MIVisionX

MIVisionX toolkit is a comprehensive computer vision and machine intelligence libraries, utilities and applications bundled into a single toolkit.

• AMD OpenVX
• AMD OpenVX Extensions
  • Loom 360 Stitch Library
  • Neural Net Library
  • OpenCV Extensions
• Applications
• Neural Net Model Compiler
• Samples
• Toolkit
• Utilities
  • Inference Generator
  • Radeon Loom Shell
  • RunCL
  • RunVX
• Pre-requisites
• Build MIVisionX
• Docker
• Release Notes

AMD OpenVX

AMD OpenVX (amd_openvx) is a highly optimized open source implementation of the Khronos OpenVX 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.

AMD OpenVX Extensions

The OpenVX framework provides a mechanism to add new vision functions to OpenVX by 3rd party vendors. This project has below OpenVX modules and utilities to extend amd_openvx project, which contains the AMD OpenVX Core Engine.

• amd_loomsl: AMD Radeon LOOM stitching library for live 360 degree video applications
• amd_nn: OpenVX neural network module
• amd_opencv: OpenVX module that implements a mechanism to access OpenCV functionality as OpenVX kernels

Applications

MIVisionX has a number of applications (apps) built on top of OpenVX modules, it uses AMD optimized libraries to build applications which can be used to prototype or used as models to develop a product.

• Cloud Inference Application
• External Applications

Model Compiler

Model compiler generates efficient inference libraries from pre-trained neural net models.

Toolkit

MIVisionX Toolkit, is a comprehensive set of help tools for neural net creation, development, training and deployment. The Toolkit provides you with help 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

If you're interested in Neural Network Inference, start with the sample cloud inference application in apps folder.

Inference Application Development Workflow	Sample Inference Application

Refer to Wiki page for further details.

Pre-requisites

• CPU: SSE4.1 or above CPU, 64-bit
• GPU: Radeon Instinct or Vega Family of Products (16GB recommended)
  • Linux: install ROCm with OpenCL development kit
  • Windows: install the latest drivers and OpenCL SDK download
• CMake 2.8 or newer download
• Qt Creator for annInferenceApp
• protobuf for inference_generator
  • install libprotobuf-dev and protobuf-compiler needed for vx_nn
• OpenCV 3 (optional) download for vx_opencv
  • Set OpenCV_DIR environment variable to OpenCV/build folder

Pre-requisites setup script - MIVisionX-setup.py

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

Prerequisites for running the scripts

1. Ubuntu 16.04/18.04 or CentOS 7.5/7.6
2. ROCm supported hardware
3. ROCm

MIVisionX-setup.py - This scipts builds all the prerequisites required by MIVisionX. The setup script creates a deps folder and installs all the prerequisites, this script only needs to be executed once. If -d option for directory is not given the script will install deps folder in '~/' directory by default, else in the user specified folder.

usage:

python MIVisionX-setup.py -s [sudo password - required] -d [setup directory - optional (default:~/)] -m [MIOpen Version - optional (default:1.6.0)]

Refer to Wiki page for developer instructions.

Build MIVisionX

Using CMake on Linux (Ubuntu 16.04/18.04 64-bit / CentOS 7.5/7.6) with ROCm

• Install ROCm
• git clone, build and install other ROCm projects (using cmake and % make install) in the below order for vx_nn.
  • rocm-cmake
  • MIOpenGEMM
  • MIOpen -- make sure to use -DMIOPEN_BACKEND=OpenCL option with cmake
• install protobuf
• install OpenCV
• git clone this project using --recursive option so that correct branch of the deps project is cloned automatically.
• build and install (using cmake and % make install)
  • executables will be placed in bin folder
  • libraries will be placed in lib folder
  • the installer will copy all executables into /opt/rocm/mivisionx/bin and libraries into /opt/rocm/mivisionx/lib
  • the installer also copies all the OpenVX and module header files into /opt/rocm/mivisionx/include folder
• add the installed library path to LD_LIBRARY_PATH environment variable (default /opt/rocm/mivisionx/lib)
• add the installed executable path to PATH environment variable (default /opt/rocm/mivisionx/bin)

Using Setup Script and CMake on Linux (Ubuntu 16.04/18.04 64-bit / CentOS 7.5/7.6) with ROCm

• Install ROCm
• Use the below commands to setup and build MIVisionX

git clone --recursive https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX.git
cd MIVisionX
python MIVisionX-setup.py -s [sudo password - required] -d [setup directory - optional (default:~/)] -m [MIOpen Version - optional (default:1.6.0)]
mkdir build
cd build
cmake ../
make -j8

Build annInferenceApp using Qt Creator

• build annInferenceApp.pro using Qt Creator
• or use annInferenceApp.py for simple tests

Build Radeon LOOM using Visual Studio Professional 2013 on 64-bit Windows 10/8.1/7

• Use loom.sln to build x64 platform

Docker

MIVisionX provides developers with docker images for Ubuntu 16.04, Ubuntu 18.04, CentOS 7.5, & CentOS 7.6. 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.

MIVisionX Docker

• Ubuntu 16.04
• Ubuntu 18.04
• CentOS 7.5
• CentOS 7.6

Docker Workflow Sample on Ubuntu 16.04

Prerequisites

• Ubuntu 16.04
• rocm supported hardware

Workflow

• Step 1 - Install rocm-dkms

sudo apt update
sudo apt dist-upgrade
sudo apt install libnuma-dev
sudo reboot

wget -qO - http://repo.radeon.com/rocm/apt/debian/rocm.gpg.key | sudo apt-key add -
echo 'deb [arch=amd64] http://repo.radeon.com/rocm/apt/debian/ xenial main' | sudo tee /etc/apt/sources.list.d/rocm.list
sudo apt update
sudo apt install rocm-dkms
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 kiritigowda/mivisionx-ubuntu-16.04

• 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 kiritigowda/mivisionx-ubuntu-16.04

• Optional: Map localhost directory on the docker image
  • option to map the localhost directory with trained caffe models to be accessed on the docker image.
  • usage: -v {LOCAL_HOST_DIRECTORY_PATH}:{DOCKER_DIRECTORY_PATH}

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 kiritigowda/mivisionx-ubuntu-16.04

Release Notes

Supported Neural Net Layers

Layer name
Activation
Argmax
Batch Normalization
Concat
Convolution
Deconvolution
Fully Connected
Local Response Normalization (LRN)
Pooling
Scale
Slice
Softmax
Tensor Add
Tensor Convert Depth
Tensor Convert from Image
Tensor Convert to Image
Tensor Multiply
Tensor Subtract
Upsample Nearest Neighborhood

Known issues

• ROCm - 1.8.151 performance degradation

Tested configurations

• Linux: Ubuntu - 16.04/18.04 & CentOS - 7.5/7.6
• ROCm: rocm-dkms - 1.9.307
• rocm-cmake - github master:ac45c6e
• MIOpenGEMM - 1.1.5
• MIOpen - 1.6.0
• Protobuf - V3.5.2
• OpenCV - 3.3.0
• Dependencies for all the above packages