OpenMMLab Model Deployment Framework
 
 
 
 
 
 
Go to file
RunningLeon 9e82851a73
[Feature] end2end yolov3 with ncnn (#248)
* support yolov3 ncnn with Yolov3DetectionOutput

* update nms

* fix contiguous in ncnn wrapper

* remove padding to detectionoutput

* format cpp

* Revert "format cpp"

This reverts commit 54050b19cd80d2f8cd851d82a755fd2c8d6c779d.

* fix zero detection

* fix yapf

* onnx2ncnn.cpp

* fix ut

* fix isort

* fix clang-format

* format cpp

* resolve comments

* resolve comments

* fix ut of ncnnend2endmodel

* fix yapf

* fix return list;

Co-authored-by: hanrui1sensetime <hanrui1@sensetime.com>
2021-12-06 19:01:36 +08:00
.github optimize markdown lint (#245) 2021-12-03 18:31:44 +08:00
backend_ops [Feature] end2end yolov3 with ncnn (#248) 2021-12-06 19:01:36 +08:00
configs [Feature] end2end yolov3 with ncnn (#248) 2021-12-06 19:01:36 +08:00
demo add a demo for rewrite (#145) 2021-10-27 11:06:42 +08:00
docs Add get_mo_command. (#247) 2021-12-06 10:31:32 +08:00
docs_zh_CN [Refactor]: Update naming style and docstring in mmseg and mmcv (#188) 2021-11-11 18:48:04 +08:00
mmdeploy [Feature] end2end yolov3 with ncnn (#248) 2021-12-06 19:01:36 +08:00
requirements [Enhancement]: Added static config and CI tests for OpenVINO. (#218) 2021-12-01 14:03:48 +08:00
tests [Feature] end2end yolov3 with ncnn (#248) 2021-12-06 19:01:36 +08:00
third_party
tools [Enhancement]: Added static config and CI tests for OpenVINO. (#218) 2021-12-01 14:03:48 +08:00
.gitignore [Docs]: Add sphinx document (#109) 2021-10-09 14:10:42 +08:00
.gitmodules
.isort.cfg add a demo for rewrite (#145) 2021-10-27 11:06:42 +08:00
.pre-commit-config.yaml optimize markdown lint (#245) 2021-12-03 18:31:44 +08:00
.pylintrc add pylint score (#119) 2021-10-11 14:52:19 +08:00
.readthedocs.yml [Docs]: Add sphinx document (#109) 2021-10-09 14:10:42 +08:00
CITATION.cff Add citation (#151) 2021-10-27 15:03:40 +08:00
CMakeLists.txt fix bugs of build doc, update cmake version (#175) 2021-11-09 17:44:42 +08:00
LICENSE
MANIFEST.in
README.md [Doc]: Fix doc (#219) 2021-11-25 14:51:21 +08:00
README_zh-CN.md [Doc]: Fix doc (#219) 2021-11-25 14:51:21 +08:00
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 get_started.md for installation.

Getting Started

Please read how_to_convert_model.md for the basic usage of MMDeploy. There are also tutorials on how to write config, how to support new models and how to measure performance of models.

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.