support v1.4.0

6f3c5f1c · limm · 6f674c7e · 6f3c5f1c · 6f674c7e · 6f3c5f1c
Commit 6f3c5f1c authored Jul 11, 2024 by limm
20 changed files
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
 ## Contributing to OpenMMLab
-Welcome to the MMCV community, we are committed to building a cutting-edge computer vision foundational library and all kinds of contributions are welcomed, including but not limited to
+All kinds of contributions are welcome, including but not limited to the following.
-**Fix bug**
+- Fix typo or bugs
+- Add documentation or translate the documentation into other languages
+- Add new features and components
-You can directly post a Pull Request to fix typo in code or documents
+### Workflow
-The steps to fix the bug of code implementation are as follows.
+1. fork and pull the latest OpenMMLab repository
+2. checkout a new branch (do not use master branch for PRs)
+3. commit your changes
+4. create a PR
-1. If the modification involve significant changes, you should create an issue first and describe the error information and how to trigger the bug. Other developers will discuss with you and propose an proper solution.
+```{note}
+If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
-2. Posting a pull request after fixing the bug and adding corresponding unit test.
-**New Feature or Enhancement**
-1. If the modification involve significant changes, you should create an issue to discuss with our developers to propose an proper design.
-2. Post a Pull Request after implementing the new feature or enhancement and add corresponding unit test.
-**Document**
-You can directly post a pull request to fix documents. If you want to add a document, you should first create an issue to check if it is reasonable.
-### Pull Request Workflow
-If you're not familiar with Pull Request, don't worry! The following guidance will tell you how to create a Pull Request step by step. If you want to dive into the develop mode of Pull Request, you can refer to the [official documents](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests)
-#### 1. Fork and clone
-If you are posting a pull request for the first time, you should fork the OpenMMLab repositories by clicking the **Fork** button in the top right corner of the GitHub page, and the forked repositories will appear under your GitHub profile.
-<img src="https://user-images.githubusercontent.com/57566630/167305749-43c7f4e9-449b-4e98-ade5-0c9276d5c9ce.png" width="1200">
-Then, you can clone the repositories to local:
-```shell
-git clone git@github.com:{username}/mmcv.git
 ```
+### Code style
-After that, you should ddd official repository as the upstream repository
+#### Python
-```bash
+We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
-git remote add upstream git@github.com:open-mmlab/mmcv
-```
-Check whether remote repository has been added successfully by `git remote -v`
+We use the following tools for linting and formatting:
-```bash
+- [flake8](http://flake8.pycqa.org/en/latest/): A wrapper around some linter tools.
-origin	git@github.com:{username}/mmcv.git (fetch)
+- [yapf](https://github.com/google/yapf): A formatter for Python files.
-origin	git@github.com:{username}/mmcv.git (push)
+- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
-upstream	git@github.com:open-mmlab/mmcv (fetch)
+- [markdownlint](https://github.com/markdownlint/markdownlint): A linter to check markdown files and flag style issues.
-upstream	git@github.com:open-mmlab/mmcv (push)
+- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
-```
-> Here's a brief introduction to origin and upstream. When we use "git clone", we create an "origin" remote by default, which points to the repository cloned from. As for "upstream", we add it ourselves to point to the target repository. Of course, if you don't like the name "upstream", you could name it as you wish. Usually, we'll push the code to "origin". If the pushed code conflicts with the latest code in official("upstream"), we should pull the latest code from upstream to resolve the conflicts, and then push to "origin" again. The posted Pull Request will be updated automatically.
+Style configurations of yapf and isort can be found in [setup.cfg](./setup.cfg).
-#### 2. Configure pre-commit
+We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
+fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
+The config for a pre-commit hook is stored in [.pre-commit-config](./.pre-commit-config.yaml).
-You should configure [pre-commit](https://pre-commit.com/#intro) in the local development environment to make sure the code style matches that of OpenMMLab. **Note**: The following code should be executed under the MMCV directory.
+After you clone the repository, you will need to install initialize pre-commit hook.
 ```shell
 pip install -U pre-commit
-pre-commit install
-```
-Check that pre-commit is configured successfully, and install the hooks defined in `.pre-commit-config.yaml`.
-```shell
-pre-commit run --all-files
-```
-<img src="https://user-images.githubusercontent.com/57566630/173660750-3df20a63-cb66-4d33-a986-1f643f1d8aaf.png" width="1200">
-<img src="https://user-images.githubusercontent.com/57566630/202368856-0465a90d-8fce-4345-918e-67b8b9c82614.png" width="1200">
-If the installation process is interrupted, you can repeatedly run `pre-commit run ... ` to continue the installation.
-If the code does not conform to the code style specification, pre-commit will raise a warning and  fixes some of the errors automatically.
-<img src="https://user-images.githubusercontent.com/57566630/202369176-67642454-0025-4023-a095-263529107aa3.png" width="1200">
-If we want to commit our code bypassing the pre-commit hook, we can use the `--no-verify` option(**only for temporarily commit**).
-```shell
-git commit -m "xxx" --no-verify
-```
-#### 3. Create a development branch
-After configuring the pre-commit, we should create a branch based on the master branch to develop the new feature or fix the bug. The proposed branch name is `username/pr_name`
-```shell
-git checkout -b yhc/refactor_contributing_doc
-```
-In subsequent development, if the master branch of the local repository is behind the master branch of "upstream", we need to pull the upstream for synchronization, and then execute the above command:
-```shell
-git pull upstream master
-```
-#### 4. Commit the code and pass the unit test
- MMCV introduces mypy to do static type checking to increase the robustness of the code. Therefore, we need to add Type Hints to our code and pass the mypy check. If you are not familiar with Type Hints, you can refer to [this tutorial](https://docs.python.org/3/library/typing.html).
- The committed code should pass through the unit test
-  ```shell
-  # Pass all unit tests
-  pytest tests
-  # Pass the unit test of runner
-  pytest tests/test_runner/test_runner.py
-  ```
-  If the unit test fails for lack of dependencies, you can install the dependencies referring to the [guidance](#unit-test)
- If the documents are modified/added, we should check the rendering result referring to [guidance](#document-rendering)
-#### 5. Push the code to remote
-We could push the local commits to remote after passing through the check of unit test and pre-commit. You can associate the local branch with remote branch by adding `-u` option.
-```shell
-git push -u origin {branch_name}
-```
-This will allow you to use the `git push` command to push code directly next time, without having to specify a branch or the remote repository.
-#### 6. Create a Pull Request
-(1) Create a pull request in GitHub's Pull request interface
-<img src="https://user-images.githubusercontent.com/57566630/201533288-516f7ac4-0b14-4dc8-afbd-912475c368b5.png" width="1200">
-(2) Modify the PR description according to the guidelines so that other developers can better understand your changes
-<img src="https://user-images.githubusercontent.com/57566630/202242953-c91a18ff-e388-4ff9-8591-5fae0ead6c1e.png" width="1200">
-Find more details about Pull Request description in [pull request guidelines](#pr-specs).
