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