mirror of https://github.com/open-mmlab/mmcv.git
[Docs] Translate building documentation (#1353)
* [Docs] Translate building documentation * polish translationpull/1351/head
Zaida Zhou
GitHub
parent
afe0794ce8
commit
f69a3a44e3
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@ -60,7 +60,9 @@ Install them first.
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- Not required for building CPU version.
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- Customize the installation if necessary. As a recommendation, skip the driver installation if a newer version is already installed.
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**You should know how to set up environment variables, especially `Path`, on Windows. The following instruction relies heavily on this skill.**
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```{note}
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You should know how to set up environment variables, especially `Path`, on Windows. The following instruction relies heavily on this skill.
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```
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#### Setup Python Environment
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@ -225,7 +227,8 @@ Check your the compute capability of your GPU from [here](https://developer.nvid
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pip list
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```
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**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).
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Follow [this pull request](https://github.com/pytorch/pytorch/pull/43380/files) to modify the source code in your local PyTorch installation.
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```{note}
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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.
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```
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If you meet issues when running or compiling mmcv, we list some common issues in [TROUBLESHOOTING](./trouble_shooting.html).
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If you meet issues when running or compiling mmcv, we list some common issues in [Frequently Asked Question](../faq.html).
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@ -1,3 +1,222 @@
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## 从源码编译 MMCV
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欢迎有兴趣的朋友一起翻译 MMCV 文档。如有兴趣，请在 [MMCV issue](https://github.com/open-mmlab/mmcv/issues) 提 issue 确定翻译的文档。
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### 在 Linux 或者 macOS 上编译 MMCV
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克隆算法库
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```bash
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git clone https://github.com/open-mmlab/mmcv.git
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cd mmcv
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```
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你可以安装 lite 版本
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```bash
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pip install -e .
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```
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也可以安装 full 版本
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```bash
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MMCV_WITH_OPS=1 pip install -e .
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```
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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
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```bash
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CC=clang CXX=clang++ CFLAGS='-stdlib=libc++'
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```
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例如
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```bash
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CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' MMCV_WITH_OPS=1 pip install -e .
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```
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```{note}
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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`
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```
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### 在 Windows 上编译 MMCV
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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。
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#### 依赖项
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请首先安装以下的依赖项：
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- [Git](https://git-scm.com/download/win：安装期间，请选择 **add git to Path**
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- [Visual Studio Community 2019](https://visualstudio.microsoft.com)：用于编译 C++ 和 CUDA 代码
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- [Miniconda](https://docs.conda.io/en/latest/miniconda.html)：包管理工具
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- [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
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```{note}
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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。
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```
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#### 设置 Python 环境
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1. 从 Windows 菜单启动 Anaconda 命令行
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```{note}
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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
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```
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2. 创建一个新的 Conda 环境
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```shell
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conda create --name mmcv python=3.7 # 经测试，3.6, 3.7, 3.8 也能通过
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conda activate mmcv # 确保做任何操作前先激活环境
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```
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3. 安装 PyTorch 时，可以根据需要安装支持 CUDA 或不支持 CUDA 的版本
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```shell
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# CUDA version
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conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
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# CPU version
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conda install pytorch torchvision cpuonly -c pytorch
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```
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4. 准备 MMCV 源代码
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```shell
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git clone https://github.com/open-mmlab/mmcv.git
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cd mmcv
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```
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5. 安装所需 Python 依赖包
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```shell
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pip3 install -r requirements.txt
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```
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#### 编译与安装 MMCV
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MMCV 有三种安装的模式：
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1. Lite 版本（不包含算子）
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这种方式下，没有算子被编译，这种模式的 mmcv 是原生的 python 包
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2. Full 版本（只包含 CPU 算子）
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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
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3. Full 版本（既包含 CPU 算子，又包含 CUDA 算子）
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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
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##### 通用步骤
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1. 设置 MSVC 编译器
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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。
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```none
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(base) PS C:\Users\xxx> cl
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Microsoft (R) C/C++ Optimizing Compiler Version 19.27.29111 for x64
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Copyright (C) Microsoft Corporation. All rights reserved.
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usage: cl [ option... ] filename... [ / link linkoption... ]
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```
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为了兼容性，我们使用 x86-hosted 以及 x64-targeted 版本，即路径中的 `Hostx86\x64` 。
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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。
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##### 安装方式一：Lite version（不包含算子）
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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
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```shell
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# 激活环境
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conda activate mmcv
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# 切换到 mmcv 根目录
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cd mmcv
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# 安装
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python setup.py develop
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# 检查是否安装成功
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pip list
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```
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##### 安装方式二：Full version（只编译 CPU 算子）
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1. 完成上述的公共步骤
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2. 设置环境变量
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```shell
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$env:MMCV_WITH_OPS = 1
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$env:MAX_JOBS = 8 # 根据你可用CPU以及内存量进行设置
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```
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3. 编译安装
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```shell
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conda activate mmcv # 激活环境
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cd mmcv # 改变路径
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python setup.py build_ext # 如果成功, cl 将被启动用于编译算子
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python setup.py develop # 安装
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pip list # 检查是否安装成功
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```
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##### 安装方式三：Full version（既编译 CPU 算子又编译 CUDA 算子）
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1. 完成上述的公共步骤
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2. 设置环境变量
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```shell
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$env:MMCV_WITH_OPS = 1
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$env:MAX_JOBS = 8 # 根据你可用CPU以及内存量进行设置
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```
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3. 检查 `CUDA_PATH` 或者 `CUDA_HOME` 环境变量已经存在在 `envs` 之中
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```none
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(base) PS C:\Users\WRH> ls env:
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Name Value
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---- -----
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CUDA_PATH C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
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CUDA_PATH_V10_1 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1
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CUDA_PATH_V10_2 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
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```
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如果没有，你可以按照下面的步骤设置
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```shell
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$env:CUDA_HOME = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2"
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# 或者
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$env:CUDA_HOME = $env:CUDA_PATH_V10_2 # CUDA_PATH_V10_2 已经在环境变量中
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```
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4. 设置 CUDA 的目标架构
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```shell
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$env:TORCH_CUDA_ARCH_LIST="6.1" # 支持 GTX 1080
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# 或者用所有支持的版本，但可能会变得很慢
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$env:TORCH_CUDA_ARCH_LIST="3.5 3.7 5.0 5.2 6.0 6.1 7.0 7.5"
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```
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```{note}
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我们可以在 [here](https://developer.nvidia.com/cuda-gpus) 查看 GPU 的计算能力
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```
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5. 编译安装
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```shell
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$env:MMCV_WITH_OPS = 1
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$env:MAX_JOBS = 8 # 根据你可用CPU以及内存量进行设置
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conda activate mmcv # 激活环境
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cd mmcv # 改变路径
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python setup.py build_ext # 如果成功, cl 将被启动用于编译算子
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python setup.py develop # 安装
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pip list # 检查是否安装成功
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```
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```{note}
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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
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```
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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
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