From 05e4bc17b2bbae6e04111972b717a5073d2640dd Mon Sep 17 00:00:00 2001 From: Hakjin Lee Date: Mon, 14 Nov 2022 18:08:31 +0900 Subject: [PATCH 1/5] [Feature] Support Activation Checkpointing for ConvNeXt. (#1152) * Support Activation Checkpointing for ConvNeXt * Add test case * Lint * Add docstring --- mmcls/models/backbones/convnext.py | 48 ++++++++++++------- .../test_backbones/test_convnext.py | 10 ++++ 2 files changed, 42 insertions(+), 16 deletions(-) diff --git a/mmcls/models/backbones/convnext.py b/mmcls/models/backbones/convnext.py index 00393638aae..02f26876efd 100644 --- a/mmcls/models/backbones/convnext.py +++ b/mmcls/models/backbones/convnext.py @@ -6,6 +6,7 @@ import torch import torch.nn as nn import torch.nn.functional as F +import torch.utils.checkpoint as cp from mmcv.cnn.bricks import (NORM_LAYERS, DropPath, build_activation_layer, build_norm_layer) from mmcv.runner import BaseModule @@ -77,8 +78,11 @@ def __init__(self, mlp_ratio=4., linear_pw_conv=True, drop_path_rate=0., - layer_scale_init_value=1e-6): + layer_scale_init_value=1e-6, + with_cp=False): super().__init__() + self.with_cp = with_cp + self.depthwise_conv = nn.Conv2d( in_channels, in_channels, @@ -108,24 +112,33 @@ def __init__(self, drop_path_rate) if drop_path_rate > 0. else nn.Identity() def forward(self, x): - shortcut = x - x = self.depthwise_conv(x) - x = self.norm(x) - if self.linear_pw_conv: - x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + def _inner_forward(x): + shortcut = x + x = self.depthwise_conv(x) + x = self.norm(x) - x = self.pointwise_conv1(x) - x = self.act(x) - x = self.pointwise_conv2(x) + if self.linear_pw_conv: + x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) - if self.linear_pw_conv: - x = x.permute(0, 3, 1, 2) # permute back + x = self.pointwise_conv1(x) + x = self.act(x) + x = self.pointwise_conv2(x) - if self.gamma is not None: - x = x.mul(self.gamma.view(1, -1, 1, 1)) + if self.linear_pw_conv: + x = x.permute(0, 3, 1, 2) # permute back + + if self.gamma is not None: + x = x.mul(self.gamma.view(1, -1, 1, 1)) + + x = shortcut + self.drop_path(x) + return x + + if self.with_cp and x.requires_grad: + x = cp.checkpoint(_inner_forward, x) + else: + x = _inner_forward(x) - x = shortcut + self.drop_path(x) return x @@ -169,6 +182,8 @@ class ConvNeXt(BaseBackbone): gap_before_final_norm (bool): Whether to globally average the feature map before the final norm layer. In the official repo, it's only used in classification task. Defaults to True. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Defaults to False. init_cfg (dict, optional): Initialization config dict """ # noqa: E501 arch_settings = { @@ -206,6 +221,7 @@ def __init__(self, out_indices=-1, frozen_stages=0, gap_before_final_norm=True, + with_cp=False, init_cfg=None): super().__init__(init_cfg=init_cfg) @@ -288,8 +304,8 @@ def __init__(self, norm_cfg=norm_cfg, act_cfg=act_cfg, linear_pw_conv=linear_pw_conv, - layer_scale_init_value=layer_scale_init_value) - for j in range(depth) + layer_scale_init_value=layer_scale_init_value, + with_cp=with_cp) for j in range(depth) ]) block_idx += depth diff --git a/tests/test_models/test_backbones/test_convnext.py b/tests/test_models/test_backbones/test_convnext.py index 35448b458b1..ccd002d1e2b 100644 --- a/tests/test_models/test_backbones/test_convnext.py +++ b/tests/test_models/test_backbones/test_convnext.py @@ -84,3 +84,13 @@ def test_convnext(): for i in range(2, 4): assert model.downsample_layers[i].training assert model.stages[i].training + + # Test