[Docs] Merge docs & docs_zh (#1342)

* master

* master 0721

* add README

* 1222 merge docs & docs_zh

* modify docs path in files

* modify docs path in files

* modify docs path in files

Author: gengenkai

.github/workflows/build.yml

@@ -12,8 +12,8 @@ on:
       - '!demo/**'
       - '!docker/**'
       - '!tools/**'
-      - '!docs/**'
-      - '!docs_zh_CN/**'
+      - '!docs/en/**'
+      - '!docs/zh_cn/**'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}

.gitignore

@@ -65,7 +65,8 @@ instance/
 .scrapy
 
 # Sphinx documentation
-docs/_build/
+docs/en/_build/
+docs/zh_cn/_build/
 
 # PyBuilder
 target/

README.md

@@ -51,24 +51,24 @@ The master branch works with **PyTorch 1.3+**.
 - (2021-10-25) We provide a [guide](https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/custom_dataset_training.md) on how to train PoseC3D with custom datasets, [bit-scientist](https://github.com/bit-scientist) authored this PR!
 - (2021-10-16) We support **PoseC3D** on UCF101 and HMDB51, achieves 87.0% and 69.3% Top-1 accuracy with 2D skeletons only. Pre-extracted 2D skeletons are also available.
 
-**Release**: v0.20.0 was released in 30/10/2021. Please refer to [changelog.md](docs/changelog.md) for details and release history.
+**Release**: v0.20.0 was released in 30/10/2021. Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
 ## Installation
 
-Please refer to [install.md](docs/install.md) for installation.
+Please refer to [install.md](docs/en/install.md) for installation.
 
 ## Get Started
 
-Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMAction2.
+Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMAction2.
 There are also tutorials:
 
-- [learn about configs](docs/tutorials/1_config.md)
-- [finetuning models](docs/tutorials/2_finetune.md)
-- [adding new dataset](docs/tutorials/3_new_dataset.md)
-- [designing data pipeline](docs/tutorials/4_data_pipeline.md)
-- [adding new modules](docs/tutorials/5_new_modules.md)
-- [exporting model to onnx](docs/tutorials/6_export_model.md)
-- [customizing runtime settings](docs/tutorials/7_customize_runtime.md)
+- [learn about configs](docs/en/tutorials/1_config.md)
+- [finetuning models](docs/en/tutorials/2_finetune.md)
+- [adding new dataset](docs/en/tutorials/3_new_dataset.md)
+- [designing data pipeline](docs/en/tutorials/4_data_pipeline.md)
+- [adding new modules](docs/en/tutorials/5_new_modules.md)
+- [exporting model to onnx](docs/en/tutorials/6_export_model.md)
+- [customizing runtime settings](docs/en/tutorials/7_customize_runtime.md)
 
 A Colab tutorial is also provided. You may preview the notebook [here](demo/mmaction2_tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmaction2/blob/master/demo/mmaction2_tutorial.ipynb) on Colab.
 
@@ -207,16 +207,16 @@ Datasets marked with * are not fully supported yet, but related dataset preparat
 
 ## Benchmark
 
-To demonstrate the efficacy and efficiency of our framework, we compare MMAction2 with some other popular frameworks and official releases in terms of speed. Details can be found in [benchmark](docs/benchmark.md).
+To demonstrate the efficacy and efficiency of our framework, we compare MMAction2 with some other popular frameworks and official releases in terms of speed. Details can be found in [benchmark](docs/en/benchmark.md).
 
 ## Data Preparation
 
-Please refer to [data_preparation.md](docs/data_preparation.md) for a general knowledge of data preparation.
-The supported datasets are listed in [supported_datasets.md](docs/supported_datasets.md)
+Please refer to [data_preparation.md](docs/en/data_preparation.md) for a general knowledge of data preparation.
+The supported datasets are listed in [supported_datasets.md](docs/en/supported_datasets.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.
 
 ## Projects built on MMAction2
 
@@ -226,7 +226,7 @@ Currently, there are many research works and projects built on MMAction2 by user
 - Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 **Oral**. [[paper]](https://arxiv.org/abs/2107.10161)[[github]](https://github.com/Cogito2012/DEAR)
 - Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 **Oral**. [[paper]](https://arxiv.org/abs/2103.17263)[[github]](https://github.com/xvjiarui/VFS)
 
-etc., check [projects.md](docs/projects.md) to see all related projects.
+etc., check [projects.md](docs/en/projects.md) to see all related projects.
 
