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OpenVINO之五:转换ONNX模型


1

2 OpenVINO支持的ONNX模型

2-1 支持的公共模型

  • bvlc_alexnet , bvlc_googlenet , bvlc_reference_caffenet , bvlc_reference_rcnn_ilsvrc13
  • inception_v1, inception_v2
  • resnet50
  • squeezenet
  • densenet121
  • emotion_ferplus
  • mnist
  • shufflenet
  • VGG19
  • zfnet512

2-2 支持的Pytorch模型

Torchvision Models:

  • alexnet,
  • densenet121, densenet161, densenet169, densenet201,
  • resnet101, resnet152, resnet18, resnet34, resnet50,
  • vgg11, vgg13, vgg16, vgg19

Pretrained Models:

  • alexnet,
  • fbresnet152,
  • resnet101, resnet152, resnet18, resnet34, resnet152, resnet18, resnet34, resnet50, resnext101_32x4d, resnext101_64x4d,
  • vgg11

2-3 支持的PaddlePaddle模型

  • fit_a_line
  • recognize_digits
  • VGG16
  • ResNet
  • MobileNet
  • SE_ResNeXt
  • Inception-v4

3 OpenVINO支持的ONNX层与其在Intermediate Representation (IR)中的对应关系

NUMBER

OPERATOR NAME IN ONNX*

LAYER TYPE IN THE INTERMEDIATE REPRESENTATION

1

Add

Eltwise(operation = sum) (added ‘axis’ support)

2

AveragePool

Pooling (pool_method=avg)

3

BatchNormalization

ScaleShift (can be fused into Convlution or FC)

4

Concat

Concat

5

Constant

Will be removed on constant propagation step

6

Conv

Convolution

7

ConvTranspose

Deconvolution (added auto_pad and output_shape attributes support))

8

Div

Eltwise(operation = mul)->Power

9

Dropout

Ignored, does not apeear in IR

10

Elu

Activation (ELU)

11

Flatten

Reshape

12

Gemm

FullyConnected

13

GlobalAveragePool

Pooling (pool_method=avg)

14

Identity

Ignored, does not appear in IR

15

LRN

Norm

16

LeakyRelu

ReLU

17

MatMul

FullyConnected

17

MaxPool

Pooling (pool_method=max)

19

Mul

Eltwise(operation = mul) (added ‘axis’ support)

20

Relu

ReLU

21

Reshape

Reshape

22

Shape

Constant propagation

23

Softmax

SoftMax

24

Squeeze

Reshape

25

Sub

Power->Eltwise(operation = sum)

26

Sum

Eltwise(operation = sum)

27

Transpose

Permute

28

Unsqueeze

Reshape

29

Upsample

Resample

30

ImageScaler

ScaleShift

31

Affine

ScaleShift

32

Reciprocal

Power(power=-1)

33

Crop

Split

34

Tanh

Activation (operation = tanh)

35

Sigmoid

Activation (operation = sigmoid)

36

Pow

Power

37

ConvTranspose

38

Gather

Constant propagation

39

Constant_fill

Constant propagation

40

ReduceMean

Reshape + Pooling(pool_method=avg) + Reshape

41

ReduceSum

Reshape + Pooling(pool_method=avg) + Power(scale=reduce_dim_size) + Reshape

42

Gather

Gather

43

Gemm

GEMM

44

GlobalMaxPool

Pooling (pool_method=max)

45

Neg

Power(scale=-1)

46

Pad

Pad

47

ArgMax

ArgMax

48

Clip

Clamp

49

DetectionOutput (experimental)

DetectionOutputONNX

50

PriorBox (experimental)

PriorBoxONNX

51

RNNSequence

TensorIterator(RNNCell)

52

GRUSequence

TensorIterator(GRUCell)

53

LSTMSequence

TensorIterator(LSTMCell)

参考资料:
1 ​​​Converting a ONNX* Model​​​

2 ​​Supported Framework Layers​​


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