acc_seg: 99.3933 +04/18 20:20:46 - mmengine - INFO - Iter(train) [116800/160000] lr: 3.1469e-03 eta: 6:38:02 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.7207 aux.loss_ce: 0.0071 aux.acc_seg: 99.1790 +04/18 20:21:14 - mmengine - INFO - Iter(train) [116850/160000] lr: 3.1438e-03 eta: 6:37:35 time: 0.5552 data_time: 0.0065 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.6984 aux.loss_ce: 0.0071 aux.acc_seg: 99.2356 +04/18 20:21:42 - mmengine - INFO - Iter(train) [116900/160000] lr: 3.1406e-03 eta: 6:37:07 time: 0.5557 data_time: 0.0065 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7245 aux.loss_ce: 0.0071 aux.acc_seg: 99.2350 +04/18 20:22:10 - mmengine - INFO - Iter(train) [116950/160000] lr: 3.1374e-03 eta: 6:36:39 time: 0.5650 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7678 aux.loss_ce: 0.0074 aux.acc_seg: 99.3468 +04/18 20:22:38 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:22:38 - mmengine - INFO - Iter(train) [117000/160000] lr: 3.1342e-03 eta: 6:36:12 time: 0.5557 data_time: 0.0061 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.7696 aux.loss_ce: 0.0068 aux.acc_seg: 99.4011 +04/18 20:23:05 - mmengine - INFO - Iter(train) [117050/160000] lr: 3.1311e-03 eta: 6:35:44 time: 0.5540 data_time: 0.0070 memory: 7635 loss: 0.0123 decode.loss_ce: 0.0059 decode.acc_seg: 99.8018 aux.loss_ce: 0.0064 aux.acc_seg: 99.4440 +04/18 20:23:33 - mmengine - INFO - Iter(train) [117100/160000] lr: 3.1279e-03 eta: 6:35:17 time: 0.5555 data_time: 0.0082 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7345 aux.loss_ce: 0.0074 aux.acc_seg: 99.0630 +04/18 20:24:01 - mmengine - INFO - Iter(train) [117150/160000] lr: 3.1247e-03 eta: 6:34:49 time: 0.5568 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7351 aux.loss_ce: 0.0072 aux.acc_seg: 99.3247 +04/18 20:24:29 - mmengine - INFO - Iter(train) [117200/160000] lr: 3.1215e-03 eta: 6:34:21 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7007 aux.loss_ce: 0.0073 aux.acc_seg: 99.0025 +04/18 20:24:56 - mmengine - INFO - Iter(train) [117250/160000] lr: 3.1184e-03 eta: 6:33:54 time: 0.5574 data_time: 0.0077 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0064 decode.acc_seg: 99.6803 aux.loss_ce: 0.0066 aux.acc_seg: 99.1085 +04/18 20:25:24 - mmengine - INFO - Iter(train) [117300/160000] lr: 3.1152e-03 eta: 6:33:26 time: 0.5548 data_time: 0.0070 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.7799 aux.loss_ce: 0.0068 aux.acc_seg: 99.2558 +04/18 20:25:52 - mmengine - INFO - Iter(train) [117350/160000] lr: 3.1120e-03 eta: 6:32:59 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.6716 aux.loss_ce: 0.0072 aux.acc_seg: 99.1094 +04/18 20:26:20 - mmengine - INFO - Iter(train) [117400/160000] lr: 3.1088e-03 eta: 6:32:31 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7077 aux.loss_ce: 0.0075 aux.acc_seg: 99.0832 +04/18 20:26:48 - mmengine - INFO - Iter(train) [117450/160000] lr: 3.1057e-03 eta: 6:32:03 time: 0.5553 data_time: 0.0079 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7317 aux.loss_ce: 0.0075 aux.acc_seg: 99.3379 +04/18 20:27:15 - mmengine - INFO - Iter(train) [117500/160000] lr: 3.1025e-03 eta: 6:31:36 time: 0.5551 data_time: 0.0071 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7749 aux.loss_ce: 0.0070 aux.acc_seg: 99.2544 +04/18 20:27:43 - mmengine - INFO - Iter(train) [117550/160000] lr: 3.0993e-03 eta: 6:31:08 time: 0.5541 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7262 aux.loss_ce: 0.0073 aux.acc_seg: 99.3453 +04/18 20:28:11 - mmengine - INFO - Iter(train) [117600/160000] lr: 3.0961e-03 eta: 6:30:41 time: 0.5570 data_time: 0.0070 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7079 aux.loss_ce: 0.0073 aux.acc_seg: 99.0181 +04/18 20:28:39 - mmengine - INFO - Iter(train) [117650/160000] lr: 3.0929e-03 eta: 6:30:13 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7430 aux.loss_ce: 0.0072 aux.acc_seg: 99.2660 +04/18 20:29:07 - mmengine - INFO - Iter(train) [117700/160000] lr: 3.0898e-03 eta: 6:29:45 time: 0.5548 data_time: 0.0072 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0059 decode.acc_seg: 99.7495 aux.loss_ce: 0.0068 aux.acc_seg: 99.3814 +04/18 20:29:34 - mmengine - INFO - Iter(train) [117750/160000] lr: 3.0866e-03 eta: 6:29:18 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7581 aux.loss_ce: 0.0074 aux.acc_seg: 99.3503 +04/18 20:30:02 - mmengine - INFO - Iter(train) [117800/160000] lr: 3.0834e-03 eta: 6:28:50 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7300 aux.loss_ce: 0.0069 aux.acc_seg: 99.1374 +04/18 20:30:30 - mmengine - INFO - Iter(train) [117850/160000] lr: 3.0802e-03 eta: 6:28:23 time: 0.5566 data_time: 0.0060 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7112 aux.loss_ce: 0.0074 aux.acc_seg: 99.1974 +04/18 20:30:58 - mmengine - INFO - Iter(train) [117900/160000] lr: 3.0770e-03 eta: 6:27:55 time: 0.5561 data_time: 0.0070 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7927 aux.loss_ce: 0.0077 aux.acc_seg: 99.3387 +04/18 20:31:26 - mmengine - INFO - Iter(train) [117950/160000] lr: 3.0738e-03 eta: 6:27:27 time: 0.5552 data_time: 0.0071 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.7211 aux.loss_ce: 0.0076 aux.acc_seg: 99.2286 +04/18 20:31:53 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:31:53 - mmengine - INFO - Iter(train) [118000/160000] lr: 3.0707e-03 eta: 6:27:00 time: 0.5555 data_time: 0.0066 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0064 decode.acc_seg: 99.7520 aux.loss_ce: 0.0070 aux.acc_seg: 99.3025 +04/18 20:32:21 - mmengine - INFO - Iter(train) [118050/160000] lr: 3.0675e-03 eta: 6:26:32 time: 0.5552 data_time: 0.0065 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.8034 aux.loss_ce: 0.0069 aux.acc_seg: 99.4008 +04/18 20:32:49 - mmengine - INFO - Iter(train) [118100/160000] lr: 3.0643e-03 eta: 6:26:05 time: 0.5573 data_time: 0.0070 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.7596 aux.loss_ce: 0.0077 aux.acc_seg: 99.3524 +04/18 20:33:17 - mmengine - INFO - Iter(train) [118150/160000] lr: 3.0611e-03 eta: 6:25:37 time: 0.5552 data_time: 0.0068 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.5859 aux.loss_ce: 0.0077 aux.acc_seg: 99.0843 +04/18 20:33:45 - mmengine - INFO - Iter(train) [118200/160000] lr: 3.0579e-03 eta: 6:25:10 time: 0.5553 data_time: 0.0061 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7565 aux.loss_ce: 0.0080 aux.acc_seg: 99.3854 +04/18 20:34:12 - mmengine - INFO - Iter(train) [118250/160000] lr: 3.0547e-03 eta: 6:24:42 time: 0.5561 data_time: 0.0070 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0077 decode.acc_seg: 99.7158 aux.loss_ce: 0.0076 aux.acc_seg: 99.0129 +04/18 20:34:40 - mmengine - INFO - Iter(train) [118300/160000] lr: 3.0516e-03 eta: 6:24:14 time: 0.5558 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.6723 aux.loss_ce: 0.0074 aux.acc_seg: 99.1504 +04/18 20:35:08 - mmengine - INFO - Iter(train) [118350/160000] lr: 3.0484e-03 eta: 6:23:47 time: 0.5561 data_time: 0.0065 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.7306 aux.loss_ce: 0.0075 aux.acc_seg: 99.3378 +04/18 20:35:36 - mmengine - INFO - Iter(train) [118400/160000] lr: 3.0452e-03 eta: 6:23:19 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0075 decode.acc_seg: 99.6934 aux.loss_ce: 0.0072 aux.acc_seg: 99.1377 +04/18 20:36:04 - mmengine - INFO - Iter(train) [118450/160000] lr: 3.0420e-03 eta: 6:22:52 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6614 aux.loss_ce: 0.0078 aux.acc_seg: 99.1085 +04/18 20:36:31 - mmengine - INFO - Iter(train) [118500/160000] lr: 3.0388e-03 eta: 6:22:24 time: 0.5557 data_time: 0.0067 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7893 aux.loss_ce: 0.0070 aux.acc_seg: 99.4375 +04/18 20:36:59 - mmengine - INFO - Iter(train) [118550/160000] lr: 3.0356e-03 eta: 6:21:56 time: 0.5642 data_time: 0.0069 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7399 aux.loss_ce: 0.0069 aux.acc_seg: 99.2958 +04/18 20:37:27 - mmengine - INFO - Iter(train) [118600/160000] lr: 3.0324e-03 eta: 6:21:29 time: 0.5631 data_time: 0.0070 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7432 aux.loss_ce: 0.0072 aux.acc_seg: 99.3347 +04/18 20:37:55 - mmengine - INFO - Iter(train) [118650/160000] lr: 3.0293e-03 eta: 6:21:01 time: 0.5561 data_time: 0.0064 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.6925 aux.loss_ce: 0.0068 aux.acc_seg: 99.2470 +04/18 20:38:23 - mmengine - INFO - Iter(train) [118700/160000] lr: 3.0261e-03 eta: 6:20:34 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0102 decode.acc_seg: 99.7601 aux.loss_ce: 0.0081 aux.acc_seg: 99.2406 +04/18 20:38:50 - mmengine - INFO - Iter(train) [118750/160000] lr: 3.0229e-03 eta: 6:20:06 time: 0.5557 data_time: 0.0069 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0084 decode.acc_seg: 99.6594 aux.loss_ce: 0.0077 aux.acc_seg: 99.0275 +04/18 20:39:18 - mmengine - INFO - Iter(train) [118800/160000] lr: 3.0197e-03 eta: 6:19:38 time: 0.5539 data_time: 0.0068 memory: 7635 loss: 0.0158 decode.loss_ce: 0.0081 decode.acc_seg: 99.6863 aux.loss_ce: 0.0078 aux.acc_seg: 99.2388 +04/18 20:39:46 - mmengine - INFO - Iter(train) [118850/160000] lr: 3.0165e-03 eta: 6:19:11 time: 0.5559 data_time: 0.0065 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0071 decode.acc_seg: 99.7490 aux.loss_ce: 0.0073 aux.acc_seg: 99.2550 +04/18 20:40:14 - mmengine - INFO - Iter(train) [118900/160000] lr: 3.0133e-03 eta: 6:18:43 time: 0.5569 data_time: 0.0073 memory: 7635 loss: 0.0165 decode.loss_ce: 0.0084 decode.acc_seg: 99.6354 aux.loss_ce: 0.0081 aux.acc_seg: 99.0667 +04/18 20:40:42 - mmengine - INFO - Iter(train) [118950/160000] lr: 3.0101e-03 eta: 6:18:16 time: 0.5560 data_time: 0.0078 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0079 decode.acc_seg: 99.6362 aux.loss_ce: 0.0073 aux.acc_seg: 99.2110 +04/18 20:41:09 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:41:09 - mmengine - INFO - Iter(train) [119000/160000] lr: 3.0069e-03 eta: 6:17:48 time: 0.5538 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0077 decode.acc_seg: 99.7345 aux.loss_ce: 0.0071 aux.acc_seg: 99.3922 +04/18 20:41:37 - mmengine - INFO - Iter(train) [119050/160000] lr: 3.0037e-03 eta: 6:17:20 time: 0.5547 data_time: 0.0065 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0066 decode.acc_seg: 99.7228 aux.loss_ce: 0.0074 aux.acc_seg: 99.0550 +04/18 20:42:05 - mmengine - INFO - Iter(train) [119100/160000] lr: 3.0006e-03 eta: 6:16:53 time: 0.5646 data_time: 0.0069 memory: 7635 loss: 0.0211 decode.loss_ce: 0.0119 decode.acc_seg: 99.7412 aux.loss_ce: 0.0092 aux.acc_seg: 99.2513 +04/18 20:42:33 - mmengine - INFO - Iter(train) [119150/160000] lr: 2.9974e-03 eta: 6:16:25 time: 0.5574 data_time: 0.0068 memory: 7635 loss: 0.0184 decode.loss_ce: 0.0097 decode.acc_seg: 99.7432 aux.loss_ce: 0.0087 aux.acc_seg: 99.3627 +04/18 20:43:01 - mmengine - INFO - Iter(train) [119200/160000] lr: 2.9942e-03 eta: 6:15:58 time: 0.5562 data_time: 0.0071 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0071 decode.acc_seg: 99.6814 aux.loss_ce: 0.0071 aux.acc_seg: 99.1776 +04/18 20:43:28 - mmengine - INFO - Iter(train) [119250/160000] lr: 2.9910e-03 eta: 6:15:30 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.6810 aux.loss_ce: 0.0072 aux.acc_seg: 99.3422 +04/18 20:43:56 - mmengine - INFO - Iter(train) [119300/160000] lr: 2.9878e-03 eta: 6:15:02 time: 0.5546 data_time: 0.0070 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0074 decode.acc_seg: 99.6921 aux.loss_ce: 0.0077 aux.acc_seg: 99.2339 +04/18 20:44:24 - mmengine - INFO - Iter(train) [119350/160000] lr: 2.9846e-03 eta: 6:14:35 time: 0.5541 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6759 aux.loss_ce: 0.0076 aux.acc_seg: 99.1552 +04/18 20:44:52 - mmengine - INFO - Iter(train) [119400/160000] lr: 2.9814e-03 eta: 6:14:07 time: 0.5553 data_time: 0.0065 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0078 decode.acc_seg: 99.7362 aux.loss_ce: 0.0082 aux.acc_seg: 99.2253 +04/18 20:45:20 - mmengine - INFO - Iter(train) [119450/160000] lr: 2.9782e-03 eta: 6:13:40 time: 0.5554 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6972 aux.loss_ce: 0.0071 aux.acc_seg: 99.1246 +04/18 20:45:47 - mmengine - INFO - Iter(train) [119500/160000] lr: 2.9750e-03 eta: 6:13:12 time: 0.5548 data_time: 0.0079 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7215 aux.loss_ce: 0.0071 aux.acc_seg: 99.3957 +04/18 20:46:15 - mmengine - INFO - Iter(train) [119550/160000] lr: 2.9718e-03 eta: 6:12:44 time: 0.5565 data_time: 0.0076 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6366 aux.loss_ce: 0.0073 aux.acc_seg: 99.0820 +04/18 20:46:43 - mmengine - INFO - Iter(train) [119600/160000] lr: 2.9686e-03 eta: 6:12:17 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0068 decode.acc_seg: 99.7691 aux.loss_ce: 0.0069 aux.acc_seg: 99.2675 +04/18 20:47:11 - mmengine - INFO - Iter(train) [119650/160000] lr: 2.9654e-03 eta: 6:11:49 time: 0.5635 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6838 aux.loss_ce: 0.0072 aux.acc_seg: 99.0096 +04/18 20:47:39 - mmengine - INFO - Iter(train) [119700/160000] lr: 2.9622e-03 eta: 6:11:22 time: 0.5639 data_time: 0.0070 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7218 aux.loss_ce: 0.0077 aux.acc_seg: 99.3238 +04/18 20:48:07 - mmengine - INFO - Iter(train) [119750/160000] lr: 2.9590e-03 eta: 6:10:54 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6610 aux.loss_ce: 0.0076 aux.acc_seg: 99.1394 +04/18 20:48:34 - mmengine - INFO - Iter(train) [119800/160000] lr: 2.9558e-03 eta: 6:10:26 time: 0.5548 data_time: 0.0075 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.6144 aux.loss_ce: 0.0074 aux.acc_seg: 98.9707 +04/18 20:49:02 - mmengine - INFO - Iter(train) [119850/160000] lr: 2.9526e-03 eta: 6:09:59 time: 0.5546 data_time: 0.0062 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6242 aux.loss_ce: 0.0080 aux.acc_seg: 98.6984 +04/18 20:49:30 - mmengine - INFO - Iter(train) [119900/160000] lr: 2.9494e-03 eta: 6:09:31 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7492 aux.loss_ce: 0.0072 aux.acc_seg: 99.2558 +04/18 20:49:58 - mmengine - INFO - Iter(train) [119950/160000] lr: 2.9462e-03 eta: 6:09:04 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.6436 aux.loss_ce: 0.0071 aux.acc_seg: 99.0393 +04/18 20:50:25 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:50:25 - mmengine - INFO - Iter(train) [120000/160000] lr: 2.9430e-03 eta: 6:08:36 time: 0.5553 data_time: 0.0068 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.7734 aux.loss_ce: 0.0074 aux.acc_seg: 99.3732 +04/18 20:50:25 - mmengine - INFO - Saving checkpoint at 120000 iterations +04/18 20:50:29 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0463 data_time: 0.0014 memory: 1657 +04/18 20:50:32 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0465 data_time: 0.0016 memory: 1657 +04/18 20:50:34 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0462 data_time: 0.0014 memory: 1657 +04/18 20:50:36 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0457 data_time: 0.0013 memory: 1657 +04/18 20:50:37 - mmengine - INFO - per class results: +04/18 20:50:37 - mmengine - INFO - ++------------+-------+------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+------+--------+-----------+--------+ +| background | 99.09 | 99.5 | 99.54 | 99.58 | 99.5 | +| contrast | 80.39 | 90.0 | 89.13 | 88.28 | 90.0 | ++------------+-------+------+--------+-----------+--------+ +04/18 20:50:37 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1200 mIoU: 89.7400 mAcc: 94.7500 mFscore: 94.3400 mPrecision: 93.9300 mRecall: 94.7500 data_time: 0.0015 time: 0.0466 +04/18 20:51:04 - mmengine - INFO - Iter(train) [120050/160000] lr: 2.9398e-03 eta: 6:08:08 time: 0.5548 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7140 aux.loss_ce: 0.0076 aux.acc_seg: 99.1138 +04/18 20:51:32 - mmengine - INFO - Iter(train) [120100/160000] lr: 2.9366e-03 eta: 6:07:41 time: 0.5565 data_time: 0.0075 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.6864 aux.loss_ce: 0.0074 aux.acc_seg: 99.1917 +04/18 20:52:00 - mmengine - INFO - Iter(train) [120150/160000] lr: 2.9334e-03 eta: 6:07:13 time: 0.5557 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7345 aux.loss_ce: 0.0074 aux.acc_seg: 99.0086 +04/18 20:52:28 - mmengine - INFO - Iter(train) [120200/160000] lr: 2.9302e-03 eta: 6:06:46 time: 0.5563 data_time: 0.0072 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.6479 aux.loss_ce: 0.0073 aux.acc_seg: 98.8611 +04/18 20:52:55 - mmengine - INFO - Iter(train) [120250/160000] lr: 2.9270e-03 eta: 6:06:18 time: 0.5556 data_time: 0.0075 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7431 aux.loss_ce: 0.0073 aux.acc_seg: 99.3074 +04/18 20:53:23 - mmengine - INFO - Iter(train) [120300/160000] lr: 2.9238e-03 eta: 6:05:50 time: 0.5546 data_time: 0.0063 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7306 aux.loss_ce: 0.0072 aux.acc_seg: 99.1041 +04/18 20:53:51 - mmengine - INFO - Iter(train) [120350/160000] lr: 2.9206e-03 eta: 6:05:23 time: 0.5562 data_time: 0.0065 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0069 decode.acc_seg: 99.7080 aux.loss_ce: 0.0072 aux.acc_seg: 99.1747 +04/18 20:54:19 - mmengine - INFO - Iter(train) [120400/160000] lr: 2.9174e-03 eta: 6:04:55 time: 0.5540 data_time: 0.0061 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7389 aux.loss_ce: 0.0071 aux.acc_seg: 99.1768 +04/18 20:54:46 - mmengine - INFO - Iter(train) [120450/160000] lr: 2.9142e-03 eta: 6:04:28 time: 0.5533 data_time: 0.0069 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.7629 aux.loss_ce: 0.0068 aux.acc_seg: 99.4340 +04/18 20:55:14 - mmengine - INFO - Iter(train) [120500/160000] lr: 2.9110e-03 eta: 6:04:00 time: 0.5652 data_time: 0.0068 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7408 aux.loss_ce: 0.0072 aux.acc_seg: 99.2261 +04/18 20:55:42 - mmengine - INFO - Iter(train) [120550/160000] lr: 2.9078e-03 eta: 6:03:32 time: 0.5540 data_time: 0.0067 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0073 decode.acc_seg: 99.7856 aux.loss_ce: 0.0071 aux.acc_seg: 99.4425 +04/18 20:56:10 - mmengine - INFO - Iter(train) [120600/160000] lr: 2.9046e-03 eta: 6:03:05 time: 0.5525 data_time: 0.0062 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0062 decode.acc_seg: 99.6795 aux.loss_ce: 0.0068 aux.acc_seg: 98.9835 +04/18 20:56:38 - mmengine - INFO - Iter(train) [120650/160000] lr: 2.9014e-03 eta: 6:02:37 time: 0.5562 data_time: 0.0064 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0060 decode.acc_seg: 99.7681 aux.loss_ce: 0.0067 aux.acc_seg: 99.3935 +04/18 20:57:06 - mmengine - INFO - Iter(train) [120700/160000] lr: 2.8982e-03 eta: 6:02:10 time: 0.5553 data_time: 0.0064 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0061 decode.acc_seg: 99.7891 aux.loss_ce: 0.0068 aux.acc_seg: 99.2044 +04/18 20:57:33 - mmengine - INFO - Iter(train) [120750/160000] lr: 2.8950e-03 eta: 6:01:42 time: 0.5548 data_time: 0.0070 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.7631 aux.loss_ce: 0.0078 aux.acc_seg: 99.3780 +04/18 20:58:01 - mmengine - INFO - Iter(train) [120800/160000] lr: 2.8918e-03 eta: 6:01:14 time: 0.5540 data_time: 0.0077 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.7164 aux.loss_ce: 0.0070 aux.acc_seg: 99.3695 +04/18 20:58:29 - mmengine - INFO - Iter(train) [120850/160000] lr: 2.8886e-03 eta: 6:00:47 time: 0.5545 data_time: 0.0072 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.6573 aux.loss_ce: 0.0076 aux.acc_seg: 98.8897 +04/18 20:58:57 - mmengine - INFO - Iter(train) [120900/160000] lr: 2.8854e-03 eta: 6:00:19 time: 0.5555 data_time: 0.0079 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7353 aux.loss_ce: 0.0075 aux.acc_seg: 99.3001 +04/18 20:59:25 - mmengine - INFO - Iter(train) [120950/160000] lr: 2.8822e-03 eta: 5:59:52 time: 0.5547 data_time: 0.0068 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7063 aux.loss_ce: 0.0071 aux.acc_seg: 99.1960 +04/18 20:59:52 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 20:59:52 - mmengine - INFO - Iter(train) [121000/160000] lr: 2.8790e-03 eta: 5:59:24 time: 0.5553 data_time: 0.0070 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7291 aux.loss_ce: 0.0071 aux.acc_seg: 99.1344 +04/18 21:00:20 - mmengine - INFO - Iter(train) [121050/160000] lr: 2.8758e-03 eta: 5:58:56 time: 0.5549 data_time: 0.0067 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0062 decode.acc_seg: 99.6985 aux.loss_ce: 0.0066 aux.acc_seg: 99.1734 +04/18 21:00:48 - mmengine - INFO - Iter(train) [121100/160000] lr: 2.8726e-03 eta: 5:58:29 time: 0.5558 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.6827 aux.loss_ce: 0.0072 aux.acc_seg: 99.1752 +04/18 21:01:16 - mmengine - INFO - Iter(train) [121150/160000] lr: 2.8694e-03 eta: 5:58:01 time: 0.5560 data_time: 0.0067 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7724 aux.loss_ce: 0.0074 aux.acc_seg: 99.2701 +04/18 21:01:43 - mmengine - INFO - Iter(train) [121200/160000] lr: 2.8662e-03 eta: 5:57:34 time: 0.5554 data_time: 0.0068 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7206 aux.loss_ce: 0.0071 aux.acc_seg: 99.1982 +04/18 21:02:11 - mmengine - INFO - Iter(train) [121250/160000] lr: 2.8630e-03 eta: 5:57:06 time: 0.5551 data_time: 0.0069 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.6980 aux.loss_ce: 0.0070 aux.acc_seg: 99.3225 +04/18 21:02:39 - mmengine - INFO - Iter(train) [121300/160000] lr: 2.8597e-03 eta: 5:56:38 time: 0.5537 data_time: 0.0067 memory: 7635 loss: 0.0213 decode.loss_ce: 0.0119 decode.acc_seg: 99.4873 aux.loss_ce: 0.0094 aux.acc_seg: 99.1308 +04/18 21:03:07 - mmengine - INFO - Iter(train) [121350/160000] lr: 2.8565e-03 eta: 5:56:11 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0086 decode.acc_seg: 99.5501 aux.loss_ce: 0.0082 aux.acc_seg: 98.8340 +04/18 21:03:34 - mmengine - INFO - Iter(train) [121400/160000] lr: 2.8533e-03 eta: 5:55:43 time: 0.5541 data_time: 0.0063 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.7892 aux.loss_ce: 0.0073 aux.acc_seg: 99.3712 +04/18 21:04:02 - mmengine - INFO - Iter(train) [121450/160000] lr: 2.8501e-03 eta: 5:55:15 time: 0.5543 data_time: 0.0067 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0081 decode.acc_seg: 99.6509 aux.loss_ce: 0.0081 aux.acc_seg: 98.9856 +04/18 21:04:30 - mmengine - INFO - Iter(train) [121500/160000] lr: 2.8469e-03 eta: 5:54:48 time: 0.5564 data_time: 0.0072 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0080 decode.acc_seg: 99.7168 aux.loss_ce: 0.0080 aux.acc_seg: 99.2508 +04/18 21:04:58 - mmengine - INFO - Iter(train) [121550/160000] lr: 2.8437e-03 eta: 5:54:20 time: 0.5551 data_time: 0.0081 memory: 7635 loss: 0.0168 decode.loss_ce: 0.0087 decode.acc_seg: 99.6490 aux.loss_ce: 0.0081 aux.acc_seg: 99.1926 +04/18 21:05:26 - mmengine - INFO - Iter(train) [121600/160000] lr: 2.8405e-03 eta: 5:53:53 time: 0.5552 data_time: 0.0062 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0076 decode.acc_seg: 99.6541 aux.loss_ce: 0.0080 aux.acc_seg: 99.0889 +04/18 21:05:53 - mmengine - INFO - Iter(train) [121650/160000] lr: 2.8373e-03 eta: 5:53:25 time: 0.5557 data_time: 0.0071 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0084 decode.acc_seg: 99.6855 aux.loss_ce: 0.0078 aux.acc_seg: 99.1094 +04/18 21:06:21 - mmengine - INFO - Iter(train) [121700/160000] lr: 2.8341e-03 eta: 5:52:57 time: 0.5566 data_time: 0.0066 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.7393 aux.loss_ce: 0.0074 aux.acc_seg: 99.1827 +04/18 21:06:49 - mmengine - INFO - Iter(train) [121750/160000] lr: 2.8309e-03 eta: 5:52:30 time: 0.5551 data_time: 0.0075 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7639 aux.loss_ce: 0.0071 aux.acc_seg: 99.2971 +04/18 21:07:17 - mmengine - INFO - Iter(train) [121800/160000] lr: 2.8276e-03 eta: 5:52:02 time: 0.5650 data_time: 0.0069 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7086 aux.loss_ce: 0.0070 aux.acc_seg: 99.1890 +04/18 21:07:45 - mmengine - INFO - Iter(train) [121850/160000] lr: 2.8244e-03 eta: 5:51:35 time: 0.5546 data_time: 0.0065 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7795 aux.loss_ce: 0.0077 aux.acc_seg: 99.3040 +04/18 21:08:12 - mmengine - INFO - Iter(train) [121900/160000] lr: 2.8212e-03 eta: 5:51:07 time: 0.5546 data_time: 0.0071 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0074 decode.acc_seg: 99.7078 aux.loss_ce: 0.0073 aux.acc_seg: 99.0706 +04/18 21:08:40 - mmengine - INFO - Iter(train) [121950/160000] lr: 2.8180e-03 eta: 5:50:39 time: 0.5542 data_time: 0.0067 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.7528 aux.loss_ce: 0.0077 aux.acc_seg: 99.2546 +04/18 21:09:08 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:09:08 - mmengine - INFO - Iter(train) [122000/160000] lr: 2.8148e-03 eta: 5:50:12 time: 0.5533 data_time: 0.0069 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7618 aux.loss_ce: 0.0071 aux.acc_seg: 99.5121 +04/18 21:09:36 - mmengine - INFO - Iter(train) [122050/160000] lr: 2.8116e-03 eta: 5:49:44 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0061 decode.acc_seg: 99.7833 aux.loss_ce: 0.0068 aux.acc_seg: 99.3933 +04/18 21:10:03 - mmengine - INFO - Iter(train) [122100/160000] lr: 2.8084e-03 eta: 5:49:17 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0079 decode.acc_seg: 99.6613 aux.loss_ce: 0.0075 aux.acc_seg: 99.2094 +04/18 21:10:31 - mmengine - INFO - Iter(train) [122150/160000] lr: 2.8051e-03 eta: 5:48:49 time: 0.5537 data_time: 0.0071 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0072 decode.acc_seg: 99.7586 aux.loss_ce: 0.0072 aux.acc_seg: 99.2103 +04/18 21:10:59 - mmengine - INFO - Iter(train) [122200/160000] lr: 2.8019e-03 eta: 5:48:21 time: 0.5521 data_time: 0.0064 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.6831 aux.loss_ce: 0.0074 aux.acc_seg: 99.1498 +04/18 21:11:27 - mmengine - INFO - Iter(train) [122250/160000] lr: 2.7987e-03 eta: 5:47:54 time: 0.5538 data_time: 0.0067 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7026 aux.loss_ce: 0.0071 aux.acc_seg: 99.1042 +04/18 21:11:54 - mmengine - INFO - Iter(train) [122300/160000] lr: 2.7955e-03 eta: 5:47:26 time: 0.5542 data_time: 0.0062 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7649 aux.loss_ce: 0.0071 aux.acc_seg: 99.4239 +04/18 21:12:22 - mmengine - INFO - Iter(train) [122350/160000] lr: 2.7923e-03 eta: 5:46:58 time: 0.5562 data_time: 0.0068 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7177 aux.loss_ce: 0.0075 aux.acc_seg: 99.2822 +04/18 21:12:50 - mmengine - INFO - Iter(train) [122400/160000] lr: 2.7890e-03 eta: 5:46:31 time: 0.5555 data_time: 0.0076 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7313 aux.loss_ce: 0.0076 aux.acc_seg: 99.1595 +04/18 21:13:18 - mmengine - INFO - Iter(train) [122450/160000] lr: 2.7858e-03 eta: 5:46:03 time: 0.5556 data_time: 0.0072 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7914 aux.loss_ce: 0.0073 aux.acc_seg: 99.1714 +04/18 21:13:45 - mmengine - INFO - Iter(train) [122500/160000] lr: 2.7826e-03 eta: 5:45:36 time: 0.5556 data_time: 0.0078 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.8101 aux.loss_ce: 0.0072 aux.acc_seg: 99.4071 +04/18 21:14:13 - mmengine - INFO - Iter(train) [122550/160000] lr: 2.7794e-03 eta: 5:45:08 time: 0.5539 data_time: 0.0069 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0073 decode.acc_seg: 99.6625 aux.loss_ce: 0.0078 aux.acc_seg: 99.1623 +04/18 21:14:41 - mmengine - INFO - Iter(train) [122600/160000] lr: 2.7762e-03 eta: 5:44:40 time: 0.5530 data_time: 0.0062 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.6551 aux.loss_ce: 0.0073 aux.acc_seg: 99.1289 +04/18 21:15:09 - mmengine - INFO - Iter(train) [122650/160000] lr: 2.7730e-03 eta: 5:44:13 time: 0.5644 data_time: 0.0063 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7834 aux.loss_ce: 0.0071 aux.acc_seg: 99.4628 +04/18 21:15:36 - mmengine - INFO - Iter(train) [122700/160000] lr: 2.7697e-03 eta: 5:43:45 time: 0.5541 data_time: 0.0070 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7046 aux.loss_ce: 0.0074 aux.acc_seg: 98.9109 +04/18 21:16:04 - mmengine - INFO - Iter(train) [122750/160000] lr: 2.7665e-03 eta: 5:43:18 time: 0.5557 data_time: 0.0070 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7723 aux.loss_ce: 0.0074 aux.acc_seg: 99.4095 +04/18 21:16:32 - mmengine - INFO - Iter(train) [122800/160000] lr: 2.7633e-03 eta: 5:42:50 time: 0.5546 data_time: 0.0072 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7140 aux.loss_ce: 0.0074 aux.acc_seg: 99.2131 +04/18 21:17:00 - mmengine - INFO - Iter(train) [122850/160000] lr: 2.7601e-03 eta: 5:42:22 time: 0.5548 data_time: 0.0071 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7643 aux.loss_ce: 0.0071 aux.acc_seg: 99.3224 +04/18 21:17:28 - mmengine - INFO - Iter(train) [122900/160000] lr: 2.7568e-03 eta: 5:41:55 time: 0.5552 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.6893 aux.loss_ce: 0.0070 aux.acc_seg: 99.1234 +04/18 21:17:55 - mmengine - INFO - Iter(train) [122950/160000] lr: 2.7536e-03 eta: 5:41:27 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7011 aux.loss_ce: 0.0073 aux.acc_seg: 99.1089 +04/18 21:18:23 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:18:23 - mmengine - INFO - Iter(train) [123000/160000] lr: 2.7504e-03 eta: 5:41:00 time: 0.5541 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7240 aux.loss_ce: 0.0072 aux.acc_seg: 99.1662 +04/18 21:18:51 - mmengine - INFO - Iter(train) [123050/160000] lr: 2.7472e-03 eta: 5:40:32 time: 0.5555 data_time: 0.0069 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7085 aux.loss_ce: 0.0071 aux.acc_seg: 99.1843 +04/18 21:19:19 - mmengine - INFO - Iter(train) [123100/160000] lr: 2.7440e-03 eta: 5:40:04 time: 0.5564 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7354 aux.loss_ce: 0.0072 aux.acc_seg: 99.2057 +04/18 21:19:47 - mmengine - INFO - Iter(train) [123150/160000] lr: 2.7407e-03 eta: 5:39:37 time: 0.5559 data_time: 0.0064 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7125 aux.loss_ce: 0.0070 aux.acc_seg: 99.1646 +04/18 21:20:14 - mmengine - INFO - Iter(train) [123200/160000] lr: 2.7375e-03 eta: 5:39:09 time: 0.5541 data_time: 0.0064 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0063 decode.acc_seg: 99.7466 aux.loss_ce: 0.0067 aux.acc_seg: 99.2326 +04/18 21:20:42 - mmengine - INFO - Iter(train) [123250/160000] lr: 2.7343e-03 eta: 5:38:41 time: 0.5550 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7112 aux.loss_ce: 0.0074 aux.acc_seg: 99.1482 +04/18 21:21:10 - mmengine - INFO - Iter(train) [123300/160000] lr: 2.7311e-03 eta: 5:38:14 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.7950 aux.loss_ce: 0.0073 aux.acc_seg: 99.2402 +04/18 21:21:38 - mmengine - INFO - Iter(train) [123350/160000] lr: 2.7278e-03 eta: 5:37:46 time: 0.5556 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6663 aux.loss_ce: 0.0073 aux.acc_seg: 99.0669 +04/18 21:22:05 - mmengine - INFO - Iter(train) [123400/160000] lr: 2.7246e-03 eta: 5:37:19 time: 0.5565 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.6578 aux.loss_ce: 0.0079 aux.acc_seg: 98.9572 +04/18 21:22:33 - mmengine - INFO - Iter(train) [123450/160000] lr: 2.7214e-03 eta: 5:36:51 time: 0.5557 data_time: 0.0062 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7147 aux.loss_ce: 0.0071 aux.acc_seg: 99.3171 +04/18 21:23:01 - mmengine - INFO - Iter(train) [123500/160000] lr: 2.7181e-03 eta: 5:36:23 time: 0.5556 data_time: 0.0063 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0064 decode.acc_seg: 99.8658 aux.loss_ce: 0.0067 aux.acc_seg: 99.5777 +04/18 21:23:29 - mmengine - INFO - Iter(train) [123550/160000] lr: 2.7149e-03 eta: 5:35:56 time: 0.5555 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7674 aux.loss_ce: 0.0070 aux.acc_seg: 99.3142 +04/18 21:23:57 - mmengine - INFO - Iter(train) [123600/160000] lr: 2.7117e-03 eta: 5:35:28 time: 0.5556 data_time: 0.0074 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0072 decode.acc_seg: 99.6979 aux.loss_ce: 0.0075 aux.acc_seg: 98.9636 +04/18 21:24:24 - mmengine - INFO - Iter(train) [123650/160000] lr: 2.7085e-03 eta: 5:35:01 time: 0.5562 data_time: 0.0073 memory: 7635 loss: 0.0152 decode.loss_ce: 0.0072 decode.acc_seg: 99.6576 aux.loss_ce: 0.0080 aux.acc_seg: 98.9332 +04/18 21:24:52 - mmengine - INFO - Iter(train) [123700/160000] lr: 2.7052e-03 eta: 5:34:33 time: 0.5555 data_time: 0.0062 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0069 decode.acc_seg: 99.7257 aux.loss_ce: 0.0072 aux.acc_seg: 99.1588 +04/18 21:25:20 - mmengine - INFO - Iter(train) [123750/160000] lr: 2.7020e-03 eta: 5:34:05 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.8016 aux.loss_ce: 0.0070 aux.acc_seg: 99.3775 +04/18 21:25:48 - mmengine - INFO - Iter(train) [123800/160000] lr: 2.6988e-03 eta: 5:33:38 time: 0.5548 data_time: 0.0064 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7156 aux.loss_ce: 0.0069 aux.acc_seg: 99.3465 +04/18 21:26:15 - mmengine - INFO - Iter(train) [123850/160000] lr: 2.6955e-03 eta: 5:33:10 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7180 aux.loss_ce: 0.0077 aux.acc_seg: 99.0951 +04/18 21:26:43 - mmengine - INFO - Iter(train) [123900/160000] lr: 2.6923e-03 eta: 5:32:42 time: 0.5556 data_time: 0.0069 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7747 aux.loss_ce: 0.0077 aux.acc_seg: 99.3084 +04/18 21:27:11 - mmengine - INFO - Iter(train) [123950/160000] lr: 2.6891e-03 eta: 5:32:15 time: 0.5559 data_time: 0.0072 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0073 decode.acc_seg: 99.7608 aux.loss_ce: 0.0073 aux.acc_seg: 99.3449 +04/18 21:27:39 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:27:39 - mmengine - INFO - Iter(train) [124000/160000] lr: 2.6858e-03 eta: 5:31:47 time: 0.5536 data_time: 0.0061 memory: 7635 loss: 0.0173 decode.loss_ce: 0.0090 decode.acc_seg: 99.6581 aux.loss_ce: 0.0083 aux.acc_seg: 99.2547 +04/18 21:28:07 - mmengine - INFO - Iter(train) [124050/160000] lr: 2.6826e-03 eta: 5:31:20 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0078 decode.acc_seg: 99.6129 aux.loss_ce: 0.0075 aux.acc_seg: 99.0871 +04/18 21:28:34 - mmengine - INFO - Iter(train) [124100/160000] lr: 2.6794e-03 eta: 5:30:52 time: 0.5558 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6805 aux.loss_ce: 0.0072 aux.acc_seg: 99.3385 +04/18 21:29:02 - mmengine - INFO - Iter(train) [124150/160000] lr: 2.6761e-03 eta: 5:30:24 time: 0.5540 data_time: 0.0066 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7404 aux.loss_ce: 0.0071 aux.acc_seg: 99.1956 +04/18 21:29:30 - mmengine - INFO - Iter(train) [124200/160000] lr: 2.6729e-03 eta: 5:29:57 time: 0.5558 data_time: 0.0069 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0074 decode.acc_seg: 99.6528 aux.loss_ce: 0.0074 aux.acc_seg: 99.0789 +04/18 21:29:58 - mmengine - INFO - Iter(train) [124250/160000] lr: 2.6697e-03 eta: 5:29:29 time: 0.5556 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0069 decode.acc_seg: 99.7506 aux.loss_ce: 0.0070 aux.acc_seg: 99.4198 +04/18 21:30:26 - mmengine - INFO - Iter(train) [124300/160000] lr: 2.6664e-03 eta: 5:29:02 time: 0.5549 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.6850 aux.loss_ce: 0.0072 aux.acc_seg: 99.1245 +04/18 21:30:53 - mmengine - INFO - Iter(train) [124350/160000] lr: 2.6632e-03 eta: 5:28:34 time: 0.5536 data_time: 0.0061 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7113 aux.loss_ce: 0.0076 aux.acc_seg: 99.2085 +04/18 21:31:21 - mmengine - INFO - Iter(train) [124400/160000] lr: 2.6600e-03 eta: 5:28:06 time: 0.5555 data_time: 0.0072 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.7376 aux.loss_ce: 0.0074 aux.acc_seg: 99.2411 +04/18 21:31:49 - mmengine - INFO - Iter(train) [124450/160000] lr: 2.6567e-03 eta: 5:27:39 time: 0.5545 data_time: 0.0069 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7454 aux.loss_ce: 0.0073 aux.acc_seg: 99.1824 +04/18 21:32:17 - mmengine - INFO - Iter(train) [124500/160000] lr: 2.6535e-03 eta: 5:27:11 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0069 decode.acc_seg: 99.7325 aux.loss_ce: 0.0075 aux.acc_seg: 99.2898 +04/18 21:32:44 - mmengine - INFO - Iter(train) [124550/160000] lr: 2.6503e-03 eta: 5:26:43 time: 0.5555 data_time: 0.0073 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7578 aux.loss_ce: 0.0069 aux.acc_seg: 99.2954 +04/18 21:33:12 - mmengine - INFO - Iter(train) [124600/160000] lr: 2.6470e-03 eta: 5:26:16 time: 0.5555 data_time: 0.0070 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0062 decode.acc_seg: 99.7704 aux.loss_ce: 0.0065 aux.acc_seg: 99.3500 +04/18 21:33:40 - mmengine - INFO - Iter(train) [124650/160000] lr: 2.6438e-03 eta: 5:25:48 time: 0.5545 data_time: 0.0066 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0072 decode.acc_seg: 99.6255 aux.loss_ce: 0.0075 aux.acc_seg: 99.0353 +04/18 21:34:08 - mmengine - INFO - Iter(train) [124700/160000] lr: 2.6405e-03 eta: 5:25:21 time: 0.5537 data_time: 0.0061 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0061 decode.acc_seg: 99.8405 aux.loss_ce: 0.0068 aux.acc_seg: 99.5062 +04/18 21:34:35 - mmengine - INFO - Iter(train) [124750/160000] lr: 2.6373e-03 eta: 5:24:53 time: 0.5538 data_time: 0.0064 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0076 decode.acc_seg: 99.7898 aux.loss_ce: 0.0078 aux.acc_seg: 99.4054 +04/18 21:35:03 - mmengine - INFO - Iter(train) [124800/160000] lr: 2.6341e-03 eta: 5:24:25 time: 0.5544 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7371 aux.loss_ce: 0.0073 aux.acc_seg: 99.2552 +04/18 21:35:31 - mmengine - INFO - Iter(train) [124850/160000] lr: 2.6308e-03 eta: 5:23:58 time: 0.5554 data_time: 0.0077 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7819 aux.loss_ce: 0.0073 aux.acc_seg: 99.3029 +04/18 21:35:59 - mmengine - INFO - Iter(train) [124900/160000] lr: 2.6276e-03 eta: 5:23:30 time: 0.5551 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.7383 aux.loss_ce: 0.0074 aux.acc_seg: 99.2082 +04/18 21:36:27 - mmengine - INFO - Iter(train) [124950/160000] lr: 2.6243e-03 eta: 5:23:02 time: 0.5545 data_time: 0.0071 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.7116 aux.loss_ce: 0.0076 aux.acc_seg: 99.3921 +04/18 21:36:55 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:36:55 - mmengine - INFO - Iter(train) [125000/160000] lr: 2.6211e-03 eta: 5:22:35 time: 0.5733 data_time: 0.0073 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.6754 aux.loss_ce: 0.0075 aux.acc_seg: 98.9037 +04/18 21:37:22 - mmengine - INFO - Iter(train) [125050/160000] lr: 2.6179e-03 eta: 5:22:07 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7577 aux.loss_ce: 0.0072 aux.acc_seg: 99.2687 +04/18 21:37:50 - mmengine - INFO - Iter(train) [125100/160000] lr: 2.6146e-03 eta: 5:21:40 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.6872 aux.loss_ce: 0.0075 aux.acc_seg: 99.3011 +04/18 21:38:18 - mmengine - INFO - Iter(train) [125150/160000] lr: 2.6114e-03 eta: 5:21:12 time: 0.5561 data_time: 0.0066 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7501 aux.loss_ce: 0.0075 aux.acc_seg: 99.2669 +04/18 21:38:46 - mmengine - INFO - Iter(train) [125200/160000] lr: 2.6081e-03 eta: 5:20:44 time: 0.5539 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.7517 aux.loss_ce: 0.0072 aux.acc_seg: 99.2521 +04/18 21:39:14 - mmengine - INFO - Iter(train) [125250/160000] lr: 2.6049e-03 eta: 5:20:17 time: 0.5546 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7034 aux.loss_ce: 0.0074 aux.acc_seg: 99.3413 +04/18 21:39:41 - mmengine - INFO - Iter(train) [125300/160000] lr: 2.6016e-03 eta: 5:19:49 time: 0.5565 data_time: 0.0078 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7685 aux.loss_ce: 0.0068 aux.acc_seg: 99.3257 +04/18 21:40:09 - mmengine - INFO - Iter(train) [125350/160000] lr: 2.5984e-03 eta: 5:19:22 time: 0.5566 data_time: 0.0069 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0069 decode.acc_seg: 99.7415 aux.loss_ce: 0.0075 aux.acc_seg: 99.2738 +04/18 21:40:37 - mmengine - INFO - Iter(train) [125400/160000] lr: 2.5952e-03 eta: 5:18:54 time: 0.5551 data_time: 0.0067 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.7213 aux.loss_ce: 0.0076 aux.acc_seg: 99.2002 +04/18 21:41:05 - mmengine - INFO - Iter(train) [125450/160000] lr: 2.5919e-03 eta: 5:18:26 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7778 aux.loss_ce: 0.0072 aux.acc_seg: 99.1993 +04/18 21:41:32 - mmengine - INFO - Iter(train) [125500/160000] lr: 2.5887e-03 eta: 5:17:59 time: 0.5523 data_time: 0.0062 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7118 aux.loss_ce: 0.0072 aux.acc_seg: 99.2544 +04/18 21:42:00 - mmengine - INFO - Iter(train) [125550/160000] lr: 2.5854e-03 eta: 5:17:31 time: 0.5545 data_time: 0.0067 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7169 aux.loss_ce: 0.0072 aux.acc_seg: 99.1631 +04/18 21:42:28 - mmengine - INFO - Iter(train) [125600/160000] lr: 2.5822e-03 eta: 5:17:03 time: 0.5552 data_time: 0.0074 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6750 aux.loss_ce: 0.0071 aux.acc_seg: 99.3027 +04/18 21:42:56 - mmengine - INFO - Iter(train) [125650/160000] lr: 2.5789e-03 eta: 5:16:36 time: 0.5554 data_time: 0.0065 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0062 decode.acc_seg: 99.7605 aux.loss_ce: 0.0066 aux.acc_seg: 99.3504 +04/18 21:43:23 - mmengine - INFO - Iter(train) [125700/160000] lr: 2.5757e-03 eta: 5:16:08 time: 0.5550 data_time: 0.0066 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.7562 aux.loss_ce: 0.0067 aux.acc_seg: 99.3752 +04/18 21:43:51 - mmengine - INFO - Iter(train) [125750/160000] lr: 2.5724e-03 eta: 5:15:41 time: 0.5557 data_time: 0.0062 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7888 aux.loss_ce: 0.0071 aux.acc_seg: 99.4412 +04/18 21:44:19 - mmengine - INFO - Iter(train) [125800/160000] lr: 2.5692e-03 eta: 5:15:13 time: 0.5564 data_time: 0.0068 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7596 aux.loss_ce: 0.0069 aux.acc_seg: 99.4021 +04/18 21:44:47 - mmengine - INFO - Iter(train) [125850/160000] lr: 2.5659e-03 eta: 5:14:45 time: 0.5560 data_time: 0.0066 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.7498 aux.loss_ce: 0.0070 aux.acc_seg: 99.2921 +04/18 21:45:15 - mmengine - INFO - Iter(train) [125900/160000] lr: 2.5627e-03 eta: 5:14:18 time: 0.5558 data_time: 0.0064 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.8037 aux.loss_ce: 