 ## License
 

README_zh-CN.md

@@ -50,23 +50,23 @@ MMAction2 是一款基于 PyTorch 的视频理解开源工具箱，是 [OpenMMLa
 - (2021-10-25) 提供使用自定义数据集训练 PoseC3D 的 [教程](https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/custom_dataset_training.md)，此 PR 由用户 [bit-scientist](https://github.com/bit-scientist) 完成！
 - (2021-10-16) 在 UCF101, HMDB51 上支持 **PoseC3D**，仅用 2D 关键点就可分别达到 87.0% 和 69.3% 的识别准确率。两数据集的预提取骨架特征可以公开下载。
 
-v0.20.0 版本已于 2021 年 10 月 30 日发布，可通过查阅 [更新日志](/docs/changelog.md) 了解更多细节以及发布历史
+v0.20.0 版本已于 2021 年 10 月 30 日发布，可通过查阅 [更新日志](/docs/en/changelog.md) 了解更多细节以及发布历史
 
 ## 安装
 
-请参考 [安装指南](/docs_zh_CN/install.md) 进行安装
+请参考 [安装指南](/docs/zh_cn/install.md) 进行安装
 
 ## 教程
 
-请参考 [基础教程](/docs_zh_CN/getting_started.md) 了解 MMAction2 的基本使用。MMAction2也提供了其他更详细的教程:
+请参考 [基础教程](/docs/zh_cn/getting_started.md) 了解 MMAction2 的基本使用。MMAction2也提供了其他更详细的教程:
 
-- [如何编写配置文件](/docs_zh_CN/tutorials/1_config.md)
-- [如何微调模型](/docs_zh_CN/tutorials/2_finetune.md)
-- [如何增加新数据集](/docs_zh_CN/tutorials/3_new_dataset.md)
-- [如何设计数据处理流程](/docs_zh_CN/tutorials/4_data_pipeline.md)
-- [如何增加新模块](/docs_zh_CN/tutorials/5_new_modules.md)
-- [如何导出模型为 onnx 格式](/docs_zh_CN/tutorials/6_export_model.md)
-- [如何自定义模型运行参数](/docs_zh_CN/tutorials/7_customize_runtime.md)
+- [如何编写配置文件](/docs/zh_cn/tutorials/1_config.md)
+- [如何微调模型](/docs/zh_cn/tutorials/2_finetune.md)
+- [如何增加新数据集](/docs/zh_cn/tutorials/3_new_dataset.md)
+- [如何设计数据处理流程](/docs/zh_cn/tutorials/4_data_pipeline.md)
+- [如何增加新模块](/docs/zh_cn/tutorials/5_new_modules.md)
+- [如何导出模型为 onnx 格式](/docs/zh_cn/tutorials/6_export_model.md)
+- [如何自定义模型运行参数](/docs/zh_cn/tutorials/7_customize_runtime.md)
 
 MMAction2 也提供了相应的中文 Colab 教程，可以点击 [这里](https://colab.research.google.com/github/open-mmlab/mmaction2/blob/master/demo/mmaction2_tutorial_zh-CN.ipynb) 进行体验！
 
@@ -203,15 +203,15 @@ MMAction2 将跟进学界的最新进展，并支持更多算法和框架。如
 
 ## 基准测试
 
-为了验证 MMAction2 框架的高精度和高效率，开发成员将其与当前其他主流框架进行速度对比。更多详情可见 [基准测试](/docs_zh_CN/benchmark.md)
+为了验证 MMAction2 框架的高精度和高效率，开发成员将其与当前其他主流框架进行速度对比。更多详情可见 [基准测试](/docs/zh_cn/benchmark.md)
 
 ## 数据集准备
 
-请参考 [数据准备](/docs_zh_CN/data_preparation.md) 了解数据集准备概况。所有支持的数据集都列于 [数据集清单](/docs_zh_CN/supported_datasets.md) 中
+请参考 [数据准备](/docs/zh_cn/data_preparation.md) 了解数据集准备概况。所有支持的数据集都列于 [数据集清单](/docs/zh_cn/supported_datasets.md) 中
 
 ## 常见问题
 
-请参考 [FAQ](/docs_zh_CN/faq.md) 了解其他用户的常见问题
+请参考 [FAQ](/docs/zh_cn/faq.md) 了解其他用户的常见问题
 
 ## 相关工作
 
@@ -221,7 +221,7 @@ MMAction2 将跟进学界的最新进展，并支持更多算法和框架。如
 - Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 **Oral**. [[论文]](https://arxiv.org/abs/2103.17263)[[代码]](https://github.com/xvjiarui/VFS)
 - Video Swin Transformer. [[论文]](https://arxiv.org/abs/2106.13230)[[代码]](https://github.com/SwinTransformer/Video-Swin-Transformer)
 
-更多详情可见 [相关工作](docs/projects.md)
+更多详情可见 [相关工作](docs/en/projects.md)
 
 ## 许可
 

configs/detection/acrn/README.md

@@ -59,7 +59,7 @@ Current state-of-the-art approaches for spatio-temporal action localization rely
 
 :::
 
-For more details on data preparation, you can refer to AVA in [Data Preparation](/docs/data_preparation.md).
+For more details on data preparation, you can refer to AVA in [Data Preparation](/docs/en/data_preparation.md).
 
