[Doc] Merge doc folder (open-mmlab#1083)

* merge folders of en/zh_cn docs

* fix merge_docs.sh

* fix index.rst

* fix merge_docs.sh

* use shared menu

* fix merge_docs

* fix zh_cn doc link

.github/workflows/build.yml

@@ -11,7 +11,6 @@ on:
       - 'README.md'
       - 'README_CN.md'
       - 'docs/**'
-      - 'docs_zh-CN/**'
       - 'examples/**'
       - '.dev_scripts/**'
 
@@ -20,7 +19,6 @@ on:
       - 'README.md'
       - 'README_CN.md'
       - 'docs/**'
-      - 'docs_zh-CN/**'
       - 'examples/**'
       - '.dev_scripts/**'
 

.gitignore

@@ -65,8 +65,8 @@ instance/
 .scrapy
 
 # Sphinx documentation
-docs/_build/
-docs_zh-CN/_build/
+docs/en/_build
+docs/zh_cn/_build
 
 # PyBuilder
 target/
@@ -116,14 +116,10 @@ mmpose/.mim
 *.log.json
 *.npy
 work_dirs/
-docs/topics/
-docs/papers/*.md
-docs/datasets.md
-docs/modelzoo.md
-docs_zh-CN/topics/
-docs_zh-CN/papers/*.md
-docs_zh-CN/datasets.md
-docs_zh-CN/modelzoo.md
+docs/**/topics/
+docs/**/papers/*.md
+docs/**/datasets.md
+docs/**/modelzoo.md
 
 !tests/data/**/*.pkl
 !tests/data/**/*.pkl.json

.pre-commit-config.yaml

@@ -59,5 +59,5 @@ repos:
         description: Add OpenMMLab copyright header to files
         entry: .dev_scripts/github/update_copyright.py
         language: python
-        files: ^(demo|docs|docs_zh-CN|mmpose|tests|tools|\.dev_scripts)/.*\.(py|c|cpp|cu|sh)$
+        files: ^(demo|docs|mmpose|tests|tools|\.dev_scripts)/.*\.(py|c|cpp|cu|sh)$
         exclude: ^demo/mm(detection|tracking)_cfg/.*$

README.md

@@ -31,12 +31,12 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 - **Higher efficiency and higher accuracy**
 
   MMPose implements multiple state-of-the-art (SOTA) deep learning models, including both top-down & bottom-up approaches. We achieve faster training speed and higher accuracy than other popular codebases, such as [HRNet](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch).
-  See [benchmark.md](docs/benchmark.md) for more information.
+  See [benchmark.md](docs/en/benchmark.md) for more information.
 
 - **Support for various datasets**
 
   The toolbox directly supports multiple popular and representative datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc.
-  See [data_preparation.md](docs/data_preparation.md) for more information.
+  See [data_preparation.md](docs/en/data_preparation.md) for more information.
 
 - **Well designed, tested and documented**
 
@@ -163,38 +163,38 @@ We demonstrate the superiority of our MMPose framework in terms of speed and acc
 | hrnet_w48  | 256x192  | **0.66** | 1.55 | **0.756** | 0.751 |
 | hrnet_w48  | 384x288  | **1.23** | 2.20 | **0.767** | 0.763 |
 
-More details about the benchmark are available on [benchmark.md](docs/benchmark.md).
+More details about the benchmark are available on [benchmark.md](docs/en/benchmark.md).
 
 ## Inference Speed
 
 We summarize the model complexity and inference speed of major models in MMPose, including FLOPs, parameter counts and inference speeds on both CPU and GPU devices with different batch sizes.
 
-Please refer to [inference_speed_summary.md](docs/inference_speed_summary.md) for more details.
+Please refer to [inference_speed_summary.md](docs/en/inference_speed_summary.md) for more details.
 
 ## Installation
 
-Please refer to [install.md](docs/install.md) for installation.
+Please refer to [install.md](docs/en/install.md) for installation.
 
 ## Data Preparation
 
-Please refer to [data_preparation.md](docs/data_preparation.md) for a general knowledge of data preparation.
+Please refer to [data_preparation.md](docs/en/data_preparation.md) for a general knowledge of data preparation.
 