-**note**
-(a) The Pull Request description should contain the reason for the change, the content of the change, and the impact of the change, and be associated with the relevant Issue (see [documentation](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)
-(b) If it is your first contribution, please sign the CLA
-<img src="https://user-images.githubusercontent.com/57566630/167307569-a794b967-6e28-4eac-a942-00deb657815f.png" width="1200">
-(c) Check whether the Pull Request pass through the CI
-<img src="https://user-images.githubusercontent.com/57566630/167307490-f9ebf9fa-63c0-4d83-8ba1-081ea169eb3a.png" width="1200">
-MMCV will run unit test for the posted Pull Request on different platforms (Linux, Window, Mac), based on different versions of Python, PyTorch, CUDA to make sure the code is correct. We can see the specific test information by clicking `Details` in the above image so that we can modify the code.
-(3) If the Pull Request passes the CI, then you can wait for the review from other developers. You'll modify the code based on the reviewer's comments, and repeat the steps [4](#4-commit-the-code-and-pass-the-unit-test)-[5](#5-push-the-code-to-remote) until all reviewers approve it. Then, we will merge it ASAP.
-<img src="https://user-images.githubusercontent.com/57566630/202145400-cc2cd8c4-10b0-472f-ba37-07e6f50acc67.png" width="1200">
-#### 7. Resolve conflicts
-If your local branch conflicts with the latest master branch of "upstream", you'll need to resolove them. There are two ways to do this:
-```shell
-git fetch --all --prune
-git rebase upstream/master
 ```
-or
+From the repository folder
-```shell
-git fetch --all --prune
-git merge upstream/master
-```
-If you are very good at handling conflicts, then you can use rebase to resolve conflicts, as this will keep your commit logs tidy. If you are not familiar with `rebase`, then you can use `merge` to resolve conflicts.
-### Guidance
-#### Unit test
-If you cannot run the unit test of some modules for lacking of some dependencies, such as [video](https://github.com/open-mmlab/mmcv/tree/master/mmcv/video) module, you can try to install the following dependencies:
 ```shell
-# Linux
+pre-commit install
-sudo apt-get update -y
-sudo apt-get install -y libturbojpeg
-sudo apt-get install -y ffmpeg
-# Windows
-conda install ffmpeg
 ```
-We should also make sure the committed code will not decrease the coverage of unit test, we could run the following command to check the coverage of unit test:
+Try the following steps to install ruby when you encounter an issue on installing markdownlint
 ```shell
-python -m coverage run -m pytest /path/to/test_file
+# install rvm
-python -m coverage html
+curl -L https://get.rvm.io | bash -s -- --autolibs=read-fail
-# check file in htmlcov/index.html
+[[ -s "$HOME/.rvm/scripts/rvm" ]] && source "$HOME/.rvm/scripts/rvm"
-```
+rvm autolibs disable
-#### Document rendering
-If the documents are modified/added, we should check the rendering result. We could install the dependencies and run the following command to render the documents and check the results:
+# install ruby
+rvm install 2.7.1
-```shell
-pip install -r requirements/docs.txt
-cd docs/zh_cn/
-# or docs/en
-make html
-# check file in ./docs/zh_cn/_build/html/index.html
 ```
-### Code style
+Or refer to [this repo](https://github.com/innerlee/setup) and take [`zzruby.sh`](https://github.com/innerlee/setup/blob/master/zzruby.sh) according its instruction.
-#### Python
+After this on every commit check code linters and formatter will be enforced.
-We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
+>Before you create a PR, make sure that your code lints and is formatted by yapf.
-We use the following tools for linting and formatting:
- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
- [yapf](https://github.com/google/yapf): A formatter for Python files.
- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
-Style configurations of yapf and isort can be found in [setup.cfg](./setup.cfg).
-We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
-fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
-The config for a pre-commit hook is stored in [.pre-commit-config](./.pre-commit-config.yaml).
 #### C++ and CUDA
 We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
-### PR Specs
-1. Use [pre-commit](https://pre-commit.com) hook to avoid issues of code style
-2. One short-time branch should be matched with only one PR
-3. Accomplish a detailed change in one PR. Avoid large PR
-   - Bad: Support Faster R-CNN
-   - Acceptable: Add a box head to Faster R-CNN
-   - Good: Add a parameter to box head to support custom conv-layer number
-4. Provide clear and significant commit message
-5. Provide clear and meaningful PR description
-   - Task name should be clarified in title. The general format is: \[Prefix\] Short description of the PR (Suffix)
-   - Prefix: add new feature \[Feature\], fix bug \[Fix\], related to documents \[Docs\], in developing \[WIP\] (which will not be reviewed temporarily)
-   - Introduce main changes, results and influences on other modules in short description
-   - Associate related issues and pull requests with a milestone
--- a/CONTRIBUTING_zh-CN.md
+++ b/CONTRIBUTING_zh-CN.md
-## 贡献代码
-欢迎加入 MMCV 社区，我们致力于打造最前沿的计算机视觉基础库，我们欢迎任何类型的贡献，包括但不限于
-**修复错误**
-修复代码实现错误的步骤如下：
-1. 如果提交的代码改动较大，建议先提交 issue，并正确描述 issue 的现象、原因和复现方式，讨论后确认修复方案。
-2. 修复错误并补充相应的单元测试，提交拉取请求。
-**新增功能或组件**
-1. 如果新功能或模块涉及较大的代码改动，建议先提交 issue，确认功能的必要性。
-2. 实现新增功能并添单元测试，提交拉取请求。
-**文档补充**
-修复文档可以直接提交拉取请求
-添加文档或将文档翻译成其他语言步骤如下
-1. 提交 issue，确认添加文档的必要性。
-2. 添加文档，提交拉取请求。
-### 拉取请求工作流
-如果你对拉取请求不了解，没关系，接下来的内容将会从零开始，一步一步地指引你如何创建一个拉取请求。如果你想深入了解拉取请求的开发模式，可以参考 github [官方文档](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests)
-#### 1. 复刻仓库
-当你第一次提交拉取请求时，先复刻 OpenMMLab 原代码库，点击 GitHub 页面右上角的 **Fork** 按钮，复刻后的代码库将会出现在你的 GitHub 个人主页下。