 ## Train
 
@@ -75,7 +75,7 @@ Example: train ACRN with SlowFast backbone on AVA with periodic validation.
 python tools/train.py configs/detection/acrn/slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py --validate
 ```
 
-For more details and optional arguments infos, you can refer to **Training setting** part in [getting_started](/docs/getting_started.md#training-setting).
+For more details and optional arguments infos, you can refer to **Training setting** part in [getting_started](/docs/en/getting_started.md#training-setting).
 
 ## Test
 
@@ -91,4 +91,4 @@ Example: test ACRN with SlowFast backbone on AVA and dump the result to a csv fi
 python tools/test.py configs/detection/acrn/slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py checkpoints/SOME_CHECKPOINT.pth --eval mAP --out results.csv
 ```
 
-For more details and optional arguments infos, you can refer to **Test a dataset** part in [getting_started](/docs/getting_started.md#test-a-dataset) .
+For more details and optional arguments infos, you can refer to **Test a dataset** part in [getting_started](/docs/en/getting_started.md#test-a-dataset) .

configs/detection/acrn/README_zh-CN.md

@@ -46,7 +46,7 @@
    依据 [线性缩放规则](https://arxiv.org/abs/1706.02677)，当用户使用不同数量的 GPU 或者每块 GPU 处理不同视频个数时，需要根据批大小等比例地调节学习率。
    如，lr=0.01 对应 4 GPUs x 2 video/gpu，以及 lr=0.08 对应 16 GPUs x 4 video/gpu。
 
-对于数据集准备的细节，用户可参考 [数据准备](/docs_zh_CN/data_preparation.md)。
+对于数据集准备的细节，用户可参考 [数据准备](/docs/zh_cn/data_preparation.md)。
 
 ## 如何训练
 
@@ -62,7 +62,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 python tools/train.py configs/detection/acrn/slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py --validate
 ```
 
-更多训练细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#训练配置) 中的 **训练配置** 部分。
+更多训练细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#训练配置) 中的 **训练配置** 部分。
 
 ## 如何测试
 
@@ -78,4 +78,4 @@ python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
 python tools/test.py configs/detection/acrn/slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py checkpoints/SOME_CHECKPOINT.pth --eval mAP --out results.csv
 ```
 
-更多测试细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。
+更多测试细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。

configs/detection/ava/README.md

@@ -86,7 +86,7 @@ AVA, with its realistic scene and action complexity, exposes the intrinsic diffi
 
 :::
 
-For more details on data preparation, you can refer to AVA in [Data Preparation](/docs/data_preparation.md).
+For more details on data preparation, you can refer to AVA in [Data Preparation](/docs/en/data_preparation.md).
 
 ## Train
 
@@ -102,7 +102,7 @@ Example: train SlowOnly model on AVA with periodic validation.
 python tools/train.py configs/detection/ava/slowonly_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py --validate
 ```
 
-For more details and optional arguments infos, you can refer to **Training setting** part in [getting_started](/docs/getting_started.md#training-setting) .
+For more details and optional arguments infos, you can refer to **Training setting** part in [getting_started](/docs/en/getting_started.md#training-setting) .
 
 ### Train Custom Classes From Ava Dataset
 
@@ -140,4 +140,4 @@ Example: test SlowOnly model on AVA and dump the result to a csv file.
 python tools/test.py configs/detection/ava/slowonly_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py checkpoints/SOME_CHECKPOINT.pth --eval mAP --out results.csv
 ```
 
-For more details and optional arguments infos, you can refer to **Test a dataset** part in [getting_started](/docs/getting_started.md#test-a-dataset) .
+For more details and optional arguments infos, you can refer to **Test a dataset** part in [getting_started](/docs/en/getting_started.md#test-a-dataset) .

configs/detection/ava/README_zh-CN.md

@@ -72,7 +72,7 @@
    如，lr=0.01 对应 4 GPUs x 2 video/gpu，以及 lr=0.08 对应 16 GPUs x 4 video/gpu。
 2. **Context** 表示同时使用 RoI 特征与全局特征进行分类，可带来约 1% mAP 的提升。
 
-对于数据集准备的细节，用户可参考 [数据准备](/docs_zh_CN/data_preparation.md)。
+对于数据集准备的细节，用户可参考 [数据准备](/docs/zh_cn/data_preparation.md)。
 
