diff options
Diffstat (limited to 'python/pyarmnn/src/pyarmnn/_tensor/const_tensor.py')
| -rw-r--r-- | python/pyarmnn/src/pyarmnn/_tensor/const_tensor.py | 159 |
1 files changed, 159 insertions, 0 deletions
diff --git a/python/pyarmnn/src/pyarmnn/_tensor/const_tensor.py b/python/pyarmnn/src/pyarmnn/_tensor/const_tensor.py new file mode 100644 index 0000000000..9735d7a63b --- /dev/null +++ b/python/pyarmnn/src/pyarmnn/_tensor/const_tensor.py @@ -0,0 +1,159 @@ +# Copyright © 2019 Arm Ltd. All rights reserved. +# SPDX-License-Identifier: MIT +import numpy as np + +from .._generated.pyarmnn import DataType_QuantisedAsymm8, DataType_QuantisedSymm16, DataType_Signed32, \ + DataType_Float32, DataType_Float16 +from .._generated.pyarmnn import ConstTensor as AnnConstTensor, TensorInfo, Tensor + + +class ConstTensor(AnnConstTensor): + """Creates a PyArmNN ConstTensor object. + + A ConstTensor is a Tensor with an immutable data store. Typically, a ConstTensor + is used to input data into a network when running inference. + + This class overrides the swig generated Tensor class. The aim of + this is to have an easy to use public API for the ConstTensor objects. + + """ + + def __init__(self, *args): + """ + Supported tensor data types: + DataType_QuantisedAsymm8, + DataType_QuantisedSymm16, + DataType_Signed32, + DataType_Float32, + DataType_Float16 + + Examples: + Create empty ConstTensor + >>> import pyarmnn as ann + >>> ann.ConstTensor() + + Create ConstTensor given tensor info and input data + >>> input_data = ... # numpy array + >>> ann.ConstTensor(ann.TensorInfo(...), input_data) + + Create ConstTensor from another ConstTensor i.e. copy ConstTensor + >>> ann.ConstTensor(ann.ConstTensor()) + + Create ConstTensor from tensor + >>> ann.ConstTensor(ann.Tensor()) + + Args: + tensor (Tensor, optional): Create a ConstTensor from a Tensor. + const_tensor (ConstTensor, optional): Create a ConstTensor from a ConstTensor i.e. copy. + tensor_info (TensorInfo, optional): Tensor information. + input_data (ndarray): Numpy array. The numpy array will be transformed to a + buffer according to type returned by `TensorInfo.GetDataType`. + Input data values type must correspond to data type returned by + `TensorInfo.GetDataType`. + + Raises: + TypeError: Unsupported input data type. + ValueError: Unsupported tensor data type and incorrect input data size. + """ + self.__memory_area = None + + # TensorInfo as first argument and numpy array as second + if len(args) > 1 and isinstance(args[0], TensorInfo): + if isinstance(args[1], np.ndarray): + self.__create_memory_area(args[0].GetDataType(), args[0].GetNumBytes(), args[0].GetNumElements(), + args[1]) + super().__init__(args[0], self.__memory_area.data) + else: + raise TypeError('Data must be provided as a numpy array.') + + # copy constructor - reference to memory area is passed from copied const + # tensor and armnn's copy constructor is called + elif len(args) > 0 and isinstance(args[0], (ConstTensor, Tensor)): + self.__memory_area = args[0].get_memory_area() + super().__init__(args[0]) + + # empty tensor + elif len(args) == 0: + super().__init__() + + else: + raise ValueError('Incorrect number of arguments or type of arguments provided to create Const Tensor.') + + def __copy__(self) -> 'ConstTensor': + """ Make copy of a const tensor. + + Make const tensor copyable using the python copy operation. + + Note: + The tensor memory area is NOT copied. Instead, the new tensor maintains a + reference to the same memory area as the old tensor. + + Example: + Copy empty tensor + >>> from copy import copy + >>> import pyarmnn as ann + >>> tensor = ann.ConstTensor() + >>> copied_tensor = copy(tensor) + + Returns: + Tensor: a copy of the tensor object provided. + + """ + return ConstTensor(self) + + @staticmethod + def __check_size(data: np.ndarray, num_bytes: int, num_elements: int): + """ Check the size of the input data against the number of bytes provided by tensor info. + + Args: + data (ndarray): Input data. + num_bytes (int): Number of bytes required by tensor info. + num_elements: Number of elements required by tensor info. + + Raises: + ValueError: number of bytes in input data does not match tensor info. + + """ + size_in_bytes = data.nbytes + elements = data.size + + if size_in_bytes != num_bytes: + raise ValueError( + "ConstTensor requires {} bytes, {} provided. " + "Is your input array data type ({}) aligned with TensorInfo?".format(num_bytes, size_in_bytes, + data.dtype)) + elif elements != num_elements: + raise ValueError("ConstTensor requires {} elements, {} provided.".format(num_elements, elements)) + + def __create_memory_area(self, data_type: int, num_bytes: int, num_elements: int, data: np.ndarray): + """ Create the memory area used by the tensor to output its results. + + Args: + data_type (int): The type of data that will be stored in the memory area. + See DataType_*. + num_bytes (int): Determines the size of the memory area that will be created. + num_elements (int): Determines number of elements in memory area. + data (ndarray): Input data as numpy array. + + """ + np_data_type_mapping = {DataType_QuantisedAsymm8: np.uint8, + DataType_Float32: np.float32, + DataType_QuantisedSymm16: np.int16, + DataType_Signed32: np.int32, + DataType_Float16: np.float16} + + if data_type not in np_data_type_mapping: + raise ValueError("The data type provided for this Tensor is not supported: {}".format(data_type)) + + self.__check_size(data, num_bytes, num_elements) + self.__memory_area = data + self.__memory_area.flags.writeable = False + + def get_memory_area(self) -> np.ndarray: + """ Get values that are stored by the tensor. + + Returns: + ndarray: Tensor data (as numpy array). + + """ + return self.__memory_area |