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yusuke-konno
Beginner
179 Views

Model Optimizer ONNX conversion error

Hello Intel Developer,

I train deeplab v3+ with Resnet-18 on MATLAB (deeplabv3plusLayers()) and export to ONNX format.

While conversion, Model Optimizer outputs following error.

Which is wrong, MATLAB's ONNX or Model Optimizer ?

I attach compressed ONNX file.

Thank you.

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python "C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer\mo.py" --steps --input_model deeplab.onnx
Model Optimizer arguments:
Common parameters:
        - Path to the Input Model:      deeplab.onnx
        - Path for generated IR:        .
        - IR output name:       deeplab
        - 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
ONNX specific parameters:
Model Optimizer version:        2019.3.0-408-gac8584cb7
[ INFO ] Model loading step
[ INFO ] Front phase execution step
[ INFO ] Middle phase execution step
[ ERROR ]  After partial shape inference were found shape collision for node Add2_ (old shape: [  1  64 128 128], new shape: [  1  -1 128 128])

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2 Replies
yusuke-konno
Beginner
179 Views

I solved conversion error.

My onnx does not contain batch normalization layer's parameters.

Because MATLAB does not support intermediate result for BN layer.

See MATLAB Answers

Thank you

179 Views

Hi yusuke-konno,

Thank you for reporting back to OpenVINO forum.

Reply