Python coremltools.proto() Examples

The following are 3 code examples of coremltools.proto(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module coremltools , or try the search function .
Example #1
Source File: coreml_graph.py    From MMdnn with MIT License 5 votes vote down vote up 
def __init__(self, model):
        from coremltools.proto import Model_pb2

        # sanity check.
        if not isinstance(model, Model_pb2.Model):
            raise TypeError("Coreml layer of type %s is not supported." % type(model))
        super(CoremlGraph, self).__init__(model)
        self.model = model
Example #2
Source File: output.py    From fast-style-transfer-coreml with MIT License 5 votes vote down vote up 
def convert_multiarray_output_to_image(spec, feature_name, is_bgr=False):  
    """  
    Convert an output multiarray to be represented as an image  
    This will modify the Model_pb spec passed in.  
    Example:  
        model = coremltools.models.MLModel('MyNeuralNetwork.mlmodel')  
        spec = model.get_spec()  
        convert_multiarray_output_to_image(spec,'imageOutput',is_bgr=False)  
        newModel = coremltools.models.MLModel(spec)  
        newModel.save('MyNeuralNetworkWithImageOutput.mlmodel')  
    Parameters  
    ----------  
    spec: Model_pb  
        The specification containing the output feature to convert  
    feature_name: str  
        The name of the multiarray output feature you want to convert  
    is_bgr: boolean  
        If multiarray has 3 channels, set to True for RGB pixel order or false for BGR  
    """  
    for output in spec.description.output:  
        if output.name != feature_name:  
            continue  
        if output.type.WhichOneof('Type') != 'multiArrayType':  
            raise ValueError("%s is not a multiarray type" % output.name)  
        array_shape = tuple(output.type.multiArrayType.shape)  
        channels, height, width = array_shape  
        from coremltools.proto import FeatureTypes_pb2 as ft  
        if channels == 1:  
            output.type.imageType.colorSpace = ft.ImageFeatureType.ColorSpace.Value('GRAYSCALE')  
        elif channels == 3:  
            if is_bgr:  
                output.type.imageType.colorSpace = ft.ImageFeatureType.ColorSpace.Value('BGR')  
            else:  
                output.type.imageType.colorSpace = ft.ImageFeatureType.ColorSpace.Value('RGB')  
        else:  
            raise ValueError("Channel Value %d not supported for image inputs" % channels)  
        output.type.imageType.width = width  
        output.type.imageType.height = height
Example #3
Source File: _k_neighbors_classifier.py    From coremltools with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
def _convert_k_neighbors_classifier(model, input_name, output_name):
    """Convert the scikit KNeighborsClassifier to CoreML. Assumes initial validation of the scikit model has been done."""

    spec = coremltools.proto.Model_pb2.Model()
    spec.specificationVersion = coremltools.SPECIFICATION_VERSION

    spec.kNearestNeighborsClassifier.numberOfNeighbors.defaultValue = model.n_neighbors
    spec.kNearestNeighborsClassifier.numberOfNeighbors.range.minValue = 1
    spec.kNearestNeighborsClassifier.numberOfNeighbors.range.maxValue = _number_of_samples(
        model, spec
    )  # is there a better heuristic to use here?

    number_of_dimensions = 0
    if _is_algorithm_brute(model):
        number_of_dimensions = model._fit_X.shape[1]
        spec.kNearestNeighborsClassifier.nearestNeighborsIndex.linearIndex.MergeFromString(
            b""
        )
    elif _is_algorithm_kd_tree(model):
        npdata = np.asarray(model._tree.data)
        number_of_dimensions = get_input_dimension(model)
        spec.kNearestNeighborsClassifier.nearestNeighborsIndex.singleKdTreeIndex.leafSize = (
            model.leaf_size
        )
    else:
        raise TypeError(
            "KNeighbors algorithm not supported for CoreML conversion: {}".format(
                model.algorithm
            )
        )
    spec.kNearestNeighborsClassifier.nearestNeighborsIndex.numberOfDimensions = (
        number_of_dimensions
    )

    # Make sure the distance function is set
    spec.kNearestNeighborsClassifier.nearestNeighborsIndex.squaredEuclideanDistance.MergeFromString(
        b""
    )

    input_features = spec.description.input.add()
    input_features.name = input_name[0][0]
    input_features.type.multiArrayType.shape.extend([number_of_dimensions])
    input_features.type.multiArrayType.dataType = (
        FeatureTypes_pb2.ArrayFeatureType.FLOAT32
    )

    output_label = spec.description.output.add()
    output_label.name = output_name[0][0]

    # predictedFeatureName is required since KNN is a classifier and it should be same as outputName.
    spec.description.predictedFeatureName = output_label.name

    # Need to confirm if scikit only accepts integer labels
    output_label.type.int64Type.MergeFromString(b"")
    spec.kNearestNeighborsClassifier.uniformWeighting.MergeFromString(b"")

    _extract_training_data(model, spec)

    return spec