(model,  # type: Union[onnx.ModelProto, Text]
            mode=None,  # type: Optional[Text]
            image_input_names=[],  # type: Sequence[Text]
            preprocessing_args={},  # type: Dict[Text, Any]
            image_output_names=[],  # type: Sequence[Text]
            deprocessing_args={},  # type: Dict[Text, Any]
            class_labels=None,  # type: Union[Text, Iterable[Text], None]
            predicted_feature_name='classLabel',  # type: Text
            add_custom_layers = False,  # type: bool
            custom_conversion_functions = {}, #type: Dict[Text, Any]
            onnx_coreml_input_shape_map = {}, # type: Dict[Text, List[int,...]]
            minimum_ios_deployment_target = '12')
return mlmodel
Convert ONNX model to CoreML.
    Parameters
    ----------
    model:
        An ONNX model with parameters loaded in onnx package or path to file
        with models.
    mode: 'classifier', 'regressor' or None
        Mode of the converted coreml model:
        'classifier', a NeuralNetworkClassifier spec will be constructed.
        'regressor', a NeuralNetworkRegressor spec will be constructed.
    preprocessing_args:
        'is_bgr', 'red_bias', 'green_bias', 'blue_bias', 'gray_bias',
        'image_scale' keys with the same meaning as
        https://apple.github.io/coremltools/generated/coremltools.models.neural_network.html#coremltools.models.neural_network.NeuralNetworkBuilder.set_pre_processing_parameters
    deprocessing_args:
        Same as 'preprocessing_args' but for deprocessing.
    class_labels:
        As a string it represents the name of the file which contains
        the classification labels (one per line).
        As a list of strings it represents a list of categories that map
        the index of the output of a neural network to labels in a classifier.
    predicted_feature_name:
        Name of the output feature for the class labels exposed in the Core ML
        model (applies to classifiers only). Defaults to 'classLabel'
    add_custom_layers: bool
        Flag to turn on addition of custom CoreML layers for unsupported ONNX ops or attributes within
        a supported op.
    custom_conversion_functions: dict()
        A dictionary with keys corresponding to the names/types of onnx ops and values as functions taking
        an object of class coreml-tools's 'NeuralNetworkBuilder', Graph' (see onnx-coreml/_graph.Graph),
        'Node' (see onnx-coreml/_graph.Node), ErrorHandling (see onnx-coreml/_error_utils.ErrorHandling).
        This custom conversion function gets full control and responsibility for converting given onnx op.
        This function returns nothing and is responsible for adding a equivalent CoreML layer via 'NeuralNetworkBuilder'
    onnx_coreml_input_shape_map: dict()
        (Optional) A dictionary with keys corresponding to the model input names. Values are a list of integers that specify
        how the shape of the input is mapped to CoreML. Convention used for CoreML shapes is
        0: Sequence, 1: Batch, 2: channel, 3: height, 4: width.
        For example, an input of rank 2 could be mapped as [3,4] (i.e. H,W) or [1,2] (i.e. B,C) etc.
        This is ignored if "minimum_ios_deployment_target" is set to 13.
    minimum_ios_deployment_target: str
        Target Deployment iOS Version (default: '12')
        Supported iOS version options: '11.2', '12', '13'        
        CoreML model produced by the converter will be compatible with the iOS version specified in this argument.
        e.g. if minimum_ios_deployment_target = '12', the converter would only utilize CoreML features released till iOS12 (equivalently macOS 10.14, watchOS 5 etc).

        iOS 11.2 (CoreML 0.8) does not support resize_bilinear, crop_resize layers 
         - (Supported features: https://github.com/apple/coremltools/releases/tag/v0.8)
        iOS 12 (CoreML 2.0)
         - (Supported features: https://github.com/apple/coremltools/releases/tag/v2.0)
        iSO 13 (CoreML 3.0)
         - (Supported features: https://github.com/apple/coremltools/releases/tag/3.0-beta6)

