deepsparse.utils package
Submodules
deepsparse.utils.data module
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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
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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
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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]
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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
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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
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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
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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
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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
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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
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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
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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
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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