308e28fcb0
* support cascade (mask) rcnn * fix docstring * support SwinTransformer * move dense_head support to this branch * fix function names * fix part of uts of mmdet * fix for mmdet ut * fix det model cfg for ut * fix test_object_detection.py * fix mmdet object_detection_model.py * fix mmdet yolov3 ort ut * fix part of uts * fix cascade bbox head ut * fix cascade bbox head ut * remove useless ssd ncnn test * fix ncnn wrapper * fix openvino ut for reppoint head * fix openvino cascade mask rcnn * sync codes * support roll * remove unused pad * fix yolox * fix isort * fix lint * fix flake8 * reply for comments and fix failed ut * fix sdk_export in dump_info * fix temp hidden xlsx bugs * fix mmdet regression test * fix lint * fix timer * fix timecount side-effect * adapt profile.py for mmdet 2.0 * hardcode report.txt for T4 benchmark test: temp version * fix no-visualizer case * fix backend_model * fix android build * adapt new mmdet 2.0 0825 * fix new 2.0 * fix test_mmdet_structures * fix test_object_detection * fix codebase import * fix ut * fix all mmdet uts * fix det * fix mmdet trt * fix ncnn onnx optimize
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Introduction
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project.
Main features
Fully support OpenMMLab models
The currently supported codebases and models are as follows, and more will be included in the future
Multiple inference backends are available
Models can be exported and run in the following backends, and more will be compatible
| ONNX Runtime | TensorRT | ppl.nn | ncnn | OpenVINO | LibTorch | snpe | more |
|---|---|---|---|---|---|---|---|
| ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | benchmark |
Efficient and scalable C/C++ SDK Framework
All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on
Get Started
Please read getting_started.md for the basic usage of MMDeploy. We also provide tutoials about:
- Build
- User Guide
- Developer Guide
- FAQ
- Contributing
Benchmark and Model zoo
You can find the supported models from here and their performance in the benchmark.
Contributing
We appreciate all contributions to 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 citing:
@misc{=mmdeploy,
title={OpenMMLab's Model Deployment Toolbox.},
author={MMDeploy Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmdeploy}},
year={2021}
}
License
This project is released under the Apache 2.0 license.
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.