[Docs] Refactor the structure of documentation (#1128)

* merge docs/ and docs_zh-CN/

* merge docs/ and docs_zh-CN/

* merge docs/ and docs_zh-CN/

* merge docs/ and docs_zh-CN/

* fix launch utility url

* fix launch utility url

* fix wrong pytorch doc url

* remove wrong links docs//

.github/workflows/build.yml

@@ -16,7 +16,6 @@ on:
       - 'docker/**'
       - 'tools/**'
       - 'docs/**'
-      - 'docs_zh-CN/**'
       - '**.md'
 
 concurrency:

.gitignore

@@ -64,7 +64,8 @@ instance/
 .scrapy
 
 # Sphinx documentation
-docs/_build/
+docs/en/_build/
+docs/zh_cn/_build/
 
 # PyBuilder
 target/

README.md

@@ -54,7 +54,7 @@ Please refer to [changelog.md](docs/changelog.md) for details and release histor
 
 ## Benchmark and model zoo
 
-Results and models are available in the [model zoo](docs/model_zoo.md).
+Results and models are available in the [model zoo](docs/en/model_zoo.md).
 
 Supported backbones:
 
@@ -105,29 +105,29 @@ Supported methods:
 
 Supported datasets:
 
-- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#cityscapes)
-- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#pascal-voc)
-- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#ade20k)
-- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#pascal-context)
-- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#coco-stuff-10k)
-- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#coco-stuff-164k)
-- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#chase-db1)
-- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#drive)
-- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#hrf)
-- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#stare)
-- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#dark-zurich)
-- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#nighttime-driving)
-- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#loveda)
+- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#cityscapes)
+- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-voc)
+- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#ade20k)
+- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-context)
+- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-10k)
+- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-164k)
+- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#chase-db1)
+- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#drive)
+- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#hrf)
+- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#stare)
+- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#dark-zurich)
+- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#nighttime-driving)
+- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#loveda)
 
 ## Installation
 
-Please refer to [get_started.md](docs/get_started.md#installation) for installation and [dataset_prepare.md](docs/dataset_prepare.md#prepare-datasets) for dataset preparation.
+Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/dataset_prepare.md#prepare-datasets) for dataset preparation.
 
 ## Get Started
 
-Please see [train.md](docs/train.md) and [inference.md](docs/inference.md) for the basic usage of MMSegmentation.
-There are also tutorials for [customizing dataset](docs/tutorials/customize_datasets.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing modules](docs/tutorials/customize_models.md), and [customizing runtime](docs/tutorials/customize_runtime.md).
-We also provide many [training tricks](docs/tutorials/training_tricks.md) for better training and [useful tools](docs/useful_tools.md) for deployment.
+Please see [train.md](docs/en/train.md) and [inference.md](docs/en/inference.md) for the basic usage of MMSegmentation.
+There are also tutorials for [customizing dataset](docs/en/tutorials/customize_datasets.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing modules](docs/en/tutorials/customize_models.md), and [customizing runtime](docs/en/tutorials/customize_runtime.md).
+We also provide many [training tricks](docs/en/tutorials/training_tricks.md) for better training and [useful tools](docs/en/useful_tools.md) for deployment.
 
 A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab.
 

README_zh-CN.md

@@ -53,7 +53,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 
 ## 基准测试和模型库
 
-测试结果和模型可以在[模型库](docs_zh-CN/model_zoo.md)中找到。
+测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
 
 已支持的骨干网络：
 
@@ -104,29 +104,29 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 
 已支持的数据集：
 
-- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#cityscapes)
-- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#pascal-voc)
-- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#ade20k)
-- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#pascal-context)
-- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#coco-stuff-10k)
-- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#coco-stuff-164k)
-- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#chase-db1)
-- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#drive)
-- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#hrf)
-- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#stare)
-- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#dark-zurich)
-- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#nighttime-driving)
-- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/dataset_prepare.md#loveda)
+- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#cityscapes)
+- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-voc)
+- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#ade20k)
+- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-context)
+- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-10k)
+- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-164k)
+- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#chase-db1)
+- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#drive)
+- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#hrf)
+- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#stare)
+- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#dark-zurich)
+- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#nighttime-driving)
+- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#loveda)
 
