[Docs] Replace markdownlint with mdformat (#311)

* Replace markdownlint with mdformat to avoid installing ruby * fix typo * add 'ba' to codespell ignore-words-list
open-mmlab · May 16, 2022 · c12c8f1 · c12c8f1
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diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -4,15 +4,14 @@ about: Suggest an idea for this project
 title: ''
 labels: ''
 assignees: ''
-
 ---
 
 **Describe the feature**
 
 **Motivation**
 A clear and concise description of the motivation of the feature.
-Ex1. It is inconvenient when [....].
-Ex2. There is a recent paper [....], which is very helpful for [....].
+Ex1. It is inconvenient when \[....\].
+Ex2. There is a recent paper \[....\], which is very helpful for \[....\].
 
 **Related resources**
 If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful.

diff --git a/.github/ISSUE_TEMPLATE/general_questions.md b/.github/ISSUE_TEMPLATE/general_questions.md
@@ -4,7 +4,6 @@ about: Ask general questions to get help
 title: ''
 labels: ''
 assignees: ''
-
 ---
 
 **Checklist**

diff --git a/.github/ISSUE_TEMPLATE/unexpected_report.md b/.github/ISSUE_TEMPLATE/unexpected_report.md
@@ -4,7 +4,6 @@ about: Create a report to help us improve
 title: ''
 labels: ''
 assignees: ''
-
 ---
 
 Thanks for reporting the unexpected results and we appreciate it a lot.
@@ -31,8 +30,8 @@ A placeholder for the command.
 
 1. Please run `python -c "from mmselfsup.utils import collect_env; print(collect_env())"` to collect necessary environment information and paste it here.
 2. You may add addition that may be helpful for locating the problem, such as
-    - How you installed PyTorch [e.g., pip, conda, source]
-    - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
+   - How you installed PyTorch \[e.g., pip, conda, source\]
+   - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
 
 **Error traceback**
 If applicable, paste the error traceback here.

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,7 +1,7 @@
 exclude: ^tests/data/
 repos:
-  - repo: https://gitlab.com/pycqa/flake8.git
-    rev: 3.8.3
+  - repo: https://github.com/PyCQA/flake8
+    rev: 4.0.1
     hooks:
       - id: flake8
   - repo: https://github.com/timothycrosley/isort
@@ -26,11 +26,6 @@ repos:
         args: ["--remove"]
       - id: mixed-line-ending
         args: ["--fix=lf"]
-  - repo: https://github.com/markdownlint/markdownlint
-    rev: v0.11.0
-    hooks:
-      - id: markdownlint
-        args: ["-r", "~MD002,~MD013,~MD024,~MD029,~MD033,~MD034,~MD036", "-t", "allow_different_nesting"]
   - repo: https://github.com/codespell-project/codespell
     rev: v2.1.0
     hooks:
@@ -40,6 +35,15 @@ repos:
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.14
+    hooks:
+      - id: mdformat
+        args: ["--number"]
+        additional_dependencies:
+          - mdformat-gfm
+          - mdformat_frontmatter
+          - linkify-it-py
   - repo: https://github.com/open-mmlab/pre-commit-hooks
     rev: v0.2.0
     hooks:

diff --git a/README.md b/README.md
@@ -30,6 +30,7 @@
 [👀Model Zoo](https://github.com/open-mmlab/mmselfsup/blob/master/docs/en/model_zoo.md) |
 [🆕Update News](https://mmselfsup.readthedocs.io/en/latest/changelog.html) |
 [🤔Reporting Issues](https://github.com/open-mmlab/mmselfsup/issues/new/choose)
+
 </div>
 
 ## Introduction
@@ -40,7 +41,6 @@ MMSelfSup is an open source self-supervised representation learning toolbox base
 
 The master branch works with **PyTorch 1.5** or higher.
 
-
 ### Major features
 
 - **Methods All in One**
@@ -59,7 +59,6 @@ The master branch works with **PyTorch 1.5** or higher.
 
   Since MMSelfSup adopts similar design of modulars and interfaces as those in other OpenMMLab projects, it supports smooth evaluation on downstream tasks with other OpenMMLab projects like object detection and segmentation.
 
-
 ## License
 
 This project is released under the [Apache 2.0 license](LICENSE).
@@ -70,8 +69,8 @@ MMSelfSup **v0.9.0** was released in 29/04/2022.
 
