1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
|
# Copyright © 2019 Arm Ltd. All rights reserved.
# SPDX-License-Identifier: MIT
import numpy as np
from .._generated.pyarmnn import Tensor as annTensor, TensorInfo, DataType_QuantisedAsymm8, \
DataType_Float32, DataType_QuantisedSymm16, DataType_Signed32, DataType_Float16
class Tensor(annTensor):
"""pyArmnn Tensor object
This class overrides the swig generated Tensor class. The aim of
this is to create an easy to use public api for the Tensor object.
Memory is allocated and managed by this class, avoiding the need to manage
a separate memory area for the tensor compared to the swig generated api.
"""
def __init__(self, *args):
""" Create Tensor object.
Supported tensor data types:
DataType_QuantisedAsymm8,
DataType_QuantisedSymm16,
DataType_Signed32,
DataType_Float32,
DataType_Float16
Examples:
Create an empty tensor
>>> import pyarmnn as ann
>>> ann.Tensor()
Create tensor given tensor information
>>> ann.Tensor(ann.TensorInfo(...))
Create tensor from another tensor i.e. copy a tensor
>>> ann.Tensor(ann.Tensor())
Args:
tensor(Tensor, optional): Create Tensor from a Tensor i.e. copy.
tensor_info (TensorInfo, optional): Tensor information.
Raises:
TypeError: unsupported input data type.
ValueError: appropriate constructor could not be found with provided arguments.
"""
self.__memory_area = None
# TensorInfo as first argument, we need to create memory area manually
if len(args) > 0 and isinstance(args[0], TensorInfo):
self.__create_memory_area(args[0].GetDataType(), args[0].GetNumElements())
super().__init__(args[0], self.__memory_area.data)
# copy constructor - reference to memory area is passed from copied tensor
# and armnn's copy constructor is called
elif len(args) > 0 and isinstance(args[0], Tensor):
self.__memory_area = args[0].get_memory_area()
super().__init__(args[0])
# empty constructor
elif len(args) == 0:
super().__init__()
else:
raise ValueError('Incorrect number of arguments or type of arguments provided to create Tensor.')
def __copy__(self) -> 'Tensor':
""" Make copy of a tensor.
Make 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.Tensor()
>>> copied_tensor = copy(tensor)
Returns:
Tensor: a copy of the tensor object provided.
"""
return Tensor(self)
def __create_memory_area(self, data_type: int, num_elements: int):
""" 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_elements (int): Determines the size of the memory area that will be created.
"""
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.")
self.__memory_area = np.empty(shape=(num_elements,), dtype=np_data_type_mapping[data_type])
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
|
