README.md

AMD WinML Extension

The AMD WinML (vx_winml) is an OpenVX module that implements a mechanism to access WinML functionality as OpenVX kernels. These kernels can be accessed from within OpenVX framework using OpenVX API call vxLoadKernels(context, "vx_winml").

The 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.

List of WinML-interop kernels

The following is a list of WinML functions that have been included in the vx_winml module.

 onnxToMivisionX com.winml.onnx_to_mivisionx
 convertImageToTensor com.winml.convert_image_to_tensor
 getTopKLabels com.winml.get_top_k_labels

NOTE: For the list of OpenVX API calls for WinML-interop refer include/vx_ext_winml.h

Build Instructions

Pre-requisites

• Windows 10 / 11, version 1809 or later
• Windows SDK, build 17763 or later
• Visual Studio 2019, version 16.11.5 or later
  • Visual Studio extension for C++/WinRT
• 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

Build using Visual Studio 2019 on 64-bit Windows 10 / 11

• Use amd_openvx_extensions/amd_winml.sln to build for x64 platform

Utilities

MIVisionX WinML Validate

This utility can be used to test and verify the ONNX model on the Windows platform. If the ONNX model is supported by this utility, the amd_winml extension can import the ONNX model and add other OpenVX nodes for pre & post-processing in a single OpenVX graph to run efficient inference.

NOTE: Samples are available

Samples

Samples to run inference on a single image and a live camera is provided in the samples folder.

• SqueezeNet
  • Single Image Inference
  • Live Inference
• FER+ Emotion Recognition
• VGG 19
• Multiple Models