diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 03c66331bd..628a9ca731 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -16,7 +16,6 @@ on:
- 'docker/**'
- 'tools/**'
- 'docs/**'
- - 'docs_zh-CN/**'
- '**.md'
concurrency:
diff --git a/.gitignore b/.gitignore
index 6bbdaec225..2c1ffb5b8d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -64,7 +64,8 @@ instance/
.scrapy
# Sphinx documentation
-docs/_build/
+docs/en/_build/
+docs/zh_cn/_build/
# PyBuilder
target/
diff --git a/README.md b/README.md
index 641479bc78..ad3d1f699b 100644
--- a/README.md
+++ b/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.
diff --git a/README_zh-CN.md b/README_zh-CN.md
index 4c0d86315a..ce02499578 100644
--- a/README_zh-CN.md
+++ b/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。
我们会在 OpenMMLab 社区为大家
diff --git a/demo/MMSegmentation_Tutorial.ipynb b/demo/MMSegmentation_Tutorial.ipynb
index b428fc9431..4bff9ad371 100644
--- a/demo/MMSegmentation_Tutorial.ipynb
+++ b/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. "
diff --git a/docs/Makefile b/docs/en/Makefile
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rename from docs/Makefile
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diff --git a/docs/_static/css/readthedocs.css b/docs/en/_static/css/readthedocs.css
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diff --git a/docs/_static/images/mmsegmentation.png b/docs/en/_static/images/mmsegmentation.png
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rename from docs/_static/images/mmsegmentation.png
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diff --git a/docs/api.rst b/docs/en/api.rst
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rename from docs/api.rst
rename to docs/en/api.rst
diff --git a/docs/changelog.md b/docs/en/changelog.md
similarity index 100%
rename from docs/changelog.md
rename to docs/en/changelog.md
diff --git a/docs/conf.py b/docs/en/conf.py
similarity index 97%
rename from docs/conf.py
rename to docs/en/conf.py
index 2d84a9620d..87b16f2667 100644
--- a/docs/conf.py
+++ b/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():
diff --git a/docs/dataset_prepare.md b/docs/en/dataset_prepare.md
similarity index 98%
rename from docs/dataset_prepare.md
rename to docs/en/dataset_prepare.md
index 70ab9b3f38..8e89f156ca 100644
--- a/docs/dataset_prepare.md
+++ b/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).
diff --git a/docs/get_started.md b/docs/en/get_started.md
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rename from docs/get_started.md
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diff --git a/docs/index.rst b/docs/en/index.rst
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rename from docs/index.rst
rename to docs/en/index.rst
diff --git a/docs/inference.md b/docs/en/inference.md
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rename from docs/inference.md
rename to docs/en/inference.md
diff --git a/docs/make.bat b/docs/en/make.bat
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rename from docs/make.bat
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diff --git a/docs/model_zoo.md b/docs/en/model_zoo.md
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rename from docs/model_zoo.md
rename to docs/en/model_zoo.md
diff --git a/docs/stat.py b/docs/en/stat.py
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diff --git a/docs/switch_language.md b/docs/en/switch_language.md
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diff --git a/docs/train.md b/docs/en/train.md
similarity index 99%
rename from docs/train.md
rename to docs/en/train.md
index b4527601a2..673b996407 100644
--- a/docs/train.md
+++ b/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
diff --git a/docs/tutorials/config.md b/docs/en/tutorials/config.md
similarity index 100%
rename from docs/tutorials/config.md
rename to docs/en/tutorials/config.md
diff --git a/docs/tutorials/customize_datasets.md b/docs/en/tutorials/customize_datasets.md
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rename from docs/tutorials/customize_datasets.md
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diff --git a/docs/tutorials/customize_models.md b/docs/en/tutorials/customize_models.md
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diff --git a/docs/tutorials/data_pipeline.md b/docs/en/tutorials/data_pipeline.md
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diff --git a/docs/tutorials/index.rst b/docs/en/tutorials/index.rst
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rename from docs/tutorials/index.rst
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diff --git a/docs/tutorials/training_tricks.md b/docs/en/tutorials/training_tricks.md
similarity index 100%
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rename to docs/en/tutorials/training_tricks.md
diff --git a/docs/useful_tools.md b/docs/en/useful_tools.md
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rename from docs/useful_tools.md
rename to docs/en/useful_tools.md
index bd3cfc66a7..cd2324ea30 100644
--- a/docs/useful_tools.md
+++ b/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
diff --git a/docs_zh-CN/Makefile b/docs/zh_cn/Makefile
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rename to docs/zh_cn/conf.py
index d551c9aee9..353b0bc725 100644
--- a/docs_zh-CN/conf.py
+++ b/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():
diff --git a/docs_zh-CN/dataset_prepare.md b/docs/zh_cn/dataset_prepare.md
similarity index 98%
rename from docs_zh-CN/dataset_prepare.md
rename to docs/zh_cn/dataset_prepare.md
index da50febeb0..bffc3d1da5 100644
--- a/docs_zh-CN/dataset_prepare.md
+++ b/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。
diff --git a/docs_zh-CN/get_started.md b/docs/zh_cn/get_started.md
similarity index 100%
rename from docs_zh-CN/get_started.md
rename to docs/zh_cn/get_started.md
diff --git a/docs_zh-CN/imgs/qq_group_qrcode.jpg b/docs/zh_cn/imgs/qq_group_qrcode.jpg
similarity index 100%
rename from docs_zh-CN/imgs/qq_group_qrcode.jpg
rename to docs/zh_cn/imgs/qq_group_qrcode.jpg
diff --git a/docs_zh-CN/imgs/seggroup_qrcode.jpg b/docs/zh_cn/imgs/seggroup_qrcode.jpg
similarity index 100%
rename from docs_zh-CN/imgs/seggroup_qrcode.jpg
rename to docs/zh_cn/imgs/seggroup_qrcode.jpg
diff --git a/docs_zh-CN/imgs/zhihu_qrcode.jpg b/docs/zh_cn/imgs/zhihu_qrcode.jpg
similarity index 100%
rename from docs_zh-CN/imgs/zhihu_qrcode.jpg
rename to docs/zh_cn/imgs/zhihu_qrcode.jpg
diff --git a/docs_zh-CN/index.rst b/docs/zh_cn/index.rst
similarity index 100%
rename from docs_zh-CN/index.rst
rename to docs/zh_cn/index.rst
diff --git a/docs_zh-CN/inference.md b/docs/zh_cn/inference.md
similarity index 100%
rename from docs_zh-CN/inference.md
rename to docs/zh_cn/inference.md
diff --git a/docs_zh-CN/make.bat b/docs/zh_cn/make.bat
similarity index 100%
rename from docs_zh-CN/make.bat
rename to docs/zh_cn/make.bat
diff --git a/docs_zh-CN/model_zoo.md b/docs/zh_cn/model_zoo.md
similarity index 100%
rename from docs_zh-CN/model_zoo.md
rename to docs/zh_cn/model_zoo.md
diff --git a/docs_zh-CN/stat.py b/docs/zh_cn/stat.py
similarity index 100%
rename from docs_zh-CN/stat.py
rename to docs/zh_cn/stat.py
diff --git a/docs_zh-CN/switch_language.md b/docs/zh_cn/switch_language.md
similarity index 100%
rename from docs_zh-CN/switch_language.md
rename to docs/zh_cn/switch_language.md
diff --git a/docs_zh-CN/train.md b/docs/zh_cn/train.md
similarity index 97%
rename from docs_zh-CN/train.md
rename to docs/zh_cn/train.md
index 7ac42284cf..520739dbe2 100644
--- a/docs_zh-CN/train.md
+++ b/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
diff --git a/docs_zh-CN/tutorials/config.md b/docs/zh_cn/tutorials/config.md
similarity index 100%
rename from docs_zh-CN/tutorials/config.md
rename to docs/zh_cn/tutorials/config.md
diff --git a/docs_zh-CN/tutorials/customize_datasets.md b/docs/zh_cn/tutorials/customize_datasets.md
similarity index 100%
rename from docs_zh-CN/tutorials/customize_datasets.md
rename to docs/zh_cn/tutorials/customize_datasets.md
diff --git a/docs_zh-CN/tutorials/customize_models.md b/docs/zh_cn/tutorials/customize_models.md
similarity index 100%
rename from docs_zh-CN/tutorials/customize_models.md
rename to docs/zh_cn/tutorials/customize_models.md
diff --git a/docs_zh-CN/tutorials/customize_runtime.md b/docs/zh_cn/tutorials/customize_runtime.md
similarity index 100%
rename from docs_zh-CN/tutorials/customize_runtime.md
rename to docs/zh_cn/tutorials/customize_runtime.md
diff --git a/docs_zh-CN/tutorials/data_pipeline.md b/docs/zh_cn/tutorials/data_pipeline.md
similarity index 100%
rename from docs_zh-CN/tutorials/data_pipeline.md
rename to docs/zh_cn/tutorials/data_pipeline.md
diff --git a/docs_zh-CN/tutorials/index.rst b/docs/zh_cn/tutorials/index.rst
similarity index 100%
rename from docs_zh-CN/tutorials/index.rst
rename to docs/zh_cn/tutorials/index.rst
diff --git a/docs_zh-CN/tutorials/training_tricks.md b/docs/zh_cn/tutorials/training_tricks.md
similarity index 100%
rename from docs_zh-CN/tutorials/training_tricks.md
rename to docs/zh_cn/tutorials/training_tricks.md
diff --git a/docs_zh-CN/useful_tools.md b/docs/zh_cn/useful_tools.md
similarity index 98%
rename from docs_zh-CN/useful_tools.md
rename to docs/zh_cn/useful_tools.md
index 1f95d71f9b..b410342ec5 100644
--- a/docs_zh-CN/useful_tools.md
+++ b/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
diff --git a/mmseg/datasets/custom.py b/mmseg/datasets/custom.py
index 872b2b8448..3eeb0dda80 100644
--- a/mmseg/datasets/custom.py
+++ b/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: