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ROCm / MIVisionX

MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX also delivers a highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions.
https://rocm.docs.amd.com/projects/MIVisionX/en/latest/
MIT License
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amd-opencl amd-opencv amd-openvx computer-vision inference inference-engine khronos-openvx machine-learning neural-network nnef onnx opencl openvx openvx-extensions openvx-neural-network rocm ryzen virtual-reality windows-machine-learning winml

readme

MIT licensed doc

MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized conformant open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning inference workloads on a wide range of computer hardware, including small embedded x86 CPUs, APUs, discrete GPUs, and heterogeneous servers.

Latest release

GitHub tag (latest SemVer)

AMD OpenVX™

AMD OpenVX™ is a highly optimized conformant open source implementation of the Khronos OpenVX™ 1.3 computer vision specification. It allows for rapid prototyping as well as fast execution on a wide range of computer hardware, including small embedded x86 CPUs and large workstation discrete GPUs.

Khronos OpenVX™ 1.0.1 conformant implementation is available in MIVisionX Lite

AMD OpenVX™ Extensions

The OpenVX framework provides a mechanism to add new vision functionality to OpenVX by vendors. This project has below listed OpenVX modules and utilities to extend amd_openvx, which contains the AMD OpenVX™ Core Engine.

Applications

MIVisionX has several applications built on top of OpenVX modules. These applications can serve as excellent prototypes and samples for developers to build upon.

Neural network model compiler and optimizer

Neural net model compiler and optimizer converts pre-trained neural net models to MIVisionX runtime code for optimized inference.

rocAL

The ROCm Augmentation Library - rocAL is designed to efficiently decode and process images and videos from a variety of storage formats and modify them through a processing graph programmable by the user.

rocAL is now available as an independent module at https://github.com/ROCm/rocAL. rocAL is deprecated in MIVisionX.

Toolkit

MIVisionX Toolkit is a comprehensive set of helpful tools for neural net creation, development, training, and deployment. The Toolkit provides useful tools to design, develop, quantize, prune, retrain, and infer your neural network work in any framework. The Toolkit has been designed to help you deploy your work on any AMD or 3rd party hardware, from embedded to servers.

MIVisionX toolkit provides tools for accomplishing your tasks throughout the whole neural net life-cycle, from creating a model to deploying them for your target platforms.

Utilities

Prerequisites

Hardware

[!IMPORTANT] Some modules in MIVisionX can be built for CPU ONLY. To take advantage of Advanced Features And Modules we recommend using AMD GPUs or AMD APUs.

Operating System

Linux

Windows

macOS

Installation instructions

Linux

The installation process uses the following steps:

Package install

Install MIVisionX runtime, development, and test packages.

Ubuntu
  sudo apt-get install mivisionx mivisionx-dev mivisionx-test
CentOS / RedHat
  sudo yum install mivisionx mivisionx-devel mivisionx-test
SLES
  sudo zypper install mivisionx mivisionx-devel mivisionx-test

[!IMPORTANT]

  • Package install supports HIP backend
  • Package install requires OpenCV V4.6 manual install
  • CentOS/RedHat/SLES requires FFMPEG Dev package manual install

Source install

Prerequisites setup script

For your convenience, we provide the setup script, MIVisionX-setup.py, which installs all required dependencies.

  python MIVisionX-setup.py --directory [setup directory - optional (default:~/)]
                            --opencv    [OpenCV Version - optional (default:4.6.0)]
                            --ffmpeg    [FFMPEG Installation - optional (default:ON) [options:ON/OFF]]
                            --amd_rpp   [MIVisionX VX RPP Dependency Install - optional (default:ON) [options:ON/OFF]]
                            --neural_net[MIVisionX Neural Net Dependency Install - optional (default:ON) [options:ON/OFF]]
                            --inference [MIVisionX Inference Dependency Install - optional (default:ON) [options:ON/OFF]]
                            --developer [Setup Developer Options - optional (default:OFF) [options:ON/OFF]]
                            --reinstall [Remove previous setup and reinstall (default:OFF)[options:ON/OFF]]
                            --backend   [MIVisionX Dependency Backend - optional (default:HIP) [options:HIP/OCL/CPU]]
                            --rocm_path [ROCm Installation Path - optional (default:/opt/rocm ROCm Installation Required)]

[!NOTE]

  • Install ROCm before running the setup script
  • This script only needs to be executed once
  • ROCm upgrade requires the setup script rerun
Using MIVisionX-setup.py

