问题
I'm trying to convert a trained Core ML model to TensorFlow Lite. I find I need convert it to Onnx first.
The problems is that I get errors. I've tried with different versions of python, onnxmltools, winmltools and it doesn't seems to work. I also tried docker image of onnx ecosystem with same result. Can any one help me with it? Thanks in advance.
Script I used:
import coremltools
import onnxmltools
input_coreml_model = '../model.mlmodel'
output_onnx_model = '../model.onnx'
coreml_model = coremltools.utils.load_spec(input_coreml_model)
onnx_model = onnxmltools.convert_coreml(coreml_model)
onnxmltools.utils.save_model(onnx_model, output_onnx_model)
IndexError Traceback (most recent call last)
<ipython-input-11-94a6dc527869> in <module>
3
4 # Convert the CoreML model into ONNX
----> 5 onnx_model = onnxmltools.convert_coreml(coreml_model)
6
7 # Save as protobuf
/usr/local/lib/python3.6/dist-packages/onnxmltools/convert/main.py in convert_coreml(model, name, initial_types, doc_string, target_opset, targeted_onnx, custom_conversion_functions, custom_shape_calculators)
16 from .coreml.convert import convert
17 return convert(model, name, initial_types, doc_string, target_opset, targeted_onnx,
---> 18 custom_conversion_functions, custom_shape_calculators)
19
20
/usr/local/lib/python3.6/dist-packages/onnxmltools/convert/coreml/convert.py in convert(model, name, initial_types, doc_string, target_opset, targeted_onnx, custom_conversion_functions, custom_shape_calculators)
58 target_opset = target_opset if target_opset else get_opset_number_from_onnx()
59 # Parse CoreML model as our internal data structure (i.e., Topology)
---> 60 topology = parse_coreml(spec, initial_types, target_opset, custom_conversion_functions, custom_shape_calculators)
61
62 # Parse CoreML description, author, and license. Those information will be attached to the final ONNX model.
/usr/local/lib/python3.6/dist-packages/onnxmltools/convert/coreml/_parse.py in parse_coreml(model, initial_types, target_opset, custom_conversion_functions, custom_shape_calculators)
465 # Instead of using CoremlModelContainer, we directly pass the model in because _parse_model is CoreML-specific.
466 _parse_model(topology, scope, model)
--> 467 topology.compile()
468
469 for variable in topology.find_root_and_sink_variables():
/usr/local/lib/python3.6/dist-packages/onnxconverter_common/topology.py in compile(self)
630 self._resolve_duplicates()
631 self._fix_shapes()
--> 632 self._infer_all_types()
633 self._check_structure()
634
/usr/local/lib/python3.6/dist-packages/onnxconverter_common/topology.py in _infer_all_types(self)
506 pass # in Keras converter, the shape calculator can be optional.
507 else:
--> 508 operator.infer_types()
509
510 def _resolve_duplicates(self):
/usr/local/lib/python3.6/dist-packages/onnxconverter_common/topology.py in infer_types(self)
108 def infer_types(self):
109 # Invoke a core inference function
--> 110 registration.get_shape_calculator(self.type)(self)
111
112
/usr/local/lib/python3.6/dist-packages/onnxmltools/convert/coreml/shape_calculators/neural_network/Concat.py in calculate_concat_output_shapes(operator)
22 if variable.type.shape[0] != 'None' and variable.type.shape[0] != output_shape[0]:
23 raise RuntimeError('Only dimensions along C-axis can be different')
---> 24 if variable.type.shape[2] != 'None' and variable.type.shape[2] != output_shape[2]:
25 raise RuntimeError('Only dimensions along C-axis can be different')
26 if variable.type.shape[3] != 'None' and variable.type.shape[3] != output_shape[3]:
IndexError: list index out of range
来源：https://stackoverflow.com/questions/60735629/cant-convert-core-ml-model-to-onnx-then-to-tensorflow-lite