[GitHub] [incubator-mxnet] dtarakanov1 commented on issue #14589: convert from mxnet to onnx failed, with Unrecognized attribute: spatial for operator BatchNormalization

[GitBox <gi...@apache.org>]

dtarakanov1 commented on issue #14589:
URL: https://github.com/apache/incubator-mxnet/issues/14589#issuecomment-692018052


   I get `Unrecognized attribute: spatial for operator BatchNormalization` in one of my scenarios.
   
   Quantize a ResNet model.
   ```Python
       model = onnx.load_model('resnet18v2_opset8.onnx')
       quantized_model = quant.quantize(model)
   ```
   The model is converted with a warning:
   ```
   Warning: The original model opset version is 7, which does not support quantized operators.
               The opset version of quantized model will be set to 10. Use onnx model checker to verify model after quantization.
   ```
   
   Save the model and try running inference
   ```Python
       onnx.save_model(quantized_model, 'quantized_resnet.onnx')
       sess = rt.InferenceSession('quantized_resnet.onnx')
   ```
   ```
   onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Error in Node:resnetv22_batchnorm0_fwd : Unrecognized attribute: spatial for operator BatchNormalization
   ```
   Run model check, just in case:
   ```Python
       onnx.checker.check_model(quantized_model)
   ```
   Something different:
   ```
   onnx.onnx_cpp2py_export.checker.ValidationError: fixed_quantization_range_uint8 in initializer but not in graph input
   ```
   If try `onnx_tf.backend.prepare` I get another problem, and if I convert the model into a later opset I also get another problem.
   Just wonder how to run a quantized model at all.


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