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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:
2-4 Semantic segmentation models:
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) |
版权声明:本文为CSDN博主「mingo_敏」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。
原文链接:https://blog.csdn.net/shanglianlm/article/details/89291009
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