[Refactor]: Refactor the directory structure of docs (#146)

* fix links of docs

* [Docs]: Use shared menu from theme instead

* [Refactor]: Refactor the directory structure of docs

* [Fix]: Fix lint

* [Fix]: Fix __version__.py file link bug

Co-authored-by: fangyixiao18 <fangyx18@hotmail.com>

.gitignore

@@ -124,3 +124,9 @@ benchmarks/detection/output
 
 # Pytorch
 *.pth
+
+
+# readthedocs
+docs/zh_cn/_build
+src/
+docs/en/_build

README.md

@@ -46,14 +46,14 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 MMSelfSup **v0.5.0** was released with refactor in 16/12/2021.
 
-Please refer to [changelog.md](docs/changelog.md) for details and release history.
+Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
-Differences between MMSelfSup and OpenSelfSup codebases can be found in [compatibility.md](docs/compatibility.md).
+Differences between MMSelfSup and OpenSelfSup codebases can be found in [compatibility.md](docs/en/compatibility.md).
 
 ## Model Zoo and Benchmark
 
 ### Model Zoo
-Please refer to [model_zoo.md](docs/model_zoo.md) for a comprehensive set of pre-trained models and benchmarks.
+Please refer to [model_zoo.md](docs/en/model_zoo.md) for a comprehensive set of pre-trained models and benchmarks.
 
 Supported algorithms:
 
@@ -74,36 +74,36 @@ 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
 
-Please refer to [install.md](docs/install.md) for installation and [prepare_data.md](docs/prepare_data.md) for dataset preparation.
+Please refer to [install.md](docs/en/install.md) for installation and [prepare_data.md](docs/en/prepare_data.md) for dataset preparation.
 
 ## Get Started
 
-Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMSelfSup.
+Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMSelfSup.
 
 We also provides tutorials for more details:
-- [config](docs/tutorials/0_config.md)
-- [add new dataset](docs/tutorials/1_new_dataset.md)
-- [data pipeline](docs/tutorials/2_data_pipeline.md)
-- [add new module](docs/tutorials/3_new_module.md)
-- [customize schedules](docs/tutorials/4_schedule.md)
-- [customize runtime](docs/tutorials/5_runtime.md)
-- [benchmarks](docs/tutorials/6_benchmarks.md)
+- [config](docs/en/tutorials/0_config.md)
+- [add new dataset](docs/en/tutorials/1_new_dataset.md)
+- [data pipeline](docs/en/tutorials/2_data_pipeline.md)
+- [add new module](docs/en/tutorials/3_new_module.md)
+- [customize schedules](docs/en/tutorials/4_schedule.md)
+- [customize runtime](docs/en/tutorials/5_runtime.md)
+- [benchmarks](docs/en/tutorials/6_benchmarks.md)
 
 ## Citation
 
@@ -120,7 +120,7 @@ If you use this toolbox or benchmark in your research, please cite this project.
 
 ## Contributing
 
-We appreciate all contributions improving MMSelfSup. Please refer to [CONTRIBUTING.md](docs/community/CONTRIBUTING.md) for more details about the contributing guideline.
+We appreciate all contributions improving MMSelfSup. Please refer to [CONTRIBUTING.md](docs/en/community/CONTRIBUTING.md) for more details about the contributing guideline.
 
 ## Acknowledgement
 

README_zh-CN.md

@@ -44,7 +44,7 @@ MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱
 
 ### 模型库
 
-请参考 [模型库](docs/model_zoo.md) 查看我们更加全面的模型基准结果。
+请参考 [模型库](docs/zh_cn/model_zoo.md) 查看我们更加全面的模型基准结果。
 
 目前已支持的算法:
 
@@ -81,24 +81,24 @@ MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱
 
 ## 安装
 
-请参考 [安装文档](docs_zh-CN/install.md) 进行安装和参考 [准备数据](docs_zh-CN/prepare_data.md) 准备数据集。
+请参考 [安装文档](docs/zh_cn/install.md) 进行安装和参考 [准备数据](docs/zh_cn/prepare_data.md) 准备数据集。
 
 ## 快速入门
 
-请参考 [入门指南](docs_zh-CN/getting_started.md) 获取 MMSelfSup 的基本使用方法.
+请参考 [入门指南](docs/zh_cn/getting_started.md) 获取 MMSelfSup 的基本使用方法.
 
