Onnx to mlmodel conversion fails to generate .mlmodel file

Question

I'm trying to convert Pytorch model to MLModel with Onnx.

My 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')

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:

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:]]

What could be the issue?