 Highlights of the new version:
 
-* Support **CAE**
-* Support **Barlow Twins**
+- Support **CAE**
+- Support **Barlow Twins**
 
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
@@ -80,6 +79,7 @@ Differences between MMSelfSup and OpenSelfSup codebases can be found in [compati
 ## Model Zoo and Benchmark
 
 ### Model Zoo
+
 Please refer to [model_zoo.md](docs/en/model_zoo.md) for a comprehensive set of pre-trained models and benchmarks.
 
 Supported algorithms:
@@ -106,19 +106,19 @@ More algorithms are in our plan.
 
 ### Benchmark
 
-  | Benchmarks                                         | Setting                                                                                                                                                              |
-  | -------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-  | ImageNet Linear Classification (Multi-head)        | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
-  | ImageNet Linear Classification (Last)              |                                                                                                                                                                      |
-  | ImageNet Semi-Sup Classification                   |                                                                                                                                                                      |
-  | Places205 Linear Classification (Multi-head)       | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
-  | iNaturalist2018 Linear Classification (Multi-head) | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
-  | PASCAL VOC07 SVM                                   | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
-  | PASCAL VOC07 Low-shot SVM                          | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
-  | PASCAL VOC07+12 Object Detection                   | [MoCo](http://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf)               |
-  | COCO17 Object Detection                            | [MoCo](http://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf)               |
-  | Cityscapes Segmentation                            | [MMSeg](configs/benchmarks/mmsegmentation/cityscapes/fcn_r50-d8_769x769_40k_cityscapes.py)                                                                           |
-  | PASCAL VOC12 Aug Segmentation                      | [MMSeg](configs/benchmarks/mmsegmentation/voc12aug/fcn_r50-d8_512x512_20k_voc12aug.py)                                                                               |
+| Benchmarks                                         | Setting                                                                                                                                                              |
+| -------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ImageNet Linear Classification (Multi-head)        | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
+| ImageNet Linear Classification (Last)              |                                                                                                                                                                      |
+| ImageNet Semi-Sup Classification                   |                                                                                                                                                                      |
+| Places205 Linear Classification (Multi-head)       | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
+| iNaturalist2018 Linear Classification (Multi-head) | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
+| PASCAL VOC07 SVM                                   | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
+| PASCAL VOC07 Low-shot SVM                          | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
+| PASCAL VOC07+12 Object Detection                   | [MoCo](http://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf)               |
+| COCO17 Object Detection                            | [MoCo](http://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf)               |
+| Cityscapes Segmentation                            | [MMSeg](configs/benchmarks/mmsegmentation/cityscapes/fcn_r50-d8_769x769_40k_cityscapes.py)                                                                           |
+| PASCAL VOC12 Aug Segmentation                      | [MMSeg](configs/benchmarks/mmsegmentation/voc12aug/fcn_r50-d8_512x512_20k_voc12aug.py)                                                                               |
 
 ## Installation
 

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -30,6 +30,7 @@
 [👀模型库](https://github.com/open-mmlab/mmselfsup/blob/master/docs/zh_cn/model_zoo.md) |
 [🆕更新日志](https://mmselfsup.readthedocs.io/zh_CN/latest/changelog.html) |
 [🤔报告问题](https://github.com/open-mmlab/mmselfsup/issues/new/choose)
+
 </div>
 
 ## 介绍
@@ -68,8 +69,8 @@ MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱
 
 新版本亮点：
 
-* 支持 **CAE**
-* 支持 **Barlow Twins**
+- 支持 **CAE**
+- 支持 **Barlow Twins**
 
 请参考 [更新日志](docs/zh_cn/changelog.md) 获取更多细节和历史版本信息。
 
@@ -105,7 +106,7 @@ MMSelfSup 和 OpenSelfSup 的不同点写在 [对比文档](docs/en/compatibilit
 
 ### 基准测试
 
-| 基准测试方法                                       | 参考设置                                                                                                                                                             |
+| 基准测试方法                                             | 参考设置                                                                                                                                                                 |
 | -------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | ImageNet Linear Classification (Multi-head)        | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
 | ImageNet Linear Classification (Last)              |                                                                                                                                                                      |
@@ -152,6 +153,7 @@ MMSelfSup 是一款由不同学校和公司共同贡献的开源项目，我们
 我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
 
 ## 引用
+
 如果您发现此项目对您的研究有用，请考虑引用：
 
 ```bibtex

diff --git a/configs/selfsup/cae/RAEDME.md b/configs/selfsup/cae/RAEDME.md
@@ -12,22 +12,19 @@ We present a novel masked image modeling (MIM) approach, context autoencoder (CA
 <img src="https://user-images.githubusercontent.com/30762564/165459947-6c6ef13c-0593-4765-b44e-6da0a079802a.png" width="40%"/>
 </div>
 
-
 ## Prerequisite
 
-Create a new folder ``cae_ckpt`` under the root directory and download the
-[weights](https://download.openmmlab.com/mmselfsup/cae/dalle_encoder.pth) for ``dalle`` encoder to that folder
+Create a new folder `cae_ckpt` under the root directory and download the
+[weights](https://download.openmmlab.com/mmselfsup/cae/dalle_encoder.pth) for `dalle` encoder to that folder
+
 ## Models and Benchmarks
 
 Here, we report the results of the model, which is pre-trained on ImageNet-1k
 for 300 epochs, the details are below:
 
-
-
-| Backbone | Pre-train epoch | Fine-tuning Top-1 |                  Pre-train Config                   |                                    Fine-tuning Config                                     |                                                                                                                        Download                                                                                                                         |
-| :------: | :-------------: | :---------------: | :-------------------------------------------------: | :---------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| ViT-B/16 |       300       |       83.2        | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/cae/cae_vit-base-p16_8xb256-fp16-coslr-300e_in1k.py) | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/cae/cae_vit-base-p16_16xb256-coslr-300e_in1k-224_20220427-4c786349.pth) &#124; [log](https://download.openmmlab.com/mmselfsup/cae/cae_vit-base-p16_16xb256-coslr-300e_in1k-224_20220427-4c786349.log.json) |
-
+| Backbone | Pre-train epoch | Fine-tuning Top-1 |                                                         Pre-train Config                                                          |                                                                   Fine-tuning Config                                                                   |                                                                                                                        Download                                                                                                                         |
+| :------: | :-------------: | :---------------: | :-------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| ViT-B/16 |       300       |       83.2        | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/cae/cae_vit-base-p16_8xb256-fp16-coslr-300e_in1k.py) | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/cae/cae_vit-base-p16_16xb256-coslr-300e_in1k-224_20220427-4c786349.pth) \| [log](https://download.openmmlab.com/mmselfsup/cae/cae_vit-base-p16_16xb256-coslr-300e_in1k-224_20220427-4c786349.log.json) |
 
 ## Citation
 

diff --git a/configs/selfsup/mae/README.md b/configs/selfsup/mae/README.md
@@ -27,18 +27,14 @@ methods that use only ImageNet-1K data. Transfer performance in downstream tasks
 <img src="https://user-images.githubusercontent.com/30762564/150733959-2959852a-c7bd-4d3f-911f-3e8d8839fe67.png" width="40%"/>
 </div>
 
-
 ## Models and Benchmarks
 
 Here, we report the results of the model, which is pre-trained on ImageNet-1k
 for 400 epochs, the details are below:
 
-
-
-| Backbone | Pre-train epoch | Fine-tuning Top-1 |                                                     Pre-train Config                                                      |                                                                 Fine-tuning Config                                                                 |                                                                                                                        Download                                                                                                                         |
-| :------: | :-------------: | :---------------: | :-----------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| ViT-B/16 |       400       |       83.1        | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/mae/mae_vit-b-p16_8xb512-coslr-400e_in1k.py) | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k-224_20220223-85be947b.pth) &#124; [log](https://download.openmmlab.com/mmselfsup/mae/mae_vit-base-p16_8xb512-coslr-300e_in1k-224_20220210_140925.log.json) |
-
+| Backbone | Pre-train epoch | Fine-tuning Top-1 |                                                     Pre-train Config                                                      |                                                                 Fine-tuning Config                                                                 |                                                                                                                      Download                                                                                                                       |
+| :------: | :-------------: | :---------------: | :-----------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| ViT-B/16 |       400       |       83.1        | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/mae/mae_vit-b-p16_8xb512-coslr-400e_in1k.py) | [config](https://github.com/open-mmlab/mmselfsup/blob/master/configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k-224_20220223-85be947b.pth) \| [log](https://download.openmmlab.com/mmselfsup/mae/mae_vit-base-p16_8xb512-coslr-300e_in1k-224_20220210_140925.log.json) |
 
 ## Citation