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[Doc] Add README_zh-CN.md and CONTRIBUTING.md (#480)
* [Docs] Add README.md and CONTRIBUTING.md * add section: Learn more * add introduction * refine * refine * resolve comments * remove a line * simplify introduction * resolve comments
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CONTRIBUTING.md
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CONTRIBUTING.md
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## Contributing to OpenMMLab
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All kinds of contributions are welcome, including but not limited to the following.
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- Fix typo or bugs
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- Add documentation or translate the documentation into other languages
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- Add new features and components
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### Workflow
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1. fork and pull the latest OpenMMLab repository
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2. checkout a new branch (do not use master branch for PRs)
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3. commit your changes
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4. create a PR
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Note: If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
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### Code style
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#### Python
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We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
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We use the following tools for linting and formatting:
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- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
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- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
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- [yapf](https://github.com/google/yapf): A formatter for Python files.
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- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
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- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
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- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
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Style configurations of yapf and isort can be found in [setup.cfg](./setup.cfg).
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We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
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fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
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The config for a pre-commit hook is stored in [.pre-commit-config](./.pre-commit-config.yaml).
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After you clone the repository, you will need to install and initialize pre-commit hook.
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```shell
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pip install -U pre-commit
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```
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From the repository folder
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```shell
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pre-commit install
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```
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After this on every commit check code linters and formatter will be enforced.
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> Before you create a PR, make sure that your code lints and is formatted by yapf.
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CONTRIBUTING_zh-CN.md
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CONTRIBUTING_zh-CN.md
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## 贡献代码
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我们欢迎任何类型的贡献，包括但不限于
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- 修改拼写错误或代码错误
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- 添加文档或将文档翻译成其他语言
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- 添加新功能和新组件
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### 工作流
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1. 复刻并拉取最新的 OpenMMLab 算法库
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2. 创建新的分支（不建议使用主分支提拉取请求）
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3. 提交你的修改
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4. 创建拉取请求
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注意：如果你的 PR 希望对代码进行大的改动（例如添加大的功能、进行代码重构等），请先开一个新的 issue 进行充分讨论，便于我们理解改动的动机和计划，并一同推动改动的合入。
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### 代码风格
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#### Python
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[PEP8](https://www.python.org/dev/peps/pep-0008/) 是 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
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- [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
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- [isort](https://github.com/timothycrosley/isort): 自动调整模块导入顺序的工具
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- [yapf](https://github.com/google/yapf): Google 发布的代码规范检查工具
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- [codespell](https://github.com/codespell-project/codespell): 检查单词拼写是否有误
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- [mdformat](https://github.com/executablebooks/mdformat): 检查 markdown 文件的工具
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- [docformatter](https://github.com/myint/docformatter): 格式化 docstring 的工具
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yapf 和 isort 的配置可以在 [setup.cfg](./setup.cfg) 找到
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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`，
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修复 `end-of-files`、`double-quoted-strings`、`python-encoding-pragma`、`mixed-line-ending`，调整 `requirments.txt` 的包顺序。
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pre-commit 钩子的配置可以在 [.pre-commit-config](./.pre-commit-config.yaml) 找到。
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在克隆算法库后，你需要安装并初始化 pre-commit 钩子
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```shell
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pip install -U pre-commit
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```
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切换算法库根目录
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```shell
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pre-commit install
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```
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> 提交拉取请求前，请确保你的代码符合 yapf 的格式
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README_zh-CN.md
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<div align="center">
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<img src="https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg" width="600"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab 官网</font></b>
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<sup>
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<a href="https://openmmlab.com">
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<i><font size="4">HOT</font></i>
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</a>
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</sup>
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<b><font size="5">OpenMMLab 开放平台</font></b>
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<sup>
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<a href="https://platform.openmmlab.com">
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<i><font size="4">TRY IT OUT</font></i>
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</a>
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</sup>
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</div>
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<div> </div>
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[](https://pypi.org/project/mmengine/)
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[](https://pypi.org/project/mmengine)
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[](https://github.com/open-mmlab/mmengine/blob/main/LICENSE)
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[](https://github.com/open-mmlab/mmengine/issues)
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[](https://github.com/open-mmlab/mmengine/issues)
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[📘使用文档](https://mmengine.readthedocs.io/zh_CN/latest/) |
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[🛠️安装教程](https://mmengine.readthedocs.io/zh_CN/latest/get_started/installation.html) |
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[🤔报告问题](https://github.com/open-mmlab/mmengine/issues/new/choose)
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</div>
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<div align="center">
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[English](README.md) | 简体中文
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</div>
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## 简介
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MMEngine 是一个用于深度学习模型训练的基础库，基于 PyTorch，支持在 Linux、Windows、MacOS 上运行。它具有如下三个亮点：
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1. 通用：MMEngine 实现了一个高级的通用训练器，它能够：
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- 支持用少量代码训练不同的任务，例如仅使用 80 行代码就可以训练 imagenet（pytorch example 400 行）
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- 轻松兼容流行的算法库如 TIMM、TorchVision 和 Detectron2 中的模型
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2. 统一：MMEngine 设计了一个接口统一的开放架构，使得
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- 用户可以仅依赖一份代码实现所有任务的轻量化，例如 MMRazor 1.x 相比 MMRazor 0.x 优化了 40% 的代码量
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- 上下游的对接更加统一便捷，在为上层算法库提供统一抽象的同时，支持多种后端设备。目前 MMEngine 支持 Nvidia CUDA、Mac MPS、AMD、MLU 等设备进行模型训练。
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3. 灵活：MMEngine 实现了“乐高”式的训练流程，支持了
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- 根据迭代数、 loss 和评测结果等动态调整的训练流程、优化策略和数据增强策略，例如早停（early stopping）机制等
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- 任意形式的模型权重平均，如 Exponential Momentum Average (EMA) 和 Stochastic Weight Averaging (SWA)
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- 训练过程中针对任意数据和任意节点的灵活可视化和日志控制
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- 对神经网络模型中各个层的优化配置进行细粒度调整
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- 混合精度训练的灵活控制
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## 安装
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在安装 MMengine 之前，请确保 PyTorch 已成功安装在环境中，可以参考 [PyTorch 官方安装文档](https://pytorch.org/get-started/locally/)。
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安装 MMEngine
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```bash
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pip install -U openmim
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mim install mmengine
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```
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验证是否安装成功
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```bash
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python -c 'from mmengine.utils.dl_utils import collect_env;print(collect_env())'
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```
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更多安装方式请阅读[安装文档](https://mmengine.readthedocs.io/zh_CN/latest/get_started/installation.html)
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## 快速上手
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<details>
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<summary>构建模型</summary>
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首先，我们需要构建一个**模型**，在 MMEngine 中，我们约定这个模型应当继承 `BaseModel`，并且其 `forward` 方法除了接受来自数据集的若干参数外，
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还需要接受额外的参数 `mode`：对于训练，我们需要 `mode` 接受字符串 "loss"，并返回一个包含 "loss" 字段的字典；
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对于验证，我们需要 `mode` 接受字符串 "predict"，并返回同时包含预测信息和真实信息的结果。
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```python
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import torch.nn.functional as F
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import torchvision
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from mmengine.model import BaseModel
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class MMResNet50(BaseModel):
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def __init__(self):
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super().__init__()
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self.resnet = torchvision.models.resnet50()
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def forward(self, imgs, labels, mode):
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x = self.resnet(imgs)
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if mode == 'loss':
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return {'loss': F.cross_entropy(x, labels)}
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elif mode == 'predict':
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return x, labels
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```
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</details>
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<details>
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<summary>构建数据集</summary>
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其次，我们需要构建训练和验证所需要的**数据集 (Dataset)**和**数据加载器 (DataLoader)**。
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对于基础的训练和验证功能，我们可以直接使用符合 PyTorch 标准的数据加载器和数据集。
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```python
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import torchvision.transforms as transforms
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from torch.utils.data import DataLoader
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norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
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train_dataloader = DataLoader(batch_size=32,
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shuffle=True,
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dataset=torchvision.datasets.CIFAR10(
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'data/cifar10',
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train=True,
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download=True,
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transform=transforms.Compose([
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transforms.RandomCrop(32, padding=4),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize(**norm_cfg)
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])))
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val_dataloader = DataLoader(batch_size=32,
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shuffle=False,
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dataset=torchvision.datasets.CIFAR10(
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'data/cifar10',
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train=False,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(**norm_cfg)
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])))
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```
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</details>
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<details>
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<summary>构建评测指标</summary>
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为了进行验证和测试，我们需要定义模型推理结果的**评测指标**。我们约定这一评测指标需要继承 `BaseMetric`，
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并实现 `process` 和 `compute_metrics` 方法。
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```python
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from mmengine.evaluator import BaseMetric
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class Accuracy(BaseMetric):
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def process(self, data_batch, data_samples):
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score, gt = data_samples
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# 将一个批次的中间结果保存至 `self.results`
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self.results.append({
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'batch_size': len(gt),
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'correct': (score.argmax(dim=1) == gt).sum().cpu(),
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})
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def compute_metrics(self, results):
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total_correct = sum(item['correct'] for item in results)
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total_size = sum(item['batch_size'] for item in results)
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# 返回保存有评测指标结果的字典，其中键为指标名称
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return dict(accuracy=100 * total_correct / total_size)
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```
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</details>
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<details>
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<summary>构建执行器</summary>
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最后，我们利用构建好的**模型**，**数据加载器**，**评测指标**构建一个**执行器 (Runner)**，同时在其中配置
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**优化器**、**工作路径**、**训练与验证配置**等选项
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```python
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from torch.optim import SGD
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from mmengine.runner import Runner
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runner = Runner(
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# 用以训练和验证的模型，需要满足特定的接口需求
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model=MMResNet50(),
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# 工作路径，用以保存训练日志、权重文件信息
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work_dir='./work_dir',
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# 训练数据加载器，需要满足 PyTorch 数据加载器协议
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train_dataloader=train_dataloader,
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# 优化器包装，用于模型优化，并提供 AMP、梯度累积等附加功能
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optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
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# 训练配置，用于指定训练周期、验证间隔等信息
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train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1),
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# 验证数据加载器，需要满足 PyTorch 数据加载器协议
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val_dataloader=val_dataloader,
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# 验证配置，用于指定验证所需要的额外参数
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val_cfg=dict(),
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# 用于验证的评测器，这里使用默认评测器，并评测指标
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val_evaluator=dict(type=Accuracy),
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)
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```
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</details>
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<details>
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<summary>开始训练</summary>
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```python
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runner.train()
|
||||||
|
```
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
## 了解更多
|
||||||
|
|
||||||
|
<details>
|
||||||
|
<summary>教程</summary>
|
||||||
|
|
||||||
|
- [注册器](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/registry.html)
|
||||||
|
- [配置](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/config.html)
|
||||||
|
- [执行器](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/runner.html)
|
||||||
|
- [钩子](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/optimizer.html)
|
||||||
|
- [优化器](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/param_scheduler.html)
|
||||||
|
- [抽象数据接口](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/data_element.html)
|
||||||
|
- [数据集基类](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/basedataset.html)
|
||||||
|
- [评测指标和评测器](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/metric_and_evaluator.html)
|
||||||
|
- [分布式通信原语](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/distributed.html)
|
||||||
|
- [记录日志](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/logging.html)
|
||||||
|
- [可视化](https://mmengine.readthedocs.io/zh_CN/latest/tutorials/visualization.html)
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
<details>
|
||||||
|
<summary>示例</summary>
|
||||||
|
|
||||||
|
- [加速训练](https://mmengine.readthedocs.io/zh_CN/latest/examples/speed_up_training.html)
|
||||||
|
- [恢复训练](https://mmengine.readthedocs.io/zh_CN/latest/examples/resume_training.html)
|
||||||
|
- [节省显存](https://mmengine.readthedocs.io/zh_CN/latest/examples/save_gpu_memory.html)
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
<details>
|
||||||
|
<summary>架构设计</summary>
|
||||||
|
|
||||||
|
- [钩子的设计](https://mmengine.readthedocs.io/zh_CN/latest/design/hook.html)
|
||||||
|
- [执行器的设计](https://mmengine.readthedocs.io/zh_CN/latest/design/runner.html)
|
||||||
|
- [可视化的设计](https://mmengine.readthedocs.io/zh_CN/latest/design/runner.html)
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
<details>
|
||||||
|
<summary>迁移指南</summary>
|
||||||
|
|
||||||
|
- [迁移 MMCV 参数调度器到 MMEngine](https://mmengine.readthedocs.io/zh_CN/latest/migration/migrate_param_scheduler_from_mmcv.html)
|
||||||
|
- [迁移 MMCV 钩子到 MMEngine](https://mmengine.readthedocs.io/zh_CN/latest/migration/migrate_hook_from_mmcv.html)
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
## 贡献指南
|
||||||
|
|
||||||
|
我们感谢所有的贡献者为改进和提升 MMEngine 所作出的努力。请参考[贡献指南](CONTRIBUTING_zh-CN.md)来了解参与项目贡献的相关指引。
|
||||||
|
|
||||||
|
## 开源许可证
|
||||||
|
|
||||||
|
该项目采用 [Apache 2.0 license](LICENSE) 开源许可证。
|
||||||
|
|
||||||
|
## OpenMMLab 的其他项目
|
||||||
|
|
||||||
|
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMLab 项目、算法、模型的统一入口
|
||||||
|
- [MMCV](https://github.com/open-mmlab/mmcv/tree/dev-2.x): OpenMMLab 计算机视觉基础库
|
||||||
|
- [MMClassification](https://github.com/open-mmlab/mmclassification/tree/dev-1.x): OpenMMLab 图像分类工具箱
|
||||||
|
- [MMDetection](https://github.com/open-mmlab/mmdetection/tree/dev-3.x): OpenMMLab 目标检测工具箱
|
||||||
|
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d/tree/dev-1.x): OpenMMLab 新一代通用 3D 目标检测平台
|
||||||
|
- [MMRotate](https://github.com/open-mmlab/mmrotate/tree/dev-1.x): OpenMMLab 旋转框检测工具箱与测试基准
|
||||||
|
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x): OpenMMLab 语义分割工具箱
|
||||||
|
- [MMOCR](https://github.com/open-mmlab/mmocr/tree/dev-1.x): OpenMMLab 全流程文字检测识别理解工具包
|
||||||
|
- [MMPose](https://github.com/open-mmlab/mmpose/tree/dev-1.x): 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/tree/dev-1.x): OpenMMLab 自监督学习工具箱与测试基准
|
||||||
|
- [MMRazor](https://github.com/open-mmlab/mmrazor/tree/dev-1.x): 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/tree/dev-1.x): OpenMMLab 新一代视频理解工具箱
|
||||||
|
- [MMTracking](https://github.com/open-mmlab/mmtracking/tree/dev-1.x): OpenMMLab 一体化视频目标感知平台
|
||||||
|
- [MMFlow](https://github.com/open-mmlab/mmflow/tree/dev-1.x): OpenMMLab 光流估计工具箱与测试基准
|
||||||
|
- [MMEditing](https://github.com/open-mmlab/mmediting/tree/dev-1.x): OpenMMLab 图像视频编辑工具箱
|
||||||
|
- [MMGeneration](https://github.com/open-mmlab/mmgeneration/tree/dev-1.x): 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=aCvMxdr3)，或通过添加微信“Open小喵Lab”加入官方交流微信群。
|
||||||
|
|
||||||
|
<div align="center">
|
||||||
|
<img src="https://user-images.githubusercontent.com/58739961/187154320-f3312cdf-31f2-4316-9dbb-8d7b0e1b7e08.jpg" height="400" /> <img src="https://user-images.githubusercontent.com/58739961/187151554-1a0748f0-a1bb-4565-84a6-ab3040247ef1.jpg" height="400" /> <img src="https://user-images.githubusercontent.com/58739961/187151778-d17c1368-125f-4fde-adbe-38cc6eb3be98.jpg" height="400" />
|
||||||
|
</div>
|
||||||
|
|
||||||
|
我们会在 OpenMMLab 社区为大家
|
||||||
|
|
||||||
|
- 📢 分享 AI 框架的前沿核心技术
|
||||||
|
- 💻 解读 PyTorch 常用模块源码
|
||||||
|
- 📰 发布 OpenMMLab 的相关新闻
|
||||||
|
- 🚀 介绍 OpenMMLab 开发的前沿算法
|
||||||
|
- 🏃 获取更高效的问题答疑和意见反馈
|
||||||
|
- 🔥 提供与各行各业开发者充分交流的平台
|
||||||
|
|
||||||
|
干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
|
||||||
12
setup.py
12
setup.py
@ -132,6 +132,18 @@ setup(
|
|||||||
author_email='openmmlab@gmail.com',
|
author_email='openmmlab@gmail.com',
|
||||||
packages=find_packages(),
|
packages=find_packages(),
|
||||||
include_package_data=True,
|
include_package_data=True,
|
||||||
|
classifiers=[
|
||||||
|
'Development Status :: 4 - Beta',
|
||||||
|
'License :: OSI Approved :: Apache Software License',
|
||||||
|
'Operating System :: OS Independent',
|
||||||
|
'Programming Language :: Python :: 3',
|
||||||
|
'Programming Language :: Python :: 3.6',
|
||||||
|
'Programming Language :: Python :: 3.7',
|
||||||
|
'Programming Language :: Python :: 3.8',
|
||||||
|
'Programming Language :: Python :: 3.9',
|
||||||
|
'Programming Language :: Python :: 3.10',
|
||||||
|
'Topic :: Utilities',
|
||||||
|
],
|
||||||
python_requires='>=3.6',
|
python_requires='>=3.6',
|
||||||
install_requires=install_requires,
|
install_requires=install_requires,
|
||||||
extras_require={
|
extras_require={
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user