Activation Checkpointing + model = ConvNeXt(arch='tiny', out_indices=-1, with_cp=True) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 1 + assert feat[0].shape == torch.Size([1, 768]) From dc8691e889843e5f81ac9000757e9dfa887c9ce1 Mon Sep 17 00:00:00 2001 From: TangYueran <65158769+Qiza-lyhm@users.noreply.github.com> Date: Tue, 15 Nov 2022 17:02:16 +0800 Subject: [PATCH 2/5] [Feature] Support MLU backend. (#1159) * Training on MLU is available --- mmcls/apis/train.py | 9 +++++---- mmcls/core/utils/dist_utils.py | 6 +++++- mmcls/datasets/samplers/distributed_sampler.py | 3 +-- mmcls/utils/distribution.py | 12 ++++++++++++ 4 files changed, 23 insertions(+), 7 deletions(-) diff --git a/mmcls/apis/train.py b/mmcls/apis/train.py index 909b116d163..c240632cb9d 100644 --- a/mmcls/apis/train.py +++ b/mmcls/apis/train.py @@ -10,11 +10,11 @@ from mmcls.core import DistEvalHook, DistOptimizerHook, EvalHook from mmcls.datasets import build_dataloader, build_dataset -from mmcls.utils import (get_root_logger, wrap_distributed_model, - wrap_non_distributed_model) +from mmcls.utils import (auto_select_device, get_root_logger, + wrap_distributed_model, wrap_non_distributed_model) -def init_random_seed(seed=None, device='cuda'): +def init_random_seed(seed=None, device=None): """Initialize random seed. If the seed is not set, the seed will be automatically randomized, @@ -30,7 +30,8 @@ def init_random_seed(seed=None, device='cuda'): """ if seed is not None: return seed - + if device is None: + device = auto_select_device() # Make sure all ranks share the same random seed to prevent # some potential bugs. Please refer to # https://github.com/open-mmlab/mmdetection/issues/6339 diff --git a/mmcls/core/utils/dist_utils.py b/mmcls/core/utils/dist_utils.py index 8912cea435a..15cf13c4d03 100644 --- a/mmcls/core/utils/dist_utils.py +++ b/mmcls/core/utils/dist_utils.py @@ -8,6 +8,8 @@ from torch._utils import (_flatten_dense_tensors, _take_tensors, _unflatten_dense_tensors) +from mmcls.utils import auto_select_device + def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): if bucket_size_mb > 0: @@ -59,7 +61,7 @@ def after_train_iter(self, runner): runner.optimizer.step() -def sync_random_seed(seed=None, device='cuda'): +def sync_random_seed(seed=None, device=None): """Make sure different ranks share the same seed. All workers must call this function, otherwise it will deadlock. @@ -81,6 +83,8 @@ def sync_random_seed(seed=None, device='cuda'): Returns: int: Seed to be used. """ + if device is None: + device = auto_select_device() if seed is None: seed = np.random.randint(2**31) assert isinstance(seed, int) diff --git a/mmcls/datasets/samplers/distributed_sampler.py b/mmcls/datasets/samplers/distributed_sampler.py index 9e78c400693..a38c5ac1d58 100644 --- a/mmcls/datasets/samplers/distributed_sampler.py +++ b/mmcls/datasets/samplers/distributed_sampler.py @@ -4,7 +4,6 @@ from mmcls.core.utils import sync_random_seed from mmcls.datasets import SAMPLERS -from mmcls.utils import auto_select_device @SAMPLERS.register_module() @@ -31,7 +30,7 @@ def __init__(self, # in the same order based on the same seed. Then different ranks # could use different indices to select non-overlapped data from the # same data list. - self.seed = sync_random_seed(seed, device=auto_select_device()) + self.seed = sync_random_seed(seed) def __iter__(self): # deterministically shuffle based on epoch diff --git a/mmcls/utils/distribution.py b/mmcls/utils/distribution.py index d57bd2b53ba..c6e4c7240d6 100644 --- a/mmcls/utils/distribution.py +++ b/mmcls/utils/distribution.py @@ -19,6 +19,9 @@ def wrap_non_distributed_model(model, device='cuda', dim=0, *args, **kwargs): if device == 'npu': from mmcv.device.npu import NPUDataParallel model = NPUDataParallel(model.npu(), dim=dim, *args, **kwargs) + elif device == 'mlu': + from mmcv.device.mlu import MLUDataParallel + model = MLUDataParallel(model.mlu(), dim=dim, *args, **kwargs) elif device == 'cuda': from mmcv.parallel import MMDataParallel model = MMDataParallel(model.cuda(), dim=dim, *args, **kwargs) @@ -57,6 +60,15 @@ def wrap_distributed_model(model, device='cuda', *args, **kwargs): from torch.npu import current_device model = NPUDistributedDataParallel( model.npu(), *args, device_ids=[current_device()], **kwargs) + elif device == 'mlu': + import os + + from mmcv.device.mlu import MLUDistributedDataParallel + model = MLUDistributedDataParallel( + model.mlu(), + *args, + device_ids=[int(os.environ['LOCAL_RANK'])], + **kwargs) elif device == 'cuda': from mmcv.parallel import MMDistributedDataParallel from torch.cuda import current_device From 96bb2dd219634a6c2ac012f7e1120c624f80d961 Mon Sep 17 00:00:00 2001 From: Ma Zerun Date: Tue, 22 Nov 2022 18:57:52 +0800 Subject: [PATCH 3/5] [Fix] Fix the redundant `device_ids` in `tools/test.py`. (#1215) --- tools/test.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/tools/test.py b/tools/test.py index 1a47174392b..74f15159039 100644 --- a/tools/test.py +++ b/tools/test.py @@ -195,10 +195,7 @@ def main(): **show_kwargs) else: model = wrap_distributed_model( - model, - device=cfg.device, - device_ids=[int(os.environ['LOCAL_RANK'])], - broadcast_buffers=False) + model, device=cfg.device, broadcast_buffers=False) outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) From 3e99395c298c4fa144f6414f6d1821c49552d93d Mon Sep 17 00:00:00 2001 From: Ma Zerun Date: Tue, 6 Dec 2022 16:50:41 +0800 Subject: [PATCH 4/5] [Fix] Fix a bug caused MMClsWandbHook stuck. (#1242) --- mmcls/core/hook/wandblogger_hook.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/mmcls/core/hook/wandblogger_hook.py b/mmcls/core/hook/wandblogger_hook.py index 61ccfe90d6e..ef67ee4d436 100644 --- a/mmcls/core/hook/wandblogger_hook.py +++ b/mmcls/core/hook/wandblogger_hook.py @@ -3,7 +3,7 @@ import numpy as np from mmcv.runner import HOOKS, BaseRunner -from mmcv.runner.dist_utils import master_only +from mmcv.runner.dist_utils import get_dist_info, master_only from mmcv.runner.hooks.checkpoint import CheckpointHook from mmcv.runner.hooks.evaluation import DistEvalHook, EvalHook from mmcv.runner.hooks.logger.wandb import WandbLoggerHook @@ -190,7 +190,6 @@ def after_train_epoch(self, runner): # Log the evaluation table self._log_eval_table(runner.epoch + 1) - @master_only def after_train_iter(self, runner): if self.get_mode(runner) == 'train': # An ugly patch. The iter-based eval hook will call the @@ -201,6 +200,10 @@ def after_train_iter(self, runner): else: super(MMClsWandbHook, self).after_train_iter(runner) + rank, _ = get_dist_info() + if rank != 0: + return + if self.by_epoch: return From c737e65164ed5d919db7bcda35e960cc850efb2c Mon Sep 17 00:00:00 2001 From: Ma Zerun Date: Tue, 6 Dec 2022 18:18:13 +0800 Subject: [PATCH 5/5] Bump version to v0.25.0. (#1244) --- .pre-commit-config.yaml | 4 +- README.md | 13 +++-- README_zh-CN.md | 5 ++ configs/t2t_vit/README.md | 2 +- docker/serve/Dockerfile | 2 +- docs/en/changelog.md | 104 ++++++++++++++++++++++++-------------- docs/en/faq.md | 3 +- docs/zh_CN/faq.md | 3 +- mmcls/version.py | 2 +- 9 files changed, 86 insertions(+), 52 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0d19d5f67bf..55138ce8c1e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,9 +29,9 @@ repos: rev: 0.7.9 hooks: - id: mdformat - args: ["--number", "--table-width", "200"] + args: ["--number", "--table-width", "200", '--disable-escape', 'backslash', '--disable-escape', 'link-enclosure'] additional_dependencies: - - mdformat-openmmlab + - "mdformat-openmmlab>=0.0.4" - mdformat_frontmatter - linkify-it-py - repo: https://github.com/codespell-project/codespell diff --git a/README.md b/README.md index 7a711c87412..f7026752da1 100644 --- a/README.md +++ b/README.md @@ -64,6 +64,12 @@ The MMClassification 1.0 has released! It's still unstable and in release candid to [the 1.x branch](https://github.com/open-mmlab/mmclassification/tree/1.x) and discuss it with us in [the discussion](https://github.com/open-mmlab/mmclassification/discussions). +v0.25.0 was released in 06/12/2022. +Highlights of the new version: + +- Support MLU backend. +- Add `dist_train_arm.sh` for ARM device. + v0.24.1 was released in 31/10/2022. Highlights of the new version: @@ -75,13 +81,6 @@ Highlights of the new version: - Support **HorNet**, **EfficientFormerm**, **SwinTransformer V2** and **MViT** backbones. - Support Standford Cars dataset. -v0.23.0 was released in 1/5/2022. -Highlights of the new version: - -- Support **DenseNet**, **VAN** and **PoolFormer**, and provide pre-trained models. -- Support training on IPU. -- New style API docs, welcome [view it](https://mmclassification.readthedocs.io/en/master/api/models.html). - Please refer to [changelog.md](docs/en/changelog.md) for more details and other release history. ## Installation diff --git a/README_zh-CN.md b/README_zh-CN.md index 60f06209d78..d37e4dba222 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -63,6 +63,11 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O MMClassification 1.0 已经发布！目前仍在公测中，如果希望试用，请切换到 [1.x 分支](https://github.com/open-mmlab/mmclassification/tree/1.x)，并在[讨论版](https://github.com/open-mmlab/mmclassification/discussions) 参加开发讨论！ +2022/12/06 发布了 v0.25.0 版本 + +- 支持 MLU 设备 +- 添加了用于 ARM 设备训练的 `dist_train_arm.sh` + 2022/10/31 发布了 v0.24.1 版本 - 支持了华为昇腾 NPU 设备。 diff --git a/configs/t2t_vit/README.md b/configs/t2t_vit/README.md index 1e3a082760e..1f80c25c60a 100644 --- a/configs/t2t_vit/README.md +++ b/configs/t2t_vit/README.md @@ -6,7 +6,7 @@ ## Abstract -Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, \\eg, the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3% top1 accuracy in image resolution 384×384 on ImageNet. +Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3% top1 accuracy in image resolution 384×384 on ImageNet.
diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile index 3056e9053e9..bdf48d12a9f 100644 --- a/docker/serve/Dockerfile +++ b/docker/serve/Dockerfile @@ -4,7 +4,7 @@ ARG CUDNN="7" FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel ARG MMCV="1.7.0" -ARG MMCLS="0.24.1" +ARG MMCLS="0.25.0" ENV PYTHONUNBUFFERED TRUE diff --git a/docs/en/changelog.md b/docs/en/changelog.md index c044f4baf5f..7928a13f1c4 100644 --- a/docs/en/changelog.md +++ b/docs/en/changelog.md @@ -1,5 +1,33 @@ # Changelog +## v0.25.0(06/12/2022) + +### Highlights + +- Support MLU backend. + +### New Features + +- Support MLU backend. ([#1159](https://github.com/open-mmlab/mmclassification/pull/1159)) +- Support Activation Checkpointing for ConvNeXt. ([#1152](https://github.com/open-mmlab/mmclassification/pull/1152)) + +### Improvements + +- Add `dist_train_arm.sh` for ARM device and update NPU results. ([#1218](https://github.com/open-mmlab/mmclassification/pull/1218)) + +### Bug Fixes + +- Fix a bug caused `MMClsWandbHook` stuck. ([#1242](https://github.com/open-mmlab/mmclassification/pull/1242)) +- Fix the redundant `device_ids` in `tools/test.py`. ([#1215](https://github.com/open-mmlab/mmclassification/pull/1215)) + +### Docs Update + +- Add version banner and version warning in master docs. ([#1216](https://github.com/open-mmlab/mmclassification/pull/1216)) +- Update NPU support doc. ([#1198](https://github.com/open-mmlab/mmclassification/pull/1198)) +- Fixed typo in `pytorch2torchscript.md`. ([#1173](https://github.com/open-mmlab/mmclassification/pull/1173)) +- Fix typo in `miscellaneous.md`. ([#1137](https://github.com/open-mmlab/mmclassification/pull/1137)) +- further detail for the doc for `ClassBalancedDataset`. ([#901](https://github.com/open-mmlab/mmclassification/pull/901)) + ## v0.24.1(31/10/2022) ### New Features @@ -28,14 +56,14 @@ ### Improvements -- \[Improve\] replace loop of progressbar in api/test. ([#878](https://github.com/open-mmlab/mmclassification/pull/878)) -- \[Enhance\] RepVGG for YOLOX-PAI. ([#1025](https://github.com/open-mmlab/mmclassification/pull/1025)) -- \[Enhancement\] Update VAN. ([#1017](https://github.com/open-mmlab/mmclassification/pull/1017)) -- \[Refactor\] Re-write `get_sinusoid_encoding` from third-party implementation. ([#965](https://github.com/open-mmlab/mmclassification/pull/965)) -- \[Improve\] Upgrade onnxsim to v0.4.0. ([#915](https://github.com/open-mmlab/mmclassification/pull/915)) -- \[Improve\] Fixed typo in `RepVGG`. ([#985](https://github.com/open-mmlab/mmclassification/pull/985)) -- \[Improve\] Using `train_step` instead of `forward` in PreciseBNHook ([#964](https://github.com/open-mmlab/mmclassification/pull/964)) -- \[Improve\] Use `forward_dummy` to calculate FLOPS. ([#953](https://github.com/open-mmlab/mmclassification/pull/953)) +- [Improve] replace loop of progressbar in api/test. ([#878](https://github.com/open-mmlab/mmclassification/pull/878)) +- [Enhance] RepVGG for YOLOX-PAI. ([#1025](https://github.com/open-mmlab/mmclassification/pull/1025)) +- [Enhancement] Update VAN. ([#1017](https://github.com/open-mmlab/mmclassification/pull/1017)) +- [Refactor] Re-write `get_sinusoid_encoding` from third-party implementation. ([#965](https://github.com/open-mmlab/mmclassification/pull/965)) +- [Improve] Upgrade onnxsim to v0.4.0. ([#915](https://github.com/open-mmlab/mmclassification/pull/915)) +- [Improve] Fixed typo in `RepVGG`. ([#985](https://github.com/open-mmlab/mmclassification/pull/985)) +- [Improve] Using `train_step` instead of `forward` in PreciseBNHook ([#964](https://github.com/open-mmlab/mmclassification/pull/964)) +- [Improve] Use `forward_dummy` to calculate FLOPS. ([#953](https://github.com/open-mmlab/mmclassification/pull/953)) ### Bug Fixes @@ -102,13 +130,13 @@ ### New Features -- \[Feature\] Support resize relative position embedding in `SwinTransformer`. ([#749](https://github.com/open-mmlab/mmclassification/pull/749)) -- \[Feature\] Add PoolFormer backbone and checkpoints. ([#746](https://github.com/open-mmlab/mmclassification/pull/746)) +- [Feature] Support resize relative position embedding in `SwinTransformer`. ([#749](https://github.com/open-mmlab/mmclassification/pull/749)) +- [Feature] Add PoolFormer backbone and checkpoints. ([#746](https://github.com/open-mmlab/mmclassification/pull/746)) ### Improvements -- \[Enhance\] Improve CPE performance by reduce memory copy. ([#762](https://github.com/open-mmlab/mmclassification/pull/762)) -- \[Enhance\] Add extra dataloader settings in configs. ([#752](https://github.com/open-mmlab/mmclassification/pull/752)) +- [Enhance] Improve CPE performance by reduce memory copy. ([#762](https://github.com/open-mmlab/mmclassification/pull/762)) +- [Enhance] Add extra dataloader settings in configs. ([#752](https://github.com/open-mmlab/mmclassification/pull/752)) ## v0.22.0(30/3/2022) @@ -120,29 +148,29 @@ ### New Features -- \[Feature\] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735)) -- \[Feature\] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738)) -- \[Feature\] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744)) -- \[Feature\] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716)) -- \[Feature\] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734)) -- \[Feature\] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709)) -- \[Feature\] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703)) -- \[Feature\] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676)) +- [Feature] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735)) +- [Feature] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738)) +- [Feature] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744)) +- [Feature] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716)) +- [Feature] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734)) +- [Feature] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709)) +- [Feature] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703)) +- [Feature] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676)) ### Improvements -- \[Enhance\] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754)) -- \[Enhance\] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721)) -- \[Enhance\] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733)) +- [Enhance] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754)) +- [Enhance] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721)) +- [Enhance] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733)) ### Bug Fixes -- \[Fix\] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743)) +- [Fix] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743)) ### Docs Update -- \[Docs\] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755)) -- \[Docs\] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732)) +- [Docs] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755)) +- [Docs] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732)) ## v0.21.0(04/03/2022) @@ -245,18 +273,18 @@ ### Improvements -- \[Reproduction\] Reproduce RegNetX training accuracy. ([#587](https://github.com/open-mmlab/mmclassification/pull/587)) -- \[Reproduction\] Reproduce training results of T2T-ViT. ([#610](https://github.com/open-mmlab/mmclassification/pull/610)) -- \[Enhance\] Provide high-acc training settings of ResNet. ([#572](https://github.com/open-mmlab/mmclassification/pull/572)) -- \[Enhance\] Set a random seed when the user does not set a seed. ([#554](https://github.com/open-mmlab/mmclassification/pull/554)) -- \[Enhance\] Added `NumClassCheckHook` and unit tests. ([#559](https://github.com/open-mmlab/mmclassification/pull/559)) -- \[Enhance\] Enhance feature extraction function. ([#593](https://github.com/open-mmlab/mmclassification/pull/593)) -- \[Enhance\] Improve efficiency of precision, recall, f1_score and