    Returns
    -------
    model: A coreml model.
Convert ONNX model to CoreML.
    Parameters
    ----------
    model:
        An ONNX model with parameters loaded in onnx package or path to file
        with models.
    mode: 'classifier', 'regressor' or None
        Mode of the converted coreml model:
        'classifier', a NeuralNetworkClassifier spec will be constructed.
        'regressor', a NeuralNetworkRegressor spec will be constructed.
    preprocessing_args:
        'is_bgr', 'red_bias', 'green_bias', 'blue_bias', 'gray_bias',
        'image_scale' keys with the same meaning as
        https://apple.github.io/coremltools/generated/coremltools.models.neural_network.html#coremltools.models.neural_network.NeuralNetworkBuilder.set_pre_processing_parameters
    deprocessing_args:
        Same as 'preprocessing_args' but for deprocessing.
    class_labels:
        As a string it represents the name of the file which contains
        the classification labels (one per line).
        As a list of strings it represents a list of categories that map
        the index of the output of a neural network to labels in a classifier.
    predicted_feature_name:
        Name of the output feature for the class labels exposed in the Core ML
        model (applies to classifiers only). Defaults to 'classLabel'
    add_custom_layers: bool
        Flag to turn on addition of custom CoreML layers for unsupported ONNX ops or attributes within
        a supported op.
    custom_conversion_functions: dict()
        A dictionary with keys corresponding to the names/types of onnx ops and values as functions taking
        an object of class coreml-tools's 'NeuralNetworkBuilder', Graph' (see onnx-coreml/_graph.Graph),
        'Node' (see onnx-coreml/_graph.Node), ErrorHandling (see onnx-coreml/_error_utils.ErrorHandling).
        This custom conversion function gets full control and responsibility for converting given onnx op.
        This function returns nothing and is responsible for adding a equivalent CoreML layer via 'NeuralNetworkBuilder'
    onnx_coreml_input_shape_map: dict()
        (Optional) A dictionary with keys corresponding to the model input names. Values are a list of integers that specify
        how the shape of the input is mapped to CoreML. Convention used for CoreML shapes is
        0: Sequence, 1: Batch, 2: channel, 3: height, 4: width.
        For example, an input of rank 2 could be mapped as [3,4] (i.e. H,W) or [1,2] (i.e. B,C) etc.
        This is ignored if "minimum_ios_deployment_target" is set to 13.
    minimum_ios_deployment_target: str
        Target Deployment iOS Version (default: '12')
        Supported iOS version options: '11.2', '12', '13'        
        CoreML model produced by the converter will be compatible with the iOS version specified in this argument.
        e.g. if minimum_ios_deployment_target = '12', the converter would only utilize CoreML features released till iOS12 (equivalently macOS 10.14, watchOS 5 etc).
Convert ONNX model to CoreML . Parameters ---------- model : An ONNX model with parameters loaded in onnx package or path to file with models . mode : classifier regressor or None Mode of the converted coreml model : classifier a NeuralNetworkClassifier spec will be constructed . regressor a NeuralNetworkRegressor spec will be constructed . preprocessing_args : is_bgr red_bias green_bias blue_bias gray_bias image_scale keys with the same meaning as https : // apple . github . io / coremltools / generated / coremltools . models . neural_network . html#coremltools . models . neural_network . NeuralNetworkBuilder . set_pre_processing_parameters deprocessing_args : Same as preprocessing_args but for deprocessing . class_labels : As a string it represents the name of the file which contains the classification labels ( one per line ) . As a list of strings it represents a list of categories that map the index of the output of a neural network to labels in a classifier . predicted_feature_name : Name of the output feature for the class labels exposed in the Core ML model ( applies to classifiers only ) . Defaults to classLabel add_custom_layers : bool Flag to turn on addition of custom CoreML layers for unsupported ONNX ops or attributes within a supported op . custom_conversion_functions : dict () A dictionary with keys corresponding to the names / types of onnx ops and values as functions taking an object of class coreml - tools s NeuralNetworkBuilder Graph ( see onnx - coreml / _graph . Graph ) Node ( see onnx - coreml / _graph . Node ) ErrorHandling ( see onnx - coreml / _error_utils . ErrorHandling ) . This custom conversion function gets full control and responsibility for converting given onnx op . This function returns nothing and is responsible for adding a equivalent CoreML layer via NeuralNetworkBuilder onnx_coreml_input_shape_map : dict () ( Optional ) A dictionary with keys corresponding to the model input names . Values are a list of integers that specify how the shape of the input is mapped to CoreML . Convention used for CoreML shapes is 0 : Sequence 1 : Batch 2 : channel 3 : height 4 : width . For example an input of rank 2 could be mapped as [ 3 4 ] ( i . e . H W ) or [ 1 2 ] ( i . e . B C ) etc . This is ignored if minimum_ios_deployment_target is set to 13 . minimum_ios_deployment_target : str Target Deployment iOS Version ( default : 12 ) Supported iOS version options : 11 . 2 12 13 CoreML model produced by the converter will be compatible with the iOS version specified in this argument . e . g . if minimum_ios_deployment_target = 12 the converter would only utilize CoreML features released till iOS12 ( equivalently macOS 10 . 14 watchOS 5 etc ) .
def convert(model,  # type: Union[onnx.ModelProto, Text]
            mode=None,  # type: Optional[Text]
            image_input_names=[],  # type: Sequence[Text]
            preprocessing_args={},  # type: Dict[Text, Any]
            image_output_names=[],  # type: Sequence[Text]
            deprocessing_args={},  # type: Dict[Text, Any]
            class_labels=None,  # type: Union[Text, Iterable[Text], None]
            predicted_feature_name='classLabel',  # type: Text
            add_custom_layers = False,  # type: bool
            custom_conversion_functions = {}, #type: Dict[Text, Any]
            onnx_coreml_input_shape_map = {}, # type: Dict[Text, List[int,...]]
            minimum_ios_deployment_target = '12'):
    # type: (...) -> MLModel
    """
    Convert ONNX model to CoreML.
    Parameters
    ----------
    model:
        An ONNX model with parameters loaded in onnx package or path to file
        with models.
    mode: 'classifier', 'regressor' or None
        Mode of the converted coreml model:
        'classifier', a NeuralNetworkClassifier spec will be constructed.
        'regressor', a NeuralNetworkRegressor spec will be constructed.
    preprocessing_args:
        'is_bgr', 'red_bias', 'green_bias', 'blue_bias', 'gray_bias',
        'image_scale' keys with the same meaning as
        https://apple.github.io/coremltools/generated/coremltools.models.neural_network.html#coremltools.models.neural_network.NeuralNetworkBuilder.set_pre_processing_parameters
    deprocessing_args:
        Same as 'preprocessing_args' but for deprocessing.
    class_labels:
        As a string it represents the name of the file which contains
        the classification labels (one per line).
        As a list of strings it represents a list of categories that map
        the index of the output of a neural network to labels in a classifier.
    predicted_feature_name:
        Name of the output feature for the class labels exposed in the Core ML
        model (applies to classifiers only). Defaults to 'classLabel'
    add_custom_layers: bool
        Flag to turn on addition of custom CoreML layers for unsupported ONNX ops or attributes within
        a supported op.
    custom_conversion_functions: dict()
        A dictionary with keys corresponding to the names/types of onnx ops and values as functions taking
        an object of class coreml-tools's 'NeuralNetworkBuilder', Graph' (see onnx-coreml/_graph.Graph),
        'Node' (see onnx-coreml/_graph.Node), ErrorHandling (see onnx-coreml/_error_utils.ErrorHandling).
        This custom conversion function gets full control and responsibility for converting given onnx op.
        This function returns nothing and is responsible for adding a equivalent CoreML layer via 'NeuralNetworkBuilder'
    onnx_coreml_input_shape_map: dict()
        (Optional) A dictionary with keys corresponding to the model input names. Values are a list of integers that specify
        how the shape of the input is mapped to CoreML. Convention used for CoreML shapes is
        0: Sequence, 1: Batch, 2: channel, 3: height, 4: width.
        For example, an input of rank 2 could be mapped as [3,4] (i.e. H,W) or [1,2] (i.e. B,C) etc.
        This is ignored if "minimum_ios_deployment_target" is set to 13.
    minimum_ios_deployment_target: str
        Target Deployment iOS Version (default: '12')
        Supported iOS version options: '11.2', '12', '13'        
        CoreML model produced by the converter will be compatible with the iOS version specified in this argument.
        e.g. if minimum_ios_deployment_target = '12', the converter would only utilize CoreML features released till iOS12 (equivalently macOS 10.14, watchOS 5 etc).