 我们也提供了更加全面的教程，包括:
-- [配置文件](docs_zh-CN/tutorials/0_config.md)
-- [添加数据集](docs_zh-CN/tutorials/1_new_dataset.md)
-- [数据处理流](docs_zh-CN/tutorials/2_data_pipeline.md)
-- [添加新模块](docs_zh-CN/tutorials/3_new_module.md)
-- [自定义流程](docs_zh-CN/tutorials/4_schedule.md)
-- [自定义运行](docs_zh-CN/tutorials/5_runtime.md)
-- [基准测试](docs_zh-CN/tutorials/6_benchmarks.md)
+- [配置文件](docs/zh_cn/tutorials/0_config.md)
+- [添加数据集](docs/zh_cn/tutorials/1_new_dataset.md)
+- [数据处理流](docs/zh_cn/tutorials/2_data_pipeline.md)
+- [添加新模块](docs/zh_cn/tutorials/3_new_module.md)
+- [自定义流程](docs/zh_cn/tutorials/4_schedule.md)
+- [自定义运行](docs/zh_cn/tutorials/5_runtime.md)
+- [基准测试](docs/zh_cn/tutorials/6_benchmarks.md)
 
 ## 参与贡献
 
-我们非常欢迎任何有助于提升 MMSelfSup 的贡献，请参考 [贡献指南](docs_zh-CN/community/CONTRIBUTING.md) 来了解如何参与贡献。
+我们非常欢迎任何有助于提升 MMSelfSup 的贡献，请参考 [贡献指南](docs/zh_cn/community/CONTRIBUTING.md) 来了解如何参与贡献。
 
 ## 致谢
 

configs/selfsup/byol/README.md

@@ -26,7 +26,7 @@
 
 ## Models and Benchmarks
 
-**Back to [model_zoo.md](../../../docs/model_zoo.md) to download models.**
+**Back to [model_zoo.md](../../../docs/en/model_zoo.md) to download models.**
 
 In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.
 

configs/selfsup/deepcluster/README.md

@@ -26,7 +26,7 @@ Clustering is a class of unsupervised learning methods that has been extensively
 
 ## Models and Benchmarks
 
-**Back to [model_zoo.md](../../../docs/model_zoo.md) to download models.**
+**Back to [model_zoo.md](../../../docs/en/model_zoo.md) to download models.**
 
 In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.
 

configs/selfsup/densecl/README.md

@@ -26,7 +26,7 @@ To date, most existing self-supervised learning methods are designed and optimiz
 
 ## Models and Benchmarks
 
-**Back to [model_zoo.md](../../../docs/model_zoo.md) to download models.**
+**Back to [model_zoo.md](../../../docs/en/model_zoo.md) to download models.**
 
 In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.
 
@@ -40,9 +40,9 @@ The **Best Layer** indicates that the best results are obtained from which layer
 
 Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
-| Self-Supervised Config                                                 | Best Layer | SVM | k=1 | k=2 | k=4 | k=8 | k=16 | k=32 | k=64 | k=96 |
-| ---------------------------------------------------------------------- | ---------- | --- | --- | --- | --- | --- | ---- | ---- | ---- | ---- |
-| [resnet50_8xb32-coslr-200e](densecl_resnet50_8xb32-coslr-200e_in1k.py) | feature5   |82.5|42.68|50.64|61.74|68.17|72.99|76.07|79.19|80.55|
+| Self-Supervised Config                                                 | Best Layer | SVM  | k=1   | k=2   | k=4   | k=8   | k=16  | k=32  | k=64  | k=96  |
+| ---------------------------------------------------------------------- | ---------- | ---- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
+| [resnet50_8xb32-coslr-200e](densecl_resnet50_8xb32-coslr-200e_in1k.py) | feature5   | 82.5 | 42.68 | 50.64 | 61.74 | 68.17 | 72.99 | 76.07 | 79.19 | 80.55 |
 
 #### ImageNet Linear Evaluation
 

configs/selfsup/moco/README.md

@@ -50,7 +50,7 @@ Contrastive unsupervised learning has recently shown encouraging progress, e.g.,
 
 ## Models and Benchmarks
 
-**Back to [model_zoo.md](../../../docs/model_zoo.md) to download models.**
+**Back to [model_zoo.md](../../../docs/en/model_zoo.md) to download models.**
 
 In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.
 

configs/selfsup/npid/README.md

@@ -30,7 +30,7 @@ Our method is also remarkable for consistently improving test performance with m
 
 ## Models and Benchmarks
 
-**Back to [model_zoo.md](../../../docs/model_zoo.md) to download models.**
+**Back to [model_zoo.md](../../../docs/en/model_zoo.md) to download models.**
 
 In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.
 
@@ -44,9 +44,9 @@ The **Best Layer** indicates that the best results are obtained from which layer
 
 Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
-| Self-Supervised Config                                                                      | Best Layer | SVM   | k=1   | k=2   | k=4   | k=8   | k=16  | k=32  | k=64  | k=96  |
-| ------------------------------------------------------------------------------------------- | ---------- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
-| [resnet50_8xb32-steplr-200e](npid_resnet50_8xb32-steplr-200e_in1k.py)                       | feature5   | 76.75 | 26.96 | 35.37 | 44.48 | 53.89 | 60.39 | 66.41 | 71.48 | 73.39 |
+| Self-Supervised Config                                                | Best Layer | SVM   | k=1   | k=2   | k=4   | k=8   | k=16  | k=32  | k=64  | k=96  |
+| --------------------------------------------------------------------- | ---------- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
+| [resnet50_8xb32-steplr-200e](npid_resnet50_8xb32-steplr-200e_in1k.py) | feature5   | 76.75 | 26.96 | 35.37 | 44.48 | 53.89 | 60.39 | 66.41 | 71.48 | 73.39 |
 
