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Moritz Pflanzerc7d15032017-07-18 16:21:16 +01001/*
2 * Copyright (c) 2017 ARM Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24#ifndef __ARM_COMPUTE_TEST_SIMPLE_TENSOR_H__
25#define __ARM_COMPUTE_TEST_SIMPLE_TENSOR_H__
26
27#include "arm_compute/core/TensorShape.h"
28#include "arm_compute/core/Types.h"
29#include "arm_compute/core/Utils.h"
30#include "support/ToolchainSupport.h"
31#include "tests/IAccessor.h"
32#include "tests/Utils.h"
33
34#include <algorithm>
35#include <array>
36#include <cstddef>
37#include <cstdint>
38#include <functional>
39#include <memory>
40#include <stdexcept>
41#include <utility>
42
43namespace arm_compute
44{
45namespace test
46{
Moritz Pflanzercde1e8a2017-09-08 09:53:14 +010047class RawTensor;
48
Moritz Pflanzerc7d15032017-07-18 16:21:16 +010049/** Simple tensor object that stores elements in a consecutive chunk of memory.
50 *
51 * It can be created by either loading an image from a file which also
52 * initialises the content of the tensor or by explcitly specifying the size.
53 * The latter leaves the content uninitialised.
54 *
55 * Furthermore, the class provides methods to convert the tensor's values into
56 * different image format.
57 */
58template <typename T>
Moritz Pflanzer82e70a12017-08-08 16:20:45 +010059class SimpleTensor : public IAccessor
Moritz Pflanzerc7d15032017-07-18 16:21:16 +010060{
61public:
62 /** Create an uninitialised tensor. */
63 SimpleTensor() = default;
64
65 /** Create an uninitialised tensor of the given @p shape and @p format.
66 *
67 * @param[in] shape Shape of the new raw tensor.
68 * @param[in] format Format of the new raw tensor.
69 * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers
70 */
71 SimpleTensor(TensorShape shape, Format format, int fixed_point_position = 0);
72
73 /** Create an uninitialised tensor of the given @p shape and @p data type.
74 *
75 * @param[in] shape Shape of the new raw tensor.
76 * @param[in] data_type Data type of the new raw tensor.
77 * @param[in] num_channels (Optional) Number of channels (default = 1).
78 * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers (default = 0).
79 */
80 SimpleTensor(TensorShape shape, DataType data_type, int num_channels = 1, int fixed_point_position = 0);
81
82 /** Create a deep copy of the given @p tensor.
83 *
84 * @param[in] tensor To be copied tensor.
85 */
86 SimpleTensor(const SimpleTensor &tensor);
87
88 /** Create a deep copy of the given @p tensor.
89 *
90 * @param[in] tensor To be copied tensor.
91 */
92 SimpleTensor &operator =(SimpleTensor tensor);
93 SimpleTensor(SimpleTensor &&) = default;
94 ~SimpleTensor() = default;
95
96 using value_type = T;
97 using Buffer = std::unique_ptr<value_type[]>;
98
Moritz Pflanzercde1e8a2017-09-08 09:53:14 +010099 friend class RawTensor;
100
Moritz Pflanzerc7d15032017-07-18 16:21:16 +0100101 /** Return value at @p offset in the buffer.
102 *
103 * @param[in] offset Offset within the buffer.
104 */
105 T &operator[](size_t offset);
106
107 /** Return constant value at @p offset in the buffer.
108 *
109 * @param[in] offset Offset within the buffer.
110 */
111 const T &operator[](size_t offset) const;
112
113 /** Shape of the tensor. */
114 TensorShape shape() const override;
115
116 /** Size of each element in the tensor in bytes. */
117 size_t element_size() const override;
118
119 /** Total size of the tensor in bytes. */
120 size_t size() const override;
121
122 /** Image format of the tensor. */
123 Format format() const override;
124
125 /** Data type of the tensor. */
126 DataType data_type() const override;
127
128 /** Number of channels of the tensor. */
129 int num_channels() const override;
130
131 /** Number of elements of the tensor. */
132 int num_elements() const override;
133
Giorgio Arenaa2611812017-07-21 10:08:48 +0100134 /** Available padding around the tensor. */
135 PaddingSize padding() const override;
136
Moritz Pflanzerc7d15032017-07-18 16:21:16 +0100137 /** The number of bits for the fractional part of the fixed point numbers. */
138 int fixed_point_position() const override;
139
140 /** Constant pointer to the underlying buffer. */
141 const T *data() const;
142
143 /** Pointer to the underlying buffer. */
144 T *data();
145
146 /** Read only access to the specified element.
147 *
148 * @param[in] coord Coordinates of the desired element.
149 *
150 * @return A pointer to the desired element.
151 */
152 const void *operator()(const Coordinates &coord) const override;
153
154 /** Access to the specified element.
155 *
156 * @param[in] coord Coordinates of the desired element.
157 *
158 * @return A pointer to the desired element.
159 */
160 void *operator()(const Coordinates &coord) override;
161
162 /** Swaps the content of the provided tensors.
163 *
164 * @param[in, out] tensor1 Tensor to be swapped.
165 * @param[in, out] tensor2 Tensor to be swapped.
166 */
167 template <typename U>
168 friend void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2);
169
Moritz Pflanzer82e70a12017-08-08 16:20:45 +0100170protected:
Moritz Pflanzerc7d15032017-07-18 16:21:16 +0100171 Buffer _buffer{ nullptr };
172 TensorShape _shape{};
173 Format _format{ Format::UNKNOWN };
174 DataType _data_type{ DataType::UNKNOWN };
175 int _num_channels{ 0 };
176 int _fixed_point_position{ 0 };
177};
178
179template <typename T>
180SimpleTensor<T>::SimpleTensor(TensorShape shape, Format format, int fixed_point_position)
181 : _buffer(nullptr),
182 _shape(shape),
183 _format(format),
184 _fixed_point_position(fixed_point_position)
185{
186 _buffer = support::cpp14::make_unique<T[]>(num_elements() * num_channels());
187}
188
189template <typename T>
190SimpleTensor<T>::SimpleTensor(TensorShape shape, DataType data_type, int num_channels, int fixed_point_position)
191 : _buffer(nullptr),
192 _shape(shape),
193 _data_type(data_type),
194 _num_channels(num_channels),
195 _fixed_point_position(fixed_point_position)
196{
197 _buffer = support::cpp14::make_unique<T[]>(num_elements() * this->num_channels());
198}
199
200template <typename T>
201SimpleTensor<T>::SimpleTensor(const SimpleTensor &tensor)
202 : _buffer(nullptr),
203 _shape(tensor.shape()),
204 _format(tensor.format()),
Moritz Pflanzer82e70a12017-08-08 16:20:45 +0100205 _data_type(tensor.data_type()),
206 _num_channels(tensor.num_channels()),
Moritz Pflanzerc7d15032017-07-18 16:21:16 +0100207 _fixed_point_position(tensor.fixed_point_position())
208{
209 _buffer = support::cpp14::make_unique<T[]>(tensor.num_elements() * num_channels());
210 std::copy_n(tensor.data(), num_elements() * num_channels(), _buffer.get());
211}
212
213template <typename T>
214SimpleTensor<T> &SimpleTensor<T>::operator=(SimpleTensor tensor)
215{
216 swap(*this, tensor);
217
218 return *this;
219}
220
221template <typename T>
222T &SimpleTensor<T>::operator[](size_t offset)
223{
224 return _buffer[offset];
225}
226
227template <typename T>
228const T &SimpleTensor<T>::operator[](size_t offset) const
229{
230 return _buffer[offset];
231}
232
233template <typename T>
234TensorShape SimpleTensor<T>::shape() const
235{
236 return _shape;
237}
238
239template <typename T>
240size_t SimpleTensor<T>::element_size() const
241{
242 return num_channels() * element_size_from_data_type(data_type());
243}
244
245template <typename T>
246int SimpleTensor<T>::fixed_point_position() const
247{
248 return _fixed_point_position;
249}
250
251template <typename T>
252size_t SimpleTensor<T>::size() const
253{
254 const size_t size = std::accumulate(_shape.cbegin(), _shape.cend(), 1, std::multiplies<size_t>());
255 return size * element_size();
256}
257
258template <typename T>
259Format SimpleTensor<T>::format() const
260{
261 return _format;
262}
263
264template <typename T>
265DataType SimpleTensor<T>::data_type() const
266{
267 if(_format != Format::UNKNOWN)
268 {
269 return data_type_from_format(_format);
270 }
271 else
272 {
273 return _data_type;
274 }
275}
276
277template <typename T>
278int SimpleTensor<T>::num_channels() const
279{
280 switch(_format)
281 {
282 case Format::U8:
283 case Format::S16:
284 case Format::U16:
285 case Format::S32:
286 case Format::U32:
Moritz Pflanzer82e70a12017-08-08 16:20:45 +0100287 case Format::F32:
Moritz Pflanzerc7d15032017-07-18 16:21:16 +0100288 return 1;
289 case Format::RGB888:
290 return 3;
291 case Format::UNKNOWN:
292 return _num_channels;
293 default:
294 ARM_COMPUTE_ERROR("NOT SUPPORTED!");
295 }
296}
297
298template <typename T>
299int SimpleTensor<T>::num_elements() const
300{
301 return _shape.total_size();
302}
303
304template <typename T>
Giorgio Arenaa2611812017-07-21 10:08:48 +0100305PaddingSize SimpleTensor<T>::padding() const
306{
307 return PaddingSize(0);
308}
309
310template <typename T>
Moritz Pflanzerc7d15032017-07-18 16:21:16 +0100311const T *SimpleTensor<T>::data() const
312{
313 return _buffer.get();
314}
315
316template <typename T>
317T *SimpleTensor<T>::data()
318{
319 return _buffer.get();
320}
321
322template <typename T>
323const void *SimpleTensor<T>::operator()(const Coordinates &coord) const
324{
325 return _buffer.get() + coord2index(_shape, coord);
326}
327
328template <typename T>
329void *SimpleTensor<T>::operator()(const Coordinates &coord)
330{
331 return _buffer.get() + coord2index(_shape, coord);
332}
333
334template <typename U>
335void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2)
336{
337 // Use unqualified call to swap to enable ADL. But make std::swap available
338 // as backup.
339 using std::swap;
340 swap(tensor1._shape, tensor2._shape);
341 swap(tensor1._format, tensor2._format);
342 swap(tensor1._data_type, tensor2._data_type);
343 swap(tensor1._num_channels, tensor2._num_channels);
344 swap(tensor1._buffer, tensor2._buffer);
345}
346} // namespace test
347} // namespace arm_compute
348#endif /* __ARM_COMPUTE_TEST_SIMPLE_TENSOR_H__ */