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MIVisionX

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

MIT licensed Build Status

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 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 workloads on a wide range of computer hardware, including small embedded x86 CPUs, APUs, discrete GPUs, and heterogeneous servers.

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

Applications

MIVisionX has a number of applications 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.

Neural Net Model Compiler & Optimizer

Neural Net Model Compiler & Optimizer (model_compiler) converts pre-trained neural net models to MIVisionX runtime code for optimized inference.

RALI

The Radeon Augmentation Library RALI 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 help 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

Prerequisites

Note: 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.

Windows

Linux

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.

MIVisionX-setup.py 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 directory option is not given, the script will install deps folder in the home directory(~/) by default, else in the user specified location.

Prerequisites for running the script
  1. Ubuntu 16.04/18.04 or CentOS 7.5/7.6
  2. ROCm supported hardware
  3. ROCm

usage:

python MIVisionX-setup.py --directory [setup directory - optional]
                          --installer [Package management tool - optional (default:apt-get) [options: Ubuntu:apt-get;CentOS:yum]]
                          --miopen    [MIOpen Version - optional (default:2.1.0)]
                          --miopengemm[MIOpenGEMM Version - optional (default:1.1.5)]
                          --ffmpeg    [FFMPEG Installation - optional (default:no) [options:Install ffmpeg - yes]]
                          --rpp       [RPP Installation - optional (default:yes) [options:yes/no]]

Note: use --installer yum for CentOS

Refer to Wiki page for developer instructions.

Build & Install MIVisionX

Windows

Using .msi packages

Using Visual Studio 2017 on 64-bit Windows 10

NOTE: vx_nn is not supported on Windows in this release

Linux

Using apt-get/yum

Prerequisites
  1. Ubuntu 16.04/18.04 or CentOS 7.5/7.6
  2. ROCm supported hardware
  3. ROCm
Ubuntu
sudo apt-get install mivisionx
CentOS
sudo yum install mivisionx

Note:

Using MIVisionX-setup.py and CMake on Linux (Ubuntu 16.04/18.04 or CentOS 7.5/7.6) with ROCm

Using CMake on Linux (Ubuntu 16.04/18.04 or CentOS 7.5/7.6) with ROCm

Verify the Installation

Linux

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

Docker Workflow Sample on Ubuntu 16.04

Prerequisites

Workflow

Note: Display option with docker

Release Notes

Known issues

Tested configurations