1 /*
2 * Copyright (c) 2017-2020 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 "tests/IAccessor.h"
31 #include "tests/Utils.h"
32
33 #include <algorithm>
34 #include <array>
35 #include <cstddef>
36 #include <cstdint>
37 #include <functional>
38 #include <memory>
39 #include <stdexcept>
40 #include <utility>
41
42 namespace arm_compute
43 {
44 namespace test
45 {
46 class RawTensor;
47
48 /** Simple tensor object that stores elements in a consecutive chunk of memory.
49 *
50 * It can be created by either loading an image from a file which also
51 * initialises the content of the tensor or by explcitly specifying the size.
52 * The latter leaves the content uninitialised.
53 *
54 * Furthermore, the class provides methods to convert the tensor's values into
55 * different image format.
56 */
57 template <typename T>
58 class SimpleTensor : public IAccessor
59 {
60 public:
61 /** Create an uninitialised tensor. */
62 SimpleTensor() = default;
63
64 /** Create an uninitialised tensor of the given @p shape and @p format.
65 *
66 * @param[in] shape Shape of the new raw tensor.
67 * @param[in] format Format of the new raw tensor.
68 */
69 SimpleTensor(TensorShape shape, Format format);
70
71 /** Create an uninitialised tensor of the given @p shape and @p data type.
72 *
73 * @param[in] shape Shape of the new raw tensor.
74 * @param[in] data_type Data type of the new raw tensor.
75 * @param[in] num_channels (Optional) Number of channels (default = 1).
76 * @param[in] quantization_info (Optional) Quantization info for asymmetric quantization (default = empty).
77 * @param[in] data_layout (Optional) Data layout of the tensor (default = NCHW).
78 */
79 SimpleTensor(TensorShape shape, DataType data_type,
80 int num_channels = 1,
81 QuantizationInfo quantization_info = QuantizationInfo(),
82 DataLayout data_layout = DataLayout::NCHW);
83
84 /** Create a deep copy of the given @p tensor.
85 *
86 * @param[in] tensor To be copied tensor.
87 */
88 SimpleTensor(const SimpleTensor &tensor);
89
90 /** Create a deep copy of the given @p tensor.
91 *
92 * @param[in] tensor To be copied tensor.
93 *
94 * @return a copy of the given tensor.
95 */
96 SimpleTensor &operator=(SimpleTensor tensor);
97 /** Allow instances of this class to be move constructed */
98 SimpleTensor(SimpleTensor &&) = default;
99 /** Default destructor. */
100 ~SimpleTensor() = default;
101
102 /** Tensor value type */
103 using value_type = T;
104 /** Tensor buffer pointer type */
105 using Buffer = std::unique_ptr<value_type[]>;
106
107 friend class RawTensor;
108
109 /** Return value at @p offset in the buffer.
110 *
111 * @param[in] offset Offset within the buffer.
112 *
113 * @return value in the buffer.
114 */
115 T &operator[](size_t offset);
116
117 /** Return constant value at @p offset in the buffer.
118 *
119 * @param[in] offset Offset within the buffer.
120 *
121 * @return constant value in the buffer.
122 */
123 const T &operator[](size_t offset) const;
124
125 /** Shape of the tensor.
126 *
127 * @return the shape of the tensor.
128 */
129 TensorShape shape() const override;
130 /** Size of each element in the tensor in bytes.
131 *
132 * @return the size of each element in the tensor in bytes.
133 */
134 size_t element_size() const override;
135 /** Total size of the tensor in bytes.
136 *
137 * @return the total size of the tensor in bytes.
138 */
139 size_t size() const override;
140 /** Image format of the tensor.
141 *
142 * @return the format of the tensor.
143 */
144 Format format() const override;
145 /** Data layout of the tensor.
146 *
147 * @return the data layout of the tensor.
148 */
149 DataLayout data_layout() const override;
150 /** Data type of the tensor.
151 *
152 * @return the data type of the tensor.
153 */
154 DataType data_type() const override;
155 /** Number of channels of the tensor.
156 *
157 * @return the number of channels of the tensor.
158 */
159 int num_channels() const override;
160 /** Number of elements of the tensor.
161 *
162 * @return the number of elements of the tensor.
163 */
164 int num_elements() const override;
165 /** Available padding around the tensor.
166 *
167 * @return the available padding around the tensor.
168 */
169 PaddingSize padding() const override;
170 /** Quantization info in case of asymmetric quantized type
171 *
172 * @return
173 */
174 QuantizationInfo quantization_info() const override;
175
176 /** Constant pointer to the underlying buffer.
177 *
178 * @return a constant pointer to the data.
