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* add ncnn test exporter in test_ops.py * add ncnn test exporter in utils.py * add onnxruntime and tensorrt ops test * fix blank line * fix comment add nms ops test * remove nms test * add test sample add dockerstring * remove nms test * fix grid_sample add type hind * fix problem * fix dockerstring * add nms batch_nms multi_level_roi_align * add test data * fix problem * rm pkl file dependent * rm file * add docstring * remove multi_level_dependce * add mmseg module unittest * add mmseg test * add mmseg model unit test * fix blankline * rename file * add syncbn2bn unit test * add apis/export * lint * lint * ?? * delete# * fix problems * add mmcv unit test * add docs about how to convert model * fix docs convert model * add list * rm mmcv test * add mmocr model in list fix decription * fix docs * fix docs * refactor docs * fix model list * add docs about how to create config file * fix how to convert model * remove other docs * add cn docs * fix args description * fix space * fix about evaluate model * fix link problems * fix cn docs Co-authored-by: SingleZombie <singlezombie@163.com>
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 inferenced with 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
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 create config, how to support new models and how to test model.
Please refer to FAQ for frequently asked questions.
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.