[Docs] Refine reamde and installation (#2340)

* [Docs] Simplify README * Simplify README_zh-CN * Update installation docs * Add mmyolo link in README * update introduction * update installation * Update build docs * remove io link in README * update

[Docs] Refine reamde and installation (#2340)
* [Docs] Simplify README * Simplify README_zh-CN * Update installation docs * Add mmyolo link in README * update introduction * update installation * Update build docs * remove io link in README * update
5b279fbd · Zaida Zhou · GitHub · cb2eb576 · 5b279fbd · 5b279fbd
Unverified Commit 5b279fbd authored Oct 22, 2022 by Zaida Zhou Committed by GitHub Oct 22, 2022
16 changed files
--- a/README.md
+++ b/README.md
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -20,6 +20,7 @@
 </div>
 [![docs](https://img.shields.io/badge/docs-2.x-blue)](https://mmcv.readthedocs.io/zh_CN/2.x/)
+[![platform](https://img.shields.io/badge/platform-Linux%7CWindows%7CmacOS-blue)](https://mmcv.readthedocs.io/zh_CN/2.x/get_started/installation.html)
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmcv)](https://pypi.org/project/mmcv/)
 [![PyPI](https://img.shields.io/pypi/v/mmcv)](https://pypi.org/project/mmcv)
 [![badge](https://github.com/open-mmlab/mmcv/workflows/build/badge.svg)](https://github.com/open-mmlab/mmcv/actions)
@@ -30,43 +31,21 @@
 ## 简介
-MMCV 是一个面向计算机视觉的基础库，它支持了很多开源项目，例如：
+MMCV 是一个面向计算机视觉的基础库，它提供了以下功能：
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMLab 项目、算法、模型的统一入口
+- [图像和视频处理](https://mmcv.readthedocs.io/zh_CN/2.x/understand_mmcv/data_process.html)
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
+- [图像和标注结果可视化](https://mmcv.readthedocs.io/zh_CN/2.x/understand_mmcv/visualization.html)
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [图像变换](https://mmcv.readthedocs.io/zh_CN/2.x/understand_mmcv/data_transform.html)
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [多种 CNN 网络结构](https://mmcv.readthedocs.io/zh_CN/2.x/understand_mmcv/cnn.html)
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [高质量实现的常见 CUDA 算子](https://mmcv.readthedocs.io/zh_CN/2.x/understand_mmcv/ops.html)
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具箱
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
-MMCV 提供了如下众多功能：
- 通用的 IO 接口
+MMCV 支持多种平台，包括：
- 图像和视频处理
- 图像和标注结果可视化
- 图像变换
- 多种 CNN 网络结构
- 高质量实现的常见 CUDA 算子
-MMCV 支持以下的系统：
 - Linux
 - Windows
 - macOS
-如想了解更多特性和使用，请参考[文档](http://mmcv.readthedocs.io/zh_CN/latest)。
+如想了解更多特性和使用，请参考[文档](http://mmcv.readthedocs.io/zh_CN/2.x)。
 提示: MMCV 需要 Python 3.6 以上版本。
@@ -79,177 +58,57 @@ MMCV 有两个版本：
 **注意**: 请不要在同一个环境中安装两个版本，否则可能会遇到类似 `ModuleNotFound` 的错误。在安装一个版本之前，需要先卸载另一个。`如果 CUDA 可用，强烈推荐安装 mmcv`。
-a. 安装完整版
+### 安装 mmcv
-在安装 mmcv 之前，请确保 PyTorch 已经成功安装在环境中，可以参考 PyTorch [官方文档](https://pytorch.org/)。对于使用 macOS M1 的用户，请确保你的 PyTorch 是 `Nightly` 版本。
-我们提供了 **Linux 和 Windows 平台** PyTorch 和 CUDA 版本组合的 mmcv 预编译包，可以大大简化用户安装编译过程。强烈推荐通过预编译包来安装。另外，安装完成后可以运行 [check_installation.py](.dev_scripts/check_installation.py) 脚本检查 mmcv 是否安装成功。
-i. 安装最新版本
+在安装 mmcv 之前，请确保 PyTorch 已经成功安装在环境中，可以参考 [PyTorch 官方安装文档](https://github.com/pytorch/pytorch#installation)。
-如下是安装最新版 `mmcv` 的命令
+在 Linux 和 Windows 平台安装 mmcv 的命令如下（如果你的系统是 macOS，请参考[源码安装 mmcv](https://mmcv.readthedocs.io/zh_CN/2.x/get_started/build.html#macos-mmcv)）
-```shell
+```bash
-pip install 'mmcv>=2.0.0rc1' -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+pip install -U openmim
+mim install 'mmcv>=2.0.0rc1'
 ```
-请将链接中的 `{cu_version}` 和 `{torch_version}` 根据自身需求替换成实际的版本号，例如想安装和 `CUDA 11.1`、`PyTorch 1.9.0` 兼容的最新版 `mmcv`，使用如下替换过的命令
+如果需要指定 mmcv 的版本，可以使用以下命令
-```shell
+```bash
-pip install 'mmcv>=2.0.0rc1' -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
+mim install mmcv==2.0.0rc1
 ```
-**注意**: PyTorch 在 1.x.0 和 1.x.1 之间通常是兼容的，故 mmcv 只提供 1.x.0 的编译包。如果你的 PyTorch 版本是 1.x.1，你可以放心地安装在 1.x.0 版本编译的 mmcv。例如，如果你的 PyTorch 版本是 1.8.1、CUDA 版本是 11.1，你可以使用以下命令安装 mmcv。
+如果发现上述的安装命令没有使用预编译包（以 `.whl` 结尾）而是使用源码包（以 `.tar.gz` 结尾）安装，则有可能是我们没有提供和当前环境的 PyTorch 版本、CUDA 版本相匹配的 mmcv 预编译包，此时，你可以[源码安装 mmcv](https://mmcv.readthedocs.io/zh_CN/2.x/get_started/build.html)。
-```shell
-pip install 'mmcv>=2.0.0rc1' -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
-```
-如果想知道更多 CUDA 和 PyTorch 版本的命令，可以参考下面的表格，将链接中的 `=={mmcv_version}` 删去即可。
+<details>
+<summary>使用预编译包的安装日志</summary>
-ii. 安装特定的版本
+Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br />
+Collecting mmcv<br />
+<b>Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv-2.0.0rc1-cp38-cp38-manylinux1_x86_64.whl</b>
-如下是安装特定版本 `mmcv` 的命令
+</details>
-```shell
+<details>
-pip install mmcv=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+<summary>使用源码包的安装日志</summary>
-```
-首先请参考版本发布信息找到想要安装的版本号，将 `{mmcv_version}` 替换成该版本号，例如 `2.0.0rc1`。
+Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br />
-然后将链接中的 `{cu_version}` 和 `{torch_version}` 根据自身需求替换成实际的版本号，例如想安装和 `CUDA 11.1`、`PyTorch 1.9.0` 兼容的 `mmcv` 2.0.0rc1 版本，使用如下替换过的命令
+Collecting mmcv==2.0.0rc1<br />
+<b>Downloading mmcv-2.0.0rc1.tar.gz</b>
-```shell
+</details>
-pip install mmcv==2.0.0rc1 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
-```
-对于更多的 PyTorch 和 CUDA 版本组合，请参考下表：
+更多安装方式请参考[安装文档](https://mmcv.readthedocs.io/zh_CN/2.x/get_started/installation.html)。
-<table class="docutils">
-  <tbody>
-    <tr>
-      <th width="80"> CUDA </th>
-      <th valign="bottom" align="left" width="120">torch 1.12</th>
-      <th valign="bottom" align="left" width="120">torch 1.11</th>
-      <th valign="bottom" align="left" width="120">torch 1.10</th>
-      <th valign="bottom" align="left" width="120">torch 1.9</th>
-      <th valign="bottom" align="left" width="120">torch 1.8</th>
-      <th valign="bottom" align="left" width="120">torch 1.7</th>
-      <th valign="bottom" align="left" width="120">torch 1.6</th>
-    </tr>
-    <tr>
-      <td align="left">11.6</td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.12.0/index.html</code></pre> </details></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-    </tr>
