* Fix include and lib paths for onnxruntime. * Fixes for SSD export test * Add onnx2openvino and OpenVINODetector. Test models: ssd, retinanet, fcos, fsaf. * Add support for two-stage models: faster_rcnn, cascade_rcnn * Add doc * Add strip_doc_string for openvino. * Fix openvino preprocess. * Add OpenVINO to test_wrapper.py. * Fix * Add openvino_execute. * Removed preprocessing. * Fix onnxruntime cmake. * Rewrote postprocessing and forward, added docstrings and fixes. * Added device type change to OpenVINOWrapper. * Update forward_of_single_roi_extractor_dynamic_openvino and fix doc. * Update docs. * Add support for masks (Mask RCNN). * Add masks to CascadeRoIHead.simple_test. * Added masks to test_OpenVINODetector. * Added test_cascade_roi_head_with_mask. * Update docs. * Fix segm_results shape. * Fix TopK in NMS and add test_multiclass_nms_with_keep_top_k. * Removed unnecessary functions. * Fix. * Fix test_multiclass_nms_with_keep_top_k. * Updated test_OpenVINODetector. |
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demo | ||
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mmdeploy | ||
requirements | ||
tests | ||
third_party | ||
tools | ||
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CITATION.cff | ||
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LICENSE | ||
MANIFEST.in | ||
README.md | ||
README_zh-CN.md | ||
requirements.txt | ||
setup.cfg | ||
setup.py |
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
-
OpenMMLab model support
Models in OpenMMLab can be deployed with this project. Such as MMClassification, MMDetection, etc.
-
Multiple inference engine support
Models can be exported and run in different backends. Such as ONNX Runtime, TensorRT, etc.
-
Model rewrite
Modules and functions used in models can be rewritten to meet the demand of different backends. It is easy to add new model support.
License
This project is released under the Apache 2.0 license.
Codebase and Backend support
Supported codebase:
- MMClassification
- MMDetection
- MMSegmentation
- MMEditing
- MMOCR
Supported backend:
- ONNX Runtime
- TensorRT
- PPL
- ncnn
- OpenVINO
Installation
Please refer to build.md for installation.
Getting Started
Please read how_to_convert_model.md for the basic usage of MMDeploy. There are also tutorials for how to write config, how to support new models and how to test model.
Please refer to FAQ for frequently asked questions.
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}
}
Contributing
We appreciate all contributions to improve MMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.
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.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
- MMGeneration: OpenMMLab image and video generative models toolbox.