0.0071 aux.acc_seg: 99.4242 +04/18 21:45:42 - mmengine - INFO - Iter(train) [125950/160000] lr: 2.5594e-03 eta: 5:13:50 time: 0.5553 data_time: 0.0062 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.7516 aux.loss_ce: 0.0070 aux.acc_seg: 99.4208 +04/18 21:46:10 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:46:10 - mmengine - INFO - Iter(train) [126000/160000] lr: 2.5562e-03 eta: 5:13:22 time: 0.5554 data_time: 0.0070 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.7361 aux.loss_ce: 0.0078 aux.acc_seg: 99.3279 +04/18 21:46:38 - mmengine - INFO - Iter(train) [126050/160000] lr: 2.5529e-03 eta: 5:12:55 time: 0.5562 data_time: 0.0071 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0061 decode.acc_seg: 99.6084 aux.loss_ce: 0.0070 aux.acc_seg: 98.8375 +04/18 21:47:06 - mmengine - INFO - Iter(train) [126100/160000] lr: 2.5497e-03 eta: 5:12:27 time: 0.5555 data_time: 0.0063 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0060 decode.acc_seg: 99.6986 aux.loss_ce: 0.0069 aux.acc_seg: 99.1886 +04/18 21:47:34 - mmengine - INFO - Iter(train) [126150/160000] lr: 2.5464e-03 eta: 5:12:00 time: 0.5556 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7086 aux.loss_ce: 0.0069 aux.acc_seg: 99.1688 +04/18 21:48:01 - mmengine - INFO - Iter(train) [126200/160000] lr: 2.5432e-03 eta: 5:11:32 time: 0.5551 data_time: 0.0073 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0073 decode.acc_seg: 99.6912 aux.loss_ce: 0.0077 aux.acc_seg: 99.2783 +04/18 21:48:29 - mmengine - INFO - Iter(train) [126250/160000] lr: 2.5399e-03 eta: 5:11:04 time: 0.5544 data_time: 0.0064 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0060 decode.acc_seg: 99.7904 aux.loss_ce: 0.0067 aux.acc_seg: 99.2697 +04/18 21:48:57 - mmengine - INFO - Iter(train) [126300/160000] lr: 2.5367e-03 eta: 5:10:37 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7656 aux.loss_ce: 0.0072 aux.acc_seg: 99.1049 +04/18 21:49:25 - mmengine - INFO - Iter(train) [126350/160000] lr: 2.5334e-03 eta: 5:10:09 time: 0.5549 data_time: 0.0069 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7588 aux.loss_ce: 0.0069 aux.acc_seg: 99.3914 +04/18 21:49:53 - mmengine - INFO - Iter(train) [126400/160000] lr: 2.5302e-03 eta: 5:09:42 time: 0.5555 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.7691 aux.loss_ce: 0.0076 aux.acc_seg: 99.2879 +04/18 21:50:20 - mmengine - INFO - Iter(train) [126450/160000] lr: 2.5269e-03 eta: 5:09:14 time: 0.5558 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7457 aux.loss_ce: 0.0072 aux.acc_seg: 99.3004 +04/18 21:50:48 - mmengine - INFO - Iter(train) [126500/160000] lr: 2.5237e-03 eta: 5:08:46 time: 0.5540 data_time: 0.0065 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.6911 aux.loss_ce: 0.0068 aux.acc_seg: 99.1219 +04/18 21:51:16 - mmengine - INFO - Iter(train) [126550/160000] lr: 2.5204e-03 eta: 5:08:19 time: 0.5541 data_time: 0.0068 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7453 aux.loss_ce: 0.0067 aux.acc_seg: 99.2411 +04/18 21:51:44 - mmengine - INFO - Iter(train) [126600/160000] lr: 2.5171e-03 eta: 5:07:51 time: 0.5542 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.6019 aux.loss_ce: 0.0074 aux.acc_seg: 99.0595 +04/18 21:52:11 - mmengine - INFO - Iter(train) [126650/160000] lr: 2.5139e-03 eta: 5:07:23 time: 0.5549 data_time: 0.0072 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0063 decode.acc_seg: 99.7804 aux.loss_ce: 0.0067 aux.acc_seg: 99.3526 +04/18 21:52:39 - mmengine - INFO - Iter(train) [126700/160000] lr: 2.5106e-03 eta: 5:06:56 time: 0.5556 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7977 aux.loss_ce: 0.0071 aux.acc_seg: 99.4468 +04/18 21:53:07 - mmengine - INFO - Iter(train) [126750/160000] lr: 2.5074e-03 eta: 5:06:28 time: 0.5566 data_time: 0.0073 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7871 aux.loss_ce: 0.0075 aux.acc_seg: 99.3344 +04/18 21:53:35 - mmengine - INFO - Iter(train) [126800/160000] lr: 2.5041e-03 eta: 5:06:01 time: 0.5546 data_time: 0.0066 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.7893 aux.loss_ce: 0.0070 aux.acc_seg: 99.4941 +04/18 21:54:03 - mmengine - INFO - Iter(train) [126850/160000] lr: 2.5008e-03 eta: 5:05:33 time: 0.5545 data_time: 0.0065 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7021 aux.loss_ce: 0.0071 aux.acc_seg: 98.9094 +04/18 21:54:30 - mmengine - INFO - Iter(train) [126900/160000] lr: 2.4976e-03 eta: 5:05:05 time: 0.5569 data_time: 0.0064 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7677 aux.loss_ce: 0.0074 aux.acc_seg: 99.2421 +04/18 21:54:58 - mmengine - INFO - Iter(train) [126950/160000] lr: 2.4943e-03 eta: 5:04:38 time: 0.5640 data_time: 0.0070 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7974 aux.loss_ce: 0.0074 aux.acc_seg: 99.5192 +04/18 21:55:26 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 21:55:26 - mmengine - INFO - Iter(train) [127000/160000] lr: 2.4911e-03 eta: 5:04:10 time: 0.5559 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.7417 aux.loss_ce: 0.0075 aux.acc_seg: 99.4516 +04/18 21:55:54 - mmengine - INFO - Iter(train) [127050/160000] lr: 2.4878e-03 eta: 5:03:43 time: 0.5547 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0075 decode.acc_seg: 99.7052 aux.loss_ce: 0.0075 aux.acc_seg: 99.0387 +04/18 21:56:22 - mmengine - INFO - Iter(train) [127100/160000] lr: 2.4845e-03 eta: 5:03:15 time: 0.5544 data_time: 0.0063 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0064 decode.acc_seg: 99.8090 aux.loss_ce: 0.0074 aux.acc_seg: 99.4081 +04/18 21:56:50 - mmengine - INFO - Iter(train) [127150/160000] lr: 2.4813e-03 eta: 5:02:47 time: 0.5738 data_time: 0.0064 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.8383 aux.loss_ce: 0.0071 aux.acc_seg: 99.4491 +04/18 21:57:17 - mmengine - INFO - Iter(train) [127200/160000] lr: 2.4780e-03 eta: 5:02:20 time: 0.5639 data_time: 0.0068 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.7498 aux.loss_ce: 0.0078 aux.acc_seg: 99.1600 +04/18 21:57:45 - mmengine - INFO - Iter(train) [127250/160000] lr: 2.4748e-03 eta: 5:01:52 time: 0.5543 data_time: 0.0070 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7391 aux.loss_ce: 0.0069 aux.acc_seg: 99.3620 +04/18 21:58:13 - mmengine - INFO - Iter(train) [127300/160000] lr: 2.4715e-03 eta: 5:01:24 time: 0.5555 data_time: 0.0068 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0074 decode.acc_seg: 99.6553 aux.loss_ce: 0.0080 aux.acc_seg: 98.9337 +04/18 21:58:41 - mmengine - INFO - Iter(train) [127350/160000] lr: 2.4682e-03 eta: 5:00:57 time: 0.5537 data_time: 0.0063 memory: 7635 loss: 0.0163 decode.loss_ce: 0.0080 decode.acc_seg: 99.6821 aux.loss_ce: 0.0083 aux.acc_seg: 99.1216 +04/18 21:59:09 - mmengine - INFO - Iter(train) [127400/160000] lr: 2.4650e-03 eta: 5:00:29 time: 0.5552 data_time: 0.0069 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0060 decode.acc_seg: 99.7319 aux.loss_ce: 0.0067 aux.acc_seg: 99.2224 +04/18 21:59:36 - mmengine - INFO - Iter(train) [127450/160000] lr: 2.4617e-03 eta: 5:00:02 time: 0.5560 data_time: 0.0068 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0071 decode.acc_seg: 99.7175 aux.loss_ce: 0.0076 aux.acc_seg: 99.2351 +04/18 22:00:04 - mmengine - INFO - Iter(train) [127500/160000] lr: 2.4584e-03 eta: 4:59:34 time: 0.5551 data_time: 0.0071 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.7566 aux.loss_ce: 0.0070 aux.acc_seg: 99.3694 +04/18 22:00:32 - mmengine - INFO - Iter(train) [127550/160000] lr: 2.4552e-03 eta: 4:59:06 time: 0.5554 data_time: 0.0069 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0071 decode.acc_seg: 99.6647 aux.loss_ce: 0.0079 aux.acc_seg: 98.8403 +04/18 22:01:00 - mmengine - INFO - Iter(train) [127600/160000] lr: 2.4519e-03 eta: 4:58:39 time: 0.5561 data_time: 0.0074 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7548 aux.loss_ce: 0.0069 aux.acc_seg: 99.2050 +04/18 22:01:27 - mmengine - INFO - Iter(train) [127650/160000] lr: 2.4486e-03 eta: 4:58:11 time: 0.5558 data_time: 0.0064 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0068 decode.acc_seg: 99.7610 aux.loss_ce: 0.0079 aux.acc_seg: 99.3730 +04/18 22:01:55 - mmengine - INFO - Iter(train) [127700/160000] lr: 2.4454e-03 eta: 4:57:44 time: 0.5555 data_time: 0.0071 memory: 7635 loss: 0.0122 decode.loss_ce: 0.0060 decode.acc_seg: 99.8035 aux.loss_ce: 0.0062 aux.acc_seg: 99.6104 +04/18 22:02:23 - mmengine - INFO - Iter(train) [127750/160000] lr: 2.4421e-03 eta: 4:57:16 time: 0.5550 data_time: 0.0071 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0070 decode.acc_seg: 99.7979 aux.loss_ce: 0.0074 aux.acc_seg: 99.4246 +04/18 22:02:51 - mmengine - INFO - Iter(train) [127800/160000] lr: 2.4388e-03 eta: 4:56:48 time: 0.5549 data_time: 0.0062 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0071 decode.acc_seg: 99.7128 aux.loss_ce: 0.0078 aux.acc_seg: 99.1030 +04/18 22:03:19 - mmengine - INFO - Iter(train) [127850/160000] lr: 2.4356e-03 eta: 4:56:21 time: 0.5565 data_time: 0.0076 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.6639 aux.loss_ce: 0.0075 aux.acc_seg: 99.2200 +04/18 22:03:47 - mmengine - INFO - Iter(train) [127900/160000] lr: 2.4323e-03 eta: 4:55:53 time: 0.5556 data_time: 0.0066 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0069 decode.acc_seg: 99.5625 aux.loss_ce: 0.0073 aux.acc_seg: 99.0203 +04/18 22:04:14 - mmengine - INFO - Iter(train) [127950/160000] lr: 2.4290e-03 eta: 4:55:25 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7223 aux.loss_ce: 0.0072 aux.acc_seg: 99.1844 +04/18 22:04:42 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:04:42 - mmengine - INFO - Iter(train) [128000/160000] lr: 2.4258e-03 eta: 4:54:58 time: 0.5557 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7183 aux.loss_ce: 0.0071 aux.acc_seg: 99.1471 +04/18 22:05:10 - mmengine - INFO - Iter(train) [128050/160000] lr: 2.4225e-03 eta: 4:54:30 time: 0.5547 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0071 decode.acc_seg: 99.6617 aux.loss_ce: 0.0078 aux.acc_seg: 99.0107 +04/18 22:05:38 - mmengine - INFO - Iter(train) [128100/160000] lr: 2.4192e-03 eta: 4:54:03 time: 0.5559 data_time: 0.0068 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.6387 aux.loss_ce: 0.0075 aux.acc_seg: 99.1412 +04/18 22:06:06 - mmengine - INFO - Iter(train) [128150/160000] lr: 2.4159e-03 eta: 4:53:35 time: 0.5531 data_time: 0.0068 memory: 7635 loss: 0.0160 decode.loss_ce: 0.0076 decode.acc_seg: 99.5480 aux.loss_ce: 0.0083 aux.acc_seg: 98.7090 +04/18 22:06:33 - mmengine - INFO - Iter(train) [128200/160000] lr: 2.4127e-03 eta: 4:53:07 time: 0.5544 data_time: 0.0070 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.6588 aux.loss_ce: 0.0074 aux.acc_seg: 99.1114 +04/18 22:07:01 - mmengine - INFO - Iter(train) [128250/160000] lr: 2.4094e-03 eta: 4:52:40 time: 0.5566 data_time: 0.0068 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7232 aux.loss_ce: 0.0073 aux.acc_seg: 99.2478 +04/18 22:07:29 - mmengine - INFO - Iter(train) [128300/160000] lr: 2.4061e-03 eta: 4:52:12 time: 0.5548 data_time: 0.0070 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7443 aux.loss_ce: 0.0073 aux.acc_seg: 99.2952 +04/18 22:07:57 - mmengine - INFO - Iter(train) [128350/160000] lr: 2.4029e-03 eta: 4:51:44 time: 0.5537 data_time: 0.0069 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0072 decode.acc_seg: 99.7016 aux.loss_ce: 0.0082 aux.acc_seg: 99.1817 +04/18 22:08:25 - mmengine - INFO - Iter(train) [128400/160000] lr: 2.3996e-03 eta: 4:51:17 time: 0.5556 data_time: 0.0071 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7628 aux.loss_ce: 0.0071 aux.acc_seg: 99.3367 +04/18 22:08:52 - mmengine - INFO - Iter(train) [128450/160000] lr: 2.3963e-03 eta: 4:50:49 time: 0.5557 data_time: 0.0069 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.8194 aux.loss_ce: 0.0069 aux.acc_seg: 99.4799 +04/18 22:09:20 - mmengine - INFO - Iter(train) [128500/160000] lr: 2.3930e-03 eta: 4:50:22 time: 0.5542 data_time: 0.0076 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0066 decode.acc_seg: 99.7598 aux.loss_ce: 0.0075 aux.acc_seg: 99.1301 +04/18 22:09:48 - mmengine - INFO - Iter(train) [128550/160000] lr: 2.3898e-03 eta: 4:49:54 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6736 aux.loss_ce: 0.0076 aux.acc_seg: 99.2837 +04/18 22:10:16 - mmengine - INFO - Iter(train) [128600/160000] lr: 2.3865e-03 eta: 4:49:26 time: 0.5555 data_time: 0.0073 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0073 decode.acc_seg: 99.7605 aux.loss_ce: 0.0079 aux.acc_seg: 99.3143 +04/18 22:10:44 - mmengine - INFO - Iter(train) [128650/160000] lr: 2.3832e-03 eta: 4:48:59 time: 0.5564 data_time: 0.0064 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0070 decode.acc_seg: 99.6891 aux.loss_ce: 0.0074 aux.acc_seg: 99.3534 +04/18 22:11:11 - mmengine - INFO - Iter(train) [128700/160000] lr: 2.3799e-03 eta: 4:48:31 time: 0.5561 data_time: 0.0069 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7433 aux.loss_ce: 0.0070 aux.acc_seg: 99.2893 +04/18 22:11:39 - mmengine - INFO - Iter(train) [128750/160000] lr: 2.3766e-03 eta: 4:48:03 time: 0.5550 data_time: 0.0071 memory: 7635 loss: 0.0124 decode.loss_ce: 0.0059 decode.acc_seg: 99.7640 aux.loss_ce: 0.0065 aux.acc_seg: 99.3477 +04/18 22:12:07 - mmengine - INFO - Iter(train) [128800/160000] lr: 2.3734e-03 eta: 4:47:36 time: 0.5568 data_time: 0.0074 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0069 decode.acc_seg: 99.7740 aux.loss_ce: 0.0082 aux.acc_seg: 99.2670 +04/18 22:12:35 - mmengine - INFO - Iter(train) [128850/160000] lr: 2.3701e-03 eta: 4:47:08 time: 0.5557 data_time: 0.0067 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.6268 aux.loss_ce: 0.0079 aux.acc_seg: 98.8998 +04/18 22:13:03 - mmengine - INFO - Iter(train) [128900/160000] lr: 2.3668e-03 eta: 4:46:41 time: 0.5543 data_time: 0.0066 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0082 decode.acc_seg: 99.6543 aux.loss_ce: 0.0081 aux.acc_seg: 99.3200 +04/18 22:13:30 - mmengine - INFO - Iter(train) [128950/160000] lr: 2.3635e-03 eta: 4:46:13 time: 0.5560 data_time: 0.0066 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0078 decode.acc_seg: 99.7135 aux.loss_ce: 0.0077 aux.acc_seg: 99.2747 +04/18 22:13:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:13:58 - mmengine - INFO - Iter(train) [129000/160000] lr: 2.3602e-03 eta: 4:45:45 time: 0.5563 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.6754 aux.loss_ce: 0.0071 aux.acc_seg: 98.9764 +04/18 22:14:26 - mmengine - INFO - Iter(train) [129050/160000] lr: 2.3570e-03 eta: 4:45:18 time: 0.5567 data_time: 0.0071 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0075 decode.acc_seg: 99.6849 aux.loss_ce: 0.0080 aux.acc_seg: 99.0967 +04/18 22:14:54 - mmengine - INFO - Iter(train) [129100/160000] lr: 2.3537e-03 eta: 4:44:50 time: 0.5653 data_time: 0.0065 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.7360 aux.loss_ce: 0.0068 aux.acc_seg: 99.1823 +04/18 22:15:22 - mmengine - INFO - Iter(train) [129150/160000] lr: 2.3504e-03 eta: 4:44:23 time: 0.5553 data_time: 0.0071 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7160 aux.loss_ce: 0.0068 aux.acc_seg: 99.1836 +04/18 22:15:49 - mmengine - INFO - Iter(train) [129200/160000] lr: 2.3471e-03 eta: 4:43:55 time: 0.5556 data_time: 0.0067 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0066 decode.acc_seg: 99.6051 aux.loss_ce: 0.0075 aux.acc_seg: 98.8124 +04/18 22:16:17 - mmengine - INFO - Iter(train) [129250/160000] lr: 2.3438e-03 eta: 4:43:27 time: 0.5571 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7875 aux.loss_ce: 0.0073 aux.acc_seg: 99.3378 +04/18 22:16:45 - mmengine - INFO - Iter(train) [129300/160000] lr: 2.3405e-03 eta: 4:43:00 time: 0.5565 data_time: 0.0066 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0066 decode.acc_seg: 99.7327 aux.loss_ce: 0.0073 aux.acc_seg: 99.0170 +04/18 22:17:13 - mmengine - INFO - Iter(train) [129350/160000] lr: 2.3373e-03 eta: 4:42:32 time: 0.5650 data_time: 0.0064 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7454 aux.loss_ce: 0.0068 aux.acc_seg: 99.3069 +04/18 22:17:41 - mmengine - INFO - Iter(train) [129400/160000] lr: 2.3340e-03 eta: 4:42:04 time: 0.5561 data_time: 0.0074 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0081 decode.acc_seg: 99.6684 aux.loss_ce: 0.0080 aux.acc_seg: 99.0279 +04/18 22:18:09 - mmengine - INFO - Iter(train) [129450/160000] lr: 2.3307e-03 eta: 4:41:37 time: 0.5534 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0077 decode.acc_seg: 99.6218 aux.loss_ce: 0.0073 aux.acc_seg: 99.3181 +04/18 22:18:36 - mmengine - INFO - Iter(train) [129500/160000] lr: 2.3274e-03 eta: 4:41:09 time: 0.5562 data_time: 0.0064 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0073 decode.acc_seg: 99.7198 aux.loss_ce: 0.0076 aux.acc_seg: 99.3068 +04/18 22:19:04 - mmengine - INFO - Iter(train) [129550/160000] lr: 2.3241e-03 eta: 4:40:42 time: 0.5559 data_time: 0.0069 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6206 aux.loss_ce: 0.0072 aux.acc_seg: 99.1624 +04/18 22:19:32 - mmengine - INFO - Iter(train) [129600/160000] lr: 2.3208e-03 eta: 4:40:14 time: 0.5543 data_time: 0.0062 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.7700 aux.loss_ce: 0.0071 aux.acc_seg: 99.3279 +04/18 22:20:00 - mmengine - INFO - Iter(train) [129650/160000] lr: 2.3175e-03 eta: 4:39:46 time: 0.5557 data_time: 0.0073 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6285 aux.loss_ce: 0.0073 aux.acc_seg: 99.0627 +04/18 22:20:28 - mmengine - INFO - Iter(train) [129700/160000] lr: 2.3143e-03 eta: 4:39:19 time: 0.5566 data_time: 0.0062 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6714 aux.loss_ce: 0.0072 aux.acc_seg: 99.1223 +04/18 22:20:55 - mmengine - INFO - Iter(train) [129750/160000] lr: 2.3110e-03 eta: 4:38:51 time: 0.5543 data_time: 0.0065 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0065 decode.acc_seg: 99.8057 aux.loss_ce: 0.0070 aux.acc_seg: 99.3267 +04/18 22:21:23 - mmengine - INFO - Iter(train) [129800/160000] lr: 2.3077e-03 eta: 4:38:23 time: 0.5568 data_time: 0.0067 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.7604 aux.loss_ce: 0.0068 aux.acc_seg: 99.4051 +04/18 22:21:51 - mmengine - INFO - Iter(train) [129850/160000] lr: 2.3044e-03 eta: 4:37:56 time: 0.5544 data_time: 0.0063 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7201 aux.loss_ce: 0.0074 aux.acc_seg: 99.1375 +04/18 22:22:19 - mmengine - INFO - Iter(train) [129900/160000] lr: 2.3011e-03 eta: 4:37:28 time: 0.5556 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7195 aux.loss_ce: 0.0071 aux.acc_seg: 99.2496 +04/18 22:22:46 - mmengine - INFO - Iter(train) [129950/160000] lr: 2.2978e-03 eta: 4:37:01 time: 0.5555 data_time: 0.0069 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7942 aux.loss_ce: 0.0073 aux.acc_seg: 99.4062 +04/18 22:23:14 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:23:14 - mmengine - INFO - Iter(train) [130000/160000] lr: 2.2945e-03 eta: 4:36:33 time: 0.5549 data_time: 0.0064 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0074 decode.acc_seg: 99.7582 aux.loss_ce: 0.0075 aux.acc_seg: 99.2439 +04/18 22:23:14 - mmengine - INFO - Saving checkpoint at 130000 iterations +04/18 22:23:18 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:07 time: 0.0461 data_time: 0.0014 memory: 1657 +04/18 22:23:21 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:04 time: 0.0466 data_time: 0.0017 memory: 1657 +04/18 22:23:23 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:02 time: 0.0466 data_time: 0.0015 memory: 1657 +04/18 22:23:25 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.0454 data_time: 0.0011 memory: 1657 +04/18 22:23:25 - mmengine - INFO - per class results: +04/18 22:23:25 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.08 | 99.52 | 99.54 | 99.56 | 99.52 | +| contrast | 80.15 | 89.42 | 88.98 | 88.55 | 89.42 | ++------------+-------+-------+--------+-----------+--------+ +04/18 22:23:25 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1200 mIoU: 89.6200 mAcc: 94.4700 mFscore: 94.2600 mPrecision: 94.0500 mRecall: 94.4700 data_time: 0.0016 time: 0.0466 +04/18 22:23:53 - mmengine - INFO - Iter(train) [130050/160000] lr: 2.2912e-03 eta: 4:36:05 time: 0.5559 data_time: 0.0062 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.6917 aux.loss_ce: 0.0068 aux.acc_seg: 99.1994 +04/18 22:24:21 - mmengine - INFO - Iter(train) [130100/160000] lr: 2.2879e-03 eta: 4:35:38 time: 0.5559 data_time: 0.0083 memory: 7635 loss: 0.0162 decode.loss_ce: 0.0080 decode.acc_seg: 99.7116 aux.loss_ce: 0.0082 aux.acc_seg: 99.2403 +04/18 22:24:49 - mmengine - INFO - Iter(train) [130150/160000] lr: 2.2846e-03 eta: 4:35:10 time: 0.5563 data_time: 0.0074 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0073 decode.acc_seg: 99.6255 aux.loss_ce: 0.0075 aux.acc_seg: 99.1024 +04/18 22:25:17 - mmengine - INFO - Iter(train) [130200/160000] lr: 2.2813e-03 eta: 4:34:42 time: 0.5563 data_time: 0.0071 memory: 7635 loss: 0.0164 decode.loss_ce: 0.0083 decode.acc_seg: 99.6431 aux.loss_ce: 0.0081 aux.acc_seg: 99.2937 +04/18 22:25:44 - mmengine - INFO - Iter(train) [130250/160000] lr: 2.2781e-03 eta: 4:34:15 time: 0.5559 data_time: 0.0067 memory: 7635 loss: 0.0167 decode.loss_ce: 0.0087 decode.acc_seg: 99.6962 aux.loss_ce: 0.0080 aux.acc_seg: 99.3298 +04/18 22:26:12 - mmengine - INFO - Iter(train) [130300/160000] lr: 2.2748e-03 eta: 4:33:47 time: 0.5547 data_time: 0.0062 memory: 7635 loss: 0.0156 decode.loss_ce: 0.0079 decode.acc_seg: 99.7221 aux.loss_ce: 0.0078 aux.acc_seg: 99.2074 +04/18 22:26:40 - mmengine - INFO - Iter(train) [130350/160000] lr: 2.2715e-03 eta: 4:33:20 time: 0.5566 data_time: 0.0071 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7002 aux.loss_ce: 0.0074 aux.acc_seg: 99.1498 +04/18 22:27:08 - mmengine - INFO - Iter(train) [130400/160000] lr: 2.2682e-03 eta: 4:32:52 time: 0.5557 data_time: 0.0063 memory: 7635 loss: 0.0154 decode.loss_ce: 0.0076 decode.acc_seg: 99.6872 aux.loss_ce: 0.0078 aux.acc_seg: 99.0938 +04/18 22:27:36 - mmengine - INFO - Iter(train) [130450/160000] lr: 2.2649e-03 eta: 4:32:24 time: 0.5570 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0066 decode.acc_seg: 99.7517 aux.loss_ce: 0.0070 aux.acc_seg: 99.2586 +04/18 22:28:04 - mmengine - INFO - Iter(train) [130500/160000] lr: 2.2616e-03 eta: 4:31:57 time: 0.5558 data_time: 0.0067 memory: 7635 loss: 0.0153 decode.loss_ce: 0.0074 decode.acc_seg: 99.6672 aux.loss_ce: 0.0079 aux.acc_seg: 99.0973 +04/18 22:28:31 - mmengine - INFO - Iter(train) [130550/160000] lr: 2.2583e-03 eta: 4:31:29 time: 0.5561 data_time: 0.0065 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.6420 aux.loss_ce: 0.0077 aux.acc_seg: 99.1776 +04/18 22:28:59 - mmengine - INFO - Iter(train) [130600/160000] lr: 2.2550e-03 eta: 4:31:01 time: 0.5545 data_time: 0.0068 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.6999 aux.loss_ce: 0.0074 aux.acc_seg: 99.2559 +04/18 22:29:27 - mmengine - INFO - Iter(train) [130650/160000] lr: 2.2517e-03 eta: 4:30:34 time: 0.5567 data_time: 0.0067 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7292 aux.loss_ce: 0.0073 aux.acc_seg: 99.2261 +04/18 22:29:55 - mmengine - INFO - Iter(train) [130700/160000] lr: 2.2484e-03 eta: 4:30:06 time: 0.5557 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0067 decode.acc_seg: 99.6858 aux.loss_ce: 0.0074 aux.acc_seg: 99.2261 +04/18 22:30:22 - mmengine - INFO - Iter(train) [130750/160000] lr: 2.2451e-03 eta: 4:29:39 time: 0.5547 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7329 aux.loss_ce: 0.0073 aux.acc_seg: 99.4963 +04/18 22:30:50 - mmengine - INFO - Iter(train) [130800/160000] lr: 2.2418e-03 eta: 4:29:11 time: 0.5563 data_time: 0.0070 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0067 decode.acc_seg: 99.6983 aux.loss_ce: 0.0075 aux.acc_seg: 99.2035 +04/18 22:31:18 - mmengine - INFO - Iter(train) [130850/160000] lr: 2.2385e-03 eta: 4:28:43 time: 0.5548 data_time: 0.0061 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0067 decode.acc_seg: 99.7300 aux.loss_ce: 0.0070 aux.acc_seg: 99.1772 +04/18 22:31:46 - mmengine - INFO - Iter(train) [130900/160000] lr: 2.2352e-03 eta: 4:28:16 time: 0.5576 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.8247 aux.loss_ce: 0.0072 aux.acc_seg: 99.4732 +04/18 22:32:14 - mmengine - INFO - Iter(train) [130950/160000] lr: 2.2319e-03 eta: 4:27:48 time: 0.5563 data_time: 0.0066 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7023 aux.loss_ce: 0.0068 aux.acc_seg: 99.1722 +04/18 22:32:42 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:32:42 - mmengine - INFO - Iter(train) [131000/160000] lr: 2.2286e-03 eta: 4:27:20 time: 0.5551 data_time: 0.0063 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7419 aux.loss_ce: 0.0077 aux.acc_seg: 99.1072 +04/18 22:33:09 - mmengine - INFO - Iter(train) [131050/160000] lr: 2.2253e-03 eta: 4:26:53 time: 0.5554 data_time: 0.0062 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.6530 aux.loss_ce: 0.0072 aux.acc_seg: 98.9323 +04/18 22:33:37 - mmengine - INFO - Iter(train) [131100/160000] lr: 2.2220e-03 eta: 4:26:25 time: 0.5562 data_time: 0.0073 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.6785 aux.loss_ce: 0.0070 aux.acc_seg: 99.1549 +04/18 22:34:05 - mmengine - INFO - Iter(train) [131150/160000] lr: 2.2187e-03 eta: 4:25:58 time: 0.5554 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0066 decode.acc_seg: 99.7717 aux.loss_ce: 0.0073 aux.acc_seg: 99.3612 +04/18 22:34:33 - mmengine - INFO - Iter(train) [131200/160000] lr: 2.2154e-03 eta: 4:25:30 time: 0.5556 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.8078 aux.loss_ce: 0.0073 aux.acc_seg: 99.1734 +04/18 22:35:00 - mmengine - INFO - Iter(train) [131250/160000] lr: 2.2120e-03 eta: 4:25:02 time: 0.5584 data_time: 0.0064 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0068 decode.acc_seg: 99.7023 aux.loss_ce: 0.0077 aux.acc_seg: 99.0439 +04/18 22:35:28 - mmengine - INFO - Iter(train) [131300/160000] lr: 2.2087e-03 eta: 4:24:35 time: 0.5549 data_time: 0.0073 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7127 aux.loss_ce: 0.0068 aux.acc_seg: 99.1218 +04/18 22:35:56 - mmengine - INFO - Iter(train) [131350/160000] lr: 2.2054e-03 eta: 4:24:07 time: 0.5555 data_time: 0.0070 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.7238 aux.loss_ce: 0.0077 aux.acc_seg: 99.0662 +04/18 22:36:24 - mmengine - INFO - Iter(train) [131400/160000] lr: 2.2021e-03 eta: 4:23:39 time: 0.5559 data_time: 0.0065 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7224 aux.loss_ce: 0.0074 aux.acc_seg: 99.1974 +04/18 22:36:52 - mmengine - INFO - Iter(train) [131450/160000] lr: 2.1988e-03 eta: 4:23:12 time: 0.5570 data_time: 0.0071 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0062 decode.acc_seg: 99.7651 aux.loss_ce: 0.0069 aux.acc_seg: 99.3143 +04/18 22:37:20 - mmengine - INFO - Iter(train) [131500/160000] lr: 2.1955e-03 eta: 4:22:44 time: 0.5560 data_time: 0.0073 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7942 aux.loss_ce: 0.0071 aux.acc_seg: 99.2578 +04/18 22:37:48 - mmengine - INFO - Iter(train) [131550/160000] lr: 2.1922e-03 eta: 4:22:17 time: 0.5547 data_time: 0.0069 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.8041 aux.loss_ce: 0.0068 aux.acc_seg: 99.4134 +04/18 22:38:15 - mmengine - INFO - Iter(train) [131600/160000] lr: 2.1889e-03 eta: 4:21:49 time: 0.5573 data_time: 0.0070 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.7626 aux.loss_ce: 0.0073 aux.acc_seg: 99.3815 +04/18 22:38:43 - mmengine - INFO - Iter(train) [131650/160000] lr: 2.1856e-03 eta: 4:21:21 time: 0.5554 data_time: 0.0072 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0061 decode.acc_seg: 99.8192 aux.loss_ce: 0.0069 aux.acc_seg: 99.4833 +04/18 22:39:11 - mmengine - INFO - Iter(train) [131700/160000] lr: 2.1823e-03 eta: 4:20:54 time: 0.5558 data_time: 0.0061 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0064 decode.acc_seg: 99.7510 aux.loss_ce: 0.0077 aux.acc_seg: 99.0620 +04/18 22:39:39 - mmengine - INFO - Iter(train) [131750/160000] lr: 2.1790e-03 eta: 4:20:26 time: 0.5547 data_time: 0.0062 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0061 decode.acc_seg: 99.7917 aux.loss_ce: 0.0071 aux.acc_seg: 99.4679 +04/18 22:40:07 - mmengine - INFO - Iter(train) [131800/160000] lr: 2.1756e-03 eta: 4:19:58 time: 0.5557 data_time: 0.0064 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.7558 aux.loss_ce: 0.0072 aux.acc_seg: 99.3421 +04/18 22:40:34 - mmengine - INFO - Iter(train) [131850/160000] lr: 2.1723e-03 eta: 4:19:31 time: 0.5559 data_time: 0.0067 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7120 aux.loss_ce: 0.0071 aux.acc_seg: 99.1834 +04/18 22:41:02 - mmengine - INFO - Iter(train) [131900/160000] lr: 2.1690e-03 eta: 4:19:03 time: 0.5555 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.6838 aux.loss_ce: 0.0073 aux.acc_seg: 98.8683 +04/18 22:41:30 - mmengine - INFO - Iter(train) [131950/160000] lr: 2.1657e-03 eta: 4:18:35 time: 0.5565 data_time: 0.0063 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7451 aux.loss_ce: 0.0071 aux.acc_seg: 99.2819 +04/18 22:41:58 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:41:58 - mmengine - INFO - Iter(train) [132000/160000] lr: 2.1624e-03 eta: 4:18:08 time: 0.5566 data_time: 0.0067 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7520 aux.loss_ce: 0.0074 aux.acc_seg: 99.1823 +04/18 22:42:25 - mmengine - INFO - Iter(train) [132050/160000] lr: 2.1591e-03 eta: 4:17:40 time: 0.5552 data_time: 0.0066 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7133 aux.loss_ce: 0.0071 aux.acc_seg: 99.2467 +04/18 22:42:53 - mmengine - INFO - Iter(train) [132100/160000] lr: 2.1558e-03 eta: 4:17:13 time: 0.5542 data_time: 0.0066 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0059 decode.acc_seg: 99.7382 aux.loss_ce: 0.0067 aux.acc_seg: 99.0999 +04/18 22:43:21 - mmengine - INFO - Iter(train) [132150/160000] lr: 2.1524e-03 eta: 4:16:45 time: 0.5556 data_time: 0.0065 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.7906 aux.loss_ce: 0.0070 aux.acc_seg: 99.3847 +04/18 22:43:49 - mmengine - INFO - Iter(train) [132200/160000] lr: 2.1491e-03 eta: 4:16:17 time: 0.5571 data_time: 0.0072 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.6938 aux.loss_ce: 0.0074 aux.acc_seg: 99.0777 +04/18 22:44:17 - mmengine - INFO - Iter(train) [132250/160000] lr: 2.1458e-03 eta: 4:15:50 time: 0.5570 data_time: 0.0067 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0071 decode.acc_seg: 99.7658 aux.loss_ce: 0.0080 aux.acc_seg: 99.4457 +04/18 22:44:44 - mmengine - INFO - Iter(train) [132300/160000] lr: 2.1425e-03 eta: 4:15:22 time: 0.5560 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7627 aux.loss_ce: 0.0073 aux.acc_seg: 99.3998 +04/18 22:45:12 - mmengine - INFO - Iter(train) [132350/160000] lr: 2.1392e-03 eta: 4:14:54 time: 0.5563 data_time: 0.0077 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7610 aux.loss_ce: 0.0075 aux.acc_seg: 99.2947 +04/18 22:45:40 - mmengine - INFO - Iter(train) [132400/160000] lr: 2.1359e-03 eta: 4:14:27 time: 0.5549 data_time: 0.0065 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7573 aux.loss_ce: 0.0071 aux.acc_seg: 99.2528 +04/18 22:46:08 - mmengine - INFO - Iter(train) [132450/160000] lr: 2.1325e-03 eta: 4:13:59 time: 0.5559 data_time: 0.0075 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.8019 aux.loss_ce: 0.0072 aux.acc_seg: 99.3565 +04/18 22:46:36 - mmengine - INFO - Iter(train) [132500/160000] lr: 2.1292e-03 eta: 4:13:32 time: 0.5549 data_time: 0.0079 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.6260 aux.loss_ce: 0.0075 aux.acc_seg: 99.0879 +04/18 22:47:03 - mmengine - INFO - Iter(train) [132550/160000] lr: 2.1259e-03 eta: 4:13:04 time: 0.5545 data_time: 0.0066 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.7648 aux.loss_ce: 0.0068 aux.acc_seg: 99.2874 +04/18 22:47:31 - mmengine - INFO - Iter(train) [132600/160000] lr: 2.1226e-03 eta: 4:12:36 time: 0.5560 data_time: 0.0077 memory: 7635 loss: 0.0148 decode.loss_ce: 0.0069 decode.acc_seg: 99.7509 aux.loss_ce: 0.0079 aux.acc_seg: 99.0533 +04/18 22:47:59 - mmengine - INFO - Iter(train) [132650/160000] lr: 2.1193e-03 eta: 4:12:09 time: 0.5552 data_time: 0.0063 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0061 decode.acc_seg: 99.5701 aux.loss_ce: 0.0072 aux.acc_seg: 98.5176 +04/18 22:48:27 - mmengine - INFO - Iter(train) [132700/160000] lr: 2.1159e-03 eta: 4:11:41 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7020 aux.loss_ce: 0.0071 aux.acc_seg: 99.2132 +04/18 22:48:54 - mmengine - INFO - Iter(train) [132750/160000] lr: 2.1126e-03 eta: 4:11:13 time: 0.5557 data_time: 0.0076 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.7324 aux.loss_ce: 0.0074 aux.acc_seg: 99.2095 +04/18 22:49:22 - mmengine - INFO - Iter(train) [132800/160000] lr: 2.1093e-03 eta: 4:10:46 time: 0.5550 data_time: 0.0072 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0060 decode.acc_seg: 99.7230 aux.loss_ce: 0.0069 aux.acc_seg: 99.1921 +04/18 22:49:50 - mmengine - INFO - Iter(train) [132850/160000] lr: 2.1060e-03 eta: 4:10:18 time: 0.5532 data_time: 0.0070 memory: 7635 loss: 0.0125 decode.loss_ce: 0.0061 decode.acc_seg: 99.7101 aux.loss_ce: 0.0064 aux.acc_seg: 99.2487 +04/18 22:50:18 - mmengine - INFO - Iter(train) [132900/160000] lr: 2.1026e-03 eta: 4:09:50 time: 0.5528 data_time: 0.0074 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0062 decode.acc_seg: 99.6808 aux.loss_ce: 0.0071 aux.acc_seg: 99.2229 +04/18 22:50:45 - mmengine - INFO - Iter(train) [132950/160000] lr: 2.0993e-03 eta: 4:09:23 time: 0.5546 data_time: 0.0073 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7187 aux.loss_ce: 0.0074 aux.acc_seg: 99.2344 +04/18 22:51:13 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 22:51:13 - mmengine - INFO - Iter(train) [133000/160000] lr: 2.0960e-03 eta: 4:08:55 time: 0.5545 data_time: 0.0064 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0075 decode.acc_seg: 99.7322 aux.loss_ce: 0.0076 aux.acc_seg: 99.3469 +04/18 22:51:41 - mmengine - INFO - Iter(train) [133050/160000] lr: 2.0927e-03 eta: 4:08:27 time: 0.5533 data_time: 0.0069 memory: 7635 loss: 0.0161 decode.loss_ce: 0.0082 decode.acc_seg: 99.7278 aux.loss_ce: 0.0079 aux.acc_seg: 99.2898 +04/18 22:52:08 - mmengine - INFO - Iter(train) [133100/160000] lr: 2.0893e-03 eta: 4:08:00 time: 0.5540 data_time: 0.0075 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0072 decode.acc_seg: 99.7031 aux.loss_ce: 0.0072 aux.acc_seg: 99.2740 +04/18 22:52:36 - mmengine - INFO - Iter(train) [133150/160000] lr: 2.0860e-03 eta: 4:07:32 time: 0.5536 data_time: 0.0074 memory: 7635 loss: 0.0159 decode.loss_ce: 0.0079 decode.acc_seg: 99.7276 aux.loss_ce: 0.0080 aux.acc_seg: 99.3108 +04/18 22:53:04 - mmengine - INFO - Iter(train) [133200/160000] lr: 2.0827e-03 eta: 4:07:04 time: 0.5529 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0067 decode.acc_seg: 99.6853 aux.loss_ce: 0.0074 aux.acc_seg: 99.2971 +04/18 22:53:32 - mmengine - INFO - Iter(train) [133250/160000] lr: 2.0793e-03 eta: 4:06:37 time: 0.5532 data_time: 0.0064 memory: 7635 loss: 0.0172 decode.loss_ce: 0.0085 decode.acc_seg: 99.5847 aux.loss_ce: 0.0087 aux.acc_seg: 99.1069 +04/18 22:53:59 - mmengine - INFO - Iter(train) [133300/160000] lr: 2.0760e-03 eta: 4:06:09 time: 0.5533 data_time: 0.0071 memory: 7635 loss: 0.0151 decode.loss_ce: 0.0076 decode.acc_seg: 99.6610 aux.loss_ce: 0.0075 aux.acc_seg: 99.1081 +04/18 22:54:27 - mmengine - INFO - Iter(train) [133350/160000] lr: 2.0727e-03 eta: 4:05:42 time: 0.5559 data_time: 0.0066 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0071 decode.acc_seg: 99.7018 aux.loss_ce: 0.0074 aux.acc_seg: 99.1064 +04/18 22:54:55 - mmengine - INFO - Iter(train) [133400/160000] lr: 2.0694e-03 eta: 4:05:14 time: 0.5542 data_time: 0.0078 memory: 7635 loss: 0.0155 decode.loss_ce: 0.0074 decode.acc_seg: 99.6956 aux.loss_ce: 0.0081 aux.acc_seg: 99.1029 +04/18 22:55:22 - mmengine - INFO - Iter(train) [133450/160000] lr: 2.0660e-03 eta: 4:04:46 time: 0.5521 data_time: 0.0069 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.7554 aux.loss_ce: 0.0075 aux.acc_seg: 99.3796 +04/18 22:55:50 - mmengine - INFO - Iter(train) [133500/160000] lr: 2.0627e-03 eta: 4:04:19 time: 0.5527 data_time: 0.0062 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.6744 aux.loss_ce: 0.0075 aux.acc_seg: 99.0935 +04/18 22:56:18 - mmengine - INFO - Iter(train) [133550/160000] lr: 2.0594e-03 eta: 4:03:51 time: 0.5545 data_time: 0.0065 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7576 aux.loss_ce: 0.0072 aux.acc_seg: 99.2503 +04/18 22:56:46 - mmengine - INFO - Iter(train) [133600/160000] lr: 2.0560e-03 eta: 4:03:23 time: 0.5538 data_time: 0.0062 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0072 decode.acc_seg: 99.6710 aux.loss_ce: 0.0078 aux.acc_seg: 99.2356 +04/18 22:57:13 - mmengine - INFO - Iter(train) [133650/160000] lr: 2.0527e-03 eta: 4:02:56 time: 0.5622 data_time: 0.0067 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0069 decode.acc_seg: 99.7643 aux.loss_ce: 0.0077 aux.acc_seg: 99.2272 +04/18 22:57:41 - mmengine - INFO - Iter(train) [133700/160000] lr: 2.0494e-03 eta: 4:02:28 time: 0.5530 data_time: 0.0063 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0065 decode.acc_seg: 99.6926 aux.loss_ce: 0.0069 aux.acc_seg: 99.0005 +04/18 22:58:09 - mmengine - INFO - Iter(train) [133750/160000] lr: 2.0460e-03 eta: 4:02:00 time: 0.5539 data_time: 0.0067 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0070 decode.acc_seg: 99.7770 aux.loss_ce: 0.0071 aux.acc_seg: 99.2735 +04/18 22:58:36 - mmengine - INFO - Iter(train) [133800/160000] lr: 2.0427e-03 eta: 4:01:33 time: 0.5540 data_time: 0.0068 memory: 7635 loss: 0.0145 decode.loss_ce: 0.0073 decode.acc_seg: 99.6379 aux.loss_ce: 0.0073 aux.acc_seg: 99.2218 +04/18 22:59:04 - mmengine - INFO - Iter(train) [133850/160000] lr: 2.0393e-03 eta: 4:01:05 time: 0.5537 data_time: 0.0075 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.7842 aux.loss_ce: 0.0073 aux.acc_seg: 99.4088 +04/18 22:59:32 - mmengine - INFO - Iter(train) [133900/160000] lr: 2.0360e-03 eta: 4:00:37 time: 0.5528 data_time: 0.0065 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.7386 aux.loss_ce: 0.0072 aux.acc_seg: 99.2549 +04/18 22:59:59 - mmengine - INFO - Iter(train) [133950/160000] lr: 2.0327e-03 eta: 4:00:10 time: 0.5541 data_time: 0.0071 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7540 aux.loss_ce: 0.0072 aux.acc_seg: 99.4673 +04/18 23:00:27 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 23:00:27 - mmengine - INFO - Iter(train) [134000/160000] lr: 2.0293e-03 eta: 3:59:42 time: 0.5534 data_time: 0.0068 memory: 7635 loss: 0.0147 decode.loss_ce: 0.0072 decode.acc_seg: 99.7256 aux.loss_ce: 0.0075 aux.acc_seg: 99.2776 +04/18 23:00:55 - mmengine - INFO - Iter(train) [134050/160000] lr: 2.0260e-03 eta: 3:59:14 time: 0.5534 data_time: 0.0066 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0068 decode.acc_seg: 99.7190 aux.loss_ce: 0.0075 aux.acc_seg: 99.4332 +04/18 23:01:23 - mmengine - INFO - Iter(train) [134100/160000] lr: 2.0226e-03 eta: 3:58:47 time: 0.5530 data_time: 0.0071 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0068 decode.acc_seg: 99.7502 aux.loss_ce: 0.0070 aux.acc_seg: 99.2365 +04/18 23:01:50 - mmengine - INFO - Iter(train) [134150/160000] lr: 2.0193e-03 eta: 3:58:19 time: 0.5527 data_time: 0.0074 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7362 aux.loss_ce: 0.0071 aux.acc_seg: 99.1766 +04/18 23:02:18 - mmengine - INFO - Iter(train) [134200/160000] lr: 2.0160e-03 eta: 3:57:51 time: 0.5536 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7310 aux.loss_ce: 0.0071 aux.acc_seg: 99.1065 +04/18 23:02:46 - mmengine - INFO - Iter(train) [134250/160000] lr: 2.0126e-03 eta: 3:57:24 time: 0.5535 data_time: 0.0072 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.8188 aux.loss_ce: 0.0075 aux.acc_seg: 99.2989 +04/18 23:03:13 - mmengine - INFO - Iter(train) [134300/160000] lr: 2.0093e-03 eta: 3:56:56 time: 0.5540 data_time: 0.0072 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0067 decode.acc_seg: 99.7257 aux.loss_ce: 0.0070 aux.acc_seg: 99.3756 +04/18 23:03:41 - mmengine - INFO - Iter(train) [134350/160000] lr: 2.0059e-03 eta: 3:56:28 time: 0.5522 data_time: 0.0067 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0066 decode.acc_seg: 99.7658 aux.loss_ce: 0.0071 aux.acc_seg: 99.3269 +04/18 23:04:09 - mmengine - INFO - Iter(train) [134400/160000] lr: 2.0026e-03 eta: 3:56:01 time: 0.5527 data_time: 0.0071 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0064 decode.acc_seg: 99.7603 aux.loss_ce: 0.0068 aux.acc_seg: 99.5281 +04/18 23:04:36 - mmengine - INFO - Iter(train) [134450/160000] lr: 1.9992e-03 eta: 3:55:33 time: 0.5536 data_time: 0.0074 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0063 decode.acc_seg: 99.8034 aux.loss_ce: 0.0068 aux.acc_seg: 99.4308 +04/18 23:05:04 - mmengine - INFO - Iter(train) [134500/160000] lr: 1.9959e-03 eta: 3:55:05 time: 0.5532 data_time: 0.0074 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7137 aux.loss_ce: 0.0077 aux.acc_seg: 99.3555 +04/18 23:05:32 - mmengine - INFO - Iter(train) [134550/160000] lr: 1.9926e-03 eta: 3:54:38 time: 0.5527 data_time: 0.0071 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0066 decode.acc_seg: 99.7665 aux.loss_ce: 0.0075 aux.acc_seg: 99.2996 +04/18 23:05:59 - mmengine - INFO - Iter(train) [134600/160000] lr: 1.9892e-03 eta: 3:54:10 time: 0.5518 data_time: 0.0063 memory: 7635 loss: 0.0146 decode.loss_ce: 0.0070 decode.acc_seg: 99.7805 aux.loss_ce: 0.0076 aux.acc_seg: 99.3905 +04/18 23:06:27 - mmengine - INFO - Iter(train) [134650/160000] lr: 1.9859e-03 eta: 3:53:43 time: 0.5613 data_time: 0.0071 memory: 7635 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.8212 aux.loss_ce: 0.0066 aux.acc_seg: 99.4777 +04/18 23:06:55 - mmengine - INFO - Iter(train) [134700/160000] lr: 1.9825e-03 eta: 3:53:15 time: 0.5635 data_time: 0.0066 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.6467 aux.loss_ce: 0.0073 aux.acc_seg: 98.8048 +04/18 23:07:22 - mmengine - INFO - Iter(train) [134750/160000] lr: 1.9792e-03 eta: 3:52:47 time: 0.5521 data_time: 0.0066 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.6914 aux.loss_ce: 0.0071 aux.acc_seg: 99.1985 +04/18 23:07:50 - mmengine - INFO - Iter(train) [134800/160000] lr: 1.9758e-03 eta: 3:52:20 time: 0.5546 data_time: 0.0069 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0066 decode.acc_seg: 99.6202 aux.loss_ce: 0.0072 aux.acc_seg: 99.2083 +04/18 23:08:18 - mmengine - INFO - Iter(train) [134850/160000] lr: 1.9725e-03 eta: 3:51:52 time: 0.5513 data_time: 0.0070 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.7522 aux.loss_ce: 0.0068 aux.acc_seg: 99.2096 +04/18 23:08:45 - mmengine - INFO - Iter(train) [134900/160000] lr: 1.9691e-03 eta: 3:51:24 time: 0.5533 data_time: 0.0063 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0065 decode.acc_seg: 99.7433 aux.loss_ce: 0.0072 aux.acc_seg: 99.1336 +04/18 23:09:13 - mmengine - INFO - Iter(train) [134950/160000] lr: 1.9658e-03 eta: 3:50:57 time: 0.5510 data_time: 0.0064 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7322 aux.loss_ce: 0.0073 aux.acc_seg: 99.1809 +04/18 23:09:41 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 23:09:41 - mmengine - INFO - Iter(train) [135000/160000] lr: 1.9624e-03 eta: 3:50:29 time: 0.5533 data_time: 0.0067 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0064 decode.acc_seg: 99.7094 aux.loss_ce: 0.0071 aux.acc_seg: 99.1610 +04/18 23:10:08 - mmengine - INFO - Iter(train) [135050/160000] lr: 1.9591e-03 eta: 3:50:01 time: 0.5515 data_time: 0.0068 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0065 decode.acc_seg: 99.7212 aux.loss_ce: 0.0069 aux.acc_seg: 99.3560 +04/18 23:10:36 - mmengine - INFO - Iter(train) [135100/160000] lr: 1.9557e-03 eta: 3:49:34 time: 0.5521 data_time: 0.0062 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0060 decode.acc_seg: 99.6983 aux.loss_ce: 0.0068 aux.acc_seg: 99.0344 +04/18 23:11:04 - mmengine - INFO - Iter(train) [135150/160000] lr: 1.9524e-03 eta: 3:49:06 time: 0.5527 data_time: 0.0064 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7859 aux.loss_ce: 0.0072 aux.acc_seg: 99.3858 +04/18 23:11:31 - mmengine - INFO - Iter(train) [135200/160000] lr: 1.9490e-03 eta: 3:48:38 time: 0.5522 data_time: 0.0065 memory: 7635 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.6347 aux.loss_ce: 0.0077 aux.acc_seg: 99.1165 +04/18 23:11:59 - mmengine - INFO - Iter(train) [135250/160000] lr: 1.9456e-03 eta: 3:48:11 time: 0.5526 data_time: 0.0061 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0064 decode.acc_seg: 99.7595 aux.loss_ce: 0.0068 aux.acc_seg: 99.3470 +04/18 23:12:27 - mmengine - INFO - Iter(train) [135300/160000] lr: 1.9423e-03 eta: 3:47:43 time: 0.5524 data_time: 0.0076 memory: 7635 loss: 0.0128 decode.loss_ce: 0.0061 decode.acc_seg: 99.7675 aux.loss_ce: 0.0067 aux.acc_seg: 99.2603 +04/18 23:12:54 - mmengine - INFO - Iter(train) [135350/160000] lr: 1.9389e-03 eta: 3:47:15 time: 0.5531 data_time: 0.0077 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0058 decode.acc_seg: 99.8325 aux.loss_ce: 0.0067 aux.acc_seg: 99.3667 +04/18 23:13:22 - mmengine - INFO - Iter(train) [135400/160000] lr: 1.9356e-03 eta: 3:46:48 time: 0.5520 data_time: 0.0060 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0068 decode.acc_seg: 99.7214 aux.loss_ce: 0.0073 aux.acc_seg: 99.1510 +04/18 23:13:49 - mmengine - INFO - Iter(train) [135450/160000] lr: 1.9322e-03 eta: 3:46:20 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0062 decode.acc_seg: 99.7041 aux.loss_ce: 0.0070 aux.acc_seg: 99.0205 +04/18 23:14:17 - mmengine - INFO - Iter(train) [135500/160000] lr: 1.9289e-03 eta: 3:45:52 time: 0.5527 data_time: 0.0069 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0060 decode.acc_seg: 99.8290 aux.loss_ce: 0.0070 aux.acc_seg: 99.3267 +04/18 23:14:45 - mmengine - INFO - Iter(train) [135550/160000] lr: 1.9255e-03 eta: 3:45:25 time: 0.5524 data_time: 0.0071 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0058 decode.acc_seg: 99.7816 aux.loss_ce: 0.0068 aux.acc_seg: 99.3285 +04/18 23:15:12 - mmengine - INFO - Iter(train) [135600/160000] lr: 1.9221e-03 eta: 3:44:57 time: 0.5514 data_time: 0.0064 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.7851 aux.loss_ce: 0.0073 aux.acc_seg: 99.2583 +04/18 23:15:40 - mmengine - INFO - Iter(train) [135650/160000] lr: 1.9188e-03 eta: 3:44:29 time: 0.5526 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0069 decode.acc_seg: 99.7872 aux.loss_ce: 0.0070 aux.acc_seg: 99.4615 +04/18 23:16:08 - mmengine - INFO - Iter(train) [135700/160000] lr: 1.9154e-03 eta: 3:44:02 time: 0.5614 data_time: 0.0072 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7512 aux.loss_ce: 0.0069 aux.acc_seg: 99.3599 +04/18 23:16:35 - mmengine - INFO - Iter(train) [135750/160000] lr: 1.9121e-03 eta: 3:43:34 time: 0.5516 data_time: 0.0064 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0060 decode.acc_seg: 99.7575 aux.loss_ce: 0.0071 aux.acc_seg: 99.1708 +04/18 23:17:03 - mmengine - INFO - Iter(train) [135800/160000] lr: 1.9087e-03 eta: 3:43:06 time: 0.5609 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0068 decode.acc_seg: 99.7773 aux.loss_ce: 0.0072 aux.acc_seg: 99.3564 +04/18 23:17:31 - mmengine - INFO - Iter(train) [135850/160000] lr: 1.9053e-03 eta: 3:42:39 time: 0.5517 data_time: 0.0066 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0066 decode.acc_seg: 99.6757 aux.loss_ce: 0.0074 aux.acc_seg: 98.9448 +04/18 23:17:58 - mmengine - INFO - Iter(train) [135900/160000] lr: 1.9020e-03 eta: 3:42:11 time: 0.5522 data_time: 0.0070 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.8109 aux.loss_ce: 0.0072 aux.acc_seg: 99.4719 +04/18 23:18:26 - mmengine - INFO - Iter(train) [135950/160000] lr: 1.8986e-03 eta: 3:41:43 time: 0.5509 data_time: 0.0069 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.7066 aux.loss_ce: 0.0069 aux.acc_seg: 99.1639 +04/18 23:18:53 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 23:18:53 - mmengine - INFO - Iter(train) [136000/160000] lr: 1.8952e-03 eta: 3:41:16 time: 0.5521 data_time: 0.0069 memory: 7635 loss: 0.0133 decode.loss_ce: 0.0063 decode.acc_seg: 99.7808 aux.loss_ce: 0.0070 aux.acc_seg: 99.3621 +04/18 23:19:21 - mmengine - INFO - Iter(train) [136050/160000] lr: 1.8919e-03 eta: 3:40:48 time: 0.5526 data_time: 0.0066 memory: 7635 loss: 0.0129 decode.loss_ce: 0.0062 decode.acc_seg: 99.7650 aux.loss_ce: 0.0067 aux.acc_seg: 99.3467 +04/18 23:19:49 - mmengine - INFO - Iter(train) [136100/160000] lr: 1.8885e-03 eta: 3:40:20 time: 0.5530 data_time: 0.0072 memory: 7635 loss: 0.0137 decode.loss_ce: 0.0065 decode.acc_seg: 99.6713 aux.loss_ce: 0.0073 aux.acc_seg: 99.1525 +04/18 23:20:16 - mmengine - INFO - Iter(train) [136150/160000] lr: 1.8851e-03 eta: 3:39:53 time: 0.5518 data_time: 0.0071 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.7138 aux.loss_ce: 0.0072 aux.acc_seg: 99.2328 +04/18 23:20:44 - mmengine - INFO - Iter(train) [136200/160000] lr: 1.8818e-03 eta: 3:39:25 time: 0.5516 data_time: 0.0071 memory: 7635 loss: 0.0124 decode.loss_ce: 0.0058 decode.acc_seg: 99.8105 aux.loss_ce: 0.0066 aux.acc_seg: 99.4086 +04/18 23:21:12 - mmengine - INFO - Iter(train) [136250/160000] lr: 1.8784e-03 eta: 3:38:57 time: 0.5508 data_time: 0.0069 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7879 aux.loss_ce: 0.0070 aux.acc_seg: 99.4346 +04/18 23:21:39 - mmengine - INFO - Iter(train) [136300/160000] lr: 1.8750e-03 eta: 3:38:30 time: 0.5519 data_time: 0.0076 memory: 7635 loss: 0.0141 decode.loss_ce: 0.0068 decode.acc_seg: 99.6236 aux.loss_ce: 0.0073 aux.acc_seg: 98.8398 +04/18 23:22:07 - mmengine - INFO - Iter(train) [136350/160000] lr: 1.8716e-03 eta: 3:38:02 time: 0.5534 data_time: 0.0063 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0063 decode.acc_seg: 99.7940 aux.loss_ce: 0.0073 aux.acc_seg: 99.3380 +04/18 23:22:34 - mmengine - INFO - Iter(train) [136400/160000] lr: 1.8683e-03 eta: 3:37:34 time: 0.5536 data_time: 0.0069 memory: 7635 loss: 0.0143 decode.loss_ce: 0.0067 decode.acc_seg: 99.8033 aux.loss_ce: 0.0076 aux.acc_seg: 99.5088 +04/18 23:23:02 - mmengine - INFO - Iter(train) [136450/160000] lr: 1.8649e-03 eta: 3:37:07 time: 0.5525 data_time: 0.0070 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0062 decode.acc_seg: 99.7760 aux.loss_ce: 0.0071 aux.acc_seg: 99.1981 +04/18 23:23:30 - mmengine - INFO - Iter(train) [136500/160000] lr: 1.8615e-03 eta: 3:36:39 time: 0.5528 data_time: 0.0067 memory: 7635 loss: 0.0126 decode.loss_ce: 0.0058 decode.acc_seg: 99.7166 aux.loss_ce: 0.0068 aux.acc_seg: 99.2714 +04/18 23:23:57 - mmengine - INFO - Iter(train) [136550/160000] lr: 1.8582e-03 eta: 3:36:11 time: 0.5515 data_time: 0.0070 memory: 7635 loss: 0.0135 decode.loss_ce: 0.0063 decode.acc_seg: 99.7321 aux.loss_ce: 0.0072 aux.acc_seg: 99.1710 +04/18 23:24:25 - mmengine - INFO - Iter(train) [136600/160000] lr: 1.8548e-03 eta: 3:35:44 time: 0.5525 data_time: 0.0077 memory: 7635 loss: 0.0132 decode.loss_ce: 0.0063 decode.acc_seg: 99.7075 aux.loss_ce: 0.0070 aux.acc_seg: 99.1806 +04/18 23:24:53 - mmengine - INFO - Iter(train) [136650/160000] lr: 1.8514e-03 eta: 3:35:16 time: 0.5527 data_time: 0.0067 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7082 aux.loss_ce: 0.0068 aux.acc_seg: 99.4295 +04/18 23:25:20 - mmengine - INFO - Iter(train) [136700/160000] lr: 1.8480e-03 eta: 3:34:48 time: 0.5517 data_time: 0.0067 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0070 decode.acc_seg: 99.6579 aux.loss_ce: 0.0070 aux.acc_seg: 99.0955 +04/18 23:25:48 - mmengine - INFO - Iter(train) [136750/160000] lr: 1.8447e-03 eta: 3:34:21 time: 0.5505 data_time: 0.0071 memory: 7635 loss: 0.0144 decode.loss_ce: 0.0067 decode.acc_seg: 99.7284 aux.loss_ce: 0.0077 aux.acc_seg: 99.1408 +04/18 23:26:16 - mmengine - INFO - Iter(train) [136800/160000] lr: 1.8413e-03 eta: 3:33:53 time: 0.5600 data_time: 0.0066 memory: 7635 loss: 0.0125 decode.loss_ce: 0.0060 decode.acc_seg: 99.7236 aux.loss_ce: 0.0065 aux.acc_seg: 99.3533 +04/18 23:26:43 - mmengine - INFO - Iter(train) [136850/160000] lr: 1.8379e-03 eta: 3:33:25 time: 0.5526 data_time: 0.0071 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0068 decode.acc_seg: 99.7611 aux.loss_ce: 0.0072 aux.acc_seg: 99.3172 +04/18 23:27:11 - mmengine - INFO - Iter(train) [136900/160000] lr: 1.8345e-03 eta: 3:32:58 time: 0.5513 data_time: 0.0068 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0067 decode.acc_seg: 99.6988 aux.loss_ce: 0.0072 aux.acc_seg: 99.2083 +04/18 23:27:38 - mmengine - INFO - Iter(train) [136950/160000] lr: 1.8311e-03 eta: 3:32:30 time: 0.5529 data_time: 0.0068 memory: 7635 loss: 0.0140 decode.loss_ce: 0.0065 decode.acc_seg: 99.7701 aux.loss_ce: 0.0075 aux.acc_seg: 99.2968 +04/18 23:28:06 - mmengine - INFO - Exp name: upernet_r101_4xb4-160k_cag-512x512_20240418_022212 +04/18 23:28:06 - mmengine - INFO - Iter(train) [137000/160000] lr: 1.8278e-03 eta: 3:32:02 time: 0.5530 data_time: 0.0071 memory: 7635 loss: 0.0150 decode.loss_ce: 0.0069 decode.acc_seg: 99.7358 aux.loss_ce: 0.0080 aux.acc_seg: 99.1114 +04/18 23:28:34 - mmengine - INFO - Iter(train) [137050/160000] lr: 1.8244e-03 eta: 3:31:35 time: 0.5517 data_time: 0.0063 memory: 7635 loss: 0.0130 decode.loss_ce: 0.0062 decode.acc_seg: 99.7225 aux.loss_ce: 0.0069 aux.acc_seg: 99.3691 +04/18 23:29:01 - mmengine - INFO - Iter(train) [137100/160000] lr: 1.8210e-03 eta: 3:31:07 time: 0.5517 data_time: 0.0069 memory: 7635 loss: 0.0136 decode.loss_ce: 0.0064 decode.acc_seg: 99.7283 aux.loss_ce: 0.0072 aux.acc_seg: 99.1338 +04/18 23:29:29 - mmengine - INFO - Iter(train) [137150/160000] lr: 1.8176e-03 eta: 3:30:39 time: 0.5522 data_time: 0.0068 memory: 7635 loss: 0.0138 decode.loss_ce: 0.0065 decode.acc_seg: 99.6776 aux.loss_ce: 0.0072 aux.acc_seg: 99.2564 +04/18 23:29:56 - mmengine - INFO - Iter(train) [137200/160000] lr: 1.8142e-03 eta: 3:30:12 time: 0.5502 data_time: 0.0063 memory: 7635 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7935 aux.loss_ce: 0.0074 aux.acc_seg: 99.3133 +04/18 23:30:24 - mmengine - INFO - Iter(train) [137250/160000] lr: 1.8109e-03 eta: 3:29:44 time: 0.5524 data_time: 0.0064 memory: 7635 loss: 0.0134 decode.loss_ce: 0.0063 decode.acc_seg: 99.7973 aux.loss_ce: 0.0071 aux.acc_seg: 99.4480 +04/18 23:30:52 - mmengine - INFO - Iter(train) [137300/160000] lr: 1.8075e-03 eta: 3:29:16 time: 0.5521 data_time: 0.0068 memory: 7635 loss: 0.0142 decode.loss_ce: 0.0069 decode.acc_seg: 99.7424 aux.loss_ce: 0.0073 aux.acc_seg: 99.1555 +04/18 23:31:19 - mmengine - INFO - Iter(train) [137350/160000] lr: 1.8041e-03 eta: 3:28:49 time: 0.5525 data_time: 0.0065 memory: 7635 loss: 0.0131 decode.loss_ce: 0.0059 decode.acc_seg: 99.7989 aux.loss_ce: 0.0072 aux.acc_seg: 99.2894 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 594961 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 594962 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 594963 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 594967 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + losses = self._run_forward(data, mode='loss') + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + results = self(**data, mode=mode) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + output = self.module(*inputs[0], **kwargs[0]) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss + losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy + correct = correct[:, target != ignore_index] +KeyboardInterrupt +Traceback (most recent call last): +Traceback (most recent call last): +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + main() + main() + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch)self.run_iter(data_batch) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step(outputs = self.runner.model.train_step( + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + losses = self._run_forward(data, mode='loss')losses = self._run_forward(data, mode='loss') + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + results = self(**data, mode=mode)results = self(**data, mode=mode) + + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + self.run_iter(data_batch) + output = self.module(*inputs[0], **kwargs[0]) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + output = self.module(*inputs[0], **kwargs[0]) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + outputs = self.runner.model.train_step( return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + +loss_decode = self._decode_head_forward_train(x, data_samples) File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step + + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss +losses = self._run_forward(data, mode='loss')loss_decode = self.decode_head.loss(inputs, data_samples, + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 161, in _run_forward + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss + losses = self.loss_by_feat(seg_logits, batch_data_samples)results = self(**data, mode=mode) + + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl +losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy +loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy + correct = correct[:, target != ignore_index] + KeyboardInterruptcorrect = correct[:, target != ignore_index] + +KeyboardInterrupt + return forward_call(*input, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 963, in forward + output = self.module(*inputs[0], **kwargs[0]) + File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl + return forward_call(*input, **kwargs) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/base.py", line 94, in forward + return self.loss(inputs, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 178, in loss + loss_decode = self._decode_head_forward_train(x, data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/segmentors/encoder_decoder.py", line 139, in _decode_head_forward_train + loss_decode = self.decode_head.loss(inputs, data_samples, + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 262, in loss + losses = self.loss_by_feat(seg_logits, batch_data_samples) + File "/workspaces/mmsegmentation-1/mmseg/models/decode_heads/decode_head.py", line 336, in loss_by_feat + loss['acc_seg'] = accuracy( + File "/workspaces/mmsegmentation-1/mmseg/models/losses/accuracy.py", line 49, in accuracy + correct = correct[:, target != ignore_index] +KeyboardInterrupt +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 594917 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:20:59 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1523973508 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1523973508 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:21:00 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained= + '/workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000_240419' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:21:02 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.cls_token as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1set param backbone.patch_embed.projection.weight as id 0 + +set param backbone.layers.0.gamma_2 as id 1set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1set param backbone.layers.0.gamma_1 as id 1 + +set param backbone.layers.0.attn.relative_position_bias_table as id 1set param backbone.layers.0.gamma_2 as id 1 + +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.0.ln1.weight as id 1 + +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1set param backbone.layers.0.attn.proj.weight as id 1 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.1.gamma_1 as id 2 + +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.1.ln2.bias as id 2set param backbone.layers.2.gamma_2 as id 3 + +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.1.ffn.layers.1.bias as id 2set param backbone.layers.2.attn.qkv.bias as id 3 + +set param backbone.layers.2.attn.proj.weight as id 3set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.2.ln1.weight as id 3 + +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.2.ln1.bias as id 3 + +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.2.ffn.layers.1.weight as id 3set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.3.attn.qkv.bias as id 4 + +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln2.weight as id 4set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.3.ln2.bias as id 4set param backbone.layers.3.ln1.bias as id 4 + +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.3.attn.qkv.bias as id 4set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.3.ln2.weight as id 4set param backbone.layers.4.gamma_2 as id 5 + +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.4.gamma_2 as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln1.weight as id 5set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.4.ln2.bias as id 5set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.4.attn.qkv.weight as id 5set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.4.ln2.bias as id 5 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.5.ln1.weight as id 6 + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7set param backbone.layers.5.attn.relative_position_bias_table as id 6 + +set param backbone.layers.6.gamma_2 as id 7set param backbone.layers.5.attn.qkv.weight as id 6 + +set param backbone.layers.6.ln1.weight as id 7set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.5.attn.proj.weight as id 6set param backbone.layers.6.attn.relative_position_bias_table as id 7 + +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.5.ln2.bias as id 6set param backbone.layers.6.attn.proj.bias as id 7 + +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7set param backbone.layers.6.attn.qkv.weight as id 7 + +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.7.gamma_1 as id 8 + +set param backbone.layers.7.gamma_2 as id 8set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.7.ln1.weight as id 8set param backbone.layers.6.ln2.bias as id 7 + +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.8.gamma_1 as id 9 + +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.8.attn.proj.bias as id 9 + +set param backbone.layers.7.gamma_2 as id 8set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.9.ln1.weight as id 10 + +set param backbone.layers.9.ln1.bias as id 10set param backbone.layers.7.attn.proj.bias as id 8 + +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8set param backbone.layers.9.ln2.weight as id 10 + +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.7.ffn.layers.1.bias as id 8 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 + +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:21:04 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/R101_4000/iter_50000.pth +04/19 00:21:05 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 00:21:05 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 00:21:05 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/MAE4000_240419. +04/19 00:22:01 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:05:43 time: 1.0235 data_time: 0.0041 memory: 8935 loss: 7.1308 decode.loss_ce: 5.1131 decode.acc_seg: 5.7117 aux.loss_ce: 2.0177 aux.acc_seg: 0.0000 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38950 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38951 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38952 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38953 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) +model = self.train_loop.run() # type: ignore File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.run_iter(data_batch)self.backward(loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward +outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params +torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38950 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38951 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38952 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 38953 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 38920 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 38920 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 38920 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:22:46 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 703928584 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 703928584 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:22:46 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000_240419' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:22:49 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} + +set param backbone.layers.0.ln2.bias as id 1 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2set param backbone.cls_token as id 0 + +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.pos_embed as id 0 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.0.gamma_1 as id 1set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.0.ln1.weight as id 1set param backbone.layers.2.ln1.weight as id 3 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3set param backbone.layers.0.attn.relative_position_bias_table as id 1 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.0.attn.qkv.weight as id 1 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.0.attn.qkv.bias as id 1 + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.1.ln1.bias as id 2 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.4.ln1.weight as id 5 + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.1.attn.proj.weight as id 2 + +set param backbone.layers.1.attn.proj.bias as id 2set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.4.ln2.bias as id 5set param backbone.layers.1.ln2.bias as id 2 + +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.2.gamma_2 as id 3set param backbone.layers.5.ln1.bias as id 6 + +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.5.attn.relative_position_bias_table as id 6 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.5.ffn.layers.1.bias as id 6 + +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.3.gamma_1 as id 4set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.4.ln1.bias as id 5set param backbone.layers.7.attn.qkv.weight as id 8 + +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.7.ffn.layers.0.0.weight as id 8 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.8.ln2.bias as id 9 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.5.attn.qkv.weight as id 6set param backbone.layers.8.ffn.layers.1.weight as id 9 + +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.9.ln1.bias as id 10 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6set param backbone.layers.9.attn.qkv.bias as id 10 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6set param backbone.layers.9.attn.proj.weight as id 10 + +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.5.ffn.layers.1.bias as id 6set param backbone.layers.9.ln2.bias as id 10 + +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.6.gamma_1 as id 7set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.6.gamma_2 as id 7set param backbone.layers.9.ffn.layers.1.weight as id 10 + +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11set param backbone.layers.6.attn.relative_position_bias_table as id 7 + +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.10.ln1.bias as id 11 + +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.10.attn.relative_position_bias_table as id 11 + +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.6.ln2.weight as id 7set param backbone.layers.10.attn.proj.bias as id 11 + +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.11.ffn.layers.1.bias as id 12 + +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param backbone.layers.7.gamma_1 as id 8 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.7.gamma_2 as id 8 +set param neck.upsample_2x.0.weight as id 13 +set param backbone.layers.7.ln1.weight as id 8 +set param neck.upsample_2x.0.bias as id 13 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param decode_head.psp_modules.0.1.conv.weight as id 13set param backbone.layers.7.ln2.weight as id 8 + +set param decode_head.psp_modules.0.1.bn.weight as id 13set param backbone.layers.7.ln2.bias as id 8 + +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param backbone.layers.7.ffn.layers.1.bias as id 8 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param backbone.layers.8.gamma_1 as id 9 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param backbone.layers.8.gamma_2 as id 9 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13set param backbone.layers.8.ln1.weight as id 9 + +set param backbone.layers.8.ln1.bias as id 9 +set param decode_head.bottleneck.conv.weight as id 13 +set param backbone.layers.8.attn.relative_position_bias_table as id 9set param decode_head.bottleneck.bn.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param backbone.layers.8.ln2.weight as id 9set param decode_head.lateral_convs.1.bn.bias as id 13 + +set param backbone.layers.8.ln2.bias as id 9 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param decode_head.fpn_convs.0.conv.weight as id 13set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param backbone.layers.9.gamma_1 as id 10 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13set param backbone.layers.9.gamma_2 as id 10 + +set param decode_head.fpn_bottleneck.conv.weight as id 13set param backbone.layers.9.ln1.weight as id 10 + +set param decode_head.fpn_bottleneck.bn.weight as id 13set param backbone.layers.9.ln1.bias as id 10 + +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10set param auxiliary_head.conv_seg.weight as id 13 + +set param auxiliary_head.conv_seg.bias as id 13 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param backbone.layers.9.ln2.weight as id 10 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param backbone.layers.9.ln2.bias as id 10 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:22:50 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Traceback (most recent call last): +Traceback (most recent call last): +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + File "tools/train.py", line 104, in + main() +main()main()main() File "tools/train.py", line 100, in main + + + + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + runner.train() runner.train() +runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + self._init_model_weights() +self._init_model_weights()self._init_model_weights() File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + model.init_weights()model.init_weights() + + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights +model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights()m.init_weights()m.init_weights()m.init_weights() + + + + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint)state_dict = self.resize_rel_pos_embed(checkpoint)state_dict = self.resize_rel_pos_embed(checkpoint)state_dict = self.resize_rel_pos_embed(checkpoint) + + + + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() dst_num_pos, _ = self.state_dict()[key].size()dst_num_pos, _ = self.state_dict()[key].size() +dst_num_pos, _ = self.state_dict()[key].size() + + +KeyErrorKeyErrorKeyError: KeyError: : 'backbone.layers.0.attn.relative_position_bias_table': 'backbone.layers.0.attn.relative_position_bias_table''backbone.layers.0.attn.relative_position_bias_table' +'backbone.layers.0.attn.relative_position_bias_table' + + +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 40810) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:22:57 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 40811) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:22:57 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 40812) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:22:57 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 40813) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:22:57 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 40810) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:33:14 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1894895559 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1894895559 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:33:14 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000_240418' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:33:17 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +set param backbone.layers.0.ln2.weight as id 1 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 + +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +set param backbone.layers.11.gamma_2 as id 12 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.11.ffn.layers.1.bias as id 12set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.6.attn.proj.bias as id 7 +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.7.gamma_1 as id 8 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param backbone.layers.7.gamma_2 as id 8 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8set param decode_head.psp_modules.1.1.conv.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.weight as id 13set param backbone.layers.7.attn.qkv.weight as id 8 + +set param decode_head.psp_modules.1.1.bn.bias as id 13set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.7.attn.proj.weight as id 8 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param backbone.layers.7.attn.proj.bias as id 8 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param backbone.layers.7.ln2.weight as id 8set param decode_head.psp_modules.2.1.bn.bias as id 13 + +set param backbone.layers.7.ln2.bias as id 8 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param decode_head.psp_modules.3.1.bn.weight as id 13set param backbone.layers.7.ffn.layers.0.0.bias as id 8 + +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param decode_head.bottleneck.conv.weight as id 13set param backbone.layers.7.ffn.layers.1.bias as id 8 + +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param decode_head.lateral_convs.0.conv.weight as id 13set param backbone.layers.8.ln1.weight as id 9 + +set param decode_head.lateral_convs.0.bn.weight as id 13set param backbone.layers.8.ln1.bias as id 9 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param backbone.layers.8.attn.proj.weight as id 9set param decode_head.lateral_convs.1.bn.bias as id 13 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param backbone.layers.8.ln2.weight as id 9 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param backbone.layers.8.ln2.bias as id 9 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param decode_head.fpn_convs.0.bn.weight as id 13set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param decode_head.fpn_convs.1.conv.weight as id 13set param backbone.layers.8.ffn.layers.1.bias as id 9 + +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.9.gamma_2 as id 10 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param backbone.layers.9.ln1.weight as id 10 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param backbone.layers.9.ln1.bias as id 10 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param auxiliary_head.conv_seg.weight as id 13set param backbone.layers.9.ln2.bias as id 10 + +set param auxiliary_head.conv_seg.bias as id 13 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param auxiliary_head.convs.0.conv.weight as id 13set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param auxiliary_head.convs.0.bn.weight as id 13set param backbone.layers.9.ffn.layers.1.weight as id 10 + +set param auxiliary_head.convs.0.bn.bias as id 13set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", 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"decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:33:18 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint) + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() +KeyError: 'backbone.layers.0.attn.relative_position_bias_table' +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint) + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() +KeyError: 'backbone.layers.0.attn.relative_position_bias_table' +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint) + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() +KeyError: 'backbone.layers.0.attn.relative_position_bias_table' +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 185, in init_weights + state_dict = self.resize_rel_pos_embed(checkpoint) + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py", line 463, in resize_rel_pos_embed + dst_num_pos, _ = self.state_dict()[key].size() +KeyError: 'backbone.layers.0.attn.relative_position_bias_table' +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 49439) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:33:20 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 49440) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:33:20 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 49441) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:33:20 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 49445) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:33:20 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 49439) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:36:34 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 135406166 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 135406166 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:36:34 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000_240418' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:36:37 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:36:38 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + self.load_or_resume()main() + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) return CheckpointLoader.load_checkpoint(filename, map_location, logger) +return CheckpointLoader.load_checkpoint(filename, map_location, logger) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError : return checkpoint_loader(filename, map_location) can not be found.return checkpoint_loader(filename, map_location)return checkpoint_loader(filename, map_location) + + + + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.')raise FileNotFoundError(f'{filename} can not be found.')raise FileNotFoundError(f'{filename} can not be found.') + + +FileNotFoundErrorFileNotFoundError: FileNotFoundError: can not be found.: can not be found. + can not be found. + +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 52295) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:36:40 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 52296) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:36:40 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 52297) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:36:40 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 52298) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:36:40 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 52295) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55354 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55355 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55356 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55357 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 93, in main +Traceback (most recent call last): + File "tools/train.py", line 104, in + runner = Runner.from_cfg(cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg + runner = cls( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 403, in __init__ + main()self._log_env(env_cfg) + + File "tools/train.py", line 93, in main + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2368, in _log_env + runner = Runner.from_cfg(cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg + runner = cls( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 403, in __init__ + self._log_env(env_cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2368, in _log_env + env = collect_env() + File "/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/collect_env.py", line 125, in collect_env + env = collect_env() + File "/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/collect_env.py", line 125, in collect_env +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 93, in main + runner = Runner.from_cfg(cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg + runner = cls( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 403, in __init__ + self._log_env(env_cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2368, in _log_env + env = collect_env() + File "/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/collect_env.py", line 125, in collect_env + cc_info = subprocess.check_output(f'{cc} --version', shell=True)cc_info = subprocess.check_output(f'{cc} --version', shell=True) + + File "/opt/conda/lib/python3.8/subprocess.py", line 415, in check_output + File "/opt/conda/lib/python3.8/subprocess.py", line 415, in check_output +cc_info = subprocess.check_output(f'{cc} --version', shell=True) + File "/opt/conda/lib/python3.8/subprocess.py", line 415, in check_output + return run(*popenargs, stdout=PIPE, timeout=timeout, check=True, + File "/opt/conda/lib/python3.8/subprocess.py", line 493, in run + return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,return run(*popenargs, stdout=PIPE, timeout=timeout, check=True, + + File "/opt/conda/lib/python3.8/subprocess.py", line 493, in run + File "/opt/conda/lib/python3.8/subprocess.py", line 493, in run + with Popen(*popenargs, **kwargs) as process: + File "/opt/conda/lib/python3.8/subprocess.py", line 858, in __init__ + with Popen(*popenargs, **kwargs) as process: + File "/opt/conda/lib/python3.8/subprocess.py", line 858, in __init__ + with Popen(*popenargs, **kwargs) as process: + File "/opt/conda/lib/python3.8/subprocess.py", line 858, in __init__ + self._execute_child(args, executable, preexec_fn, close_fds, + File "/opt/conda/lib/python3.8/subprocess.py", line 1639, in _execute_child + self._execute_child(args, executable, preexec_fn, close_fds, + File "/opt/conda/lib/python3.8/subprocess.py", line 1639, in _execute_child + self._execute_child(args, executable, preexec_fn, close_fds, + File "/opt/conda/lib/python3.8/subprocess.py", line 1639, in _execute_child + self.pid = _posixsubprocess.fork_exec(self.pid = _posixsubprocess.fork_exec( + +self.pid = _posixsubprocess.fork_exec( +KeyboardInterruptKeyboardInterrupt + +KeyboardInterrupt +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 93, in main + runner = Runner.from_cfg(cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg + runner = cls( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 403, in __init__ + self._log_env(env_cfg) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2368, in _log_env + env = collect_env() + File "/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/collect_env.py", line 125, in collect_env + cc_info = subprocess.check_output(f'{cc} --version', shell=True) + File "/opt/conda/lib/python3.8/subprocess.py", line 415, in check_output + return run(*popenargs, stdout=PIPE, timeout=timeout, check=True, + File "/opt/conda/lib/python3.8/subprocess.py", line 493, in run + with Popen(*popenargs, **kwargs) as process: + File "/opt/conda/lib/python3.8/subprocess.py", line 858, in __init__ + self._execute_child(args, executable, preexec_fn, close_fds, + File "/opt/conda/lib/python3.8/subprocess.py", line 1639, in _execute_child + self.pid = _posixsubprocess.fork_exec( +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55354 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55355 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55356 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 55357 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 55324 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 55324 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 55324 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:38:45 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1757742453 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1757742453 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:38:45 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:38:48 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14set param backbone.patch_embed.projection.weight as id 0 + +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.cls_token as id 0 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.pos_embed as id 0 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.0.attn.proj.bias as id 1 + +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.0.ffn.layers.0.0.bias as id 1 + +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.0.ffn.layers.1.weight as id 1 + +set param backbone.layers.1.ln2.bias as id 2set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.1.gamma_2 as id 2 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.2.ln2.bias as id 3 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.gamma_1 as id 3set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.2.ffn.layers.1.bias as id 3 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.2.attn.qkv.weight as id 3set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.3.attn.relative_position_bias_table as id 4 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.2.ffn.layers.0.0.weight as id 3 + +set param backbone.layers.3.attn.proj.bias as id 4set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4set param backbone.layers.3.ln1.bias as id 4 + +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.4.attn.qkv.weight as id 5set param backbone.layers.3.ln2.bias as id 4 + +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.4.ln2.bias as id 5 + +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.4.gamma_2 as id 5set param backbone.layers.4.ffn.layers.1.bias as id 5 + +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.4.attn.qkv.bias as id 5 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5set param backbone.layers.5.attn.proj.bias as id 6 + +set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.5.ffn.layers.0.0.weight as id 6 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.5.ffn.layers.1.weight as id 6 + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.7.ln2.bias as id 8 + +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.8.gamma_2 as id 9set param backbone.layers.7.ln1.bias as id 8 + +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.7.attn.relative_position_bias_table as id 8 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.8.attn.relative_position_bias_table as id 9set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.7.attn.proj.weight as id 8 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.8.ln2.bias as id 9 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.8.attn.relative_position_bias_table as id 9set param backbone.layers.9.ln1.weight as id 10 + +set param backbone.layers.9.ln1.bias as id 10set param backbone.layers.8.attn.qkv.weight as id 9 + +set param backbone.layers.8.attn.qkv.bias as id 9set param backbone.layers.9.attn.relative_position_bias_table as id 10 + +set param backbone.layers.8.attn.proj.weight as id 9set param backbone.layers.9.attn.qkv.weight as id 10 + +set param backbone.layers.8.attn.proj.bias as id 9set param backbone.layers.9.attn.qkv.bias as id 10 + +set param backbone.layers.8.ln2.weight as id 9set param backbone.layers.9.attn.proj.weight as id 10 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.9.ffn.layers.0.0.weight as id 10 + +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.10.ln1.bias as id 11 + +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11set param backbone.layers.9.ffn.layers.0.0.weight as id 10 + +set param backbone.layers.10.ln2.bias as id 11set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11set param backbone.layers.10.gamma_1 as id 11 + +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12set param backbone.layers.10.attn.qkv.weight as id 11 + +set param backbone.layers.10.attn.qkv.bias as id 11set param backbone.layers.11.attn.relative_position_bias_table as id 12 + +set param backbone.layers.10.attn.proj.weight as id 11set param backbone.layers.11.attn.qkv.weight as id 12 + +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.10.ln2.weight as id 11set param backbone.layers.11.attn.proj.weight as id 12 + +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12set param backbone.layers.10.ffn.layers.1.weight as id 11 + +set param backbone.layers.11.ffn.layers.0.0.bias as id 12set param backbone.layers.10.ffn.layers.1.bias as id 11 + +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param neck.upsample_4x.1.weight as id 13set param backbone.layers.11.attn.proj.weight as id 12 + +set param neck.upsample_4x.1.bias as id 13set param backbone.layers.11.attn.proj.bias as id 12 + +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.11.ln2.weight as id 12 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.11.ln2.bias as id 12 +set param neck.upsample_2x.0.