[Fix] Fix docs link (#1121)

* [Fix] Fix docs link

* Fix English doc conf link
Co-authored-by: Tai-Wang <tab_wang@outlook.com>

README.md

@@ -48,11 +48,11 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
 
 - **Natural integration with 2D detection**
 
-  All the about **300+ models, methods of 40+ papers**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
+  All the about **300+ models, methods of 40+ papers**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
 
 - **High efficiency**
 
-  It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
+  It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
 
   | Methods | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) |[votenet](https://github.com/facebookresearch/votenet)| [Det3D](https://github.com/poodarchu/Det3D) |
   |:-------:|:-------------:|:---------:|:-----:|:-----:|
@@ -71,14 +71,14 @@ This project is released under the [Apache 2.0 license](LICENSE).
 ## Changelog
 
 v0.17.3 was released in 1/12/2021.
-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.
 
 For branch v1.0.0.dev0, please refer to [changelog_v1.0.md](https://github.com/Tai-Wang/mmdetection3d/blob/v1.0.0.dev0-changelog/docs/changelog_v1.0.md) for our latest features and more details.
 
 ## Benchmark and model zoo
 
 Supported methods and backbones are shown in the below table.
-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).
 
 Support backbones:
 
@@ -127,17 +127,17 @@ Support methods
 Other features
 - [x] [Dynamic Voxelization](configs/dynamic_voxelization/README.md)
 
-**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
+**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
 
 ## Installation
 
-Please refer to [getting_started.md](docs/getting_started.md) for installation.
+Please refer to [getting_started.md](docs/en/getting_started.md) for installation.
 
 ## Get Started
 
-Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/1_exist_data_model.md) and [with customized dataset](docs/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/tutorials/config.md), [adding new dataset](docs/tutorials/customize_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing models](docs/tutorials/customize_models.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [Waymo dataset](docs/datasets/waymo_det.md).
+Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/1_exist_data_model.md) and [with customized dataset](docs/en/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/tutorials/config.md), [adding new dataset](docs/en/tutorials/customize_dataset.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing models](docs/en/tutorials/customize_models.md), [customizing runtime settings](docs/en/tutorials/customize_runtime.md) and [Waymo dataset](docs/en/datasets/waymo_det.md).
 
-Please refer to [FAQ](docs/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
+Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/en/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
 
 ## Citation
 

README_zh-CN.md

@@ -48,11 +48,11 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 
 - **与 2D 检测器的自然整合**
 
-   [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) 支持的**300+个模型 , 40+的论文算法**, 和相关模块都可以在此代码库中训练或使用。
+   [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/model_zoo.md) 支持的**300+个模型 , 40+的论文算法**, 和相关模块都可以在此代码库中训练或使用。
 
 - **性能高**
 
-   训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
+   训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/zh_cn/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
 
   | Methods | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) |[votenet](https://github.com/facebookresearch/votenet)| [Det3D](https://github.com/poodarchu/Det3D) |
   |:-------:|:-------------:|:---------:|:-----:|:-----:|
@@ -71,13 +71,13 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 ## 更新日志
 
 最新的版本 v0.17.3 在 2021.12.01 发布。
-如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/changelog.md)。
+如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/zh_cn/changelog.md)。
 
 对于分支 [v1.0.0.dev0](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0) ，请参考 [v1.0 更新日志](https://github.com/Tai-Wang/mmdetection3d/blob/v1.0.0.dev0-changelog/docs/changelog_v1.0.md) 来了解我们的最新功能和更多细节。
 
 ## 基准测试和模型库
 
-测试结果和模型可以在[模型库](docs/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：
 
@@ -126,17 +126,17 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 其他特性
 - [x] [Dynamic Voxelization](configs/dynamic_voxelization/README.md)
 
-**注意：** [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) 支持的基于2D检测的**300+个模型 , 40+的论文算法**在 MMDetection3D 中都可以被训练或使用。
+**注意：** [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/model_zoo.md) 支持的基于2D检测的**300+个模型 , 40+的论文算法**在 MMDetection3D 中都可以被训练或使用。
 
 ## 安装
 
-请参考[快速入门文档](docs/getting_started.md)进行安装。
+请参考[快速入门文档](docs/zh_cn/getting_started.md)进行安装。
 
 ## 快速入门
 
-请参考[快速入门文档](docs/getting_started.md)学习 MMDetection3D 的基本使用。 我们为新手提供了分别针对[已有数据集](docs/1_exist_data_model.md)和[新数据集](docs/2_new_data_model.md)的使用指南。我们也提供了一些进阶教程，内容覆盖了[学习配置文件](docs/tutorials/config.md), [增加数据集支持](docs/tutorials/customize_dataset.md), [设计新的数据预处理流程](docs/tutorials/data_pipeline.md), [增加自定义模型](docs/tutorials/customize_models.md), [增加自定义的运行时配置](docs/tutorials/customize_runtime.md)和 [Waymo 数据集](docs/tutorials/waymo.md).
+请参考[快速入门文档](docs/zh_cn/getting_started.md)学习 MMDetection3D 的基本使用。 我们为新手提供了分别针对[已有数据集](docs/zh_cn/1_exist_data_model.md)和[新数据集](docs/zh_cn/2_new_data_model.md)的使用指南。我们也提供了一些进阶教程，内容覆盖了[学习配置文件](docs/zh_cn/tutorials/config.md), [增加数据集支持](docs/zh_cn/tutorials/customize_dataset.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)和 [Waymo 数据集](docs/zh_cn/tutorials/waymo.md).
 
-请参考 [FAQ](docs/faq.md) 查看一些常见的问题与解答。在升级 MMDetection3D 的版本时，请查看[兼容性文档](docs/compatibility.md)以知晓每个版本引入的不与之前版本兼容的更新。
+请参考 [FAQ](docs/zh_cn/faq.md) 查看一些常见的问题与解答。在升级 MMDetection3D 的版本时，请查看[兼容性文档](docs/zh_cn/compatibility.md)以知晓每个版本引入的不与之前版本兼容的更新。
 
 ## 引用
 

configs/h3dnet/README.md

@@ -29,4 +29,4 @@ We implement H3DNet and provide the result and checkpoints on ScanNet datasets.
 python ./tools/model_converters/convert_h3dnet_checkpoints.py ${ORIGINAL_CHECKPOINT_PATH} --out=${NEW_CHECKPOINT_PATH}
 ```
 
-Then you can use the converted checkpoints following [getting_started.md](../../docs/getting_started.md).
+Then you can use the converted checkpoints following [getting_started.md](../../docs/en/getting_started.md).

configs/votenet/README.md

@@ -35,7 +35,7 @@ We implement VoteNet and provide the result and checkpoints on ScanNet and SUNRG
 python ./tools/model_converters/convert_votenet_checkpoints.py ${ORIGINAL_CHECKPOINT_PATH} --out=${NEW_CHECKPOINT_PATH}
 ```
 
-Then you can use the converted checkpoints following [getting_started.md](../../docs/getting_started.md).
+Then you can use the converted checkpoints following [getting_started.md](../../docs/en/getting_started.md).
 
 ## Indeterminism
 

docs/en/conf.py

@@ -17,7 +17,7 @@ import sys
 from m2r import MdInclude
 from recommonmark.transform import AutoStructify
 
-sys.path.insert(0, os.path.abspath('..'))
+sys.path.insert(0, os.path.abspath('../../'))
 
 # -- Project information -----------------------------------------------------
 
@@ -25,7 +25,7 @@ project = 'MMDetection3D'
 copyright = '2020-2023, OpenMMLab'
 author = 'MMDetection3D Authors'
 
-version_file = '../mmdet3d/version.py'
+version_file = '../../mmdet3d/version.py'
 
 
 def get_version():

docs/en/datasets/lyft_det.md

@@ -41,7 +41,7 @@ Note that we follow the original folder names for clear organization. Please ren
 ## Dataset Preparation
 
 The way to organize Lyft dataset is similar to nuScenes. We also generate the .pkl and .json files which share almost the same structure.
-Next, we will mainly focus on the difference between these two datasets. For a more detailed explanation of the info structure, please refer to [nuScenes tutorial](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/datasets/nuscenes_det.md).
+Next, we will mainly focus on the difference between these two datasets. For a more detailed explanation of the info structure, please refer to [nuScenes tutorial](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/datasets/nuscenes_det.md).
 
 To prepare info files for Lyft, run the following commands:
 

docs/en/datasets/scannet_sem_seg.md

@@ -2,7 +2,7 @@
 
 ## Dataset preparation
 
-The overall process is similar to ScanNet 3D detection task. Please refer to this [section](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/datasets/scannet_det.md#dataset-preparation). Only a few differences and additional information about the 3D semantic segmentation data will be listed below.
+The overall process is similar to ScanNet 3D detection task. Please refer to this [section](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/datasets/scannet_det.md#dataset-preparation). Only a few differences and additional information about the 3D semantic segmentation data will be listed below.
 
 ### Export ScanNet data
 

docs/en/demo.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-We provide scripts for multi-modality/single-modality (LiDAR-based/vision-based), indoor/outdoor 3D detection and 3D semantic segmentation demos. The pre-trained models can be downloaded from [model zoo](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/model_zoo.md/). We provide pre-processed sample data from KITTI, SUN RGB-D, nuScenes and ScanNet dataset. You can use any other data following our pre-processing steps.
+We provide scripts for multi-modality/single-modality (LiDAR-based/vision-based), indoor/outdoor 3D detection and 3D semantic segmentation demos. The pre-trained models can be downloaded from [model zoo](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/model_zoo.md/). We provide pre-processed sample data from KITTI, SUN RGB-D, nuScenes and ScanNet dataset. You can use any other data following our pre-processing steps.
 
 ## Testing