title: Onnx to mlmodel conversion fails to generate .mlmodel file
body: <p>I'm trying to convert Pytorch model to MLModel with Onnx.</p>
  
  <p>My code:</p>
  
  <pre><code>import torch
  from onnx_coreml import convert
  import coremltools
  
  net = BiSeNet(19)
  net.cuda()
  net.load_state_dict(torch.load('model.pth'))
  #net.eval()
  
  dummy = torch.rand(1,3,512,512).cuda()
  
  
  torch.onnx.export(net, dummy, "Model.onnx", input_names=["image"], output_names=["output"], opset_version=11)
  
  finalModel = convert(model='Model.onnx', minimum_ios_deployment_target='12')
  finalModel.save('ModelML.mlmodel')
  </code></pre>
  
  <p>After the code runs Model.onnx is generated, however, .mlmodel file is not generated. There're no errors in the console. This is the output:</p>
  
  <pre><code>2020-04-15 21:49:32.367179: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
  WARNING:root:TensorFlow version 2.2.0-rc2 detected. Last version known to be fully compatible is 1.14.0 .
  WARNING:root:Keras version 2.3.1 detected. Last version known to be fully compatible of Keras is 2.2.4 .
  1.4.0
  /content/drive/My Drive/Collab/fp/model.py:116: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
    size_array = [int(s) for s in feat32.size()[2:]]
  /content/drive/My Drive/Collab/fp/model.py:80: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
    size_array = [int(s) for s in feat.size()[2:]]
  /content/drive/My Drive/Collab/fp/model.py:211: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
    size_array = [int(s) for s in feat.size()[2:]]
  </code></pre>
  
  <p>What could be the issue?</p>