        iOS 11.2 (CoreML 0.8) does not support resize_bilinear, crop_resize layers 
         - (Supported features: https://github.com/apple/coremltools/releases/tag/v0.8)
        iOS 12 (CoreML 2.0)
         - (Supported features: https://github.com/apple/coremltools/releases/tag/v2.0)
        iSO 13 (CoreML 3.0)
         - (Supported features: https://github.com/apple/coremltools/releases/tag/3.0-beta6)

    Returns
    -------
    model: A coreml model.
    """
    if isinstance(model, Text):
        onnx_model = onnx.load(model)
    elif isinstance(model, onnx.ModelProto):
        onnx_model = model
    else:
        raise TypeError(
            "Model must be file path to .onnx file or onnx loaded model"
        )

    if not SupportedVersion.ios_support_check(minimum_ios_deployment_target):
        raise TypeError('{} not supported. Please provide one of target iOS: {}', minimum_ios_deployment_target, SupportedVersion.get_supported_ios())
        

    global USE_SHAPE_MAPPING
    disable_coreml_rank5_mapping = False
    if SupportedVersion.is_nd_array_supported(minimum_ios_deployment_target):
        disable_coreml_rank5_mapping = True
    
    if disable_coreml_rank5_mapping:
        USE_SHAPE_MAPPING = False
    else:
        USE_SHAPE_MAPPING = True

    '''
    First, apply a few optimizations to the ONNX graph,
    in preparation for conversion to CoreML. 
    '''

    # Using Dummy transformation to conditionally disable certain transformation
    class  DummyTransformation(object):
        def __call__(self, graph):
            return graph

    transformers = [
        ConstantsToInitializers(),
        ShapeOpRemover(),
        ConstantRemover(),
        CastOpRemover(),
        PaddingOpRemover(),
        ReshapeInitTensorFuser(),
        DropoutRemover(),
        DeadCodeElimination(),
        ConvAddFuser(),
        BNBroadcastedMulFuser(),
        BNBroadcastedAddFuser(),
        ReshapeTransposeReshape_pattern1(),
        PixelShuffleFuser(),
        AddModelInputsOutputs() if not disable_coreml_rank5_mapping else DummyTransformation(),
        ConstantFillToInitializers(),
    ]  # type: Iterable[Transformer]


    onnx_model = onnx.shape_inference.infer_shapes(onnx_model)
    graph = _prepare_onnx_graph(onnx_model.graph, transformers, onnx_model.ir_version)

    '''
    Check for ImageScalar nodes in ONNX, this will indicate whether input image preprocessing needs
    to be added to the CoreML graph or not. 
    '''
    # are there ImageScaler nodes in the Graph?
    # If yes then add the info from it to the "preprocessing_args" dictionary, if the dictionary is not
    # already provided by the user
    if not bool(preprocessing_args):
        for node in graph.nodes:
            if node.op_type == 'ImageScaler':
                inp_name = node.inputs[0]
                scale = node.attrs.get('scale', 1.0)
                bias = node.attrs.get('bias', [0,0,0])
                if not (len(bias) == 1 or len(bias) == 3):
                    continue
                if 'image_scale' in preprocessing_args:
                    preprocessing_args['image_scale'][inp_name] = scale
                else:
                    preprocessing_args['image_scale'] = {inp_name: scale}
                if len(bias) == 3:
                    for i, color in enumerate(['red', 'green', 'blue']):
                        if color + '_bias' in preprocessing_args:
                            preprocessing_args[color + '_bias'][inp_name] = bias[i]
                        else:
                            preprocessing_args[color + '_bias'] = {inp_name: bias[i]}
                else:
                    if 'gray_bias' in preprocessing_args:
                        preprocessing_args['gray_bias'][inp_name] = bias[0]
                    else:
                        preprocessing_args['gray_bias'] = {inp_name: bias[0]}
                if inp_name not in image_input_names:
                    image_input_names.append(inp_name) # type: ignore

    # remove all ImageScaler ops
    graph = graph.transformed([ImageScalerRemover()])

    '''
    Gather information (name, shape) for model inputs and outputs
    This information is then used to initialize the neural network builder object of coremltools. 
    The builder object is later used to add layers to the CoreML model. 
    '''

    #Make CoreML input and output features by gathering shape info and
    #interpreting it for CoreML
    input_features = _make_coreml_input_features(graph, onnx_coreml_input_shape_map, disable_coreml_rank5_mapping)
    if len( image_output_names) > 0:
        output_features = _make_coreml_output_features(graph, forceShape=True, disable_coreml_rank5_mapping=disable_coreml_rank5_mapping)
    else:
        output_features = _make_coreml_output_features(graph, disable_coreml_rank5_mapping=disable_coreml_rank5_mapping)

    builder = NeuralNetworkBuilder(input_features, output_features, mode=mode, disable_rank5_shape_mapping=disable_coreml_rank5_mapping)

    # TODO: To be removed once, auto-downgrading of spec version is enabled
    builder.spec.specificationVersion = SupportedVersion.get_specification_version(minimum_ios_deployment_target)

    '''
    Set CoreML input,output types (float, double, int) same as onnx types, if supported
    '''
    _transform_coreml_dtypes(builder, graph.inputs, graph.outputs)


    '''what follows is some book-keeping to support outputs of type image. 
    '''

    is_deprocess_bgr_only = (len(deprocessing_args) == 1) and \
                            ("is_bgr" in deprocessing_args)
    add_deprocess = (len(image_output_names) > 0) and \
                    (len(deprocessing_args) > 0) and \
                    (not is_deprocess_bgr_only)

    if add_deprocess:
        mapping = {}
        for f in output_features:
            output_name = f[0]
            mapping[output_name] = graph.get_unique_edge_name(output_name)
        graph = OutputRenamer(mapping)(graph)



    if len(image_input_names) > 0:
        builder.set_pre_processing_parameters(
            image_input_names=image_input_names,
            is_bgr=preprocessing_args.get('is_bgr', False),
            red_bias=preprocessing_args.get('red_bias', 0.0),
            green_bias=preprocessing_args.get('green_bias', 0.0),
            blue_bias=preprocessing_args.get('blue_bias', 0.0),
            gray_bias=preprocessing_args.get('gray_bias', 0.0),
            image_scale=preprocessing_args.get('image_scale', 1.0)
        )

    preprocessing_args.clear()

    if len(image_output_names) > 0:
        for f in output_features:
            f_name = f[0]
            if f_name in image_output_names:
                is_bgr = deprocessing_args.get('is_bgr', False)
                _convert_multiarray_output_to_image(
                    builder.spec, f_name, is_bgr=is_bgr
                )

    '''
    Iterate through all the ONNX ops and translate them to CoreML layers, one by one. 
    '''

    '''
    before proceeding to start the layer translation process,
    check whether there is an op in the ONNX graph, whose translation function is not yet
    implemented in the converter or which is not supported in the CoreML framework. If so, 
    raise an error before starting the process.
    (if the user desires to add a custom layer then this check is not required)
    '''
    if not add_custom_layers:
        _check_unsupported_ops(graph.nodes, disable_coreml_rank5_mapping)

    '''
    ErrorHandling is a generic class, useful to store a variety of parameters during the conversion process  
    '''
    err = ErrorHandling(add_custom_layers,
                        custom_conversion_functions)

    for i, node in enumerate(graph.nodes):
        print("%d/%d: Converting Node Type %s" %(i+1, len(graph.nodes), node.op_type))
        if disable_coreml_rank5_mapping:
            _convert_node_nd(builder, node, graph, err)
        else:
            _add_const_inputs_if_required(builder, node, graph, err)
            _convert_node(builder, node, graph, err)

    if DEBUG:
        plot_graph(graph, graph_img_path='/tmp/after_conversion.pdf', show_coreml_mapped_shapes=not disable_coreml_rank5_mapping) 

    if add_deprocess:
        for f in output_features:
            output_name = f[0]
            if output_name not in image_output_names:
                continue
            output_shape = f[1].dimensions
            if len(output_shape) == 2 or output_shape[0] == 1:
                is_grayscale = True
            elif output_shape[0] == 3:
                is_grayscale = False
            else:
                raise ValueError('Output must be RGB image or Grayscale')
            _set_deprocessing(
                is_grayscale,
                builder,
                deprocessing_args,
                mapping[output_name],
                output_name
            )

    if class_labels is not None:
        if isinstance(class_labels, Text):
            labels = [l.strip() for l in open(class_labels).readlines()]  # type: Sequence[Text]
        elif isinstance(class_labels, list):
            labels = class_labels
        else:
            raise TypeError(
                "synset variable of unknown type. Type found: {}. \
                Expected either string or list of strings."
                .format(type(class_labels),))

        builder.set_class_labels(
            class_labels=labels,
            predicted_feature_name=predicted_feature_name
        )

    def _add_informative_description(feature, raise_error=True):
        if feature.type.WhichOneof('Type') == 'multiArrayType':
            if feature.name in graph.onnx_coreml_shape_mapping and feature.name in graph.shape_dict:
                mapp = graph.onnx_coreml_shape_mapping[feature.name]
                onnx_shape = graph.shape_dict[feature.name]
                if raise_error: assert len(mapp) == len(onnx_shape), "Something wrong in shape"
                if len(mapp) == len(onnx_shape):
                    shape = []
                    for i in range(5):
                        if i in mapp:
                            shape += [int(onnx_shape[mapp.index(i)])]
                        else:
                            shape += [1]
                    msg = 'MultiArray of shape {}. The first and second dimensions correspond to sequence and batch size, respectively'.format(str(tuple(shape)))
                    feature.shortDescription += msg

    optional_input_names = []
    for tup in graph.optional_inputs:
        optional_input_names.append(tup[0])
    optional_output_names = []
    for tup in graph.optional_outputs:
        optional_output_names.append(tup[0])

    # add description for inputs and outputs shapes
    remove_input_id = []
    for i, input_ in enumerate(builder.spec.description.input):
        if input_.name not in optional_input_names:
            if not disable_coreml_rank5_mapping:
                _add_informative_description(input_)
        else:
            remove_input_id.append(i)
    remove_output_id = []
    for i, output_ in enumerate(builder.spec.description.output):
        if output_.name not in optional_output_names:
            if not disable_coreml_rank5_mapping:
                _add_informative_description(output_, raise_error=False)
        else:
            remove_output_id.append(i)

    for index in sorted(remove_input_id, reverse=True):
        del builder.spec.description.input[index]
    for index in sorted(remove_output_id, reverse=True):
        del builder.spec.description.output[index]


    if len(graph.optional_inputs) > 0 or len(graph.optional_outputs):
        builder.add_optionals(graph.optional_inputs, graph.optional_outputs)

    # Check for specification version and target ios compatibility
    if minimum_ios_deployment_target == '11.2' and builder.spec.WhichOneof('Type') == 'neuralNetwork':
        nn_spec = builder.spec.neuralNetwork
        for layer in nn_spec.layers:
            if layer.WhichOneof('layer') == 'resizeBilinear' or layer.WhichOneof('layer') == 'cropResize':
                raise TypeError('{} not supported with target iOS 11.2 please provide higher target iOS'.format(layer.WhichOneof('layer')))

    # Optimize ML Model Spec
    ml_model_passes = [remove_disconnected_layers, transform_conv_crop]
    for opt in ml_model_passes:
        opt(builder.spec)

    print("Translation to CoreML spec completed. Now compiling the CoreML model.")
    try:
        if DEBUG:
            import coremltools
            coremltools.models.utils.save_spec(builder.spec, '/tmp/node_model_raw_spec.mlmodel')
            from  coremltools.models.neural_network.printer import print_network_spec
            print_network_spec(builder.spec, style='coding')
        mlmodel = MLModel(builder.spec)
    except RuntimeError as e:
        raise ValueError('Compilation failed: {}'.format(str(e)))
    print('Model Compilation done.')


    # print information about all ops for which custom layers have been added
    if len(err.custom_layer_nodes) > 0:
        print('\n')
        print("Custom layers have been added to the CoreML model "
              "corresponding to the following ops in the onnx model: ")
        for i, node in enumerate(err.custom_layer_nodes):
            input_info = []
            for input_ in node.inputs:
                input_info.append((str(input_), graph.shape_dict.get(input_, str("Shape not available"))))
            output_info = []
            for output_ in node.outputs:
                output_info.append((str(output_), graph.shape_dict.get(output_, str("Shape not available"))))
            print("{}/{}: op type: {}, op input names and shapes: {}, op output names and shapes: {}".
                  format(i+1, len(err.custom_layer_nodes), node.op_type, str(input_info), str(output_info)))

    return mlmodel
def convert ( model , # type: Union[onnx.ModelProto, Text] mode = None , # type: Optional[Text] image_input_names = [ ] , # type: Sequence[Text] preprocessing_args = { } , # type: Dict[Text, Any] image_output_names = [ ] , # type: Sequence[Text] deprocessing_args = { } , # type: Dict[Text, Any] class_labels = None , # type: Union[Text, Iterable[Text], None] predicted_feature_name = 'classLabel' , # type: Text add_custom_layers = False , # type: bool custom_conversion_functions = { } , #type: Dict[Text, Any] onnx_coreml_input_shape_map = { } , # type: Dict[Text, List[int,...]] minimum_ios_deployment_target = '12' ) : # type: (...) -> MLModel if isinstance ( model , Text ) : onnx_model = onnx . load ( model ) elif isinstance ( model , onnx . ModelProto ) : onnx_model = model else : raise TypeError ( "Model must be file path to .onnx file or onnx loaded model" ) if not SupportedVersion . ios_support_check ( minimum_ios_deployment_target ) : raise TypeError ( '{} not supported. Please provide one of target iOS: {}' , minimum_ios_deployment_target , SupportedVersion . get_supported_ios ( ) ) global USE_SHAPE_MAPPING disable_coreml_rank5_mapping = False if SupportedVersion . is_nd_array_supported ( minimum_ios_deployment_target ) : disable_coreml_rank5_mapping = True if disable_coreml_rank5_mapping : USE_SHAPE_MAPPING = False else : USE_SHAPE_MAPPING = True '''
    First, apply a few optimizations to the ONNX graph,
    in preparation for conversion to CoreML. 
    ''' # Using Dummy transformation to conditionally disable certain transformation class DummyTransformation ( object ) : def __call__ ( self , graph ) : return graph transformers = [ ConstantsToInitializers ( ) , ShapeOpRemover ( ) , ConstantRemover ( ) , CastOpRemover ( ) , PaddingOpRemover ( ) , ReshapeInitTensorFuser ( ) , DropoutRemover ( ) , DeadCodeElimination ( ) , ConvAddFuser ( ) , BNBroadcastedMulFuser ( ) , BNBroadcastedAddFuser ( ) , ReshapeTransposeReshape_pattern1 ( ) , PixelShuffleFuser ( ) , AddModelInputsOutputs ( ) if not disable_coreml_rank5_mapping else DummyTransformation ( ) , ConstantFillToInitializers ( ) , ] # type: Iterable[Transformer] onnx_model = onnx . shape_inference . infer_shapes ( onnx_model ) graph = _prepare_onnx_graph ( onnx_model . graph , transformers , onnx_model . ir_version ) '''
    Check for ImageScalar nodes in ONNX, this will indicate whether input image preprocessing needs
    to be added to the CoreML graph or not. 
    ''' # are there ImageScaler nodes in the Graph? # If yes then add the info from it to the "preprocessing_args" dictionary, if the dictionary is not # already provided by the user if not bool ( preprocessing_args ) : for node in graph . nodes : if node . op_type == 'ImageScaler' : inp_name = node . inputs [ 0 ] scale = node . attrs . get ( 'scale' , 1.0 ) bias = node . attrs . get ( 'bias' , [ 0 , 0 , 0 ] ) if not ( len ( bias ) == 1 or len ( bias ) == 3 ) : continue if 'image_scale' in preprocessing_args : preprocessing_args [ 'image_scale' ] [ inp_name ] = scale else : preprocessing_args [ 'image_scale' ] = { inp_name : scale } if len ( bias ) == 3 : for i , color in enumerate ( [ 'red' , 'green' , 'blue' ] ) : if color + '_bias' in preprocessing_args : preprocessing_args [ color + '_bias' ] [ inp_name ] = bias [ i ] else : preprocessing_args [ color + '_bias' ] = { inp_name : bias [ i ] } else : if 'gray_bias' in preprocessing_args : preprocessing_args [ 'gray_bias' ] [ inp_name ] = bias [ 0 ] else : preprocessing_args [ 'gray_bias' ] = { inp_name : bias [ 0 ] } if inp_name not in image_input_names : image_input_names . append ( inp_name ) # type: ignore # remove all ImageScaler ops graph = graph . transformed ( [ ImageScalerRemover ( ) ] ) '''