-    <tr>
-      <td align="left">11.5</td>
-      <td align="left"></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu115/torch1.11.0/index.html</code></pre> </details></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-    </tr>
-    <tr>
-      <td align="left">11.3</td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.12.0/index.html</code></pre> </details></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html</code></pre> </details></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html</code></pre> </details></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-    </tr>
-    <tr>
-      <td align="left">11.1</td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.10.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html</code></pre> </details> </td>
-      <td align="left"> </td>
-      <td align="left"> </td>
-    </tr>
-    <tr>
-      <td align="left">11.0</td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html</code></pre> </details> </td>
-      <td align="left"></td>
-    </tr>
-    <tr>
-      <td align="left">10.2</td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.12.0/index.html</code></pre> </details></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.11.0/index.html</code></pre> </details></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.10.0/index.html</code></pre> </details></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.9.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.7.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.6.0/index.html</code></pre> </details> </td>
-    </tr>
-    <tr>
-      <td align="left">10.1</td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html</code></pre> </details> </td>
-    </tr>
-    <tr>
-      <td align="left">9.2</td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu92/torch1.7.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu92/torch1.6.0/index.html</code></pre> </details> </td>
-    </tr>
-    <tr>
-      <td align="left">cpu</td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.12.0/index.html</code></pre> </details></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.11.0/index.html</code></pre> </details></td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.9.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.7.0/index.html</code></pre> </details> </td>
-      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.6.0/index.html</code></pre> </details> </td>
-    </tr>
-  </tbody>
-</table>
-**注意**：mmcv 没有提供 Windows 平台 `cu102-torch1.8.0` 和 `cu92-torch*` 的预编译包。
-除了使用预编译包之外，另一种方式是在本地进行编译，直接运行下述命令
-```python
-pip install 'mmcv>=2.0.0rc1'
-```
-但注意本地编译可能会耗时 10 分钟以上。
+### 安装 mmcv-lite
-b. 安装精简版
+如果你需要使用和 PyTorch 相关的模块，请确保 PyTorch 已经成功安装在环境中，可以参考 [PyTorch 官方安装文档](https://github.com/pytorch/pytorch#installation)。
-```python
+```bash
-pip install mmcv-lite
+pip install -U openmim
+mim install 'mmcv-lite>=2.0.0rc1'
 ```
-如果想从源码编译 MMCV，请参考[该文档](https://mmcv.readthedocs.io/zh_CN/2.x/get_started/build.html)。
 ## FAQ
-如果你遇到了安装问题，CUDA 相关的问题或者 RuntimeErrors，可以首先参考[问题解决页面](https://mmcv.readthedocs.io/zh_CN/2.x/faq.html) 看是否已经有解决方案。
+如果你遇到了安装问题或者运行时问题，请查看[问题解决页面](https://mmcv.readthedocs.io/zh_CN/2.x/faq.html)是否已有解决方案。如果问题仍然没有解决，欢迎提 [issue](https://github.com/open-mmlab/mmcv/issues)。
 ## 贡献指南
@@ -259,6 +118,30 @@ pip install mmcv-lite
 `MMCV` 目前以 Apache 2.0 的许可证发布，但是其中有一部分功能并不是使用的 Apache2.0 许可证，我们在 [许可证](LICENSES.md) 中详细地列出了这些功能以及他们对应的许可证，如果您正在从事盈利性活动，请谨慎参考此文档。
+## OpenMMLab 的其他项目
+- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
+- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱与测试基准
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具箱
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
 ## 欢迎加入 OpenMMLab 社区
 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=3ijNTqfg)，或添加微信小助手”OpenMMLabwx“加入官方交流微信群。

--- a/docker/release/Dockerfile
+++ b/docker/release/Dockerfile
@@ -16,5 +16,8 @@ RUN apt-get update && apt-get install -y libgl1 libglib2.0-0 \
    && rm -rf /var/lib/apt/lists/*
 # Install mmcv
-ARG MMCV="2.0.0rc1"
+ARG MMCV=""
-RUN pip install openmim && mim install mmcv==${MMCV} && python -c 'import mmcv;print(mmcv.__version__)'
+RUN if [ "${MMCV}" = "" ]; then pip install -U openmim && mim install 'mmcv>=2.0.0rc1'; else pip install -U openmim && mim install mmcv==${MMCV}; fi
+# Verify the installation
+RUN python -c 'import mmcv;print(mmcv.__version__)'
--- a/docs/en/_static/version.json
+++ b/docs/en/_static/version.json
+{
+    "Linux": [
+        {
+            "cuda": "11.6",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.5",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.0",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "9.2",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "9.2",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        }
+    ],
+    "Windows": [
+        {
+            "cuda": "11.6",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.5",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        }
+    ]
+}
--- a/docs/en/conf.py
+++ b/docs/en/conf.py
@@ -58,6 +58,8 @@ extensions = [
 myst_heading_anchors = 4
+myst_enable_extensions = ['colon_fence']
 # Configuration for intersphinx
 intersphinx_mapping = {
    'python': ('https://docs.python.org/3', None),

--- a/docs/en/get_started/build.md
+++ b/docs/en/get_started/build.md
 ## Build MMCV from source
-### Build on Linux or macOS
+### Build mmcv
-Clone the repo with
+Before installing mmcv, make sure that PyTorch has been successfully installed following the [PyTorch official installation guide](https://pytorch.org/get-started/locally/#start-locally). This can be verified using the following command
 ```bash
-git clone https://github.com/open-mmlab/mmcv.git
+python -c 'import torch;print(torch.__version__)'
-cd mmcv
-git checkout 2.x
 ```
-It is recommended to install `ninja` to speed up the compilation
+If version information is output, then PyTorch is installed.
