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[Docs] Add docs contents at README.md (#3083)
Add docs contents at README.md to easily find documents. Issue: #2664    
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README.md
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README.md
@ -110,6 +110,60 @@ A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSe
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To migrate from MMSegmentation 0.x, please refer to [migration](docs/en/migration).
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## Tutorial
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<details>
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<summary>Get Started</summary>
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- [MMSeg overview](docs/en/overview.md)
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- [MMSeg Installation](docs/en/get_started.md)
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- [FAQ](docs/en/notes/faq.md)
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</details>
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<details>
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<summary>MMSeg Basic Tutorial</summary>
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- [Tutorial 1: Learn about Configs](docs/en/user_guides/1_config.md)
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- [Tutorial 2: Prepare datasets](docs/en/user_guides/2_dataset_prepare.md)
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- [Tutorial 3: Inference with existing models](docs/en/user_guides/3_inference.md)
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- [Tutorial 4: Train and test with existing models](docs/en/user_guides/4_train_test.md)
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- [Tutorial 5: Model deployment](docs/en/user_guides/5_deployment.md)
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- [Useful Tools](docs/en/user_guides/useful_tools.md)
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- [Feature Map Visualization](docs/en/user_guides/visualization_feature_map.md)
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- [Visualization](docs/en/user_guides/visualization.md)
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</details>
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<details>
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<summary>MMSeg Detail Tutorial</summary>
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- [MMSeg Dataset](docs/en/advanced_guides/datasets.md)
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- [MMSeg Models](docs/en/advanced_guides/models.md)
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- [MMSeg Dataset Structures](docs/en/advanced_guides/structures.md)
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- [MMSeg Data Transforms](docs/en/advanced_guides/transforms.md)
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- [MMSeg Dataflow](docs/en/advanced_guides/data_flow.md)
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- [MMSeg Training Engine](docs/en/advanced_guides/engine.md)
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- [MMSeg Evaluation](docs/en/advanced_guides/evaluation.md)
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</details>
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<details>
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<summary>MMSeg Development Tutorial</summary>
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- [Add New Datasets](docs/en/advanced_guides/add_datasets.md)
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- [Add New Metrics](docs/en/advanced_guides/add_metrics.md)
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- [Add New Modules](docs/en/advanced_guides/add_models.md)
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- [Add New Data Transforms](docs/en/advanced_guides/add_transforms.md)
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- [Customize Runtime Settings](docs/en/advanced_guides/customize_runtime.md)
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- [Training Tricks](docs/en/advanced_guides/training_tricks.md)
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- [Contribute code to MMSeg](.github/CONTRIBUTING.md)
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- [Contribute a standard dataset in projects](docs/zh_cn/advanced_guides/contribute_dataset.md)
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- [NPU (HUAWEI Ascend)](docs/en/device/npu.md)
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- [0.x → 1.x migration](docs/en/migration/interface.md)，[0.x → 1.x package](docs/en/migration/package.md)
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</details>
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## Benchmark and model zoo
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Results and models are available in the [model zoo](docs/en/model_zoo.md).
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@ -104,6 +104,60 @@ MMSegmentation v1.x 在 0.x 版本的基础上有了显著的提升，提供了
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若需要将 0.x 版本的代码迁移至新版，请参考[迁移文档](docs/zh_cn/migration)。
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## 教程文档
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<details>
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<summary>开启 MMSeg 之旅</summary>
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- [MMSeg 概述](docs/zh_cn/overview.md)
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- [安装和验证](docs/zh_cn/get_started.md)
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- [常见问题解答](docs/zh_cn/notes/faq.md)
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</details>
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<details>
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<summary>MMSeg 快速入门教程</summary>
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- [教程1：了解配置文件](docs/zh_cn/user_guides/1_config.md)
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- [教程2：准备数据集](docs/zh_cn/user_guides/2_dataset_prepare.md)
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- [教程3：使用预训练模型推理](docs/zh_cn/user_guides/3_inference.md)
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- [教程4：使用现有模型进行训练和测试](docs/zh_cn/user_guides/4_train_test.md)
