OpenVINO-ExecutionProvider.md

Hardware Enabled with OpenVINO Execution Provider

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

ONNX Layers supported using OpenVINO

Below table shows the ONNX layers supported using OpenVINO Execution Provider and the mapping between ONNX layers and OpenVINO layers. 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^® Movidius^TM VPUs as well as Intel^® Vision accelerator Design with Intel Movidius ^TM MyriadX VPU.

ONNX Layers	OpenVINO Layers	CPU	GPU	VPU
Add	Eltwise (operation=sum)	Yes	Yes	Yes
AveragePool	Pooling(pool_method=avg)	Yes	Yes	Yes
BatchNormalization	Scaleshift (can be fused into Convlution or Fully Connected)	Yes	Yes	Yes
Concat	Concat	Yes	Yes	Yes
Conv	Convolution	Yes	Yes	Yes
Dropout	Ignored	Yes	Yes	Yes
Flatten	Reshape	Yes	Yes	Yes
Gemm	FullyConnected	Yes	Yes	Yes
GlobalAveragePool	Pooling	Yes	Yes	Yes
Identity	Ignored	Yes	Yes	Yes
ImageScaler	ScaleShift	Yes	Yes	Yes
LRN	Norm	Yes	Yes	Yes
MatMul	FullyConnected	Yes	Yes*	No
MaxPool	Pooling(pool_method=max)	Yes	Yes	Yes
Mul	Eltwise(operation=mul)	Yes	Yes	Yes
Relu	ReLU	Yes	Yes	Yes
Reshape	Reshape	Yes	Yes	Yes
Softmax	SoftMax	Yes	Yes	Yes
Sum	Eltwise(operation=sum)	Yes	Yes	Yes
Transpose	Permute	Yes	Yes	Yes
UnSqueeze	Reshape	Yes	Yes	Yes
LeakyRelu	ReLU	Yes	Yes	Yes

*MatMul is supported in GPU only when the following layer is an Add layer in the topology.

Topology Support

Below topologies are supported from ONNX open model zoo using OpenVINO Execution Provider

Image Classification Networks

Topology	CPU	GPU	VPU
bvlc_alexnet	Yes	Yes	Yes
bvlc_googlenet	Yes	Yes	Yes
bvlc_reference_caffenet	Yes	Yes	Yes
bvlc_reference_rcnn_ilsvrc13	Yes	Yes	Yes
densenet121	Yes	Yes	Yes
Inception_v1	Yes	Yes	Yes**
Inception_v2	Yes	Yes	Yes
Shufflenet	Yes	Yes	Yes
Zfnet512	Yes	Yes	Yes
Squeeznet 1.1	Yes	Yes	Yes
Resnet18v1	Yes	Yes	Yes
Resnet34v1	Yes	Yes	Yes
Resnet50v1	Yes	Yes	Yes
Resnet101v1	Yes	Yes	Yes
Resnet152v1	Yes	Yes	Yes
Resnet18v2	Yes	Yes	Yes
Resnet34v2	Yes	Yes	Yes
Resnet50v2	Yes	Yes	Yes
Resnet101v2	Yes	Yes	Yes
Resnet152v2	Yes	Yes	Yes
Mobilenetv2	Yes	Yes	Yes
vgg16	Yes	Yes	Yes
vgg19	Yes	Yes	Yes

Image Recognition Networks

Topology	CPU	GPU	VPU
MNIST	Yes	Yes	Yes**

**Inception_v1 and MNIST are supported in OpenVINO R1.1 and are not supported in OpenVINO R5.0.1.

Object Detection Networks

Topology	CPU	GPU	VPU
TinyYOLOv2	Yes	Yes	Yes
ResNet101_DUC_HDC	Yes	No	No

Application code changes for VAD-M performance scaling

VAD-M has 8 VPUs and is suitable for applications that require multiple inferences to run in parallel. We use batching approach for performance scaling on VAD-M.

Below python code snippets provide sample classification code to batch input images, load a model and process the output results.

import onnxruntime as rt
from onnxruntime import get_device
import os
import os.path
import sys
import cv2
import numpy
import time
import glob

Load the input onnx model

sess = rt.InferenceSession(str(sys.argv[1]))
print("\n")

Preprocessing input images

for i in range(iters):
   y = None
   images = [cv2.imread(file) for file in glob.glob(str(sys.argv[2])+'/*.jpg')]
   for img in images:
     # resizing the image
     img = cv2.resize(img, (224,224))
     # convert image to numpy
     x = numpy.asarray(img).astype(numpy.float32)
     x = numpy.transpose(x, (2,0,1))
     # expand the dimension and batch the images
     x = numpy.expand_dims(x,axis=0)
     if y is None:
        y = x
     else:
        y = numpy.concatenate((y,x), axis=0)

Start Inference

   res = sess.run([sess.get_outputs()[0].name], {sess.get_inputs()[0].name: y})

Post-processing output results

   print("Output probabilities:")
   i = 0
   for k in range(batch_size):
       for prob in res[0][k][0]:
          print("%d : %7.4f" % (i, prob))