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OpenVINO Execution Provider

OpenVINO Execution Provider enables deep learning inference on Intel CPUs, Intel integrated GPUs and Intel® MovidiusTM Vision Processing Units (VPUs). Please refer to this page for details on the Intel hardware supported.

Contents

Build

For build instructions, please see the BUILD page.

Onnxruntime Graph Optimization level

OpenVINO backend performs both hardware dependent as well as independent optimizations to the graph to infer it with on the target hardware with best possible performance. In most of the cases it has been observed that passing in the graph from the input model as is would lead to best possible optimizations by OpenVINO. For this reason, it is advised to turn off high level optimizations performed by ONNX Runtime before handing the graph over to OpenVINO backend. This can be done using Session options as shown below:-

  1. Python API
    options = onnxruntime.SessionOptions()
    options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL
    sess = onnxruntime.InferenceSession(<path_to_model_file>, options)
    
  2. C++ API
    SessionOptions::SetGraphOptimizationLevel(ORT_DISABLE_ALL);
    

Dynamic device selection

When ONNX Runtime is built with OpenVINO Execution Provider, a target hardware option needs to be provided. This build time option becomes the default target harware the EP schedules inference on. However, this target may be overriden at runtime to schedule inference on a different hardware as shown below.

Note. This dynamic hardware selection is optional. The EP falls back to the build-time default selection if no dynamic hardware option value is specified.

  1. Python API
import onnxruntime
onnxruntime.capi._pybind_state.set_openvino_device("<harware_option>")
# Create session after this
  1. C/C++ API
Ort::ThrowOnError(OrtSessionOptionsAppendExecutionProvider_OpenVINO(sf, "<hardware_option>"));

ONNX Layers supported using OpenVINO

The table below shows the ONNX layers supported and validated using OpenVINO Execution Provider.The below table also lists the Intel hardware support for each of the layers. CPU refers to Intel® Atom, Core, and Xeon processors. GPU refers to the Intel Integrated Graphics. VPU refers to USB based Intel® MovidiusTM VPUs as well as Intel® Vision accelerator Design with Intel Movidius TM MyriadX VPU.

ONNX Layers CPU GPU VPU
Add Yes Yes Yes
ArgMax Yes Yes Yes
AveragePool Yes Yes Yes
BatchNormalization Yes Yes Yes
Cast Yes Yes Yes
Clip Yes Yes Yes
Concat Yes Yes Yes
Constant Yes Yes Yes
Conv Yes Yes Yes
ConvTranspose Yes Yes Yes
Div Yes Yes Yes
Dropout Yes Yes Yes
Flatten Yes Yes Yes
Floor Yes Yes Yes
Gather Yes Yes Yes
GatherND Yes Yes Yes
Gemm Yes Yes Yes
GlobalAveragePool Yes Yes Yes
Identity Yes Yes Yes
LeakyRelu Yes Yes Yes
Log Yes Yes Yes
LRN Yes Yes Yes
LSTM Yes Yes Yes
MatMul Yes Yes Yes
Max Yes Yes Yes
MaxPool Yes Yes Yes
Min Yes Yes Yes
Mul Yes Yes Yes
Pad Yes Yes Yes
Pow Yes Yes Yes
PRelu Yes Yes Yes
ReduceMax Yes Yes Yes
ReduceMean Yes Yes Yes
ReduceMin Yes Yes Yes
ReduceSum Yes Yes Yes
Relu Yes Yes Yes
Reshape Yes Yes Yes
Sigmoid Yes Yes Yes
Slice Yes Yes Yes
Softmax Yes Yes Yes
Squeeze Yes Yes Yes
Sub Yes Yes Yes
Sum Yes Yes Yes
Tanh Yes Yes Yes
TopK Yes Yes Yes
Transpose Yes Yes Yes
Unsqueeze Yes Yes Yes

Topology Support

Below topologies from ONNX open model zoo are fully supported on OpenVINO Execution Provider and many more are supported through sub-graph partitioning

Image Classification Networks

MODEL NAME CPU GPU VPU FPGA
bvlc_alexnet Yes Yes Yes Yes*
bvlc_googlenet Yes Yes Yes Yes*
bvlc_reference_caffenet Yes Yes Yes Yes*
bvlc_reference_rcnn_ilsvrc13 Yes Yes Yes Yes*
emotion ferplus Yes Yes Yes Yes*
densenet121 Yes Yes Yes Yes*
inception_v1 Yes Yes Yes Yes*
inception_v2 Yes Yes Yes Yes*
mobilenetv2 Yes Yes Yes Yes*
resnet18v1 Yes Yes Yes Yes*
resnet34v1 Yes Yes Yes Yes*
resnet101v1 Yes Yes Yes Yes*
resnet152v1 Yes Yes Yes Yes*
resnet18v2 Yes Yes Yes Yes*
resnet34v2 Yes Yes Yes Yes*
resnet101v2 Yes Yes Yes Yes*
resnet152v2 Yes Yes Yes Yes*
resnet50 Yes Yes Yes Yes*
resnet50v2 Yes Yes Yes Yes*
shufflenet Yes Yes Yes Yes*
squeezenet1.1 Yes Yes Yes Yes*
vgg19 Yes Yes Yes Yes*
vgg16 Yes Yes Yes Yes*
zfnet512 Yes Yes Yes Yes*
arcface Yes Yes Yes Yes*

Image Recognition Networks

| MODEL NAME | CPU | GPU | VPU | FPGA | | — | — | — | — | — | | mnist | Yes | Yes | Yes | Yes* |

Object Detection Networks

| MODEL NAME | CPU | GPU | VPU | FPGA | | — | — | — | — | — | | tiny_yolov2 | Yes | Yes | Yes | Yes* |

*FPGA only runs in HETERO mode wherein the layers that are not supported on FPGA fall back to OpenVINO CPU.

CSharp API

To use csharp api for openvino execution provider create a custom nuget package. Two nuget packages will be created Microsoft.ML.OnnxRuntime.Managed and Microsoft.ML.OnnxRuntime.Openvino.

  1. Windows

Build a custom nuget package for windows.

.\build.bat --config Debug --build --use_openvino $Device --build_csharp
msbuild csharp\OnnxRuntime.CSharp.proj /p:OrtPackageId=Microsoft.ML.OnnxRuntime.Openvino /p:Configuration=Debug /t:CreatePackage

The msbuild log will show the paths of the nuget packages created.

  1. Linux

We currently do not have a process to build directly in Linux. But we can copy shared library /build/Linux//libonnxruntime.so to onnxruntime source repository in windows and execute the same commands above to get custom nuget package for linux. Two nuget packages will be created Microsoft.ML.OnnxRuntime.Managed and Microsoft.ML.OnnxRuntime.Openvino.

On Linux Machine

./build.sh --config Debug --build_shared_lib --use_openvino $Device 

On Windows Machine

cp libonnxruntime.so onnxruntime/ 
.\build.bat --config Debug --build --use_openvino $Device --build_csharp
msbuild csharp\OnnxRuntime.CSharp.proj /p:OrtPackageId=Microsoft.ML.OnnxRuntime.Openvino /p:Configuration=Debug /t:CreatePackage

The msbuild log will show the path of the nuget packages created.