179 */
180 const T *data() const;
181
182 /** Pointer to the underlying buffer.
183 *
184 * @return a pointer to the data.
185 */
186 T *data();
187
188 /** Read only access to the specified element.
189 *
190 * @param[in] coord Coordinates of the desired element.
191 *
192 * @return A pointer to the desired element.
193 */
194 const void *operator()(const Coordinates &coord) const override;
195
196 /** Access to the specified element.
197 *
198 * @param[in] coord Coordinates of the desired element.
199 *
200 * @return A pointer to the desired element.
201 */
202 void *operator()(const Coordinates &coord) override;
203
204 /** Swaps the content of the provided tensors.
205 *
206 * @param[in, out] tensor1 Tensor to be swapped.
207 * @param[in, out] tensor2 Tensor to be swapped.
208 */
209 template <typename U>
210 friend void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2);
211
212 protected:
213 Buffer _buffer{ nullptr };
214 TensorShape _shape{};
215 Format _format{ Format::UNKNOWN };
216 DataType _data_type{ DataType::UNKNOWN };
217 int _num_channels{ 0 };
218 QuantizationInfo _quantization_info{};
219 DataLayout _data_layout{ DataLayout::UNKNOWN };
220 };
221
222 template <typename T1, typename T2>
copy_tensor(const SimpleTensor<T2> & tensor)223 SimpleTensor<T1> copy_tensor(const SimpleTensor<T2> &tensor)
224 {
225 SimpleTensor<T1> st(tensor.shape(), tensor.data_type(),
226 tensor.num_channels(),
227 tensor.quantization_info(),
228 tensor.data_layout());
229 for(size_t n = 0; n < size_t(st.num_elements()); n++)
230 {
231 st.data()[n] = static_cast<T1>(tensor.data()[n]);
232 }
233 return st;
234 }
235
236 template <typename T1, typename T2, typename std::enable_if<std::is_same<T1, T2>::value, int>::type = 0>
copy_tensor(const SimpleTensor<half> & tensor)237 SimpleTensor<T1> copy_tensor(const SimpleTensor<half> &tensor)
238 {
239 SimpleTensor<T1> st(tensor.shape(), tensor.data_type(),
240 tensor.num_channels(),
241 tensor.quantization_info(),
242 tensor.data_layout());
243 memcpy((void *)st.data(), (const void *)tensor.data(), size_t(st.num_elements() * sizeof(T1)));
244 return st;
245 }
246
247 template < typename T1, typename T2, typename std::enable_if < (std::is_same<T1, half>::value || std::is_same<T2, half>::value), int >::type = 0 >
copy_tensor(const SimpleTensor<half> & tensor)248 SimpleTensor<T1> copy_tensor(const SimpleTensor<half> &tensor)
249 {
250 SimpleTensor<T1> st(tensor.shape(), tensor.data_type(),
251 tensor.num_channels(),
252 tensor.quantization_info(),
253 tensor.data_layout());
254 for(size_t n = 0; n < size_t(st.num_elements()); n++)
255 {
256 st.data()[n] = half_float::detail::half_cast<T1, T2>(tensor.data()[n]);
257 }
258 return st;
259 }
260
261 template <typename T>
SimpleTensor(TensorShape shape,Format format)262 SimpleTensor<T>::SimpleTensor(TensorShape shape, Format format)
263 : _buffer(nullptr),
264 _shape(shape),
265 _format(format),
266 _quantization_info(),
267 _data_layout(DataLayout::NCHW)
268 {
269 _num_channels = num_channels();
270 _buffer = std::make_unique<T[]>(num_elements() * _num_channels);
271 }
272
273 template <typename T>
SimpleTensor(TensorShape shape,DataType data_type,int num_channels,QuantizationInfo quantization_info,DataLayout data_layout)274 SimpleTensor<T>::SimpleTensor(TensorShape shape, DataType data_type, int num_channels, QuantizationInfo quantization_info, DataLayout data_layout)
275 : _buffer(nullptr),
276 _shape(shape),
277 _data_type(data_type),
278 _num_channels(num_channels),
279 _quantization_info(quantization_info),
280 _data_layout(data_layout)
281 {
282 _buffer = std::make_unique<T[]>(this->_shape.total_size() * _num_channels);
283 }
284
285 template <typename T>
SimpleTensor(const SimpleTensor & tensor)286 SimpleTensor<T>::SimpleTensor(const SimpleTensor &tensor)
287 : _buffer(nullptr),
288 _shape(tensor.shape()),
289 _format(tensor.format()),
290 _data_type(tensor.data_type()),
291 _num_channels(tensor.num_channels()),
292 _quantization_info(tensor.quantization_info()),
293 _data_layout(tensor.data_layout())
294 {
295 _buffer = std::make_unique<T[]>(tensor.num_elements() * _num_channels);
296 std::copy_n(tensor.data(), this->_shape.total_size() * _num_channels, _buffer.get());
297 }
298
299 template <typename T>
300 SimpleTensor<T> &SimpleTensor<T>::operator=(SimpleTensor tensor)
301 {
302 swap(*this, tensor);
303
304 return *this;
305 }
306
307 template <typename T>
308 T &SimpleTensor<T>::operator[](size_t offset)
309 {
310 return _buffer[offset];
311 }
312
313 template <typename T>
314 const T &SimpleTensor<T>::operator[](size_t offset) const
315 {
316 return _buffer[offset];
317 }
318
319 template <typename T>
shape()320 TensorShape SimpleTensor<T>::shape() const
321 {
322 return _shape;
323 }
324
325 template <typename T>
element_size()326 size_t SimpleTensor<T>::element_size() const
327 {
328 return num_channels() * element_size_from_data_type(data_type());
329 }
330
331 template <typename T>
quantization_info()332 QuantizationInfo SimpleTensor<T>::quantization_info() const
333 {
334 return _quantization_info;
335 }
336
337 template <typename T>
size()338 size_t SimpleTensor<T>::size() const
339 {
340 const size_t size = std::accumulate(_shape.cbegin(), _shape.cend(), 1, std::multiplies<size_t>());
341 return size * element_size();
342 }
343
344 template <typename T>
format()345 Format SimpleTensor<T>::format() const
346 {
347 return _format;
348 }
349
350 template <typename T>
data_layout()351 DataLayout SimpleTensor<T>::data_layout() const
352 {
353 return _data_layout;
354 }
355
356 template <typename T>
data_type()357 DataType SimpleTensor<T>::data_type() const
358 {
359 if(_format != Format::UNKNOWN)
360 {
361 return data_type_from_format(_format);
362 }
363 else
364 {
365 return _data_type;
366 }
367 }
368
369 template <typename T>
num_channels()370 int SimpleTensor<T>::num_channels() const
371 {
372 switch(_format)
373 {
374 case Format::U8:
375 case Format::U16:
376 case Format::S16:
377 case Format::U32:
378 case Format::S32:
379 case Format::F16:
380 case Format::F32:
381 return 1;
382 // Because the U and V channels are subsampled
383 // these formats appear like having only 2 channels:
384 case Format::YUYV422:
385 case Format::UYVY422:
386 return 2;
387 case Format::UV88:
388 return 2;
389 case Format::RGB888:
390 return 3;
391 case Format::RGBA8888:
392 return 4;
393 case Format::UNKNOWN:
394 return _num_channels;
395 //Doesn't make sense for planar formats:
396 case Format::NV12:
397 case Format::NV21:
398 case Format::IYUV:
399 case Format::YUV444:
400 default:
401 return 0;
402 }
403 }
404
405 template <typename T>
num_elements()406 int SimpleTensor<T>::num_elements() const
407 {
408 return _shape.total_size();
409 }
410
411 template <typename T>
padding()412 PaddingSize SimpleTensor<T>::padding() const
413 {
414 return PaddingSize(0);
415 }
416
417 template <typename T>
data()418 const T *SimpleTensor<T>::data() const
419 {
420 return _buffer.get();
421 }
422
423 template <typename T>
data()424 T *SimpleTensor<T>::data()
425 {
426 return _buffer.get();
427 }
428
429 template <typename T>
operator()430 const void *SimpleTensor<T>::operator()(const Coordinates &coord) const
431 {
432 return _buffer.get() + coord2index(_shape, coord) * _num_channels;
433 }
434
435 template <typename T>
operator()436 void *SimpleTensor<T>::operator()(const Coordinates &coord)
437 {
438 return _buffer.get() + coord2index(_shape, coord) * _num_channels;
439 }
440
441 template <typename U>
swap(SimpleTensor<U> & tensor1,SimpleTensor<U> & tensor2)442 void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2)
443 {
444 // Use unqualified call to swap to enable ADL. But make std::swap available
445 // as backup.
446 using std::swap;
447 swap(tensor1._shape, tensor2._shape);
448 swap(tensor1._format, tensor2._format);
449 swap(tensor1._data_type, tensor2._data_type);
450 swap(tensor1._num_channels, tensor2._num_channels);
451 swap(tensor1._quantization_info, tensor2._quantization_info);
452 swap(tensor1._buffer, tensor2._buffer);
453 }
454 } // namespace test
455 } // namespace arm_compute
456 #endif /* ARM_COMPUTE_TEST_SIMPLE_TENSOR_H */
457