-```bash
+```{note}
-pip install -r requirements/optional.txt
+If you would like to use `opencv-python-headless` instead of `opencv-python`,
+e.g., in a minimum container environment or servers without GUI,
+you can first install it before installing MMCV to skip the installation of `opencv-python`.
 ```
-You can either
+#### Build on Linux
- install the lite version
+1. Clone the repo
-  ```bash
+   ```bash
-  MMCV_WITH_OPS=0 pip install -e .
+   git clone https://github.com/open-mmlab/mmcv.git
-  ```
+   cd mmcv
+   ```
- or install the full version
+2. Install `ninja` and `psutil` to speed up the compilation
-  ```bash
+   ```bash
-  pip install -e .
+   pip install -r requirements/optional.txt
-  ```
+   ```
-If you are on macOS, add the following environment variables before the installing command. Meanwhile, please make sure you are using `PyTorch Nightly` in macOS M1.
+3. Check the nvcc version (requires 9.2+. Skip if no GPU available.)
-```bash
+   ```bash
-CC=clang CXX=clang++ CFLAGS='-stdlib=libc++'
+   nvcc --version
-```
+   ```
-e.g.,
+   If the above command outputs the following message, it means that the nvcc setting is OK, otherwise you need to set CUDA_HOME.
-```bash
+   ```
-CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .
+   nvcc: NVIDIA (R) Cuda compiler driver
-```
+   Copyright (c) 2005-2020 NVIDIA Corporation
+   Built on Mon_Nov_30_19:08:53_PST_2020
+   Cuda compilation tools, release 11.2, V11.2.67
+   Build cuda_11.2.r11.2/compiler.29373293_0
+   ```
+   :::{note}
+   If you want to support ROCm, you can refer to [AMD ROCm](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html) to install ROCm.
+   :::
+4. Check the gcc version (requires 5.4+)
+   ```bash
+   gcc --version
+   ```
+5. Start building (takes 10+ min)
+   ```bash
+   pip install -e . -v
+   ```
+6. Validate the installation
+   ```bash
+   python .dev_scripts/check_installation.py
+   ```
+   If no error is reported by the above command, the installation is successful. If there is an error reported, please check [Frequently Asked Questions](../faq.md) to see if there is already a solution.
+   If no solution is found, please feel free to open an [issue](https://github.com/open-mmlab/mmcv/issues).
+#### Build on macOS
 ```{note}
-If you would like to use `opencv-python-headless` instead of `opencv-python`,
+If you are using a mac with an M1 chip, install the nightly version of PyTorch, otherwise you will encounter the problem in [issues#2218](https://github.com/open-mmlab/mmcv/issues/2218).
-e.g., in a minimum container environment or servers without GUI,
-you can first install it before installing MMCV to skip the installation of `opencv-python`.
 ```
-### Build on Windows
+1. Clone the repo
+   ```bash
+   git clone https://github.com/open-mmlab/mmcv.git
+   cd mmcv
+   ```
+2. Install `ninja` and `psutil` to speed up the compilation
+   ```bash
+   pip install -r requirements/optional.txt
+   ```
+3. Start building
+   ```bash
+   MMCV_WITH_OPS=1 pip install -e .
+   ```
+4. Validate the installation
+   ```bash
+   python .dev_scripts/check_installation.py
+   ```
+   If no error is reported by the above command, the installation is successful. If there is an error reported, please check [Frequently Asked Questions](../faq.md) to see if there is already a solution.
+   If no solution is found, please feel free to open an [issue](https://github.com/open-mmlab/mmcv/issues).
+#### Build on Windows
 Building MMCV on Windows is a bit more complicated than that on Linux.
 The following instructions show how to get this accomplished.
-#### Prerequisite
+##### Prerequisite
 The following software is required for building MMCV on windows.
 Install them first.
@@ -72,7 +132,7 @@ Install them first.
 You should know how to set up environment variables, especially `Path`, on Windows. The following instruction relies heavily on this skill.
 ```
-#### Setup Python Environment
+##### Common steps
 1. Launch Anaconda prompt from Windows Start menu
@@ -80,63 +140,39 @@ You should know how to set up environment variables, especially `Path`, on Windo
 2. Create a new conda environment
-   ```shell
+   ```powershell
-   conda create --name mmcv python=3.7  # 3.6, 3.7, 3.8 should work too as tested
+   (base) PS C:\Users\xxx> conda create --name mmcv python=3.7
-   conda activate mmcv  # make sure to activate environment before any operation
+   (base) PS C:\Users\xxx> conda activate mmcv  # make sure to activate environment before any operation
   ```
 3. Install PyTorch. Choose a version based on your need.
-   ```shell
+   ```powershell
-   conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
+   # CUDA version
+   (mmcv) PS C:\Users\xxx> conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
+   # CPU version
+   (mmcv) PS C:\Users\xxx> conda install install pytorch torchvision cpuonly -c pytorch
   ```
-   We only tested PyTorch version >= 1.6.0.