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12set param neck.upsample_2x.0.bias as id 13 + +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13set param neck.upsample_4x.1.bias as id 13 + +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13set param neck.upsample_2x.0.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13set param decode_head.psp_modules.3.1.conv.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13set param decode_head.psp_modules.0.1.conv.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13set param decode_head.bottleneck.conv.weight as id 13 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13set param decode_head.fpn_convs.1.bn.bias as id 13 + +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13set param decode_head.lateral_convs.2.conv.weight as id 13 + +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13set param decode_head.fpn_convs.0.conv.weight as id 13 + +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13set param auxiliary_head.convs.0.conv.weight as id 13 + +set param auxiliary_head.convs.0.bn.weight as id 13set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param auxiliary_head.convs.0.bn.bias as id 13set param decode_head.fpn_convs.1.bn.weight as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:38:49 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 55633) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:38:51 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 55634) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:38:51 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 55635) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:38:51 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 55636) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:38:51 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 55633) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:41:23 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 856117106 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 856117106 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:41:23 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:41:26 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.cls_token as id 0 + +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.cls_token as id 0 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.pos_embed as id 0 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.1.gamma_2 as id 2 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1set param backbone.layers.1.attn.proj.weight as id 2 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1set param backbone.layers.1.attn.proj.bias as id 2 + +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.1.gamma_1 as id 2 + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3set param backbone.layers.1.ln2.weight as id 2 + +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.1.ffn.layers.0.0.bias as id 2 + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 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"decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:41:27 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 58086) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:41:29 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 58087) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:41:29 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 58088) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:41:29 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 58089) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:41:29 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 58086) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: argument --resume: ignored explicit argument 'auto' +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: argument --resume: ignored explicit argument 'auto' +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: argument --resume: ignored explicit argument 'auto' +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: argument --resume: ignored explicit argument 'auto' +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 2) local_rank: 0 (pid: 59317) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:42:18 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 2 (pid: 59318) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:42:18 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 2 (pid: 59319) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:42:18 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 2 (pid: 59320) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:42:18 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 2 (pid: 59317) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: --load_from= +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: --load_from= +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: --load_from= +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: --load_from= +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 2) local_rank: 0 (pid: 61127) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:44:54 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 2 (pid: 61128) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:44:54 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 2 (pid: 61129) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:44:54 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 2 (pid: 61130) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:44:54 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 2 (pid: 61127) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:45:35 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 172032146 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 172032146 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:45:35 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = '' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:45:38 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.layers.2.ln1.weight as id 3 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 + +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4set param backbone.cls_token as id 0 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.pos_embed as id 0 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.cls_token as id 0set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.pos_embed as id 0set param backbone.patch_embed.projection.weight as id 0set param backbone.layers.3.attn.proj.weight as id 4 + + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.patch_embed.projection.weight as id 0set param backbone.layers.3.ffn.layers.0.0.weight as id 4 + +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.gamma_2 as id 1set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.gamma_1 as id 1 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.0.ln1.bias as id 1set param backbone.layers.4.ln1.weight as id 5set param backbone.layers.0.gamma_2 as id 1 + + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.0.attn.relative_position_bias_table as id 1 + +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.layers.0.attn.relative_position_bias_table as id 1 + + +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.0.ln2.weight as id 1set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.0.ln2.bias as id 1 + + +set param backbone.layers.0.attn.proj.bias as id 1set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.0.ln2.weight as id 1set param backbone.layers.5.ln1.weight as id 6 + +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.5.ln1.bias as id 6set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.0.ffn.layers.1.bias as id 1set param backbone.layers.5.ffn.layers.1.weight as id 6 + + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.1.ln1.bias as id 2set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.6.ln1.weight as id 7set param backbone.layers.1.attn.relative_position_bias_table as id 2 + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.6.attn.relative_position_bias_table as id 7 + +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.1.ln1.bias as id 2set param backbone.layers.6.attn.qkv.bias as id 7 + +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.6.attn.proj.weight as id 7set param backbone.layers.1.attn.relative_position_bias_table as id 2 + + +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.1.attn.proj.bias as id 2 + +set param backbone.layers.6.ln2.weight as id 7set param backbone.layers.1.attn.qkv.weight as id 2 + +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.6.ln2.bias as id 7 + +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.2.gamma_2 as id 3set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + + +set param backbone.layers.1.ffn.layers.1.bias as id 2set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.gamma_2 as id 3 + +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.7.ln1.weight as id 8 + +set param backbone.layers.7.ln1.bias as id 8set param backbone.layers.2.ln1.bias as id 3set param backbone.layers.2.attn.proj.weight as id 3 + + +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.2.attn.qkv.bias as id 3 + + +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.2.ffn.layers.0.0.bias as id 3set param backbone.layers.2.attn.proj.bias as id 3 + + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.2.ffn.layers.1.bias as id 3 + +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.3.gamma_1 as id 4set param backbone.layers.8.ln1.weight as id 9 + + +set param backbone.layers.8.ln1.bias as id 9set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.3.ln1.weight as id 4set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.3.ln1.bias as id 4set param backbone.layers.8.attn.qkv.bias as id 9 + +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.8.attn.proj.weight as id 9set param backbone.layers.3.attn.relative_position_bias_table as id 4 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.8.ln2.bias as id 9set param backbone.layers.3.attn.qkv.bias as id 4 + +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.3.ln1.bias as id 4set param backbone.layers.8.ffn.layers.0.0.bias as id 9set param backbone.layers.3.attn.proj.weight as id 4 + + +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.8.ffn.layers.1.bias as id 9 + +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.ln2.weight as id 4set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.9.gamma_2 as id 10set param backbone.layers.3.ln2.bias as id 4 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.3.attn.proj.bias as id 4set param backbone.layers.9.attn.relative_position_bias_table as id 10 + +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 + +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.3.ffn.layers.1.weight as id 4set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.3.ffn.layers.1.bias as id 4set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.3.ffn.layers.0.0.weight as id 4 + + +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.4.gamma_1 as id 5 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.10.gamma_2 as id 11 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.10.ln1.weight as id 11 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.10.attn.qkv.weight as id 11 + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.10.attn.proj.weight as id 11set param backbone.layers.4.attn.relative_position_bias_table as id 5 + + +set param backbone.layers.10.attn.proj.bias as id 11set param backbone.layers.4.attn.proj.bias as id 5 + +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.10.ln2.bias as id 11 + + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11set param backbone.layers.4.attn.proj.bias as id 5 + +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.11.ln1.weight as id 12set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + + +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.5.gamma_1 as id 6 + + +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.4.ffn.layers.1.weight as id 5set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.11.attn.qkv.bias as id 12 + + +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.11.attn.proj.weight as id 12 + +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.5.ln1.bias as id 6set param backbone.layers.11.ln2.weight as id 12set param backbone.layers.5.gamma_1 as id 6 + + +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.5.attn.relative_position_bias_table as id 6set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.5.attn.qkv.weight as id 6set param backbone.layers.5.ln1.weight as id 6 + +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.5.attn.qkv.bias as id 6set param backbone.layers.11.ffn.layers.1.bias as id 12set param backbone.layers.5.ln1.bias as id 6 + + +set param backbone.layers.5.attn.proj.weight as id 6set param backbone.layers.5.attn.relative_position_bias_table as id 6 + +set param backbone.layers.5.attn.proj.bias as id 6 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.5.attn.qkv.weight as id 6 + +set param backbone.layers.5.ln2.weight as id 6set param neck.upsample_4x.0.bias as id 13 + +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param neck.upsample_4x.1.weight as id 13 +set param backbone.layers.5.attn.proj.weight as id 6set param neck.upsample_4x.1.bias as id 13 + +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6set param neck.upsample_4x.3.weight as id 13 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.5.ln2.weight as id 6 +set param neck.upsample_2x.0.weight as id 13set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 + +set param neck.upsample_2x.0.bias as id 13 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param decode_head.conv_seg.weight as id 13set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.6.gamma_2 as id 7set param decode_head.conv_seg.bias as id 13 + +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.ln1.bias as id 7 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.gamma_1 as id 7 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.ln1.weight as id 7set param backbone.layers.6.attn.qkv.bias as id 7set param decode_head.psp_modules.1.1.conv.weight as id 13 + + +set param backbone.layers.6.ln1.bias as id 7set param decode_head.psp_modules.1.1.bn.weight as id 13 + +set param backbone.layers.6.attn.proj.weight as id 7 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param decode_head.psp_modules.2.1.bn.bias as id 13set param backbone.layers.6.ln2.bias as id 7 + +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7set param decode_head.psp_modules.3.1.conv.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param backbone.layers.6.ln2.weight as id 7set param decode_head.psp_modules.3.1.bn.bias as id 13 + +set param backbone.layers.6.ln2.bias as id 7 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param backbone.layers.6.ffn.layers.1.weight as id 7set param decode_head.fpn_convs.0.bn.bias as id 13 + +set param backbone.layers.6.ffn.layers.1.bias as id 7set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param decode_head.fpn_convs.1.bn.weight as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7set param backbone.layers.7.gamma_1 as id 8 +set param decode_head.fpn_convs.2.conv.weight as id 13 + +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param backbone.layers.7.gamma_2 as id 8set param backbone.layers.6.ffn.layers.1.weight as id 7set param decode_head.fpn_convs.2.bn.bias as id 13 + + +set param backbone.layers.6.ffn.layers.1.bias as id 7set param backbone.layers.7.ln1.weight as id 8set param decode_head.fpn_bottleneck.conv.weight as id 13 + + +set param backbone.layers.7.ln1.bias as id 8set param decode_head.fpn_bottleneck.bn.weight as id 13 + +set param decode_head.fpn_bottleneck.bn.bias as id 13set param backbone.layers.7.gamma_1 as id 8 + +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8set param auxiliary_head.conv_seg.weight as id 13 + +set param backbone.layers.7.ln1.weight as id 8 +set param auxiliary_head.conv_seg.bias as id 13set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.7.ln1.bias as id 8 +set param auxiliary_head.convs.0.conv.weight as id 13set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 + +set param auxiliary_head.convs.0.bn.weight as id 13 +set param backbone.layers.7.attn.proj.bias as id 8set param auxiliary_head.convs.0.bn.bias as id 13 + +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.ln2.weight as id 8set param backbone.layers.7.attn.qkv.bias as id 8 + +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8set param backbone.layers.7.ln2.weight as id 8 + +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9set param backbone.layers.7.ffn.layers.1.weight as id 8 + +set param backbone.layers.7.ffn.layers.1.bias as id 8set param backbone.layers.8.ln1.weight as id 9 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.8.ln1.weight as id 9 + +set param backbone.layers.8.attn.qkv.bias as id 9set param backbone.layers.8.ln1.bias as id 9 + +set param backbone.layers.8.attn.proj.weight as id 9set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.8.ln2.bias as id 9set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.9.ln2.weight as id 10 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10set param backbone.layers.9.ln2.bias as id 10 + +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10set param backbone.layers.10.gamma_1 as id 11 + +set param backbone.layers.9.ffn.layers.1.bias as id 10set param backbone.layers.10.gamma_2 as id 11 + +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ln2.bias as id 11set param backbone.layers.10.ffn.layers.0.0.bias as id 11 + +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11set param backbone.layers.11.gamma_1 as id 12 + +set param backbone.layers.10.ffn.layers.1.bias as id 11set param backbone.layers.11.gamma_2 as id 12 + +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12set param backbone.layers.11.ln1.bias as id 12 + +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12set param backbone.layers.11.attn.proj.weight as id 12 + +set param backbone.layers.11.ln2.bias as id 12set param backbone.layers.11.attn.proj.bias as id 12 + +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12set param backbone.layers.11.ffn.layers.0.0.weight as id 12 + +set param backbone.layers.11.ffn.layers.1.bias as id 12set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13set param neck.upsample_4x.1.weight as id 13 + +set param neck.upsample_4x.3.bias as id 13set param neck.upsample_4x.1.bias as id 13 + +set param neck.upsample_4x.3.weight as id 13set param neck.upsample_2x.0.weight as id 13 + +set param neck.upsample_4x.3.bias as id 13set param neck.upsample_2x.0.bias as id 13 + +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13set param decode_head.psp_modules.1.1.bn.bias as id 13 + +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param decode_head.psp_modules.1.1.bn.bias as id 13 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13set param decode_head.psp_modules.2.1.conv.weight as id 13 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.bottleneck.conv.weight as id 13set param decode_head.psp_modules.3.1.bn.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.bias as id 13set param decode_head.bottleneck.bn.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13set param decode_head.bottleneck.bn.bias as id 13 + +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13set param decode_head.lateral_convs.0.conv.weight as id 13 + +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13set param decode_head.fpn_convs.0.conv.weight as id 13 + +set param decode_head.fpn_convs.0.bn.weight as id 13set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13set param decode_head.fpn_convs.1.conv.weight as id 13 + +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13set param decode_head.fpn_convs.2.conv.weight as id 13 + +set param decode_head.fpn_convs.2.bn.weight as id 13set param decode_head.fpn_bottleneck.conv.weight as id 13 + +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 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+ "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 00:45:39 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +Loads checkpoint by local backend from path: +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1765, in train + self.load_or_resume() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1699, in load_or_resume + self.load_checkpoint(self._load_from) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 2127, in load_checkpoint + checkpoint = _load_checkpoint(filename, map_location=map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 61752) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:45:41 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 61753) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:45:41 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 61754) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:45:41 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 61755) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:45:41 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 61752) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: auto +train.py: error: unrecognized arguments: auto +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: auto +usage: train.py [-h] [--work-dir WORK_DIR] [--resume] [--amp] + [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] + [--launcher {none,pytorch,slurm,mpi}] + [--local_rank LOCAL_RANK] + config +train.py: error: unrecognized arguments: auto +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 2) local_rank: 0 (pid: 62754) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_00:46:07 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 2 (pid: 62755) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_00:46:07 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 2 (pid: 62756) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_00:46:07 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 2 (pid: 62757) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_00:46:07 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 2 (pid: 62754) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 00:49:00 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1281961478 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1281961478 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 00:49:00 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = True +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 00:49:03 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +set param backbone.layers.0.gamma_1 as id 1 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.cls_token as id 0 +set param backbone.layers.0.attn.qkv.weight as id 1set param backbone.pos_embed as id 0 + +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.patch_embed.projection.weight as id 0set param backbone.layers.0.attn.proj.weight as id 1 + +set param backbone.patch_embed.projection.bias as id 0set param backbone.layers.0.attn.proj.bias as id 1 + +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.gamma_1 as id 1set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1set param backbone.layers.0.attn.qkv.bias as id 1 + +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1set param backbone.layers.1.gamma_1 as id 2 + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.0.ln2.bias as id 1set param backbone.layers.1.ln1.weight as id 2 + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.0.ffn.layers.0.0.weight as id 1 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.0.ffn.layers.1.weight as id 1 + +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2set param backbone.layers.1.attn.relative_position_bias_table as id 2 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.1.attn.qkv.weight as id 2 + +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.1.ffn.layers.0.0.weight as id 2 + +set param backbone.layers.2.ln1.bias as id 3set param backbone.layers.1.ffn.layers.0.0.bias as id 2 + +set param backbone.layers.2.attn.relative_position_bias_table as id 3set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.1.ffn.layers.1.bias as id 2set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.3.gamma_2 as id 4set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.3.ln1.weight as id 4set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4set param backbone.layers.3.attn.proj.weight as id 4 + +set param backbone.layers.3.attn.qkv.bias as id 4set param backbone.layers.3.attn.proj.bias as id 4 + +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.3.ln2.weight as id 4 + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.gamma_2 as id 5set param backbone.layers.4.ln1.weight as id 5 + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.4.attn.proj.bias as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.4.ln2.bias as id 5set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.5.gamma_1 as id 6set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.4.ffn.layers.1.bias as id 5 + +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.5.attn.relative_position_bias_table as id 6set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6set param backbone.layers.5.attn.proj.bias as id 6 + +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.5.attn.qkv.weight as id 6 + +set param backbone.layers.5.ln2.bias as id 6set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.5.ffn.layers.0.0.weight as id 6set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.5.ffn.layers.1.bias as id 6 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.6.attn.relative_position_bias_table as id 7set param backbone.layers.5.ffn.layers.1.weight as id 6 + +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.5.ffn.layers.1.bias as id 6 + +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7set param backbone.layers.6.gamma_1 as id 7 + +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param backbone.layers.6.ffn.layers.1.bias as id 7set param backbone.layers.7.ffn.layers.0.0.weight as id 8 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8set param backbone.layers.7.gamma_1 as id 8 + +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.7.attn.relative_position_bias_table as id 8 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.7.attn.qkv.bias as id 8set param backbone.layers.8.attn.qkv.weight as id 9 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9set param backbone.layers.7.ln2.weight as id 8 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.8.ffn.layers.1.weight as id 9 + +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.7.ffn.layers.0.0.bias as id 8 + +set param backbone.layers.7.ffn.layers.1.weight as id 8set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.7.ffn.layers.1.bias as id 8set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10set param backbone.layers.8.gamma_1 as id 9 + +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.8.gamma_2 as id 9 + +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.8.ln1.weight as id 9 + +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.8.attn.qkv.weight as id 9 + +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.8.attn.qkv.bias as id 9 + +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.8.ln2.weight as id 9set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11set param backbone.layers.8.ffn.layers.1.weight as id 9 + +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.10.attn.qkv.weight as id 11 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.10.attn.proj.weight as id 11 + +set param backbone.layers.10.attn.proj.bias as id 11set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.10.ln2.weight as id 11set param backbone.layers.9.ln1.weight as id 10 + +set param backbone.layers.10.ln2.bias as id 11set param backbone.layers.9.ln1.bias as id 10 + +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.9.attn.qkv.bias as id 10set param backbone.layers.10.ffn.layers.1.bias as id 11 + +set param backbone.layers.9.attn.proj.weight as id 10set param backbone.layers.11.gamma_1 as id 12 + +set param backbone.layers.9.attn.proj.bias as id 10set param backbone.layers.11.gamma_2 as id 12 + +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.11.attn.relative_position_bias_table as id 12 + +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.11.ln2.weight as id 12set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.10.gamma_1 as id 11set param backbone.layers.11.ffn.layers.0.0.weight as id 12 + +set param backbone.layers.11.ffn.layers.0.0.bias as id 12set param backbone.layers.10.gamma_2 as id 11 + +set param backbone.layers.11.ffn.layers.1.weight as id 12set param backbone.layers.10.ln1.weight as id 11 + +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.10.attn.qkv.weight as id 11 + +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param neck.upsample_4x.1.weight as id 13set param backbone.layers.10.attn.proj.weight as id 11 + +set param neck.upsample_4x.1.bias as id 13 +set param backbone.layers.10.attn.proj.bias as id 11 +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.10.ln2.weight as id 11 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.10.ln2.bias as id 11 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12set param decode_head.psp_modules.0.1.conv.weight as id 13 + +set param backbone.layers.11.ln1.weight as id 12set param decode_head.psp_modules.0.1.bn.weight as id 13 + +set param backbone.layers.11.ln1.bias as id 12set param decode_head.psp_modules.0.1.bn.bias as id 13 + +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12set param decode_head.psp_modules.1.1.conv.weight as id 13 + +set param backbone.layers.11.attn.qkv.bias as id 12set param decode_head.psp_modules.1.1.bn.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.11.attn.proj.weight as id 12 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param backbone.layers.11.attn.proj.bias as id 12 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13set param backbone.layers.11.ln2.weight as id 12 + +set param backbone.layers.11.ln2.bias as id 12 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.bottleneck.conv.weight as id 13set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param decode_head.bottleneck.bn.weight as id 13 +set param backbone.layers.11.ffn.layers.1.weight as id 12set param decode_head.bottleneck.bn.bias as id 13 + +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param neck.upsample_4x.0.bias as id 13set param decode_head.lateral_convs.1.bn.bias as id 13 + +set param decode_head.lateral_convs.2.conv.weight as id 13set param neck.upsample_4x.1.weight as id 13 + +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param neck.upsample_4x.3.bias as id 13set param decode_head.fpn_convs.0.bn.weight as id 13 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13set param auxiliary_head.conv_seg.bias as id 13 + +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 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INFO - Auto resumed from the latest checkpoint /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +04/19 00:49:06 - mmengine - INFO - Load checkpoint from /workspaces/mmsegmentation-1/work_dirs/MAE4000/iter_60000.pth +04/19 00:49:06 - mmengine - INFO - resumed epoch: 0, iter: 60000 +04/19 00:49:06 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 00:49:06 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 00:49:06 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/MAE4000. +04/19 00:49:06 - mmengine - WARNING - Advance dataloader 60000 steps to skip data that has already been trained +04/19 01:03:46 - mmengine - INFO - Iter(train) [ 60050/160000] base_lr: 6.3060e-05 lr: 2.3315e-07 eta: 20 days, 9:05:08 time: 0.9809 data_time: 0.0044 memory: 8930 loss: 0.0164 decode.loss_ce: 0.0071 decode.acc_seg: 99.5728 aux.loss_ce: 0.0092 aux.acc_seg: 98.9300 +04/19 01:04:36 - mmengine - INFO - Iter(train) [ 60100/160000] base_lr: 6.3029e-05 lr: 2.3303e-07 eta: 10 days, 18:05:59 time: 0.9869 data_time: 0.0046 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.6572 aux.loss_ce: 0.0076 aux.acc_seg: 98.8302 +04/19 01:05:25 - mmengine - INFO - Iter(train) [ 60150/160000] base_lr: 6.2997e-05 lr: 2.3291e-07 eta: 7 days, 13:07:00 time: 0.9888 data_time: 0.0043 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0062 decode.acc_seg: 99.7871 aux.loss_ce: 0.0071 aux.acc_seg: 99.3629 +04/19 01:06:15 - mmengine - INFO - Iter(train) [ 60200/160000] base_lr: 6.2966e-05 lr: 2.3280e-07 eta: 5 days, 22:38:11 time: 0.9928 data_time: 0.0046 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0060 decode.acc_seg: 99.8487 aux.loss_ce: 0.0078 aux.acc_seg: 99.4499 +04/19 01:07:04 - mmengine - INFO - Iter(train) [ 60250/160000] base_lr: 6.2934e-05 lr: 2.3268e-07 eta: 4 days, 23:33:04 time: 0.9917 data_time: 0.0044 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0056 decode.acc_seg: 99.8344 aux.loss_ce: 0.0069 aux.acc_seg: 99.4980 +04/19 01:07:54 - mmengine - INFO - Iter(train) [ 60300/160000] base_lr: 6.2903e-05 lr: 2.3256e-07 eta: 4 days, 8:09:31 time: 0.9937 data_time: 0.0045 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0060 decode.acc_seg: 99.7702 aux.loss_ce: 0.0072 aux.acc_seg: 99.2840 +04/19 01:08:44 - mmengine - INFO - Iter(train) [ 60350/160000] base_lr: 6.2871e-05 lr: 2.3245e-07 eta: 3 days, 21:09:39 time: 0.9942 data_time: 0.0044 memory: 8457 loss: 0.0135 decode.loss_ce: 0.0062 decode.acc_seg: 99.6994 aux.loss_ce: 0.0073 aux.acc_seg: 99.1688 +04/19 01:09:33 - mmengine - INFO - Iter(train) [ 60400/160000] base_lr: 6.2840e-05 lr: 2.3233e-07 eta: 3 days, 12:54:36 time: 0.9924 data_time: 0.0044 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0061 decode.acc_seg: 99.7244 aux.loss_ce: 0.0076 aux.acc_seg: 99.0751 +04/19 01:10:23 - mmengine - INFO - Iter(train) [ 60450/160000] base_lr: 6.2808e-05 lr: 2.3221e-07 eta: 3 days, 6:29:25 time: 0.9936 data_time: 0.0041 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0061 decode.acc_seg: 99.6300 aux.loss_ce: 0.0072 aux.acc_seg: 98.7514 +04/19 01:11:13 - mmengine - INFO - Iter(train) [ 60500/160000] base_lr: 6.2776e-05 lr: 2.3210e-07 eta: 3 days, 1:21:13 time: 0.9934 data_time: 0.0047 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.6748 aux.loss_ce: 0.0070 aux.acc_seg: 99.3412 +04/19 01:12:02 - mmengine - INFO - Iter(train) [ 60550/160000] base_lr: 6.2745e-05 lr: 2.3198e-07 eta: 2 days, 21:08:55 time: 0.9952 data_time: 0.0043 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.7656 aux.loss_ce: 0.0076 aux.acc_seg: 99.2676 +04/19 01:12:52 - mmengine - INFO - Iter(train) [ 60600/160000] base_lr: 6.2713e-05 lr: 2.3186e-07 eta: 2 days, 17:38:32 time: 0.9947 data_time: 0.0046 memory: 8457 loss: 0.0157 decode.loss_ce: 0.0070 decode.acc_seg: 99.6742 aux.loss_ce: 0.0086 aux.acc_seg: 98.7873 +04/19 01:13:42 - mmengine - INFO - Iter(train) [ 60650/160000] base_lr: 6.2682e-05 lr: 2.3175e-07 eta: 2 days, 14:40:24 time: 0.9943 data_time: 0.0048 memory: 8457 loss: 0.0146 decode.loss_ce: 0.0071 decode.acc_seg: 99.7372 aux.loss_ce: 0.0075 aux.acc_seg: 99.4942 +04/19 01:14:32 - mmengine - INFO - Iter(train) [ 60700/160000] base_lr: 6.2650e-05 lr: 2.3163e-07 eta: 2 days, 12:07:39 time: 0.9953 data_time: 0.0050 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0057 decode.acc_seg: 99.8459 aux.loss_ce: 0.0069 aux.acc_seg: 99.5266 +04/19 01:15:21 - mmengine - INFO - Iter(train) [ 60750/160000] base_lr: 6.2619e-05 lr: 2.3151e-07 eta: 2 days, 9:55:12 time: 0.9950 data_time: 0.0046 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0062 decode.acc_seg: 99.8405 aux.loss_ce: 0.0066 aux.acc_seg: 99.4726 +04/19 01:16:11 - mmengine - INFO - Iter(train) [ 60800/160000] base_lr: 6.2587e-05 lr: 2.3140e-07 eta: 2 days, 7:59:14 time: 0.9963 data_time: 0.0047 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.6958 aux.loss_ce: 0.0077 aux.acc_seg: 98.9441 +04/19 01:17:01 - mmengine - INFO - Iter(train) [ 60850/160000] base_lr: 6.2556e-05 lr: 2.3128e-07 eta: 2 days, 6:16:51 time: 0.9958 data_time: 0.0045 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0063 decode.acc_seg: 99.7522 aux.loss_ce: 0.0077 aux.acc_seg: 98.9187 +04/19 01:17:51 - mmengine - INFO - Iter(train) [ 60900/160000] base_lr: 6.2524e-05 lr: 2.3116e-07 eta: 2 days, 4:45:41 time: 0.9943 data_time: 0.0047 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0056 decode.acc_seg: 99.8035 aux.loss_ce: 0.0067 aux.acc_seg: 99.2311 +04/19 01:18:40 - mmengine - INFO - Iter(train) [ 60950/160000] base_lr: 6.2493e-05 lr: 2.3105e-07 eta: 2 days, 3:24:03 time: 0.9961 data_time: 0.0044 memory: 8457 loss: 0.0149 decode.loss_ce: 0.0065 decode.acc_seg: 99.6803 aux.loss_ce: 0.0083 aux.acc_seg: 98.6212 +04/19 01:19:30 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 01:19:30 - mmengine - INFO - Iter(train) [ 61000/160000] base_lr: 6.2461e-05 lr: 2.3093e-07 eta: 2 days, 2:10:35 time: 0.9963 data_time: 0.0042 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0057 decode.acc_seg: 99.7381 aux.loss_ce: 0.0067 aux.acc_seg: 99.1875 +04/19 01:20:20 - mmengine - INFO - Iter(train) [ 61050/160000] base_lr: 6.2429e-05 lr: 2.3081e-07 eta: 2 days, 1:03:59 time: 0.9958 data_time: 0.0046 memory: 8457 loss: 0.0152 decode.loss_ce: 0.0071 decode.acc_seg: 99.5174 aux.loss_ce: 0.0081 aux.acc_seg: 98.8636 +04/19 01:21:10 - mmengine - INFO - Iter(train) [ 61100/160000] base_lr: 6.2398e-05 lr: 2.3070e-07 eta: 2 days, 0:03:25 time: 0.9957 data_time: 0.0043 memory: 8457 loss: 0.0155 decode.loss_ce: 0.0076 decode.acc_seg: 99.6033 aux.loss_ce: 0.0079 aux.acc_seg: 99.1104 +04/19 01:22:00 - mmengine - INFO - Iter(train) [ 61150/160000] base_lr: 6.2366e-05 lr: 2.3058e-07 eta: 1 day, 23:08:02 time: 0.9969 data_time: 0.0045 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0061 decode.acc_seg: 99.7784 aux.loss_ce: 0.0079 aux.acc_seg: 99.1186 +04/19 01:22:50 - mmengine - INFO - Iter(train) [ 61200/160000] base_lr: 6.2335e-05 lr: 2.3046e-07 eta: 1 