651eec239b
* Initial commit of build script * Fixed lint * * Set prefix depending on install arch * Auto detect installed version of cmake, gcc. Update if necessary * Prompt to reinstall venv if exists * Disable .tar packing * Enable MMDEPLOY SDK Python * Misc cleanup * Fixed lint * Renamed script * * Set env variables without restarting script. * OpenCV install optional. * Fix numpy OPENBLAS_CORETYPE on numpy >1.19.4. * Remove specific build files. * Fix prebuild output .tar.gz. * Comments added. exec bash when closing script * * Use root dir as MMDeploy dir. * Use pip3. * Remove explicit MMDeploy checkout version. * Use MMDEPLOY_DIR for examples path * Make PyTorch, torchvision, mmcv install optional * * Do not deactivate python venv. * Make prebuild archive optional. * Add venv* to gitignore * * Add support for unattended, interactive install. * Cleanup misc. comments. * Check input arguments before continuing. * Added additional log messages, comments added * Added default option when pressing [ENTER] * * Rearranged cmake, gcc build/install order. * Mention default option when reinstalling venv * sudo make install instead of cmake --install * Moved script to tools/scripts * Fixed relative path when generating WORKING_DIR |
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README.md
Introduction
English | 简体中文
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project.
Major features
-
Fully support OpenMMLab models
We provide a unified model deployment toolbox for the codebases in OpenMMLab. The supported codebases are listed as below, and more will be added in the future
- MMClassification
- MMDetection
- MMSegmentation
- MMEditing
- MMOCR
- MMPose
-
Multiple inference backends are available
Models can be exported and run in different backends. The following ones are supported, and more will be taken into consideration
- ONNX Runtime
- TensorRT
- PPLNN
- ncnn
- OpenVINO
-
Efficient and highly scalable SDK Framework by C/C++
All kinds of modules in SDK can be extensible, such as
Transformfor image processing,Netfor Neural Network inference,Modulefor postprocessing and so on
License
This project is released under the Apache 2.0 license.
Installation
Please refer to build.md for installation.
Getting Started
Please see getting_started.md for the basic usage of MMDeploy. We also provide other tutorials for:
- how to convert model
- how to write config
- how to support new models
- how to measure performance of models
Please refer to FAQ for frequently asked questions.
Benchmark and model zoo
Results and supported model list are available in the benchmark and model list.
Contributing
We appreciate all contributions to improve MMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.
Acknowledgement
We would like to sincerely thank the following teams for their contributions to MMDeploy:
Citation
If you find this project useful in your research, please consider cite:
@misc{=mmdeploy,
title={OpenMMLab's Model Deployment Toolbox.},
author={MMDeploy Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmdeploy}},
year={2021}
}
Projects in OpenMMLab
- MMCV: OpenMMLab foundational library for computer vision.
- MIM: MIM installs OpenMMLab packages.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
- MMDetection: OpenMMLab detection toolbox and benchmark.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
- MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
- MMRazor: OpenMMLab model compression toolbox and benchmark.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMGeneration: OpenMMLab image and video generative models toolbox.
- MMDeploy: OpenMMLab model deployment framework.