+4. Clone the repo
-4. Prepare MMCV source code
+   ```powershell
+   (mmcv) PS C:\Users\xxx> git clone https://github.com/open-mmlab/mmcv.git
-   ```shell
+   (mmcv) PS C:\Users\xxx\mmcv> cd mmcv
-   git clone https://github.com/open-mmlab/mmcv.git
-   cd mmcv
-   git checkout 2.x
   ```
-5. Install required Python packages
+5. Install `ninja` and `psutil` to speed up the compilation
-   ```shell
+   ```powershell
-   pip install -r requirements/runtime.txt
+   (mmcv) PS C:\Users\xxx\mmcv> pip install -r requirements/optional.txt
   ```
-6. It is recommended to install `ninja` to speed up the compilation
+6. Set up MSVC compiler
-   ```bash
-   pip install -r requirements/optional.txt
-   ```
-#### Build and install MMCV
-MMCV can be built in three ways:
-1. Lite version (without ops)
-   In this way, no custom ops are compiled and mmcv is a pure python package.
-2. Full version (CPU ops)
-   Module `ops` will be compiled as a pytorch extension, but only x86 code will be compiled. The compiled ops can be executed on CPU only.
-3. Full version (CUDA ops)
-   Both x86 and CUDA codes of `ops` module will be compiled. The compiled version can be run on both CPU and CUDA-enabled GPU (if implemented).
-##### Common steps
-1. Set up MSVC compiler
   Set Environment variable, add `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\Hostx86\x64` to `PATH`, so that `cl.exe` will be available in prompt, as shown below.
-   ```none
+   ```powershell
-   (base) PS C:\Users\xxx> cl
+   (mmcv) PS C:\Users\xxx\mmcv> cl
   Microsoft (R) C/C++ Optimizing  Compiler Version 19.27.29111 for x64
   Copyright (C) Microsoft Corporation.   All rights reserved.
@@ -147,128 +183,110 @@ MMCV can be built in three ways:
   You may want to change the system language to English because pytorch will parse text output from `cl.exe` to check its version. However only utf-8 is recognized. Navigate to Control Panel -> Region -> Administrative -> Language for Non-Unicode programs and change it to English.
-##### Option 1: Build MMCV (lite version)
+##### Build and install MMCV
-After finishing above common steps, launch Anaconda shell from Start menu and issue the following commands:
-```shell
+mmcv can be built in two ways:
-# activate environment
-conda activate mmcv
-# change directory
-cd mmcv
-git checkout 2.x
-# install
-python setup.py develop
-# check
-pip list
-```
-##### Option 2: Build MMCV (full version with CPU)
+1. Full version (CPU ops)
-1. Finish above common steps
+   Module `ops` will be compiled as a pytorch extension, but only x86 code will be compiled. The compiled ops can be executed on CPU only.
-2. Set up environment variables
+2. Full version (CUDA ops)
-   ```shell
+   Both x86 and CUDA codes of `ops` module will be compiled. The compiled version can be run on both CPU and CUDA-enabled GPU (if implemented).
-   $env:MMCV_WITH_OPS = 1
-   $env:MAX_JOBS = 8  # based on your available number of CPU cores and amount of memory
-   ```
-3. Following build steps of the lite version
+###### CPU version
-   ```shell
+Build and install
-   # activate environment
-   conda activate mmcv
-   # change directory
-   cd mmcv
-   git checkout 2.x
-   # build
-   python setup.py build_ext # if success, cl will be launched to compile ops
-   # install
-   python setup.py develop
-   # check
-   pip list
-   ```
-##### Option 3: Build MMCV (full version with CUDA)
+```powershell
+(mmcv) PS C:\Users\xxx\mmcv> python setup.py build_ext
+(mmcv) PS C:\Users\xxx\mmcv> python setup.py develop
+```
-1. Finish above common steps
+###### GPU version
-2. Make sure `CUDA_PATH` or `CUDA_HOME` is already set in `envs` via `ls env:`, desired output is shown as below:
+1. Make sure `CUDA_PATH` or `CUDA_HOME` is already set in `envs` via `ls env:`, desired output is shown as below:
-   ```none
+   ```powershell
-   (base) PS C:\Users\WRH> ls env:
+   (mmcv) PS C:\Users\xxx\mmcv> ls env:
   Name                           Value
   ----                           -----
-   <... omit some lines ...>
   CUDA_PATH                      C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
   CUDA_PATH_V10_1                C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1
   CUDA_PATH_V10_2                C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
-   <... omit some lines ...>
   ```
   This should already be done by CUDA installer. If not, or you have multiple version of CUDA toolkit installed, set it with
-   ```shell
+   ```powershell
-   $env:CUDA_HOME = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2"
+   (mmcv) PS C:\Users\xxx\mmcv> $env:CUDA_HOME = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2"
   # OR
-   $env:CUDA_HOME = $env:CUDA_PATH_V10_2 # if CUDA_PATH_V10_2 is in envs:
+   (mmcv) PS C:\Users\xxx\mmcv> $env:CUDA_HOME = $env:CUDA_PATH_V10_2 # if CUDA_PATH_V10_2 is in envs:
   ```
-3. Set CUDA target arch
+2. Set CUDA target arch
   ```shell
-   # Suppose you are using GTX 1080, which is of capability 6.1
+   # Here you need to change to the target architecture corresponding to your GPU
-   $env:TORCH_CUDA_ARCH_LIST="6.1"
+   (mmcv) PS C:\Users\xxx\mmcv> $env:TORCH_CUDA_ARCH_LIST="7.5"
-   # OR build all supported arch, will be slow
-   $env:TORCH_CUDA_ARCH_LIST="3.5 3.7 5.0 5.2 6.0 6.1 7.0 7.5"
   ```
-```{note}
+   :::{note}
-Check your the compute capability of your GPU from [here](https://developer.nvidia.com/cuda-gpus).
+   Check your the compute capability of your GPU from [here](https://developer.nvidia.com/cuda-gpus).
-```
-4. Launch compiling the same way as CPU
+   ```powershell
+   (mmcv) PS C:\Users\xxx\mmcv> &"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\extras\demo_suite\deviceQuery.exe"
+   Device 0: "NVIDIA GeForce GTX 1660 SUPER"
+   CUDA Driver Version / Runtime Version          11.7 / 11.1
+   CUDA Capability Major/Minor version number:    7.5
+   ```
-   ```shell
+   The 7.5 above indicates the target architecture. Note: You need to replace v10.2 with your CUDA version in the above command.
-   $env:MMCV_WITH_OPS = 1
+   :::
-   $env:MAX_JOBS = 8  # based on available number of CPU cores and amount of memory
-   # activate environment
+3. Build and install
-   conda activate mmcv
-   # change directory
+   ```powershell
-   cd mmcv
-   git checkout 2.x
   # build
   python setup.py build_ext # if success, cl will be launched to compile ops
   # install
   python setup.py develop
-   # check
-   pip list
   ```
-```{note}
+   ```{note}
-If you are compiling against PyTorch 1.6.0, you might meet some errors from PyTorch as described in [this issue](https://github.com/pytorch/pytorch/issues/42467). Follow [this pull request](https://github.com/pytorch/pytorch/pull/43380/files) to modify the source code in your local PyTorch installation.