[!IMPORTANT] MIVisionX has support for two GPU backends: OPENCL and HIP

Windows

Using Visual Studio

[!IMPORTANT] Some modules in MIVisionX are only supported on Linux

macOS

macOS build instructions

[!IMPORTANT] macOS only supports MIVisionX CPU backend

Verify installation

Linux / macOS

Verify with sample application

Canny Edge Detection

  export PATH=$PATH:/opt/rocm/bin
  export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib
  runvx /opt/rocm/share/mivisionx/samples/gdf/canny.gdf

[!NOTE]

  • More samples are available here
  • For macOS use export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/opt/rocm/lib

Verify with mivisionx-test package

Test package will install ctest module to test MIVisionX. Follow below steps to test packge install

mkdir mivisionx-test && cd mivisionx-test
cmake /opt/rocm/share/mivisionx/test/
ctest -VV

Windows

Docker

MIVisionX provides developers with docker images for Ubuntu 20.04 / 22.04. Using docker images developers can quickly prototype and build applications without having to be locked into a single system setup or lose valuable time figuring out the dependencies of the underlying software.

Docker files to build MIVisionX containers and suggested workflow are available

MIVisionX docker

Documentation

Run the steps below to build documentation locally.

Technical support

Please email mivisionx.support@amd.com for questions, and feedback on MIVisionX.

Please submit your feature requests, and bug reports on the GitHub issues page.

Release notes

Latest release version

GitHub tag (latest SemVer)

Changelog

Review all notable changes with the latest release

Tested configurations

Known issues

MIVisionX dependency map

HIP Backend

Docker Image: sudo docker build -f docker/ubuntu20/{DOCKER_LEVEL_FILE_NAME}.dockerfile -t {mivisionx-level-NUMBER} .

Build Level MIVisionX Dependencies Modules Libraries and Executables Docker Tag
Level_1 cmake
gcc
g++		 amd_openvx
utilities		 #c5f015 libopenvx.so - OpenVX™ Lib - CPU
#c5f015 libvxu.so - OpenVX™ immediate node Lib - CPU
#c5f015 runvx - OpenVX™ Graph Executor - CPU with Display OFF		 Docker Image Version (tag latest semver)
Level_2 ROCm HIP
+Level 1		 amd_openvx
amd_openvx_extensions
utilities		 #c5f015 libopenvx.so - OpenVX™ Lib - CPU/GPU
#c5f015 libvxu.so - OpenVX™ immediate node Lib - CPU/GPU
#c5f015 runvx - OpenVX™ Graph Executor - Display OFF		 Docker Image Version (tag latest semver)
Level_3 OpenCV
FFMPEG
+Level 2		 amd_openvx
amd_openvx_extensions
utilities		 #1589F0 libopenvx.so - OpenVX™ Lib
#1589F0 libvxu.so - OpenVX™ immediate node Lib
#c5f015 libvx_amd_media.so - OpenVX™ Media Extension
#c5f015 libvx_opencv.so - OpenVX™ OpenCV InterOp Extension
#c5f015 mv_compile - Neural Net Model Compile
#c5f015 runvx - OpenVX™ Graph Executor - Display ON		 Docker Image Version (tag latest semver)
Level_4 MIOpen
MIGraphX
ProtoBuf
+Level 3		 amd_openvx
amd_openvx_extensions
apps
utilities		 #1589F0 libopenvx.so - OpenVX™ Lib
#1589F0 libvxu.so - OpenVX™ immediate node Lib
#1589F0 libvx_amd_media.so - OpenVX™ Media Extension
#1589F0 libvx_opencv.so - OpenVX™ OpenCV InterOp Extension
#1589F0 mv_compile - Neural Net Model Compile
#1589F0 runvx - OpenVX™ Graph Executor - Display ON
#c5f015 libvx_nn.so - OpenVX™ Neural Net Extension		 Docker Image Version (tag latest semver)
Level_5 AMD_RPP
RPP deps
+Level 4		 amd_openvx
amd_openvx_extensions
apps
AMD VX RPP
utilities		 #1589F0 libopenvx.so - OpenVX™ Lib
#1589F0 libvxu.so - OpenVX™ immediate node Lib
#1589F0 libvx_amd_media.so - OpenVX™ Media Extension
#1589F0 libvx_opencv.so - OpenVX™ OpenCV InterOp Extension
#1589F0 mv_compile - Neural Net Model Compile
#1589F0 runvx - OpenVX™ Graph Executor - Display ON
#1589F0 libvx_nn.so - OpenVX™ Neural Net Extension
#c5f015 libvx_rpp.so - OpenVX™ RPP Extension		 Docker Image Version (tag latest semver)

[!IMPORTANT] OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc.