Error in converting retinaface mobilenetv2 backend to CoreML

[N-Harish]

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/framework/importer.py:496, in _import_graph_def_internal(graph_def, input_map, return_elements, validate_colocation_constraints, name, producer_op_list)
    495 try:
--> 496   results = c_api.TF_GraphImportGraphDefWithResults(
    497       graph._c_graph, serialized, options)  # pylint: disable=protected-access
    498   results = c_api_util.ScopedTFImportGraphDefResults(results)

InvalidArgumentError: Input 0 of node RetinaFaceModel/FPN/ConvBN/bn/AssignNewValue was passed float from RetinaFaceModel/FPN/ConvBN/bn/FusedBatchNormV3/ReadVariableOp/resource:0 incompatible with expected resource.

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
Cell In[19], line 1
----> 1 ct.convert(model, source='tensorflow')

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/_converters_entry.py:492, in convert(model, source, inputs, outputs, classifier_config, minimum_deployment_target, convert_to, compute_precision, skip_model_load, compute_units, package_dir, debug, pass_pipeline)
    489 if specification_version is None:
    490     specification_version = _set_default_specification_version(exact_target)
--> 492 mlmodel = mil_convert(
    493     model,
    494     convert_from=exact_source,
    495     convert_to=exact_target,
    496     inputs=inputs,
    497     outputs=outputs_as_tensor_or_image_types,  # None or list[ct.ImageType/ct.TensorType]
    498     classifier_config=classifier_config,
    499     skip_model_load=skip_model_load,
    500     compute_units=compute_units,
    501     package_dir=package_dir,
    502     debug=debug,
    503     specification_version=specification_version,
    504     main_pipeline=pass_pipeline,
    505 )
    507 if exact_target == 'milinternal':
    508     return mlmodel  # Returns the MIL program

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/converter.py:188, in mil_convert(model, convert_from, convert_to, compute_units, **kwargs)
    149 @_profile
    150 def mil_convert(
    151     model,
   (...)
    155     **kwargs
    156 ):
    157     """
    158     Convert model from a specified frontend `convert_from` to a specified
    159     converter backend `convert_to`.
   (...)
    186         See `coremltools.converters.convert`
    187     """
--> 188     return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/converter.py:212, in _mil_convert(model, convert_from, convert_to, registry, modelClass, compute_units, **kwargs)
    209     weights_dir = _tempfile.TemporaryDirectory()
    210     kwargs["weights_dir"] = weights_dir.name
--> 212 proto, mil_program = mil_convert_to_proto(
    213                         model,
    214                         convert_from,
    215                         convert_to,
    216                         registry,
    217                         **kwargs
    218                      )
    220 _reset_conversion_state()
    222 if convert_to == 'milinternal':

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/converter.py:285, in mil_convert_to_proto(model, convert_from, convert_to, converter_registry, main_pipeline, **kwargs)
    280 frontend_pipeline, backend_pipeline = _construct_other_pipelines(
    281     main_pipeline, convert_from, convert_to
    282 )
    284 frontend_converter = frontend_converter_type()
--> 285 prog = frontend_converter(model, **kwargs)
    286 PipelineManager.apply_pipeline(prog, frontend_pipeline)
    288 PipelineManager.apply_pipeline(prog, main_pipeline)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/converter.py:98, in TensorFlow2Frontend.__call__(self, *args, **kwargs)
     95 from .frontend.tensorflow2.load import TF2Loader
     97 tf2_loader = TF2Loader(*args, **kwargs)
---> 98 return tf2_loader.load()

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/load.py:61, in TFLoader.load(self)
     59 outputs = self.kwargs.get("outputs", None)
     60 output_names = get_output_names(outputs)
---> 61 self._graph_def = self._graph_def_from_model(output_names)
     63 if self._graph_def is not None and len(self._graph_def.node) == 0:
     64     msg = "tf.Graph should have at least 1 node, Got empty graph."

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow2/load.py:133, in TF2Loader._graph_def_from_model(self, output_names)
    131 def _graph_def_from_model(self, output_names=None):
    132     """Overwrites TFLoader._graph_def_from_model()"""
--> 133     cfs, graph_def = self._get_concrete_functions_and_graph_def()
    134     if isinstance(self.model, _tf.keras.Model) and self.kwargs.get("outputs", None) is None:
    135         # For the keras model, check if the outputs is provided by the user.
    136         # If not, we make sure the coreml model outputs order is the same as
    137         # the original keras model
    138         cf = cfs[0]

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow2/load.py:127, in TF2Loader._get_concrete_functions_and_graph_def(self)
    124 else:
    125     raise NotImplementedError(msg.format(self.model))
--> 127 graph_def = self._graph_def_from_concrete_fn(cfs)
    129 return cfs, graph_def

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow2/load.py:328, in TF2Loader._graph_def_from_concrete_fn(self, cfs)
    325     raise NotImplementedError("Only a single concrete function is supported.")
    327 if _get_version(_tf.__version__) >= _StrictVersion("2.2.0"):
--> 328     frozen_fn = _convert_variables_to_constants_v2(cfs[0], lower_control_flow=False, aggressive_inlining=True)
    329 else:
    330     frozen_fn = _convert_variables_to_constants_v2(cfs[0], lower_control_flow=False)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/framework/convert_to_constants.py:1083, in convert_variables_to_constants_v2(func, lower_control_flow, aggressive_inlining)
   1075 converter_data = _FunctionConverterData(
   1076     func=func,
   1077     lower_control_flow=lower_control_flow,
   1078     aggressive_inlining=aggressive_inlining)
   1080 output_graph_def, converted_input_indices = _replace_variables_by_constants(
   1081     converter_data=converter_data)
-> 1083 return _construct_concrete_function(func, output_graph_def,
   1084                                     converted_input_indices)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/framework/convert_to_constants.py:1008, in _construct_concrete_function(func, output_graph_def, converted_input_indices)
   1005   if context.context().has_function(f.signature.name):
   1006     context.context().remove_function(f.signature.name)
-> 1008 new_func = wrap_function.function_from_graph_def(output_graph_def,
   1009                                                  new_input_names,
   1010                                                  new_output_names)
   1012 # Manually propagate shape for input tensors where the shape is not correctly
   1013 # propagated. Scalars shapes are lost when wrapping the function.
   1014 for input_tensor in new_func.inputs:

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/eager/wrap_function.py:650, in function_from_graph_def(graph_def, inputs, outputs)
    647 def _imports_graph_def():
    648   importer.import_graph_def(graph_def, name="")
--> 650 wrapped_import = wrap_function(_imports_graph_def, [])
    651 import_graph = wrapped_import.graph
    652 return wrapped_import.prune(
    653     nest.map_structure(import_graph.as_graph_element, inputs),
    654     nest.map_structure(import_graph.as_graph_element, outputs))

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/eager/wrap_function.py:621, in wrap_function(fn, signature, name)
    618 if name is not None:
    619   func_graph_name = "wrapped_function_" + name
    620 return WrappedFunction(
--> 621     func_graph.func_graph_from_py_func(
    622         func_graph_name,
    623         holder,
    624         args=None,
    625         kwargs=None,
    626         signature=signature,
    627         add_control_dependencies=False,
    628         collections={}),
    629     variable_holder=holder,
    630     signature=signature)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/framework/func_graph.py:999, in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    996 else:
    997   _, original_func = tf_decorator.unwrap(python_func)
--> 999 func_outputs = python_func(*func_args, **func_kwargs)
   1001 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
   1002 # TensorArrays and `None`s.
   1003 func_outputs = nest.map_structure(convert, func_outputs,
   1004                                   expand_composites=True)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/eager/wrap_function.py:87, in VariableHolder.__call__(self, *args, **kwargs)
     86 def __call__(self, *args, **kwargs):
---> 87   return self.call_with_variable_creator_scope(self._fn)(*args, **kwargs)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/eager/wrap_function.py:93, in VariableHolder.call_with_variable_creator_scope.<locals>.wrapped(*args, **kwargs)
     91 def wrapped(*args, **kwargs):
     92   with variable_scope.variable_creator_scope(self.variable_creator_scope):
---> 93     return fn(*args, **kwargs)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/eager/wrap_function.py:648, in function_from_graph_def.<locals>._imports_graph_def()
    647 def _imports_graph_def():
--> 648   importer.import_graph_def(graph_def, name="")

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/util/deprecation.py:535, in deprecated_args.<locals>.deprecated_wrapper.<locals>.new_func(*args, **kwargs)
    527         _PRINTED_WARNING[(func, arg_name)] = True
    528       logging.warning(
    529           'From %s: calling %s (from %s) with %s is deprecated and will '
    530           'be removed %s.\nInstructions for updating:\n%s',
   (...)
    533           'in a future version' if date is None else ('after %s' % date),
    534           instructions)
--> 535 return func(*args, **kwargs)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/framework/importer.py:400, in import_graph_def(***failed resolving arguments***)
    357 """Imports the graph from `graph_def` into the current default `Graph`.
    358 
    359 This function provides a way to import a serialized TensorFlow
   (...)
    397     it refers to an unknown tensor).
    398 """
    399 del op_dict
--> 400 return _import_graph_def_internal(
    401     graph_def,
    402     input_map=input_map,
    403     return_elements=return_elements,
    404     name=name,
    405     producer_op_list=producer_op_list)

File ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/tensorflow/python/framework/importer.py:501, in _import_graph_def_internal(graph_def, input_map, return_elements, validate_colocation_constraints, name, producer_op_list)
    498     results = c_api_util.ScopedTFImportGraphDefResults(results)
    499   except errors.InvalidArgumentError as e:
    500     # Convert to ValueError for backwards compatibility.
--> 501     raise ValueError(str(e))
    503 # Create _DefinedFunctions for any imported functions.
    504 #
    505 # We do this by creating _DefinedFunctions directly from `graph_def`, and
   (...)
    510 # TODO(skyewm): fetch the TF_Functions directly from the TF_Graph
    511 # TODO(skyewm): avoid sending serialized FunctionDefs back to the TF_Graph
    513 _ProcessNewOps(graph)

ValueError: Input 0 of node RetinaFaceModel/FPN/ConvBN/bn/AssignNewValue was passed float from RetinaFaceModel/FPN/ConvBN/bn/FusedBatchNormV3/ReadVariableOp/resource:0 incompatible with expected resource.

While trying to convert model to CoreMl format, We are getting this error and the conversion process stops @peteryuX can you let us know how to fix this ?