+   If you are compiling against PyTorch 1.6.0, you might meet some errors from PyTorch as described in [this issue](https://github.com/pytorch/pytorch/issues/42467). Follow [this pull request](https://github.com/pytorch/pytorch/pull/43380/files) to modify the source code in your local PyTorch installation.
-```
+   ```
-If you meet issues when running or compiling mmcv, we list some common issues in [Frequently Asked Question](../faq.html).
+##### Validate installation
-## \[Optional\] Build MMCV on IPU machine
+```powershell
+(mmcv) PS C:\Users\xxx\mmcv> python .dev_scripts/check_installation.py
+```
-Firstly, you need to apply for an IPU cloud machine, see [here](https://www.graphcore.ai/ipus-in-the-cloud).
+If no error is reported by the above command, the installation is successful. If there is an error reported, please check [Frequently Asked Questions](../faq.md) to see if there is already a solution.
+If no solution is found, please feel free to open an [issue](https://github.com/open-mmlab/mmcv/issues).
-### Option 1: Docker
+### Build mmcv-lite
-1. Pull docker
+If you need to use PyTorch-related modules, make sure PyTorch has been successfully installed in your environment by referring to the [PyTorch official installation guide](https://github.com/pytorch/pytorch#installation).
-```shell
+1. Clone the repo
-  docker pull graphcore/pytorch
-```
-2. Build MMCV under same python environment
+   ```bash
+   git clone https://github.com/open-mmlab/mmcv.git
+   cd mmcv
+   ```
-### Option 2: Install from SDK
+2. Start building
-1. Build MMCV
+   ```bash
+   MMCV_WITH_OPS=0 pip install -e . -v
+   ```
-2. Use pip to install sdk according to [IPU PyTorch document](https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/installation.html). Also, you need to apply for machine and sdk to Graphcore.
+3. Validate installation
+   ```bash
+   python -c 'import mmcv;print(mmcv.__version__)'
+   ```
--- a/docs/en/get_started/installation.md
+++ b/docs/en/get_started/installation.md
--- a/docs/en/get_started/introduction.md
+++ b/docs/en/get_started/introduction.md
 ## Introduction
-MMCV is a foundational library for computer vision research and supports many
+MMCV is a foundational library for computer vision research and provides the following functionalities.
-research projects as below:
+- [Image/Video processing](../understand_mmcv/data_process.md)
+- [Image and annotation visualization](../understand_mmcv/visualization.md)
+- [Image transformation](../understand_mmcv/data_transform.md)
+- [Various CNN architectures](../understand_mmcv/cnn.md)
+- [High-quality implementation of common CUDA ops](../understand_mmcv/ops.md)
+It supports the following systems:
+- Linux
+- Windows
+- macOS
+It supports many research projects as below:
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
 - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
 - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
 - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
 - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
 - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
 - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
 - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
@@ -21,22 +34,3 @@ research projects as below:
 - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
 - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
 - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
-It provides the following functionalities:
- Universal IO APIs
- Image/Video processing
- Image and annotation visualization
- Image transformation
- Various CNN architectures
- High-quality implementation of common CUDA ops
-It supports the following systems.
- Linux
- Windows
- macOS
-```{note}
-MMCV requires Python 3.6+.
-```
--- a/docs/en/index.rst
+++ b/docs/en/index.rst
@@ -16,6 +16,7 @@ You can switch between Chinese and English documents in the lower-left corner of
   :caption: Understand MMCV
   understand_mmcv/data_process.md
+   understand_mmcv/data_transform.md
   understand_mmcv/visualization.md
   understand_mmcv/cnn.md
   understand_mmcv/ops.md

--- a/docs/zh_cn/_static/version.json
+++ b/docs/zh_cn/_static/version.json
+{
+    "Linux": [
+        {
+            "cuda": "11.6",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.5",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.0",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "9.2",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "9.2",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        }
+    ],
+    "Windows": [
+        {
+            "cuda": "11.6",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.5",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.3",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "11.1",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.2",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "10.1",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.12.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.11.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.10.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.9.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.8.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.7.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        },
+        {
+            "cuda": "cpu",
+            "torch": "1.6.x",
+            "mmcv": [
+                "2.0.0rc1"
+            ]
+        }
+    ]
+}
--- a/docs/zh_cn/conf.py
+++ b/docs/zh_cn/conf.py
@@ -59,6 +59,8 @@ extensions = [
 myst_heading_anchors = 4
+myst_enable_extensions = ['colon_fence']
 # Configuration for intersphinx
 intersphinx_mapping = {
    'python': ('https://docs.python.org/3', None),

--- a/docs/zh_cn/get_started/build.md
+++ b/docs/zh_cn/get_started/build.md
 ## 从源码编译 MMCV
-### 在 Linux 或者 macOS 上编译 MMCV
+### 编译 mmcv
-克隆算法库
+在编译 mmcv 之前，请确保 PyTorch 已经成功安装在环境中，可以参考 [PyTorch 官方安装文档](https://pytorch.org/get-started/locally/#start-locally)。可使用以下命令验证
 ```bash
-git clone https://github.com/open-mmlab/mmcv.git
+python -c 'import torch;print(torch.__version__)'
-cd mmcv
-git checkout 2.x
 ```
-建议安装 `ninja` 以加快编译速度
+:::{note}
-```bash
+- 如果克隆代码仓库的速度过慢，可以使用以下命令克隆（注意：gitee 的 mmcv 不一定和 github 的保持一致，因为每天只同步一次）
-pip install -r requirements/optional.txt
-```
-你可以安装 lite 版本
 ```bash
-MMCV_WITH_OPS=0 pip install -e .
+git clone https://gitee.com/open-mmlab/mmcv.git
 ```
-也可以安装 full 版本
+- 如果打算使用 `opencv-python-headless` 而不是 `opencv-python`，例如在一个很小的容器环境或者没有图形用户界面的服务器中，你可以先安装 `opencv-python-headless`，这样在安装 mmcv 依赖的过程中会跳过 `opencv-python`。
+- 如果编译过程安装依赖库的时间过长，可以[设置 pypi 源](https://mirrors.tuna.tsinghua.edu.cn/help/pypi/)
 ```bash
-pip install -e .
+pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
 ```
-如果是在 macOS 上编译，则需要在安装命令前添加一些环境变量，同时对于使用 macOS M1 的用户，请确保你的 PyTorch 是  `Nightly` 版本。
+:::
-```bash
+#### 在 Linux 上编译 mmcv
-CC=clang CXX=clang++ CFLAGS='-stdlib=libc++'
-```
-例如
+| TODO: 视频教程
-```bash
+1. 克隆代码仓库
-CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .
-```
-```{note}
+   ```bash
-如果你打算使用 `opencv-python-headless` 而不是 `opencv-python`，例如在一个很小的容器环境或者没有图形用户界面的服务器中，你可以先安装 `opencv-python-headless`，这样在安装 mmcv 依赖的过程中会跳过 `opencv-python`
+   git clone https://github.com/open-mmlab/mmcv.git
-```
+   cd mmcv
+   ```
-### 在 Windows 上编译 MMCV
+2. 安装 `ninja` 和 `psutil` 以加快编译速度
-在 Windows 上编译 MMCV 比 Linux 复杂，本节将一步步介绍如何在 Windows 上编译 MMCV。
+   ```bash
+   pip install -r requirements/optional.txt
+   ```
-#### 依赖项
+3. 检查 nvcc 的版本（要求大于等于 9.2，如果没有 GPU，可以跳过）
-请首先安装以下的依赖项：
+   ```bash
+   nvcc --version
+   ```
- [Git](https://git-scm.com/download/win)：安装期间，请选择 **add git to Path**
+   上述命令如果输出以下信息，表示 nvcc 的设置没有问题，否则需要设置 CUDA_HOME
- [Visual Studio Community 2019](https://visualstudio.microsoft.com)：用于编译 C++ 和 CUDA 代码
- [Miniconda](https://docs.conda.io/en/latest/miniconda.html)：包管理工具
- [CUDA 10.2](https://developer.nvidia.com/cuda-10.2-download-archive)：如果只需要 CPU 版本可以不安装 CUDA，安装CUDA时，可根据需要进行自定义安装。如果已经安装新版本的显卡驱动，建议取消驱动程序的安装
-```{note}
+   ```
-您需要知道如何在 Windows 上设置变量环境，尤其是 "PATH" 的设置，以下安装过程都会用到。
+   nvcc: NVIDIA (R) Cuda compiler driver
-```
+   Copyright (c) 2005-2020 NVIDIA Corporation
+   Built on Mon_Nov_30_19:08:53_PST_2020
+   Cuda compilation tools, release 11.2, V11.2.67
+   Build cuda_11.2.r11.2/compiler.29373293_0
+   ```
-#### 设置 Python 环境
+   :::{note}
+   如果想要支持 ROCm，可以参考 [AMD ROCm](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html) 安装 ROCm。
+   :::
-1. 从 Windows 菜单启动 Anaconda 命令行
+4. 检查 gcc 的版本（要求大于等于**5.4**）
-```{note}
+   ```bash
-如 Miniconda 安装程序建议，不要使用原始的 `cmd.exe` 或是 `powershell.exe`。命令行有两个版本，一个基于 PowerShell，一个基于传统的 `cmd.exe`。请注意以下说明都是使用的基于 PowerShell
+   gcc --version
-```
+   ```
-2. 创建一个新的 Conda 环境
+5. 开始编译（预估耗时 10 分钟）
-   ```shell
+   ```bash
-   conda create --name mmcv python=3.7  # 经测试，3.6, 3.7, 3.8 也能通过
+   pip install -e . -v
-   conda activate mmcv  # 确保做任何操作前先激活环境
   ```
-3. 安装 PyTorch 时，可以根据需要安装支持 CUDA 或不支持 CUDA 的版本
+6. 验证安装
-   ```shell
+   ```bash
-   # CUDA version
+   python .dev_scripts/check_installation.py
-   conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
-   # CPU version
-   conda install pytorch torchvision cpuonly -c pytorch
   ```
-4. 准备 MMCV 源代码
+   如果上述命令没有报错，说明安装成功。如有报错，请查看[问题解决页面](../faq.html)是否已经有解决方案。
+   如果没有找到解决方案，欢迎提 [issue](https://github.com/open-mmlab/mmcv/issues)。
+#### 在 macOS 上编译 mmcv
-   ```shell
+| TODO: 视频教程
+```{note}
+如果你使用的 mac 是 M1 芯片，请安装 PyTorch 的 nightly 版本，否则会遇到 [issues#2218](https://github.com/open-mmlab/mmcv/issues/2218) 中的问题。
+```
+1. 克隆代码仓库
+   ```bash
   git clone https://github.com/open-mmlab/mmcv.git
   cd mmcv
   ```
-5. 安装所需 Python 依赖包
+2. 安装 `ninja` 和 `psutil` 以加快编译速度
-   ```shell
+   ```bash
-   pip3 install -r requirements/runtime.txt
+   pip install -r requirements/optional.txt
   ```
-6. 建议安装 `ninja` 以加快编译速度
+3. 开始编译
   ```bash
-   pip install -r requirements/optional.txt
+   pip install -e .