    Gather information (name, shape) for model inputs and outputs
    This information is then used to initialize the neural network builder object of coremltools. 
    The builder object is later used to add layers to the CoreML model. 
    ''' #Make CoreML input and output features by gathering shape info and #interpreting it for CoreML input_features = _make_coreml_input_features ( graph , onnx_coreml_input_shape_map , disable_coreml_rank5_mapping ) if len ( image_output_names ) > 0 : output_features = _make_coreml_output_features ( graph , forceShape = True , disable_coreml_rank5_mapping = disable_coreml_rank5_mapping ) else : output_features = _make_coreml_output_features ( graph , disable_coreml_rank5_mapping = disable_coreml_rank5_mapping ) builder = NeuralNetworkBuilder ( input_features , output_features , mode = mode , disable_rank5_shape_mapping = disable_coreml_rank5_mapping ) # TODO: To be removed once, auto-downgrading of spec version is enabled builder . spec . specificationVersion = SupportedVersion . get_specification_version ( minimum_ios_deployment_target ) '''
    Set CoreML input,output types (float, double, int) same as onnx types, if supported
    ''' _transform_coreml_dtypes ( builder , graph . inputs , graph . outputs ) '''what follows is some book-keeping to support outputs of type image. 
    ''' is_deprocess_bgr_only = ( len ( deprocessing_args ) == 1 ) and ( "is_bgr" in deprocessing_args ) add_deprocess = ( len ( image_output_names ) > 0 ) and ( len ( deprocessing_args ) > 0 ) and ( not is_deprocess_bgr_only ) if add_deprocess : mapping = { } for f in output_features : output_name = f [ 0 ] mapping [ output_name ] = graph . get_unique_edge_name ( output_name ) graph = OutputRenamer ( mapping ) ( graph ) if len ( image_input_names ) > 0 : builder . set_pre_processing_parameters ( image_input_names = image_input_names , is_bgr = preprocessing_args . get ( 'is_bgr' , False ) , red_bias = preprocessing_args . get ( 'red_bias' , 0.0 ) , green_bias = preprocessing_args . get ( 'green_bias' , 0.0 ) , blue_bias = preprocessing_args . get ( 'blue_bias' , 0.0 ) , gray_bias = preprocessing_args . get ( 'gray_bias' , 0.0 ) , image_scale = preprocessing_args . get ( 'image_scale' , 1.0 ) ) preprocessing_args . clear ( ) if len ( image_output_names ) > 0 : for f in output_features : f_name = f [ 0 ] if f_name in image_output_names : is_bgr = deprocessing_args . get ( 'is_bgr' , False ) _convert_multiarray_output_to_image ( builder . spec , f_name , is_bgr = is_bgr ) '''
    Iterate through all the ONNX ops and translate them to CoreML layers, one by one. 
    ''' '''
    before proceeding to start the layer translation process,
    check whether there is an op in the ONNX graph, whose translation function is not yet
    implemented in the converter or which is not supported in the CoreML framework. If so, 
    raise an error before starting the process.
    (if the user desires to add a custom layer then this check is not required)
    ''' if not add_custom_layers : _check_unsupported_ops ( graph . nodes , disable_coreml_rank5_mapping ) '''
    ErrorHandling is a generic class, useful to store a variety of parameters during the conversion process  
    ''' err = ErrorHandling ( add_custom_layers , custom_conversion_functions ) for i , node in enumerate ( graph . nodes ) : print ( "%d/%d: Converting Node Type %s" % ( i + 1 , len ( graph . nodes ) , node . op_type ) ) if disable_coreml_rank5_mapping : _convert_node_nd ( builder , node , graph , err ) else : _add_const_inputs_if_required ( builder , node , graph , err ) _convert_node ( builder , node , graph , err ) if DEBUG : plot_graph ( graph , graph_img_path = '/tmp/after_conversion.pdf' , show_coreml_mapped_shapes = not disable_coreml_rank5_mapping ) if add_deprocess : for f in output_features : output_name = f [ 0 ] if output_name not in image_output_names : continue output_shape = f [ 1 ] . dimensions if len ( output_shape ) == 2 or output_shape [ 0 ] == 1 : is_grayscale = True elif output_shape [ 0 ] == 3 : is_grayscale = False else : raise ValueError ( 'Output must be RGB image or Grayscale' ) _set_deprocessing ( is_grayscale , builder , deprocessing_args , mapping [ output_name ] , output_name ) if class_labels is not None : if isinstance ( class_labels , Text ) : labels = [ l . strip ( ) for l in open ( class_labels ) . readlines ( ) ] # type: Sequence[Text] elif isinstance ( class_labels , list ) : labels = class_labels else : raise TypeError ( "synset variable of unknown type. Type found: {}. \
