Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1 | /* |
Michele Di Giorgio | d9eaf61 | 2020-07-08 11:12:57 +0100 | [diff] [blame] | 2 | * Copyright (c) 2017-2020 Arm Limited. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 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 | */ |
Michalis Spyrou | f464337 | 2019-11-29 16:17:13 +0000 | [diff] [blame] | 24 | #ifndef ARM_COMPUTE_TEST_TENSOR_LIBRARY_H |
| 25 | #define ARM_COMPUTE_TEST_TENSOR_LIBRARY_H |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 26 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 27 | #include "arm_compute/core/Coordinates.h" |
| 28 | #include "arm_compute/core/Error.h" |
| 29 | #include "arm_compute/core/Helpers.h" |
| 30 | #include "arm_compute/core/TensorInfo.h" |
| 31 | #include "arm_compute/core/TensorShape.h" |
| 32 | #include "arm_compute/core/Types.h" |
| 33 | #include "arm_compute/core/Window.h" |
Sang-Hoon Park | 68dd25f | 2020-10-19 16:00:11 +0100 | [diff] [blame] | 34 | #include "support/Random.h" |
Moritz Pflanzer | e49e266 | 2017-07-21 15:55:28 +0100 | [diff] [blame] | 35 | #include "tests/RawTensor.h" |
| 36 | #include "tests/TensorCache.h" |
| 37 | #include "tests/Utils.h" |
Anthony Barbier | f6705ec | 2017-09-28 12:01:10 +0100 | [diff] [blame] | 38 | #include "tests/framework/Exceptions.h" |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 39 | |
| 40 | #include <algorithm> |
| 41 | #include <cstddef> |
| 42 | #include <fstream> |
| 43 | #include <random> |
| 44 | #include <string> |
| 45 | #include <type_traits> |
SiCong Li | 86b5333 | 2017-08-23 11:02:43 +0100 | [diff] [blame] | 46 | #include <vector> |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 47 | |
| 48 | namespace arm_compute |
| 49 | { |
| 50 | namespace test |
| 51 | { |
| 52 | /** Factory class to create and fill tensors. |
| 53 | * |
| 54 | * Allows to initialise tensors from loaded images or by specifying the shape |
| 55 | * explicitly. Furthermore, provides methods to fill tensors with the content of |
| 56 | * loaded images or with random values. |
| 57 | */ |
Moritz Pflanzer | fb5aabb | 2017-07-18 14:39:55 +0100 | [diff] [blame] | 58 | class AssetsLibrary final |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 59 | { |
| 60 | public: |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 61 | using RangePair = std::pair<float, float>; |
| 62 | |
| 63 | public: |
John Richardson | 70f946b | 2017-10-02 16:52:16 +0100 | [diff] [blame] | 64 | /** Initialises the library with a @p path to the assets directory. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 65 | * Furthermore, sets the seed for the random generator to @p seed. |
| 66 | * |
John Richardson | 70f946b | 2017-10-02 16:52:16 +0100 | [diff] [blame] | 67 | * @param[in] path Path to load assets from. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 68 | * @param[in] seed Seed used to initialise the random number generator. |
| 69 | */ |
Moritz Pflanzer | fb5aabb | 2017-07-18 14:39:55 +0100 | [diff] [blame] | 70 | AssetsLibrary(std::string path, std::random_device::result_type seed); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 71 | |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 72 | /** Path to assets directory used to initialise library. |
| 73 | * |
| 74 | * @return the path to the assets directory. |
| 75 | */ |
John Richardson | 70f946b | 2017-10-02 16:52:16 +0100 | [diff] [blame] | 76 | std::string path() const; |
| 77 | |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 78 | /** Seed that is used to fill tensors with random values. |
| 79 | * |
| 80 | * @return the initial random seed. |
| 81 | */ |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 82 | std::random_device::result_type seed() const; |
| 83 | |
Giorgio Arena | fda4618 | 2017-06-16 13:57:33 +0100 | [diff] [blame] | 84 | /** Provides a tensor shape for the specified image. |
| 85 | * |
| 86 | * @param[in] name Image file used to look up the raw tensor. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 87 | * |
| 88 | * @return the tensor shape for the specified image. |
Giorgio Arena | fda4618 | 2017-06-16 13:57:33 +0100 | [diff] [blame] | 89 | */ |
| 90 | TensorShape get_image_shape(const std::string &name); |
