MMDeploy Release V0.4.0

Features

• Support MMPose model inference in SDK: HRNet, LiteHRNet and MSPN
• Support MMDetection3D: PointPillars and CenterPoint(pillar)
• Support Andoid platform so as to benefit the development of android apps
• Support fcn_unet deployment with dynamic shape
• Support TorchScript

Improvements

• Optimize TRTMultiLevelRoiAlign plugin
• Remove RoiAlign plugin for ONNXRuntime
• Add DCN TensorRT plugin
• Update pad logic in detection heads
• Refactor the rewriter module of Model Converter
• Suppress CMAKE_CUDA_ARCHITECTURES warnings
• Update cmake scripts to ensure that the thirdparty packages are relocatable

Bug fixes

• Fix the crash on the headless installation
• Correct the deployment configs for MMSegmentation
• Optimize prepocess module and fix the potential use-after-free issue
• Resolve the compatibility with torch 1.11
• Fix the errors when deploying yolox model
• Fix the errors occurred during docker build

Documents

• Reorganize the build documents. Add more details about how to build MMDeploy on Linx, Windows and Android platforms
• Publish two chapters about the knowledge of model deployment
• Update the supported model list, including MMSegmentation，MMPose and MMDetection3D
• Translate the tutorial of "How to support new backends" into Chinese
• Update the FAQ

Contributors

@irexyc @lvhan028 @RunningLeon @hanrui1sensetime @AllentDan @grimoire @lzhangzz @SemyonBevzuk @VVsssssk @SingleZombie @raykindle @yydc-0 @haofanwang @LJoson @PeterH0323

MMDeploy Release V0.3.0

Features

• Support for windows platform.(#106)
• Support mmpose codebase.(#94)
• Support GFL model from mmdetection.(#124)
• Support export hardsigmoid in torch<=1.8.(#169)

Improvements

• Support mmocr v0.4+.(#115)
• Upgrade isort in pre-commit config.(#141)
• Opimize delta2bboxes.(#152)

Bug fixes

• Fix onnxruntime wrapper for gpu inference. (#123)
• Fix ci.(#144)
• Fix tests for OpenVINO with python 3.6. (#125)
• Added TensorRT version check. (#133)
• Fix a type error when computing scale_factor in rewriting interpolate.(#185)

Documents

• Add Chinese documents How_to_support_new_model.md and How_to_write_config.md
  (#147,#137)

Contributors

A total of 19 developers contributed to this release.

@grimoire @RunningLeon @AllentDan @lvhan028 @hhaAndroid @SingleZombie @lzhangzz @hanrui1sensetime @VVsssssk @SemyonBevzuk @ypwhs @TheSeriousProgrammer @matrixgame2018 @tehkillerbee @uniyushu @haofanwang @ypwhs @zhouzaida @q3394101

MMDeploy Release V0.2.0

Features

• Support Nvidia Jetson deployment. (Nano, TX2, Xavier)
• Add Python interface for SDK inference. (#27)
• Support yolox on ncnn. (#29)
• Support segmentation model UNet. (#77)
• Add docker files. (#67)

Improvements

• Add coverage report, CI to GitHub repository. (#16, #34, #35)
• Refactor the config utilities. (#12, #36)
• Remove redundant copy operation when converting model. (#61)
• Simplify single batch NMS. (#99)

Documents

• Now our English and Chinese documents are available on readthedocs: English 简体中文
• Benchmark and tutorial for Nvidia Jetson Nano. (#71)
• Fix docstring, links in documents. (#18, #32, #60, #84)
• More documents for TensorRT and OpenVINO. (#96, #102)

Bug fixes

• Avoid outputting empty tensor in NMS for ONNX Runtime. (#42)
• Fix TensorRT 7 SSD. (#49)
• Fix mmseg dynamic shape. (#57)
• Fix bugs about pplnn. (#40, #74)

Contributors

A total of 14 developers contributed to this release.

@grimoire @RunningLeon @AllentDan @SemyonBevzuk @lvhan028 @hhaAndroid @Stephenfang51 @SingleZombie @lzhangzz @hanrui1sensetime @VVsssssk @zhiqwang @tehkillerbee @Echo-minn

MMDeploy Release V0.1.0

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 (== 0.19.0)
  • MMDetection (== 2.19.0)
  • MMSegmentation (== 0.19.0)
  • MMEditing (== 0.11.0)
  • MMOCR (== 0.3.0)

• 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 (>= 1.8.0)
  • TensorRT (>= 7.2)
  • PPLNN (== 0.3.0)
  • ncnn (== 20211208)
  • OpenVINO (2021 4 LTS)

• Efficient and highly scalable SDK Framework by C/C++

  All kinds of modules in SDK can be extensible, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on.

Contributors

A total of 11 developers contributed to this release.

@grimoire @lvhan028 @AllentDan @VVsssssk @SemyonBevzuk @lzhangzz @RunningLeon @SingleZombie @del-zhenwu @zhouzaida @hanrui1sensetime