 ## 安装
 
-请参考[快速入门文档](docs_zh-CN/get_started.md#installation)进行安装，参考[数据集准备](docs_zh-CN/dataset_prepare.md)处理数据。
+请参考[快速入门文档](docs/zh_cn/get_started.md#installation)进行安装，参考[数据集准备](docs/zh_cn/dataset_prepare.md)处理数据。
 
 ## 快速入门
 
-请参考[训练教程](docs_zh-CN/train.md)和[测试教程](docs_zh-CN/inference.md)学习 MMSegmentation 的基本使用。
-我们也提供了一些进阶教程，内容覆盖了[增加自定义数据集](docs_zh-CN/tutorials/customize_datasets.md)，[设计新的数据预处理流程](docs_zh-CN/tutorials/data_pipeline.md)，[增加自定义模型](docs_zh-CN/tutorials/customize_models.md)，[增加自定义的运行时配置](docs_zh-CN/tutorials/customize_runtime.md)。
-除此之外，我们也提供了很多实用的[训练技巧说明](docs_zh-CN/tutorials/training_tricks.md)和模型部署相关的[有用的工具](docs_zh-CN/useful_tools.md)。
+请参考[训练教程](docs/zh_cn/train.md)和[测试教程](docs/zh_cn/inference.md)学习 MMSegmentation 的基本使用。
+我们也提供了一些进阶教程，内容覆盖了[增加自定义数据集](docs/zh_cn/tutorials/customize_datasets.md)，[设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md)，[增加自定义模型](docs/zh_cn/tutorials/customize_models.md)，[增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)。
+除此之外，我们也提供了很多实用的[训练技巧说明](docs/zh_cn/tutorials/training_tricks.md)和模型部署相关的[有用的工具](docs/zh_cn/useful_tools.md)。
 
 同时，我们提供了 Colab 教程。你可以在[这里](demo/MMSegmentation_Tutorial.ipynb)浏览教程，或者直接在 Colab 上[运行](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb)。
 
@@ -173,7 +173,7 @@ MMSegmentation 是一个由来自不同高校和企业的研发人员共同参
  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](https://jq.qq.com/?_wv=1027&k=aCvMxdr3) 以及 [MMSegmentation](https://jq.qq.com/?_wv=1027&k=ukevz6Ie) 的 QQ 群。
 
  <div align="center">
- <img src="docs_zh-CN/imgs/zhihu_qrcode.jpg" height="400" />  <img src="docs_zh-CN/imgs/qq_group_qrcode.jpg" height="400" />  <img src="docs_zh-CN/imgs/seggroup_qrcode.jpg" height="400" />
+ <img src="docs/zh_cn/imgs/zhihu_qrcode.jpg" height="400" />  <img src="docs/zh_cn/imgs/qq_group_qrcode.jpg" height="400" />  <img src="docs/zh_cn/imgs/seggroup_qrcode.jpg" height="400" />
  </div>
 
  我们会在 OpenMMLab 社区为大家

demo/MMSegmentation_Tutorial.ipynb

@@ -230,7 +230,7 @@
     "\n",
     "Datasets in MMSegmentation require image and semantic segmentation maps to be placed in folders with the same perfix. To support a new dataset, we may need to modify the original file structure. \n",
     "\n",
-    "In this tutorial, we give an example of converting the dataset. You may refer to [docs](https://github.com/open-mmlab/mmsegmentation/docs/tutorials/new_dataset.md) for details about dataset reorganization. \n",
+    "In this tutorial, we give an example of converting the dataset. You may refer to [docs](https://github.com/open-mmlab/mmsegmentation/docs/en/tutorials/new_dataset.md) for details about dataset reorganization. \n",
     "\n",
     "We use [Standord Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) as an example. The dataset contains 715 images chosen from existing public datasets [LabelMe](http://labelme.csail.mit.edu), [MSRC](http://research.microsoft.com/en-us/projects/objectclassrecognition), [PASCAL VOC](http://pascallin.ecs.soton.ac.uk/challenges/VOC) and [Geometric Context](http://www.cs.illinois.edu/homes/dhoiem/). Images from these datasets are mainly outdoor scenes, each containing approximately 320-by-240 pixels. \n",
     "In this tutorial, we use the region annotations as labels. There are 8 classes in total, i.e. sky, tree, road, grass, water, building, mountain, and foreground object. "

