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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.

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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)

Table of Contents

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 mentioned OpenVX modules and utilities to extend amd_openvx, which contains the AMD OpenVX Core Engine.

  • amd_loomsl: AMD Radeon Loom stitching library for live 360 degree video applications
  • amd_media: vx_amd_media is an OpenVX AMD media extension module for encode and decode
  • amd_nn: OpenVX neural network module
  • amd_opencv: OpenVX module that implements a mechanism to access OpenCV functionality as OpenVX kernels
  • amd_rpp: OpenVX extension providing an interface to some of the RPP's (Radeon Performance Primitives) functions. This extension is used to enable rocAL to perform image augmentation.
  • amd_winml: WinML extension will allow developers to import a pre-trained ONNX model into an OpenVX graph and add hundreds of different pre & post processing vision / generic / user-defined functions, available in OpenVX and OpenCV interop, to the input and output of the neural net model. This will allow developers to build an end to end application for inference.

Applications

MIVisionX has several applications built on top of OpenVX modules, it uses AMD optimized libraries to build applications that can be used to prototype or use as a model to develop products.

  • Bubble Pop: This sample application creates bubbles and donuts to pop using OpenVX & OpenCV functionality.
  • Cloud Inference Application: This sample application does inference using a client-server system.
  • Digit Test: This sample application is used to recognize hand written digits.
  • Image Augmentation: This sample application demonstrates the basic usage of rocAL's C API to load JPEG images from the disk and modify them in different possible ways and displays the output images.
  • MIVisionX Inference Analyzer: This sample application uses pre-trained ONNX / NNEF / Caffe models to analyze and summarize images.
  • MIVisionX OpenVX Classsification: This sample application shows how to run supported pre-trained caffe models with MIVisionX RunTime.
  • MIVisionX Validation Tool: This sample application uses pre-trained ONNX / NNEF / Caffe models to analyze, summarize and validate models.
  • MIVisionX WinML Classification: This sample application shows how to run supported ONNX models with MIVisionX RunTime on Windows.
  • MIVisionX WinML YoloV2: This sample application shows how to run tiny yolov2(20 classes) with MIVisionX RunTime on Windows.
  • Optical Flow: This sample application creates an OpenVX graph to run Optical Flow on a video/live.
  • External Applications

Neural Net Model Compiler & Optimizer

Neural Net Model Compiler & 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.

Toolkit

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

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

Utilities

  • inference_generator: generate inference library from pre-trained CAFFE models
  • loom_shell: an interpreter to prototype 360 degree video stitching applications using a script
  • RunVX: command-line utility to execute OpenVX graph described in GDF text file
  • RunCL: command-line utility to build, execute, and debug OpenCL programs

Prerequisites

Hardware

Operating System

Windows

  • Windows 10 / 11
  • Windows SDK
  • Visual Studio 2019 or later
  • Install the latest AMD drivers
  • Install OpenCL SDK
  • Install OpenCV 4.5.5
    • Set OpenCV_DIR environment variable to OpenCV/build folder
    • Add %OpenCV_DIR%\x64\vc14\bin or %OpenCV_DIR%\x64\vc15\bin to your PATH

macOS

Linux

  • Linux distribution
    • Ubuntu - 18.04 / 20.04
    • CentOS - 7 / 8
  • Install ROCm
  • CMake 3.0 or later
  • ROCm CMake, MIOpenGEMM & MIOpen for Neural Net Extensions (vx_nn)
  • Qt Creator for Cloud Inference Client
  • Protobuf for inference generator & model compiler
    • install libprotobuf-dev and protobuf-compiler needed for vx_nn
  • OpenCV 4.5.5
    • Set OpenCV_DIR environment variable to OpenCV/build folder
  • FFMPEG n4.0.4
    • FFMPEG is required for amd_media & mv_deploy modules
  • rocAL Prerequisites
Prerequisites setup script for Linux - MIVisionX-setup.py

For the convenience of the developer, we here provide the setup script which will install all the dependencies required by this project.

NOTE: This script only needs to be executed once.