 ## 如何训练
 
@@ -88,7 +88,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 python tools/train.py configs/detection/ava/slowonly_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py --validate
 ```
 
-更多训练细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#训练配置) 中的 **训练配置** 部分。
+更多训练细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#训练配置) 中的 **训练配置** 部分。
 
 ### 训练 AVA 数据集中的自定义类别
 
@@ -126,4 +126,4 @@ python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
 python tools/test.py configs/detection/ava/slowonly_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py checkpoints/SOME_CHECKPOINT.pth --eval mAP --out results.csv
 ```
 
-更多测试细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。
+更多测试细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。

configs/detection/lfb/README.md

@@ -98,7 +98,7 @@ python tools/train.py configs/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_
   --validate --seed 0 --deterministic
 ```
 
-For more details and optional arguments infos, you can refer to **Training setting** part in [getting_started](/docs/getting_started.md#training-setting).
+For more details and optional arguments infos, you can refer to **Training setting** part in [getting_started](/docs/en/getting_started.md#training-setting).
 
 ## Test
 
@@ -123,4 +123,4 @@ python tools/test.py configs/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_r
     checkpoints/SOME_CHECKPOINT.pth --eval mAP --out results.csv
 ```
 
-For more details, you can refer to **Test a dataset** part in [getting_started](/docs/getting_started.md#test-a-dataset).
+For more details, you can refer to **Test a dataset** part in [getting_started](/docs/en/getting_started.md#test-a-dataset).

configs/detection/lfb/README_zh-CN.md

@@ -75,7 +75,7 @@ python tools/train.py configs/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_
   --validate --seed 0 --deterministic
 ```
 
-更多训练细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#训练配置) 中的 **训练配置** 部分。
+更多训练细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#训练配置) 中的 **训练配置** 部分。
 
 ## 测试
 
@@ -100,4 +100,4 @@ python tools/test.py configs/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_r
     checkpoints/SOME_CHECKPOINT.pth --eval mAP --out results.csv
 ```
 
-更多测试细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。
+更多测试细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。

configs/localization/bmn/README.md

@@ -60,7 +60,7 @@ Temporal action proposal generation is an challenging and promising task which a
 
 *We train BMN with the [official repo](https://github.com/JJBOY/BMN-Boundary-Matching-Network), evaluate its proposal generation and action detection performance with [anet_cuhk_2017](https://download.openmmlab.com/mmaction/localization/cuhk_anet17_pred.json) for label assigning.
 
-For more details on data preparation, you can refer to ActivityNet feature in [Data Preparation](/docs/data_preparation.md).
+For more details on data preparation, you can refer to ActivityNet feature in [Data Preparation](/docs/en/data_preparation.md).
 
 ## Train
 
@@ -76,7 +76,7 @@ Example: train BMN model on ActivityNet features dataset.
 python tools/train.py configs/localization/bmn/bmn_400x100_2x8_9e_activitynet_feature.py
 ```
 
-For more details and optional arguments infos, you can refer to **Training setting** part in [getting_started](/docs/getting_started.md#training-setting) .
+For more details and optional arguments infos, you can refer to **Training setting** part in [getting_started](/docs/en/getting_started.md#training-setting) .
 
 ## Test
 
@@ -109,4 +109,4 @@ python tools/analysis/report_map.py --proposal path/to/proposal_file
 
 :::
 
-For more details and optional arguments infos, you can refer to **Test a dataset** part in [getting_started](/docs/getting_started.md#test-a-dataset) .
+For more details and optional arguments infos, you can refer to **Test a dataset** part in [getting_started](/docs/en/getting_started.md#test-a-dataset) .

configs/localization/bmn/README_zh-CN.md

@@ -48,7 +48,7 @@
 
 *MMAction2 在 [原始代码库](https://github.com/JJBOY/BMN-Boundary-Matching-Network) 上训练 BMN，并且在 [anet_cuhk_2017](https://download.openmmlab.com/mmaction/localization/cuhk_anet17_pred.json) 的对应标签上评估时序动作候选生成和时序检测的结果。
 
-对于数据集准备的细节，用户可参考 [数据集准备文档](/docs_zh_CN/data_preparation.md) 中的 ActivityNet 特征部分。
+对于数据集准备的细节，用户可参考 [数据集准备文档](/docs/zh_cn/data_preparation.md) 中的 ActivityNet 特征部分。
 
 ## 如何训练
 
@@ -64,7 +64,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 python tools/train.py configs/localization/bmn/bmn_400x100_2x8_9e_activitynet_feature.py
 ```
 
-更多训练细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#训练配置) 中的 **训练配置** 部分。
+更多训练细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#训练配置) 中的 **训练配置** 部分。
 
 ## 如何测试
 
@@ -95,4 +95,4 @@ python tools/analysis/report_map.py --proposal path/to/proposal_file
     python tools/data/activitynet/convert_proposal_format.py
     ```
 
-更多测试细节，可参考 [基础教程](/docs_zh_CN/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。
+更多测试细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。