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- [教程5：模型部署](docs/zh_cn/user_guides/5_deployment.md)
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- [常用工具](docs/zh_cn/user_guides/useful_tools.md)
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- [特征图可视化](docs/zh_cn/user_guides/visualization_feature_map.md)
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- [可视化](docs/zh_cn/user_guides/visualization.md)
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</details>
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<details>
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<summary>MMSeg 细节介绍</summary>
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- [MMSeg 数据集介绍](docs/zh_cn/advanced_guides/datasets.md)
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- [MMSeg 模型介绍](docs/zh_cn/advanced_guides/models.md)
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- [MMSeg 数据结构介绍](docs/zh_cn/advanced_guides/structures.md)
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- [MMSeg 数据增强介绍](docs/zh_cn/advanced_guides/transforms.md)
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- [MMSeg 数据流介绍](docs/zh_cn/advanced_guides/data_flow.md)
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- [MMSeg 训练引擎介绍](docs/zh_cn/advanced_guides/engine.md)
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- [MMSeg 模型评测介绍](docs/zh_cn/advanced_guides/evaluation.md)
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</details>
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<details>
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<summary>MMSeg 开发教程</summary>
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- [新增自定义数据集](docs/zh_cn/advanced_guides/add_datasets.md)
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- [新增评测指标](docs/zh_cn/advanced_guides/add_metrics.md)
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- [新增自定义模型](docs/zh_cn/advanced_guides/add_models.md)
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- [新增自定义数据增强](docs/zh_cn/advanced_guides/add_transforms.md)
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- [自定义运行设定](docs/zh_cn/advanced_guides/customize_runtime.md)
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- [训练技巧](docs/zh_cn/advanced_guides/training_tricks.md)
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- [如何给 MMSeg贡献代码](.github/CONTRIBUTING.md)
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- [在 projects 给 MMSeg 贡献一个标准数据集](docs/zh_cn/advanced_guides/contribute_dataset.md)
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- [NPU (华为 昇腾)](docs/zh_cn/device/npu.md)
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- [0.x → 1.x 迁移文档](docs/zh_cn/migration/interface.md)，[0.x → 1.x 库变更文档](docs/zh_cn/migration/package.md)
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</details>
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## 基准测试和模型库
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测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
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461
docs/zh_cn/advanced_guides/contribute_dataset.md
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461
docs/zh_cn/advanced_guides/contribute_dataset.md
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@ -0,0 +1,461 @@
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# 在 mmsegmentation projects 中贡献一个标准格式的数据集
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- 在开始您的贡献流程前，请先阅读[《OpenMMLab 贡献代码指南》](https://mmcv.readthedocs.io/zh_CN/latest/community/contributing.html)，以详细的了解 OpenMMLab 代码库的代码贡献流程。
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- 该教程以 [Gaofen Image Dataset (GID)](https://www.sciencedirect.com/science/article/pii/S0034425719303414) 高分 2 号卫星所拍摄的遥感图像语义分割数据集作为样例，来演示在 mmsegmentation 中的数据集贡献流程。
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## 步骤 1： 配置 mmsegmentation 开发所需必要环境
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- 开发所必需的环境安装请参考[中文快速入门指南](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/zh_cn/get_started.md)或[英文 get_started](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/get_started.md)。
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- 如果您已安装了最新版的 pytorch、mmcv、mmengine，那么您可以跳过步骤 1 至[步骤 2](<#[步骤-2](#%E6%AD%A5%E9%AA%A4-2%E4%BB%A3%E7%A0%81%E8%B4%A1%E7%8C%AE%E5%89%8D%E7%9A%84%E5%87%86%E5%A4%87%E5%B7%A5%E4%BD%9C)>)。
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- **注：** 在此处无需安装 mmsegmentation，只需安装开发 mmsegmentation 所必需的 pytorch、mmcv、mmengine 等即可。
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**新建虚拟环境（如已有合适的开发环境，可跳过）**
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- 从[官方网站](https://docs.conda.io/en/latest/miniconda.html)下载并安装 Miniconda
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- 创建一个 conda 环境，并激活
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```shell
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conda create --name openmmlab python=3.8 -y
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conda activate openmmlab
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```
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**安装 pytorch （如环境下已安装 pytorch，可跳过）**
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- 参考 [official instructions](https://pytorch.org/get-started/locally/) 安装 **PyTorch**
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**使用 mim 安装 mmcv、mmengine**
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- 使用 [MIM](https://github.com/open-mmlab/mim) 安装 [MMCV](https://github.com/open-mmlab/mmcv)
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```shell
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pip install -U openmim
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mim install mmengine
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mim install "mmcv>=2.0.0"
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```
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## 步骤 2：代码贡献前的准备工作
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### 2.1 Fork mmsegmentation 仓库
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- 通过浏览器打开[mmsegmentation 官方仓库](https://github.com/open-mmlab/mmsegmentation/tree/main)。
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- 登录您的 GitHub 账户，以下步骤均需在 GitHub 登录的情况下进行。
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- Fork mmsegmentation 仓库
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- Fork 之后，mmsegmentation 仓库将会出现在您的个人仓库中。
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### 2.2 在您的代码编写软件中 git clone mmsegmentation
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这里以 VSCODE 为例
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- 打开 VSCODE，新建终端窗口并激活您在[步骤 1 ](#%E6%AD%A5%E9%AA%A4-1-%E9%85%8D%E7%BD%AE-mmsegmentation-%E5%BC%80%E5%8F%91%E6%89%80%E9%9C%80%E5%BF%85%E8%A6%81%E7%8E%AF%E5%A2%83)中所安装的虚拟环境。
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- 在您 GitHub 的个人仓库中找到您 Fork 的 mmsegmentation 仓库，复制其链接。
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- 在终端中执行命令
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```bash
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git clone {您所复制的个人仓库的链接}
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```
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**注：** 如提示以下信息，请在 GitHub 中添加 [SSH 秘钥](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent)
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- 进入 mmsegmentation 目录（之后的操作均在 mmsegmentation 目录下）。
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```bash
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cd mmsegmentation
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```
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- 在终端中执行以下命令，添加官方仓库为上游仓库。
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```bash
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git remote add upstream git@github.com:open-mmlab/mmsegmentation.git
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```
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- 使用以下命令检查 remote 是否添加成功。
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```bash
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git remote -v
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```
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### 2.3 切换目录至 mmsegmentation 并从源码安装mmsegmentation
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在`mmsegmentation`目录下执行`pip install -v -e .`，通过源码构建方式安装 mmsegmentaion 库。
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安装完成后，您将能看到如下图所示的文件树。
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<img src="https://user-images.githubusercontent.com/50650583/233826064-4b111358-8f97-44dd-955c-df3204410b8b.png" alt="image" style="zoom:67%;" />
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### 2.4 切换分支为 dev-1.x
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正如您在[ mmsegmentation 官网](https://github.com/open-mmlab/mmsegmentation/tree/main)所见，该仓库有许多分支，默认分支`main`为稳定的发行版本，以及用于贡献者进行开发的`dev-1.x`分支。`dev-1.x`分支是贡献者们用来提交创意和 PR 的分支，`dev-1.x`分支的内容会被周期性的合入到`main`分支。
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回到 VSCODE 中，在终端执行命令
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```bash
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git checkout dev-1.x
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```
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### 2.5 创新属于自己的新分支
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在基于`dev-1.x`分支下，使用如下命令，创建属于您自己的分支。
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```bash
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# git checkout -b 您的GitHubID/您的分支想要实现的功能的名字
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# git checkout -b AI-Tianlong/support_GID_dataset
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git checkout -b {您的GitHubID/您的分支想要实现的功能的名字}
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```
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### 2.6 配置 pre-commit
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OpenMMLab 仓库对代码质量有着较高的要求，所有提交的 PR 必须要通过代码格式检查。pre-commit 详细配置参阅[配置 pre-commit](https://mmcv.readthedocs.io/zh_CN/latest/community/contributing.html#pre-commit)。
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## 步骤 3：在`mmsegmentation/projects`下贡献您的代码
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**先对 GID 数据集进行分析**
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这里以贡献高分 2 号遥感图像语义分割数据集 GID 为例，GID 数据集是由我国自主研发的高分 2 号卫星所拍摄的光学遥感图像所创建，经图像预处理后共提供了 150 张 6800x7200 像素的 RGB 三通道遥感图像。并提供了两种不同类别数的数据标注，一种是包含 5 类有效物体的 RGB 标签，另一种是包含 15 类有效物体的 RGB 标签。本教程将针对 5 类标签进行数据集贡献流程讲解。
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GID 的 5 类有效标签分别为：0-背景-\[0,0,0\](mask 标签值-标签名称-RGB 标签值)、1-建筑-\[255,0,0\]、2-农田-\[0,255,0\]、3-森林-\[0,0,255\]、4-草地-\[255,255,0\]、5-水-\[0,0,255\]。在语义分割任务中，标签是与原图尺寸一致的单通道图像，标签图像中的像素值为真实样本图像中对应像素所包含的物体的类别。GID 数据集提供的是具有 RGB 三通道的彩色标签，为了模型的训练需要将 RGB 标签转换为 mask 标签。并且由于图像尺寸为 6800x7200 像素，对于神经网络的训练来有些过大，所以将每张图像裁切成了没有重叠的 512x512 的图像以便进行训练。
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<img align='center' src="https://user-images.githubusercontent.com/50650583/234192183-83ee4209-e181-4a18-90ca-4d71757cd2c7.png" alt="image" style="zoom:67%;" />
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### 3.1 在`mmsegmentation/projects`下创建新的项目文件夹
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在`mmsegmentation/projects`下创建文件夹`gid_dataset`
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### 3.2 贡献您的数据集代码
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为了最终能将您在 projects 中贡献的代码更加顺畅的移入核心库中（对代码要求质量更高），非常建议按照核心库的目录来编辑您的数据集文件。
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关于数据集有 4 个必要的文件：
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- **1** `mmseg/datasets/gid.py` 定义了数据集的尾缀、CLASSES、PALETTE、reduce_zero_label等
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- **2** `configs/_base_/gid.py` GID 数据集的配置文件，定义了数据集的`dataset_type`（数据集类型，`mmseg/datasets/gid.py`中注册的数据集的类名）、`data_root`(数据集所在的根目录，建议将数据集通过软连接的方式将数据集放至`mmsegmentation/data`)、`train_pipline`(训练的数据流)、`test_pipline`(测试和验证时的数据流)、`img_rations`(多尺度预测时的多尺度配置)、`tta_pipeline`（多尺度预测）、`train_dataloader`(训练集的数据加载器)、`val_dataloader`(验证集的数据加载器)、`test_dataloader`(测试集的数据加载器)、`val_evaluator`(验证集的评估器)、`test_evaluator`(测试集的评估器)。
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- **3** 使用了 GID 数据集的模型训练配置文件
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这个是可选的，但是强烈建议您添加。在核心库中，所贡献的数据集需要和参考文献中所提出的结果精度对齐，为了后期将您贡献的代码合并入核心库。如您的算力充足，最好能提供对应的模型配置文件在您贡献的数据集上所验证的结果以及相应的权重文件，并撰写较为详细的README.md文档。[示例参考结果](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/deeplabv3plus#mapillary-vistas-v12)
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- **4** 使用如下命令格式： 撰写`docs/zh_cn/user_guides/2_dataset_prepare.md`来添加您的数据集介绍，包括但不限于数据集的下载方式，数据集目录结构、数据集生成等一些必要性的文字性描述和运行命令。以更好地帮助用户能更快的实现数据集的准备工作。
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### 3.3 贡献`tools/dataset_converters/gid.py`
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由于 GID 数据集是由未经过切分的 6800x7200 图像所构成的数据集，并且没有划分训练集、验证集与测试集。以及其标签为 RGB 彩色标签，需要将标签转换为单通道的 mask label。为了方便训练，首先将 GID 数据集进行裁切和标签转换，并进行数据集划分，构建为 mmsegmentation 所支持的格式。
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```python
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# tools/dataset_converters/gid.py
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import argparse
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import glob
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import math
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import os
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import os.path as osp
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from PIL import Image
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import mmcv
|
||||
import numpy as np
|
||||
from mmengine.utils import ProgressBar, mkdir_or_exist
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Convert GID dataset to mmsegmentation format')
|
||||
parser.add_argument('dataset_img_path', help='GID images folder path')
|
||||
parser.add_argument('dataset_label_path', help='GID labels folder path')
|
||||
parser.add_argument('--tmp_dir', help='path of the temporary directory')
|
||||
parser.add_argument('-o', '--out_dir', help='output path', default='data/gid')
|
||||
parser.add_argument(
|
||||
'--clip_size',
|
||||
type=int,
|
||||
help='clipped size of image after preparation',
|
||||
default=256)
|
||||
parser.add_argument(
|
||||
'--stride_size',
|
||||
type=int,
|
||||
help='stride of clipping original images',
|
||||
default=256)
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
GID_COLORMAP = dict(
|
||||
Background=(0, 0, 0), #0-背景-黑色
|
||||
Building=(255, 0, 0), #1-建筑-红色
|
||||
Farmland=(0, 255, 0), #2-农田-绿色
|
||||
Forest=(0, 0, 255), #3-森林-蓝色
|
||||
Meadow=(255, 255, 0),#4-草地-黄色
|
||||
Water=(0, 0, 255)#5-水-蓝色
|
||||
)
|
||||
palette = list(GID_COLORMAP.values())
|
||||
classes = list(GID_COLORMAP.keys())
|
||||
|
||||
#############用列表来存一个 RGB 和一个类别的对应################
|
||||
def colormap2label(palette):
|
||||
colormap2label_list = np.zeros(256**3, dtype = np.longlong)
|
||||
for i, colormap in enumerate(palette):
|
||||
colormap2label_list[(colormap[0] * 256 + colormap[1])*256+colormap[2]] = i
|
||||
return colormap2label_list
|
||||
|
||||
#############给定那个列表，和vis_png然后生成masks_png################
|
||||
def label_indices(RGB_label, colormap2label_list):
|
||||
RGB_label = RGB_label.astype('int32')
|
||||
idx = (RGB_label[:, :, 0] * 256 + RGB_label[:, :, 1]) * 256 + RGB_label[:, :, 2]
|
||||
# print(idx.shape)
|
||||
return colormap2label_list[idx]
|
||||
|
||||
def RGB2mask(RGB_label, colormap2label_list):
|
||||
# RGB_label = np.array(Image.open(RGB_label).convert('RGB')) #打开RGB_png
|
||||
mask_label = label_indices(RGB_label, colormap2label_list) # .numpy()
|
||||
return mask_label
|
||||
|
||||
colormap2label_list = colormap2label(palette)
|
||||
|
||||
def clip_big_image(image_path, clip_save_dir, args, to_label=False):
|
||||
"""
|
||||
Original image of GID dataset is very large, thus pre-processing
|
||||
of them is adopted. Given fixed clip size and stride size to generate
|
||||
clipped image, the intersection of width and height is determined.
|
||||
For example, given one 6800 x 7200 original image, the clip size is
|
||||
256 and stride size is 256, thus it would generate 29 x 27 = 783 images
|
||||
whose size are all 256 x 256.
|
||||
|
||||
"""
|
||||
|
||||
image = mmcv.imread(image_path, channel_order='rgb')
|
||||
# image = mmcv.bgr2gray(image)
|
||||
|
||||
h, w, c = image.shape
|
||||
clip_size = args.clip_size
|
||||
stride_size = args.stride_size
|
||||
|
||||
num_rows = math.ceil((h - clip_size) / stride_size) if math.ceil(
|
||||
(h - clip_size) /
|
||||
stride_size) * stride_size + clip_size >= h else math.ceil(
|
||||
(h - clip_size) / stride_size) + 1
|
||||
num_cols = math.ceil((w - clip_size) / stride_size) if math.ceil(
|
||||
(w - clip_size) /
|
||||
stride_size) * stride_size + clip_size >= w else math.ceil(
|
||||
(w - clip_size) / stride_size) + 1
|
||||
|
||||
x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1))
|
||||
xmin = x * clip_size
|
||||
ymin = y * clip_size
|
||||
|
||||
xmin = xmin.ravel()
|
||||
ymin = ymin.ravel()
|
||||
xmin_offset = np.where(xmin + clip_size > w, w - xmin - clip_size,
|
||||
np.zeros_like(xmin))
|
||||
ymin_offset = np.where(ymin + clip_size > h, h - ymin - clip_size,
|
||||
np.zeros_like(ymin))
|
||||
boxes = np.stack([
|
||||
xmin + xmin_offset, ymin + ymin_offset,
|
||||
np.minimum(xmin + clip_size, w),
|
||||
np.minimum(ymin + clip_size, h)
|
||||
], axis=1)
|
||||
|
||||
if to_label:
|
||||
image = RGB2mask(image, colormap2label_list) #这里得改一下
|
||||
|
||||
for count, box in enumerate(boxes):
|
||||
start_x, start_y, end_x, end_y = box
|
||||
clipped_image = image[start_y:end_y,
|
||||
start_x:end_x] if to_label else image[
|
||||
start_y:end_y, start_x:end_x, :]
|
||||
img_name = osp.basename(image_path).replace('.tif', '')
|
||||
img_name = img_name.replace('_label', '')
|
||||
if count % 3 == 0:
|
||||
mmcv.imwrite(
|
||||
clipped_image.astype(np.uint8),
|
||||
osp.join(
|
||||
clip_save_dir.replace('train', 'val'),
|
||||
f'{img_name}_{start_x}_{start_y}_{end_x}_{end_y}.png'))
|
||||
else:
|
||||
mmcv.imwrite(
|
||||
clipped_image.astype(np.uint8),
|
||||
osp.join(
|
||||
clip_save_dir,
|
||||
f'{img_name}_{start_x}_{start_y}_{end_x}_{end_y}.png'))
|
||||
count += 1
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
"""
|
||||
According to this paper: https://ieeexplore.ieee.org/document/9343296/
|
||||
select 15 images contained in GID, , which cover the whole six
|
||||
categories, to generate train set and validation set.
|
||||
|
||||
According to Paper: https://ieeexplore.ieee.org/document/9343296/
|
||||
|
||||
"""
|
||||
|
||||
if args.out_dir is None:
|
||||
out_dir = osp.join('data', 'gid')
|
||||
else:
|
||||
out_dir = args.out_dir
|
||||
|
||||
print('Making directories...')
|
||||
mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
|
||||
mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
|
||||
mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
|
||||
mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
|
||||
|
||||
src_path_list = glob.glob(os.path.join(args.dataset_img_path, '*.tif'))
|
||||
print(f'Find {len(src_path_list)} pictures')
|
||||
|
||||
prog_bar = ProgressBar(len(src_path_list))
|
||||
|
||||
dst_img_dir = osp.join(out_dir, 'img_dir', 'train')
|
||||
dst_label_dir = osp.join(out_dir, 'ann_dir', 'train')
|
||||
|
||||
for i, img_path in enumerate(src_path_list):
|
||||
label_path = osp.join(args.dataset_label_path, osp.basename(img_path.replace('.tif', '_label.tif')))
|
||||
|
||||
clip_big_image(img_path, dst_img_dir, args, to_label=False)
|
||||
clip_big_image(label_path, dst_label_dir, args, to_label=True)
|
||||
prog_bar.update()
|
||||
|
||||
print('Done!')
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
```
|
||||
|
||||
### 3.4 贡献`mmseg/datasets/gid.py`
|
||||
|
||||
可参考[`projects/mapillary_dataset/mmseg/datasets/mapillary.py`](https://github.com/open-mmlab/mmsegmentation/blob/main/projects/mapillary_dataset/mmseg/datasets/mapillary.py)并在此基础上修改相应变量以适配您的数据集。
|
||||
|
||||
```python
|
||||
# mmseg/datasets/gid.py
|
||||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
from mmseg.datasets.basesegdataset import BaseSegDataset
|
||||
from mmseg.registry import DATASETS
|
||||
|
||||
# 注册数据集类
|
||||
@DATASETS.register_module()
|
||||
class GID_Dataset(BaseSegDataset):
|
||||
"""Gaofen Image Dataset (GID)
|
||||
|
||||
Dataset paper link:
|
||||
https://www.sciencedirect.com/science/article/pii/S0034425719303414
|
||||
https://x-ytong.github.io/project/GID.html
|
||||
|
||||
GID 6 classes: background(others), built-up, farmland, forest, meadow, water
|
||||
|
||||
In This example, select 10 images from GID dataset as training set,
|
||||
and select 5 images as validation set.
|
||||
The selected images are listed as follows:
|
||||
|
||||
GF2_PMS1__L1A0000647767-MSS1
|
||||
GF2_PMS1__L1A0001064454-MSS1
|
||||
GF2_PMS1__L1A0001348919-MSS1
|
||||
GF2_PMS1__L1A0001680851-MSS1
|
||||
GF2_PMS1__L1A0001680853-MSS1
|
||||
GF2_PMS1__L1A0001680857-MSS1
|
||||
GF2_PMS1__L1A0001757429-MSS1
|
||||
GF2_PMS2__L1A0000607681-MSS2
|
||||
GF2_PMS2__L1A0000635115-MSS2
|
||||
GF2_PMS2__L1A0000658637-MSS2
|
||||
GF2_PMS2__L1A0001206072-MSS2
|
||||
GF2_PMS2__L1A0001471436-MSS2
|
||||
GF2_PMS2__L1A0001642620-MSS2
|
||||
GF2_PMS2__L1A0001787089-MSS2
|
||||
GF2_PMS2__L1A0001838560-MSS2
|
||||
|
||||
The ``img_suffix`` is fixed to '.tif' and ``seg_map_suffix`` is
|
||||
fixed to '.tif' for GID.
|
||||
"""
|
||||
METAINFO = dict(
|
||||
classes=('Others', 'Built-up', 'Farmland', 'Forest',
|
||||
'Meadow', 'Water'),
|
||||
|
||||
palette=[[0, 0, 0], [255, 0, 0], [0, 255, 0], [0, 255, 255],
|
||||
[255, 255, 0], [0, 0, 255]])
|
||||
|
||||
def __init__(self,
|
||||
img_suffix='.png',
|
||||
seg_map_suffix='.png',
|
||||
reduce_zero_label=None,
|
||||
**kwargs) -> None:
|
||||
super().__init__(
|
||||
img_suffix=img_suffix,
|
||||
seg_map_suffix=seg_map_suffix,
|
||||
reduce_zero_label=reduce_zero_label,
|
||||
**kwargs)
|
||||
```
|
||||
|
||||
### 3.5 贡献使用 GID 的训练 config file
|
||||
|
||||
```python
|
||||
_base_ = [
|
||||
'../../../configs/_base_/models/deeplabv3plus_r50-d8.py',
|
||||
'./_base_/datasets/gid.py',
|
||||
'../../../configs/_base_/default_runtime.py',
|
||||
'../../../configs/_base_/schedules/schedule_240k.py'
|
||||
]
|
||||
custom_imports = dict(
|
||||
imports=['projects.gid_dataset.mmseg.datasets.gid'])
|
||||
|
||||
crop_size = (256, 256)
|
||||
data_preprocessor = dict(size=crop_size)
|
||||
model = dict(
|
||||
data_preprocessor=data_preprocessor,
|
||||
pretrained='open-mmlab://resnet101_v1c',
|
||||
backbone=dict(depth=101),
|
||||
decode_head=dict(num_classes=6),
|
||||
auxiliary_head=dict(num_classes=6))
|
||||
|
||||
```
|
||||
|
||||
### 3.6 撰写`docs/zh_cn/user_guides/2_dataset_prepare.md`
|
||||
|
||||
**Gaofen Image Dataset (GID)**
|
||||
|
||||
- GID 数据集可在[此处](https://x-ytong.github.io/project/Five-Billion-Pixels.html)进行下载。
|
||||
- GID 数据集包含 150 张 6800x7200 的大尺寸图像，标签为 RGB 标签。
|
||||
- 此处选择 15 张图像生成训练集和验证集，该 15 张图像包含了所有六类信息。所选的图像名称如下：
|
||||
|
||||
```None
|
||||
GF2_PMS1__L1A0000647767-MSS1
|
||||
GF2_PMS1__L1A0001064454-MSS1
|
||||
GF2_PMS1__L1A0001348919-MSS1
|
||||
GF2_PMS1__L1A0001680851-MSS1
|
||||
GF2_PMS1__L1A0001680853-MSS1
|
||||
GF2_PMS1__L1A0001680857-MSS1
|
||||
GF2_PMS1__L1A0001757429-MSS1
|
||||
GF2_PMS2__L1A0000607681-MSS2
|
||||
GF2_PMS2__L1A0000635115-MSS2
|
||||
GF2_PMS2__L1A0000658637-MSS2
|
||||
GF2_PMS2__L1A0001206072-MSS2
|
||||
GF2_PMS2__L1A0001471436-MSS2
|
||||
GF2_PMS2__L1A0001642620-MSS2
|
||||
GF2_PMS2__L1A0001787089-MSS2
|
||||
GF2_PMS2__L1A0001838560-MSS2
|
||||
```
|
||||
|
||||
执行以下命令进行裁切及标签的转换，需要修改为您所存储 15 张图像及标签的路径。
|
||||
|
||||
```
|
||||
python projects/gid_dataset/tools/dataset_converters/gid.py [15 张图像的路径] [15 张标签的路径]
|
||||
```
|
||||
|
||||
完成裁切后的 GID 数据结构如下：
|
||||
|
||||
```none
|
||||
mmsegmentation
|
||||
├── mmseg
|
||||
├── tools
|
||||
├── configs
|
||||
├── data
|
||||
│ ├── gid
|
||||
│ │ ├── ann_dir
|
||||
| │ │ │ ├── train
|
||||
| │ │ │ ├── val
|
||||
│ │ ├── img_dir
|
||||
| │ │ │ ├── train
|
||||
| │ │ │ ├── val
|
||||
|
||||
```
|
||||
|
||||
### 3.7 贡献的代码及文档通过`pre-commit`检查
|
||||
|
||||
使用命令
|
||||
|
||||
```bash
|
||||
git add .
|
||||
git commit -m "添加描述"
|
||||
git push
|
||||
```
|
||||
|
||||
### 3.8 在 GitHub 中向 mmsegmentation 提交 PR
|
||||
|
||||
具体步骤可见[《OpenMMLab 贡献代码指南》](https://mmcv.readthedocs.io/zh_CN/latest/community/contributing.html)
|
||||
242
docs/zh_cn/user_guides/5_deployment.md
Normal file
242
docs/zh_cn/user_guides/5_deployment.md
Normal file
@ -0,0 +1,242 @@
|
||||
# 教程5：模型部署
|
||||
|
||||
# MMSegmentation 模型部署
|
||||
|
||||
- [教程5：模型部署](#教程5模型部署)
|
||||
- [MMSegmentation 模型部署](#mmsegmentation-模型部署)
|
||||
- [安装](#安装)
|
||||
- [安装 mmseg](#安装-mmseg)
|
||||
- [安装 mmdeploy](#安装-mmdeploy)
|
||||
- [模型转换](#模型转换)
|
||||
- [模型规范](#模型规范)
|
||||
- [模型推理](#模型推理)
|
||||
- [后端模型推理](#后端模型推理)
|
||||
- [SDK 模型推理](#sdk-模型推理)
|
||||
- [模型支持列表](#模型支持列表)
|
||||
- [注意事项](#注意事项)
|
||||
|
||||
______________________________________________________________________
|
||||
|
||||
[MMSegmentation](https://github.com/open-mmlab/mmsegmentation/tree/main) 又称`mmseg`，是一个基于 PyTorch 的开源对象分割工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
|
||||
|
||||
## 安装
|
||||
|
||||
### 安装 mmseg
|
||||
|
||||
请参考[官网安装指南](https://mmsegmentation.readthedocs.io/en/latest/get_started.html)。
|
||||
|
||||
### 安装 mmdeploy
|
||||
|
||||
mmdeploy 有以下几种安装方式:
|
||||
|
||||
**方式一：** 安装预编译包
|
||||
|
||||
请参考[安装概述](https://mmdeploy.readthedocs.io/zh_CN/latest/get_started.html#mmdeploy)
|
||||
|
||||
**方式二：** 一键式脚本安装
|
||||
|
||||
如果部署平台是 **Ubuntu 18.04 及以上版本**， 请参考[脚本安装说明](../01-how-to-build/build_from_script.md)，完成安装过程。
|
||||
比如，以下命令可以安装 mmdeploy 以及配套的推理引擎——`ONNX Runtime`.
|
||||
|
||||
```shell
|
||||
git clone --recursive -b main https://github.com/open-mmlab/mmdeploy.git
|
||||
cd mmdeploy
|
||||
python3 tools/scripts/build_ubuntu_x64_ort.py $(nproc)
|
||||
export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH
|
||||
export LD_LIBRARY_PATH=$(pwd)/../mmdeploy-dep/onnxruntime-linux-x64-1.8.1/lib/:$LD_LIBRARY_PATH
|
||||
```
|
||||
|
||||
**说明**:
|
||||
|
||||
- 把 `$(pwd)/build/lib` 添加到 `PYTHONPATH`，目的是为了加载 mmdeploy SDK python 包 `mmdeploy_runtime`，在章节 [SDK模型推理](#sdk模型推理)中讲述其用法。
|
||||
- 在[使用 ONNX Runtime推理后端模型](#后端模型推理)时，需要加载自定义算子库，需要把 ONNX Runtime 库的路径加入环境变量 `LD_LIBRARY_PATH`中。
|
||||
**方式三：** 源码安装
|
||||
|
||||
在方式一、二都满足不了的情况下，请参考[源码安装说明](../01-how-to-build/build_from_source.md) 安装 mmdeploy 以及所需推理引擎。
|
||||
|
||||
## 模型转换
|
||||
|
||||
你可以使用 [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/tree/main/tools/deploy.py) 把 mmseg 模型一键式转换为推理后端模型。
|
||||
该工具的详细使用说明请参考[这里](https://github.com/open-mmlab/mmdeploy/tree/main/docs/en/02-how-to-run/convert_model.md#usage).
|
||||
|
||||
以下，我们将演示如何把 `unet` 转换为 onnx 模型。
|
||||
|
||||
```shell
|
||||
cd mmdeploy
|
||||
|
||||
# download unet model from mmseg model zoo
|
||||
mim download mmsegmentation --config unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024 --dest .
|
||||
|
||||
# convert mmseg model to onnxruntime model with dynamic shape
|
||||
python tools/deploy.py \
|
||||
configs/mmseg/segmentation_onnxruntime_dynamic.py \
|
||||
unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py \
|
||||
fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth \
|
||||
demo/resources/cityscapes.png \
|
||||
--work-dir mmdeploy_models/mmseg/ort \
|
||||
--device cpu \
|
||||
--show \
|
||||
--dump-info
|
||||
```
|
||||
|
||||
转换的关键之一是使用正确的配置文件。项目中已内置了各后端部署[配置文件](https://github.com/open-mmlab/mmdeploy/tree/main/configs/mmseg)。
|
||||
文件的命名模式是：
|
||||
|
||||
```
|
||||
segmentation_{backend}-{precision}_{static | dynamic}_{shape}.py
|
||||
```
|
||||
|
||||
其中：
|
||||
|
||||
- **{backend}:** 推理后端名称。比如，onnxruntime、tensorrt、pplnn、ncnn、openvino、coreml 等等
|
||||
- **{precision}:** 推理精度。比如，fp16、int8。不填表示 fp32
|
||||
- **{static | dynamic}:** 动态、静态 shape
|
||||
- **{shape}:** 模型输入的 shape 或者 shape 范围
|
||||
|
||||
在上例中，你也可以把 `unet` 转为其他后端模型。比如使用`segmentation_tensorrt-fp16_dynamic-512x1024-2048x2048.py`，把模型转为 tensorrt-fp16 模型。
|
||||
|
||||
```{tip}
|
||||
当转 tensorrt 模型时, --device 需要被设置为 "cuda"
|
||||
```
|
||||
|
||||
## 模型规范
|
||||
|
||||
在使用转换后的模型进行推理之前，有必要了解转换结果的结构。 它存放在 `--work-dir` 指定的路路径下。
|
||||
|
||||
上例中的`mmdeploy_models/mmseg/ort`，结构如下：
|
||||
|
||||
```
|
||||
mmdeploy_models/mmseg/ort
|
||||
├── deploy.json
|
||||
├── detail.json
|
||||
├── end2end.onnx
|
||||
└── pipeline.json
|
||||
```
|
||||
|
||||
重要的是：
|
||||
|
||||
- **end2end.onnx**: 推理引擎文件。可用 ONNX Runtime 推理
|
||||
- \***.json**: mmdeploy SDK 推理所需的 meta 信息
|
||||
|
||||
整个文件夹被定义为**mmdeploy SDK model**。换言之，**mmdeploy SDK model**既包括推理引擎，也包括推理 meta 信息。
|
||||
|
||||
## 模型推理
|
||||
|
||||
### 后端模型推理
|
||||
|
||||
以上述模型转换后的 `end2end.onnx` 为例，你可以使用如下代码进行推理：
|
||||
|
||||
```python
|
||||
from mmdeploy.apis.utils import build_task_processor
|
||||
from mmdeploy.utils import get_input_shape, load_config
|
||||
import torch
|
||||
|
||||
deploy_cfg = 'configs/mmseg/segmentation_onnxruntime_dynamic.py'
|
||||
model_cfg = './unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py'
|
||||
device = 'cpu'
|
||||
backend_model = ['./mmdeploy_models/mmseg/ort/end2end.onnx']
|
||||
image = './demo/resources/cityscapes.png'
|
||||
|
||||
# read deploy_cfg and model_cfg
|
||||
deploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)
|
||||
|
||||
# build task and backend model
|
||||
task_processor = build_task_processor(model_cfg, deploy_cfg, device)
|
||||
model = task_processor.build_backend_model(backend_model)
|
||||
|
||||
# process input image
|
||||
input_shape = get_input_shape(deploy_cfg)
|
||||
model_inputs, _ = task_processor.create_input(image, input_shape)
|
||||
|
||||
# do model inference
|
||||
with torch.no_grad():
|
||||
result = model.test_step(model_inputs)
|
||||
|
||||
# visualize results
|
||||
task_processor.visualize(
|
||||
image=image,
|
||||
model=model,
|
||||
result=result[0],
|
||||
window_name='visualize',
|
||||
output_file='./output_segmentation.png')
|
||||
```
|
||||
|
||||
### SDK 模型推理
|
||||
|
||||
你也可以参考如下代码，对 SDK model 进行推理：
|
||||
|
||||
```python
|
||||
from mmdeploy_runtime import Segmentor
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
img = cv2.imread('./demo/resources/cityscapes.png')
|
||||
|
||||
# create a classifier
|
||||
segmentor = Segmentor(model_path='./mmdeploy_models/mmseg/ort', device_name='cpu', device_id=0)
|
||||
# perform inference
|
||||
seg = segmentor(img)
|
||||
|
||||
# visualize inference result
|
||||
## random a palette with size 256x3
|
||||
palette = np.random.randint(0, 256, size=(256, 3))
|
||||
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
|
||||
for label, color in enumerate(palette):
|
||||
color_seg[seg == label, :] = color
|
||||
# convert to BGR
|
||||
color_seg = color_seg[..., ::-1]
|
||||
img = img * 0.5 + color_seg * 0.5
|
||||
img = img.astype(np.uint8)
|
||||
cv2.imwrite('output_segmentation.png', img)
|
||||
```
|
||||
|
||||
除了python API，mmdeploy SDK 还提供了诸如 C、C++、C#、Java等多语言接口。
|
||||
你可以参考[样例](https://github.com/open-mmlab/mmdeploy/tree/main/demo)学习其他语言接口的使用方法。
|
||||
|
||||
## 模型支持列表
|
||||
|
||||
| Model | TorchScript | OnnxRuntime | TensorRT | ncnn | PPLNN | OpenVino |
|
||||
| :-------------------------------------------------------------------------------------------------------- | :---------: | :---------: | :------: | :--: | :---: | :------: |
|
||||
| [FCN](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/fcn) | Y | Y | Y | Y | Y | Y |
|
||||
| [PSPNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/pspnet)[\*](#static_shape) | Y | Y | Y | Y | Y | Y |
|
||||
| [DeepLabV3](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/deeplabv3) | Y | Y | Y | Y | Y | Y |
|
||||
| [DeepLabV3+](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/deeplabv3plus) | Y | Y | Y | Y | Y | Y |
|
||||
| [Fast-SCNN](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/fastscnn)[\*](#static_shape) | Y | Y | Y | N | Y | Y |
|
||||
| [UNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/unet) | Y | Y | Y | Y | Y | Y |
|
||||
| [ANN](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/ann)[\*](#static_shape) | Y | Y | Y | N | N | N |
|
||||
| [APCNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/apcnet) | Y | Y | Y | Y | N | N |
|
||||
| [BiSeNetV1](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/bisenetv1) | Y | Y | Y | Y | N | Y |
|
||||
| [BiSeNetV2](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/bisenetv2) | Y | Y | Y | Y | N | Y |
|
||||
| [CGNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/cgnet) | Y | Y | Y | Y | N | Y |
|
||||
| [DMNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/dmnet) | ? | Y | N | N | N | N |
|
||||
| [DNLNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/dnlnet) | ? | Y | Y | Y | N | Y |
|
||||
| [EMANet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/emanet) | Y | Y | Y | N | N | Y |
|
||||
| [EncNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/encnet) | Y | Y | Y | N | N | Y |
|
||||
| [ERFNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/erfnet) | Y | Y | Y | Y | N | Y |
|
||||
| [FastFCN](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/fastfcn) | Y | Y | Y | Y | N | Y |
|
||||
| [GCNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/gcnet) | Y | Y | Y | N | N | N |
|
||||
| [ICNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/icnet)[\*](#static_shape) | Y | Y | Y | N | N | Y |
|
||||
| [ISANet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/isanet)[\*](#static_shape) | N | Y | Y | N | N | Y |
|
||||
| [NonLocal Net](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/nonlocal_net) | ? | Y | Y | Y | N | Y |
|
||||
| [OCRNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/ocrnet) | Y | Y | Y | Y | N | Y |
|
||||
| [PointRend](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/point_rend)[\*](#static_shape) | Y | Y | Y | N | N | N |
|
||||
| [Semantic FPN](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/sem_fpn) | Y | Y | Y | Y | N | Y |
|
||||
| [STDC](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/stdc) | Y | Y | Y | Y | N | Y |
|
||||
| [UPerNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/upernet)[\*](#static_shape) | N | Y | Y | N | N | N |
|
||||
| [DANet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/danet) | ? | Y | Y | N | N | Y |
|
||||
| [Segmenter](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/segmenter)[\*](#static_shape) | N | Y | Y | Y | N | Y |
|
||||
| [SegFormer](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/segformer)[\*](#static_shape) | ? | Y | Y | N | N | Y |
|
||||
| [SETR](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/setr) | ? | Y | N | N | N | Y |
|
||||
| [CCNet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/ccnet) | ? | N | N | N | N | N |
|
||||
| [PSANet](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/psanet) | ? | N | N | N | N | N |
|
||||
| [DPT](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/dpt) | ? | N | N | N | N | N |
|
||||
|
||||
## 注意事项
|
||||
|
||||
- 所有 mmseg 模型仅支持 "whole" 推理模式。
|
||||
|
||||
- <i id=“static_shape”>PSPNet，Fast-SCNN</i> 仅支持静态输入，因为多数推理框架的 [nn.AdaptiveAvgPool2d](https://github.com/open-mmlab/mmsegmentation/blob/0c87f7a0c9099844eff8e90fa3db5b0d0ca02fee/mmseg/models/decode_heads/psp_head.py#L38) 不支持动态输入。
|
||||
|
||||
- 对于仅支持静态形状的模型，应使用静态形状的部署配置文件，例如 `configs/mmseg/segmentation_tensorrt_static-1024x2048.py`
|
||||
|
||||
- 对于喜欢部署模型生成概率特征图的用户，将 `codebase_config = dict(with_argmax=False)` 放在部署配置中就足够了。
|
||||
@ -1 +0,0 @@
|
||||
# 模型部署
|
||||
Loading…
x
Reference in New Issue
Block a user