docs/conf.py → docs/en/conf.py

@@ -17,14 +17,14 @@
 
 import pytorch_sphinx_theme
 
-sys.path.insert(0, os.path.abspath('..'))
+sys.path.insert(0, os.path.abspath('../../'))
 
 # -- Project information -----------------------------------------------------
 
 project = 'MMSegmentation'
 copyright = '2020-2021, OpenMMLab'
 author = 'MMSegmentation Authors'
-version_file = '../mmseg/version.py'
+version_file = '../../mmseg/version.py'
 
 
 def get_version():

docs/dataset_prepare.md → docs/en/dataset_prepare.md

@@ -143,7 +143,7 @@ If you would like to use augmented VOC dataset, please run following command to
 python tools/convert_datasets/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug --nproc 8
 ```
 
-Please refer to [concat dataset](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/tutorials/customize_datasets.md#concatenate-dataset) for details about how to concatenate them and train them together.
+Please refer to [concat dataset](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/tutorials/customize_datasets.md#concatenate-dataset) for details about how to concatenate them and train them together.
 
 ### ADE20K
 
@@ -283,6 +283,6 @@ For LoveDA dataset, please run the following command to download and re-organize
 python tools/convert_datasets/loveda.py /path/to/loveDA
 ```
 
-Using trained model to predict test set of LoveDA and submit it to server can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/inference.md).
+Using trained model to predict test set of LoveDA and submit it to server can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/inference.md).
 
 More details about LoveDA can be found [here](https://github.com/Junjue-Wang/LoveDA).

docs/train.md → docs/en/train.md

@@ -68,7 +68,7 @@ GPUS=16 ./tools/slurm_train.sh dev pspr50 configs/pspnet/pspnet_r50-d8_512x1024_
 You can check [slurm_train.sh](../tools/slurm_train.sh) for full arguments and environment variables.
 
 If you have just multiple machines connected with ethernet, you can refer to
-PyTorch [launch utility](https://pytorch.org/docs/stable/distributed_deprecated.html#launch-utility).
+PyTorch [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility).
 Usually it is slow if you do not have high speed networking like InfiniBand.
 
 ### Launch multiple jobs on a single machine

docs/useful_tools.md → docs/en/useful_tools.md

@@ -189,7 +189,7 @@ A script to convert [ONNX](https://github.com/onnx/onnx) model to [TensorRT](htt
 
 Prerequisite
 
-- install `mmcv-full` with ONNXRuntime custom ops and TensorRT plugins follow [ONNXRuntime in mmcv](https://mmcv.readthedocs.io/en/latest/onnxruntime_op.html) and [TensorRT plugin in mmcv](https://github.com/open-mmlab/mmcv/blob/master/docs/tensorrt_plugin.md).
+- install `mmcv-full` with ONNXRuntime custom ops and TensorRT plugins follow [ONNXRuntime in mmcv](https://mmcv.readthedocs.io/en/latest/deployment/onnxruntime_op.html) and [TensorRT plugin in mmcv](https://github.com/open-mmlab/mmcv/blob/master/docs/en/deployment/tensorrt_plugin.md).
 - Use [pytorch2onnx](#convert-to-onnx-experimental) to convert the model from PyTorch to ONNX.
 