day, 22:17:13 time: 0.9957 data_time: 0.0048 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0065 decode.acc_seg: 99.7452 aux.loss_ce: 0.0079 aux.acc_seg: 99.3444 +04/19 01:23:39 - mmengine - INFO - Iter(train) [ 61250/160000] base_lr: 6.2303e-05 lr: 2.3035e-07 eta: 1 day, 21:30:22 time: 0.9961 data_time: 0.0044 memory: 8457 loss: 0.0153 decode.loss_ce: 0.0069 decode.acc_seg: 99.7793 aux.loss_ce: 0.0085 aux.acc_seg: 99.4120 +04/19 01:24:29 - mmengine - INFO - Iter(train) [ 61300/160000] base_lr: 6.2272e-05 lr: 2.3023e-07 eta: 1 day, 20:47:05 time: 0.9964 data_time: 0.0050 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0061 decode.acc_seg: 99.7265 aux.loss_ce: 0.0072 aux.acc_seg: 99.2550 +04/19 01:25:19 - mmengine - INFO - Iter(train) [ 61350/160000] base_lr: 6.2240e-05 lr: 2.3011e-07 eta: 1 day, 20:06:58 time: 0.9965 data_time: 0.0042 memory: 8457 loss: 0.0125 decode.loss_ce: 0.0057 decode.acc_seg: 99.7890 aux.loss_ce: 0.0067 aux.acc_seg: 99.2046 +04/19 01:26:09 - mmengine - INFO - Iter(train) [ 61400/160000] base_lr: 6.2209e-05 lr: 2.3000e-07 eta: 1 day, 19:29:38 time: 0.9963 data_time: 0.0046 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0059 decode.acc_seg: 99.7929 aux.loss_ce: 0.0074 aux.acc_seg: 99.2052 +04/19 01:26:59 - mmengine - INFO - Iter(train) [ 61450/160000] base_lr: 6.2177e-05 lr: 2.2988e-07 eta: 1 day, 18:54:50 time: 0.9971 data_time: 0.0044 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0066 decode.acc_seg: 99.7149 aux.loss_ce: 0.0075 aux.acc_seg: 99.1526 +04/19 01:27:49 - mmengine - INFO - Iter(train) [ 61500/160000] base_lr: 6.2146e-05 lr: 2.2976e-07 eta: 1 day, 18:22:20 time: 0.9974 data_time: 0.0043 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0066 decode.acc_seg: 99.7095 aux.loss_ce: 0.0079 aux.acc_seg: 98.9267 +04/19 01:28:39 - mmengine - INFO - Iter(train) [ 61550/160000] base_lr: 6.2114e-05 lr: 2.2965e-07 eta: 1 day, 17:51:51 time: 0.9969 data_time: 0.0050 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0063 decode.acc_seg: 99.7963 aux.loss_ce: 0.0079 aux.acc_seg: 99.4560 +04/19 01:29:28 - mmengine - INFO - Iter(train) [ 61600/160000] base_lr: 6.2082e-05 lr: 2.2953e-07 eta: 1 day, 17:23:13 time: 0.9973 data_time: 0.0046 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.7114 aux.loss_ce: 0.0077 aux.acc_seg: 98.9637 +04/19 01:30:18 - mmengine - INFO - Iter(train) [ 61650/160000] base_lr: 6.2051e-05 lr: 2.2941e-07 eta: 1 day, 16:56:18 time: 0.9978 data_time: 0.0046 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0068 decode.acc_seg: 99.7086 aux.loss_ce: 0.0076 aux.acc_seg: 98.9288 +04/19 01:31:08 - mmengine - INFO - Iter(train) [ 61700/160000] base_lr: 6.2019e-05 lr: 2.2930e-07 eta: 1 day, 16:30:53 time: 0.9968 data_time: 0.0045 memory: 8457 loss: 0.0149 decode.loss_ce: 0.0068 decode.acc_seg: 99.5613 aux.loss_ce: 0.0081 aux.acc_seg: 98.4583 +04/19 01:31:58 - mmengine - INFO - Iter(train) [ 61750/160000] base_lr: 6.1988e-05 lr: 2.2918e-07 eta: 1 day, 16:06:54 time: 0.9974 data_time: 0.0049 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0056 decode.acc_seg: 99.7421 aux.loss_ce: 0.0071 aux.acc_seg: 99.1478 +04/19 01:32:48 - mmengine - INFO - Iter(train) [ 61800/160000] base_lr: 6.1956e-05 lr: 2.2906e-07 eta: 1 day, 15:44:10 time: 0.9975 data_time: 0.0045 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.7810 aux.loss_ce: 0.0070 aux.acc_seg: 99.4858 +04/19 01:33:38 - mmengine - INFO - Iter(train) [ 61850/160000] base_lr: 6.1925e-05 lr: 2.2895e-07 eta: 1 day, 15:22:38 time: 0.9975 data_time: 0.0044 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0060 decode.acc_seg: 99.8119 aux.loss_ce: 0.0072 aux.acc_seg: 99.4720 +04/19 01:34:28 - mmengine - INFO - Iter(train) [ 61900/160000] base_lr: 6.1893e-05 lr: 2.2883e-07 eta: 1 day, 15:02:13 time: 0.9977 data_time: 0.0044 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0059 decode.acc_seg: 99.7007 aux.loss_ce: 0.0075 aux.acc_seg: 99.0156 +04/19 01:35:17 - mmengine - INFO - Iter(train) [ 61950/160000] base_lr: 6.1862e-05 lr: 2.2872e-07 eta: 1 day, 14:42:45 time: 0.9968 data_time: 0.0045 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0060 decode.acc_seg: 99.7168 aux.loss_ce: 0.0066 aux.acc_seg: 99.2653 +04/19 01:36:07 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 01:36:07 - mmengine - INFO - Iter(train) [ 62000/160000] base_lr: 6.1830e-05 lr: 2.2860e-07 eta: 1 day, 14:24:15 time: 0.9968 data_time: 0.0047 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0061 decode.acc_seg: 99.7591 aux.loss_ce: 0.0077 aux.acc_seg: 99.4114 +04/19 01:36:57 - mmengine - INFO - Iter(train) [ 62050/160000] base_lr: 6.1798e-05 lr: 2.2848e-07 eta: 1 day, 14:06:34 time: 0.9956 data_time: 0.0048 memory: 8457 loss: 0.0146 decode.loss_ce: 0.0066 decode.acc_seg: 99.6675 aux.loss_ce: 0.0079 aux.acc_seg: 98.9210 +04/19 01:37:47 - mmengine - INFO - Iter(train) [ 62100/160000] base_lr: 6.1767e-05 lr: 2.2837e-07 eta: 1 day, 13:49:42 time: 0.9973 data_time: 0.0043 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.6946 aux.loss_ce: 0.0073 aux.acc_seg: 99.0738 +04/19 01:38:37 - mmengine - INFO - Iter(train) [ 62150/160000] base_lr: 6.1735e-05 lr: 2.2825e-07 eta: 1 day, 13:33:36 time: 0.9988 data_time: 0.0043 memory: 8457 loss: 0.0121 decode.loss_ce: 0.0057 decode.acc_seg: 99.7206 aux.loss_ce: 0.0064 aux.acc_seg: 99.2146 +04/19 01:39:27 - mmengine - INFO - Iter(train) [ 62200/160000] base_lr: 6.1704e-05 lr: 2.2813e-07 eta: 1 day, 13:18:14 time: 0.9995 data_time: 0.0047 memory: 8457 loss: 0.0125 decode.loss_ce: 0.0057 decode.acc_seg: 99.7284 aux.loss_ce: 0.0068 aux.acc_seg: 99.0816 +04/19 01:40:17 - mmengine - INFO - Iter(train) [ 62250/160000] base_lr: 6.1672e-05 lr: 2.2802e-07 eta: 1 day, 13:03:29 time: 0.9986 data_time: 0.0046 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0056 decode.acc_seg: 99.7137 aux.loss_ce: 0.0073 aux.acc_seg: 99.1442 +04/19 01:41:07 - mmengine - INFO - Iter(train) [ 62300/160000] base_lr: 6.1641e-05 lr: 2.2790e-07 eta: 1 day, 12:49:20 time: 0.9961 data_time: 0.0049 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0055 decode.acc_seg: 99.6380 aux.loss_ce: 0.0065 aux.acc_seg: 98.8453 +04/19 01:41:56 - mmengine - INFO - Iter(train) [ 62350/160000] base_lr: 6.1609e-05 lr: 2.2778e-07 eta: 1 day, 12:35:48 time: 0.9994 data_time: 0.0047 memory: 8457 loss: 0.0160 decode.loss_ce: 0.0076 decode.acc_seg: 99.7267 aux.loss_ce: 0.0084 aux.acc_seg: 99.2651 +04/19 01:42:46 - mmengine - INFO - Iter(train) [ 62400/160000] base_lr: 6.1578e-05 lr: 2.2767e-07 eta: 1 day, 12:22:46 time: 0.9967 data_time: 0.0049 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0070 decode.acc_seg: 99.6550 aux.loss_ce: 0.0076 aux.acc_seg: 98.7083 +04/19 01:43:36 - mmengine - INFO - Iter(train) [ 62450/160000] base_lr: 6.1546e-05 lr: 2.2755e-07 eta: 1 day, 12:10:14 time: 0.9978 data_time: 0.0043 memory: 8457 loss: 0.0149 decode.loss_ce: 0.0072 decode.acc_seg: 99.7229 aux.loss_ce: 0.0076 aux.acc_seg: 99.4339 +04/19 01:44:26 - mmengine - INFO - Iter(train) [ 62500/160000] base_lr: 6.1515e-05 lr: 2.2743e-07 eta: 1 day, 11:58:09 time: 0.9980 data_time: 0.0042 memory: 8457 loss: 0.0160 decode.loss_ce: 0.0074 decode.acc_seg: 99.7686 aux.loss_ce: 0.0086 aux.acc_seg: 99.4291 +04/19 01:45:16 - mmengine - INFO - Iter(train) [ 62550/160000] base_lr: 6.1483e-05 lr: 2.2732e-07 eta: 1 day, 11:46:30 time: 0.9972 data_time: 0.0043 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0059 decode.acc_seg: 99.7599 aux.loss_ce: 0.0069 aux.acc_seg: 99.1179 +04/19 01:46:06 - mmengine - INFO - Iter(train) [ 62600/160000] base_lr: 6.1451e-05 lr: 2.2720e-07 eta: 1 day, 11:35:17 time: 0.9975 data_time: 0.0045 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0057 decode.acc_seg: 99.6805 aux.loss_ce: 0.0071 aux.acc_seg: 99.0713 +04/19 01:46:56 - mmengine - INFO - Iter(train) [ 62650/160000] base_lr: 6.1420e-05 lr: 2.2708e-07 eta: 1 day, 11:24:25 time: 0.9953 data_time: 0.0046 memory: 8457 loss: 0.0151 decode.loss_ce: 0.0067 decode.acc_seg: 99.6197 aux.loss_ce: 0.0084 aux.acc_seg: 99.1144 +04/19 01:47:46 - mmengine - INFO - Iter(train) [ 62700/160000] base_lr: 6.1388e-05 lr: 2.2697e-07 eta: 1 day, 11:13:55 time: 0.9955 data_time: 0.0043 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0064 decode.acc_seg: 99.6607 aux.loss_ce: 0.0082 aux.acc_seg: 99.1154 +04/19 01:48:35 - mmengine - INFO - Iter(train) [ 62750/160000] base_lr: 6.1357e-05 lr: 2.2685e-07 eta: 1 day, 11:03:46 time: 0.9950 data_time: 0.0042 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0065 decode.acc_seg: 99.7042 aux.loss_ce: 0.0077 aux.acc_seg: 99.1091 +04/19 01:49:25 - mmengine - INFO - Iter(train) [ 62800/160000] base_lr: 6.1325e-05 lr: 2.2673e-07 eta: 1 day, 10:53:58 time: 0.9973 data_time: 0.0043 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.6748 aux.loss_ce: 0.0077 aux.acc_seg: 99.3170 +04/19 01:50:15 - mmengine - INFO - Iter(train) [ 62850/160000] base_lr: 6.1294e-05 lr: 2.2662e-07 eta: 1 day, 10:44:30 time: 0.9962 data_time: 0.0049 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0060 decode.acc_seg: 99.7328 aux.loss_ce: 0.0077 aux.acc_seg: 99.1205 +04/19 01:51:05 - mmengine - INFO - Iter(train) [ 62900/160000] base_lr: 6.1262e-05 lr: 2.2650e-07 eta: 1 day, 10:35:19 time: 0.9975 data_time: 0.0046 memory: 8457 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+04/19 01:54:24 - mmengine - INFO - Iter(train) [ 63100/160000] base_lr: 6.1136e-05 lr: 2.2603e-07 eta: 1 day, 10:01:24 time: 0.9978 data_time: 0.0045 memory: 8457 loss: 0.0148 decode.loss_ce: 0.0070 decode.acc_seg: 99.5815 aux.loss_ce: 0.0078 aux.acc_seg: 99.1236 +04/19 01:55:14 - mmengine - INFO - Iter(train) [ 63150/160000] base_lr: 6.1104e-05 lr: 2.2592e-07 eta: 1 day, 9:53:32 time: 0.9985 data_time: 0.0044 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0061 decode.acc_seg: 99.7086 aux.loss_ce: 0.0075 aux.acc_seg: 98.6923 +04/19 01:56:04 - mmengine - INFO - Iter(train) [ 63200/160000] base_lr: 6.1073e-05 lr: 2.2580e-07 eta: 1 day, 9:45:53 time: 0.9989 data_time: 0.0046 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0057 decode.acc_seg: 99.8119 aux.loss_ce: 0.0067 aux.acc_seg: 99.3879 +04/19 01:56:54 - mmengine - INFO - Iter(train) [ 63250/160000] base_lr: 6.1041e-05 lr: 2.2568e-07 eta: 1 day, 9:38:26 time: 0.9975 data_time: 0.0048 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0061 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aux.loss_ce: 0.0066 aux.acc_seg: 99.3591 +04/19 02:19:23 - mmengine - INFO - Iter(train) [ 64600/160000] base_lr: 6.0190e-05 lr: 2.2253e-07 eta: 1 day, 7:12:15 time: 0.9984 data_time: 0.0043 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0061 decode.acc_seg: 99.6122 aux.loss_ce: 0.0081 aux.acc_seg: 98.8277 +04/19 02:20:13 - mmengine - INFO - Iter(train) [ 64650/160000] base_lr: 6.0158e-05 lr: 2.2242e-07 eta: 1 day, 7:08:13 time: 0.9981 data_time: 0.0045 memory: 8457 loss: 0.0147 decode.loss_ce: 0.0067 decode.acc_seg: 99.6441 aux.loss_ce: 0.0080 aux.acc_seg: 99.1022 +04/19 02:21:03 - mmengine - INFO - Iter(train) [ 64700/160000] base_lr: 6.0127e-05 lr: 2.2230e-07 eta: 1 day, 7:04:16 time: 1.0002 data_time: 0.0048 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0063 decode.acc_seg: 99.7303 aux.loss_ce: 0.0076 aux.acc_seg: 99.0334 +04/19 02:21:53 - mmengine - INFO - Iter(train) [ 64750/160000] base_lr: 6.0095e-05 lr: 2.2218e-07 eta: 1 day, 7:00:22 time: 1.0001 data_time: 0.0045 memory: 8457 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INFO - Iter(train) [ 65700/160000] base_lr: 5.9496e-05 lr: 2.1997e-07 eta: 1 day, 5:56:37 time: 1.0004 data_time: 0.0047 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0063 decode.acc_seg: 99.6353 aux.loss_ce: 0.0080 aux.acc_seg: 98.9258 +04/19 02:38:32 - mmengine - INFO - Iter(train) [ 65750/160000] base_lr: 5.9464e-05 lr: 2.1985e-07 eta: 1 day, 5:53:42 time: 0.9989 data_time: 0.0045 memory: 8457 loss: 0.0135 decode.loss_ce: 0.0060 decode.acc_seg: 99.7807 aux.loss_ce: 0.0075 aux.acc_seg: 99.2249 +04/19 02:39:22 - mmengine - INFO - Iter(train) [ 65800/160000] base_lr: 5.9433e-05 lr: 2.1973e-07 eta: 1 day, 5:50:50 time: 0.9999 data_time: 0.0051 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0057 decode.acc_seg: 99.7437 aux.loss_ce: 0.0074 aux.acc_seg: 99.3151 +04/19 02:40:12 - mmengine - INFO - Iter(train) [ 65850/160000] base_lr: 5.9401e-05 lr: 2.1962e-07 eta: 1 day, 5:47:59 time: 1.0005 data_time: 0.0052 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0064 decode.acc_seg: 99.7002 aux.loss_ce: 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mmengine - INFO - Iter(train) [ 66250/160000] base_lr: 5.9149e-05 lr: 2.1868e-07 eta: 1 day, 5:26:26 time: 0.9992 data_time: 0.0045 memory: 8457 loss: 0.0153 decode.loss_ce: 0.0071 decode.acc_seg: 99.7150 aux.loss_ce: 0.0081 aux.acc_seg: 99.1611 +04/19 02:47:42 - mmengine - INFO - Iter(train) [ 66300/160000] base_lr: 5.9117e-05 lr: 2.1857e-07 eta: 1 day, 5:23:52 time: 1.0002 data_time: 0.0047 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0063 decode.acc_seg: 99.7278 aux.loss_ce: 0.0078 aux.acc_seg: 99.1610 +04/19 02:48:32 - mmengine - INFO - Iter(train) [ 66350/160000] base_lr: 5.9086e-05 lr: 2.1845e-07 eta: 1 day, 5:21:20 time: 0.9987 data_time: 0.0044 memory: 8457 loss: 0.0110 decode.loss_ce: 0.0052 decode.acc_seg: 99.8354 aux.loss_ce: 0.0058 aux.acc_seg: 99.5626 +04/19 02:49:22 - mmengine - INFO - Iter(train) [ 66400/160000] base_lr: 5.9054e-05 lr: 2.1833e-07 eta: 1 day, 5:18:49 time: 0.9990 data_time: 0.0042 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0061 decode.acc_seg: 99.8220 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memory: 8457 loss: 0.0125 decode.loss_ce: 0.0057 decode.acc_seg: 99.7541 aux.loss_ce: 0.0068 aux.acc_seg: 99.2014 +04/19 03:02:42 - mmengine - INFO - Iter(train) [ 67200/160000] base_lr: 5.8549e-05 lr: 2.1647e-07 eta: 1 day, 4:41:52 time: 0.9990 data_time: 0.0045 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.6986 aux.loss_ce: 0.0076 aux.acc_seg: 99.0858 +04/19 03:03:32 - mmengine - INFO - Iter(train) [ 67250/160000] base_lr: 5.8518e-05 lr: 2.1635e-07 eta: 1 day, 4:39:44 time: 1.0010 data_time: 0.0044 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0058 decode.acc_seg: 99.7366 aux.loss_ce: 0.0070 aux.acc_seg: 99.3134 +04/19 03:04:22 - mmengine - INFO - Iter(train) [ 67300/160000] base_lr: 5.8486e-05 lr: 2.1624e-07 eta: 1 day, 4:37:37 time: 1.0003 data_time: 0.0044 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0067 decode.acc_seg: 99.6817 aux.loss_ce: 0.0078 aux.acc_seg: 99.2720 +04/19 03:05:12 - mmengine - INFO - Iter(train) [ 67350/160000] base_lr: 5.8455e-05 lr: 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INFO - Iter(train) [ 67550/160000] base_lr: 5.8328e-05 lr: 2.1565e-07 eta: 1 day, 4:27:17 time: 0.9990 data_time: 0.0042 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0055 decode.acc_seg: 99.7091 aux.loss_ce: 0.0067 aux.acc_seg: 99.1322 +04/19 03:09:22 - mmengine - INFO - Iter(train) [ 67600/160000] base_lr: 5.8297e-05 lr: 2.1554e-07 eta: 1 day, 4:25:16 time: 1.0010 data_time: 0.0051 memory: 8457 loss: 0.0147 decode.loss_ce: 0.0066 decode.acc_seg: 99.6870 aux.loss_ce: 0.0081 aux.acc_seg: 98.9845 +04/19 03:10:12 - mmengine - INFO - Iter(train) [ 67650/160000] base_lr: 5.8265e-05 lr: 2.1542e-07 eta: 1 day, 4:23:16 time: 0.9992 data_time: 0.0044 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0062 decode.acc_seg: 99.6229 aux.loss_ce: 0.0080 aux.acc_seg: 99.0772 +04/19 03:11:02 - mmengine - INFO - Iter(train) [ 67700/160000] base_lr: 5.8234e-05 lr: 2.1530e-07 eta: 1 day, 4:21:16 time: 0.9984 data_time: 0.0047 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0059 decode.acc_seg: 99.8013 aux.loss_ce: 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decode.loss_ce: 0.0066 decode.acc_seg: 99.6967 aux.loss_ce: 0.0079 aux.acc_seg: 98.7911 +04/19 03:15:12 - mmengine - INFO - Iter(train) [ 67950/160000] base_lr: 5.8076e-05 lr: 2.1472e-07 eta: 1 day, 4:11:33 time: 1.0002 data_time: 0.0044 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0059 decode.acc_seg: 99.8369 aux.loss_ce: 0.0071 aux.acc_seg: 99.5193 +04/19 03:16:02 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 03:16:02 - mmengine - INFO - Iter(train) [ 68000/160000] base_lr: 5.8045e-05 lr: 2.1460e-07 eta: 1 day, 4:09:39 time: 0.9999 data_time: 0.0047 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0063 decode.acc_seg: 99.7166 aux.loss_ce: 0.0078 aux.acc_seg: 99.2054 +04/19 03:16:52 - mmengine - INFO - Iter(train) [ 68050/160000] base_lr: 5.8013e-05 lr: 2.1449e-07 eta: 1 day, 4:07:46 time: 1.0013 data_time: 0.0046 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0063 decode.acc_seg: 99.6471 aux.loss_ce: 0.0081 aux.acc_seg: 98.9067 +04/19 03:17:42 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aux.loss_ce: 0.0075 aux.acc_seg: 99.1526 +04/19 03:21:02 - mmengine - INFO - Iter(train) [ 68300/160000] base_lr: 5.7855e-05 lr: 2.1390e-07 eta: 1 day, 3:58:31 time: 1.0000 data_time: 0.0043 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0057 decode.acc_seg: 99.6550 aux.loss_ce: 0.0073 aux.acc_seg: 98.7543 +04/19 03:21:52 - mmengine - INFO - Iter(train) [ 68350/160000] base_lr: 5.7824e-05 lr: 2.1379e-07 eta: 1 day, 3:56:42 time: 1.0000 data_time: 0.0047 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0060 decode.acc_seg: 99.8215 aux.loss_ce: 0.0074 aux.acc_seg: 99.4900 +04/19 03:22:42 - mmengine - INFO - Iter(train) [ 68400/160000] base_lr: 5.7792e-05 lr: 2.1367e-07 eta: 1 day, 3:54:53 time: 0.9983 data_time: 0.0047 memory: 8457 loss: 0.0153 decode.loss_ce: 0.0067 decode.acc_seg: 99.6662 aux.loss_ce: 0.0086 aux.acc_seg: 98.7791 +04/19 03:23:32 - mmengine - INFO - Iter(train) [ 68450/160000] base_lr: 5.7761e-05 lr: 2.1355e-07 eta: 1 day, 3:53:05 time: 1.0010 data_time: 0.0047 memory: 8457 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INFO - Iter(train) [ 69400/160000] base_lr: 5.7161e-05 lr: 2.1134e-07 eta: 1 day, 3:20:57 time: 0.9998 data_time: 0.0048 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0058 decode.acc_seg: 99.7053 aux.loss_ce: 0.0075 aux.acc_seg: 98.9986 +04/19 03:40:12 - mmengine - INFO - Iter(train) [ 69450/160000] base_lr: 5.7130e-05 lr: 2.1122e-07 eta: 1 day, 3:19:21 time: 0.9990 data_time: 0.0045 memory: 8457 loss: 0.0125 decode.loss_ce: 0.0057 decode.acc_seg: 99.7320 aux.loss_ce: 0.0067 aux.acc_seg: 99.3029 +04/19 03:41:02 - mmengine - INFO - Iter(train) [ 69500/160000] base_lr: 5.7098e-05 lr: 2.1110e-07 eta: 1 day, 3:17:45 time: 0.9993 data_time: 0.0045 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0066 decode.acc_seg: 99.7351 aux.loss_ce: 0.0073 aux.acc_seg: 99.2998 +04/19 03:41:52 - mmengine - INFO - Iter(train) [ 69550/160000] base_lr: 5.7067e-05 lr: 2.1099e-07 eta: 1 day, 3:16:10 time: 0.9989 data_time: 0.0048 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0058 decode.acc_seg: 99.8100 aux.loss_ce: 0.0071 aux.acc_seg: 99.4490 +04/19 03:42:42 - mmengine - INFO - Iter(train) [ 69600/160000] base_lr: 5.7035e-05 lr: 2.1087e-07 eta: 1 day, 3:14:35 time: 0.9978 data_time: 0.0046 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0060 decode.acc_seg: 99.7519 aux.loss_ce: 0.0074 aux.acc_seg: 99.4026 +04/19 03:43:32 - mmengine - INFO - Iter(train) [ 69650/160000] base_lr: 5.7004e-05 lr: 2.1075e-07 eta: 1 day, 3:13:00 time: 0.9979 data_time: 0.0044 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0055 decode.acc_seg: 99.7826 aux.loss_ce: 0.0069 aux.acc_seg: 99.2519 +04/19 03:44:22 - mmengine - INFO - Iter(train) [ 69700/160000] base_lr: 5.6972e-05 lr: 2.1064e-07 eta: 1 day, 3:11:25 time: 0.9978 data_time: 0.0045 memory: 8457 loss: 0.0121 decode.loss_ce: 0.0054 decode.acc_seg: 99.7658 aux.loss_ce: 0.0066 aux.acc_seg: 99.1362 +04/19 03:45:12 - mmengine - INFO - Iter(train) [ 69750/160000] base_lr: 5.6940e-05 lr: 2.1052e-07 eta: 1 day, 3:09:52 time: 0.9974 data_time: 0.0046 memory: 8457 loss: 0.0111 decode.loss_ce: 0.0051 decode.acc_seg: 99.7854 aux.loss_ce: 0.0060 aux.acc_seg: 99.4383 +04/19 03:46:02 - mmengine - INFO - Iter(train) [ 69800/160000] base_lr: 5.6909e-05 lr: 2.1040e-07 eta: 1 day, 3:08:18 time: 1.0000 data_time: 0.0047 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0065 decode.acc_seg: 99.7082 aux.loss_ce: 0.0073 aux.acc_seg: 99.2994 +04/19 03:46:51 - mmengine - INFO - Iter(train) [ 69850/160000] base_lr: 5.6877e-05 lr: 2.1029e-07 eta: 1 day, 3:06:45 time: 0.9984 data_time: 0.0047 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0063 decode.acc_seg: 99.6773 aux.loss_ce: 0.0078 aux.acc_seg: 99.0072 +04/19 03:47:41 - mmengine - INFO - Iter(train) [ 69900/160000] base_lr: 5.6846e-05 lr: 2.1017e-07 eta: 1 day, 3:05:12 time: 0.9976 data_time: 0.0046 memory: 8457 loss: 0.0146 decode.loss_ce: 0.0063 decode.acc_seg: 99.7482 aux.loss_ce: 0.0083 aux.acc_seg: 99.1983 +04/19 03:48:31 - mmengine - INFO - Iter(train) [ 69950/160000] base_lr: 5.6814e-05 lr: 2.1005e-07 eta: 1 day, 3:03:40 time: 0.9979 data_time: 0.0051 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0059 decode.acc_seg: 99.7807 aux.loss_ce: 0.0072 aux.acc_seg: 99.3937 +04/19 03:49:21 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 03:49:21 - mmengine - INFO - Iter(train) [ 70000/160000] base_lr: 5.6783e-05 lr: 2.0994e-07 eta: 1 day, 3:02:07 time: 0.9969 data_time: 0.0045 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0059 decode.acc_seg: 99.7444 aux.loss_ce: 0.0075 aux.acc_seg: 99.2407 +04/19 03:49:21 - mmengine - INFO - Saving checkpoint at 70000 iterations +04/19 03:49:32 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:21 time: 0.1162 data_time: 0.0015 memory: 7120 +04/19 03:49:38 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:12 time: 0.1160 data_time: 0.0015 memory: 3999 +04/19 03:49:44 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:06 time: 0.1168 data_time: 0.0015 memory: 3999 +04/19 03:49:50 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1156 data_time: 0.0013 memory: 3999 +04/19 03:49:50 - mmengine - INFO - per class results: +04/19 03:49:50 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.17 | 99.56 | 99.58 | 99.61 | 99.56 | +| contrast | 81.94 | 90.71 | 90.07 | 89.45 | 90.71 | ++------------+-------+-------+--------+-----------+--------+ +04/19 03:49:50 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.2000 mIoU: 90.5600 mAcc: 95.1300 mFscore: 94.8300 mPrecision: 94.5300 mRecall: 95.1300 data_time: 0.0024 time: 0.1227 +04/19 03:50:40 - mmengine - INFO - Iter(train) [ 70050/160000] base_lr: 5.6751e-05 lr: 2.0982e-07 eta: 1 day, 3:00:37 time: 0.9962 data_time: 0.0044 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0066 decode.acc_seg: 99.6668 aux.loss_ce: 0.0070 aux.acc_seg: 99.2702 +04/19 03:51:30 - mmengine - INFO - Iter(train) [ 70100/160000] base_lr: 5.6720e-05 lr: 2.0970e-07 eta: 1 day, 2:59:06 time: 0.9969 data_time: 0.0048 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0059 decode.acc_seg: 99.7742 aux.loss_ce: 0.0071 aux.acc_seg: 99.4482 +04/19 03:52:20 - mmengine - INFO - Iter(train) [ 70150/160000] base_lr: 5.6688e-05 lr: 2.0959e-07 eta: 1 day, 2:57:35 time: 0.9974 data_time: 0.0047 memory: 8457 loss: 0.0112 decode.loss_ce: 0.0050 decode.acc_seg: 99.7908 aux.loss_ce: 0.0062 aux.acc_seg: 99.1558 +04/19 03:53:10 - mmengine - INFO - Iter(train) [ 70200/160000] base_lr: 5.6657e-05 lr: 2.0947e-07 eta: 1 day, 2:56:05 time: 0.9973 data_time: 0.0044 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0060 decode.acc_seg: 99.7467 aux.loss_ce: 0.0072 aux.acc_seg: 99.3105 +04/19 03:53:59 - mmengine - INFO - Iter(train) [ 70250/160000] base_lr: 5.6625e-05 lr: 2.0935e-07 eta: 1 day, 2:54:35 time: 0.9976 data_time: 0.0047 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0066 decode.acc_seg: 99.5974 aux.loss_ce: 0.0079 aux.acc_seg: 99.0288 +04/19 03:54:49 - mmengine - INFO - Iter(train) [ 70300/160000] base_lr: 5.6593e-05 lr: 2.0924e-07 eta: 1 day, 2:53:05 time: 0.9987 data_time: 0.0046 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7795 aux.loss_ce: 0.0073 aux.acc_seg: 99.4055 +04/19 03:55:39 - mmengine - INFO - Iter(train) [ 70350/160000] base_lr: 5.6562e-05 lr: 2.0912e-07 eta: 1 day, 2:51:36 time: 0.9970 data_time: 0.0048 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0058 decode.acc_seg: 99.7595 aux.loss_ce: 0.0069 aux.acc_seg: 99.1049 +04/19 03:56:29 - mmengine - INFO - Iter(train) [ 70400/160000] base_lr: 5.6530e-05 lr: 2.0900e-07 eta: 1 day, 2:50:08 time: 0.9981 data_time: 0.0047 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0051 decode.acc_seg: 99.7854 aux.loss_ce: 0.0064 aux.acc_seg: 99.1919 +04/19 03:57:19 - mmengine - INFO - Iter(train) [ 70450/160000] base_lr: 5.6499e-05 lr: 2.0889e-07 eta: 1 day, 2:48:39 time: 0.9968 data_time: 0.0046 memory: 8457 loss: 0.0157 decode.loss_ce: 0.0073 decode.acc_seg: 99.7282 aux.loss_ce: 0.0084 aux.acc_seg: 99.1796 +04/19 03:58:09 - mmengine - INFO - Iter(train) [ 70500/160000] base_lr: 5.6467e-05 lr: 2.0877e-07 eta: 1 day, 2:47:11 time: 0.9973 data_time: 0.0046 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0064 decode.acc_seg: 99.5211 aux.loss_ce: 0.0079 aux.acc_seg: 98.7547 +04/19 03:58:59 - mmengine - INFO - Iter(train) [ 70550/160000] base_lr: 5.6436e-05 lr: 2.0865e-07 eta: 1 day, 2:45:43 time: 0.9971 data_time: 0.0048 memory: 8457 loss: 0.0152 decode.loss_ce: 0.0066 decode.acc_seg: 99.7805 aux.loss_ce: 0.0086 aux.acc_seg: 99.4310 +04/19 03:59:49 - mmengine - INFO - Iter(train) [ 70600/160000] base_lr: 5.6404e-05 lr: 2.0854e-07 eta: 1 day, 2:44:16 time: 0.9974 data_time: 0.0045 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0064 decode.acc_seg: 99.6746 aux.loss_ce: 0.0079 aux.acc_seg: 98.9529 +04/19 04:00:39 - mmengine - INFO - Iter(train) [ 70650/160000] base_lr: 5.6373e-05 lr: 2.0842e-07 eta: 1 day, 2:42:49 time: 0.9985 data_time: 0.0046 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0054 decode.acc_seg: 99.7784 aux.loss_ce: 0.0066 aux.acc_seg: 99.1545 +04/19 04:01:28 - mmengine - INFO - Iter(train) [ 70700/160000] base_lr: 5.6341e-05 lr: 2.0830e-07 eta: 1 day, 2:41:22 time: 0.9983 data_time: 0.0047 memory: 8457 loss: 0.0119 decode.loss_ce: 0.0054 decode.acc_seg: 99.7101 aux.loss_ce: 0.0064 aux.acc_seg: 99.1886 +04/19 04:02:18 - mmengine - INFO - Iter(train) [ 70750/160000] base_lr: 5.6310e-05 lr: 2.0819e-07 eta: 1 day, 2:39:56 time: 0.9975 data_time: 0.0045 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0058 decode.acc_seg: 99.7566 aux.loss_ce: 0.0071 aux.acc_seg: 99.3814 +04/19 04:03:08 - mmengine - INFO - Iter(train) [ 70800/160000] base_lr: 5.6278e-05 lr: 2.0807e-07 eta: 1 day, 2:38:30 time: 0.9976 data_time: 0.0049 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0056 decode.acc_seg: 99.7635 aux.loss_ce: 0.0074 aux.acc_seg: 99.0618 +04/19 04:03:58 - mmengine - INFO - Iter(train) [ 70850/160000] base_lr: 5.6246e-05 lr: 2.0795e-07 eta: 1 day, 2:37:05 time: 0.9962 data_time: 0.0045 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0059 decode.acc_seg: 99.6508 aux.loss_ce: 0.0070 aux.acc_seg: 98.9620 +04/19 04:04:48 - mmengine - INFO - Iter(train) [ 70900/160000] base_lr: 5.6215e-05 lr: 2.0784e-07 eta: 1 day, 2:35:40 time: 0.9984 data_time: 0.0044 memory: 8457 loss: 0.0150 decode.loss_ce: 0.0068 decode.acc_seg: 99.5358 aux.loss_ce: 0.0082 aux.acc_seg: 98.5611 +04/19 04:05:38 - mmengine - INFO - Iter(train) [ 70950/160000] base_lr: 5.6183e-05 lr: 2.0772e-07 eta: 1 day, 2:34:15 time: 0.9982 data_time: 0.0047 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0056 decode.acc_seg: 99.7366 aux.loss_ce: 0.0064 aux.acc_seg: 99.2506 +04/19 04:06:28 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 04:06:28 - mmengine - INFO - Iter(train) [ 71000/160000] base_lr: 5.6152e-05 lr: 2.0760e-07 eta: 1 day, 2:32:50 time: 0.9985 data_time: 0.0052 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0059 decode.acc_seg: 99.7271 aux.loss_ce: 0.0072 aux.acc_seg: 99.3591 +04/19 04:07:18 - mmengine - INFO - Iter(train) [ 71050/160000] base_lr: 5.6120e-05 lr: 2.0749e-07 eta: 1 day, 2:31:26 time: 0.9993 data_time: 0.0045 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0061 decode.acc_seg: 99.7278 aux.loss_ce: 0.0077 aux.acc_seg: 99.2346 +04/19 04:08:08 - mmengine - INFO - Iter(train) [ 71100/160000] base_lr: 5.6089e-05 lr: 2.0737e-07 eta: 1 day, 2:30:02 time: 0.9973 data_time: 0.0044 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0054 decode.acc_seg: 99.7286 aux.loss_ce: 0.0066 aux.acc_seg: 99.2502 +04/19 04:08:58 - mmengine - INFO - Iter(train) [ 71150/160000] base_lr: 5.6057e-05 lr: 2.0725e-07 eta: 1 day, 2:28:38 time: 0.9982 data_time: 0.0048 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0063 decode.acc_seg: 99.6756 aux.loss_ce: 0.0082 aux.acc_seg: 99.1711 +04/19 04:09:47 - mmengine - INFO - Iter(train) [ 71200/160000] base_lr: 5.6026e-05 lr: 2.0714e-07 eta: 1 day, 2:27:15 time: 0.9988 data_time: 0.0046 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0058 decode.acc_seg: 99.7915 aux.loss_ce: 0.0074 aux.acc_seg: 99.2517 +04/19 04:10:37 - mmengine - INFO - Iter(train) [ 71250/160000] base_lr: 5.5994e-05 lr: 2.0702e-07 eta: 1 day, 2:25:52 time: 0.9971 data_time: 0.0051 memory: 8457 loss: 0.0117 decode.loss_ce: 0.0056 decode.acc_seg: 99.8142 aux.loss_ce: 0.0061 aux.acc_seg: 99.4389 +04/19 04:11:27 - mmengine - INFO - Iter(train) [ 71300/160000] base_lr: 5.5962e-05 lr: 2.0690e-07 eta: 1 day, 2:24:29 time: 0.9973 data_time: 0.0047 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0062 decode.acc_seg: 99.6574 aux.loss_ce: 0.0074 aux.acc_seg: 99.0965 +04/19 04:12:17 - mmengine - INFO - Iter(train) [ 71350/160000] base_lr: 5.5931e-05 lr: 2.0679e-07 eta: 1 day, 2:23:07 time: 0.9980 data_time: 0.0053 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.6717 aux.loss_ce: 0.0073 aux.acc_seg: 99.0242 +04/19 04:13:07 - mmengine - INFO - Iter(train) [ 71400/160000] base_lr: 5.5899e-05 lr: 2.0667e-07 eta: 1 day, 2:21:45 time: 0.9986 data_time: 0.0048 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0060 decode.acc_seg: 99.8230 aux.loss_ce: 0.0071 aux.acc_seg: 99.4883 +04/19 04:13:57 - mmengine - INFO - Iter(train) [ 71450/160000] base_lr: 5.5868e-05 lr: 2.0655e-07 eta: 1 day, 2:20:23 time: 0.9985 data_time: 0.0050 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.7934 aux.loss_ce: 0.0075 aux.acc_seg: 99.3322 +04/19 04:14:47 - mmengine - INFO - Iter(train) [ 71500/160000] base_lr: 5.5836e-05 lr: 2.0644e-07 eta: 1 day, 2:19:01 time: 0.9977 data_time: 0.0046 memory: 8457 loss: 0.0115 decode.loss_ce: 0.0050 decode.acc_seg: 99.7473 aux.loss_ce: 0.0066 aux.acc_seg: 99.1293 +04/19 04:15:37 - mmengine - INFO - Iter(train) [ 71550/160000] base_lr: 5.5805e-05 lr: 2.0632e-07 eta: 1 day, 2:17:39 time: 0.9969 data_time: 0.0049 memory: 8457 loss: 0.0155 decode.loss_ce: 0.0068 decode.acc_seg: 99.8236 aux.loss_ce: 0.0087 aux.acc_seg: 99.4001 +04/19 04:16:27 - mmengine - INFO - Iter(train) [ 71600/160000] base_lr: 5.5773e-05 lr: 2.0621e-07 eta: 1 day, 2:16:18 time: 0.9972 data_time: 0.0047 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0060 decode.acc_seg: 99.8430 aux.loss_ce: 0.0071 aux.acc_seg: 99.5842 +04/19 04:17:16 - mmengine - INFO - Iter(train) [ 71650/160000] base_lr: 5.5742e-05 lr: 2.0609e-07 eta: 1 day, 2:14:57 time: 0.9974 data_time: 0.0046 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0061 decode.acc_seg: 99.7597 aux.loss_ce: 0.0071 aux.acc_seg: 99.4097 +04/19 04:18:06 - mmengine - INFO - Iter(train) [ 71700/160000] base_lr: 5.5710e-05 lr: 2.0597e-07 eta: 1 day, 2:13:36 time: 0.9976 data_time: 0.0048 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0059 decode.acc_seg: 99.7728 aux.loss_ce: 0.0075 aux.acc_seg: 99.3156 +04/19 04:18:56 - mmengine - INFO - Iter(train) [ 71750/160000] base_lr: 5.5679e-05 lr: 2.0586e-07 eta: 1 day, 2:12:16 time: 0.9975 data_time: 0.0047 memory: 8457 loss: 0.0113 decode.loss_ce: 0.0051 decode.acc_seg: 99.7702 aux.loss_ce: 0.0061 aux.acc_seg: 99.1955 +04/19 04:19:46 - mmengine - INFO - Iter(train) [ 71800/160000] base_lr: 5.5647e-05 lr: 2.0574e-07 eta: 1 day, 2:10:57 time: 0.9991 data_time: 0.0047 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0056 decode.acc_seg: 99.7063 aux.loss_ce: 0.0076 aux.acc_seg: 99.0969 +04/19 04:20:36 - mmengine - INFO - Iter(train) [ 71850/160000] base_lr: 5.5615e-05 lr: 2.0562e-07 eta: 1 day, 2:09:37 time: 0.9976 data_time: 0.0045 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0065 decode.acc_seg: 99.8339 aux.loss_ce: 0.0079 aux.acc_seg: 99.6168 +04/19 04:21:26 - mmengine - INFO - Iter(train) [ 71900/160000] base_lr: 5.5584e-05 lr: 2.0551e-07 eta: 1 day, 2:08:18 time: 0.9980 data_time: 0.0047 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0055 decode.acc_seg: 99.7591 aux.loss_ce: 0.0074 aux.acc_seg: 99.0673 +04/19 04:22:16 - mmengine - INFO - Iter(train) [ 71950/160000] base_lr: 5.5552e-05 lr: 2.0539e-07 eta: 1 day, 2:06:59 time: 0.9987 data_time: 0.0049 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0061 decode.acc_seg: 99.6096 aux.loss_ce: 0.0076 aux.acc_seg: 98.9359 +04/19 04:23:06 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 04:23:06 - mmengine - INFO - Iter(train) [ 72000/160000] base_lr: 5.5521e-05 lr: 2.0527e-07 eta: 1 day, 2:05:40 time: 0.9981 data_time: 0.0044 memory: 8457 loss: 0.0149 decode.loss_ce: 0.0067 decode.acc_seg: 99.6532 aux.loss_ce: 0.0082 aux.acc_seg: 98.9784 +04/19 04:23:56 - mmengine - INFO - Iter(train) [ 72050/160000] base_lr: 5.5489e-05 lr: 2.0516e-07 eta: 1 day, 2:04:22 time: 1.0004 data_time: 0.0043 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0056 decode.acc_seg: 99.7738 aux.loss_ce: 0.0064 aux.acc_seg: 99.4066 +04/19 04:24:46 - mmengine - INFO - Iter(train) [ 72100/160000] base_lr: 5.5458e-05 lr: 2.0504e-07 eta: 1 day, 2:03:04 time: 0.9988 data_time: 0.0048 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0053 decode.acc_seg: 99.7711 aux.loss_ce: 0.0073 aux.acc_seg: 99.2994 +04/19 04:25:36 - mmengine - INFO - Iter(train) [ 72150/160000] base_lr: 5.5426e-05 lr: 2.0492e-07 eta: 1 day, 2:01:47 time: 1.0004 data_time: 0.0052 memory: 8457 loss: 0.0118 decode.loss_ce: 0.0052 decode.acc_seg: 99.7536 aux.loss_ce: 0.0066 aux.acc_seg: 99.1631 +04/19 04:26:26 - mmengine - INFO - Iter(train) [ 72200/160000] base_lr: 5.5395e-05 lr: 2.0481e-07 eta: 1 day, 2:00:29 time: 1.0000 data_time: 0.0046 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0061 decode.acc_seg: 99.7293 aux.loss_ce: 0.0078 aux.acc_seg: 98.9708 +04/19 04:27:16 - mmengine - INFO - Iter(train) [ 72250/160000] base_lr: 5.5363e-05 lr: 2.0469e-07 eta: 1 day, 1:59:12 time: 0.9989 data_time: 0.0044 memory: 8457 loss: 0.0121 decode.loss_ce: 0.0053 decode.acc_seg: 99.8394 aux.loss_ce: 0.0068 aux.acc_seg: 99.4120 +04/19 04:28:06 - mmengine - INFO - Iter(train) [ 72300/160000] base_lr: 5.5332e-05 lr: 2.0457e-07 eta: 1 day, 1:57:55 time: 0.9999 data_time: 0.0048 memory: 8457 loss: 0.0163 decode.loss_ce: 0.0072 decode.acc_seg: 99.6603 aux.loss_ce: 0.0091 aux.acc_seg: 99.1877 +04/19 04:28:56 - mmengine - INFO - Iter(train) [ 72350/160000] base_lr: 5.5300e-05 lr: 2.0446e-07 eta: 1 day, 1:56:38 time: 1.0006 data_time: 0.0048 memory: 8457 loss: 0.0140 decode.loss_ce: 0.0058 decode.acc_seg: 99.7231 aux.loss_ce: 0.0081 aux.acc_seg: 99.1499 +04/19 04:29:46 - mmengine - INFO - Iter(train) [ 72400/160000] base_lr: 5.5268e-05 lr: 2.0434e-07 eta: 1 day, 1:55:22 time: 1.0005 data_time: 0.0049 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0054 decode.acc_seg: 99.6748 aux.loss_ce: 0.0067 aux.acc_seg: 99.1932 +04/19 04:30:36 - mmengine - INFO - Iter(train) [ 72450/160000] base_lr: 5.5237e-05 lr: 2.0422e-07 eta: 1 day, 1:54:06 time: 1.0009 data_time: 0.0045 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0056 decode.acc_seg: 99.7463 aux.loss_ce: 0.0073 aux.acc_seg: 99.0011 +04/19 04:31:26 - mmengine - INFO - Iter(train) [ 72500/160000] base_lr: 5.5205e-05 lr: 2.0411e-07 eta: 1 day, 1:52:50 time: 1.0010 data_time: 0.0047 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0064 decode.acc_seg: 99.8198 aux.loss_ce: 0.0071 aux.acc_seg: 99.5687 +04/19 04:32:16 - mmengine - INFO - Iter(train) [ 72550/160000] base_lr: 5.5174e-05 lr: 2.0399e-07 eta: 1 day, 1:51:35 time: 0.9999 data_time: 0.0048 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0060 decode.acc_seg: 99.7398 aux.loss_ce: 0.0073 aux.acc_seg: 99.0044 +04/19 04:33:06 - mmengine - INFO - Iter(train) [ 72600/160000] base_lr: 5.5142e-05 lr: 2.0387e-07 eta: 1 day, 1:50:19 time: 0.9998 data_time: 0.0044 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0053 decode.acc_seg: 99.7574 aux.loss_ce: 0.0070 aux.acc_seg: 99.3227 +04/19 04:33:56 - mmengine - INFO - Iter(train) [ 72650/160000] base_lr: 5.5111e-05 lr: 2.0376e-07 eta: 1 day, 1:49:04 time: 1.0023 data_time: 0.0045 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.7295 aux.loss_ce: 0.0077 aux.acc_seg: 99.1224 +04/19 04:34:46 - mmengine - INFO - Iter(train) [ 72700/160000] base_lr: 5.5079e-05 lr: 2.0364e-07 eta: 1 day, 1:47:50 time: 1.0013 data_time: 0.0044 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0061 decode.acc_seg: 99.7032 aux.loss_ce: 0.0075 aux.acc_seg: 98.8962 +04/19 04:35:36 - mmengine - INFO - Iter(train) [ 72750/160000] base_lr: 5.5048e-05 lr: 2.0352e-07 eta: 1 day, 1:46:35 time: 1.0004 data_time: 0.0046 memory: 8457 loss: 0.0117 decode.loss_ce: 0.0053 decode.acc_seg: 99.8070 aux.loss_ce: 0.0064 aux.acc_seg: 99.4915 +04/19 04:36:27 - mmengine - INFO - Iter(train) [ 72800/160000] base_lr: 5.5016e-05 lr: 2.0341e-07 eta: 1 day, 1:45:21 time: 1.0012 data_time: 0.0044 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0062 decode.acc_seg: 99.8421 aux.loss_ce: 0.0073 aux.acc_seg: 99.3183 +04/19 04:37:17 - mmengine - INFO - Iter(train) [ 72850/160000] base_lr: 5.4985e-05 lr: 2.0329e-07 eta: 1 day, 1:44:07 time: 1.0010 data_time: 0.0044 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7499 aux.loss_ce: 0.0073 aux.acc_seg: 99.2165 +04/19 04:38:07 - mmengine - INFO - Iter(train) [ 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memory: 8457 loss: 0.0121 decode.loss_ce: 0.0056 decode.acc_seg: 99.7995 aux.loss_ce: 0.0065 aux.acc_seg: 99.4841 +04/19 04:41:27 - mmengine - INFO - Iter(train) [ 73100/160000] base_lr: 5.4827e-05 lr: 2.0271e-07 eta: 1 day, 1:37:58 time: 1.0006 data_time: 0.0050 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0058 decode.acc_seg: 99.7210 aux.loss_ce: 0.0069 aux.acc_seg: 99.0213 +04/19 04:42:17 - mmengine - INFO - Iter(train) [ 73150/160000] base_lr: 5.4795e-05 lr: 2.0259e-07 eta: 1 day, 1:36:45 time: 1.0021 data_time: 0.0050 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0055 decode.acc_seg: 99.6876 aux.loss_ce: 0.0073 aux.acc_seg: 98.7301 +04/19 04:43:07 - mmengine - INFO - Iter(train) [ 73200/160000] base_lr: 5.4764e-05 lr: 2.0247e-07 eta: 1 day, 1:35:32 time: 0.9996 data_time: 0.0047 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0058 decode.acc_seg: 99.6916 aux.loss_ce: 0.0072 aux.acc_seg: 99.1751 +04/19 04:43:57 - mmengine - INFO - Iter(train) [ 73250/160000] base_lr: 5.4732e-05 lr: 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INFO - Iter(train) [ 73450/160000] base_lr: 5.4606e-05 lr: 2.0189e-07 eta: 1 day, 1:29:28 time: 1.0004 data_time: 0.0049 memory: 8457 loss: 0.0145 decode.loss_ce: 0.0065 decode.acc_seg: 99.7345 aux.loss_ce: 0.0080 aux.acc_seg: 99.1789 +04/19 04:48:07 - mmengine - INFO - Iter(train) [ 73500/160000] base_lr: 5.4574e-05 lr: 2.0177e-07 eta: 1 day, 1:28:16 time: 1.0005 data_time: 0.0044 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0062 decode.acc_seg: 99.7120 aux.loss_ce: 0.0070 aux.acc_seg: 99.2508 +04/19 04:48:57 - mmengine - INFO - Iter(train) [ 73550/160000] base_lr: 5.4543e-05 lr: 2.0166e-07 eta: 1 day, 1:27:04 time: 1.0006 data_time: 0.0044 memory: 8457 loss: 0.0152 decode.loss_ce: 0.0068 decode.acc_seg: 99.7654 aux.loss_ce: 0.0084 aux.acc_seg: 99.1514 +04/19 04:49:47 - mmengine - INFO - Iter(train) [ 73600/160000] base_lr: 5.4511e-05 lr: 2.0154e-07 eta: 1 day, 1:25:52 time: 1.0010 data_time: 0.0045 memory: 8457 loss: 0.0118 decode.loss_ce: 0.0052 decode.acc_seg: 99.7936 aux.loss_ce: 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decode.loss_ce: 0.0056 decode.acc_seg: 99.7301 aux.loss_ce: 0.0066 aux.acc_seg: 99.3900 +04/19 04:53:57 - mmengine - INFO - Iter(train) [ 73850/160000] base_lr: 5.4354e-05 lr: 2.0096e-07 eta: 1 day, 1:19:55 time: 1.0005 data_time: 0.0043 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0060 decode.acc_seg: 99.7335 aux.loss_ce: 0.0073 aux.acc_seg: 99.1028 +04/19 04:54:47 - mmengine - INFO - Iter(train) [ 73900/160000] base_lr: 5.4322e-05 lr: 2.0084e-07 eta: 1 day, 1:18:43 time: 1.0005 data_time: 0.0044 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0058 decode.acc_seg: 99.8169 aux.loss_ce: 0.0069 aux.acc_seg: 99.5962 +04/19 04:55:37 - mmengine - INFO - Iter(train) [ 73950/160000] base_lr: 5.4291e-05 lr: 2.0072e-07 eta: 1 day, 1:17:32 time: 1.0002 data_time: 0.0050 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.7305 aux.loss_ce: 0.0073 aux.acc_seg: 99.1680 +04/19 04:56:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 04:56:27 - mmengine - INFO - Iter(train) [ 74000/160000] base_lr: 5.4259e-05 lr: 2.0061e-07 eta: 1 day, 1:16:21 time: 0.9996 data_time: 0.0045 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0056 decode.acc_seg: 99.7553 aux.loss_ce: 0.0073 aux.acc_seg: 99.1318 +04/19 04:57:17 - mmengine - INFO - Iter(train) [ 74050/160000] base_lr: 5.4227e-05 lr: 2.0049e-07 eta: 1 day, 1:15:11 time: 1.0020 data_time: 0.0044 memory: 8457 loss: 0.0099 decode.loss_ce: 0.0046 decode.acc_seg: 99.7950 aux.loss_ce: 0.0053 aux.acc_seg: 99.3742 +04/19 04:58:07 - mmengine - INFO - Iter(train) [ 74100/160000] base_lr: 5.4196e-05 lr: 2.0037e-07 eta: 1 day, 1:14:00 time: 1.0002 data_time: 0.0046 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0054 decode.acc_seg: 99.7715 aux.loss_ce: 0.0067 aux.acc_seg: 99.3795 +04/19 04:58:57 - mmengine - INFO - Iter(train) [ 74150/160000] base_lr: 5.4164e-05 lr: 2.0026e-07 eta: 1 day, 1:12:50 time: 1.0006 data_time: 0.0045 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0054 decode.acc_seg: 99.7684 aux.loss_ce: 0.0077 aux.acc_seg: 99.3389 +04/19 04:59:47 - mmengine - INFO - Iter(train) [ 74200/160000] base_lr: 5.4133e-05 lr: 2.0014e-07 eta: 1 day, 1:11:40 time: 1.0006 data_time: 0.0046 memory: 8457 loss: 0.0142 decode.loss_ce: 0.0061 decode.acc_seg: 99.7543 aux.loss_ce: 0.0081 aux.acc_seg: 99.1320 +04/19 05:00:37 - mmengine - INFO - Iter(train) [ 74250/160000] base_lr: 5.4101e-05 lr: 2.0002e-07 eta: 1 day, 1:10:30 time: 1.0003 data_time: 0.0051 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0056 decode.acc_seg: 99.8207 aux.loss_ce: 0.0068 aux.acc_seg: 99.3891 +04/19 05:01:27 - mmengine - INFO - Iter(train) [ 74300/160000] base_lr: 5.4070e-05 lr: 1.9991e-07 eta: 1 day, 1:09:20 time: 1.0001 data_time: 0.0048 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0058 decode.acc_seg: 99.7780 aux.loss_ce: 0.0066 aux.acc_seg: 99.2271 +04/19 05:02:17 - mmengine - INFO - Iter(train) [ 74350/160000] base_lr: 5.4038e-05 lr: 1.9979e-07 eta: 1 day, 1:08:10 time: 0.9995 data_time: 0.0049 memory: 8457 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INFO - Iter(train) [ 75300/160000] base_lr: 5.3439e-05 lr: 1.9757e-07 eta: 1 day, 0:46:26 time: 0.9989 data_time: 0.0045 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0064 decode.acc_seg: 99.7427 aux.loss_ce: 0.0080 aux.acc_seg: 98.9557 +04/19 05:18:57 - mmengine - INFO - Iter(train) [ 75350/160000] base_lr: 5.3407e-05 lr: 1.9746e-07 eta: 1 day, 0:45:18 time: 0.9993 data_time: 0.0045 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0063 decode.acc_seg: 99.8064 aux.loss_ce: 0.0082 aux.acc_seg: 99.4492 +04/19 05:19:47 - mmengine - INFO - Iter(train) [ 75400/160000] base_lr: 5.3376e-05 lr: 1.9734e-07 eta: 1 day, 0:44:11 time: 0.9991 data_time: 0.0045 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0050 decode.acc_seg: 99.7866 aux.loss_ce: 0.0066 aux.acc_seg: 99.4564 +04/19 05:20:37 - mmengine - INFO - Iter(train) [ 75450/160000] base_lr: 5.3344e-05 lr: 1.9722e-07 eta: 1 day, 0:43:04 time: 0.9992 data_time: 0.0046 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0065 decode.acc_seg: 99.7326 aux.loss_ce: 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decode.loss_ce: 0.0058 decode.acc_seg: 99.7952 aux.loss_ce: 0.0077 aux.acc_seg: 99.4871 +04/19 05:24:47 - mmengine - INFO - Iter(train) [ 75700/160000] base_lr: 5.3186e-05 lr: 1.9664e-07 eta: 1 day, 0:37:29 time: 1.0001 data_time: 0.0049 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0063 decode.acc_seg: 99.6210 aux.loss_ce: 0.0075 aux.acc_seg: 98.9208 +04/19 05:25:36 - mmengine - INFO - Iter(train) [ 75750/160000] base_lr: 5.3155e-05 lr: 1.9652e-07 eta: 1 day, 0:36:22 time: 0.9981 data_time: 0.0049 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0066 decode.acc_seg: 99.6876 aux.loss_ce: 0.0077 aux.acc_seg: 99.2889 +04/19 05:26:26 - mmengine - INFO - Iter(train) [ 75800/160000] base_lr: 5.3123e-05 lr: 1.9641e-07 eta: 1 day, 0:35:16 time: 0.9984 data_time: 0.0048 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0056 decode.acc_seg: 99.7414 aux.loss_ce: 0.0067 aux.acc_seg: 99.2846 +04/19 05:27:16 - mmengine - INFO - Iter(train) [ 75850/160000] base_lr: 5.3092e-05 lr: 1.9629e-07 eta: 1 day, 0:34:09 time: 0.9990 data_time: 0.0048 memory: 8457 loss: 0.0163 decode.loss_ce: 0.0072 decode.acc_seg: 99.7507 aux.loss_ce: 0.0091 aux.acc_seg: 99.0150 +04/19 05:28:06 - mmengine - INFO - Iter(train) [ 75900/160000] base_lr: 5.3060e-05 lr: 1.9617e-07 eta: 1 day, 0:33:03 time: 0.9985 data_time: 0.0053 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0053 decode.acc_seg: 99.7759 aux.loss_ce: 0.0069 aux.acc_seg: 99.4860 +04/19 05:28:56 - mmengine - INFO - Iter(train) [ 75950/160000] base_lr: 5.3029e-05 lr: 1.9606e-07 eta: 1 day, 0:31:56 time: 0.9987 data_time: 0.0044 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0055 decode.acc_seg: 99.8390 aux.loss_ce: 0.0072 aux.acc_seg: 99.5131 +04/19 05:29:46 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 05:29:46 - mmengine - INFO - Iter(train) [ 76000/160000] base_lr: 5.2997e-05 lr: 1.9594e-07 eta: 1 day, 0:30:50 time: 0.9990 data_time: 0.0045 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0063 decode.acc_seg: 99.6550 aux.loss_ce: 0.0074 aux.acc_seg: 98.8285 +04/19 05:30:36 - mmengine - INFO - Iter(train) [ 76050/160000] base_lr: 5.2966e-05 lr: 1.9582e-07 eta: 1 day, 0:29:44 time: 0.9975 data_time: 0.0047 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0061 decode.acc_seg: 99.7934 aux.loss_ce: 0.0073 aux.acc_seg: 99.1962 +04/19 05:31:26 - mmengine - INFO - Iter(train) [ 76100/160000] base_lr: 5.2934e-05 lr: 1.9571e-07 eta: 1 day, 0:28:37 time: 0.9982 data_time: 0.0054 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0061 decode.acc_seg: 99.7311 aux.loss_ce: 0.0076 aux.acc_seg: 99.2887 +04/19 05:32:16 - mmengine - INFO - Iter(train) [ 76150/160000] base_lr: 5.2903e-05 lr: 1.9559e-07 eta: 1 day, 0:27:31 time: 0.9984 data_time: 0.0049 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0061 decode.acc_seg: 99.7318 aux.loss_ce: 0.0075 aux.acc_seg: 99.2664 +04/19 05:33:06 - mmengine - INFO - Iter(train) [ 76200/160000] base_lr: 5.2871e-05 lr: 1.9547e-07 eta: 1 day, 0:26:26 time: 0.9985 data_time: 0.0050 memory: 8457 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decode.acc_seg: 99.7515 aux.loss_ce: 0.0085 aux.acc_seg: 99.0440 +04/19 05:46:24 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 05:46:24 - mmengine - INFO - Iter(train) [ 77000/160000] base_lr: 5.2366e-05 lr: 1.9361e-07 eta: 1 day, 0:09:02 time: 0.9980 data_time: 0.0046 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0057 decode.acc_seg: 99.7608 aux.loss_ce: 0.0072 aux.acc_seg: 99.3055 +04/19 05:47:14 - mmengine - INFO - Iter(train) [ 77050/160000] base_lr: 5.2335e-05 lr: 1.9349e-07 eta: 1 day, 0:07:58 time: 0.9988 data_time: 0.0049 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.7597 aux.loss_ce: 0.0076 aux.acc_seg: 99.3515 +04/19 05:48:04 - mmengine - INFO - Iter(train) [ 77100/160000] base_lr: 5.2303e-05 lr: 1.9338e-07 eta: 1 day, 0:06:53 time: 0.9975 data_time: 0.0045 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0060 decode.acc_seg: 99.7835 aux.loss_ce: 0.0077 aux.acc_seg: 98.8512 +04/19 05:48:54 - mmengine - INFO - 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aux.acc_seg: 99.0391 +04/19 05:52:14 - mmengine - INFO - Iter(train) [ 77350/160000] base_lr: 5.2145e-05 lr: 1.9279e-07 eta: 1 day, 0:01:34 time: 0.9991 data_time: 0.0050 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0056 decode.acc_seg: 99.7997 aux.loss_ce: 0.0067 aux.acc_seg: 99.4102 +04/19 05:53:04 - mmengine - INFO - Iter(train) [ 77400/160000] base_lr: 5.2114e-05 lr: 1.9268e-07 eta: 1 day, 0:00:30 time: 0.9982 data_time: 0.0048 memory: 8457 loss: 0.0110 decode.loss_ce: 0.0051 decode.acc_seg: 99.8072 aux.loss_ce: 0.0059 aux.acc_seg: 99.2752 +04/19 05:53:54 - mmengine - INFO - Iter(train) [ 77450/160000] base_lr: 5.2082e-05 lr: 1.9256e-07 eta: 23:59:26 time: 0.9987 data_time: 0.0050 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0060 decode.acc_seg: 99.6710 aux.loss_ce: 0.0069 aux.acc_seg: 98.9635 +04/19 05:54:43 - mmengine - INFO - Iter(train) [ 77500/160000] base_lr: 5.2051e-05 lr: 1.9244e-07 eta: 23:58:23 time: 0.9988 data_time: 0.0046 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0059 decode.acc_seg: 99.7751 aux.loss_ce: 0.0073 aux.acc_seg: 99.2783 +04/19 05:55:33 - mmengine - INFO - Iter(train) [ 77550/160000] base_lr: 5.2019e-05 lr: 1.9233e-07 eta: 23:57:19 time: 0.9999 data_time: 0.0046 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0054 decode.acc_seg: 99.7700 aux.loss_ce: 0.0068 aux.acc_seg: 99.3414 +04/19 05:56:23 - mmengine - INFO - Iter(train) [ 77600/160000] base_lr: 5.1988e-05 lr: 1.9221e-07 eta: 23:56:16 time: 0.9991 data_time: 0.0045 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0055 decode.acc_seg: 99.7419 aux.loss_ce: 0.0071 aux.acc_seg: 99.2914 +04/19 05:57:13 - mmengine - INFO - Iter(train) [ 77650/160000] base_lr: 5.1956e-05 lr: 1.9209e-07 eta: 23:55:13 time: 1.0000 data_time: 0.0042 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0049 decode.acc_seg: 99.8108 aux.loss_ce: 0.0066 aux.acc_seg: 99.4274 +04/19 05:58:03 - mmengine - INFO - Iter(train) [ 77700/160000] base_lr: 5.1925e-05 lr: 1.9198e-07 eta: 23:54:09 time: 0.9986 data_time: 0.0045 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0056 decode.acc_seg: 99.7299 aux.loss_ce: 0.0071 aux.acc_seg: 99.0665 +04/19 05:58:53 - mmengine - INFO - Iter(train) [ 77750/160000] base_lr: 5.1893e-05 lr: 1.9186e-07 eta: 23:53:06 time: 0.9981 data_time: 0.0050 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0059 decode.acc_seg: 99.6883 aux.loss_ce: 0.0071 aux.acc_seg: 99.0023 +04/19 05:59:43 - mmengine - INFO - Iter(train) [ 77800/160000] base_lr: 5.1862e-05 lr: 1.9174e-07 eta: 23:52:03 time: 0.9973 data_time: 0.0047 memory: 8457 loss: 0.0137 decode.loss_ce: 0.0064 decode.acc_seg: 99.7408 aux.loss_ce: 0.0073 aux.acc_seg: 99.2992 +04/19 06:00:33 - mmengine - INFO - Iter(train) [ 77850/160000] base_lr: 5.1830e-05 lr: 1.9163e-07 eta: 23:51:00 time: 1.0011 data_time: 0.0049 memory: 8457 loss: 0.0123 decode.loss_ce: 0.0057 decode.acc_seg: 99.7818 aux.loss_ce: 0.0066 aux.acc_seg: 99.3597 +04/19 06:01:23 - mmengine - INFO - Iter(train) [ 77900/160000] base_lr: 5.1798e-05 lr: 1.9151e-07 eta: 23:49:57 time: 0.9979 data_time: 0.0047 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0060 decode.acc_seg: 99.7086 aux.loss_ce: 0.0079 aux.acc_seg: 99.0963 +04/19 06:02:13 - mmengine - INFO - Iter(train) [ 77950/160000] base_lr: 5.1767e-05 lr: 1.9139e-07 eta: 23:48:54 time: 0.9972 data_time: 0.0045 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0052 decode.acc_seg: 99.7627 aux.loss_ce: 0.0064 aux.acc_seg: 99.4083 +04/19 06:03:03 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 06:03:03 - mmengine - INFO - Iter(train) [ 78000/160000] base_lr: 5.1735e-05 lr: 1.9128e-07 eta: 23:47:51 time: 0.9979 data_time: 0.0048 memory: 8457 loss: 0.0130 decode.loss_ce: 0.0056 decode.acc_seg: 99.7499 aux.loss_ce: 0.0075 aux.acc_seg: 99.2113 +04/19 06:03:53 - mmengine - INFO - Iter(train) [ 78050/160000] base_lr: 5.1704e-05 lr: 1.9116e-07 eta: 23:46:48 time: 0.9980 data_time: 0.0050 memory: 8457 loss: 0.0121 decode.loss_ce: 0.0054 decode.acc_seg: 99.6878 aux.loss_ce: 0.0067 aux.acc_seg: 99.0467 +04/19 06:04:43 - mmengine - INFO - Iter(train) [ 78100/160000] base_lr: 5.1672e-05 lr: 1.9104e-07 eta: 23:45:45 time: 0.9996 data_time: 0.0046 memory: 8457 loss: 0.0129 decode.loss_ce: 0.0056 decode.acc_seg: 99.7698 aux.loss_ce: 0.0073 aux.acc_seg: 99.2567 +04/19 06:05:33 - mmengine - INFO - Iter(train) [ 78150/160000] base_lr: 5.1641e-05 lr: 1.9093e-07 eta: 23:44:43 time: 0.9991 data_time: 0.0046 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0054 decode.acc_seg: 99.7957 aux.loss_ce: 0.0066 aux.acc_seg: 99.4730 +04/19 06:06:23 - mmengine - INFO - Iter(train) [ 78200/160000] base_lr: 5.1609e-05 lr: 1.9081e-07 eta: 23:43:41 time: 0.9976 data_time: 0.0045 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0055 decode.acc_seg: 99.7253 aux.loss_ce: 0.0065 aux.acc_seg: 99.3870 +04/19 06:07:13 - mmengine - INFO - Iter(train) [ 78250/160000] base_lr: 5.1578e-05 lr: 1.9069e-07 eta: 23:42:39 time: 0.9995 data_time: 0.0048 memory: 8457 loss: 0.0118 decode.loss_ce: 0.0051 decode.acc_seg: 99.7387 aux.loss_ce: 0.0067 aux.acc_seg: 99.0322 +04/19 06:08:03 - mmengine - INFO - Iter(train) [ 78300/160000] base_lr: 5.1546e-05 lr: 1.9058e-07 eta: 23:41:36 time: 0.9991 data_time: 0.0044 memory: 8457 loss: 0.0138 decode.loss_ce: 0.0062 decode.acc_seg: 99.7467 aux.loss_ce: 0.0077 aux.acc_seg: 99.2725 +04/19 06:08:53 - mmengine - INFO - Iter(train) [ 78350/160000] base_lr: 5.1515e-05 lr: 1.9046e-07 eta: 23:40:34 time: 0.9999 data_time: 0.0051 memory: 8457 loss: 0.0144 decode.loss_ce: 0.0066 decode.acc_seg: 99.7419 aux.loss_ce: 0.0078 aux.acc_seg: 99.2062 +04/19 06:09:43 - mmengine - INFO - Iter(train) [ 78400/160000] base_lr: 5.1483e-05 lr: 1.9034e-07 eta: 23:39:32 time: 0.9978 data_time: 0.0046 memory: 8457 loss: 0.0132 decode.loss_ce: 0.0057 decode.acc_seg: 99.6658 aux.loss_ce: 0.0075 aux.acc_seg: 99.2020 +04/19 06:10:32 - mmengine - INFO - Iter(train) [ 78450/160000] base_lr: 5.1451e-05 lr: 1.9023e-07 eta: 23:38:30 time: 0.9990 data_time: 0.0046 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0059 decode.acc_seg: 99.7873 aux.loss_ce: 0.0075 aux.acc_seg: 99.1709 +04/19 06:11:22 - mmengine - INFO - Iter(train) [ 78500/160000] base_lr: 5.1420e-05 lr: 1.9011e-07 eta: 23:37:28 time: 0.9997 data_time: 0.0048 memory: 8457 loss: 0.0143 decode.loss_ce: 0.0064 decode.acc_seg: 99.7080 aux.loss_ce: 0.0079 aux.acc_seg: 99.2508 +04/19 06:12:12 - mmengine - INFO - Iter(train) [ 78550/160000] base_lr: 5.1388e-05 lr: 1.8999e-07 eta: 23:36:26 time: 0.9989 data_time: 0.0047 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0054 decode.acc_seg: 99.7654 aux.loss_ce: 0.0074 aux.acc_seg: 99.3689 +04/19 06:13:02 - mmengine - INFO - Iter(train) [ 78600/160000] base_lr: 5.1357e-05 lr: 1.8988e-07 eta: 23:35:24 time: 0.9972 data_time: 0.0046 memory: 8457 loss: 0.0155 decode.loss_ce: 0.0067 decode.acc_seg: 99.7011 aux.loss_ce: 0.0087 aux.acc_seg: 98.7347 +04/19 06:13:52 - mmengine - INFO - Iter(train) [ 78650/160000] base_lr: 5.1325e-05 lr: 1.8976e-07 eta: 23:34:22 time: 1.0000 data_time: 0.0047 memory: 8457 loss: 0.0128 decode.loss_ce: 0.0059 decode.acc_seg: 99.6748 aux.loss_ce: 0.0069 aux.acc_seg: 99.0702 +04/19 06:14:42 - mmengine - INFO - Iter(train) [ 78700/160000] base_lr: 5.1294e-05 lr: 1.8964e-07 eta: 23:33:20 time: 0.9996 data_time: 0.0047 memory: 8457 loss: 0.0125 decode.loss_ce: 0.0058 decode.acc_seg: 99.7826 aux.loss_ce: 0.0066 aux.acc_seg: 99.4392 +04/19 06:15:32 - mmengine - INFO - Iter(train) [ 78750/160000] base_lr: 5.1262e-05 lr: 1.8953e-07 eta: 23:32:18 time: 0.9989 data_time: 0.0047 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0056 decode.acc_seg: 99.7278 aux.loss_ce: 0.0070 aux.acc_seg: 99.1493 +04/19 06:16:22 - mmengine - INFO - Iter(train) [ 78800/160000] base_lr: 5.1231e-05 lr: 1.8941e-07 eta: 23:31:17 time: 0.9985 data_time: 0.0046 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0057 decode.acc_seg: 99.7591 aux.loss_ce: 0.0067 aux.acc_seg: 99.1268 +04/19 06:17:12 - mmengine - INFO - Iter(train) [ 78850/160000] base_lr: 5.1199e-05 lr: 1.8929e-07 eta: 23:30:15 time: 0.9996 data_time: 0.0048 memory: 8457 loss: 0.0113 decode.loss_ce: 0.0050 decode.acc_seg: 99.6885 aux.loss_ce: 0.0063 aux.acc_seg: 98.7377 +04/19 06:18:02 - mmengine - INFO - Iter(train) [ 78900/160000] base_lr: 5.1168e-05 lr: 1.8918e-07 eta: 23:29:14 time: 0.9994 data_time: 0.0045 memory: 8457 loss: 0.0124 decode.loss_ce: 0.0056 decode.acc_seg: 99.6813 aux.loss_ce: 0.0068 aux.acc_seg: 99.1846 +04/19 06:18:52 - mmengine - INFO - Iter(train) [ 78950/160000] base_lr: 5.1136e-05 lr: 1.8906e-07 eta: 23:28:13 time: 1.0016 data_time: 0.0043 memory: 8457 loss: 0.0157 decode.loss_ce: 0.0069 decode.acc_seg: 99.7581 aux.loss_ce: 0.0088 aux.acc_seg: 99.3343 +04/19 06:19:42 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 06:19:42 - mmengine - INFO - Iter(train) [ 79000/160000] base_lr: 5.1104e-05 lr: 1.8894e-07 eta: 23:27:12 time: 1.0016 data_time: 0.0051 memory: 8457 loss: 0.0110 decode.loss_ce: 0.0051 decode.acc_seg: 99.8230 aux.loss_ce: 0.0059 aux.acc_seg: 99.5138 +04/19 06:20:32 - mmengine - INFO - Iter(train) [ 79050/160000] base_lr: 5.1073e-05 lr: 1.8883e-07 eta: 23:26:11 time: 1.0001 data_time: 0.0048 memory: 8457 loss: 0.0136 decode.loss_ce: 0.0061 decode.acc_seg: 99.7364 aux.loss_ce: 0.0075 aux.acc_seg: 98.9046 +04/19 06:21:22 - mmengine - INFO - Iter(train) [ 79100/160000] base_lr: 5.1041e-05 lr: 1.8871e-07 eta: 23:25:10 time: 1.0014 data_time: 0.0050 memory: 8457 loss: 0.0133 decode.loss_ce: 0.0058 decode.acc_seg: 99.6956 aux.loss_ce: 0.0075 aux.acc_seg: 99.1325 +04/19 06:22:12 - mmengine - INFO - Iter(train) [ 79150/160000] base_lr: 5.1010e-05 lr: 1.8859e-07 eta: 23:24:09 time: 1.0004 data_time: 0.0058 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0055 decode.acc_seg: 99.7379 aux.loss_ce: 0.0076 aux.acc_seg: 98.9176 +04/19 06:23:02 - mmengine - INFO - Iter(train) [ 79200/160000] base_lr: 5.0978e-05 lr: 1.8848e-07 eta: 23:23:08 time: 1.0009 data_time: 0.0047 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0059 decode.acc_seg: 99.6513 aux.loss_ce: 0.0075 aux.acc_seg: 98.9153 +04/19 06:23:52 - mmengine - INFO - Iter(train) [ 79250/160000] base_lr: 5.0947e-05 lr: 1.8836e-07 eta: 23:22:07 time: 0.9978 data_time: 0.0049 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7013 aux.loss_ce: 0.0073 aux.acc_seg: 99.3008 +04/19 06:24:42 - mmengine - INFO - Iter(train) [ 79300/160000] base_lr: 5.0915e-05 lr: 1.8824e-07 eta: 23:21:06 time: 0.9985 data_time: 0.0046 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0061 decode.acc_seg: 99.7015 aux.loss_ce: 0.0066 aux.acc_seg: 99.2834 +04/19 06:25:32 - mmengine - INFO - Iter(train) [ 79350/160000] base_lr: 5.0884e-05 lr: 1.8813e-07 eta: 23:20:05 time: 0.9995 data_time: 0.0046 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7406 aux.loss_ce: 0.0072 aux.acc_seg: 99.1667 +04/19 06:26:22 - mmengine - INFO - Iter(train) [ 79400/160000] base_lr: 5.0852e-05 lr: 1.8801e-07 eta: 23:19:04 time: 0.9991 data_time: 0.0047 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0054 decode.acc_seg: 99.8453 aux.loss_ce: 0.0069 aux.acc_seg: 99.5214 +04/19 06:27:12 - mmengine - INFO - Iter(train) [ 79450/160000] base_lr: 5.0821e-05 lr: 1.8789e-07 eta: 23:18:03 time: 0.9974 data_time: 0.0045 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0052 decode.acc_seg: 99.7797 aux.loss_ce: 0.0064 aux.acc_seg: 99.2872 +04/19 06:28:02 - mmengine - INFO - Iter(train) [ 79500/160000] base_lr: 5.0789e-05 lr: 1.8778e-07 eta: 23:17:02 time: 0.9990 data_time: 0.0047 memory: 8457 loss: 0.0127 decode.loss_ce: 0.0054 decode.acc_seg: 99.7972 aux.loss_ce: 0.0073 aux.acc_seg: 99.3031 +04/19 06:28:52 - mmengine - INFO - Iter(train) [ 79550/160000] base_lr: 5.0757e-05 lr: 1.8766e-07 eta: 23:16:01 time: 0.9999 data_time: 0.0048 memory: 8457 loss: 0.0116 decode.loss_ce: 0.0052 decode.acc_seg: 99.8238 aux.loss_ce: 0.0064 aux.acc_seg: 99.3664 +04/19 06:29:42 - mmengine - INFO - Iter(train) [ 79600/160000] base_lr: 5.0726e-05 lr: 1.8754e-07 eta: 23:15:01 time: 0.9977 data_time: 0.0046 memory: 8457 loss: 0.0122 decode.loss_ce: 0.0052 decode.acc_seg: 99.8024 aux.loss_ce: 0.0070 aux.acc_seg: 99.2453 +04/19 06:30:32 - mmengine - INFO - Iter(train) [ 79650/160000] base_lr: 5.0694e-05 lr: 1.8743e-07 eta: 23:14:00 time: 0.9991 data_time: 0.0047 memory: 8457 loss: 0.0141 decode.loss_ce: 0.0059 decode.acc_seg: 99.7225 aux.loss_ce: 0.0082 aux.acc_seg: 98.9305 +04/19 06:31:22 - mmengine - INFO - Iter(train) [ 79700/160000] base_lr: 5.0663e-05 lr: 1.8731e-07 eta: 23:12:59 time: 0.9990 data_time: 0.0048 memory: 8457 loss: 0.0126 decode.loss_ce: 0.0058 decode.acc_seg: 99.6677 aux.loss_ce: 0.0069 aux.acc_seg: 99.2506 +04/19 06:32:12 - mmengine - INFO - Iter(train) [ 79750/160000] base_lr: 5.0631e-05 lr: 1.8719e-07 eta: 23:11:59 time: 1.0000 data_time: 0.0048 memory: 8457 loss: 0.0139 decode.loss_ce: 0.0062 decode.acc_seg: 99.7698 aux.loss_ce: 0.0077 aux.acc_seg: 99.1795 +04/19 06:33:02 - mmengine - INFO - Iter(train) [ 79800/160000] base_lr: 5.0600e-05 lr: 1.8708e-07 eta: 23:10:59 time: 0.9991 data_time: 0.0052 memory: 8457 loss: 0.0120 decode.loss_ce: 0.0056 decode.acc_seg: 99.6943 aux.loss_ce: 0.0063 aux.acc_seg: 99.2014 +04/19 06:33:52 - mmengine - INFO - Iter(train) [ 79850/160000] base_lr: 5.0568e-05 lr: 1.8696e-07 eta: 23:09:58 time: 0.9993 data_time: 0.0048 memory: 8457 loss: 0.0135 decode.loss_ce: 0.0060 decode.acc_seg: 99.6765 aux.loss_ce: 0.0076 aux.acc_seg: 98.8350 +04/19 06:34:42 - mmengine - INFO - Iter(train) [ 79900/160000] base_lr: 5.0537e-05 lr: 1.8684e-07 eta: 23:08:58 time: 0.9978 data_time: 0.0048 memory: 8457 loss: 0.0131 decode.loss_ce: 0.0058 decode.acc_seg: 99.7200 aux.loss_ce: 0.0073 aux.acc_seg: 99.0034 +04/19 06:35:32 - mmengine - INFO - Iter(train) [ 79950/160000] base_lr: 5.0505e-05 lr: 1.8673e-07 eta: 23:07:57 time: 0.9982 data_time: 0.0043 memory: 8457 loss: 0.0135 decode.loss_ce: 0.0058 decode.acc_seg: 99.7614 aux.loss_ce: 0.0077 aux.acc_seg: 99.4556 +04/19 06:36:22 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_cag-512x512_20240419_004857 +04/19 06:36:22 - mmengine - INFO - Iter(train) [ 80000/160000] base_lr: 5.0474e-05 lr: 1.8661e-07 eta: 23:06:57 time: 0.9971 data_time: 0.0045 memory: 8457 loss: 0.0134 decode.loss_ce: 0.0060 decode.acc_seg: 99.7063 aux.loss_ce: 0.0074 aux.acc_seg: 99.2573 +04/19 06:36:22 - mmengine - INFO - Saving checkpoint at 80000 iterations +04/19 06:36:32 - mmengine - INFO - Iter(val) [ 50/200] eta: 0:00:17 time: 0.1156 data_time: 0.0014 memory: 3999 +04/19 06:36:37 - mmengine - INFO - Iter(val) [100/200] eta: 0:00:11 time: 0.1158 data_time: 0.0015 memory: 3999 +04/19 06:36:43 - mmengine - INFO - Iter(val) [150/200] eta: 0:00:05 time: 0.1158 data_time: 0.0014 memory: 3999 +04/19 06:36:49 - mmengine - INFO - Iter(val) [200/200] eta: 0:00:00 time: 0.1160 data_time: 0.0016 memory: 3999 +04/19 06:36:49 - mmengine - INFO - per class results: +04/19 06:36:49 - mmengine - INFO - ++------------+-------+-------+--------+-----------+--------+ +| Class | IoU | Acc | Fscore | Precision | Recall | ++------------+-------+-------+--------+-----------+--------+ +| background | 99.16 | 99.51 | 99.58 | 99.64 | 99.51 | +| contrast | 81.8 | 91.37 | 89.99 | 88.65 | 91.37 | ++------------+-------+-------+--------+-----------+--------+ +04/19 06:36:49 - mmengine - INFO - Iter(val) [200/200] aAcc: 99.1900 mIoU: 90.4800 mAcc: 95.4400 mFscore: 94.7800 mPrecision: 94.1500 mRecall: 95.4400 data_time: 0.0015 time: 0.1160 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64757 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64758 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64759 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64760 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +main()runner.train() + + File "tools/train.py", line 100, in main + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + model = self.train_loop.run() # type: ignoreoptim_wrapper.update_params(parsed_loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + model = self.train_loop.run() # type: ignore + loss.backward(**kwargs) File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter +self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward +loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64757 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64758 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64759 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 64760 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 64727 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 64727 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 64727 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 06:46:48 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1862928832 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1862928832 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 06:46:49 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag_10000' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 06:46:51 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + 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+ "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + 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"backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 06:46:52 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +Loads checkpoint by local backend from path: /workspaces/mmsegmentation-1/converted_model.pth +04/19 06:46:53 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 06:46:53 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 06:46:53 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/mae-base_upernet_8xb2-amp-160k_cag-512x512. +04/19 06:47:50 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:31:49 time: 1.0291 data_time: 0.0041 memory: 8935 loss: 6.8105 decode.loss_ce: 4.8183 decode.acc_seg: 19.2308 aux.loss_ce: 1.9922 aux.acc_seg: 0.0448 +04/19 06:48:41 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 2 days, 0:07:14 time: 1.0283 data_time: 0.0045 memory: 8462 loss: 6.4806 decode.loss_ce: 4.5186 decode.acc_seg: 27.7260 aux.loss_ce: 1.9619 aux.acc_seg: 15.1159 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303901 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303902 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303903 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303904 closing signal SIGINT +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +Traceback (most recent call last): + File "tools/train.py", line 104, in +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train +Traceback (most recent call last): + File "tools/train.py", line 104, in + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + main()optim_wrapper.update_params(parsed_loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + File "tools/train.py", line 100, in main + main()self.backward(loss) + + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + File "tools/train.py", line 100, in main + loss.backward(**kwargs)runner.train() + + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + runner.train() + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train + + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + model = self.train_loop.run() # type: ignore + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 286, in run + self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step +self.run_iter(data_batch) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/loops.py", line 309, in run_iter + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + outputs = self.runner.model.train_step( + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 123, in train_step + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + optim_wrapper.update_params(parsed_loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params + loss.backward(**kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + self.backward(loss) + File "/opt/conda/lib/python3.8/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) +loss.backward(**kwargs) File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + + File "/opt/conda/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) + File "/opt/conda/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass +KeyboardInterrupt +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303901 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303902 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303903 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 303904 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 303871 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 303871 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 303871 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 06:52:22 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1322765965 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1322765965 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 06:52:23 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag_10000' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='./pretrain/mae_pretrain_vit_base_mmcls.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_ade20k-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 06:52:25 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} + +set param backbone.layers.0.gamma_2 as id 1 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.cls_token as id 0 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.pos_embed as id 0 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1set param backbone.patch_embed.projection.bias as id 0 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.0.attn.qkv.weight as id 1 + +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.0.attn.qkv.bias as id 1 + +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.1.attn.proj.bias as id 2 + +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.2.gamma_1 as id 3set param backbone.layers.0.ffn.layers.1.weight as id 1 + +set param backbone.layers.2.gamma_2 as id 3set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.gamma_1 as id 2set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.2.attn.qkv.bias as id 3 + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.2.ln2.weight as id 3 + +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.2.ln2.bias as id 3 + +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.1.ffn.layers.1.bias as id 2set param backbone.layers.3.ln1.bias as id 4 + +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.2.ln1.bias as id 3 + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.2.ln2.bias as id 3set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.4.gamma_1 as id 5 + +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.2.ffn.layers.1.weight as id 3set param backbone.layers.4.ln1.weight as id 5 + +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.4.attn.qkv.weight as id 5set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.4.attn.proj.bias as id 5set param backbone.layers.3.attn.relative_position_bias_table as id 4 + +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.3.attn.qkv.weight as id 4 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4set param backbone.layers.4.ffn.layers.0.0.weight as id 5 + +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.4.ffn.layers.1.weight as id 5set param backbone.layers.3.ln2.bias as id 4 + +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.5.attn.qkv.bias as id 6set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.5.attn.proj.bias as id 6set param backbone.layers.4.attn.qkv.weight as id 5 + +set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.6.gamma_1 as id 7 + +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.6.attn.qkv.weight as id 7set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.6.ffn.layers.0.0.bias as id 7set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.6.attn.proj.weight as id 7set param backbone.layers.7.gamma_2 as id 8 + +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9set param backbone.layers.7.gamma_2 as id 8 + +set param backbone.layers.7.ln1.weight as id 8set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.7.ln1.bias as id 8set param backbone.layers.8.ln2.bias as id 9 + +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.7.ln2.weight as id 8 + +set param backbone.layers.9.gamma_2 as id 10set param backbone.layers.7.ln2.bias as id 8 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.9.ln1.bias as id 10 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.9.ffn.layers.1.weight as id 10set param backbone.layers.8.attn.qkv.bias as id 9 + +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9set param backbone.layers.10.gamma_1 as id 11 + +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.10.ln2.weight as id 11 + +set param backbone.layers.9.gamma_2 as id 10set param backbone.layers.10.ln2.bias as id 11 + +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.10.ffn.layers.0.0.bias as id 11 + +set param backbone.layers.10.ffn.layers.1.weight as id 11set param backbone.layers.9.attn.qkv.weight as id 10 + +set param backbone.layers.10.ffn.layers.1.bias as id 11set param backbone.layers.9.attn.qkv.bias as id 10 + +set param backbone.layers.9.attn.proj.weight as id 10set param backbone.layers.11.gamma_1 as id 12 + +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.11.ln1.weight as id 12 + +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.11.ln1.bias as id 12 + +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.11.attn.qkv.weight as id 12set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.11.attn.proj.weight as id 12set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.10.gamma_1 as id 11set param backbone.layers.11.ln2.weight as id 12 + +set param backbone.layers.10.gamma_2 as id 11set param backbone.layers.11.ln2.bias as id 12 + +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12set param backbone.layers.10.ln1.bias as id 11 + +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param neck.upsample_4x.0.weight as id 13set param backbone.layers.10.ln2.weight as id 11 + +set param neck.upsample_4x.0.bias as id 13set param backbone.layers.10.ln2.bias as id 11 + +set param neck.upsample_4x.1.weight as id 13set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param neck.upsample_4x.1.bias as id 13set param backbone.layers.10.ffn.layers.0.0.bias as id 11 + +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.10.ffn.layers.1.weight as id 11set param neck.upsample_4x.3.bias as id 13 + +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.11.attn.relative_position_bias_table as id 12set param decode_head.conv_seg.bias as id 13 + +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.11.attn.proj.weight as id 12 +set param decode_head.psp_modules.0.1.bn.weight as id 13set param backbone.layers.11.attn.proj.bias as id 12 + +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param backbone.layers.11.ffn.layers.1.bias as id 12 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param neck.upsample_4x.0.weight as id 13set param decode_head.psp_modules.3.1.conv.weight as id 13 + +set param neck.upsample_4x.0.bias as id 13set param decode_head.psp_modules.3.1.bn.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param decode_head.bottleneck.conv.weight as id 13set param neck.upsample_4x.1.bias as id 13 + +set param decode_head.bottleneck.bn.weight as id 13set param neck.upsample_4x.3.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13set param neck.upsample_4x.3.bias as id 13 + +set param neck.upsample_2x.0.weight as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13set param decode_head.psp_modules.0.1.bn.weight as id 13 + +set param decode_head.fpn_convs.0.bn.weight as id 13set param decode_head.psp_modules.0.1.bn.bias as id 13 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param decode_head.fpn_convs.2.bn.weight as id 13 + +set param decode_head.psp_modules.2.1.bn.weight as id 13set param decode_head.fpn_convs.2.bn.bias as id 13 + +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13set param decode_head.fpn_bottleneck.bn.weight as id 13 + +set param decode_head.psp_modules.3.1.bn.weight as id 13set param decode_head.fpn_bottleneck.bn.bias as id 13 + +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13set param auxiliary_head.conv_seg.bias as id 13 + +set param decode_head.bottleneck.bn.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13set param decode_head.lateral_convs.0.conv.weight as id 13 + +set param auxiliary_head.convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 06:52:26 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: ./pretrain/mae_pretrain_vit_base_mmcls.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./pretrain/mae_pretrain_vit_base_mmcls.pth can not be found. +Loads checkpoint by local backend from path: ./pretrain/mae_pretrain_vit_base_mmcls.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./pretrain/mae_pretrain_vit_base_mmcls.pth can not be found. +Loads checkpoint by local backend from path: ./pretrain/mae_pretrain_vit_base_mmcls.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./pretrain/mae_pretrain_vit_base_mmcls.pth can not be found. +Loads checkpoint by local backend from path: ./pretrain/mae_pretrain_vit_base_mmcls.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./pretrain/mae_pretrain_vit_base_mmcls.pth can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 308287) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_06:52:29 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 308288) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_06:52:29 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 308289) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_06:52:29 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 308290) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_06:52:29 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 308287) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 06:57:54 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1351088420 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1351088420 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 06:57:54 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag_10000' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained='./mae_pretrain_vit_base.