support. ([#595](https://github.com/open-mmlab/mmclassification/pull/595)) -- \[Enhance\] Improve accuracy calculation performance. ([#592](https://github.com/open-mmlab/mmclassification/pull/592)) -- \[Refactor\] Refactor `analysis_log.py`. ([#529](https://github.com/open-mmlab/mmclassification/pull/529)) -- \[Refactor\] Use new API of matplotlib to handle blocking input in visualization. ([#568](https://github.com/open-mmlab/mmclassification/pull/568)) -- \[CI\] Cancel previous runs that are not completed. ([#583](https://github.com/open-mmlab/mmclassification/pull/583)) -- \[CI\] Skip build CI if only configs or docs modification. ([#575](https://github.com/open-mmlab/mmclassification/pull/575)) +- [Reproduction] Reproduce RegNetX training accuracy. ([#587](https://github.com/open-mmlab/mmclassification/pull/587)) +- [Reproduction] Reproduce training results of T2T-ViT. ([#610](https://github.com/open-mmlab/mmclassification/pull/610)) +- [Enhance] Provide high-acc training settings of ResNet. ([#572](https://github.com/open-mmlab/mmclassification/pull/572)) +- [Enhance] Set a random seed when the user does not set a seed. ([#554](https://github.com/open-mmlab/mmclassification/pull/554)) +- [Enhance] Added `NumClassCheckHook` and unit tests. ([#559](https://github.com/open-mmlab/mmclassification/pull/559)) +- [Enhance] Enhance feature extraction function. ([#593](https://github.com/open-mmlab/mmclassification/pull/593)) +- [Enhance] Improve efficiency of precision, recall, f1_score and support. ([#595](https://github.com/open-mmlab/mmclassification/pull/595)) +- [Enhance] Improve accuracy calculation performance. ([#592](https://github.com/open-mmlab/mmclassification/pull/592)) +- [Refactor] Refactor `analysis_log.py`. ([#529](https://github.com/open-mmlab/mmclassification/pull/529)) +- [Refactor] Use new API of matplotlib to handle blocking input in visualization. ([#568](https://github.com/open-mmlab/mmclassification/pull/568)) +- [CI] Cancel previous runs that are not completed. ([#583](https://github.com/open-mmlab/mmclassification/pull/583)) +- [CI] Skip build CI if only configs or docs modification. ([#575](https://github.com/open-mmlab/mmclassification/pull/575)) ### Bug Fixes diff --git a/docs/en/faq.md b/docs/en/faq.md index 81f32c5f558..6bd9822afae 100644 --- a/docs/en/faq.md +++ b/docs/en/faq.md @@ -18,7 +18,8 @@ and make sure you fill in all required information in the template. | MMClassification version | MMCV version | | :----------------------: | :--------------------: | | dev | mmcv>=1.7.0, \<1.9.0 | - | 0.24.1 (master) | mmcv>=1.4.2, \<1.9.0 | + | 0.25.0 (master) | mmcv>=1.4.2, \<1.9.0 | + | 0.24.1 | mmcv>=1.4.2, \<1.9.0 | | 0.23.2 | mmcv>=1.4.2, \<1.7.0 | | 0.22.1 | mmcv>=1.4.2, \<1.6.0 | | 0.21.0 | mmcv>=1.4.2, \<=1.5.0 | diff --git a/docs/zh_CN/faq.md b/docs/zh_CN/faq.md index 4f9572226ac..03243a24c8c 100644 --- a/docs/zh_CN/faq.md +++ b/docs/zh_CN/faq.md @@ -16,7 +16,8 @@ | MMClassification version | MMCV version | | :----------------------: | :--------------------: | | dev | mmcv>=1.7.0, \<1.9.0 | - | 0.24.1 (master) | mmcv>=1.4.2, \<1.9.0 | + | 0.25.0 (master) | mmcv>=1.4.2, \<1.9.0 | + | 0.24.1 | mmcv>=1.4.2, \<1.9.0 | | 0.23.2 | mmcv>=1.4.2, \<1.7.0 | | 0.22.1 | mmcv>=1.4.2, \<1.6.0 | | 0.21.0 | mmcv>=1.4.2, \<=1.5.0 | diff --git a/mmcls/version.py b/mmcls/version.py index 42df0388647..91d1cf5a634 100644 --- a/mmcls/version.py +++ b/mmcls/version.py @@ -1,6 +1,6 @@ # Copyright (c) OpenMMLab. All rights reserved -__version__ = '0.24.1' +__version__ = '0.25.0' def parse_version_info(version_str):