 ## Get Started
 
-Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMPose.
+Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMPose.
 There are also tutorials:
 
-- [learn about configs](docs/tutorials/0_config.md)
-- [finetune model](docs/tutorials/1_finetune.md)
-- [add new dataset](docs/tutorials/2_new_dataset.md)
-- [customize data pipelines](docs/tutorials/3_data_pipeline.md)
-- [add new modules](docs/tutorials/4_new_modules.md)
-- [export a model to ONNX](docs/tutorials/5_export_model.md)
-- [customize runtime settings](docs/tutorials/6_customize_runtime.md)
+- [learn about configs](docs/en/tutorials/0_config.md)
+- [finetune model](docs/en/tutorials/1_finetune.md)
+- [add new dataset](docs/en/tutorials/2_new_dataset.md)
+- [customize data pipelines](docs/en/tutorials/3_data_pipeline.md)
+- [add new modules](docs/en/tutorials/4_new_modules.md)
+- [export a model to ONNX](docs/en/tutorials/5_export_model.md)
+- [customize runtime settings](docs/en/tutorials/6_customize_runtime.md)
 
 ## FAQ
 
-Please refer to [FAQ](docs/faq.md) for frequently asked questions.
+Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
 
 ## License
 

README_CN.md

@@ -30,11 +30,11 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 - **更高的精度和更快的速度**
 
   MMPose 复现了多种学界最先进的人体姿态分析模型，包括“自顶向下”和“自底向上”两大类算法。MMPose 相比于其他主流的代码库，具有更高的模型精度和训练速度。
-  具体请参考 [基准测试](docs/benchmark.md)。
+  具体请参考 [基准测试](docs/en/benchmark.md)（英文）。
 
 - **支持多样的数据集**
 
-  MMPose 支持了很多主流数据集的准备和构建，如 COCO、 MPII 等。 具体请参考 [数据集准备](docs/data_preparation.md)。
+  MMPose 支持了很多主流数据集的准备和构建，如 COCO、 MPII 等。 具体请参考 [数据集准备](docs/en/data_preparation.md)。
 
 - **模块化设计**
 
@@ -162,38 +162,38 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 | hrnet_w48  |  256x192   |    **0.66**     |                                       1.55                                       |  **0.756**   |                                     0.751                                     |
 | hrnet_w48  |  384x288   |    **1.23**     |                                       2.20                                       |  **0.767**   |                                     0.763                                     |
 
-更多详情可见 [基准测试](docs/benchmark.md)。
+更多详情可见 [基准测试](docs/en/benchmark.md)(英文)。
 
 ## 推理速度
 
 我们总结了 MMPose 中主要模型的复杂度信息和推理速度，包括模型的计算复杂度、参数数量，以及以不同的批处理大小在 CPU 和 GPU 上的推理速度。
 
-详细信息可见 [模型推理速度](docs_zh-CN/inference_speed_summary.md).
+详细信息可见 [模型推理速度](docs/zh_cn/inference_speed_summary.md).
 
 ## 安装
 
-请参考 [安装指南](docs/install.md) 进行安装。
+请参考 [安装指南](docs/zh_cn/install.md) 进行安装。
 
 ## 数据准备
 
-请参考 [data_preparation.md](docs/data_preparation.md) 进行数据集准备。
+请参考 [data_preparation.md](docs/en/data_preparation.md)（英文） 进行数据集准备。
 
 ## 教程
 
-请参考 [getting_started.md](docs/getting_started.md) 了解 MMPose 的基本使用。
+请参考 [getting_started.md](docs/zh_cn/getting_started.md) 了解 MMPose 的基本使用。
 MMPose 也提供了其他更详细的教程:
 
-- [如何编写配置文件](docs/tutorials/0_config.md)
-- [如何微调模型](docs/tutorials/1_finetune.md)
-- [如何增加新数据集](docs/tutorials/2_new_dataset.md)
-- [如何设计数据处理流程](docs/tutorials/3_data_pipeline.md)
-- [如何增加新模块](docs/tutorials/4_new_modules.md)
-- [如何导出模型为 onnx 格式](docs/tutorials/5_export_model.md)
-- [如何自定义模型运行参数](docs/tutorials/6_customize_runtime.md)
+- [如何编写配置文件](docs/zh_cn/tutorials/0_config.md)
+- [如何微调模型](docs/zh_cn/tutorials/1_finetune.md)
+- [如何增加新数据集](docs/zh_cn/tutorials/2_new_dataset.md)
+- [如何设计数据处理流程](docs/zh_cn/tutorials/3_data_pipeline.md)
+- [如何增加新模块](docs/zh_cn/tutorials/4_new_modules.md)
+- [如何导出模型为 onnx 格式](docs/zh_cn/tutorials/5_export_model.md)
+- [如何自定义模型运行参数](docs/en/tutorials/6_customize_runtime.md)(英文)
 
 ## 常见问题
 
-请参考 [FAQ](docs/faq.md) 了解其他用户的常见问题。
+请参考 [FAQ](docs/en/faq.md) 了解其他用户的常见问题。
 
 ## 许可
 
@@ -244,7 +244,7 @@ MMPose 是一款由不同学校和公司共同贡献的开源项目。我们感
 扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=GJP18SjI)
 
 <div align="center">
-<img src="docs/imgs/zhihu_qrcode.jpg" height="400" />  <img src="docs/imgs/qq_group2_qrcode.jpg" height="400" />
+<img src="docs/en/imgs/zhihu_qrcode.jpg" height="400" />  <img src="docs/en/imgs/qq_group2_qrcode.jpg" height="400" />
 </div>
 
 我们会在 OpenMMLab 社区为大家

configs/animal/2d_kpt_sview_rgb_img/README.md

@@ -5,11 +5,11 @@ dogs, macaques, and cheetah. It provides detailed behavioral analysis for neuros
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/2d_animal_keypoint.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/2d_animal_keypoint.md) to prepare data.
 
 ## Demo
 
-Please follow [DEMO](/demo/docs/2d_animal_demo.md) to generate fancy demos.
+Please follow [DEMO](/demo/docs/en/2d_animal_demo.md) to generate fancy demos.
 
 <img src="https://user-images.githubusercontent.com/11788150/114201893-4446ec00-9989-11eb-808b-5718c47c7b23.gif" height="140px" alt><br>
 

configs/body/2d_kpt_sview_rgb_img/README.md

@@ -10,10 +10,10 @@ Bottom-up approaches (e.g. AE) first detect all the keypoints and then group/ass
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/2d_body_keypoint.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/2d_body_keypoint.md) to prepare data.
 
 ## Demo
 
-Please follow [Demo](/demo/docs/2d_human_pose_demo.md#2d-human-pose-demo) to run demos.
+Please follow [Demo](/demo/docs/en/2d_human_pose_demo.md#2d-human-pose-demo) to run demos.
 
 <img src="demo/resources/demo_coco.gif" width="600px" alt>

configs/body/2d_kpt_sview_rgb_vid/README.md

@@ -6,4 +6,4 @@ For this task, we currently support [PoseWarper](/configs/body/2d_kpt_sview_rgb_
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/2d_body_keypoint.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/2d_body_keypoint.md) to prepare data.

configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_posetrack18_posewarper.md

@@ -85,4 +85,4 @@ Results on PoseTrack2018 val with precomputed human bounding boxes from PoseWarp
 | :--- | :--------: | :------: |:------: |:------: |:------: |:------: |:------: | :------: | :------: |:------: |:------: |
 | [pose_hrnet_w48](/configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_w48_posetrack18_384x288_posewarper_stage2.py) | 384x288 | 81.8 | 85.6 | 82.7 | 77.2 | 76.8 | 79.0 | 74.4 | 79.8 | [ckpt](https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2-4abf88db_20211130.pth) | [log](https://download.openmmlab.com/mmpose/top_down/posewarper/hrnet_w48_posetrack18_384x288_posewarper_stage2_20211130.log.json) |
 
-<sup>1</sup> Please download the precomputed human bounding boxes on PoseTrack2018 val from `$PoseWarper_supp_files/posetrack18_precomputed_boxes/val_boxes.json` and place it here: `$mmpose/data/posetrack18/posetrack18_precomputed_boxes/val_boxes.json` to be consistent with the [config](/configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_w48_posetrack18_384x288_posewarper_stage2.py). Please refer to [DATA Preparation](/docs/tasks/2d_body_keypoint.md) for more detail about data preparation.
+<sup>1</sup> Please download the precomputed human bounding boxes on PoseTrack2018 val from `$PoseWarper_supp_files/posetrack18_precomputed_boxes/val_boxes.json` and place it here: `$mmpose/data/posetrack18/posetrack18_precomputed_boxes/val_boxes.json` to be consistent with the [config](/configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_w48_posetrack18_384x288_posewarper_stage2.py). Please refer to [DATA Preparation](/docs/en/tasks/2d_body_keypoint.md) for more detail about data preparation.

configs/body/3d_kpt_sview_rgb_img/README.md

@@ -8,10 +8,10 @@ For single-person 3D pose estimation from a monocular camera, existing works can
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/3d_body_keypoint.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/3d_body_keypoint.md) to prepare data.
 
 ## Demo
 
-Please follow [Demo](/demo/docs/3d_human_pose_demo.md) to run demos.
+Please follow [Demo](/demo/docs/en/3d_human_pose_demo.md) to run demos.
 
 <img src="https://user-images.githubusercontent.com/15977946/118820606-02df2000-b8e9-11eb-9984-b9228101e780.gif" width="600px" alt><br>

configs/body/3d_kpt_sview_rgb_vid/README.md

@@ -8,4 +8,4 @@ For single-person 3D pose estimation from a monocular camera, existing works can
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/3d_body_keypoint.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/3d_body_keypoint.md) to prepare data.

configs/body/3d_mesh_sview_rgb_img/README.md

@@ -11,7 +11,7 @@ The preparation for human mesh recovery mainly includes:
 - Annotations
 - SMPL Model
 
-Please follow [DATA Preparation](/docs/tasks/3d_body_mesh.md) to prepare them.
+Please follow [DATA Preparation](/docs/en/tasks/3d_body_mesh.md) to prepare them.
 
 ## Prepare Pretrained Models
 

configs/face/2d_kpt_sview_rgb_img/README.md

@@ -7,10 +7,10 @@ or the rough location (or the bounding box) of the hand is provided.
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/2d_face_keypoint.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/2d_face_keypoint.md) to prepare data.
 
 ## Demo
 
-Please follow [Demo](/demo/docs/2d_face_demo.md) to run demos.
+Please follow [Demo](/demo/docs/en/2d_face_demo.md) to run demos.
 
 <img src="https://user-images.githubusercontent.com/11788150/109144943-ccd44900-779c-11eb-9e9d-8682e7629654.gif" width="600px" alt><br>

configs/fashion/2d_kpt_sview_rgb_img/README.md

@@ -4,4 +4,4 @@
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/2d_fashion_landmark.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/2d_fashion_landmark.md) to prepare data.

configs/hand/2d_kpt_sview_rgb_img/README.md

@@ -7,10 +7,10 @@ or the rough location (or the bounding box) of the hand is provided.
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/2d_hand_keypoint.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/2d_hand_keypoint.md) to prepare data.
 
 ## Demo
 
-Please follow [Demo](/demo/docs/2d_hand_demo.md) to run demos.
+Please follow [Demo](/demo/docs/en/2d_hand_demo.md) to run demos.
 
 <img src="https://user-images.githubusercontent.com/11788150/109098558-8c54db00-775c-11eb-8966-85df96b23dc5.gif" width="600px" alt><br>

configs/hand/3d_kpt_sview_rgb_img/README.md

@@ -4,4 +4,4 @@
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/3d_hand_keypoint.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/3d_hand_keypoint.md) to prepare data.

configs/wholebody/2d_kpt_sview_rgb_img/README.md

@@ -10,10 +10,10 @@ Bottom-up approaches (e.g. AE) first detect all the whole-body keypoints and the
 
 ## Data preparation
 
-Please follow [DATA Preparation](/docs/tasks/2d_wholebody_keypoint.md) to prepare data.
+Please follow [DATA Preparation](/docs/en/tasks/2d_wholebody_keypoint.md) to prepare data.
 
 ## Demo
 
-Please follow [Demo](/demo/docs/2d_wholebody_pose_demo.md) to run demos.
+Please follow [Demo](/demo/docs/en/2d_wholebody_pose_demo.md) to run demos.
 
 <img src="https://user-images.githubusercontent.com/9464825/95552839-00a61080-0a40-11eb-818c-b8dad7307217.gif" width="600px" alt><br>

demo/docs/2d_pose_tracking_demo.md

@@ -91,7 +91,7 @@ For top-down models, try to edit the config file. For example,
 
 1. set `flip_test=False` in [topdown-res50](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/top_down/resnet/coco/res50_coco_256x192.py#L51).
 1. set `post_process='default'` in [topdown-res50](https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/top_down/resnet/coco/res50_coco_256x192.py#L52).
-1. use faster human detector or human tracker, see [MMDetection](https://mmdetection.readthedocs.io/en/latest/model_zoo.html) or [MMTracking](https://github.com/open-mmlab/mmtracking/blob/master/docs/model_zoo.md).
+1. use faster human detector or human tracker, see [MMDetection](https://mmdetection.readthedocs.io/en/latest/model_zoo.html) or [MMTracking](https://mmtracking.readthedocs.io/en/latest/model_zoo.html).
 
 For bottom-up models, try to edit the config file. For example,