From 7c5ddb1e5bee68d52ff8b5622cdbd75c02792c07 Mon Sep 17 00:00:00 2001 From: Ma Zerun Date: Sun, 1 May 2022 21:58:33 +0800 Subject: [PATCH] Bump version to v0.23.0 (#809) --- README.md | 13 ++++++------- README_zh-CN.md | 14 +++++++------- configs/van/README.md | 8 ++++---- configs/van/metafile.yml | 8 ++++---- docker/serve/Dockerfile | 2 +- docs/en/changelog.md | 15 +++++++++++++++ docs/en/install.md | 5 +++-- docs/en/model_zoo.md | 8 ++++---- docs/zh_CN/install.md | 3 ++- mmcls/models/backbones/swin_transformer.py | 17 ++++++++++------- mmcls/version.py | 2 +- 11 files changed, 57 insertions(+), 38 deletions(-) diff --git a/README.md b/README.md index a0ff08fd..f090b81b 100644 --- a/README.md +++ b/README.md @@ -59,6 +59,12 @@ The master branch works with **PyTorch 1.5+**. ## What's new +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). + v0.22.0 was released in 30/3/2022. Highlights of the new version: @@ -66,13 +72,6 @@ Highlights of the new version: - A new `CustomDataset` class to help you **build dataset of yourself**! - Support new backbones - **ConvMixer**, **RepMLP** and new dataset - **CUB dataset**. -v0.21.0 was released in 4/3/2022. - -Highlights of the new version: -- Support **ResNetV1c** and **Wide-ResNet**, and provide pre-trained models. -- Support **dynamic input shape** for ViT-based algorithms. Now our ViT, DeiT, Swin-Transformer and T2T-ViT support forwarding with any input shape. -- Reproduce training results of DeiT. And our DeiT-T and DeiT-S have **higher accuracy** comparing with the official weights. - 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 0354d578..20720f31 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -57,6 +57,13 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O ## 更新日志 +2022/5/1 发布了 v0.23.0 版本 + +新版本亮点： +- 支持了 **DenseNet**，**VAN** 和 **PoolFormer** 三个网络，并提供了预训练模型。 +- 支持在 IPU 上进行训练。 +- 更新了 API 文档的样式，更方便查阅，[欢迎查阅](https://mmclassification.readthedocs.io/en/master/api/models.html)。 + 2022/3/30 发布了 v0.22.0 版本 新版本亮点： @@ -64,13 +71,6 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O - 我们提供了一个新的 `CustomDataset` 类，这个类将帮助你轻松使用**自己的数据集**！ - 支持了新的主干网络 **ConvMixer**、**RepMLP** 和一个新的数据集 **CUB dataset**。 -2022/3/4 发布了 v0.21.0 版本 - -新版本亮点： -- 支持了 **ResNetV1c** 和 **Wide-ResNet** 两个 ResNet 变种，并提供了预训练模型 -- ViT 相关模型支持 **动态输入尺寸**。现在我们的 ViT，DeiT，Swin-Transformer 和 T2T-ViT 支持任意尺寸的输入。 -- 复现了 DeiT 的训练结果，并且我们的 DeiT-T 和 DeiT-S 拥有比官方权重 **更高的精度**。 - 发布历史和更新细节请参考 [更新日志](docs/en/changelog.md) ## 安装 diff --git a/configs/van/README.md b/configs/van/README.md index b356621c..99ac9e07 100644 --- a/configs/van/README.md +++ b/configs/van/README.md @@ -18,10 +18,10 @@ While originally designed for natural language processing (NLP) tasks, the self- | Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | |:---------:|:------------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:------:|:--------:| -| VAN-T\* | From scratch | 224x224 | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220427-8ac0feec.pth) | -| VAN-S\* | From scratch | 224x224 | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220427-bd6a9edd.pth) | -| VAN-B\* | From scratch | 224x224 | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220427-5275471d.pth) | -| VAN-L\* | From scratch | 224x224 | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220427-56159105.pth) | +| VAN-T\* | From scratch | 224x224 | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | +| VAN-S\* | From scratch | 224x224 | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | +| VAN-B\* | From scratch | 224x224 | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | +| VAN-L\* | From scratch | 224x224 | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | *Models with \* are converted from [the official repo](https://github.com/Visual-Attention-Network/VAN-Classification). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. diff --git a/configs/van/metafile.yml b/configs/van/metafile.yml index 26b40558..13e28c16 100644 --- a/configs/van/metafile.yml +++ b/configs/van/metafile.yml @@ -27,7 +27,7 @@ Models: Top 1 Accuracy: 75.41 Top 5 Accuracy: 93.02 Task: Image Classification - Weights: https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220427-8ac0feec.pth + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth Config: configs/van/van-tiny_8xb128_in1k.py - Name: van-small_8xb128_in1k Metadata: @@ -40,7 +40,7 @@ Models: Top 1 Accuracy: 81.01 Top 5 Accuracy: 95.63 Task: Image Classification - Weights: https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220427-bd6a9edd.pth + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth Config: configs/van/van-small_8xb128_in1k.py - Name: van-base_8xb128_in1k Metadata: @@ -53,7 +53,7 @@ Models: Top 1 Accuracy: 82.80 Top 5 Accuracy: 96.21 Task: Image Classification - Weights: https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220427-5275471d.pth + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth Config: configs/van/van-base_8xb128_in1k.py - Name: van-large_8xb128_in1k Metadata: @@ -66,5 +66,5 @@ Models: Top 1 Accuracy: 83.86 Top 5 Accuracy: 96.73 Task: Image Classification - Weights: https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220427-56159105.pth + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth Config: configs/van/van-large_8xb128_in1k.py diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile index e8ea11f8..26e306d6 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="1.4.2" -ARG MMCLS="0.22.1" +ARG MMCLS="0.23.0" ENV PYTHONUNBUFFERED TRUE diff --git a/docs/en/changelog.md b/docs/en/changelog.md index 6f89726a..e3b0bf6f 100644 --- a/docs/en/changelog.md +++ b/docs/en/changelog.md @@ -1,5 +1,20 @@ # Changelog +## v0.23.0(1/5/2022) + +### New Features + +- Support DenseNet. ([#750](https://github.com/open-mmlab/mmclassification/pull/750)) +- Support VAN. ([#739](https://github.com/open-mmlab/mmclassification/pull/739)) + +### Improvements + +- Support training on IPU and add fine-tuning configs of ViT. ([#723](https://github.com/open-mmlab/mmclassification/pull/723)) + +### Docs Update + +- New style API reference, and easier to use! Welcome [view it](https://mmclassification.readthedocs.io/en/master/api/models.html). ([#774](https://github.com/open-mmlab/mmclassification/pull/774)) + ## v0.22.1(15/4/2022) ### New Features diff --git a/docs/en/install.md b/docs/en/install.md index a6404b6a..df130111 100644 --- a/docs/en/install.md +++ b/docs/en/install.md @@ -10,8 +10,9 @@ The compatible MMClassification and MMCV versions are as below. Please install t | MMClassification version | MMCV version | |:------------------------:|:---------------------:| -| dev | mmcv>=1.4.8, <1.6.0 | -| 0.22.1 (master) | mmcv>=1.4.2, <1.6.0 | +| dev | mmcv>=1.5.0, <1.6.0 | +| 0.23.0 (master) | mmcv>=1.4.2, <1.6.0 | +| 0.22.1 | mmcv>=1.4.2, <1.6.0 | | 0.21.0 | mmcv>=1.4.2, <=1.5.0 | | 0.20.1 | mmcv>=1.4.2, <=1.5.0 | | 0.19.0 | mmcv>=1.3.16, <=1.5.0 | diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md index d2690b7a..8a039dd8 100644 --- a/docs/en/model_zoo.md +++ b/docs/en/model_zoo.md @@ -137,10 +137,10 @@ The ResNet family models below are trained by standard data augmentations, i.e., | DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) | | DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) | | DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) | -| VAN-T\* | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220427-8ac0feec.pth) | -| VAN-S\* | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220427-bd6a9edd.pth) | -| VAN-B\* | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220427-5275471d.pth) | -| VAN-L\* | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220427-56159105.pth) | +| VAN-T\* | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | +| VAN-S\* | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | +| VAN-B\* | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | +| VAN-L\* | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | *Models with \* are converted from other repos, others are trained by ourselves.* diff --git a/docs/zh_CN/install.md b/docs/zh_CN/install.md index 654830f0..c6630300 100644 --- a/docs/zh_CN/install.md +++ b/docs/zh_CN/install.md @@ -11,7 +11,8 @@ MMClassification 和 MMCV 的适配关系如下，请安装正确版本的 MMCV | MMClassification 版本 | MMCV 版本 | |:------------------------:|:---------------------:| | dev | mmcv>=1.4.8, <1.6.0 | -| 0.22.1 (master) | mmcv>=1.4.2, <1.6.0 | +| 0.23.0 (master) | mmcv>=1.4.2, <1.6.0 | +| 0.22.1 | mmcv>=1.4.2, <1.6.0 | | 0.21.0 | mmcv>=1.4.2, <=1.5.0 | | 0.20.1 | mmcv>=1.4.2, <=1.5.0 | | 0.19.0 | mmcv>=1.3.16, <=1.5.0 | diff --git a/mmcls/models/backbones/swin_transformer.py b/mmcls/models/backbones/swin_transformer.py index 2295c267..0ab82f19 100644 --- a/mmcls/models/backbones/swin_transformer.py +++ b/mmcls/models/backbones/swin_transformer.py @@ -506,11 +506,14 @@ class SwinTransformer(BaseBackbone): def _prepare_relative_position_bias_table(self, state_dict, prefix, *args, **kwargs): - all_keys = list(state_dict.keys()) state_dict_model = self.state_dict() + all_keys = list(state_dict_model.keys()) for key in all_keys: if 'relative_position_bias_table' in key: - relative_position_bias_table_pretrained = state_dict[key] + ckpt_key = prefix + key + if ckpt_key not in state_dict: + continue + relative_position_bias_table_pretrained = state_dict[ckpt_key] relative_position_bias_table_current = state_dict_model[key] L1, nH1 = relative_position_bias_table_pretrained.size() L2, nH2 = relative_position_bias_table_current.size() @@ -522,11 +525,11 @@ class SwinTransformer(BaseBackbone): relative_position_bias_table_pretrained, nH1) from mmcls.utils import get_root_logger logger = get_root_logger() - logger.info( - f'Resize the relative_position_bias_table from \ - {state_dict[key].shape} to {new_rel_pos_bias.shape}') - state_dict[key] = new_rel_pos_bias + logger.info('Resize the relative_position_bias_table from ' + f'{state_dict[ckpt_key].shape} to ' + f'{new_rel_pos_bias.shape}') + state_dict[ckpt_key] = new_rel_pos_bias # The index buffer need to be re-generated. - index_buffer = key.replace('bias_table', 'index') + index_buffer = ckpt_key.replace('bias_table', 'index') del state_dict[index_buffer] diff --git a/mmcls/version.py b/mmcls/version.py index 527ec004..de30bc3c 100644 --- a/mmcls/version.py +++ b/mmcls/version.py @@ -1,6 +1,6 @@ # Copyright (c) OpenMMLab. All rights reserved -__version__ = '0.22.1' +__version__ = '0.23.0' def parse_version_info(version_str):