                Expected either string or list of strings." . format ( type ( class_labels ) , ) ) builder . set_class_labels ( class_labels = labels , predicted_feature_name = predicted_feature_name ) def _add_informative_description ( feature , raise_error = True ) : if feature . type . WhichOneof ( 'Type' ) == 'multiArrayType' : if feature . name in graph . onnx_coreml_shape_mapping and feature . name in graph . shape_dict : mapp = graph . onnx_coreml_shape_mapping [ feature . name ] onnx_shape = graph . shape_dict [ feature . name ] if raise_error : assert len ( mapp ) == len ( onnx_shape ) , "Something wrong in shape" if len ( mapp ) == len ( onnx_shape ) : shape = [ ] for i in range ( 5 ) : if i in mapp : shape += [ int ( onnx_shape [ mapp . index ( i ) ] ) ] else : shape += [ 1 ] msg = 'MultiArray of shape {}. The first and second dimensions correspond to sequence and batch size, respectively' . format ( str ( tuple ( shape ) ) ) feature . shortDescription += msg optional_input_names = [ ] for tup in graph . optional_inputs : optional_input_names . append ( tup [ 0 ] ) optional_output_names = [ ] for tup in graph . optional_outputs : optional_output_names . append ( tup [ 0 ] ) # add description for inputs and outputs shapes remove_input_id = [ ] for i , input_ in enumerate ( builder . spec . description . input ) : if input_ . name not in optional_input_names : if not disable_coreml_rank5_mapping : _add_informative_description ( input_ ) else : remove_input_id . append ( i ) remove_output_id = [ ] for i , output_ in enumerate ( builder . spec . description . output ) : if output_ . name not in optional_output_names : if not disable_coreml_rank5_mapping : _add_informative_description ( output_ , raise_error = False ) else : remove_output_id . append ( i ) for index in sorted ( remove_input_id , reverse = True ) : del builder . spec . description . input [ index ] for index in sorted ( remove_output_id , reverse = True ) : del builder . spec . description . output [ index ] if len ( graph . optional_inputs ) > 0 or len ( graph . optional_outputs ) : builder . add_optionals ( graph . optional_inputs , graph . optional_outputs ) # Check for specification version and target ios compatibility if minimum_ios_deployment_target == '11.2' and builder . spec . WhichOneof ( 'Type' ) == 'neuralNetwork' : nn_spec = builder . spec . neuralNetwork for layer in nn_spec . layers : if layer . WhichOneof ( 'layer' ) == 'resizeBilinear' or layer . WhichOneof ( 'layer' ) == 'cropResize' : raise TypeError ( '{} not supported with target iOS 11.2 please provide higher target iOS' . format ( layer . WhichOneof ( 'layer' ) ) ) # Optimize ML Model Spec ml_model_passes = [ remove_disconnected_layers , transform_conv_crop ] for opt in ml_model_passes : opt ( builder . spec ) print ( "Translation to CoreML spec completed. Now compiling the CoreML model." ) try : if DEBUG : import coremltools coremltools . models . utils . save_spec ( builder . spec , '/tmp/node_model_raw_spec.mlmodel' ) from coremltools . models . neural_network . printer import print_network_spec print_network_spec ( builder . spec , style = 'coding' ) mlmodel = MLModel ( builder . spec ) except RuntimeError as e : raise ValueError ( 'Compilation failed: {}' . format ( str ( e ) ) ) print ( 'Model Compilation done.' ) # print information about all ops for which custom layers have been added if len ( err . custom_layer_nodes ) > 0 : print ( '\n' ) print ( "Custom layers have been added to the CoreML model " "corresponding to the following ops in the onnx model: " ) for i , node in enumerate ( err . custom_layer_nodes ) : input_info = [ ] for input_ in node . inputs : input_info . append ( ( str ( input_ ) , graph . shape_dict . get ( input_ , str ( "Shape not available" ) ) ) ) output_info = [ ] for output_ in node . outputs : output_info . append ( ( str ( output_ ) , graph . shape_dict . get ( output_ , str ( "Shape not available" ) ) ) ) print ( "{}/{}: op type: {}, op input names and shapes: {}, op output names and shapes: {}" . format ( i + 1 , len ( err . custom_layer_nodes ) , node . op_type , str ( input_info ) , str ( output_info ) ) ) return mlmodel