OpenVINO之七:转换Caffe模型

1 OpenVINO转换caffe模型步骤

进入目录

C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer

python mo_caffe.py -h

参数如下:
–input_proto:prototxt文件所在位置
-k:CustomLayersMapping.xml文件所在位置
–input_mode:
–mean_file:
–mean_file_offsets:

–output_dir
–scale SCALE
–log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}]
–input
–mean_values
–data_type {FP16,FP32,half,float}] [–disable_fusing]
–disable_resnet_optimization
–finegrain_fusing
–enable_concat_optimization
–extensions
–silent
–freeze_placeholder_with_value
–generate_deprecated_IR_V2

–mean_file_offsets
–disable_omitting_optional
–enable_flattening_nested_params

2 OpenVINO支持的caffe模型

2-1 Classification models:

  • AlexNet
  • VGG-16, VGG-19
  • SqueezeNet v1.0, SqueezeNet v1.1
  • ResNet-50, ResNet-101, Res-Net-152
  • Inception v1, Inception v2, Inception v3, Inception v4
  • CaffeNet
  • MobileNet
  • Squeeze-and-Excitation Networks: SE-BN-Inception, SE-Resnet-101, SE-ResNet-152, SE-ResNet-50, SE-ResNeXt-101, SE-ResNeXt-50
  • ShuffleNet v2

2-2 Object detection models:

  • SSD300-VGG16, SSD500-VGG16
  • Faster-RCNN
  • RefineDet (Myriad plugin only)

2-3 Face detection models:

  • VGG Face

2-4 Semantic segmentation models:

  • FCN8

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

NUMBER LAYER NAME IN CAFFE* LAYER NAME IN THE INTERMEDIATE REPRESENTATION
1 Input Input
2 GlobalInput Input
3 InnerProduct FullyConnected
4 Dropout Ignored, does not appear in IR
5 Convolution Convolution
6 Deconvolution Deconvolution
7 Pooling Pooling
8 BatchNorm BatchNormalization
9 LRN Norm
10 Power Power
11 ReLU ReLU
12 Scale ScaleShift
13 Concat Concat
14 Eltwise Eltwise
15 Flatten Flatten
16 Reshape Reshape
17 Slice Slice
18 Softmax SoftMax
19 Permute Permute
20 ROIPooling ROIPooling
21 Tile Tile
22 ShuffleChannel Reshape + Split + Permute + Concat
23 Axpy ScaleShift + Eltwise
24 BN ScaleShift
25 DetectionOutput DetectionOutput
26 StridedSlice StridedSlice
27 Bias Eltwise(operation = sum)

参考资料:
1 Converting a Caffe* Model
2 Supported Framework Layers

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