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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#

Documentation#

Run the steps below to build documentation locally.

  • sphinx documentation

cd docs
pip3 install -r .sphinx/requirements.txt
python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html

  • Doxygen

doxygen .Doxyfile

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_migraphx: amd_migraphx extension integrates the AMD’s MIGraphx into an OpenVX graph. This extension allows developers to combine the vision funcions in OpenVX with the MIGraphX and build an end-to-end application for inference.

  • 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 (ROCm 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 Classification: 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 4.6.0

    • 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 - 20.04 / 22.04

    • CentOS - 7 / 8

    • RedHat - 8 / 9

    • SLES - 15-SP4

  • Install ROCm

  • CMake 3.5 or later

  • MIOpen for vx_nn extension

  • MIGraphX for vx_migraphx extension

  • Protobuf

  • OpenCV 4.6.0

  • FFMPEG n4.4.2

  • rocAL Prerequisites

Prerequisites setup script for Linux#

For the convenience of the developer, we provide the setup script MIVisionX-setup.py 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 - 20.04 / 22.04

    • CentOS - 7 / 8

    • RedHat - 8 / 9

    • SLES - 15-SP4

  • ROCm supported hardware

  • ROCm

    usage:

    python MIVisionX-setup.py --directory [setup directory - optional (default:~/)]
                          --opencv    [OpenCV Version - optional (default:4.6.0)]
                          --protobuf  [ProtoBuf Version - optional (default:3.12.4)]
                          --rpp       [RPP Version - optional (default:1.0.0)]
                          --pybind11  [PyBind11 Version - optional (default:v2.10.4)]
                          --ffmpeg    [FFMPEG V4.4.2 Installation - optional (default:ON) [options:ON/OFF]]
                          --rocal     [MIVisionX rocAL Dependency Install - optional (default:ON) [options:ON/OFF]]
                          --neural_net[MIVisionX Neural Net Dependency Install - optional (default:ON) [options:ON/OFF]]
                          --inference [MIVisionX Neural Net Inference Dependency Install - optional (default:ON) [options:ON/OFF]]
                          --developer [Setup Developer Options - optional (default:ON) [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:

    • ROCm upgrade with sudo apt upgrade requires the setup script rerun.

    • use X Window / X11 for remote GUI app control

Build & Install MIVisionX#

Building on Windows#

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

Building on macOS#

macOS build instructions

Building on Linux#

Using apt-get / yum / zypper#

  • ROCm supported hardware

  • Install ROCm

  • On Ubuntu

    sudo apt-get install mivisionx

  • On CentOS/RedHat

    sudo yum install mivisionx

  • On SLES

    sudo zypper install mivisionxF

    Note:

    • vx_winml is not supported on Linux

    • source code will not available with apt-get / yum / zypper 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 v4.6 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 cmake --build . --target PyPackageInstall
sudo make install

      Note:

      • PyPackageInstall used for rocal_pybind installation

      • rocal_pybind not supported on windows.

      • sudo required for pybind installation

    • Instructions for building MIVisionX with OPENCL GPU backend

Verify the Installation#

Verifying on 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

    Note: For macOS use export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/opt/rocm/lib

Verifying on 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 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 are available

MIVisionX Docker#

Docker Workflow on Ubuntu 20.04/22.04#

Prerequisites#

Workflow#

  • Step 1 - Get latest docker image

    sudo docker pull mivisionx/ubuntu-20.04:latest

    • NOTE: Use the above command to bring in latest changes from upstream

  • Step 2 - Run docker image

Run docker image: Local Machine#

sudo docker run -it --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mem --cap-add=SYS_RAWIO  --group-add video --shm-size=4g --ipc="host" --network=host mivisionx/ubuntu-20.04:latest

  • Test - Computer Vision Workflow

    python3 /workspace/MIVisionX/tests/vision_tests/runVisionTests.py --num_frames 1

  • Test - Neural Network Workflow

    python3 /workspace/MIVisionX/tests/neural_network_tests/runNeuralNetworkTests.py --profiler_level 1

  • Test - Khronos OpenVX 1.3.0 Conformance Test

    python3 /workspace/MIVisionX/tests/conformance_tests/runConformanceTests.py --backend_type HOST

Option 1: Map localhost directory on the docker image#

  • option to map the localhost directory with data to be accessed on the docker image

  • usage: -v {LOCAL_HOST_DIRECTORY_PATH}:{DOCKER_DIRECTORY_PATH}

    sudo docker run -it -v /home/:/root/hostDrive/ -privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mem --cap-add=SYS_RAWIO  --group-add video --shm-size=4g --ipc="host" --network=host mivisionx/ubuntu-20.04:latest

Option 2: Display with docker#

  • Using host display for docker

    xhost +local:root
sudo docker run -it --privileged --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host --env DISPLAY=$DISPLAY --volume="$HOME/.Xauthority:/root/.Xauthority:rw" --volume /tmp/.X11-unix/:/tmp/.X11-unix mivisionx/ubuntu-20.04:latest

  • Test display with MIVisionX sample

    runvx -v /opt/rocm/share/mivisionx/samples/gdf/canny.gdf

Run docker image with display: Remote Server Machine#

sudo docker run -it --privileged --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host --env DISPLAY=$DISPLAY --volume="$HOME/.Xauthority:/root/.Xauthority:rw" --volume /tmp/.X11-unix/:/tmp/.X11-unix mivisionx/ubuntu-20.04:latest

  • Test display with MIVisionX sample

    runvx -v /opt/rocm/share/mivisionx/samples/gdf/canny.gdf

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#

  • Windows 10 / 11

  • Linux distribution

    • Ubuntu - 20.04 / 22.04

    • CentOS - 7 / 8

    • RHEL - 8 / 9

    • SLES - 15-SP4

  • ROCm: rocm-core - 5.4.3.50403-121

  • miopen-hip - 2.19.0.50403-121

  • miopen-opencl - 2.18.0.50300-63

  • migraphx - 2.4.0.50403-121

  • Protobuf - V3.12.4

  • OpenCV - 4.6.0

  • RPP - 1.2.0

  • FFMPEG - n4.4.2

  • Dependencies for all the above packages

  • MIVisionX Setup Script - V2.5.5

Known issues#

  • Package install requires OpenCV V-4.6.0 to execute AMD OpenCV extensions

MIVisionX Dependency Map#

HIP Backend#

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 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

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_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
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_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
#c5f015 librocal.so - Radeon Augmentation Library
#c5f015 rocal_pybind.so - rocAL Pybind Lib

Docker Image Version (tag latest semver)

NOTE: OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc.

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