coremltools.models.neural_network.flexible_shape_utils¶

Utilities to annotate Neural Network Features with flexible shape information. Only available in coremltools 2.0b1 and onwards

Functions

Classes

class coremltools.models.neural_network.flexible_shape_utils.NeuralNetworkImageSize(height=None, width=None)¶

An object representing a size for an image feature inside a neural network. Valid sizess for height and width are > 0.

__init__(self, height=None, width=None)¶

class coremltools.models.neural_network.flexible_shape_utils.NeuralNetworkImageSizeRange(height_range=None, width_range=None)¶

An object representing a range of sizes for an image feature inside a neural network. Valid ranges for height and width are > 0. A “-1” upper bound value for either width or height represents an unbounded size for that dimension.

__init__(self, height_range=None, width_range=None)¶

class coremltools.models.neural_network.flexible_shape_utils.NeuralNetworkMultiArrayShape(channel=None, height=None, width=None)¶

An object representing a shape for a multiArray feature in a neural network. Valid shapes must have have only the Channel [C] shape or the Channel, Height and Width [C, H, W] shapes populated

__init__(self, channel=None, height=None, width=None)¶

class coremltools.models.neural_network.flexible_shape_utils.NeuralNetworkMultiArrayShapeRange(input_ranges=None)¶

An object representing a range of shapes for a multiArray feature in a neural network. Valid shape ranges must have have only the Channel [C] range or the Channel, Height and Width [C, H, W] ranges populated. A “-1” value in an upper bound represents an unbounded range.

__init__(self, input_ranges=None)¶

isFlexible(self)¶

Returns true if any one of the channel, height, or width ranges of this shape allow more than one input value.

coremltools.models.neural_network.flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name, sizes)¶

Annotate an input or output image feature in a Neural Network spec to to accommodate a list of enumerated image sizes

Parameters:	spec – MLModel The MLModel spec containing the feature feature_name – str The name of the image feature for which to add size information. If the feature is not found in the input or output descriptions then an exception is thrown sizes – [] | NeuralNetworkImageSize A single or a list of NeuralNetworkImageSize objects which encode valid size information for a image feature

Examples

>>> import coremltools
>>> from coremltools.models.neural_network import flexible_shape_utils
>>> spec = coremltools.utils.load_spec('mymodel.mlmodel')
>>> image_sizes = [flexible_shape_utils.NeuralNetworkImageSize(128, 128)]
>>> image_sizes.append(flexible_shape_utils.NeuralNetworkImageSize(256, 256))
>>> flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='my_multiarray_featurename', sizes=image_sizes)

Returns:	None. The spec object is updated

coremltools.models.neural_network.flexible_shape_utils.add_enumerated_multiarray_shapes(spec, feature_name, shapes)¶

Annotate an input or output multiArray feature in a Neural Network spec to to accommodate a list of enumerated array shapes

Parameters:	spec – MLModel The MLModel spec containing the feature feature_name – str The name of the image feature for which to add shape information. If the feature is not found in the input or output descriptions then an exception is thrown shapes – [] | NeuralNetworkMultiArrayShape A single or a list of NeuralNetworkImageSize objects which encode valid size information for a image feature

Examples

>>> import coremltools
>>> from coremltools.models.neural_network import flexible_shape_utils
>>> spec = coremltools.utils.load_spec('mymodel.mlmodel')
>>> array_shapes = [flexible_shape_utils.NeuralNetworkMultiArrayShape(3)]
>>> second_shape = flexible_shape_utils.NeuralNetworkMultiArrayShape()
>>> second_shape.set_channel_shape(3)
>>> second_shape.set_height_shape(10)
>>> second_shape.set_width_shape(15)
>>> array_shapes.append(second_shape)
>>> flexible_shape_utils.add_enumerated_multiarray_shapes(spec, feature_name='my_multiarray_featurename', shapes=array_shapes)

Returns:	None. The spec object is updated

coremltools.models.neural_network.flexible_shape_utils.add_multiarray_ndshape_enumeration(spec, feature_name, enumerated_shapes)¶

Annotate an input or output MLMultiArray feature in a Neural Network spec to accommodate a range of shapes. Add provided enumerated shapes to the list of shapes already present. This method is different from “add_enumerated_multiarray_shapes”, which is applicable for rank 5 mapping, SBCHW, arrays.

Parameters:	spec – MLModel The MLModel spec containing the feature feature_name – str The name of the feature for which to add shape range information. If the feature is not found in the input or output descriptions then an exception is thrown enumerated_shapes – List[Tuple(int)] list of shapes, where each shape is specified as a tuple of integers.

Examples

>>> import coremltools
>>> from coremltools.models.neural_network import flexible_shape_utils
>>> spec = coremltools.utils.load_spec('mymodel.mlmodel')
>>> # say, the default shape of "my_multiarray_featurename" is (2,3)
>>> flexible_shape_utils.add_multiarray_ndshape_enumeration(spec, feature_name='my_multiarray_featurename', enumerated_shapes=[(2,4), (2,6)])

Returns:	None. The spec is updated

coremltools.models.neural_network.flexible_shape_utils.can_allow_multiple_input_shapes(*args, **kwargs)¶

Examines a model specification and determines if it can compute results for more than one output shape.

Parameters:	spec – MLModel The protobuf specification of the model.
Returns:	Bool Returns True if the model can allow multiple input shapes, False otherwise.

coremltools.models.neural_network.flexible_shape_utils.get_allowed_shape_ranges(*args, **kwargs)¶

For a given model specification, returns a dictionary with a shape range object for each input feature name.

coremltools.models.neural_network.flexible_shape_utils.set_multiarray_ndshape_range(spec, feature_name, lower_bounds, upper_bounds)¶

Annotate an input or output MLMultiArray feature in a Neural Network spec to accommodate a range of shapes. This is different from “update_multiarray_shape_range”, which works with rank 5 SBCHW mapping.

Parameters:

spec – MLModel The MLModel spec containing the feature feature_name – str The name of the feature for which to add shape range information. If the feature is not found in the input or output descriptions then an exception is thrown lower_bounds – List[int] list of integers specifying the lower bounds of each dimension. Length must be same as the rank (length of shape) of the feature_name. upper_bounds – List[int] list of integers specifying the upper bounds of each dimension. -1 corresponds to unbounded range. Length must be same as the rank (length of shape) of the feature_name.

Examples

>>> import coremltools
>>> from coremltools.models.neural_network import flexible_shape_utils
>>> spec = coremltools.utils.load_spec('mymodel.mlmodel')
>>> # say, the default shape of "my_multiarray_featurename" is (2,3)
>>> flexible_shape_utils.set_multiarray_ndshape_range(spec, feature_name='my_multiarray_featurename', lower_bounds=[1,2], upper_bounds=[10,-1])

Returns:	None. The spec is updated

coremltools.models.neural_network.flexible_shape_utils.update_image_size_range(spec, feature_name, size_range)¶

Annotate an input or output Image feature in a Neural Network spec to to accommodate a range of image sizes

Parameters:	spec – MLModel The MLModel spec containing the feature feature_name – str The name of the Image feature for which to add shape information. If the feature is not found in the input or output descriptions then an exception is thrown size_range – NeuralNetworkImageSizeRange A NeuralNetworkImageSizeRange object with the populated image size range information.

Examples

>>> import coremltools
>>> from coremltools.models.neural_network import flexible_shape_utils
>>> spec = coremltools.utils.load_spec('mymodel.mlmodel')
>>> img_size_ranges = flexible_shape_utils.NeuralNetworkImageSizeRange()
>>> img_size_ranges.add_height_range(64, 128)
>>> img_size_ranges.add_width_range(128, -1)
>>> flexible_shape_utils.update_image_size_range(spec, feature_name='my_multiarray_featurename', size_range=img_size_ranges)

Returns:	None. The spec object is updated

coremltools.models.neural_network.flexible_shape_utils.update_multiarray_shape_range(spec, feature_name, shape_range)¶

Annotate an input or output MLMultiArray feature in a Neural Network spec to accommodate a range of shapes

Parameters:

spec – MLModel The MLModel spec containing the feature feature_name – str The name of the feature for which to add shape range information. If the feature is not found in the input or output descriptions then an exception is thrown shape_range – NeuralNetworkMultiArrayShapeRange A NeuralNetworkMultiArrayShapeRange object with the populated shape range information. The shape_range object must either contain only shape information for channel or channel, height and width. If the object is invalid then an exception is thrown

Examples

>>> import coremltools
>>> from coremltools.models.neural_network import flexible_shape_utils
>>> spec = coremltools.utils.load_spec('mymodel.mlmodel')
>>> shape_range = flexible_shape_utils.NeuralNetworkMultiArrayShapeRange()
>>> shape_range.add_channel_range((1, 3))
>>> shape_range.add_width_range((128, 256))
>>> shape_range.add_height_range((128, 256))
>>> flexible_shape_utils.update_multiarray_shape_range(spec, feature_name='my_multiarray_featurename', shape_range=shape_range)

Returns:	None. The spec is updated