-<img src="https://user-images.githubusercontent.com/57566630/167305749-43c7f4e9-449b-4e98-ade5-0c9276d5c9ce.png" width="1200">
-将代码克隆到本地
-```shell
-git clone git@github.com:{username}/mmcv.git
-```
-添加原代码库为上游代码库
-```bash
-git remote add upstream git@github.com:open-mmlab/mmcv
-```
-检查 remote 是否添加成功，在终端输入 `git remote -v`
-```bash
-origin	git@github.com:{username}/mmcv.git (fetch)
-origin	git@github.com:{username}/mmcv.git (push)
-upstream	git@github.com:open-mmlab/mmcv (fetch)
-upstream	git@github.com:open-mmlab/mmcv (push)
-```
-> 这里对 origin 和 upstream 进行一个简单的介绍，当我们使用 git clone 来克隆代码时，会默认创建一个 origin 的 remote，它指向我们克隆的代码库地址，而 upstream 则是我们自己添加的，用来指向原始代码库地址。当然如果你不喜欢他叫 upstream，也可以自己修改，比如叫 open-mmlab。我们通常向 origin 提交代码（即 fork 下来的远程仓库），然后向 upstream 提交一个 pull request。如果提交的代码和最新的代码发生冲突，再从 upstream 拉取最新的代码，和本地分支解决冲突，再提交到 origin。
-#### 2. 配置 pre-commit
-在本地开发环境中，我们使用 [pre-commit](https://pre-commit.com/#intro) 来检查代码风格，以确保代码风格的统一。在提交代码，需要先安装 pre-commit（需要在 MMCV 目录下执行）:
-```shell
-pip install -U pre-commit
-pre-commit install
-```
-检查 pre-commit 是否配置成功，并安装 `.pre-commit-config.yaml` 中的钩子：
-```shell
-pre-commit run --all-files
-```
-<img src="https://user-images.githubusercontent.com/57566630/173660750-3df20a63-cb66-4d33-a986-1f643f1d8aaf.png" width="1200">
-<img src="https://user-images.githubusercontent.com/57566630/202368856-0465a90d-8fce-4345-918e-67b8b9c82614.png" width="1200">
-> 如果你是中国用户，由于网络原因，可能会出现安装失败的情况，这时可以使用国内源
-> pre-commit install -c .pre-commit-config-zh-cn.yaml
-> pre-commit run --all-files -c .pre-commit-config-zh-cn.yaml
-如果安装过程被中断，可以重复执行 `pre-commit run ...` 继续安装。
-如果提交的代码不符合代码风格规范，pre-commit 会发出警告，并自动修复部分错误。
-<img src="https://user-images.githubusercontent.com/57566630/202369176-67642454-0025-4023-a095-263529107aa3.png" width="1200">
-如果我们想临时绕开 pre-commit 的检查提交一次代码，可以在 `git commit` 时加上 `--no-verify`（需要保证最后推送至远程仓库的代码能够通过 pre-commit 检查）。
-```shell
-git commit -m "xxx" --no-verify
-```
-#### 3. 创建开发分支
-安装完 pre-commit 之后，我们需要基于 master 创建开发分支，建议的分支命名规则为 `username/pr_name`。
-```shell
-git checkout -b yhc/refactor_contributing_doc
-```
-在后续的开发中，如果本地仓库的 master 分支落后于 upstream 的 master 分支，我们需要先拉取 upstream 的代码进行同步，再执行上面的命令
-```shell
-git pull upstream master
-```
-#### 4. 提交代码并在本地通过单元测试
- MMCV 引入了 mypy 来做静态类型检查，以增加代码的鲁棒性。因此我们在提交代码时，需要补充 Type Hints。具体规则可以参考[教程](https://zhuanlan.zhihu.com/p/519335398)。
- 提交的代码同样需要通过单元测试
-  ```shell
-  # 通过全量单元测试
-  pytest tests
-  # 我们需要保证提交的代码能够通过修改模块的单元测试，以 runner 为例
-  pytest tests/test_runner/test_runner.py
-  ```
-  如果你由于缺少依赖无法运行修改模块的单元测试，可以参考[指引-单元测试](#单元测试)
- 如果修改/添加了文档，参考[指引](#文档渲染)确认文档渲染正常。
-#### 5. 推送代码到远程
-代码通过单元测试和 pre-commit 检查后，将代码推送到远程仓库，如果是第一次推送，可以在 `git push` 后加上 `-u` 参数以关联远程分支
-```shell
-git push -u origin {branch_name}
-```
-这样下次就可以直接使用 `git push` 命令推送代码了，而无需指定分支和远程仓库。
-#### 6. 提交拉取请求（PR）
-(1) 在 GitHub 的 Pull request 界面创建拉取请求
-<img src="https://user-images.githubusercontent.com/57566630/201533288-516f7ac4-0b14-4dc8-afbd-912475c368b5.png" width="1200">
-(2) 根据指引修改 PR 描述，以便于其他开发者更好地理解你的修改
-<img src="https://user-images.githubusercontent.com/57566630/202242953-c91a18ff-e388-4ff9-8591-5fae0ead6c1e.png" width="1200">
-描述规范详见[拉取请求规范](#拉取请求规范)
-&#160;
-**注意事项**
-(a) PR 描述应该包含修改理由、修改内容以及修改后带来的影响，并关联相关 Issue（具体方式见[文档](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)）
-(b) 如果是第一次为 OpenMMLab 做贡献，需要签署 CLA
-<img src="https://user-images.githubusercontent.com/57566630/167307569-a794b967-6e28-4eac-a942-00deb657815f.png" width="1200">
-(c) 检查提交的 PR 是否通过 CI（集成测试）
-<img src="https://user-images.githubusercontent.com/57566630/167307490-f9ebf9fa-63c0-4d83-8ba1-081ea169eb3a.png" width="1200">
-MMCV 会在不同的平台（Linux、Window、Mac），基于不同版本的 Python、PyTorch、CUDA 对提交的代码进行单元测试，以保证代码的正确性，如果有任何一个没有通过，我们可点击上图中的 `Details` 来查看具体的测试信息，以便于我们修改代码。
-(3) 如果 PR 通过了 CI，那么就可以等待其他开发者的 review，并根据 reviewer 的意见，修改代码，并重复 [4](#4-提交代码并本地通过单元测试)-[5](#5-推送代码到远程) 步骤，直到 reviewer 同意合入 PR。
-<img src="https://user-images.githubusercontent.com/57566630/202145400-cc2cd8c4-10b0-472f-ba37-07e6f50acc67.png" width="1200">
-所有 reviewer 同意合入 PR 后，我们会尽快将 PR 合并到主分支。
-#### 7. 解决冲突
-随着时间的推移，我们的代码库会不断更新，这时候，如果你的 PR 与主分支存在冲突，你需要解决冲突，解决冲突的方式有两种：
-```shell
-git fetch --all --prune
-git rebase upstream/master
-```
-或者
-```shell
-git fetch --all --prune
-git merge upstream/master
-```
-如果你非常善于处理冲突，那么可以使用 rebase 的方式来解决冲突，因为这能够保证你的 commit log 的整洁。如果你不太熟悉 `rebase` 的使用，那么可以使用 `merge` 的方式来解决冲突。
-### 指引
-#### 单元测试
-如果你无法正常执行部分模块的单元测试，例如 [video](https://github.com/open-mmlab/mmcv/tree/master/mmcv/video) 模块，可能是你的当前环境没有安装以下依赖
-```shell
-# Linux
-sudo apt-get update -y
-sudo apt-get install -y libturbojpeg
-sudo apt-get install -y ffmpeg
-# Windows
-conda install ffmpeg
-```
-在提交修复代码错误或新增特性的拉取请求时，我们应该尽可能的让单元测试覆盖所有提交的代码，计算单元测试覆盖率的方法如下
-```shell
-python -m coverage run -m pytest /path/to/test_file
-python -m coverage html
-# check file in htmlcov/index.html
-```
-#### 文档渲染
-在提交修复代码错误或新增特性的拉取请求时，可能会需要修改/新增模块的 docstring。我们需要确认渲染后的文档样式是正确的。
-本地生成渲染后的文档的方法如下
-```shell
-pip install -r requirements/docs.txt
-cd docs/zh_cn/
-# or docs/en
-make html
-# check file in ./docs/zh_cn/_build/html/index.html
-```
-### 代码风格
-#### Python
-[PEP8](https://www.python.org/dev/peps/pep-0008/) 作为 OpenMMLab 算法库首选的代码规范，我们使用以下工具检查和格式化代码
- [flake8](https://github.com/PyCQA/flake8): Python 官方发布的代码规范检查工具，是多个检查工具的封装
- [isort](https://github.com/timothycrosley/isort): 自动调整模块导入顺序的工具
- [yapf](https://github.com/google/yapf): Google 发布的代码规范检查工具
- [codespell](https://github.com/codespell-project/codespell): 检查单词拼写是否有误
- [mdformat](https://github.com/executablebooks/mdformat): 检查 markdown 文件的工具
- [docformatter](https://github.com/myint/docformatter): 格式化 docstring 的工具
-yapf 和 isort 的配置可以在 [setup.cfg](./setup.cfg) 找到
-通过配置 [pre-commit hook](https://pre-commit.com/) ，我们可以在提交代码时自动检查和格式化 `flake8`、`yapf`、`isort`、`trailing whitespaces`、`markdown files`，
-修复 `end-of-files`、`double-quoted-strings`、`python-encoding-pragma`、`mixed-line-ending`，调整 `requirments.txt` 的包顺序。
-pre-commit 钩子的配置可以在 [.pre-commit-config](./.pre-commit-config.yaml) 找到。
-pre-commit 具体的安装使用方式见[拉取请求](#2-配置-pre-commit)。
-更具体的规范请参考 [OpenMMLab 代码规范](code_style.md)。
-#### C++ and CUDA
-C++ 和 CUDA 的代码规范遵从 [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html)
-### 拉取请求规范
-1. 使用 [pre-commit hook](https://pre-commit.com)，尽量减少代码风格相关问题
-2. 一个`拉取请求`对应一个短期分支
-3. 粒度要细，一个`拉取请求`只做一件事情，避免超大的`拉取请求`
-   - Bad：实现 Faster R-CNN
-   - Acceptable：给 Faster R-CNN 添加一个 box head
-   - Good：给 box head 增加一个参数来支持自定义的 conv 层数
-4. 每次 Commit 时需要提供清晰且有意义 commit 信息
-5. 提供清晰且有意义的`拉取请求`描述
-   - 标题写明白任务名称，一般格式:\[Prefix\] Short description of the pull request (Suffix)
-   - prefix: 新增功能 \[Feature\], 修 bug \[Fix\], 文档相关 \[Docs\], 开发中 \[WIP\] (暂时不会被review)
-   - 描述里介绍`拉取请求`的主要修改内容，结果，以及对其他部分的影响, 参考`拉取请求`模板
-   - 关联相关的`议题` (issue) 和其他`拉取请求`
-6. 如果引入了其他三方库，或借鉴了三方库的代码，请确认他们的许可证和 mmcv 兼容，并在借鉴的代码上补充 `This code is inspired from http://`
--- a/Dockerfile
+++ b/Dockerfile
+FROM python:3.7
+WORKDIR /mmcv
+COPY . /mmcv
+RUN pip install -e .
--- a/LICENSES.md
+++ b/LICENSES.md
@@ -2,10 +2,7 @@
 In this file, we list the operations with other licenses instead of Apache 2.0. Users should be careful about adopting these operations in any commercial matters.
-|    Operation     |                                                                             Files                                                                              |    License     |
+|    Operation     |                                                                    Files                                                                              |    License     |
-| :--------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------: |
+| :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------: | :------------: |
-|    upfirdn2d     |          [mmcv/ops/csrc/pytorch/cuda/upfirdn2d_kernel.cu](https://github.com/open-mmlab/mmcv/tree/2.x/mmcv/ops/csrc/pytorch/cuda/upfirdn2d_kernel.cu)          | NVIDIA License |
+|    upfirdn2d     |          [mmcv/ops/csrc/pytorch/cuda/upfirdn2d_kernel.cu](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/csrc/pytorch/cuda/upfirdn2d_kernel.cu)          | NVIDIA License |
-| fused_leaky_relu | [mmcv/ops/csrc/pytorch/cuda/fused_bias_leakyrelu_cuda.cu](https://github.com/open-mmlab/mmcv/tree/2.x/mmcv/ops/csrc/pytorch/cuda/fused_bias_leakyrelu_cuda.cu) | NVIDIA License |
+| fused_leaky_relu | [mmcv/ops/csrc/pytorch/cuda/fused_bias_leakyrelu_cuda.cu](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/csrc/pytorch/cuda/fused_bias_leakyrelu_cuda.cu) | NVIDIA License |
-|     bias_act     |             [mmcv/ops/csrc/pytorch/cuda/bias_act_cuda.cu](https://github.com/open-mmlab/mmcv/tree/2.x/mmcv/ops/csrc/pytorch/cuda/bias_act_cuda.cu)             | NVIDIA License |
-|  filtered_lrelu  |            [mmcv/ops/csrc/pytorch/cuda/filtered_lrelu.cu](https://github.com/open-mmlab/mmcv/tree/2.x/mmcv/ops/csrc/pytorch/cuda/filtered_lrelu.cu)            | NVIDIA License |
-|  conv2d_gradfix  |                              [mmcv/ops/conv2d_gradfix.py](https://github.com/open-mmlab/mmcv/tree/2.x/mmcv/ops/conv2d_gradfix.py)                              | NVIDIA License |
--- a/MANIFEST.in
+++ b/MANIFEST.in
 include requirements/runtime.txt
+include mmcv/model_zoo/open_mmlab.json mmcv/model_zoo/deprecated.json mmcv/model_zoo/mmcls.json
 include mmcv/ops/csrc/common/cuda/*.cuh mmcv/ops/csrc/common/cuda/*.hpp mmcv/ops/csrc/common/*.hpp
 include mmcv/ops/csrc/pytorch/*.cpp mmcv/ops/csrc/pytorch/cuda/*.cu mmcv/ops/csrc/pytorch/cuda/*.cpp mmcv/ops/csrc/pytorch/cpu/*.cpp
 include mmcv/ops/csrc/parrots/*.h mmcv/ops/csrc/parrots/*.cpp
-include mmcv/ops/csrc/pytorch/mps/*.mm mmcv/ops/csrc/common/mps/*.h mmcv/ops/csrc/common/mps/*.mm
-recursive-include mmcv/ops/csrc/ *.h *.hpp *.cpp *.cuh *.cu *.mm
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ MMCV是计算机视觉研究的基础库，主要提供以下功能：图像处
 + Python 3.7、3.8、3.9
 ### 1、使用pip方式安装
-mmcv whl包下载目录：[https://cancon.hpccube.com:65024/4/main/mmcv/dtk23.04](https://cancon.hpccube.com:65024/4/main/mmcv/dtk23.04)，选择对应的pytorch版本和python版本下载对应mmcv的whl包
+mmcv whl包下载目录：[https://cancon.hpccube.com:65024/4/main/mmcv](https://cancon.hpccube.com:65024/4/main/mmcv)，选择对应的pytorch版本和python版本下载对应mmcv的whl包
 ```shell
 pip install mmcv* (下载的mmcv的whl包)
 ```
@@ -18,7 +18,7 @@ pip install mmcv* (下载的mmcv的whl包)
 1. 基于光源pytorch基础镜像环境：镜像下载地址：[https://sourcefind.cn/#/image/dcu/pytorch](https://sourcefind.cn/#/image/dcu/pytorch)，根据pytorch、python、dtk及系统下载对应的镜像版本。
-2. 基于现有python环境：安装pytorch，pytorch whl包下载目录：[https://cancon.hpccube.com:65024/4/main/pytorch/dtk23.04](https://cancon.hpccube.com:65024/4/main/pytorch/dtk23.04)，根据python、dtk版本,下载对应pytorch的whl包。安装命令如下：
+2. 基于现有python环境：安装pytorch，pytorch whl包下载目录：[https://cancon.hpccube.com:65024/4/main/pytorch/dtk24.04.1](https://cancon.hpccube.com:65024/4/main/pytorch/dtk24.04.1)，根据python、dtk版本,下载对应pytorch的whl包。安装命令如下：
 ```shell
 pip install torch* (下载的torch的whl包)
 pip install setuptools==59.5.0 wheel
@@ -32,11 +32,17 @@ git clone https://developer.hpccube.com/codes/aicomponent/mmcv # 根据编译需
 - 提供2种源码编译方式（进入mmcv目录）：
 ```
 1. 编译whl包并安装
-MMCV_WITH_OPS=1 ROCM_HOME=${ROCM_PATH} python3 setup.py -v bdist_wheel
+MMCV_WITH_OPS=1 python3 setup.py -v bdist_wheel
 pip install dist/mmcv*
 2. 源码编译安装
-MMCV_WITH_OPS=1 ROCM_HOME=${ROCM_PATH} python3 setup.py install
+MMCV_WITH_OPS=1 python3 setup.py install
+```
+3. 测试验证
+```
+cd test
+pytest -s ./test_arraymisc.py
+pytest -s ./test_ops
 ```
 #### 注意事项
 + 若使用pip install下载安装过慢，可添加pypi清华源：-i https://pypi.tuna.tsinghua.edu.cn/simple/
@@ -52,3 +58,4 @@ MMCV_WITH_OPS=1 ROCM_HOME=${ROCM_PATH} python3 setup.py install
 - [README_ORIGIN](README_ORIGIN.md)
 - [README_zh-CN](README_zh-CN.md)
 - [https://github.com/open-mmlab/mmcv](https://github.com/open-mmlab/mmcv)
--- a/README_ORIGIN.md
+++ b/README_ORIGIN.md
 <div align="center">
-  <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/en/mmcv-logo.png" width="300"/>
+    <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/mmcv-logo.png" width="300"/>
-  <div>&nbsp;</div>
-  <div align="center">
-    <b><font size="5">OpenMMLab website</font></b>
-    <sup>
-      <a href="https://openmmlab.com">
-        <i><font size="4">HOT</font></i>
-      </a>
-    </sup>
-    &nbsp;&nbsp;&nbsp;&nbsp;
-    <b><font size="5">OpenMMLab platform</font></b>
-    <sup>
-      <a href="https://platform.openmmlab.com">
-        <i><font size="4">TRY IT OUT</font></i>
-      </a>
-    </sup>
-  </div>
-  <div>&nbsp;</div>
 </div>
-[![docs](https://img.shields.io/badge/docs-2.x-blue)](https://mmcv.readthedocs.io/en/2.x/)
+[![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) [![codecov](https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmcv) [![license](https://img.shields.io/github/license/open-mmlab/mmcv.svg)](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
-[![platform](https://img.shields.io/badge/platform-Linux%7CWindows%7CmacOS-blue)](https://mmcv.readthedocs.io/en/2.x/get_started/installation.html)
-[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmcv)](https://pypi.org/project/mmcv/)
-[![pytorch](https://img.shields.io/badge/pytorch-1.6~1.13-orange)](https://pytorch.org/get-started/previous-versions/)
-[![cuda](https://img.shields.io/badge/cuda-9.2~11.7-green)](https://developer.nvidia.com/cuda-downloads)
-[![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)
-[![codecov](https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmcv)
-[![license](https://img.shields.io/github/license/open-mmlab/mmcv.svg)](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
 English | [简体中文](README_zh-CN.md)
 ## Introduction
-MMCV is a foundational library for computer vision research and it provides the following functionalities:
+MMCV is a foundational library for computer vision research and supports many
+research projects as below:
- [Image/Video processing](https://mmcv.readthedocs.io/en/2.x/understand_mmcv/data_process.html)
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [Image and annotation visualization](https://mmcv.readthedocs.io/en/2.x/understand_mmcv/visualization.html)
+- [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages.
- [Image transformation](https://mmcv.readthedocs.io/en/2.x/understand_mmcv/data_transform.html)
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [Various CNN architectures](https://mmcv.readthedocs.io/en/2.x/understand_mmcv/cnn.html)
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [High-quality implementation of common CPU and CUDA ops](https://mmcv.readthedocs.io/en/2.x/understand_mmcv/ops.html)
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
+- [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab FewShot Learning Toolbox and Benchmark.
-It supports the following systems:
+It provides the following functionalities.
- Linux
+- Universal IO APIs
- Windows
+- Image/Video processing
- macOS
+- Image and annotation visualization
+- Useful utilities (progress bar, timer, ...)
+- PyTorch runner with hooking mechanism
+- Various CNN architectures
+- High-quality implementation of common CUDA ops
-See the [documentation](http://mmcv.readthedocs.io/en/2.x) for more features and usage.
+See the [documentation](http://mmcv.readthedocs.io/en/latest) for more features and usage.
-Note: MMCV requires Python 3.7+.
+Note: MMCV requires Python 3.6+.
 ## Installation
 There are two versions of MMCV:
- **mmcv**: comprehensive, with full features and various CUDA ops out of the box. It takes longer time to build.
+- **mmcv-full**: comprehensive, with full features and various CUDA ops out of box. It takes longer time to build.
- **mmcv-lite**: lite, without CUDA ops but all other features, similar to mmcv\<1.0.0. It is useful when you do not need those CUDA ops.
+- **mmcv**: lite, without CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops.
 **Note**: Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other. `Installing the full version is highly recommended if CUDA is available`.
-### Install mmcv
+a. Install the full version.
+Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/).
-Before installing mmcv, make sure that PyTorch has been successfully installed following the [PyTorch official installation guide](https://github.com/pytorch/pytorch#installation). For apple silicon users, please use PyTorch 1.13+.
+We provide pre-built mmcv packages (recommended) with different PyTorch and CUDA versions to simplify the building. In addition, you can run [check_installation.py](.dev_scripts/check_installation.py) to check the installation of mmcv-full after running the installation commands.
-The command to install mmcv:
+i. Install the latest version.
-```bash
+The rule for installing the latest ``mmcv-full`` is as follows:
-pip install -U openmim
-mim install "mmcv>=2.0.0rc1"
+```shell
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
 ```
-If you need to specify the version of mmcv, you can use the following command:
+Please replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired one. For example,
+to install the latest ``mmcv-full`` with ``CUDA 11.1`` and ``PyTorch 1.9.0``, use the following command:
-```bash
+```shell
-mim install mmcv==2.0.0rc3
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
 ```
-If you find that the above installation command does not use a pre-built package ending with `.whl` but a source package ending with `.tar.gz`, you may not have a pre-build package corresponding to the PyTorch or CUDA or mmcv version, in which case you can [build mmcv from source](https://mmcv.readthedocs.io/en/2.x/get_started/build.html).
+**Note**: mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well. For example, if your PyTorch version is 1.8.1 and CUDA version is 11.1, you can use the following command to install mmcv-full.
-<details>
+```shell
-<summary>Installation log using pre-built packages</summary>
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
+```
-Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br />
+For more details, please refer the the following tables and delete ``=={mmcv_version}``.
-Collecting mmcv<br />
-<b>Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv-2.0.0rc3-cp38-cp38-manylinux1_x86_64.whl</b>
-</details>
+ii. Install a specified version.
-<details>
+The rule for installing a specified ``mmcv-full`` is as follows:
-<summary>Installation log using source packages</summary>
-Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br />
+```shell
-Collecting mmcv==2.0.0rc3<br />
+pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
-<b>Downloading mmcv-2.0.0rc3.tar.gz</b>
+```
+First of all, please refer to the Releases and replace ``{mmcv_version}`` a specified one. e.g. ``1.3.9``.
+Then replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired versions. For example,
+to install ``mmcv-full==1.3.9`` with ``CUDA 11.1`` and ``PyTorch 1.9.0``, use the following command:
-</details>
+```shell
+pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
+```
-For more installation methods, please refer to the [Installation documentation](https://mmcv.readthedocs.io/en/2.x/get_started/installation.html).
+For more details, please refer the the following tables.
+<table class="docutils">
+  <tbody>
+    <tr>
+      <th width="80"> CUDA </th>
+      <th valign="bottom" align="left" width="120">torch1.10</th>
+      <th valign="bottom" align="left" width="100">torch1.9</th>
+      <th valign="bottom" align="left" width="100">torch1.8</th>
+      <th valign="bottom" align="left" width="100">torch1.7</th>
+      <th valign="bottom" align="left" width="100">torch1.6</th>
+      <th valign="bottom" align="left" width="100">torch1.5</th>
+    </tr>
+    <tr>
+      <td align="left">11.3</td>
+      <td align="left"><details><summary> install </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"></code></pre> </details> </td>
+      <td align="left"> </td>
+      <td align="left"> </td>
+      <td align="left"> </td>
+    </tr>
+    <tr>
+      <td align="left">11.1</td>
+      <td align="left"><details><summary> install </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> install </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> install </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>
+      <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"><details><summary> install </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>
+      <td align="left"> </td>
+    </tr>
+    <tr>
+      <td align="left">10.2</td>
+      <td align="left"><details><summary> install </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> install </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> install </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> install </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> install </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>
+      <td align="left"><details><summary> install </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.5.0/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"><details><summary> install </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> install </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> install </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>
+      <td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.5.0/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"><details><summary> install </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> install </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>
+      <td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu92/torch1.5.0/index.html</code></pre> </details> </td>
+    </tr>
+    <tr>
+      <td align="left">cpu</td>
+      <td align="left"><details><summary> install </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> install </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> install </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> install </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> install </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>
+      <td align="left"><details><summary> install </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.5.0/index.html</code></pre> </details> </td>
+    </tr>
+  </tbody>
+</table>
+**Note**: The pre-built packages provided above do not include all versions of mmcv-full, you can click on the corresponding links to see the supported versions. For example, you can click [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html) and you can see that `cu102-torch1.8.0` only provides 1.3.0 and above versions of mmcv-full. In addition, We no longer provide `mmcv-full` pre-built packages compiled with `PyTorch 1.3 & 1.4` since v1.3.17. You can find previous versions that compiled with PyTorch 1.3 & 1.4 [here](./docs/get_started/previous_versions.md). The compatibility is still ensured in our CI, but we will discard the support of PyTorch 1.3 & 1.4 next year.
+Another way is to compile locally by running
+```python
+pip install mmcv-full
+```
-### Install mmcv-lite
+Note that the local compiling may take up to 10 mins.
-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).
+b. Install the lite version.
-```bash
+```python
-pip install -U openmim
+pip install mmcv
-mim install "mmcv-lite>=2.0.0rc1"
 ```
+c. Install full version with custom operators for onnxruntime
+- Check [here](docs/deployment/onnxruntime_op.md) for detailed instruction.
+If you would like to build MMCV from source, please refer to the [guide](https://mmcv.readthedocs.io/en/latest/get_started/build.html).
 ## FAQ
 If you face some installation issues, CUDA related issues or RuntimeErrors,
-you may first refer to this [Frequently Asked Questions](https://mmcv.readthedocs.io/en/2.x/faq.html).
+you may first refer to this [Frequently Asked Questions](https://mmcv.readthedocs.io/en/latest/faq.html).
-If you face installation problems or runtime issues, you may first refer to this [Frequently Asked Questions](https://mmcv.readthedocs.io/en/2.x/faq.html) to see if there is a solution. If the problem is still not solved, feel free to open an [issue](https://github.com/open-mmlab/mmcv/issues).
 ## Citation
@@ -135,27 +220,3 @@ We appreciate all contributions to improve MMCV. Please refer to [CONTRIBUTING.m
 ## License
 MMCV is released under the Apache 2.0 license, while some specific operations in this library are with other licenses. Please refer to [LICENSES.md](LICENSES.md) for the careful check, if you are using our code for commercial matters.
-## Projects in OpenMMLab
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [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.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [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.
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
 <div align="center">
-  <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/en/mmcv-logo.png" width="300"/>
+    <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/mmcv-logo.png" width="300"/>
-  <div>&nbsp;</div>
-  <div align="center">
-    <b><font size="5">OpenMMLab 官网</font></b>
-    <sup>
-      <a href="https://openmmlab.com">
-        <i><font size="4">HOT</font></i>
-      </a>
-    </sup>
-    &nbsp;&nbsp;&nbsp;&nbsp;
-    <b><font size="5">OpenMMLab 开放平台</font></b>
-    <sup>
-      <a href="https://platform.openmmlab.com">
-        <i><font size="4">TRY IT OUT</font></i>
-      </a>
-    </sup>
-  </div>
-  <div>&nbsp;</div>
 </div>
-[![docs](https://img.shields.io/badge/docs-2.x-blue)](https://mmcv.readthedocs.io/zh_CN/2.x/)
+[![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) [![codecov](https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmcv) [![license](https://img.shields.io/github/license/open-mmlab/mmcv.svg)](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
-[![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/)
-[![pytorch](https://img.shields.io/badge/pytorch-1.6~1.13-orange)](https://pytorch.org/get-started/previous-versions/)
-[![cuda](https://img.shields.io/badge/cuda-9.2~11.7-green)](https://developer.nvidia.com/cuda-downloads)
-[![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)
-[![codecov](https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmcv)
-[![license](https://img.shields.io/github/license/open-mmlab/mmcv.svg)](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
 [English](README.md) | 简体中文
 ## 简介
-MMCV 是一个面向计算机视觉的基础库，它提供了以下功能：
+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：
- [图像和视频处理](https://mmcv.readthedocs.io/zh_CN/2.x/understand_mmcv/data_process.html)
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
- [图像和标注结果可视化](https://mmcv.readthedocs.io/zh_CN/2.x/understand_mmcv/visualization.html)
+- [MIM](https://github.com/open-mmlab/mim): OpenMMLab 项目、算法、模型的统一入口
- [图像变换](https://mmcv.readthedocs.io/zh_CN/2.x/understand_mmcv/data_transform.html)
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱与测试基准
- [多种 CNN 网络结构](https://mmcv.readthedocs.io/zh_CN/2.x/understand_mmcv/cnn.html)
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 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)
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用3D目标检测平台
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱与测试基准
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态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
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 新一代生成模型工具箱
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
-MMCV 支持多种平台，包括：
+MMCV 提供了如下众多功能：
- Linux
+- 通用的 IO 接口
- Windows
+- 图像和视频处理
- macOS
+- 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
-如想了解更多特性和使用，请参考[文档](http://mmcv.readthedocs.io/zh_CN/2.x)。
+如想了解更多特性和使用，请参考[文档](http://mmcv.readthedocs.io/en/latest)。
-提示: MMCV 需要 Python 3.7 以上版本。
+提示: MMCV 需要 Python 3.6 以上版本。
 ## 安装
 MMCV 有两个版本：
- **mmcv**: 完整版，包含所有的特性以及丰富的开箱即用的 CUDA 算子。注意完整版本可能需要更长时间来编译。
+- **mmcv-full**: 完整版，包含所有的特性以及丰富的开箱即用的 CUDA 算子。注意完整版本可能需要更长时间来编译。
- **mmcv-lite**: 精简版，不包含 CUDA 算子但包含其余所有特性和功能，类似 MMCV 1.0 之前的版本。如果你不需要使用 CUDA 算子的话，精简版可以作为一个考虑选项。
+- **mmcv**: 精简版，不包含 CUDA 算子但包含其余所有特性和功能，类似 MMCV 1.0 之前的版本。如果你不需要使用 CUDA 算子的话，精简版可以作为一个考虑选项。
+**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-full`。
-**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-full 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/)。
-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)。如果你使用的是搭载 apple silicon 的 mac 设备，请安装 PyTorch 1.13+ 的版本。
+我们提供了不同 PyTorch 和 CUDA 版本的 mmcv-full 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-full 是否安装成功。
-安装 mmcv 的命令如下：
+i. 安装最新版本
-```bash
+如下是安装最新版 ``mmcv-full`` 的命令
-pip install -U openmim
-mim install "mmcv>=2.0.0rc1"
+```shell
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
 ```
-如果需要指定 mmcv 的版本，可以使用以下命令
+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-full``，使用如下替换过的命令
-```bash
+```shell
-mim install mmcv==2.0.0rc3
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
 ```
-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)。
+**注意**: PyTorch 在 1.x.0 和 1.x.1 之间通常是兼容的，故 mmcv-full 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-full。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-full。
+```shell
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
+```
-<details>
+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。
-<summary>使用预编译包的安装日志</summary>
-Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br />
+ii. 安装特定的版本
-Collecting mmcv<br />
-<b>Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv-2.0.0rc3-cp38-cp38-manylinux1_x86_64.whl</b>
-</details>
+如下是安装特定版本 ``mmcv-full`` 的命令
-<details>
+```shell
-<summary>使用源码包的安装日志</summary>
+pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+```
-Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br />
+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 ``1.3.9``。
-Collecting mmcv==2.0.0rc3<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-full`` 1.3.9 版本，使用如下替换过的命令
-<b>Downloading mmcv-2.0.0rc3.tar.gz</b>
-</details>
+```shell
+pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
+```
-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)。
+对于更多的 PyTorch 和 CUDA 版本组合，请参考下表：
+<table class="docutils">
+  <tbody>
+    <tr>
+      <th width="80"> CUDA </th>
+      <th valign="bottom" align="left" width="120">torch1.10</th>
+      <th valign="bottom" align="left" width="100">torch1.9</th>
+      <th valign="bottom" align="left" width="100">torch1.8</th>
+      <th valign="bottom" align="left" width="100">torch1.7</th>
+      <th valign="bottom" align="left" width="100">torch1.6</th>
+      <th valign="bottom" align="left" width="100">torch1.5</th>
+    </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/torch1.10.0/index.html</code></pre> </details> </td>
+      <td align="left"></td>
+      <td align="left"></code></pre> </details> </td>
+      <td align="left"> </td>
+      <td align="left"> </td>
+      <td align="left"> </td>
+    </tr>
+    <tr>
+      <td align="left">11.1</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>
+      <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"><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>
+      <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/torch1.10.0/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>
+      <td align="left"><details><summary> 安装 </summary><pre><code>pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.5.0/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"><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>
+      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.5.0/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"><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>
+      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cu92/torch1.5.0/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/torch1.10.0/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>
+      <td align="left"><details><summary> 安装 </summary><pre><code> pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.5.0/index.html</code></pre> </details> </td>
+    </tr>
+  </tbody>
+</table>
+**注意**：以上提供的预编译包并不囊括所有的 mmcv-full 版本，你可以点击对应链接查看支持的版本。例如，点击 [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html)，可以看到 `cu102-torch1.8.0` 只提供了 1.3.0 及以上的 mmcv-full 版本。另外，从 `mmcv v1.3.17` 开始，我们不再提供`PyTorch 1.3 & 1.4` 对应的 mmcv-full 预编译包。你可以在 [这](./docs_zh_CN/get_started/previous_versions.md) 找到 `PyTorch 1.3 & 1.4` 对应的预编包。虽然我们不再提供 `PyTorch 1.3 & 1.4` 对应的预编译包，但是我们依然在 CI 中保证对它们的兼容持续到下一年。
+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-full
+```
-### 安装 mmcv-lite
+但注意本地编译可能会耗时 10 分钟以上。
-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)。
+b. 安装精简版
-```bash
+```python
-pip install -U openmim
+pip install mmcv
-mim install "mmcv-lite>=2.0.0rc1"
 ```
+c. 安装完整版并且编译 onnxruntime 的自定义算子
+- 详细的指南请查看 [这里](docs/deployment/onnxruntime_op.md)。
+如果想从源码编译 MMCV，请参考[该文档](https://mmcv.readthedocs.io/en/latest/get_started/build.html)。
 ## FAQ
-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)。
+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/en/latest/faq.html) 看是否已经有解决方案。
 ## 贡献指南
@@ -119,37 +203,12 @@ mim install "mmcv-lite>=2.0.0rc1"
 ## 许可证
 `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=K0QI8ByU)，或添加微信小助手”OpenMMLabwx“加入官方交流微信群。
+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=GJP18SjI)
 <div align="center">
-<img src="https://user-images.githubusercontent.com/25839884/205870927-39f4946d-8751-4219-a4c0-740117558fd7.jpg" height="400" />  <img src="https://user-images.githubusercontent.com/25839884/203904835-62392033-02d4-4c73-a68c-c9e4c1e2b07f.jpg" height="400" /> <img src="https://user-images.githubusercontent.com/25839884/205872898-e2e6009d-c6bb-4d27-8d07-117e697a3da8.jpg" height="400" />
+<img src="docs/_static/zhihu_qrcode.jpg" height="400" />  <img src="docs/_static/qq_group_qrcode.jpg" height="400" />
 </div>
 我们会在 OpenMMLab 社区为大家

--- a/TERMINOLOGY.md
+++ b/TERMINOLOGY.md
@@ -4,27 +4,27 @@ This document is used as a reference for English-Chinese terminology translation
 该文档用作中英文翻译对照参考。
-|      English      |     中文     |
+| English | 中文 |
-| :---------------: | :----------: |
+| :-----: | :---:|
-|    annotation     |     标注     |
+| annotation | 标注 |
-|     backbone      |   主干网络   |
+| backbone | 主干网络 |
-|     benchmark     |   基准测试   |
+| benchmark | 基准测试 |
-|    checkpoint     | 模型权重文件 |
+| checkpoint | 模型权重文件 |
-|    classifier     |    分类器    |
+| classifier | 分类器 |
-|     cls_head      |    分类头    |
+| cls_head | 分类头 |
-|      decoder      |    解码器    |
+| decoder | 解码器 |
-|     detector      |    检测器    |
+| detector | 检测器 |
-|      encoder      |    编码器    |
+| encoder | 编码器 |
-|     finetune      |     微调     |
+| finetune | 微调 |
-|   ground truth    |   真实标签   |
+| ground truth | 真实标签 |
-|       hook        |     钩子     |
+| hook | 钩子 |
-|     localizer     |    定位器    |
+| localizer | 定位器 |
-|       neck        |   模型颈部   |
+| neck | 模型颈部 |
-|     pipeline      |    流水线    |
+| pipeline | 流水线 |
-|    recognizer     |    识别器    |
+| recognizer | 识别器 |
-|     register      |    注册器    |
+| register | 注册器 |
-|     schedule      |     调整     |
+| schedule | 调整 |
-|     scheduler     |    调度器    |
+| scheduler | 调度器 |
-|     segmentor     |    分割器    |
+| segmentor | 分割器 |
-|      tensor       |     张量     |
+| tensor | 张量 |
-| training schedule |   训练策略   |
+| training schedule | 训练策略 |
--- a/docker/README.md
+++ b/docker/README.md
-# Docker images
-There are two `Dockerfile` files to build docker images, one to build an image with the mmcv pre-built package and the other with the mmcv development environment.
-```text
-.
-|-- README.md
-|-- dev  # build with mmcv development environment
-|   `-- Dockerfile
-`-- release  # build with mmcv pre-built package
-    `-- Dockerfile
-```
-## Build docker images
-### Build with mmcv pre-built package
-Build with local repository
-```bash
-git clone https://github.com/open-mmlab/mmcv.git && cd mmcv
-docker build -t mmcv -f docker/release/Dockerfile .
-```
-Or build with remote repository
-```bash
-docker build -t mmcv https://github.com/open-mmlab/mmcv.git#master:docker/release
-```
-The [Dockerfile](release/Dockerfile) installs latest released version of mmcv by default, but you can specify mmcv versions to install expected versions.
-```bash
-docker image build -t mmcv -f docker/release/Dockerfile --build-arg MMCV=2.0.0rc1 .
-```
-If you also want to use other versions of PyTorch and CUDA, you can also pass them when building docker images.
-An example to build an image with PyTorch 1.11 and CUDA 11.3.
-```bash
-docker build -t mmcv -f docker/release/Dockerfile \
-    --build-arg PYTORCH=1.9.0 \
-    --build-arg CUDA=11.1 \
-    --build-arg CUDNN=8 \
-    --build-arg MMCV=2.0.0rc1 .
-```
-More available versions of PyTorch and CUDA can be found at [dockerhub/pytorch](https://hub.docker.com/r/pytorch/pytorch/tags).
-### Build with mmcv development environment
-If you want to build an docker image with the mmcv development environment, you can use the following command
-```bash
-git clone https://github.com/open-mmlab/mmcv.git && cd mmcv
-docker build -t mmcv -f docker/dev/Dockerfile --build-arg CUDA_ARCH=7.5 .
-```
-Note that `CUDA_ARCH` is the cumpute capability of your GPU and you can find it at [Compute Capability](https://developer.nvidia.com/cuda-gpus#compute).
-The building process may take 10 minutes or more.
-## Run images
-```bash
-docker run --gpus all --shm-size=8g -it mmcv
-```
-See [docker run](https://docs.docker.com/engine/reference/commandline/run/) for more usages.
--- a/docker/dev/Dockerfile
+++ b/docker/dev/Dockerfile
-ARG PYTORCH="1.8.1"
-ARG CUDA="10.2"
-ARG CUDNN="7"
-FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
-# To fix GPG key error when running apt-get update
-RUN rm /etc/apt/sources.list.d/cuda.list \
-    && rm /etc/apt/sources.list.d/nvidia-ml.list \
-    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub \
-    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
-# Install git and system dependencies for opencv-python
-RUN apt-get update && apt-get install -y git \
-    && apt-get update && apt-get install -y libgl1 libglib2.0-0
-# Install system dependencies for unit tests
-RUN apt-get install -y ffmpeg libturbojpeg \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
-# build mmcv from source with develop mode
-ARG HTTPS_PROXY=""
-ENV https_proxy=${HTTPS_PROXY}
-ENV FORCE_CUDA="1"
-ARG CUDA_ARCH=""
-ENV TORCH_CUDA_ARCH_LIST=${CUDA_ARCH}
-RUN git clone https://github.com/open-mmlab/mmcv.git /mmcv
-WORKDIR /mmcv
-RUN git checkout 2.x && git rev-parse --short HEAD
-RUN pip install --no-cache-dir -e .[all] -v && pip install pre-commit && pre-commit install
--- a/docker/release/Dockerfile
+++ b/docker/release/Dockerfile
-ARG PYTORCH="1.8.1"
-ARG CUDA="10.2"
-ARG CUDNN="7"
-FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
-# To fix GPG key error when running apt-get update
-RUN rm /etc/apt/sources.list.d/cuda.list \
-    && rm /etc/apt/sources.list.d/nvidia-ml.list \
-    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub \
-    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
-# Install system dependencies for opencv-python
-RUN apt-get update && apt-get install -y libgl1 libglib2.0-0 \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
-# Install mmcv
-ARG MMCV=""
-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/Makefile
+++ b/docs/en/Makefile
--- a/docs/en/_static/community/1.png
+++ b/docs/en/_static/community/1.png
--- a/docs/en/_static/community/2.png
+++ b/docs/en/_static/community/2.png
--- a/docs/en/_static/community/3.png
+++ b/docs/en/_static/community/3.png
--- a/docs/en/_static/css/readthedocs.css
+++ b/docs/en/_static/css/readthedocs.css
@@ -4,7 +4,3 @@
    height: 40px;
    width: 85px;
 }
-table.colwidths-auto td {
-    width: 50%
-}
--- a/docs/en/_static/flow_img2toimg1.png
+++ b/docs/en/_static/flow_img2toimg1.png
--- a/docs/en/_static/flow_raw_images.png
+++ b/docs/en/_static/flow_raw_images.png
--- a/docs/en/_static/flow_visualization.png
+++ b/docs/en/_static/flow_visualization.png