Prerequisites for running the script
  • Linux distribution

    • Ubuntu - 18.04 / 20.04
    • CentOS - 7 / 8
  • ROCm supported hardware

  • ROCm

    usage:

    python MIVisionX-setup.py --directory [setup directory - optional (default:~/)]
                              --opencv    [OpenCV Version - optional (default:4.5.5)]
                              --protobuf  [ProtoBuf Version - optional (default:3.12.0)]
                              --rpp       [RPP Version - optional (default:0.91)]
                              --ffmpeg    [FFMPEG V4.0.4 Installation - optional (default:no) [options:yes/no]]
                              --rocal     [MIVisionX rocAL Dependency Install - optional (default:yes) [options:yes/no]]
                              --neural_net[MIVisionX Neural Net Dependency Install - optional (default:yes) [options:yes/no]]
                              --reinstall [Remove previous setup and reinstall (default:no)[options:yes/no]]
                              --backend   [MIVisionX Dependency Backend - optional (default:HIP) [options:OCL/HIP]]
                              --rocm_path [ROCm Installation Path - optional (default:/opt/rocm) - ROCm Installation Required]
    

    Note:

    • ROCm upgrade with sudo apt upgrade requires the setup script rerun.
    • use X Window / X11 for remote GUI app control

Build & Install MIVisionX

Windows

Using .msi packages

Using Visual Studio

  • Install Windows Prerequisites

  • Use MIVisionX.sln to build for x64 platform

    NOTE: vx_nn is not supported on Windows in this release

macOS

macOS build instructions

Linux

Using apt-get / yum

  • ROCm supported hardware

  • Install ROCm

  • On Ubuntu

    sudo apt-get install mivisionx
    
  • On CentOS

    sudo yum install mivisionx
    

    Note:

    • vx_winml is not supported on Linux
    • source code will not available with apt-get / yum install
    • the installer will copy
      • Executables into /opt/rocm/bin
      • Libraries into /opt/rocm/lib
      • OpenVX and module header files into /opt/rocm/include/mivisionx
      • Model compiler, & toolkit folders into /opt/rocm/libexec/mivisionx
      • Apps, & samples folder into /opt/rocm/share/mivisionx
      • Docs folder into /opt/rocm/share/doc/mivisionx
    • Package (.deb & .rpm) install requires OpenCV v3+ to execute AMD OpenCV extensions

Using MIVisionX-setup.py

  • Install ROCm

  • Use the below commands to set up and build MIVisionX

    git clone https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX.git
    cd MIVisionX
    

    Note: MIVisionX has support for two GPU backends: OPENCL and HIP:

    • Instructions for building MIVisionX with the HIP GPU backend (i.e., default GPU backend):

      • run the setup script to install all the dependencies required by the HIP GPU backend:
      python MIVisionX-setup.py
      
      • run the below commands to build MIVisionX with the HIP GPU backend:
      mkdir build-hip
      cd build-hip
      cmake ../
      make -j8
      sudo make install
      
    • Instructions for building MIVisionX with OPENCL GPU backend:

      • run the setup script to install all the dependencies required by the OPENCL GPU backend:
      python MIVisionX-setup.py --reinstall yes --backend OCL
      
      • run the below commands to build MIVisionX with the OPENCL GPU backend:
      mkdir build-ocl
      cd build-ocl
      cmake -DBACKEND=OPENCL ../
      make -j8
      sudo make install
      

    Note:

    • MIVisionX cannot be installed for both GPU backends in the same default folder (i.e., /opt/rocm/) if an app interested in installing MIVisionX with both GPU backends, then add -DCMAKE_INSTALL_PREFIX in the cmake commands to install MIVisionX with OPENCL and HIP backends into two separate custom folders.
    • vx_winml is not supported on Linux

Verify the Installation

Linux / macOS

  • The installer will copy

    • Executables into /opt/rocm/bin
    • Libraries into /opt/rocm/lib
    • OpenVX and OpenVX module header files into /opt/rocm/include/mivisionx
    • Apps, & Samples folder into /opt/rocm/share/mivisionx
    • Documents folder into /opt/rocm/share/doc/mivisionx
    • Model Compiler, and Toolkit folder into /opt/rocm/libexec/mivisionx
  • Run the below sample to verify the installation

    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

Windows

  • MIVisionX.sln builds the libraries & executables in the folder MIVisionX/x64

  • Use RunVX to test the build

    ./runvx.exe PATH_TO/MIVisionX/samples/gdf/skintonedetect.gdf
    

Docker

MIVisionX provides developers with docker images for Ubuntu 18.04 / 20.04 and CentOS 7 / 8. 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 are available

MIVisionX Docker

Docker Workflow Sample on Ubuntu 18.04 / 20.04

Prerequisites

Workflow

  • Step 1 - Install rocm-dkms
sudo apt update -y
sudo apt dist-upgrade -y
sudo apt install libnuma-dev wget
sudo reboot
wget https://repo.radeon.com/amdgpu-install/21.50/ubuntu/focal/amdgpu-install_21.50.50000-1_all.deb
sudo apt-get install -y ./amdgpu-install_21.50.50000-1_all.deb
sudo apt-get update -y
sudo amdgpu-install -y --usecase=rocm
sudo reboot
  • Step 2 - Setup Docker
sudo apt-get install curl
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
apt-cache policy docker-ce
sudo apt-get install -y docker-ce
sudo systemctl status docker
  • Step 3 - Get Docker Image
sudo docker pull mivisionx/ubuntu-18.04
  • Step 4 - Run the docker image
sudo docker run -it --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host mivisionx/ubuntu-18.04:latest

Note:

  • Map host directory on the docker image

    • map the localhost directory to be accessed on the docker image.
    • use -v option with docker run command: -v {LOCAL_HOST_DIRECTORY_PATH}:{DOCKER_DIRECTORY_PATH}
    • usage:
    sudo docker run -it -v /home/:/root/hostDrive/ --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host mivisionx/ubuntu-18.04:latest
    
  • Display option with docker

    • Using host display
    xhost +local:root
    sudo docker run -it --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host --env DISPLAY=unix$DISPLAY --privileged --volume $XAUTH:/root/.Xauthority --volume /tmp/.X11-unix/:/tmp/.X11-unix mivisionx/ubuntu-18.04:latest
    
    • Test display with MIVisionX sample
    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
    

Technical Support

Please email [email protected] for questions, issues, and feedback on MIVisionX.

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

Release Notes

Latest Release

GitHub tag (latest SemVer)

Changelog

Review all notable changes with the latest release

Tested configurations

  • Windows 10 / 11
  • Linux distribution
    • Ubuntu - 18.04 / 20.04
    • CentOS - 7 / 8
    • SLES - 15-SP2
  • ROCm: rocm-core - 5.1.1.50101-48
  • miopen-hip - 2.16.0.50101-48
  • miopen-opencl - 2.16.0.50101-48
  • migraphx - 2.1.0.50101-48
  • Protobuf - V3.12.0
  • OpenCV - 4.5.5
  • RPP - 0.93
  • FFMPEG - n4.0.4
  • Dependencies for all the above packages
  • MIVisionX Setup Script - V2.3.0

Known issues

  • Package install requires OpenCV V-3+ to execute AMD OpenCV extensions

MIVisionX Dependency Map

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

  • #c5f015 new component added to the level
  • #1589F0 existing component from the previous level
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 OpenCL
+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 libvx_loomsl.so - Loom 360 Stitch Lib
#c5f015 loom_shell - 360 Stitch App
#c5f015 runcl - OpenCL™ program debug App
#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
#1589F0 libvx_loomsl.so - Loom 360 Stitch Lib
#1589F0 loom_shell - 360 Stitch App
#1589F0 runcl - OpenCL™ program debug App
#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 MIOpenGEMM
MIOpen
ProtoBuf
+Level 3
amd_openvx
amd_openvx_extensions
apps
utilities
#1589F0 libopenvx.so - OpenVX™ Lib
#1589F0 libvxu.so - OpenVX™ immediate node Lib
#1589F0 libvx_loomsl.so - Loom 360 Stitch Lib
#1589F0 loom_shell - 360 Stitch App
#1589F0 libvx_amd_media.so - OpenVX™ Media Extension
#1589F0 libvx_opencv.so - OpenVX™ OpenCV InterOp Extension
#1589F0 mv_compile - Neural Net Model Compile
#1589F0 runcl - OpenCL™ program debug App
#1589F0 runvx - OpenVX™ Graph Executor - Display ON
#c5f015 libvx_nn.so - OpenVX™ Neural Net Extension
#c5f015 inference_server_app - Cloud Inference App
Docker Image Version (tag latest semver)
Level_5 AMD_RPP
rocAL deps
+Level 4
amd_openvx
amd_openvx_extensions
apps
rocAL
utilities
#1589F0 libopenvx.so - OpenVX™ Lib
#1589F0 libvxu.so - OpenVX™ immediate node Lib
#1589F0 libvx_loomsl.so - Loom 360 Stitch Lib
#1589F0 loom_shell - 360 Stitch App
#1589F0 libvx_amd_media.so - OpenVX™ Media Extension
#1589F0 libvx_opencv.so - OpenVX™ OpenCV InterOp Extension
#1589F0 mv_compile - Neural Net Model Compile
#1589F0 runcl - OpenCL™ program debug App
#1589F0 runvx - OpenVX™ Graph Executor - Display ON
#1589F0 libvx_nn.so - OpenVX™ Neural Net Extension
#1589F0 inference_server_app - Cloud Inference App
#c5f015 libvx_rpp.so - OpenVX™ RPP Extension
#c5f015 librali.so - Radeon Augmentation Library
#c5f015 rali_pybind.so - rocAL Pybind Lib
Docker Image Version (tag latest semver)