 #### ImageNet Linear Evaluation
 
@@ -56,7 +56,7 @@ The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePool
 
 | Self-Supervised Config                                                | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
 | --------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |
-| [resnet50_8xb32-steplr-200e](npid_resnet50_8xb32-steplr-200e_in1k.py) | 14.68    | 31.98    | 42.85    |  56.95   | 58.41    | 58.16   |
+| [resnet50_8xb32-steplr-200e](npid_resnet50_8xb32-steplr-200e_in1k.py) | 14.68    | 31.98    | 42.85    | 56.95    | 58.41    | 58.16   |
 
 ### Detection
 
@@ -66,9 +66,9 @@ The detection benchmarks includes 2 downstream task datasets, **Pascal VOC 2007
 
 Please refer to [faster_rcnn_r50_c4_mstrain_24k_voc0712.py](../../benchmarks/mmdetection/voc0712/faster_rcnn_r50_c4_mstrain_24k_voc0712.py) for details of config.
 
-| Self-Supervised Config                                                                      | AP50 |
-| ------------------------------------------------------------------------------------------- | ---- |
-| [resnet50_8xb32-steplr-200e](npid_resnet50_8xb32-steplr-200e_in1k.py)                       |79.52 |
+| Self-Supervised Config                                                | AP50  |
+| --------------------------------------------------------------------- | ----- |
+| [resnet50_8xb32-steplr-200e](npid_resnet50_8xb32-steplr-200e_in1k.py) | 79.52 |
 
 #### COCO2017
 
@@ -86,6 +86,6 @@ The segmentation benchmarks includes 2 downstream task datasets, **Cityscapes**
 
 Please refer to [fcn_r50-d8_512x512_20k_voc12aug.py](../../benchmarks/mmsegmentation/voc12aug/fcn_r50-d8_512x512_20k_voc12aug.py) for details of config.
 
-| Self-Supervised Config                                                                      | mIOU  |
-| ------------------------------------------------------------------------------------------- | ----- |
-| [resnet50_8xb32-steplr-200e](npid_resnet50_8xb32-steplr-200e_in1k.py)                       | 65.45 |
+| Self-Supervised Config                                                | mIOU  |
+| --------------------------------------------------------------------- | ----- |
+| [resnet50_8xb32-steplr-200e](npid_resnet50_8xb32-steplr-200e_in1k.py) | 65.45 |

configs/selfsup/odc/README.md

@@ -26,7 +26,7 @@ Joint clustering and feature learning methods have shown remarkable performance
 
 ## Models and Benchmarks
 
-**Back to [model_zoo.md](../../../docs/model_zoo.md) to download models.**
+**Back to [model_zoo.md](../../../docs/en/model_zoo.md) to download models.**
 
 In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.
 

configs/selfsup/relative_loc/README.md

@@ -26,7 +26,7 @@ This work explores the use of spatial context as a source of free and plentiful
 
 ## Models and Benchmarks
 
-**Back to [model_zoo.md](../../../docs/model_zoo.md) to download models.**
+**Back to [model_zoo.md](../../../docs/en/model_zoo.md) to download models.**
 
 In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.
 
@@ -62,8 +62,8 @@ The detection benchmarks includes 2 downstream task datasets, **Pascal VOC 2007
 
 Please refer to [faster_rcnn_r50_c4_mstrain_24k_voc0712.py](../../benchmarks/mmdetection/voc0712/faster_rcnn_r50_c4_mstrain_24k_voc0712.py) for details of config.
 
-| Self-Supervised Config                                                      | AP50 |
-| --------------------------------------------------------------------------- | ---- |
+| Self-Supervised Config                                                      | AP50  |
+| --------------------------------------------------------------------------- | ----- |
 | [resnet50_8xb64-steplr-70e](relative-loc_resnet50_8xb64-steplr-70e_in1k.py) | 79.70 |
 
 #### COCO2017
@@ -72,7 +72,7 @@ Please refer to [mask_rcnn_r50_fpn_mstrain_1x_coco.py](../../benchmarks/mmdetect
 
 | Self-Supervised Config                                                      | mAP(Box) | AP50(Box) | AP75(Box) | mAP(Mask) | AP50(Mask) | AP75(Mask) |
 | --------------------------------------------------------------------------- | -------- | --------- | --------- | --------- | ---------- | ---------- |
-| [resnet50_8xb64-steplr-70e](relative-loc_resnet50_8xb64-steplr-70e_in1k.py) | 37.5     | 56.2      | 41.3      | 33.7      | 53.3       |  36.1      |
+| [resnet50_8xb64-steplr-70e](relative-loc_resnet50_8xb64-steplr-70e_in1k.py) | 37.5     | 56.2      | 41.3      | 33.7      | 53.3       | 36.1       |
 
 ### Segmentation