   ```
-#### 编译与安装 MMCV
+4. 验证安装
-MMCV 有三种安装的模式：
+   ```bash
+   python .dev_scripts/check_installation.py
+   ```
+   如果上述命令没有报错，说明安装成功。如有报错，请查看[问题解决页面](../faq.md)是否已经有解决方案。
+   如果没有找到解决方案，欢迎提 [issue](https://github.com/open-mmlab/mmcv/issues)。
-1. Lite 版本（不包含算子）
+#### 在 Windows 上编译 mmcv
-   这种方式下，没有算子被编译，这种模式的 mmcv 是原生的 python 包
+| TODO: 视频教程
-2. Full 版本（只包含 CPU 算子）
+在 Windows 上编译 mmcv 比 Linux 复杂，本节将一步步介绍如何在 Windows 上编译 mmcv。
-   编译 CPU 算子，但只有 x86 将会被编译，并且编译版本只能在 CPU only 情况下运行
+##### 依赖项
-3. Full 版本（既包含 CPU 算子，又包含 CUDA 算子）
+请先安装以下的依赖项：
+- [Git](https://git-scm.com/download/win)：安装期间，请选择 **add git to Path**
+- [Visual Studio Community 2019](https://visualstudio.microsoft.com)：用于编译 C++ 和 CUDA 代码
+- [Miniconda](https://docs.conda.io/en/latest/miniconda.html)：包管理工具
+- [CUDA 10.2](https://developer.nvidia.com/cuda-10.2-download-archive)：如果只需要 CPU 版本可以不安装 CUDA，安装 CUDA 时，可根据需要进行自定义安装。如果已经安装新版本的显卡驱动，建议取消驱动程序的安装
-   同时编译 CPU 和 CUDA 算子，`ops` 模块的 x86 与 CUDA 的代码都可以被编译。同时编译的版本可以在 CUDA 上调用 GPU
+```{note}
+如果不清楚如何安装以上依赖，请参考[Windows 环境从零安装 mmcv](https://zhuanlan.zhihu.com/p/434491590)。
+另外，你需要知道如何在 Windows 上设置变量环境，尤其是 "PATH" 的设置，以下安装过程都会用到。
+```
 ##### 通用步骤
-1. 设置 MSVC 编译器
+1. 从 Windows 菜单启动 Anaconda 命令行
+   如 Miniconda 安装程序建议，不要使用原始的 `cmd.exe` 或是 `powershell.exe`。命令行有两个版本，一个基于 PowerShell，一个基于传统的 `cmd.exe`。请注意以下说明都是使用的基于 PowerShell
+2. 创建一个新的 Conda 环境
+   ```powershell
+   (base) PS C:\Users\xxx> conda create --name mmcv python=3.7
+   (base) PS C:\Users\xxx> conda activate mmcv  # 确保做任何操作前先激活环境
+   ```
+3. 安装 PyTorch 时，可以根据需要安装支持 CUDA 或不支持 CUDA 的版本
+   ```powershell
+   # CUDA version
+   (mmcv) PS C:\Users\xxx> conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
+   # CPU version
+   (mmcv) PS C:\Users\xxx> conda install install pytorch torchvision cpuonly -c pytorch
+   ```
+4. 克隆代码仓库
+   ```powershell
+   (mmcv) PS C:\Users\xxx> git clone https://github.com/open-mmlab/mmcv.git
+   (mmcv) PS C:\Users\xxx> cd mmcv
+   ```
+5. 安装 `ninja` 和 `psutil` 以加快编译速度
+   ```powershell
+   (mmcv) PS C:\Users\xxx\mmcv> pip install -r requirements/optional.txt
+   ```
+6. 设置 MSVC 编译器
   设置环境变量。添加 `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\Hostx86\x64` 到 `PATH`，则 `cl.exe` 可以在命令行中运行，如下所示。
-   ```none
+   ```powershell
-   (base) PS C:\Users\xxx> cl
+   (mmcv) PS C:\Users\xxx\mmcv> cl
   Microsoft (R) C/C++ Optimizing  Compiler Version 19.27.29111 for x64
   Copyright (C) Microsoft Corporation.   All rights reserved.
@@ -138,60 +193,33 @@ MMCV 有三种安装的模式：
   因为 PyTorch 将解析 `cl.exe` 的输出以检查其版本，只有 utf-8 将会被识别，你可能需要将系统语言更改为英语。控制面板 -> 地区-> 管理-> 非 Unicode 来进行语言转换。
-##### 安装方式一：Lite version（不包含算子）
+##### 编译与安装 mmcv
-在完成上述的公共步骤后，从菜单打开 Anaconda 命令框，输入以下命令
-```shell
-# 激活环境
-conda activate mmcv
-# 切换到 mmcv 根目录
-cd mmcv
-# 切换到 2.x 分支
-git checkout 2.x
-# 安装
-python setup.py develop
-# 检查是否安装成功
-pip list
-```
-##### 安装方式二：Full version（只编译 CPU 算子）
+mmcv 有两个版本：
-1. 完成上述的公共步骤
+- 只包含 CPU 算子的版本
-2. 设置环境变量
+  编译 CPU 算子，但只有 x86 将会被编译，并且编译版本只能在 CPU only 情况下运行
-   ```shell
+- 既包含 CPU 算子，又包含 CUDA 算子的版本
-   $env:MMCV_WITH_OPS = 1
-   $env:MAX_JOBS = 8  # 根据你可用CPU以及内存量进行设置
-   ```
-3. 编译安装
+  同时编译 CPU 和 CUDA 算子，`ops` 模块的 x86 与 CUDA 的代码都可以被编译。同时编译的版本可以在 CUDA 上调用 GPU
-   ```shell
+###### CPU 版本
-   conda activate mmcv  # 激活环境
-   cd mmcv  # 改变路径
-   git checkout 2.x  # 切换到 2.x 分支
-   python setup.py build_ext  # 如果成功, cl 将被启动用于编译算子
-   python setup.py develop  # 安装
-   pip list  # 检查是否安装成功
-   ```
-##### 安装方式三：Full version（既编译 CPU 算子又编译 CUDA 算子）
+编译安装
-1. 完成上述的公共步骤
+```powershell
+(mmcv) PS C:\Users\xxx\mmcv> python setup.py build_ext  # 如果成功, cl 将被启动用于编译算子
+(mmcv) PS C:\Users\xxx\mmcv> python setup.py develop  # 安装
+```
-2. 设置环境变量
+###### GPU 版本
-   ```shell
+1. 检查 `CUDA_PATH` 或者 `CUDA_HOME` 环境变量已经存在在 `envs` 之中
-   $env:MMCV_WITH_OPS = 1
-   $env:MAX_JOBS = 8  # 根据你可用CPU以及内存量进行设置
-   ```
-3. 检查 `CUDA_PATH` 或者 `CUDA_HOME` 环境变量已经存在在 `envs` 之中
+   ```powershell
+   (mmcv) PS C:\Users\xxx\mmcv> ls env:
-   ```none
-   (base) PS C:\Users\WRH> ls env:
   Name                           Value
   ----                           -----
@@ -202,39 +230,71 @@ pip list
   如果没有，你可以按照下面的步骤设置
-   ```shell
+   ```powershell
-   $env:CUDA_HOME = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2"
+   (mmcv) PS C:\Users\xxx\mmcv> $env:CUDA_HOME = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2"
   # 或者
-   $env:CUDA_HOME = $env:CUDA_PATH_V10_2  # CUDA_PATH_V10_2 已经在环境变量中
