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.

OpenVX MIGraphX Extension Library

vx_amd_migraphx is an OpenVX AMD extension module which has one node (com.amd.amd_migraphx_node). This node enables importing the AMD’s MIGraphx library into an OpenVX graph for inference.

Build Instructions


This module is built by default when building the MIVisionX.

Example 1: vision inference example with the MNIST

Following is an example gdf to perform inference using the vx_amd_migraphx extension. The model used is a CNN pre-trained on the MNIST dataset.

import vx_amd_migraphx
import vx_nn

data input = image:28,28,U008
read input image_4.jpg
data a = scalar:FLOAT32,0.00392157
data b = scalar:FLOAT32,0.0
data reverse_channel_order = scalar:BOOL,0
data image_tensor = tensor:4,{28,28,1,1},VX_TYPE_FLOAT32,0
node com.amd.nn_extension.convert_image_to_tensor input image_tensor a b reverse_channel_order

data model = scalar:STRING,"mnist-8.onnx"
data output_tensor = tensor:2,{10,1},VX_TYPE_FLOAT32,0

node com.amd.amd_migraphx_node model image_tensor output_tensor
write output_tensor out_mnist.f32

For additional examples for using the vx_amd_migraphx extension, please see amd_migraphx_test section.

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