[MMSIG] 334 The new version of config adapts ResNet algorithm

mmpretrain/configs/_base_/datasets/imagenet_bs64.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.dataset import DefaultSampler
+
+from mmpretrain.datasets import (CenterCrop, ImageNet, LoadImageFromFile,
+                                 PackInputs, RandomFlip, RandomResizedCrop,
+                                 ResizeEdge)
+from mmpretrain.evaluation import Accuracy
+
+# dataset settings
+dataset_type = ImageNet
+data_preprocessor = dict(
+    num_classes=1000,
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=RandomResizedCrop, scale=224),
+    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
+    dict(type=PackInputs),
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=ResizeEdge, scale=256, edge='short'),
+    dict(type=CenterCrop, crop_size=224),
+    dict(type=PackInputs),
+]
+
+train_dataloader = dict(
+    batch_size=64,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        split='train',
+        pipeline=train_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=True),
+)
+
+val_dataloader = dict(
+    batch_size=64,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        split='val',
+        pipeline=test_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=False),
+)
+val_evaluator = dict(type=Accuracy, topk=(1, 5))
+
+# If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

mmpretrain/configs/_base_/models/resnet101.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, ResNet)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet,
+        depth=101,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5),
+    ))

mmpretrain/configs/_base_/models/resnet101_cifar.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, ResNet_CIFAR)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet_CIFAR,
+        depth=101,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+    ))

mmpretrain/configs/_base_/models/resnet152.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, ResNet)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet,
+        depth=152,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5),
+    ))

mmpretrain/configs/_base_/models/resnet152_cifar.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, ResNet_CIFAR)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet_CIFAR,
+        depth=152,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+    ))

mmpretrain/configs/_base_/models/resnet18_cifar.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, ResNet_CIFAR)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet_CIFAR,
+        depth=18,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=10,
+        in_channels=512,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+    ))

mmpretrain/configs/_base_/models/resnet34.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, ResNet)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet,
+        depth=34,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=512,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5),
+    ))

mmpretrain/configs/_base_/models/resnet34_cifar.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, ResNet_CIFAR)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet_CIFAR,
+        depth=34,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=10,
+        in_channels=512,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+    ))

mmpretrain/configs/_base_/models/resnet34_gem.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GeneralizedMeanPooling,
+                               ImageClassifier, LinearClsHead, ResNet)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet,
+        depth=34,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GeneralizedMeanPooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=512,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5),
+    ))

mmpretrain/configs/_base_/models/resnet50.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, ResNet)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet,
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+        topk=(1, 5),
+    ))

mmpretrain/configs/_base_/models/resnet50_cifar.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, LinearClsHead, ResNet_CIFAR)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet_CIFAR,
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=LinearClsHead,
+        num_classes=10,
+        in_channels=2048,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0),
+    ))

mmpretrain/configs/_base_/models/resnet50_cifar_mixup.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling,
+                               ImageClassifier, Mixup, MultiLabelLinearClsHead,
+                               ResNet_CIFAR)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet_CIFAR,
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=MultiLabelLinearClsHead,
+        num_classes=10,
+        in_channels=2048,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0, use_soft=True)),
+    train_cfg=dict(augments=dict(type=Mixup, alpha=1.)),
+)

mmpretrain/configs/_base_/models/resnet50_cutmix.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmpretrain.models import (CrossEntropyLoss, CutMix, GlobalAveragePooling,
+                               ImageClassifier, MultiLabelLinearClsHead,
+                               ResNet)
+
+# model settings
+model = dict(
+    type=ImageClassifier,
+    backbone=dict(
+        type=ResNet,
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type=GlobalAveragePooling),
+    head=dict(
+        type=MultiLabelLinearClsHead,
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type=CrossEntropyLoss, loss_weight=1.0, use_soft=True)),
+    train_cfg=dict(
+        augments=dict(type=CutMix, alpha=1.0, num_classes=1000, prob=1.0)),
+)

mmpretrain/configs/_base_/schedules/imagenet_bs2048.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.optim import LinearLR, MultiStepLR
+from torch.optim import SGD
+
+# optimizer
+optim_wrapper = dict(
+    optimizer=dict(
+        type=SGD, lr=0.8, momentum=0.9, weight_decay=0.0001, nesterov=True))
+
+# learning policy
+param_scheduler = [
+    dict(type=LinearLR, start_factor=0.25, by_epoch=False, begin=0, end=2500),
+    dict(type=MultiStepLR, by_epoch=True, milestones=[30, 60, 90], gamma=0.1)
+]
+
+# train, val, test setting
+train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
+val_cfg = dict()
+test_cfg = dict()
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# based on the actual training batch size.
+auto_scale_lr = dict(base_batch_size=2048)

mmpretrain/configs/_base_/schedules/imagenet_bs256_coslr.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.optim import CosineAnnealingLR
+from torch.optim import SGD
+
+# optimizer
+optim_wrapper = dict(
+    optimizer=dict(type=SGD, lr=0.1, momentum=0.9, weight_decay=0.0001))
+
+# learning policy
+param_scheduler = dict(
+    type=CosineAnnealingLR, T_max=100, by_epoch=True, begin=0, end=100)
+
+# train, val, test setting
+train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
+val_cfg = dict()
+test_cfg = dict()
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# based on the actual training batch size.
+auto_scale_lr = dict(base_batch_size=256)

mmpretrain/configs/resnet/resnet101_8xb16_cifar10.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+# This is a BETA new format config file, and the usage may change recently.
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.datasets.cifar10_bs16 import *
+    from .._base_.default_runtime import *
+    from .._base_.models.resnet101_cifar import *
+    from .._base_.schedules.cifar10_bs128 import *