+   (mmcv) PS C:\Users\xxx\mmcv> $env:CUDA_HOME = $env:CUDA_PATH_V10_2  # CUDA_PATH_V10_2 已经在环境变量中
   ```
-4. 设置 CUDA 的目标架构
+2. 设置 CUDA 的目标架构
-   ```shell
+   ```powershell
-   $env:TORCH_CUDA_ARCH_LIST="6.1" # 支持 GTX 1080
+   # 这里需要改成你的显卡对应的目标架构
-   # 或者用所有支持的版本，但可能会变得很慢
+   (mmcv) PS C:\Users\xxx\mmcv> $env:TORCH_CUDA_ARCH_LIST="7.5"
-   $env:TORCH_CUDA_ARCH_LIST="3.5 3.7 5.0 5.2 6.0 6.1 7.0 7.5"
   ```
-```{note}
+   :::{note}
-我们可以在 [here](https://developer.nvidia.com/cuda-gpus) 查看 GPU 的计算能力
+   可以点击 [cuda-gpus](https://developer.nvidia.com/cuda-gpus) 查看 GPU 的计算能力，也可以通过 CUDA 目录下的 deviceQuery.exe 工具查看
-```
+   ```powershell
+   (mmcv) PS C:\Users\xxx\mmcv> &"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\extras\demo_suite\deviceQuery.exe"
+   Device 0: "NVIDIA GeForce GTX 1660 SUPER"
+   CUDA Driver Version / Runtime Version          11.7 / 11.1
+   CUDA Capability Major/Minor version number:    7.5
+   ```
+   上面的 7.5 表示目标架构。注意：需把上面命令的 v10.2 换成你的 CUDA 版本。
+   :::
+3. 编译安装
-5. 编译安装
+   ```powershell
+   (mmcv) PS C:\Users\xxx\mmcv> python setup.py build_ext  # 如果成功, cl 将被启动用于编译算子
+   (mmcv) PS C:\Users\xxx\mmcv> python setup.py develop # 安装
+   ```
-   ```shell
+   ```{note}
-   $env:MMCV_WITH_OPS = 1
+   如果你的 PyTorch 版本是 1.6.0，你可能会遇到一些 [issue](https://github.com/pytorch/pytorch/issues/42467) 提到的错误，你可以参考这个 [pull request](https://github.com/pytorch/pytorch/pull/43380/files) 修改本地环境的 PyTorch 源代码
-   $env:MAX_JOBS = 8 # 根据你可用CPU以及内存量进行设置
-   conda activate mmcv # 激活环境
-   cd mmcv  # 改变路径
-   git checkout 2.x  # 切换到 2.x 分支
-   python setup.py build_ext  # 如果成功, cl 将被启动用于编译算子
-   python setup.py develop # 安装
-   pip list # 检查是否安装成功
   ```
-```{note}
+##### 验证安装
-如果你的 PyTorch 版本是 1.6.0，你可能会遇到一些这个 [issue](https://github.com/pytorch/pytorch/issues/42467) 提到的错误，则可以参考这个 [pull request](https://github.com/pytorch/pytorch/pull/43380/files) 修改 本地环境的 PyTorch 源代码
+```powershell
+(mmcv) PS C:\Users\xxx\mmcv> python .dev_scripts/check_installation.py
 ```
-如果编译安装 mmcv 的过程中遇到了问题，你也许可以在 [Frequently Asked Question](../faq.html) 找到解决方法
+如果上述命令没有报错，说明安装成功。如有报错，请查看[问题解决页面](../faq.md)是否已经有解决方案。
+如果没有找到解决方案，欢迎提 [issue](https://github.com/open-mmlab/mmcv/issues)。
+### 编译 mmcv-lite
+如果你需要使用和 PyTorch 相关的模块，请确保 PyTorch 已经成功安装在环境中，可以参考 [PyTorch 官方安装文档](https://pytorch.org/get-started/locally/#start-locally)。
+1. 克隆代码仓库
+   ```bash
+   git clone https://github.com/open-mmlab/mmcv.git
+   cd mmcv
+   ```
+2. 开始编译
+   ```bash
+   MMCV_WITH_OPS=0 pip install -e . -v
+   ```
+3. 验证安装
+   ```bash
+   python -c 'import mmcv;print(mmcv.__version__)'
+   ```
--- a/docs/zh_cn/get_started/installation.md
+++ b/docs/zh_cn/get_started/installation.md
--- a/docs/zh_cn/get_started/introduction.md
+++ b/docs/zh_cn/get_started/introduction.md
 ## 介绍 MMCV
-MMCV 是一个面向计算机视觉的基础库，它支持了很多开源项目，例如：
+MMCV 是一个面向计算机视觉的基础库，它提供了以下功能：
+- [图像和视频处理](../understand_mmcv/data_process.md)
+- [图像和标注结果可视化](../understand_mmcv/visualization.md)
+- [图像变换](../understand_mmcv/data_transform.md)
+- [多种 CNN 网络结构](../understand_mmcv/cnn.md)
+- [高质量实现的常见 CUDA 算子](../understand_mmcv/ops.md)
+MMCV 支持多种平台，包括：
+- Linux
+- Windows
+- macOS
+它支持的 OpenMMLab 项目：
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
 - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
 - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
 - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
 - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱与测试基准
 - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
 - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具箱
 - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
@@ -20,25 +34,3 @@ MMCV 是一个面向计算机视觉的基础库，它支持了很多开源项目
 - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
 - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
 - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
-MMCV 提供了如下众多功能：
- 通用的 IO 接口
- 图像和视频处理
- 图像和标注结果可视化
- 常用小工具（进度条，计时器等）
- 基于 PyTorch 的通用训练框架
- 多种 CNN 网络结构
- 高质量实现的常见 CUDA 算子
-MMCV 支持以下的系统：
- Linux
- Windows
- macOS
-如想了解更多特性和使用，请参考[文档](https://mmcv.readthedocs.io/zh_CN/latest)。
-```{note}
-MMCV 需要 Python 3.6 以上版本。
-```
--- a/docs/zh_cn/switch_language.md
+++ b/docs/zh_cn/switch_language.md
-## <a href='https://mmcv.readthedocs.io/en/latest/'>English</a>
+## <a href='https://mmcv.readthedocs.io/en/2.x/'>English</a>
-## <a href='https://mmcv.readthedocs.io/zh_CN/latest/'>简体中文</a>
+## <a href='https://mmcv.readthedocs.io/zh_CN/2.x/'>简体中文</a>
--- a/requirements/optional.txt
+++ b/requirements/optional.txt
 ninja
+psutil