Comments (1)
I would recommend printing all node names to debug this further. To print all, try this.
Also, from this thread, I had success converting a pytorch model -> onnx -> keras using this command:
keras_model = onnx_to_keras(onnx_model, input_names=['input'], name_policy="renumerate")
Setting the name_policy
was crucial to get it working. Might be that you want to do something similar, but I would try printing the node names to see if you are able to extract them. Might be that something went wrong during tfjs -> onnx conversion.
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Related Issues (20)
- Indentation error in onnx2keras/converter.py
- KeyError: 'Resize' while converting onnx to keras HOT 2
- Getting strange split and Lambda function when converting onnx HOT 5
- ValueError: 'sequential/conv2d/BiasAdd__6:0/' is not a valid scope name HOT 2
- Not able to pip install in Apple M1
- Error with BatchNorm conversions in training mode
- Deserialization error with `Lambda` layers when loading saved model in new environment
- Use of bare except clause in `convert_concat`
- Not able to convert onnx model HOT 1
- ValueError: `padding` should have 3 elements. Received: [0]. HOT 1
- AttributeError: Number of inputs is not equal 1 for unsqueeze layer HOT 1
- Dynamic shapes
- Trying to convert from Yolov5 pt to onxx and then to h5 keras
- TypeError: unhashable type: 'google._upb._message.RepeatedScalarContainer' HOT 3
- Relu6 Operation not Converting
- '/Reshape_output_0/' is not a valid root scope name. HOT 2
- BatchNormalization throws error when change_ordering=True
- ValueError: Exception encountered when calling layer "LAYER_0" (type Conv2D). HOT 1
- Requirements Depends on tensorflow - does not work with tf-nightly
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