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Hi,
I'm trying to run model optimizer on my VGG16_faster_rcnn_final.caffemodel
I a working in a ubuntu 16.04 VM
I run like it was said on Faster RCNN demo :
python3 mo_caffe.py --input_model VGG16_faster_rcnn_final.caffemodel --input_proto test.prototxt
And i got this result :
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: /home/beno/intel/VGG16_faster_rcnn_final.caffemodel
- Path for generated IR: /home/beno/intel/.
- IR output name: VGG16_faster_rcnn_final
- Log level: ERROR
- Batch: Not specified, inherited from the model
- Input layers: Not specified, inherited from the model
- Output layers: Not specified, inherited from the model
- Input shapes: Not specified, inherited from the model
- Mean values: Not specified
- Scale values: Not specified
- Scale factor: Not specified
- Precision of IR: FP32
- Enable fusing: True
- Enable grouped convolutions fusing: True
- Move mean values to preprocess section: False
- Reverse input channels: False
Caffe specific parameters:
- Enable resnet optimization: True
- Path to the Input prototxt: /home/beno/intel/test.prototxt
- Path to CustomLayersMapping.xml: Default
- Path to a mean file: Not specified
- Offsets for a mean file: Not specified
Model Optimizer version: 2019.1.1-83-g28dfbfd
[ ERROR ] Cannot infer shapes or values for node "conv1".
[ ERROR ] -1
[ ERROR ]
[ ERROR ] It can happen due to bug in custom shape infer function <function Convolution.infer at 0x7f0575880a60>.
[ ERROR ] Or because the node inputs have incorrect values/shapes.
[ ERROR ] Or because input shapes are incorrect (embedded to the model or passed via --input_shape).
[ ERROR ] Run Model Optimizer with --log_level=DEBUG for more information.
[ ERROR ] Exception occurred during running replacer "REPLACEMENT_ID" (<class 'extensions.middle.PartialInfer.PartialInfer'>): Stopped shape/value propagation at "conv1" node.
For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #38.
Any idea of what's wrong ?
Regards.
Reno
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I was following tutorial on this url ; https://docs.openvinotoolkit.org/2018_R5/_samples_object_detection_demo_README.html#how_it_works
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add the flag --batch 1
the issue looks to be in the shape model optimizer is unable to handle a -1 in the input shape.
Try that and let me know if it completes.
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Still got the same error
Input :
python3 mo_caffe.py --input_model VGG16_faster_rcnn_final.caffemodel --input_proto test.prototxt --batch 1
Output :
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: /home/beno/intel/VGG16_faster_rcnn_final.caffemodel
- Path for generated IR: /home/beno/intel/.
- IR output name: VGG16_faster_rcnn_final
- Log level: ERROR
- Batch: 1
- Input layers: Not specified, inherited from the model
- Output layers: Not specified, inherited from the model
- Input shapes: Not specified, inherited from the model
- Mean values: Not specified
- Scale values: Not specified
- Scale factor: Not specified
- Precision of IR: FP32
- Enable fusing: True
- Enable grouped convolutions fusing: True
- Move mean values to preprocess section: False
- Reverse input channels: False
Caffe specific parameters:
- Enable resnet optimization: True
- Path to the Input prototxt: /home/beno/intel/test.prototxt
- Path to CustomLayersMapping.xml: Default
- Path to a mean file: Not specified
- Offsets for a mean file: Not specified
Model Optimizer version: 2019.1.1-83-g28dfbfd
[ ERROR ] Cannot infer shapes or values for node "conv1".
[ ERROR ] -1
[ ERROR ]
[ ERROR ] It can happen due to bug in custom shape infer function <function Convolution.infer at 0x7f4c46546b70>.
[ ERROR ] Or because the node inputs have incorrect values/shapes.
[ ERROR ] Or because input shapes are incorrect (embedded to the model or passed via --input_shape).
[ ERROR ] Run Model Optimizer with --log_level=DEBUG for more information.
[ ERROR ] Exception occurred during running replacer "REPLACEMENT_ID" (<class 'extensions.middle.PartialInfer.PartialInfer'>): Stopped shape/value propagation at "conv1" node.
For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #38.

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