deepsparse.utils package

Submodules

deepsparse.utils.data module

deepsparse.utils.data. arrays_to_bytes ( arrays : List [ numpy.array ] ) bytearray [source]
Parameters

arrays – List of numpy arrays to serialize as bytes

Returns

bytearray representation of list of numpy arrays

deepsparse.utils.data. bytes_to_arrays ( serialized_arr : bytearray ) List [ numpy.array ] [source]
Parameters

serialized_arr – bytearray representation of list of numpy arrays

Returns

List of numpy arrays decoded from input

deepsparse.utils.data. parse_input_shapes ( shape_string : str ) List [ List [ int ] ] [source]

Reduces a string representation of a list of shapes to an actual list of shapes. .. rubric:: Examples

“[1,2,3]” -> input0=[1,2,3] “[1,2,3],[4,5,6],[7,8,9]” -> input0=[1,2,3] input1=[4,5,6] input2=[7,8,9]

deepsparse.utils.data. verify_outputs ( outputs : List [ numpy.array ] , gt_outputs : List [ numpy.array ] , atol : float = 0.0008 , rtol : float = 0.0 ) List [ float ] [source]

Compares two lists of output tensors, checking that they are sufficiently similar :param outputs: List of numpy arrays, usually model outputs :param gt_outputs: List of numpy arrays, usually reference outputs :param atol: Absolute tolerance for allclose :param rtol: Relative tolerance for allclose :return: The list of max differences for each pair of outputs

deepsparse.utils.log module

deepsparse.utils.onnx module

deepsparse.utils.onnx. generate_random_inputs ( onnx_filepath : str , batch_size : Optional [ int ] = None ) List [ numpy.array ] [source]

Generate random data that matches the type and shape of ONNX model, with a batch size override :param onnx_filepath: File path to ONNX model :param batch_size: If provided, override for the batch size dimension :return: List of random tensors

deepsparse.utils.onnx. get_external_inputs ( onnx_filepath : str ) List [source]

Gather external inputs of ONNX model :param onnx_filepath: File path to ONNX model :return: List of input objects

deepsparse.utils.onnx. get_external_outputs ( onnx_filepath : str ) List [source]

Gather external outputs of ONNX model :param onnx_filepath: File path to ONNX model :return: List of output objects

deepsparse.utils.onnx. get_input_names ( onnx_filepath : str ) List [ str ] [source]

Gather names of all external inputs of ONNX model :param onnx_filepath: File path to ONNX model :return: List of string names

deepsparse.utils.onnx. get_output_names ( onnx_filepath : str ) List [ str ] [source]

Gather names of all external outputs of ONNX model :param onnx_filepath: File path to ONNX model :return: List of string names

deepsparse.utils.onnx. model_to_path ( model : Union [ str , sparsezoo.objects.model.Model , sparsezoo.objects.file.File ] ) str [source]

Deals with the various forms a model can take. Either an ONNX file, a SparseZoo model stub prefixed by ‘zoo:’, a SparseZoo Model object, or a SparseZoo ONNX File object that defines the neural network

deepsparse.utils.onnx. override_onnx_batch_size ( onnx_filepath : str , batch_size : int ) str [source]

Rewrite batch sizes of ONNX model, saving the modified model and returning its path :param onnx_filepath: File path to ONNX model :param batch_size: Override for the batch size dimension :return: File path to modified ONNX model

deepsparse.utils.onnx. override_onnx_input_shapes ( onnx_filepath : str , input_shapes : Union [ List [ int ] , List [ List [ int ] ] ] ) str [source]

Rewrite input shapes of ONNX model, saving the modified model and returning its path :param onnx_filepath: File path to ONNX model :param input_shapes: Override for model’s input shapes :return: File path to modified ONNX model

Module contents