 Usage

docs_zh-CN/conf.py → docs/zh_cn/conf.py

@@ -17,14 +17,14 @@
 
 import pytorch_sphinx_theme
 
-sys.path.insert(0, os.path.abspath('..'))
+sys.path.insert(0, os.path.abspath('../../'))
 
 # -- Project information -----------------------------------------------------
 
 project = 'MMSegmentation'
 copyright = '2020-2021, OpenMMLab'
 author = 'MMSegmentation Authors'
-version_file = '../mmseg/version.py'
+version_file = '../../mmseg/version.py'
 
 
 def get_version():

docs_zh-CN/dataset_prepare.md → docs/zh_cn/dataset_prepare.md

@@ -125,7 +125,7 @@ Pascal VOC 2012 可以在 [这里](http://host.robots.ox.ac.uk/pascal/VOC/voc201
 python tools/convert_datasets/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug --nproc 8
 ```
 
-关于如何拼接数据集 (concatenate) 并一起训练它们，更多细节请参考 [拼接连接数据集](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/tutorials/customize_datasets.md#%E6%8B%BC%E6%8E%A5%E6%95%B0%E6%8D%AE%E9%9B%86) 。
+关于如何拼接数据集 (concatenate) 并一起训练它们，更多细节请参考 [拼接连接数据集](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/tutorials/customize_datasets.md#%E6%8B%BC%E6%8E%A5%E6%95%B0%E6%8D%AE%E9%9B%86) 。
 
 ### ADE20K
 
@@ -225,6 +225,6 @@ wget https://zenodo.org/record/5706578/files/Test.zip
 python tools/convert_datasets/loveda.py /path/to/loveDA
 ```
 
-请参照 [这里](https://github.com/open-mmlab/mmsegmentation/blob/master/docs_zh-CN/inference.md) 来使用训练好的模型去预测 LoveDA 测试集并且提交到官网。
+请参照 [这里](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/inference.md) 来使用训练好的模型去预测 LoveDA 测试集并且提交到官网。
 
 关于 LoveDA 的更多细节可以在[这里](https://github.com/Junjue-Wang/LoveDA) 找到。

docs_zh-CN/train.md → docs/zh_cn/train.md

@@ -59,7 +59,7 @@ GPUS=16 ./tools/slurm_train.sh dev pspr50 configs/pspnet/pspnet_r50-d8_512x1024_
 您可以查看 [slurm_train.sh](../tools/slurm_train.sh) 以熟悉全部的参数与环境变量。
 
 如果您多个机器已经有以太网连接， 您可以参考 PyTorch
-[launch utility](https://pytorch.org/docs/stable/distributed_deprecated.html#launch-utility) 。
+[launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility) 。
 若您没有像 InfiniBand 这样高速的网络连接，多机器训练通常会比较慢。
 
 ### 在单个机器上启动多个任务

docs_zh-CN/useful_tools.md → docs/zh_cn/useful_tools.md

@@ -184,7 +184,7 @@ python tools/pytorch2torchscript.py \
 
 先决条件
 
-- 按照 [ONNXRuntime in mmcv](https://mmcv.readthedocs.io/en/latest/onnxruntime_op.html) 和 [TensorRT plugin in mmcv](https://github.com/open-mmlab/mmcv/blob/master/docs/tensorrt_plugin.md) ，用 ONNXRuntime 自定义运算 (custom ops) 和 TensorRT 插件安装 `mmcv-full`
+- 按照 [ONNXRuntime in mmcv](https://mmcv.readthedocs.io/en/latest/deployment/onnxruntime_op.html) 和 [TensorRT plugin in mmcv](https://github.com/open-mmlab/mmcv/blob/master/docs/en/deployment/tensorrt_plugin.md) ，用 ONNXRuntime 自定义运算 (custom ops) 和 TensorRT 插件安装 `mmcv-full`
 - 使用 [pytorch2onnx](#convert-to-onnx-experimental) 将模型从 PyTorch 转成 ONNX
 
 使用方法

mmseg/datasets/custom.py

@@ -42,7 +42,7 @@ class CustomDataset(Dataset):
     ``xxx{img_suffix}`` and ``xxx{seg_map_suffix}`` (extension is also included
     in the suffix). If split is given, then ``xxx`` is specified in txt file.
     Otherwise, all files in ``img_dir/``and ``ann_dir`` will be loaded.
-    Please refer to ``docs/tutorials/new_dataset.md`` for more details.
+    Please refer to ``docs/en/tutorials/new_dataset.md`` for more details.
 
 
     Args: