234 lines
20 KiB
Markdown
234 lines
20 KiB
Markdown
<div align="center">
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<img src="resources/mmdeploy-logo.png" width="450"/>
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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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</div>
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[](https://mmdeploy.readthedocs.io/zh_CN/latest/)
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[](https://github.com/open-mmlab/mmdeploy/actions)
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[](https://codecov.io/gh/open-mmlab/mmdeploy)
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[](https://github.com/open-mmlab/mmdeploy/blob/master/LICENSE)
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[](https://github.com/open-mmlab/mmdeploy/issues)
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[](https://github.com/open-mmlab/mmdeploy/issues)
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[English](README.md) | 简体中文
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## MMDeploy 1.x 版本
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全新的 MMDeploy 1.x 已发布，该版本适配OpenMMLab 2.0生态体系，使用时务必**对齐版本**。
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MMDeploy 代码库默认分支从`master`切换至`main`。 MMDeploy 0.x (`master`)将逐步废弃，新特性将只添加到 MMDeploy 1.x (`main`)。
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| mmdeploy | mmengine | mmcv | mmdet | mmcls and others |
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| :------: | :------: | :------: | :------: | :--------------: |
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| 0.x.y | - | \<=1.x.y | \<=2.x.y | 0.x.y |
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| 1.x.y | 0.x.y | 2.x.y | 3.x.y | 1.x.y |
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## 介绍
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MMDeploy 是 [OpenMMLab](https://openmmlab.com/) 模型部署工具箱，**为各算法库提供统一的部署体验**。基于 MMDeploy，开发者可以轻松从训练 repo 生成指定硬件所需 SDK，省去大量适配时间。
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## 架构简析
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<div align="center">
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<img src="resources/introduction.png"/>
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</div>
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## 特性简介
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### 支持超多 OpenMMLab 算法库
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- [mmcls](docs/zh_cn/04-supported-codebases/mmcls.md)
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- [mmdet](docs/zh_cn/04-supported-codebases/mmdet.md)
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- [mmseg](docs/zh_cn/04-supported-codebases/mmseg.md)
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- [mmedit](docs/zh_cn/04-supported-codebases/mmedit.md)
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- [mmocr](docs/zh_cn/04-supported-codebases/mmocr.md)
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- [mmpose](docs/zh_cn/04-supported-codebases/mmpose.md)
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- [mmdet3d](docs/zh_cn/04-supported-codebases/mmdet3d.md)
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- [mmrotate](docs/zh_cn/04-supported-codebases/mmrotate.md)
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- [mmaction2](docs/zh_cn/04-supported-codebases/mmaction2.md)
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### 支持多种推理后端
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支持的设备平台和推理引擎如下表所示。benchmark请参考[这里](docs/zh_cn/03-benchmark/benchmark.md)
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| Device / Platform | Linux | Windows | macOS | Android |
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| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- |
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| x86_64 CPU | [![Build Status][pass-backend-ort]][ci-backend-ort]ONNXRuntime<br>[![Build Status][pass-backend-pplnn]][ci-backend-pplnn]pplnn<br>[![Build Status][pass-backend-ncnn]][ci-backend-ncnn]ncnn<br>[![Build Status][pass-backend-torchscript]][ci-backend-torchscript]LibTorch<br>[![Build Status][pass-build-rknpu]][ci-build-rknpu]OpenVINO<br>[![Build Status][pass-build-tvm]][ci-build-tvm]TVM | ![][pass-no-status]ONNXRuntime<br>![][pass-no-status]OpenVINO | - | - |
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| ARM CPU | [![Build Status][pass-build-rknpu]][ci-build-rknpu]ncnn | - | - | [![Build Status][pass-build-rknpu]][ci-build-rknpu]ncnn |
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| RISC-V | [![Build Status][pass-build-riscv64-gcc]][ci-build-riscv64-gcc]ncnn | - | - | - |
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| NVIDIA GPU | ![Build Status][pass-no-status]ONNXRuntime<br>![Build Status][pass-no-status]TensorRT<br>![Build Status][pass-no-status]pplnn<br>![Build Status][pass-no-status]LibTorch<br>![Build Status][pass-no-status]TVM | ![Build Status][pass-no-status]ONNXRuntime<br>![Build Status][pass-no-status]TensorRT<br>![Build Status][pass-no-status]pplnn | - | - |
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| NVIDIA Jetson | [![Build Status][pass-build-rknpu]][ci-build-rknpu]TensorRT | - | - | - |
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| Huawei ascend310 | [![Build Status][pass-backend-ascend]][ci-backend-ascend]CANN | - | - | - |
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| Rockchip | [![Build Status][pass-backend-rknn]][ci-backend-rknn]RKNN | - | - | - |
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| Apple M1 | - | - | [![Build Status][pass-backend-coreml]][ci-backend-coreml]CoreML | - |
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| Adreno GPU | - | - | - | [![Build Status][pass-backend-snpe]][ci-backend-snpe]SNPE<br>[![Build Status][pass-build-rknpu]][ci-build-rknpu]ncnn |
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| Hexagon DSP | - | - | - | [![Build Status][pass-backend-snpe]][ci-backend-snpe]SNPE |
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```
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```
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### SDK 可高度定制化
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- Transform 数据预处理
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- Net 推理
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- Module 后处理
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## [中文文档](https://mmdeploy.readthedocs.io/zh_CN/latest/)
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- [快速上手](docs/zh_cn/get_started.md)
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- [编译](docs/zh_cn/01-how-to-build/build_from_source.md)
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- [一键式脚本安装](docs/zh_cn/01-how-to-build/build_from_script.md)
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- [Build from Docker](docs/zh_cn/01-how-to-build/build_from_docker.md)
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- [Build for Linux](docs/zh_cn/01-how-to-build/linux-x86_64.md)
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- [Build for Windows](docs/zh_cn/01-how-to-build/windows.md)
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- [Build for macOS](docs/zh_cn/01-how-to-build/macos-arm64.md)
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- [Build for Win10](docs/zh_cn/01-how-to-build/windows.md)
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- [Build for Android](docs/zh_cn/01-how-to-build/android.md)
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- [Build for Jetson](docs/zh_cn/01-how-to-build/jetsons.md)
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- [Build for SNPE](docs/zh_cn/01-how-to-build/snpe.md)
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- [Build for Rockchip](docs/zh_cn/01-how-to-build/rockchip.md)
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- [Cross Build for aarch64](docs/zh_cn/01-how-to-build/cross_build_ncnn_aarch64.md)
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- 使用
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- [把模型转换到推理 Backend](docs/zh_cn/02-how-to-run/convert_model.md)
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- [配置转换参数](docs/zh_cn/02-how-to-run/write_config.md)
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- [量化](docs/zh_cn/02-how-to-run/quantize_model.md)
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- [测试转换完成的模型](docs/zh_cn/02-how-to-run/profile_model.md)
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- [工具集介绍](docs/zh_cn/02-how-to-run/useful_tools.md)
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- 开发指南
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- [软件架构](docs/zh_cn/07-developer-guide/architecture.md)
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- [支持新模型](docs/zh_cn/07-developer-guide/support_new_model.md)
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- [增加推理 backend](docs/zh_cn/07-developer-guide/support_new_backend.md)
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- [模型分块](docs/zh_cn/07-developer-guide/partition_model.md)
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- [测试重写模型](docs/zh_cn/07-developer-guide/test_rewritten_models.md)
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- [backend 算子测试](docs/zh_cn/07-developer-guide/add_backend_ops_unittest.md)
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- [回归测试](docs/zh_cn/07-developer-guide/regression_test.md)
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- 各 backend 自定义算子列表
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- [ncnn](docs/zh_cn/06-custom-ops/ncnn.md)
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- [onnxruntime](docs/zh_cn/06-custom-ops/onnxruntime.md)
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- [tensorrt](docs/zh_cn/06-custom-ops/tensorrt.md)
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- [FAQ](docs/zh_cn/faq.md)
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- [贡献者手册](.github/CONTRIBUTING.md)
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## 新人解说
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- [01 术语解释、加载第一个模型](docs/zh_cn/tutorial/01_introduction_to_model_deployment.md)
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- [02 部署常见问题](docs/zh_cn/tutorial/02_challenges.md)
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- [03 torch转onnx](docs/zh_cn/tutorial/03_pytorch2onnx.md)
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- [04 让torch支持更多onnx算子](docs/zh_cn/tutorial/04_onnx_custom_op.md)
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- [05 调试onnx模型](docs/zh_cn/tutorial/05_onnx_model_editing.md)
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## 基准与模型库
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基准和支持的模型列表可以在[基准](https://mmdeploy.readthedocs.io/zh_CN/latest/benchmark.html)和[模型列表](https://mmdeploy.readthedocs.io/en/latest/supported_models.html)中获得。
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## 贡献指南
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我们感谢所有的贡献者为改进和提升 MMDeploy 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
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## 致谢
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- [OpenPPL](https://github.com/openppl-public/ppl.nn): 高性能推理框架底层库
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- [OpenVINO](https://github.com/openvinotoolkit/openvino): AI 推理优化和部署框架
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- [ncnn](https://github.com/tencent/ncnn): 为手机端极致优化的高性能神经网络前向计算框架
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## 引用
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如果您在研究中使用了本项目的代码或者性能基准，请参考如下 bibtex 引用 MMDeploy:
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```BibTeX
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@misc{=mmdeploy,
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title={OpenMMLab's Model Deployment Toolbox.},
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author={MMDeploy Contributors},
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howpublished = {\url{https://github.com/open-mmlab/mmdeploy}},
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year={2021}
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}
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```
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## 开源许可证
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该项目采用 [Apache 2.0 开源许可证](LICENSE)。
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## OpenMMLab 的其他项目
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
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- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
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- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
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- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱和基准测试
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- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
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- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
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- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
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- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
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- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
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- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
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- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
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- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
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- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
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- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
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- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
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## 欢迎加入 OpenMMLab 社区
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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=MSMAfWOe)，或添加微信小助手”OpenMMLabwx“加入官方交流微信群。
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<div align="center">
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<img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/en/_static/zhihu_qrcode.jpg" height="400" />
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<img src="resources/qq_group_qrcode.jpg" height="400" />
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<img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/en/_static/wechat_qrcode.jpg" height="400" />
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</div>
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我们会在 OpenMMLab 社区为大家
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- 📢 分享 AI 框架的前沿核心技术
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- 💻 解读 PyTorch 常用模块源码
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- 📰 发布 OpenMMLab 的相关新闻
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- 🚀 介绍 OpenMMLab 开发的前沿算法
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- 🏃 获取更高效的问题答疑和意见反馈
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- 🔥 提供与各行各业开发者充分交流的平台
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干货满满 📘，等您来撩 💗，OpenMMLab 社区期待您的加入 👬
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[ci-backend-ascend]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-ascend.yml
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[ci-backend-coreml]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-coreml.yml
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[ci-backend-ncnn]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-ncnn.yml
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[ci-backend-ort]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-ort.yml
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[ci-backend-pplnn]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-pplnn.yml
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[ci-backend-rknn]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-rknn.yml
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[ci-backend-snpe]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-snpe.yml
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[ci-backend-torchscript]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-torchscript.yml
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[ci-build-riscv64-gcc]: https://github.com/open-mmlab/mmdeploy/actions/workflows/linux-riscv64-gcc.yml
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[ci-build-rknpu]: https://github.com/open-mmlab/mmdeploy/actions/workflows/linux-rknpu.yml
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[ci-build-tvm]: https://github.com/open-mmlab/mmdeploy/actions/workflows/backend-tvm.yml
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[pass-backend-ascend]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ascend.yml?branch=master
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[pass-backend-coreml]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-coreml.yml?branch=master
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[pass-backend-ncnn]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ncnn.yml?branch=master
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[pass-backend-ort]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ort.yml?branch=master
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[pass-backend-pplnn]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-pplnn.yml?branch=master
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[pass-backend-rknn]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-rknn.yml?branch=master
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[pass-backend-snpe]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-snpe.yml?branch=master
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[pass-backend-torchscript]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-ort.yml?branch=master
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[pass-build-riscv64-gcc]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/linux-riscv64-gcc.yml?branch=master
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[pass-build-rknpu]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-rknn.yml?branch=master
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[pass-build-tvm]: https://img.shields.io/github/actions/workflow/status/open-mmlab/mmdeploy/backend-tvm.yml?branch=master
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[pass-no-status]: https://img.shields.io/badge/build-no%20status-lightgrey
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