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_ade20k-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 06:57:57 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.layers.0.attn.proj.weight as id 1 + +set param backbone.layers.0.attn.proj.bias as id 1 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.cls_token as id 0 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.pos_embed as id 0set param backbone.layers.1.gamma_2 as id 2 + +set param backbone.layers.1.ln1.weight as id 2 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.layers.1.ln1.bias as id 2 + +set param backbone.patch_embed.projection.weight as id 0set param backbone.layers.1.attn.relative_position_bias_table as id 2 +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 + +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1set param backbone.layers.1.ffn.layers.0.0.weight as id 2 + +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.cls_token as id 0 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.pos_embed as id 0set param backbone.layers.0.attn.qkv.bias as id 1 + +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.2.ln1.weight as id 3 + +set param backbone.layers.0.attn.proj.bias as id 1set param backbone.layers.2.ln1.bias as id 3 + +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.2.attn.relative_position_bias_table as id 3set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.2.attn.qkv.weight as id 3set param backbone.layers.0.ln2.bias as id 1 + +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.0.gamma_1 as id 1set param backbone.layers.2.attn.proj.bias as id 3 + +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.0.gamma_2 as id 1set param backbone.layers.0.ffn.layers.0.0.bias as id 1 + + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.0.ln1.weight as id 1set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.0.ffn.layers.1.weight as id 1 + + +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.0.ln1.bias as id 1set param backbone.layers.0.ffn.layers.1.bias as id 1 + +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.0.attn.qkv.weight as id 1 + +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.0.attn.qkv.bias as id 1set param backbone.layers.3.ln1.bias as id 4 + + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.0.attn.proj.weight as id 1set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.3.attn.qkv.bias as id 4 + + +set param backbone.layers.0.attn.proj.bias as id 1set param backbone.layers.1.attn.qkv.bias as id 2 + +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.1.attn.proj.weight as id 2set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.0.ln2.bias as id 1set param backbone.layers.3.ln2.bias as id 4 + +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.0.ffn.layers.1.bias as id 1set param backbone.layers.2.gamma_1 as id 3set param backbone.layers.4.gamma_2 as id 5 + + +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.2.ln1.weight as id 3set param backbone.layers.4.ln1.bias as id 5 + +set param backbone.layers.1.gamma_1 as id 2set param backbone.layers.2.ln1.bias as id 3set param backbone.layers.4.attn.relative_position_bias_table as id 5 + + +set param backbone.layers.1.gamma_2 as id 2set param backbone.layers.2.attn.relative_position_bias_table as id 3set param backbone.layers.4.attn.qkv.weight as id 5 + + +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.4.attn.qkv.bias as id 5set param backbone.layers.2.attn.qkv.weight as id 3 + + +set param backbone.layers.1.ln1.bias as id 2set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.4.attn.proj.weight as id 5 + +set param backbone.layers.1.attn.relative_position_bias_table as id 2set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.2.attn.proj.weight as id 3 + +set param backbone.layers.1.attn.qkv.weight as id 2set param backbone.layers.2.attn.proj.bias as id 3set param backbone.layers.4.ln2.weight as id 5 + + +set param backbone.layers.1.attn.qkv.bias as id 2set param backbone.layers.4.ln2.bias as id 5set param backbone.layers.2.ln2.weight as id 3 + + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 + +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.1.ln2.weight as id 2set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.4.ffn.layers.1.bias as id 5 + +set param backbone.layers.3.gamma_1 as id 4set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.5.gamma_2 as id 6 + +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.1.ffn.layers.1.weight as id 2 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.3.attn.relative_position_bias_table as id 4set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.2.gamma_1 as id 3set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.3.attn.proj.bias as id 4set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.5.attn.proj.bias as id 6set param backbone.layers.3.ln2.weight as id 4 + +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.3.ln2.bias as id 4set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.2.attn.qkv.bias as id 3set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.3.ffn.layers.1.bias as id 4 + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.6.gamma_1 as id 7 + +set param backbone.layers.2.ln2.weight as id 3set param backbone.layers.4.gamma_2 as id 5set param backbone.layers.6.gamma_2 as id 7 + + +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.4.ln1.weight as id 5set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.4.ln1.bias as id 5set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.2.ffn.layers.1.bias as id 3set param backbone.layers.6.attn.proj.weight as id 7set param backbone.layers.4.attn.proj.bias as id 5 + + +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.3.gamma_1 as id 4set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.4.ln2.bias as id 5 + +set param backbone.layers.3.gamma_2 as id 4set param backbone.layers.6.ln2.bias as id 7 + +set param backbone.layers.4.ffn.layers.0.0.weight as id 5set param backbone.layers.3.ln1.weight as id 4 + +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6set param backbone.layers.3.attn.proj.weight as id 4 + +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.3.ln2.bias as id 4set param backbone.layers.5.attn.qkv.bias as id 6 + +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.5.attn.proj.bias as id 6 + +set param backbone.layers.6.ffn.layers.1.weight as id 7set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.5.ln2.weight as id 6 + +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.7.gamma_2 as id 8set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.7.ln1.bias as id 8set param backbone.layers.4.gamma_1 as id 5 + + +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.7.attn.qkv.weight as id 8set param backbone.layers.6.gamma_1 as id 7 + +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.7.attn.qkv.bias as id 8set param backbone.layers.6.gamma_2 as id 7 + +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.7.ln2.weight as id 8set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.4.attn.qkv.bias as id 5 + +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.4.attn.proj.weight as id 5set param backbone.layers.6.attn.qkv.bias as id 7 + +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.6.attn.proj.bias as id 7set param backbone.layers.7.ffn.layers.1.weight as id 8 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.7.ffn.layers.1.bias as id 8set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.8.gamma_2 as id 9 + +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.5.attn.qkv.weight as id 6set param backbone.layers.8.ffn.layers.0.0.bias as id 9 + +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + +set param backbone.layers.8.ffn.layers.1.bias as id 9set param backbone.layers.5.attn.proj.weight as id 6 + +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.5.attn.proj.bias as id 6set param backbone.layers.6.ffn.layers.1.bias as id 7 + +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.5.ln2.bias as id 6set param backbone.layers.9.ln1.weight as id 10 + +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.9.attn.relative_position_bias_table as id 10set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.7.ln1.bias as id 8 + +set param backbone.layers.9.attn.qkv.weight as id 10set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.5.ffn.layers.1.weight as id 6set param backbone.layers.9.attn.proj.weight as id 10 + +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.5.ffn.layers.1.bias as id 6set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.9.ln2.weight as id 10 + +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.7.ln2.weight as id 8 + +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.6.ln1.bias as id 7 + +set param backbone.layers.9.ffn.layers.1.weight as id 10set param backbone.layers.7.ffn.layers.0.0.bias as id 8 + +set param backbone.layers.6.attn.relative_position_bias_table as id 7set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8set param backbone.layers.6.attn.qkv.weight as id 7 + +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.8.gamma_1 as id 9 + +set param backbone.layers.10.ln1.weight as id 11set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.6.attn.proj.weight as id 7 + +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.8.ln1.weight as id 9set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 + +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.8.attn.qkv.weight as id 9 + +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.11.gamma_1 as id 12set param backbone.layers.8.ffn.layers.1.bias as id 9 + +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.11.ln1.weight as id 12 + +set param backbone.layers.9.gamma_2 as id 10set param backbone.layers.11.ln1.bias as id 12 + +set param backbone.layers.9.ln1.weight as id 10set param backbone.layers.11.attn.relative_position_bias_table as id 12 + +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7set param backbone.layers.11.attn.proj.bias as id 12 + +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 + +set param backbone.layers.9.ln2.weight as id 10set param backbone.layers.11.ln2.bias as id 12 + +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.11.ffn.layers.1.weight as id 12 + +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.11.ffn.layers.1.bias as id 12set param backbone.layers.7.gamma_2 as id 8 + +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.10.gamma_1 as id 11set param backbone.layers.7.ln1.bias as id 8 + +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param neck.upsample_4x.0.weight as id 13 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.7.attn.qkv.weight as id 8set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.10.ln1.bias as id 11 + +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.10.attn.relative_position_bias_table as id 11set param neck.upsample_4x.1.weight as id 13 + +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.10.attn.qkv.weight as id 11 + +set param neck.upsample_4x.1.bias as id 13 +set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.10.attn.qkv.bias as id 11 + +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.7.ln2.weight as id 8set param backbone.layers.10.attn.proj.weight as id 11set param neck.upsample_4x.3.bias as id 13 + + +set param backbone.layers.7.ln2.bias as id 8set param backbone.layers.10.attn.proj.bias as id 11 + +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13set param backbone.layers.10.ln2.weight as id 11 + +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param decode_head.conv_seg.weight as id 13set param backbone.layers.10.ffn.layers.0.0.bias as id 11 + +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.11.gamma_1 as id 12 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.8.attn.relative_position_bias_table as id 9set param backbone.layers.11.gamma_2 as id 12 + +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.11.ln1.weight as id 12 +set param decode_head.psp_modules.0.1.bn.bias as id 13 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.8.attn.proj.bias as id 9set param decode_head.psp_modules.1.1.conv.weight as id 13 + +set param backbone.layers.11.attn.qkv.weight as id 12 +set param decode_head.psp_modules.1.1.bn.weight as id 13set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.11.attn.qkv.bias as id 12 + +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.11.attn.proj.weight as id 12 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param backbone.layers.11.attn.proj.bias as id 12set param backbone.layers.8.ffn.layers.0.0.weight as id 9 + +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9set param backbone.layers.11.ln2.weight as id 12 +set param decode_head.psp_modules.2.1.bn.bias as id 13 + +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.11.ln2.bias as id 12 + +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param backbone.layers.9.gamma_1 as id 10set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param decode_head.bottleneck.conv.weight as id 13 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param decode_head.bottleneck.bn.weight as id 13 +set param backbone.layers.9.ln1.weight as id 10set param backbone.layers.11.ffn.layers.1.bias as id 12 + +set param decode_head.bottleneck.bn.bias as id 13 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param decode_head.lateral_convs.0.bn.weight as id 13set param neck.upsample_4x.0.weight as id 13 +set param backbone.layers.9.attn.qkv.bias as id 10 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10set param decode_head.lateral_convs.1.conv.weight as id 13 +set param neck.upsample_4x.1.weight as id 13 + +set param backbone.layers.9.ln2.weight as id 10set param neck.upsample_4x.1.bias as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 + +set param backbone.layers.9.ln2.bias as id 10 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10set param neck.upsample_4x.3.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 + +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param neck.upsample_2x.0.weight as id 13set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param decode_head.lateral_convs.2.bn.bias as id 13 + +set param neck.upsample_2x.0.bias as id 13 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13set param backbone.layers.10.gamma_1 as id 11 + +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param backbone.layers.10.gamma_2 as id 11 +set param decode_head.fpn_convs.1.conv.weight as id 13set param backbone.layers.10.ln1.weight as id 11 +set param decode_head.conv_seg.weight as id 13 + +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param backbone.layers.10.ln1.bias as id 11set param decode_head.conv_seg.bias as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param backbone.layers.10.attn.qkv.bias as id 11set param decode_head.fpn_convs.2.bn.bias as id 13 + +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param backbone.layers.10.attn.proj.weight as id 11 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13set param backbone.layers.10.attn.proj.bias as id 11 + +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13set param backbone.layers.10.ln2.weight as id 11 + +set param backbone.layers.10.ln2.bias as id 11 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11set param decode_head.psp_modules.1.1.bn.weight as id 13 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11set param decode_head.psp_modules.1.1.bn.bias as id 13set param auxiliary_head.conv_seg.weight as id 13 + + +set param auxiliary_head.conv_seg.bias as id 13set param backbone.layers.10.ffn.layers.1.weight as id 11 + +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param backbone.layers.11.gamma_1 as id 12set param auxiliary_head.convs.0.bn.weight as id 13 + +set param backbone.layers.11.gamma_2 as id 12set param auxiliary_head.convs.0.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 + +set param backbone.layers.11.ln1.weight as id 12 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param backbone.layers.11.ln1.bias as id 12 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param decode_head.bottleneck.conv.weight as id 13 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param decode_head.bottleneck.bn.weight as id 13set param backbone.layers.11.attn.qkv.bias as id 12 + +set param decode_head.bottleneck.bn.bias as id 13 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param backbone.layers.11.ln2.weight as id 12 +set param decode_head.lateral_convs.0.bn.weight as id 13set param backbone.layers.11.ln2.bias as id 12 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.lateral_convs.1.conv.weight as id 13set param backbone.layers.11.ffn.layers.0.0.bias as id 12 + +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13set param neck.upsample_4x.1.weight as id 13 + +set param decode_head.fpn_convs.1.bn.weight as id 13set param neck.upsample_4x.1.bias as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13set param neck.upsample_4x.3.bias as id 13 + +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param neck.upsample_2x.0.weight as id 13set param decode_head.fpn_convs.2.bn.bias as id 13 + +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13set param auxiliary_head.convs.0.conv.weight as id 13 + +set param decode_head.psp_modules.0.1.bn.weight as id 13set param auxiliary_head.convs.0.bn.weight as id 13 + +set param decode_head.psp_modules.0.1.bn.bias as id 13set param auxiliary_head.convs.0.bn.bias as id 13 + +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + "backbone.layers.2.ffn.layers.0.0.weight", + "backbone.layers.2.ffn.layers.1.weight" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.05 + }, + "layer_4_no_decay": { + "param_names": [ + "backbone.layers.3.gamma_1", + "backbone.layers.3.gamma_2", + "backbone.layers.3.ln1.weight", + "backbone.layers.3.ln1.bias", + "backbone.layers.3.attn.qkv.bias", + "backbone.layers.3.attn.proj.bias", + "backbone.layers.3.ln2.weight", + "backbone.layers.3.ln2.bias", + "backbone.layers.3.ffn.layers.0.0.bias", + "backbone.layers.3.ffn.layers.1.bias" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.0 + }, + "layer_4_decay": { + "param_names": [ + "backbone.layers.3.attn.relative_position_bias_table", + "backbone.layers.3.attn.qkv.weight", + "backbone.layers.3.attn.proj.weight", + "backbone.layers.3.ffn.layers.0.0.weight", + "backbone.layers.3.ffn.layers.1.weight" + ], + "lr_scale": 0.02071191283789063, + "lr": 2.0711912837890633e-06, + "weight_decay": 0.05 + }, + "layer_5_no_decay": { + "param_names": [ + "backbone.layers.4.gamma_1", + "backbone.layers.4.gamma_2", + "backbone.layers.4.ln1.weight", + "backbone.layers.4.ln1.bias", + "backbone.layers.4.attn.qkv.bias", + "backbone.layers.4.attn.proj.bias", + "backbone.layers.4.ln2.weight", + "backbone.layers.4.ln2.bias", + "backbone.layers.4.ffn.layers.0.0.bias", + "backbone.layers.4.ffn.layers.1.bias" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.0 + }, + "layer_5_decay": { + "param_names": [ + "backbone.layers.4.attn.relative_position_bias_table", + "backbone.layers.4.attn.qkv.weight", + "backbone.layers.4.attn.proj.weight", + "backbone.layers.4.ffn.layers.0.0.weight", + "backbone.layers.4.ffn.layers.1.weight" + ], + "lr_scale": 0.03186448128906251, + "lr": 3.1864481289062513e-06, + "weight_decay": 0.05 + }, + "layer_6_no_decay": { + "param_names": [ + "backbone.layers.5.gamma_1", + "backbone.layers.5.gamma_2", + "backbone.layers.5.ln1.weight", + "backbone.layers.5.ln1.bias", + "backbone.layers.5.attn.qkv.bias", + "backbone.layers.5.attn.proj.bias", + "backbone.layers.5.ln2.weight", + "backbone.layers.5.ln2.bias", + "backbone.layers.5.ffn.layers.0.0.bias", + "backbone.layers.5.ffn.layers.1.bias" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.0 + }, + "layer_6_decay": { + "param_names": [ + "backbone.layers.5.attn.relative_position_bias_table", + "backbone.layers.5.attn.qkv.weight", + "backbone.layers.5.attn.proj.weight", + "backbone.layers.5.ffn.layers.0.0.weight", + "backbone.layers.5.ffn.layers.1.weight" + ], + "lr_scale": 0.049022278906250015, + "lr": 4.9022278906250015e-06, + "weight_decay": 0.05 + }, + "layer_7_no_decay": { + "param_names": [ + "backbone.layers.6.gamma_1", + "backbone.layers.6.gamma_2", + "backbone.layers.6.ln1.weight", + "backbone.layers.6.ln1.bias", + "backbone.layers.6.attn.qkv.bias", + "backbone.layers.6.attn.proj.bias", + "backbone.layers.6.ln2.weight", + "backbone.layers.6.ln2.bias", + "backbone.layers.6.ffn.layers.0.0.bias", + "backbone.layers.6.ffn.layers.1.bias" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.0 + }, + "layer_7_decay": { + "param_names": [ + "backbone.layers.6.attn.relative_position_bias_table", + "backbone.layers.6.attn.qkv.weight", + "backbone.layers.6.attn.proj.weight", + "backbone.layers.6.ffn.layers.0.0.weight", + "backbone.layers.6.ffn.layers.1.weight" + ], + "lr_scale": 0.07541889062500001, + "lr": 7.541889062500002e-06, + "weight_decay": 0.05 + }, + "layer_8_no_decay": { + "param_names": [ + "backbone.layers.7.gamma_1", + "backbone.layers.7.gamma_2", + "backbone.layers.7.ln1.weight", + "backbone.layers.7.ln1.bias", + "backbone.layers.7.attn.qkv.bias", + "backbone.layers.7.attn.proj.bias", + "backbone.layers.7.ln2.weight", + "backbone.layers.7.ln2.bias", + "backbone.layers.7.ffn.layers.0.0.bias", + "backbone.layers.7.ffn.layers.1.bias" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.0 + }, + "layer_8_decay": { + "param_names": [ + "backbone.layers.7.attn.relative_position_bias_table", + "backbone.layers.7.attn.qkv.weight", + "backbone.layers.7.attn.proj.weight", + "backbone.layers.7.ffn.layers.0.0.weight", + "backbone.layers.7.ffn.layers.1.weight" + ], + "lr_scale": 0.11602906250000002, + "lr": 1.1602906250000002e-05, + "weight_decay": 0.05 + }, + "layer_9_no_decay": { + "param_names": [ + "backbone.layers.8.gamma_1", + "backbone.layers.8.gamma_2", + "backbone.layers.8.ln1.weight", + "backbone.layers.8.ln1.bias", + "backbone.layers.8.attn.qkv.bias", + "backbone.layers.8.attn.proj.bias", + "backbone.layers.8.ln2.weight", + "backbone.layers.8.ln2.bias", + "backbone.layers.8.ffn.layers.0.0.bias", + "backbone.layers.8.ffn.layers.1.bias" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.0 + }, + "layer_9_decay": { + "param_names": [ + "backbone.layers.8.attn.relative_position_bias_table", + "backbone.layers.8.attn.qkv.weight", + "backbone.layers.8.attn.proj.weight", + "backbone.layers.8.ffn.layers.0.0.weight", + "backbone.layers.8.ffn.layers.1.weight" + ], + "lr_scale": 0.17850625000000003, + "lr": 1.7850625000000003e-05, + "weight_decay": 0.05 + }, + "layer_10_no_decay": { + "param_names": [ + "backbone.layers.9.gamma_1", + "backbone.layers.9.gamma_2", + "backbone.layers.9.ln1.weight", + "backbone.layers.9.ln1.bias", + "backbone.layers.9.attn.qkv.bias", + "backbone.layers.9.attn.proj.bias", + "backbone.layers.9.ln2.weight", + "backbone.layers.9.ln2.bias", + "backbone.layers.9.ffn.layers.0.0.bias", + "backbone.layers.9.ffn.layers.1.bias" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.0 + }, + "layer_10_decay": { + "param_names": [ + "backbone.layers.9.attn.relative_position_bias_table", + "backbone.layers.9.attn.qkv.weight", + "backbone.layers.9.attn.proj.weight", + "backbone.layers.9.ffn.layers.0.0.weight", + "backbone.layers.9.ffn.layers.1.weight" + ], + "lr_scale": 0.274625, + "lr": 2.7462500000000003e-05, + "weight_decay": 0.05 + }, + "layer_11_no_decay": { + "param_names": [ + "backbone.layers.10.gamma_1", + "backbone.layers.10.gamma_2", + "backbone.layers.10.ln1.weight", + "backbone.layers.10.ln1.bias", + "backbone.layers.10.attn.qkv.bias", + "backbone.layers.10.attn.proj.bias", + "backbone.layers.10.ln2.weight", + "backbone.layers.10.ln2.bias", + "backbone.layers.10.ffn.layers.0.0.bias", + "backbone.layers.10.ffn.layers.1.bias" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.0 + }, + "layer_11_decay": { + "param_names": [ + "backbone.layers.10.attn.relative_position_bias_table", + "backbone.layers.10.attn.qkv.weight", + "backbone.layers.10.attn.proj.weight", + "backbone.layers.10.ffn.layers.0.0.weight", + "backbone.layers.10.ffn.layers.1.weight" + ], + "lr_scale": 0.42250000000000004, + "lr": 4.2250000000000004e-05, + "weight_decay": 0.05 + }, + "layer_12_no_decay": { + "param_names": [ + "backbone.layers.11.gamma_1", + "backbone.layers.11.gamma_2", + "backbone.layers.11.ln1.weight", + "backbone.layers.11.ln1.bias", + "backbone.layers.11.attn.qkv.bias", + "backbone.layers.11.attn.proj.bias", + "backbone.layers.11.ln2.weight", + "backbone.layers.11.ln2.bias", + "backbone.layers.11.ffn.layers.0.0.bias", + "backbone.layers.11.ffn.layers.1.bias" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.0 + }, + "layer_12_decay": { + "param_names": [ + "backbone.layers.11.attn.relative_position_bias_table", + "backbone.layers.11.attn.qkv.weight", + "backbone.layers.11.attn.proj.weight", + "backbone.layers.11.ffn.layers.0.0.weight", + "backbone.layers.11.ffn.layers.1.weight" + ], + "lr_scale": 0.65, + "lr": 6.500000000000001e-05, + "weight_decay": 0.05 + }, + "layer_13_decay": { + "param_names": [ + "neck.upsample_4x.0.weight", + "neck.upsample_4x.3.weight", + "neck.upsample_2x.0.weight", + "decode_head.conv_seg.weight", + "decode_head.psp_modules.0.1.conv.weight", + "decode_head.psp_modules.1.1.conv.weight", + "decode_head.psp_modules.2.1.conv.weight", + "decode_head.psp_modules.3.1.conv.weight", + "decode_head.bottleneck.conv.weight", + "decode_head.lateral_convs.0.conv.weight", + "decode_head.lateral_convs.1.conv.weight", + "decode_head.lateral_convs.2.conv.weight", + "decode_head.fpn_convs.0.conv.weight", + "decode_head.fpn_convs.1.conv.weight", + "decode_head.fpn_convs.2.conv.weight", + "decode_head.fpn_bottleneck.conv.weight", + "auxiliary_head.conv_seg.weight", + "auxiliary_head.convs.0.conv.weight" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.05 + }, + "layer_13_no_decay": { + "param_names": [ + "neck.upsample_4x.0.bias", + "neck.upsample_4x.1.weight", + "neck.upsample_4x.1.bias", + "neck.upsample_4x.3.bias", + "neck.upsample_2x.0.bias", + "decode_head.conv_seg.bias", + "decode_head.psp_modules.0.1.bn.weight", + "decode_head.psp_modules.0.1.bn.bias", + "decode_head.psp_modules.1.1.bn.weight", + "decode_head.psp_modules.1.1.bn.bias", + "decode_head.psp_modules.2.1.bn.weight", + "decode_head.psp_modules.2.1.bn.bias", + "decode_head.psp_modules.3.1.bn.weight", + "decode_head.psp_modules.3.1.bn.bias", + "decode_head.bottleneck.bn.weight", + "decode_head.bottleneck.bn.bias", + "decode_head.lateral_convs.0.bn.weight", + "decode_head.lateral_convs.0.bn.bias", + "decode_head.lateral_convs.1.bn.weight", + "decode_head.lateral_convs.1.bn.bias", + "decode_head.lateral_convs.2.bn.weight", + "decode_head.lateral_convs.2.bn.bias", + "decode_head.fpn_convs.0.bn.weight", + "decode_head.fpn_convs.0.bn.bias", + "decode_head.fpn_convs.1.bn.weight", + "decode_head.fpn_convs.1.bn.bias", + "decode_head.fpn_convs.2.bn.weight", + "decode_head.fpn_convs.2.bn.bias", + "decode_head.fpn_bottleneck.bn.weight", + "decode_head.fpn_bottleneck.bn.bias", + "auxiliary_head.conv_seg.bias", + "auxiliary_head.convs.0.bn.weight", + "auxiliary_head.convs.0.bn.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +04/19 06:57:58 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Loads checkpoint by local backend from path: ./mae_pretrain_vit_base.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./mae_pretrain_vit_base.pth can not be found. +Loads checkpoint by local backend from path: ./mae_pretrain_vit_base.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./mae_pretrain_vit_base.pth can not be found. +Loads checkpoint by local backend from path: ./mae_pretrain_vit_base.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./mae_pretrain_vit_base.pth can not be found. +Loads checkpoint by local backend from path: ./mae_pretrain_vit_base.pth +Traceback (most recent call last): + File "tools/train.py", line 104, in + main() + File "tools/train.py", line 100, in main + runner.train() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1748, in train + self._init_model_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/runner.py", line 923, in _init_model_weights + model.init_weights() + File "/opt/conda/lib/python3.8/site-packages/mmengine/model/base_module.py", line 136, in init_weights + m.init_weights() + File "/workspaces/mmsegmentation-1/mmseg/models/backbones/mae.py", line 183, in init_weights + checkpoint = _load_checkpoint( + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 548, in _load_checkpoint + return CheckpointLoader.load_checkpoint(filename, map_location, logger) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 330, in load_checkpoint + return checkpoint_loader(filename, map_location) + File "/opt/conda/lib/python3.8/site-packages/mmengine/runner/checkpoint.py", line 346, in load_from_local + raise FileNotFoundError(f'{filename} can not be found.') +FileNotFoundError: ./mae_pretrain_vit_base.pth can not be found. +ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 312536) of binary: /opt/conda/bin/python +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +tools/train.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2024-04-19_06:58:00 + host : 06540a9609a4 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 312537) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2024-04-19_06:58:00 + host : 06540a9609a4 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 312538) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2024-04-19_06:58:00 + host : 06540a9609a4 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 312539) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2024-04-19_06:58:00 + host : 06540a9609a4 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 312536) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 06:59:25 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 330997872 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 330997872 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 06:59:25 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag_10000' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained= + '/workspaces/mmsegmentation-1/configs/mae/mae_pretrain_vit_base.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag_10000', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mae-base_upernet_8xb2-amp-160k_ade20k-512x512' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 06:59:28 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.0 + }, + "layer_1_decay": { + "param_names": [ + "backbone.layers.0.attn.relative_position_bias_table", + "backbone.layers.0.attn.qkv.weight", + "backbone.layers.0.attn.proj.weight", + "backbone.layers.0.ffn.layers.0.0.weight", + "backbone.layers.0.ffn.layers.1.weight" + ], + "lr_scale": 0.005688009063105715, + "lr": 5.688009063105716e-07, + "weight_decay": 0.05 + }, + "layer_2_no_decay": { + "param_names": [ + "backbone.layers.1.gamma_1", + "backbone.layers.1.gamma_2", + "backbone.layers.1.ln1.weight", + "backbone.layers.1.ln1.bias", + "backbone.layers.1.attn.qkv.bias", + "backbone.layers.1.attn.proj.bias", + "backbone.layers.1.ln2.weight", + "backbone.layers.1.ln2.bias", + "backbone.layers.1.ffn.layers.0.0.bias", + "backbone.layers.1.ffn.layers.1.bias" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.0 + }, + "layer_2_decay": { + "param_names": [ + "backbone.layers.1.attn.relative_position_bias_table", + "backbone.layers.1.attn.qkv.weight", + "backbone.layers.1.attn.proj.weight", + "backbone.layers.1.ffn.layers.0.0.weight", + "backbone.layers.1.ffn.layers.1.weight" + ], + "lr_scale": 0.008750783174008792, + "lr": 8.750783174008792e-07, + "weight_decay": 0.05 + }, + "layer_3_no_decay": { + "param_names": [ + "backbone.layers.2.gamma_1", + "backbone.layers.2.gamma_2", + "backbone.layers.2.ln1.weight", + "backbone.layers.2.ln1.bias", + "backbone.layers.2.attn.qkv.bias", + "backbone.layers.2.attn.proj.bias", + "backbone.layers.2.ln2.weight", + "backbone.layers.2.ln2.bias", + "backbone.layers.2.ffn.layers.0.0.bias", + "backbone.layers.2.ffn.layers.1.bias" + ], + "lr_scale": 0.013462743344628911, + "lr": 1.3462743344628912e-06, + "weight_decay": 0.0 + }, + "layer_3_decay": { + "param_names": [ + "backbone.layers.2.attn.relative_position_bias_table", + "backbone.layers.2.attn.qkv.weight", + "backbone.layers.2.attn.proj.weight", + 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Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 06:59:30 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 06:59:30 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/mae-base_upernet_8xb2-amp-160k_ade20k-512x512. +04/19 07:00:27 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:23:31 time: 1.0255 data_time: 0.0040 memory: 8935 loss: 6.8883 decode.loss_ce: 4.8707 decode.acc_seg: 29.3745 aux.loss_ce: 2.0176 aux.acc_seg: 0.3349 +04/19 07:01:18 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 1 day, 23:58:07 time: 1.0259 data_time: 0.0044 memory: 8462 loss: 6.5402 decode.loss_ce: 4.5599 decode.acc_seg: 36.9930 aux.loss_ce: 1.9803 aux.acc_seg: 2.7327 +04/19 07:02:09 - mmengine - INFO - Iter(train) [ 150/160000] base_lr: 9.9401e-06 lr: 3.6750e-08 eta: 1 day, 23:09:52 time: 1.0258 data_time: 0.0043 memory: 8462 loss: 6.1158 decode.loss_ce: 4.1781 decode.acc_seg: 36.4723 aux.loss_ce: 1.9377 aux.acc_seg: 38.2418 +04/19 07:03:00 - mmengine - INFO - Iter(train) [ 200/160000] base_lr: 1.3276e-05 lr: 4.9083e-08 eta: 1 day, 22:43:21 time: 1.0203 data_time: 0.0039 memory: 8462 loss: 5.6012 decode.loss_ce: 3.7162 decode.acc_seg: 58.7185 aux.loss_ce: 1.8850 aux.acc_seg: 35.2327 +04/19 07:03:51 - mmengine - INFO - Iter(train) [ 250/160000] base_lr: 1.6611e-05 lr: 6.1415e-08 eta: 1 day, 22:24:47 time: 1.0181 data_time: 0.0044 memory: 8462 loss: 5.1370 decode.loss_ce: 3.3229 decode.acc_seg: 60.3662 aux.loss_ce: 1.8141 aux.acc_seg: 37.0596 +04/19 07:04:42 - mmengine - INFO - Iter(train) [ 300/160000] base_lr: 1.9947e-05 lr: 7.3747e-08 eta: 1 day, 22:11:16 time: 1.0159 data_time: 0.0044 memory: 8462 loss: 4.5380 decode.loss_ce: 2.7961 decode.acc_seg: 86.9333 aux.loss_ce: 1.7419 aux.acc_seg: 43.2981 +04/19 07:05:33 - mmengine - INFO - 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aux.acc_seg: 82.0532 +04/19 07:08:55 - mmengine - INFO - Iter(train) [ 550/160000] base_lr: 3.6624e-05 lr: 1.3541e-07 eta: 1 day, 21:31:53 time: 1.0116 data_time: 0.0042 memory: 8462 loss: 1.5581 decode.loss_ce: 0.4585 decode.acc_seg: 97.1006 aux.loss_ce: 1.0996 aux.acc_seg: 89.4524 +04/19 07:09:46 - mmengine - INFO - Iter(train) [ 600/160000] base_lr: 3.9960e-05 lr: 1.4774e-07 eta: 1 day, 21:26:46 time: 1.0079 data_time: 0.0042 memory: 8462 loss: 1.2520 decode.loss_ce: 0.3212 decode.acc_seg: 97.2136 aux.loss_ce: 0.9308 aux.acc_seg: 95.9038 +04/19 07:10:36 - mmengine - INFO - Iter(train) [ 650/160000] base_lr: 4.3296e-05 lr: 1.6007e-07 eta: 1 day, 21:21:33 time: 1.0020 data_time: 0.0038 memory: 8462 loss: 1.0009 decode.loss_ce: 0.2412 decode.acc_seg: 96.5857 aux.loss_ce: 0.7596 aux.acc_seg: 95.6675 +04/19 07:11:26 - mmengine - INFO - Iter(train) [ 700/160000] base_lr: 4.6631e-05 lr: 1.7240e-07 eta: 1 day, 21:16:14 time: 1.0006 data_time: 0.0042 memory: 8462 loss: 0.7967 decode.loss_ce: 0.1877 decode.acc_seg: 97.4550 aux.loss_ce: 0.6090 aux.acc_seg: 96.2496 +04/19 07:12:16 - mmengine - INFO - Iter(train) [ 750/160000] base_lr: 4.9967e-05 lr: 1.8474e-07 eta: 1 day, 21:11:03 time: 0.9981 data_time: 0.0041 memory: 8462 loss: 0.5978 decode.loss_ce: 0.1384 decode.acc_seg: 97.7348 aux.loss_ce: 0.4594 aux.acc_seg: 97.1413 +04/19 07:13:06 - mmengine - INFO - Iter(train) [ 800/160000] base_lr: 5.3302e-05 lr: 1.9707e-07 eta: 1 day, 21:06:08 time: 0.9967 data_time: 0.0040 memory: 8462 loss: 0.5116 decode.loss_ce: 0.1474 decode.acc_seg: 95.7693 aux.loss_ce: 0.3642 aux.acc_seg: 95.3093 +04/19 07:13:56 - mmengine - INFO - Iter(train) [ 850/160000] base_lr: 5.6638e-05 lr: 2.0940e-07 eta: 1 day, 21:01:39 time: 0.9975 data_time: 0.0044 memory: 8462 loss: 0.3884 decode.loss_ce: 0.1213 decode.acc_seg: 98.1377 aux.loss_ce: 0.2671 aux.acc_seg: 96.9400 +04/19 07:14:45 - mmengine - INFO - Iter(train) [ 900/160000] base_lr: 5.9973e-05 lr: 2.2173e-07 eta: 1 day, 20:57:36 time: 0.9968 data_time: 0.0042 memory: 8462 loss: 0.3336 decode.loss_ce: 0.1094 decode.acc_seg: 97.8647 aux.loss_ce: 0.2242 aux.acc_seg: 96.1260 +04/19 07:15:35 - mmengine - INFO - Iter(train) [ 950/160000] base_lr: 6.3309e-05 lr: 2.3407e-07 eta: 1 day, 20:53:56 time: 0.9966 data_time: 0.0043 memory: 8462 loss: 0.2584 decode.loss_ce: 0.0931 decode.acc_seg: 97.6976 aux.loss_ce: 0.1653 aux.acc_seg: 97.0474 +04/19 07:16:25 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_065922 +04/19 07:16:25 - mmengine - INFO - Iter(train) [ 1000/160000] base_lr: 6.6644e-05 lr: 2.4640e-07 eta: 1 day, 20:50:32 time: 0.9976 data_time: 0.0051 memory: 8462 loss: 0.2484 decode.loss_ce: 0.1043 decode.acc_seg: 96.5818 aux.loss_ce: 0.1441 aux.acc_seg: 96.0867 +04/19 07:17:15 - mmengine - INFO - Iter(train) [ 1050/160000] base_lr: 6.9980e-05 lr: 2.5873e-07 eta: 1 day, 20:47:24 time: 0.9968 data_time: 0.0044 memory: 8462 loss: 0.2135 decode.loss_ce: 0.0918 decode.acc_seg: 97.1264 aux.loss_ce: 0.1217 aux.acc_seg: 96.5210 +04/19 07:18:05 - mmengine - INFO - Iter(train) [ 1100/160000] base_lr: 7.3316e-05 lr: 2.7106e-07 eta: 1 day, 20:44:25 time: 0.9965 data_time: 0.0043 memory: 8462 loss: 0.2053 decode.loss_ce: 0.0867 decode.acc_seg: 96.3804 aux.loss_ce: 0.1187 aux.acc_seg: 95.7396 +04/19 07:18:55 - mmengine - INFO - Iter(train) [ 1150/160000] base_lr: 7.6651e-05 lr: 2.8339e-07 eta: 1 day, 20:41:40 time: 0.9979 data_time: 0.0045 memory: 8462 loss: 0.1674 decode.loss_ce: 0.0742 decode.acc_seg: 97.7476 aux.loss_ce: 0.0932 aux.acc_seg: 96.7743 +04/19 07:19:45 - mmengine - INFO - Iter(train) [ 1200/160000] base_lr: 7.9987e-05 lr: 2.9573e-07 eta: 1 day, 20:39:08 time: 0.9967 data_time: 0.0042 memory: 8462 loss: 0.1592 decode.loss_ce: 0.0774 decode.acc_seg: 98.5477 aux.loss_ce: 0.0818 aux.acc_seg: 97.7438 +04/19 07:20:34 - mmengine - INFO - Iter(train) [ 1250/160000] base_lr: 8.3322e-05 lr: 3.0806e-07 eta: 1 day, 20:36:46 time: 0.9985 data_time: 0.0044 memory: 8462 loss: 0.1593 decode.loss_ce: 0.0811 decode.acc_seg: 97.6845 aux.loss_ce: 0.0782 aux.acc_seg: 96.8628 +04/19 07:21:24 - mmengine - INFO - Iter(train) [ 1300/160000] base_lr: 8.6658e-05 lr: 3.2039e-07 eta: 1 day, 20:34:27 time: 0.9981 data_time: 0.0043 memory: 8462 loss: 0.1575 decode.loss_ce: 0.0764 decode.acc_seg: 98.2693 aux.loss_ce: 0.0811 aux.acc_seg: 97.4092 +04/19 07:22:14 - mmengine - INFO - Iter(train) [ 1350/160000] base_lr: 8.9993e-05 lr: 3.3272e-07 eta: 1 day, 20:32:15 time: 0.9968 data_time: 0.0042 memory: 8462 loss: 0.1320 decode.loss_ce: 0.0655 decode.acc_seg: 98.1331 aux.loss_ce: 0.0666 aux.acc_seg: 97.2891 +04/19 07:23:04 - mmengine - INFO - Iter(train) [ 1400/160000] base_lr: 9.3329e-05 lr: 3.4506e-07 eta: 1 day, 20:30:10 time: 0.9974 data_time: 0.0048 memory: 8462 loss: 0.1308 decode.loss_ce: 0.0717 decode.acc_seg: 98.7215 aux.loss_ce: 0.0591 aux.acc_seg: 97.6765 +04/19 07:23:54 - mmengine - INFO - Iter(train) [ 1450/160000] base_lr: 9.6664e-05 lr: 3.5739e-07 eta: 1 day, 20:28:10 time: 0.9978 data_time: 0.0047 memory: 8462 loss: 0.1409 decode.loss_ce: 0.0761 decode.acc_seg: 97.6967 aux.loss_ce: 0.0648 aux.acc_seg: 96.7949 +04/19 07:24:44 - mmengine - INFO - Iter(train) [ 1500/160000] base_lr: 1.0000e-04 lr: 3.6972e-07 eta: 1 day, 20:26:20 time: 0.9988 data_time: 0.0042 memory: 8462 loss: 0.1213 decode.loss_ce: 0.0638 decode.acc_seg: 97.8680 aux.loss_ce: 0.0575 aux.acc_seg: 97.0081 +04/19 07:25:34 - mmengine - INFO - Iter(train) [ 1550/160000] base_lr: 9.9969e-05 lr: 3.6961e-07 eta: 1 day, 20:24:31 time: 0.9977 data_time: 0.0038 memory: 8462 loss: 0.1194 decode.loss_ce: 0.0642 decode.acc_seg: 98.4535 aux.loss_ce: 0.0553 aux.acc_seg: 96.6642 +04/19 07:26:24 - mmengine - INFO - Iter(train) [ 1600/160000] base_lr: 9.9938e-05 lr: 3.6949e-07 eta: 1 day, 20:22:50 time: 0.9984 data_time: 0.0045 memory: 8462 loss: 0.1192 decode.loss_ce: 0.0650 decode.acc_seg: 98.8091 aux.loss_ce: 0.0542 aux.acc_seg: 97.6425 +04/19 07:27:14 - mmengine - INFO - Iter(train) [ 1650/160000] base_lr: 9.9906e-05 lr: 3.6937e-07 eta: 1 day, 20:21:09 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.1103 decode.loss_ce: 0.0602 decode.acc_seg: 97.7407 aux.loss_ce: 0.0500 aux.acc_seg: 96.1254 +04/19 07:28:04 - mmengine - INFO - Iter(train) [ 1700/160000] base_lr: 9.9874e-05 lr: 3.6926e-07 eta: 1 day, 20:19:34 time: 0.9983 data_time: 0.0041 memory: 8462 loss: 0.0946 decode.loss_ce: 0.0512 decode.acc_seg: 98.4478 aux.loss_ce: 0.0435 aux.acc_seg: 98.2590 +WARNING:torch.distributed.elastic.agent.server.api:Received 2 death signal, shutting down workers +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314189 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314190 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314191 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314192 closing signal SIGINT +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314189 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314190 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314191 closing signal SIGTERM +WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 314192 closing signal SIGTERM +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 709, in run + result = self._invoke_run(role) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 850, in _invoke_run + time.sleep(monitor_interval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 314155 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 716, in run + self._shutdown(e.sigval) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 314155 got signal: 2 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main + return _run_code(code, main_globals, None, + File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code + exec(code, run_globals) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in + main() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main + launch(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch + run(args) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 715, in run + elastic_launch( + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 236, in launch_agent + result = agent.run() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 125, in wrapper + result = f(*args, **kwargs) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 721, in run + self._shutdown() + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py", line 190, in _shutdown + self._pcontext.close(death_sig) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 330, in close + self._close(death_sig=death_sig, timeout=timeout) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 720, in _close + handler.proc.wait(time_to_wait) + File "/opt/conda/lib/python3.8/subprocess.py", line 1083, in wait + return self._wait(timeout=timeout) + File "/opt/conda/lib/python3.8/subprocess.py", line 1800, in _wait + time.sleep(delay) + File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 60, in _terminate_process_handler + raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval) +torch.distributed.elastic.multiprocessing.api.SignalException: Process 314155 got signal: 2 +/opt/conda/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated +and will be removed in future. Use torchrun. +Note that --use_env is set by default in torchrun. +If your script expects `--local_rank` argument to be set, please +change it to read from `os.environ['LOCAL_RANK']` instead. See +https://pytorch.org/docs/stable/distributed.html#launch-utility for +further instructions + + warnings.warn( +WARNING:torch.distributed.run: +***************************************** +Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. +***************************************** +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +/opt/conda/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. + warnings.warn( +04/19 07:32:08 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 930945155 + GPU 0,1,2,3: NVIDIA GeForce RTX 2080 Ti + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.3, V11.3.109 + GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 + PyTorch: 1.11.0 + PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.12.0 + OpenCV: 4.9.0 + MMEngine: 0.10.3 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 930945155 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +04/19 07:32:09 - mmengine - INFO - Config: +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cag' +dataset_type = 'CoronaryAngiographyDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=10000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +fp16 = dict(loss_scale='dynamic') +img_ratios = [ + 1.0, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=768, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + drop_path_rate=0.1, + embed_dims=768, + img_size=( + 512, + 512, + ), + in_channels=3, + init_values=1.0, + mlp_ratio=4, + norm_cfg=dict(eps=1e-06, type='LN'), + norm_eval=False, + num_heads=12, + num_layers=12, + out_indices=[ + 3, + 5, + 7, + 11, + ], + patch_size=16, + type='MAE'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=768, + dropout_ratio=0.1, + in_channels=[ + 768, + 768, + 768, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='UPerHead'), + neck=dict( + embed_dim=768, rescales=[ + 4, + 2, + 1, + 0.5, + ], type='Feature2Pyramid'), + pretrained= + '/workspaces/mmsegmentation-1/configs/mae/mae_pretrain_vit_base.pth', + test_cfg=dict(crop_size=( + 512, + 512, + ), mode='slide', stride=( + 341, + 341, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + constructor='LayerDecayOptimizerConstructor', + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0001, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict(layer_decay_rate=0.65, num_layers=12), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/test', seg_map_path='annotations/test'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=10000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict( + border_mode=0, + p=1, + rotate_limit=20, + scale_limit=( + -0.2, + 0, + ), + shift_limit=0.1, + type='AlbuShiftScaleRotateTransform', + value=[ + 0.3, + 0.4, + 0.5, + ]), + dict( + brightness_limit=( + -0.2, + 0.2, + ), + contrast_limit=( + -0.2, + 0.2, + ), + p=0.5, + type='AlbuRandomContrastTransform'), + dict(p=0.5, type='AlbuGaussNoiseTransform', var_limit=( + 0, + 0.01, + )), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/cag', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 512, + 512, + ), type='Resize'), + dict(reduce_zero_label=False, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + reduce_zero_label=False, + type='CoronaryAngiographyDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + 'mFscore', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = '/workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000' + +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/backbones/beit.py:299: UserWarning: DeprecationWarning: pretrained is deprecated, please use "init_cfg" instead + warnings.warn('DeprecationWarning: pretrained is deprecated, ' +/opt/conda/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755903507/work/aten/src/ATen/native/TensorShape.cpp:2228.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/models/builder.py:36: UserWarning: ``build_loss`` would be deprecated soon, please use ``mmseg.registry.MODELS.build()`` + warnings.warn('``build_loss`` would be deprecated soon, please use ' +/workspaces/mmsegmentation-1/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``. + warnings.warn( +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +/workspaces/mmsegmentation-1/mmseg/engine/hooks/visualization_hook.py:61: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored. + warnings.warn('The draw is False, it means that the ' +04/19 07:32:12 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65}set param backbone.layers.0.ln1.bias as id 1 + +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.cls_token as id 0set param backbone.layers.0.ln2.weight as id 1 + +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.pos_embed as id 0 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.0.ln1.weight as id 1set param backbone.layers.1.gamma_2 as id 2 + +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2set param backbone.layers.0.attn.relative_position_bias_table as id 1 + +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.1.ffn.layers.1.weight as id 2set param backbone.layers.0.ffn.layers.0.0.bias as id 1 + +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.1.ln1.weight as id 2set param backbone.layers.2.attn.qkv.bias as id 3 + +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2set param backbone.layers.2.ffn.layers.0.0.weight as id 3 + +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2set param backbone.layers.2.ffn.layers.1.weight as id 3 + +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.3.ln1.weight as id 4set param backbone.layers.1.ffn.layers.1.bias as id 2 + +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4set param backbone.layers.2.gamma_1 as id 3 + +set param backbone.layers.3.attn.qkv.weight as id 4set param backbone.layers.2.gamma_2 as id 3 + +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3set param backbone.layers.3.attn.proj.weight as id 4 + +set param backbone.layers.3.attn.proj.bias as id 4set param backbone.layers.2.attn.relative_position_bias_table as id 3 + +set param backbone.layers.3.ln2.weight as id 4set param backbone.layers.2.attn.qkv.weight as id 3 + +set param backbone.layers.3.ln2.bias as id 4set param backbone.layers.2.attn.qkv.bias as id 3 + +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.4.gamma_1 as id 5set param backbone.layers.2.ffn.layers.0.0.bias as id 3 + +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5set param backbone.layers.3.gamma_1 as id 4 + +set param backbone.layers.4.attn.qkv.weight as id 5set param backbone.layers.3.gamma_2 as id 4 + +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.3.attn.relative_position_bias_table as id 4set param backbone.layers.4.attn.proj.bias as id 5 + +set param backbone.layers.3.attn.qkv.weight as id 4set param backbone.layers.4.ln2.weight as id 5 + +set param backbone.layers.3.attn.qkv.bias as id 4set param backbone.layers.4.ln2.bias as id 5 + +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5set param backbone.layers.3.attn.proj.bias as id 4 + +set param backbone.layers.4.ffn.layers.0.0.bias as id 5set param backbone.layers.3.ln2.weight as id 4 + +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4set param backbone.layers.5.gamma_1 as id 6 + +set param backbone.layers.5.gamma_2 as id 6set param backbone.layers.3.ffn.layers.1.weight as id 4 + +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.5.attn.proj.weight as id 6set param backbone.layers.4.attn.relative_position_bias_table as id 5 + +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.5.ln2.weight as id 6set param backbone.layers.4.attn.qkv.bias as id 5 + +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.4.ln2.weight as id 5set param backbone.layers.5.ffn.layers.0.0.bias as id 6 + +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5set param backbone.layers.5.ffn.layers.1.bias as id 6 + +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7set param backbone.layers.4.ffn.layers.1.weight as id 5 + +set param backbone.layers.4.ffn.layers.1.bias as id 5set param backbone.layers.6.ln1.weight as id 7 + +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7set param backbone.layers.6.ffn.layers.1.weight as id 7 + +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8set param backbone.layers.6.ln2.weight as id 7 + +set param backbone.layers.6.ln2.bias as id 7set param backbone.layers.7.gamma_2 as id 8 + +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8set param backbone.layers.6.ffn.layers.0.0.weight as id 7 + +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9set param backbone.layers.7.gamma_1 as id 8 + +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.7.attn.qkv.weight as id 8set param backbone.layers.8.attn.proj.weight as id 9 + +set param backbone.layers.7.attn.qkv.bias as id 8set param backbone.layers.8.attn.proj.bias as id 9 + +set param backbone.layers.7.attn.proj.weight as id 8set param backbone.layers.8.ln2.weight as id 9 + +set param backbone.layers.7.attn.proj.bias as id 8set param backbone.layers.8.ln2.bias as id 9 + +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9set param backbone.layers.7.ln2.bias as id 8 + +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.8.attn.qkv.weight as id 9set param backbone.layers.9.attn.qkv.bias as id 10 + +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.8.attn.proj.weight as id 9set param backbone.layers.9.attn.proj.bias as id 10 + +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.8.ln2.weight as id 9set param backbone.layers.9.ln2.bias as id 10 + +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.8.ffn.layers.1.weight as id 9set param backbone.layers.9.ffn.layers.1.bias as id 10 + +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11set param backbone.layers.9.gamma_1 as id 10 + +set param backbone.layers.10.ln1.weight as id 11set param backbone.layers.9.gamma_2 as id 10 + +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.10.attn.qkv.weight as id 11set param backbone.layers.9.attn.relative_position_bias_table as id 10 + +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10set param backbone.layers.10.ffn.layers.0.0.weight as id 11 + +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.10.ffn.layers.1.weight as id 11set param backbone.layers.9.ffn.layers.0.0.bias as id 10 + +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12set param backbone.layers.10.attn.qkv.weight as id 11 + +set param backbone.layers.11.attn.proj.weight as id 12set param backbone.layers.10.attn.qkv.bias as id 11 + +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.11.ln2.weight as id 12set param backbone.layers.10.attn.proj.bias as id 11 + +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param backbone.layers.11.gamma_2 as id 12 +set param neck.upsample_4x.0.bias as id 13 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param neck.upsample_4x.1.weight as id 13 +set param backbone.layers.11.attn.relative_position_bias_table as id 12set param neck.upsample_4x.1.bias as id 13 + +set param neck.upsample_4x.3.weight as id 13 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param neck.upsample_4x.3.bias as id 13 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param neck.upsample_2x.0.weight as id 13set param backbone.layers.11.attn.proj.weight as id 12 + +set param neck.upsample_2x.0.bias as id 13set param backbone.layers.11.attn.proj.bias as id 12 + +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param decode_head.conv_seg.weight as id 13 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param decode_head.conv_seg.bias as id 13 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param neck.upsample_4x.0.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13set param neck.upsample_4x.1.weight as id 13 + +set param neck.upsample_4x.1.bias as id 13set param decode_head.psp_modules.1.1.bn.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.bias as id 13set param neck.upsample_4x.3.weight as id 13 + +set param neck.upsample_4x.3.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13set param neck.upsample_2x.0.weight as id 13 + +set param decode_head.psp_modules.2.1.bn.weight as id 13set param neck.upsample_2x.0.bias as id 13 + +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13set param decode_head.psp_modules.0.1.bn.weight as id 13 + +set param decode_head.lateral_convs.0.bn.weight as id 13set param decode_head.psp_modules.0.1.bn.bias as id 13 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13set param decode_head.lateral_convs.1.conv.weight as id 13 + +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13set param decode_head.psp_modules.2.1.conv.weight as id 13 + +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13set param decode_head.fpn_convs.0.bn.bias as id 13 + +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.bottleneck.conv.weight as id 13set param decode_head.fpn_convs.1.bn.weight as id 13 + +set param decode_head.fpn_convs.1.bn.bias as id 13set param decode_head.bottleneck.bn.weight as id 13 + +set param decode_head.bottleneck.bn.bias as id 13set param decode_head.fpn_convs.2.conv.weight as id 13 + +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13set param decode_head.fpn_bottleneck.conv.weight as id 13 + +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +/workspaces/mmsegmentation-1/mmseg/datasets/transforms/loading.py:83: UserWarning: `reduce_zero_label` will be deprecated, if you would like to ignore the zero label, please set `reduce_zero_label=True` when dataset initialized + warnings.warn('`reduce_zero_label` will be deprecated, ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:198: UserWarning: DeprecationWarning: Original LayerDecayOptimizerConstructor of BEiT will be deprecated. Please use LearningRateDecayOptimizerConstructor instead, and set decay_type = layer_wise_vit in paramwise_cfg. + warnings.warn('DeprecationWarning: Original ' +/workspaces/mmsegmentation-1/mmseg/engine/optimizers/layer_decay_optimizer_constructor.py:204: UserWarning: DeprecationWarning: Layer_decay_rate will be deleted, please use decay_rate instead. + warnings.warn('DeprecationWarning: Layer_decay_rate will ' +self.paramwise_cfg is {'num_layers': 12, 'decay_type': 'layer_wise_vit', 'decay_rate': 0.65} +Build LearningRateDecayOptimizerConstructor layer_wise_vit 0.65 - 14 +set param backbone.cls_token as id 0 +set param backbone.pos_embed as id 0 +set param backbone.patch_embed.projection.weight as id 0 +set param backbone.patch_embed.projection.bias as id 0 +set param backbone.layers.0.gamma_1 as id 1 +set param backbone.layers.0.gamma_2 as id 1 +set param backbone.layers.0.ln1.weight as id 1 +set param backbone.layers.0.ln1.bias as id 1 +set param backbone.layers.0.attn.relative_position_bias_table as id 1 +set param backbone.layers.0.attn.qkv.weight as id 1 +set param backbone.layers.0.attn.qkv.bias as id 1 +set param backbone.layers.0.attn.proj.weight as id 1 +set param backbone.layers.0.attn.proj.bias as id 1 +set param backbone.layers.0.ln2.weight as id 1 +set param backbone.layers.0.ln2.bias as id 1 +set param backbone.layers.0.ffn.layers.0.0.weight as id 1 +set param backbone.layers.0.ffn.layers.0.0.bias as id 1 +set param backbone.layers.0.ffn.layers.1.weight as id 1 +set param backbone.layers.0.ffn.layers.1.bias as id 1 +set param backbone.layers.1.gamma_1 as id 2 +set param backbone.layers.1.gamma_2 as id 2 +set param backbone.layers.1.ln1.weight as id 2 +set param backbone.layers.1.ln1.bias as id 2 +set param backbone.layers.1.attn.relative_position_bias_table as id 2 +set param backbone.layers.1.attn.qkv.weight as id 2 +set param backbone.layers.1.attn.qkv.bias as id 2 +set param backbone.layers.1.attn.proj.weight as id 2 +set param backbone.layers.1.attn.proj.bias as id 2 +set param backbone.layers.1.ln2.weight as id 2 +set param backbone.layers.1.ln2.bias as id 2 +set param backbone.layers.1.ffn.layers.0.0.weight as id 2 +set param backbone.layers.1.ffn.layers.0.0.bias as id 2 +set param backbone.layers.1.ffn.layers.1.weight as id 2 +set param backbone.layers.1.ffn.layers.1.bias as id 2 +set param backbone.layers.2.gamma_1 as id 3 +set param backbone.layers.2.gamma_2 as id 3 +set param backbone.layers.2.ln1.weight as id 3 +set param backbone.layers.2.ln1.bias as id 3 +set param backbone.layers.2.attn.relative_position_bias_table as id 3 +set param backbone.layers.2.attn.qkv.weight as id 3 +set param backbone.layers.2.attn.qkv.bias as id 3 +set param backbone.layers.2.attn.proj.weight as id 3 +set param backbone.layers.2.attn.proj.bias as id 3 +set param backbone.layers.2.ln2.weight as id 3 +set param backbone.layers.2.ln2.bias as id 3 +set param backbone.layers.2.ffn.layers.0.0.weight as id 3 +set param backbone.layers.2.ffn.layers.0.0.bias as id 3 +set param backbone.layers.2.ffn.layers.1.weight as id 3 +set param backbone.layers.2.ffn.layers.1.bias as id 3 +set param backbone.layers.3.gamma_1 as id 4 +set param backbone.layers.3.gamma_2 as id 4 +set param backbone.layers.3.ln1.weight as id 4 +set param backbone.layers.3.ln1.bias as id 4 +set param backbone.layers.3.attn.relative_position_bias_table as id 4 +set param backbone.layers.3.attn.qkv.weight as id 4 +set param backbone.layers.3.attn.qkv.bias as id 4 +set param backbone.layers.3.attn.proj.weight as id 4 +set param backbone.layers.3.attn.proj.bias as id 4 +set param backbone.layers.3.ln2.weight as id 4 +set param backbone.layers.3.ln2.bias as id 4 +set param backbone.layers.3.ffn.layers.0.0.weight as id 4 +set param backbone.layers.3.ffn.layers.0.0.bias as id 4 +set param backbone.layers.3.ffn.layers.1.weight as id 4 +set param backbone.layers.3.ffn.layers.1.bias as id 4 +set param backbone.layers.4.gamma_1 as id 5 +set param backbone.layers.4.gamma_2 as id 5 +set param backbone.layers.4.ln1.weight as id 5 +set param backbone.layers.4.ln1.bias as id 5 +set param backbone.layers.4.attn.relative_position_bias_table as id 5 +set param backbone.layers.4.attn.qkv.weight as id 5 +set param backbone.layers.4.attn.qkv.bias as id 5 +set param backbone.layers.4.attn.proj.weight as id 5 +set param backbone.layers.4.attn.proj.bias as id 5 +set param backbone.layers.4.ln2.weight as id 5 +set param backbone.layers.4.ln2.bias as id 5 +set param backbone.layers.4.ffn.layers.0.0.weight as id 5 +set param backbone.layers.4.ffn.layers.0.0.bias as id 5 +set param backbone.layers.4.ffn.layers.1.weight as id 5 +set param backbone.layers.4.ffn.layers.1.bias as id 5 +set param backbone.layers.5.gamma_1 as id 6 +set param backbone.layers.5.gamma_2 as id 6 +set param backbone.layers.5.ln1.weight as id 6 +set param backbone.layers.5.ln1.bias as id 6 +set param backbone.layers.5.attn.relative_position_bias_table as id 6 +set param backbone.layers.5.attn.qkv.weight as id 6 +set param backbone.layers.5.attn.qkv.bias as id 6 +set param backbone.layers.5.attn.proj.weight as id 6 +set param backbone.layers.5.attn.proj.bias as id 6 +set param backbone.layers.5.ln2.weight as id 6 +set param backbone.layers.5.ln2.bias as id 6 +set param backbone.layers.5.ffn.layers.0.0.weight as id 6 +set param backbone.layers.5.ffn.layers.0.0.bias as id 6 +set param backbone.layers.5.ffn.layers.1.weight as id 6 +set param backbone.layers.5.ffn.layers.1.bias as id 6 +set param backbone.layers.6.gamma_1 as id 7 +set param backbone.layers.6.gamma_2 as id 7 +set param backbone.layers.6.ln1.weight as id 7 +set param backbone.layers.6.ln1.bias as id 7 +set param backbone.layers.6.attn.relative_position_bias_table as id 7 +set param backbone.layers.6.attn.qkv.weight as id 7 +set param backbone.layers.6.attn.qkv.bias as id 7 +set param backbone.layers.6.attn.proj.weight as id 7 +set param backbone.layers.6.attn.proj.bias as id 7 +set param backbone.layers.6.ln2.weight as id 7 +set param backbone.layers.6.ln2.bias as id 7 +set param backbone.layers.6.ffn.layers.0.0.weight as id 7 +set param backbone.layers.6.ffn.layers.0.0.bias as id 7 +set param backbone.layers.6.ffn.layers.1.weight as id 7 +set param backbone.layers.6.ffn.layers.1.bias as id 7 +set param backbone.layers.7.gamma_1 as id 8 +set param backbone.layers.7.gamma_2 as id 8 +set param backbone.layers.7.ln1.weight as id 8 +set param backbone.layers.7.ln1.bias as id 8 +set param backbone.layers.7.attn.relative_position_bias_table as id 8 +set param backbone.layers.7.attn.qkv.weight as id 8 +set param backbone.layers.7.attn.qkv.bias as id 8 +set param backbone.layers.7.attn.proj.weight as id 8 +set param backbone.layers.7.attn.proj.bias as id 8 +set param backbone.layers.7.ln2.weight as id 8 +set param backbone.layers.7.ln2.bias as id 8 +set param backbone.layers.7.ffn.layers.0.0.weight as id 8 +set param backbone.layers.7.ffn.layers.0.0.bias as id 8 +set param backbone.layers.7.ffn.layers.1.weight as id 8 +set param backbone.layers.7.ffn.layers.1.bias as id 8 +set param backbone.layers.8.gamma_1 as id 9 +set param backbone.layers.8.gamma_2 as id 9 +set param backbone.layers.8.ln1.weight as id 9 +set param backbone.layers.8.ln1.bias as id 9 +set param backbone.layers.8.attn.relative_position_bias_table as id 9 +set param backbone.layers.8.attn.qkv.weight as id 9 +set param backbone.layers.8.attn.qkv.bias as id 9 +set param backbone.layers.8.attn.proj.weight as id 9 +set param backbone.layers.8.attn.proj.bias as id 9 +set param backbone.layers.8.ln2.weight as id 9 +set param backbone.layers.8.ln2.bias as id 9 +set param backbone.layers.8.ffn.layers.0.0.weight as id 9 +set param backbone.layers.8.ffn.layers.0.0.bias as id 9 +set param backbone.layers.8.ffn.layers.1.weight as id 9 +set param backbone.layers.8.ffn.layers.1.bias as id 9 +set param backbone.layers.9.gamma_1 as id 10 +set param backbone.layers.9.gamma_2 as id 10 +set param backbone.layers.9.ln1.weight as id 10 +set param backbone.layers.9.ln1.bias as id 10 +set param backbone.layers.9.attn.relative_position_bias_table as id 10 +set param backbone.layers.9.attn.qkv.weight as id 10 +set param backbone.layers.9.attn.qkv.bias as id 10 +set param backbone.layers.9.attn.proj.weight as id 10 +set param backbone.layers.9.attn.proj.bias as id 10 +set param backbone.layers.9.ln2.weight as id 10 +set param backbone.layers.9.ln2.bias as id 10 +set param backbone.layers.9.ffn.layers.0.0.weight as id 10 +set param backbone.layers.9.ffn.layers.0.0.bias as id 10 +set param backbone.layers.9.ffn.layers.1.weight as id 10 +set param backbone.layers.9.ffn.layers.1.bias as id 10 +set param backbone.layers.10.gamma_1 as id 11 +set param backbone.layers.10.gamma_2 as id 11 +set param backbone.layers.10.ln1.weight as id 11 +set param backbone.layers.10.ln1.bias as id 11 +set param backbone.layers.10.attn.relative_position_bias_table as id 11 +set param backbone.layers.10.attn.qkv.weight as id 11 +set param backbone.layers.10.attn.qkv.bias as id 11 +set param backbone.layers.10.attn.proj.weight as id 11 +set param backbone.layers.10.attn.proj.bias as id 11 +set param backbone.layers.10.ln2.weight as id 11 +set param backbone.layers.10.ln2.bias as id 11 +set param backbone.layers.10.ffn.layers.0.0.weight as id 11 +set param backbone.layers.10.ffn.layers.0.0.bias as id 11 +set param backbone.layers.10.ffn.layers.1.weight as id 11 +set param backbone.layers.10.ffn.layers.1.bias as id 11 +set param backbone.layers.11.gamma_1 as id 12 +set param backbone.layers.11.gamma_2 as id 12 +set param backbone.layers.11.ln1.weight as id 12 +set param backbone.layers.11.ln1.bias as id 12 +set param backbone.layers.11.attn.relative_position_bias_table as id 12 +set param backbone.layers.11.attn.qkv.weight as id 12 +set param backbone.layers.11.attn.qkv.bias as id 12 +set param backbone.layers.11.attn.proj.weight as id 12 +set param backbone.layers.11.attn.proj.bias as id 12 +set param backbone.layers.11.ln2.weight as id 12 +set param backbone.layers.11.ln2.bias as id 12 +set param backbone.layers.11.ffn.layers.0.0.weight as id 12 +set param backbone.layers.11.ffn.layers.0.0.bias as id 12 +set param backbone.layers.11.ffn.layers.1.weight as id 12 +set param backbone.layers.11.ffn.layers.1.bias as id 12 +set param neck.upsample_4x.0.weight as id 13 +set param neck.upsample_4x.0.bias as id 13 +set param neck.upsample_4x.1.weight as id 13 +set param neck.upsample_4x.1.bias as id 13 +set param neck.upsample_4x.3.weight as id 13 +set param neck.upsample_4x.3.bias as id 13 +set param neck.upsample_2x.0.weight as id 13 +set param neck.upsample_2x.0.bias as id 13 +set param decode_head.conv_seg.weight as id 13 +set param decode_head.conv_seg.bias as id 13 +set param decode_head.psp_modules.0.1.conv.weight as id 13 +set param decode_head.psp_modules.0.1.bn.weight as id 13 +set param decode_head.psp_modules.0.1.bn.bias as id 13 +set param decode_head.psp_modules.1.1.conv.weight as id 13 +set param decode_head.psp_modules.1.1.bn.weight as id 13 +set param decode_head.psp_modules.1.1.bn.bias as id 13 +set param decode_head.psp_modules.2.1.conv.weight as id 13 +set param decode_head.psp_modules.2.1.bn.weight as id 13 +set param decode_head.psp_modules.2.1.bn.bias as id 13 +set param decode_head.psp_modules.3.1.conv.weight as id 13 +set param decode_head.psp_modules.3.1.bn.weight as id 13 +set param decode_head.psp_modules.3.1.bn.bias as id 13 +set param decode_head.bottleneck.conv.weight as id 13 +set param decode_head.bottleneck.bn.weight as id 13 +set param decode_head.bottleneck.bn.bias as id 13 +set param decode_head.lateral_convs.0.conv.weight as id 13 +set param decode_head.lateral_convs.0.bn.weight as id 13 +set param decode_head.lateral_convs.0.bn.bias as id 13 +set param decode_head.lateral_convs.1.conv.weight as id 13 +set param decode_head.lateral_convs.1.bn.weight as id 13 +set param decode_head.lateral_convs.1.bn.bias as id 13 +set param decode_head.lateral_convs.2.conv.weight as id 13 +set param decode_head.lateral_convs.2.bn.weight as id 13 +set param decode_head.lateral_convs.2.bn.bias as id 13 +set param decode_head.fpn_convs.0.conv.weight as id 13 +set param decode_head.fpn_convs.0.bn.weight as id 13 +set param decode_head.fpn_convs.0.bn.bias as id 13 +set param decode_head.fpn_convs.1.conv.weight as id 13 +set param decode_head.fpn_convs.1.bn.weight as id 13 +set param decode_head.fpn_convs.1.bn.bias as id 13 +set param decode_head.fpn_convs.2.conv.weight as id 13 +set param decode_head.fpn_convs.2.bn.weight as id 13 +set param decode_head.fpn_convs.2.bn.bias as id 13 +set param decode_head.fpn_bottleneck.conv.weight as id 13 +set param decode_head.fpn_bottleneck.bn.weight as id 13 +set param decode_head.fpn_bottleneck.bn.bias as id 13 +set param auxiliary_head.conv_seg.weight as id 13 +set param auxiliary_head.conv_seg.bias as id 13 +set param auxiliary_head.convs.0.conv.weight as id 13 +set param auxiliary_head.convs.0.bn.weight as id 13 +set param auxiliary_head.convs.0.bn.bias as id 13 +Param groups = { + "layer_0_decay": { + "param_names": [ + "backbone.cls_token", + "backbone.pos_embed", + "backbone.patch_embed.projection.weight" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.05 + }, + "layer_0_no_decay": { + "param_names": [ + "backbone.patch_embed.projection.bias" + ], + "lr_scale": 0.003697205891018715, + "lr": 3.697205891018715e-07, + "weight_decay": 0.0 + }, + "layer_1_no_decay": { + "param_names": [ + "backbone.layers.0.gamma_1", + "backbone.layers.0.gamma_2", + "backbone.layers.0.ln1.weight", + "backbone.layers.0.ln1.bias", + "backbone.layers.0.attn.qkv.bias", + "backbone.layers.0.attn.proj.bias", + "backbone.layers.0.ln2.weight", + "backbone.layers.0.ln2.bias", + "backbone.layers.0.ffn.layers.0.0.bias", + "backbone.layers.0.ffn.layers.1.bias" + ], + "lr_scale": 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Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +04/19 07:32:14 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +04/19 07:32:14 - mmengine - INFO - Checkpoints will be saved to /workspaces/mmsegmentation-1/work_dirs/MAE_Pre_4000. +04/19 07:33:10 - mmengine - INFO - Iter(train) [ 50/160000] base_lr: 3.2689e-06 lr: 1.2086e-08 eta: 2 days, 2:26:12 time: 1.0282 data_time: 0.0044 memory: 8935 loss: 6.8929 decode.loss_ce: 4.8994 decode.acc_seg: 13.5880 aux.loss_ce: 1.9936 aux.acc_seg: 0.0000 +04/19 07:34:02 - mmengine - INFO - Iter(train) [ 100/160000] base_lr: 6.6045e-06 lr: 2.4418e-08 eta: 2 days, 0:05:00 time: 1.0281 data_time: 0.0044 memory: 8462 loss: 6.5884 decode.loss_ce: 4.6293 decode.acc_seg: 34.7708 aux.loss_ce: 1.9591 aux.acc_seg: 11.9486 +04/19 07:34:53 - mmengine - INFO - Iter(train) [ 150/160000] base_lr: 9.9401e-06 lr: 3.6750e-08 eta: 1 day, 23:14:54 time: 1.0264 data_time: 0.0038 memory: 8462 loss: 6.0648 decode.loss_ce: 4.1524 decode.acc_seg: 36.6734 aux.loss_ce: 1.9124 aux.acc_seg: 23.0017 +04/19 07:35:45 - mmengine - INFO - Iter(train) [ 200/160000] base_lr: 1.3276e-05 lr: 4.9083e-08 eta: 1 day, 22:47:50 time: 1.0232 data_time: 0.0040 memory: 8462 loss: 5.5490 decode.loss_ce: 3.6809 decode.acc_seg: 73.1098 aux.loss_ce: 1.8681 aux.acc_seg: 61.9604 +04/19 07:36:35 - mmengine - INFO - Iter(train) [ 250/160000] base_lr: 1.6611e-05 lr: 6.1415e-08 eta: 1 day, 22:28:20 time: 1.0183 data_time: 0.0051 memory: 8462 loss: 5.0071 decode.loss_ce: 3.2241 decode.acc_seg: 74.2155 aux.loss_ce: 1.7830 aux.acc_seg: 79.6803 +04/19 07:37:26 - mmengine - INFO - Iter(train) [ 300/160000] base_lr: 1.9947e-05 lr: 7.3747e-08 eta: 1 day, 22:14:09 time: 1.0157 data_time: 0.0040 memory: 8462 loss: 4.4336 decode.loss_ce: 2.7250 decode.acc_seg: 81.5327 aux.loss_ce: 1.7086 aux.acc_seg: 69.2970 +04/19 07:38:17 - mmengine - INFO - Iter(train) [ 350/160000] base_lr: 2.3282e-05 lr: 8.6079e-08 eta: 1 day, 22:02:23 time: 1.0125 data_time: 0.0042 memory: 8462 loss: 3.8821 decode.loss_ce: 2.2462 decode.acc_seg: 86.4904 aux.loss_ce: 1.6358 aux.acc_seg: 69.9341 +04/19 07:39:08 - mmengine - INFO - Iter(train) [ 400/160000] base_lr: 2.6618e-05 lr: 9.8412e-08 eta: 1 day, 21:52:57 time: 1.0134 data_time: 0.0043 memory: 8462 loss: 3.1226 decode.loss_ce: 1.6190 decode.acc_seg: 94.7613 aux.loss_ce: 1.5036 aux.acc_seg: 83.1755 +04/19 07:39:58 - mmengine - INFO - Iter(train) [ 450/160000] base_lr: 2.9953e-05 lr: 1.1074e-07 eta: 1 day, 21:45:08 time: 1.0109 data_time: 0.0041 memory: 8462 loss: 2.5017 decode.loss_ce: 1.1098 decode.acc_seg: 96.2904 aux.loss_ce: 1.3919 aux.acc_seg: 81.9899 +04/19 07:40:49 - mmengine - INFO - Iter(train) [ 500/160000] base_lr: 3.3289e-05 lr: 1.2308e-07 eta: 1 day, 21:38:21 time: 1.0099 data_time: 0.0041 memory: 8462 loss: 1.9654 decode.loss_ce: 0.7101 decode.acc_seg: 98.1623 aux.loss_ce: 1.2554 aux.acc_seg: 88.5754 +04/19 07:41:39 - mmengine - INFO - Iter(train) [ 550/160000] base_lr: 3.6624e-05 lr: 1.3541e-07 eta: 1 day, 21:32:18 time: 1.0070 data_time: 0.0040 memory: 8462 loss: 1.5712 decode.loss_ce: 0.4877 decode.acc_seg: 98.3290 aux.loss_ce: 1.0835 aux.acc_seg: 93.5476 +04/19 07:42:30 - mmengine - INFO - Iter(train) [ 600/160000] base_lr: 3.9960e-05 lr: 1.4774e-07 eta: 1 day, 21:26:52 time: 1.0063 data_time: 0.0044 memory: 8462 loss: 1.2105 decode.loss_ce: 0.3179 decode.acc_seg: 96.7295 aux.loss_ce: 0.8926 aux.acc_seg: 95.1063 +04/19 07:43:20 - mmengine - INFO - Iter(train) [ 650/160000] base_lr: 4.3296e-05 lr: 1.6007e-07 eta: 1 day, 21:21:34 time: 1.0028 data_time: 0.0043 memory: 8462 loss: 0.9846 decode.loss_ce: 0.2559 decode.acc_seg: 96.0199 aux.loss_ce: 0.7287 aux.acc_seg: 95.8773 +04/19 07:44:10 - mmengine - INFO - Iter(train) [ 700/160000] base_lr: 4.6631e-05 lr: 1.7240e-07 eta: 1 day, 21:16:11 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.7521 decode.loss_ce: 0.1831 decode.acc_seg: 97.2086 aux.loss_ce: 0.5690 aux.acc_seg: 96.8592 +04/19 07:45:00 - mmengine - INFO - Iter(train) [ 750/160000] base_lr: 4.9967e-05 lr: 1.8474e-07 eta: 1 day, 21:11:06 time: 1.0000 data_time: 0.0042 memory: 8462 loss: 0.6118 decode.loss_ce: 0.1588 decode.acc_seg: 97.6543 aux.loss_ce: 0.4529 aux.acc_seg: 97.6311 +04/19 07:45:50 - mmengine - INFO - Iter(train) [ 800/160000] base_lr: 5.3302e-05 lr: 1.9707e-07 eta: 1 day, 21:06:10 time: 0.9954 data_time: 0.0041 memory: 8462 loss: 0.4727 decode.loss_ce: 0.1355 decode.acc_seg: 96.8500 aux.loss_ce: 0.3372 aux.acc_seg: 96.2122 +04/19 07:46:39 - mmengine - INFO - Iter(train) [ 850/160000] base_lr: 5.6638e-05 lr: 2.0940e-07 eta: 1 day, 21:01:45 time: 0.9967 data_time: 0.0042 memory: 8462 loss: 0.4170 decode.loss_ce: 0.1385 decode.acc_seg: 98.6187 aux.loss_ce: 0.2784 aux.acc_seg: 97.8769 +04/19 07:47:29 - mmengine - INFO - Iter(train) [ 900/160000] base_lr: 5.9973e-05 lr: 2.2173e-07 eta: 1 day, 20:57:43 time: 0.9982 data_time: 0.0044 memory: 8462 loss: 0.3119 decode.loss_ce: 0.1100 decode.acc_seg: 96.6267 aux.loss_ce: 0.2019 aux.acc_seg: 96.7194 +04/19 07:48:19 - mmengine - INFO - Iter(train) [ 950/160000] base_lr: 6.3309e-05 lr: 2.3407e-07 eta: 1 day, 20:54:01 time: 0.9974 data_time: 0.0042 memory: 8462 loss: 0.2706 decode.loss_ce: 0.1000 decode.acc_seg: 97.2006 aux.loss_ce: 0.1706 aux.acc_seg: 96.2858 +04/19 07:49:09 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_073206 +04/19 07:49:09 - mmengine - INFO - Iter(train) [ 1000/160000] base_lr: 6.6644e-05 lr: 2.4640e-07 eta: 1 day, 20:50:37 time: 0.9962 data_time: 0.0046 memory: 8462 loss: 0.2227 decode.loss_ce: 0.0922 decode.acc_seg: 98.8609 aux.loss_ce: 0.1306 aux.acc_seg: 97.1130 +04/19 07:49:59 - mmengine - INFO - Iter(train) [ 1050/160000] base_lr: 6.9980e-05 lr: 2.5873e-07 eta: 1 day, 20:47:26 time: 0.9968 data_time: 0.0046 memory: 8462 loss: 0.2316 decode.loss_ce: 0.0922 decode.acc_seg: 97.4508 aux.loss_ce: 0.1394 aux.acc_seg: 96.8807 +04/19 07:50:49 - mmengine - INFO - Iter(train) [ 1100/160000] base_lr: 7.3316e-05 lr: 2.7106e-07 eta: 1 day, 20:44:28 time: 0.9962 data_time: 0.0041 memory: 8462 loss: 0.1786 decode.loss_ce: 0.0795 decode.acc_seg: 97.5788 aux.loss_ce: 0.0991 aux.acc_seg: 97.0448 +04/19 07:51:39 - mmengine - INFO - Iter(train) [ 1150/160000] base_lr: 7.6651e-05 lr: 2.8339e-07 eta: 1 day, 20:41:37 time: 0.9960 data_time: 0.0041 memory: 8462 loss: 0.1799 decode.loss_ce: 0.0788 decode.acc_seg: 96.7302 aux.loss_ce: 0.1011 aux.acc_seg: 95.9280 +04/19 07:52:28 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_073206 +04/19 07:52:28 - mmengine - INFO - Iter(train) [ 1200/160000] base_lr: 7.9987e-05 lr: 2.9573e-07 eta: 1 day, 20:39:01 time: 0.9964 data_time: 0.0042 memory: 8462 loss: 0.1665 decode.loss_ce: 0.0772 decode.acc_seg: 98.2353 aux.loss_ce: 0.0893 aux.acc_seg: 97.0778 +04/19 07:53:18 - mmengine - INFO - Iter(train) [ 1250/160000] base_lr: 8.3322e-05 lr: 3.0806e-07 eta: 1 day, 20:36:34 time: 0.9961 data_time: 0.0044 memory: 8462 loss: 0.1596 decode.loss_ce: 0.0793 decode.acc_seg: 98.0207 aux.loss_ce: 0.0804 aux.acc_seg: 96.4180 +04/19 07:54:08 - mmengine - INFO - Iter(train) [ 1300/160000] base_lr: 8.6658e-05 lr: 3.2039e-07 eta: 1 day, 20:34:14 time: 0.9980 data_time: 0.0041 memory: 8462 loss: 0.1510 decode.loss_ce: 0.0773 decode.acc_seg: 96.0957 aux.loss_ce: 0.0737 aux.acc_seg: 95.7628 +04/19 07:54:58 - mmengine - INFO - Iter(train) [ 1350/160000] base_lr: 8.9993e-05 lr: 3.3272e-07 eta: 1 day, 20:32:00 time: 0.9967 data_time: 0.0043 memory: 8462 loss: 0.1365 decode.loss_ce: 0.0683 decode.acc_seg: 97.9652 aux.loss_ce: 0.0682 aux.acc_seg: 97.5304 +04/19 07:55:48 - mmengine - INFO - Iter(train) [ 1400/160000] base_lr: 9.3329e-05 lr: 3.4506e-07 eta: 1 day, 20:29:56 time: 0.9980 data_time: 0.0042 memory: 8462 loss: 0.1231 decode.loss_ce: 0.0644 decode.acc_seg: 97.4878 aux.loss_ce: 0.0587 aux.acc_seg: 95.0882 +04/19 07:56:38 - mmengine - INFO - Iter(train) [ 1450/160000] base_lr: 9.6664e-05 lr: 3.5739e-07 eta: 1 day, 20:27:59 time: 0.9969 data_time: 0.0040 memory: 8462 loss: 0.1321 decode.loss_ce: 0.0677 decode.acc_seg: 96.7474 aux.loss_ce: 0.0644 aux.acc_seg: 95.5687 +04/19 07:57:28 - mmengine - INFO - Iter(train) [ 1500/160000] base_lr: 1.0000e-04 lr: 3.6972e-07 eta: 1 day, 20:26:09 time: 0.9976 data_time: 0.0041 memory: 8462 loss: 0.1305 decode.loss_ce: 0.0714 decode.acc_seg: 97.9761 aux.loss_ce: 0.0591 aux.acc_seg: 96.3438 +04/19 07:58:18 - mmengine - INFO - Iter(train) [ 1550/160000] base_lr: 9.9969e-05 lr: 3.6961e-07 eta: 1 day, 20:24:27 time: 0.9994 data_time: 0.0040 memory: 8462 loss: 0.1054 decode.loss_ce: 0.0567 decode.acc_seg: 97.9790 aux.loss_ce: 0.0487 aux.acc_seg: 96.3348 +04/19 07:59:08 - mmengine - INFO - Iter(train) [ 1600/160000] base_lr: 9.9938e-05 lr: 3.6949e-07 eta: 1 day, 20:22:48 time: 0.9983 data_time: 0.0041 memory: 8462 loss: 0.1074 decode.loss_ce: 0.0558 decode.acc_seg: 97.5245 aux.loss_ce: 0.0516 aux.acc_seg: 97.0989 +04/19 07:59:57 - mmengine - INFO - Iter(train) [ 1650/160000] base_lr: 9.9906e-05 lr: 3.6937e-07 eta: 1 day, 20:21:15 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.1062 decode.loss_ce: 0.0588 decode.acc_seg: 98.6660 aux.loss_ce: 0.0473 aux.acc_seg: 97.1249 +04/19 08:00:47 - mmengine - INFO - Iter(train) [ 1700/160000] base_lr: 9.9874e-05 lr: 3.6926e-07 eta: 1 day, 20:19:43 time: 0.9997 data_time: 0.0045 memory: 8462 loss: 0.1060 decode.loss_ce: 0.0581 decode.acc_seg: 98.2771 aux.loss_ce: 0.0478 aux.acc_seg: 97.3316 +04/19 08:01:37 - mmengine - INFO - Iter(train) [ 1750/160000] base_lr: 9.9843e-05 lr: 3.6914e-07 eta: 1 day, 20:18:12 time: 0.9998 data_time: 0.0044 memory: 8462 loss: 0.1101 decode.loss_ce: 0.0612 decode.acc_seg: 98.4524 aux.loss_ce: 0.0489 aux.acc_seg: 97.2885 +04/19 08:02:27 - mmengine - INFO - Iter(train) [ 1800/160000] base_lr: 9.9811e-05 lr: 3.6902e-07 eta: 1 day, 20:16:44 time: 0.9986 data_time: 0.0043 memory: 8462 loss: 0.1074 decode.loss_ce: 0.0601 decode.acc_seg: 98.3191 aux.loss_ce: 0.0473 aux.acc_seg: 97.3679 +04/19 08:03:17 - mmengine - INFO - Iter(train) [ 1850/160000] base_lr: 9.9780e-05 lr: 3.6891e-07 eta: 1 day, 20:15:20 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.1145 decode.loss_ce: 0.0646 decode.acc_seg: 96.8958 aux.loss_ce: 0.0499 aux.acc_seg: 95.6266 +04/19 08:04:07 - mmengine - INFO - Iter(train) [ 1900/160000] base_lr: 9.9748e-05 lr: 3.6879e-07 eta: 1 day, 20:13:58 time: 0.9999 data_time: 0.0045 memory: 8462 loss: 0.0987 decode.loss_ce: 0.0551 decode.acc_seg: 98.0410 aux.loss_ce: 0.0437 aux.acc_seg: 97.2126 +04/19 08:04:57 - mmengine - INFO - Iter(train) [ 1950/160000] base_lr: 9.9717e-05 lr: 3.6867e-07 eta: 1 day, 20:12:40 time: 1.0001 data_time: 0.0042 memory: 8462 loss: 0.1058 decode.loss_ce: 0.0595 decode.acc_seg: 98.5418 aux.loss_ce: 0.0463 aux.acc_seg: 96.3310 +04/19 08:05:47 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_073206 +04/19 08:05:47 - mmengine - INFO - Iter(train) [ 2000/160000] base_lr: 9.9685e-05 lr: 3.6856e-07 eta: 1 day, 20:11:20 time: 1.0007 data_time: 0.0041 memory: 8462 loss: 0.1130 decode.loss_ce: 0.0679 decode.acc_seg: 96.9311 aux.loss_ce: 0.0452 aux.acc_seg: 95.4798 +04/19 08:06:37 - mmengine - INFO - Iter(train) [ 2050/160000] base_lr: 9.9654e-05 lr: 3.6844e-07 eta: 1 day, 20:10:02 time: 0.9987 data_time: 0.0041 memory: 8462 loss: 0.0946 decode.loss_ce: 0.0536 decode.acc_seg: 98.1184 aux.loss_ce: 0.0410 aux.acc_seg: 97.6820 +04/19 08:07:27 - mmengine - INFO - Iter(train) [ 2100/160000] base_lr: 9.9622e-05 lr: 3.6832e-07 eta: 1 day, 20:08:43 time: 0.9996 data_time: 0.0043 memory: 8462 loss: 0.0973 decode.loss_ce: 0.0559 decode.acc_seg: 98.9077 aux.loss_ce: 0.0414 aux.acc_seg: 97.5845 +04/19 08:08:17 - mmengine - INFO - Iter(train) [ 2150/160000] base_lr: 9.9591e-05 lr: 3.6821e-07 eta: 1 day, 20:07:30 time: 1.0001 data_time: 0.0046 memory: 8462 loss: 0.0883 decode.loss_ce: 0.0493 decode.acc_seg: 98.4428 aux.loss_ce: 0.0390 aux.acc_seg: 97.5756 +04/19 08:09:07 - mmengine - INFO - Iter(train) [ 2200/160000] base_lr: 9.9559e-05 lr: 3.6809e-07 eta: 1 day, 20:06:16 time: 1.0000 data_time: 0.0041 memory: 8462 loss: 0.0858 decode.loss_ce: 0.0488 decode.acc_seg: 98.9340 aux.loss_ce: 0.0370 aux.acc_seg: 98.4314 +04/19 08:09:57 - mmengine - INFO - Iter(train) [ 2250/160000] base_lr: 9.9527e-05 lr: 3.6797e-07 eta: 1 day, 20:05:02 time: 0.9992 data_time: 0.0041 memory: 8462 loss: 0.0857 decode.loss_ce: 0.0492 decode.acc_seg: 97.9370 aux.loss_ce: 0.0365 aux.acc_seg: 96.0619 +04/19 08:10:47 - mmengine - INFO - Iter(train) [ 2300/160000] base_lr: 9.9496e-05 lr: 3.6786e-07 eta: 1 day, 20:03:48 time: 0.9986 data_time: 0.0042 memory: 8462 loss: 0.0937 decode.loss_ce: 0.0543 decode.acc_seg: 99.1913 aux.loss_ce: 0.0394 aux.acc_seg: 97.6168 +04/19 08:11:37 - mmengine - INFO - Iter(train) [ 2350/160000] base_lr: 9.9464e-05 lr: 3.6774e-07 eta: 1 day, 20:02:34 time: 0.9974 data_time: 0.0042 memory: 8462 loss: 0.0839 decode.loss_ce: 0.0480 decode.acc_seg: 96.4069 aux.loss_ce: 0.0359 aux.acc_seg: 95.8021 +04/19 08:12:27 - mmengine - INFO - Iter(train) [ 2400/160000] base_lr: 9.9433e-05 lr: 3.6762e-07 eta: 1 day, 20:01:23 time: 0.9996 data_time: 0.0042 memory: 8462 loss: 0.0740 decode.loss_ce: 0.0406 decode.acc_seg: 98.9935 aux.loss_ce: 0.0333 aux.acc_seg: 97.8920 +04/19 08:13:17 - mmengine - INFO - Iter(train) [ 2450/160000] base_lr: 9.9401e-05 lr: 3.6751e-07 eta: 1 day, 20:00:12 time: 0.9989 data_time: 0.0042 memory: 8462 loss: 0.0819 decode.loss_ce: 0.0465 decode.acc_seg: 97.6427 aux.loss_ce: 0.0354 aux.acc_seg: 95.5507 +04/19 08:14:07 - mmengine - INFO - Iter(train) [ 2500/160000] base_lr: 9.9370e-05 lr: 3.6739e-07 eta: 1 day, 19:59:02 time: 0.9996 data_time: 0.0041 memory: 8462 loss: 0.0806 decode.loss_ce: 0.0457 decode.acc_seg: 98.3541 aux.loss_ce: 0.0350 aux.acc_seg: 97.8451 +04/19 08:14:57 - mmengine - INFO - Iter(train) [ 2550/160000] base_lr: 9.9338e-05 lr: 3.6727e-07 eta: 1 day, 19:57:55 time: 1.0000 data_time: 0.0046 memory: 8462 loss: 0.0901 decode.loss_ce: 0.0521 decode.acc_seg: 96.8052 aux.loss_ce: 0.0380 aux.acc_seg: 95.4477 +04/19 08:15:47 - mmengine - INFO - Iter(train) [ 2600/160000] base_lr: 9.9307e-05 lr: 3.6716e-07 eta: 1 day, 19:56:46 time: 0.9992 data_time: 0.0044 memory: 8462 loss: 0.0891 decode.loss_ce: 0.0503 decode.acc_seg: 98.3551 aux.loss_ce: 0.0388 aux.acc_seg: 96.2929 +04/19 08:16:37 - mmengine - INFO - Iter(train) [ 2650/160000] base_lr: 9.9275e-05 lr: 3.6704e-07 eta: 1 day, 19:55:38 time: 0.9993 data_time: 0.0042 memory: 8462 loss: 0.0795 decode.loss_ce: 0.0454 decode.acc_seg: 97.8434 aux.loss_ce: 0.0341 aux.acc_seg: 97.0446 +04/19 08:17:27 - mmengine - INFO - Iter(train) [ 2700/160000] base_lr: 9.9244e-05 lr: 3.6692e-07 eta: 1 day, 19:54:33 time: 0.9995 data_time: 0.0044 memory: 8462 loss: 0.0840 decode.loss_ce: 0.0501 decode.acc_seg: 98.5741 aux.loss_ce: 0.0339 aux.acc_seg: 96.9194 +04/19 08:18:17 - mmengine - INFO - Iter(train) [ 2750/160000] base_lr: 9.9212e-05 lr: 3.6681e-07 eta: 1 day, 19:53:27 time: 0.9998 data_time: 0.0043 memory: 8462 loss: 0.0900 decode.loss_ce: 0.0518 decode.acc_seg: 98.2058 aux.loss_ce: 0.0381 aux.acc_seg: 96.4069 +04/19 08:19:07 - mmengine - INFO - Iter(train) [ 2800/160000] base_lr: 9.9180e-05 lr: 3.6669e-07 eta: 1 day, 19:52:24 time: 1.0003 data_time: 0.0045 memory: 8462 loss: 0.0752 decode.loss_ce: 0.0434 decode.acc_seg: 98.1392 aux.loss_ce: 0.0318 aux.acc_seg: 97.6873 +04/19 08:19:57 - mmengine - INFO - Iter(train) [ 2850/160000] base_lr: 9.9149e-05 lr: 3.6657e-07 eta: 1 day, 19:51:18 time: 0.9991 data_time: 0.0041 memory: 8462 loss: 0.0800 decode.loss_ce: 0.0476 decode.acc_seg: 98.0701 aux.loss_ce: 0.0324 aux.acc_seg: 95.6417 +04/19 08:20:47 - mmengine - INFO - Iter(train) [ 2900/160000] base_lr: 9.9117e-05 lr: 3.6646e-07 eta: 1 day, 19:50:15 time: 1.0000 data_time: 0.0045 memory: 8462 loss: 0.0739 decode.loss_ce: 0.0421 decode.acc_seg: 98.0650 aux.loss_ce: 0.0317 aux.acc_seg: 97.2357 +04/19 08:21:37 - mmengine - INFO - Iter(train) [ 2950/160000] base_lr: 9.9086e-05 lr: 3.6634e-07 eta: 1 day, 19:49:14 time: 1.0000 data_time: 0.0042 memory: 8462 loss: 0.0739 decode.loss_ce: 0.0434 decode.acc_seg: 98.9803 aux.loss_ce: 0.0306 aux.acc_seg: 97.1418 +04/19 08:22:27 - mmengine - INFO - Exp name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512_20240419_073206 +04/19 08:22:27 - mmengine - INFO - Iter(train) [ 3000/160000] base_lr: 9.9054e-05 lr: 3.6622e-07 eta: 1 day, 19:48:15 time: 1.0009 data_time: 0.0045 memory: 8462 loss: 0.0741 decode.loss_ce: 0.0436 decode.acc_seg: 99.0244 aux.loss_ce: 0.0305 aux.acc_seg: 98.1958 From bc8501a2657bafd5170b7da631ab24230236fb12 Mon Sep 17 00:00:00 2001 From: Jaeofbum Date: Thu, 25 Apr 2024 04:56:33 +0000 Subject: [PATCH 24/24] 2024.04.24 --- .gitignore | 2 +- configs/_base_/datasets/cag.py | 2 +- configs/_base_/models/upernet_mae.py | 4 ++-- configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py | 2 +- mmseg/datasets/transforms/formatting.py | 4 ---- 5 files changed, 5 insertions(+), 9 deletions(-) diff --git a/.gitignore b/.gitignore index 9f4acc0a01..b1aea68659 100644 --- a/.gitignore +++ b/.gitignore @@ -122,4 +122,4 @@ mmseg/.mim logs/ *.png -nohup.out \ No newline at end of file +*.out \ No newline at end of file diff --git a/configs/_base_/datasets/cag.py b/configs/_base_/datasets/cag.py index b8935bdcbe..08e6785fdf 100644 --- a/configs/_base_/datasets/cag.py +++ b/configs/_base_/datasets/cag.py @@ -1,6 +1,6 @@ # dataset settings dataset_type = 'CoronaryAngiographyDataset' -data_root = 'data/cag' +data_root = 'data/cag_2100' # augmentation setting from YoungIn's jupyter notebook train_pipeline = [ dict(type='LoadImageFromFile'), diff --git a/configs/_base_/models/upernet_mae.py b/configs/_base_/models/upernet_mae.py index 9ea5cda984..b833b67645 100644 --- a/configs/_base_/models/upernet_mae.py +++ b/configs/_base_/models/upernet_mae.py @@ -1,8 +1,8 @@ norm_cfg = dict(type='SyncBN', requires_grad=True) data_preprocessor = dict( type='SegDataPreProcessor', - # mean=[123.675, 116.28, 103.53], - # std=[58.395, 57.12, 57.375], + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) diff --git a/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py b/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py index e28a03ec88..0225c1e9f7 100644 --- a/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py +++ b/configs/mae/mae-base_upernet_8xb2-amp-160k_cag-512x512.py @@ -6,7 +6,7 @@ data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, - pretrained='/workspaces/mmsegmentation/converted_model.pth', + pretrained='/workspaces/mmsegmentation-1/converted_model.pth', backbone=dict( type='MAE', img_size=(512, 512), diff --git a/mmseg/datasets/transforms/formatting.py b/mmseg/datasets/transforms/formatting.py index 07a58321c8..83f566d03c 100644 --- a/mmseg/datasets/transforms/formatting.py +++ b/mmseg/datasets/transforms/formatting.py @@ -66,13 +66,9 @@ def transform(self, results: dict) -> dict: if len(img.shape) < 3: img = np.stack([img] * 3, axis=-1) if not img.flags.c_contiguous: - img = (img - np.min(img)) - img = img /np.max(img) img = to_tensor(np.ascontiguousarray(img.transpose(2, 0, 1))) else: img = img.transpose(2, 0, 1) - img = (img - np.min(img)) - img = img /np.max(img) img = to_tensor(img).contiguous() packed_results['inputs'] = img data_sample = SegDataSample()