| 91 | |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 92 | /** Provides a constant raw tensor for the specified image. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 93 | * |
| 94 | * @param[in] name Image file used to look up the raw tensor. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 95 | * |
| 96 | * @return a raw tensor for the specified image. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 97 | */ |
| 98 | const RawTensor &get(const std::string &name) const; |
| 99 | |
| 100 | /** Provides a raw tensor for the specified image. |
| 101 | * |
| 102 | * @param[in] name Image file used to look up the raw tensor. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 103 | * |
| 104 | * @return a raw tensor for the specified image. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 105 | */ |
| 106 | RawTensor get(const std::string &name); |
| 107 | |
| 108 | /** Creates an uninitialised raw tensor with the given @p data_type and @p |
| 109 | * num_channels. The shape is derived from the specified image. |
| 110 | * |
| 111 | * @param[in] name Image file used to initialise the tensor. |
| 112 | * @param[in] data_type Data type used to initialise the tensor. |
| 113 | * @param[in] num_channels Number of channels used to initialise the tensor. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 114 | * |
| 115 | * @return a raw tensor for the specified image. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 116 | */ |
| 117 | RawTensor get(const std::string &name, DataType data_type, int num_channels = 1) const; |
| 118 | |
| 119 | /** Provides a contant raw tensor for the specified image after it has been |
| 120 | * converted to @p format. |
| 121 | * |
| 122 | * @param[in] name Image file used to look up the raw tensor. |
| 123 | * @param[in] format Format used to look up the raw tensor. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 124 | * |
| 125 | * @return a raw tensor for the specified image. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 126 | */ |
| 127 | const RawTensor &get(const std::string &name, Format format) const; |
| 128 | |
| 129 | /** Provides a raw tensor for the specified image after it has been |
| 130 | * converted to @p format. |
| 131 | * |
| 132 | * @param[in] name Image file used to look up the raw tensor. |
| 133 | * @param[in] format Format used to look up the raw tensor. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 134 | * |
| 135 | * @return a raw tensor for the specified image. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 136 | */ |
| 137 | RawTensor get(const std::string &name, Format format); |
| 138 | |
| 139 | /** Provides a contant raw tensor for the specified channel after it has |
| 140 | * been extracted form the given image. |
| 141 | * |
| 142 | * @param[in] name Image file used to look up the raw tensor. |
| 143 | * @param[in] channel Channel used to look up the raw tensor. |
| 144 | * |
| 145 | * @note The channel has to be unambiguous so that the format can be |
| 146 | * inferred automatically. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 147 | * |
| 148 | * @return a raw tensor for the specified image channel. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 149 | */ |
| 150 | const RawTensor &get(const std::string &name, Channel channel) const; |
| 151 | |
| 152 | /** Provides a raw tensor for the specified channel after it has been |
| 153 | * extracted form the given image. |
| 154 | * |
| 155 | * @param[in] name Image file used to look up the raw tensor. |
| 156 | * @param[in] channel Channel used to look up the raw tensor. |
| 157 | * |
| 158 | * @note The channel has to be unambiguous so that the format can be |
| 159 | * inferred automatically. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 160 | * |
| 161 | * @return a raw tensor for the specified image channel. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 162 | */ |
| 163 | RawTensor get(const std::string &name, Channel channel); |
| 164 | |
| 165 | /** Provides a constant raw tensor for the specified channel after it has |
| 166 | * been extracted form the given image formatted to @p format. |
| 167 | * |
| 168 | * @param[in] name Image file used to look up the raw tensor. |
| 169 | * @param[in] format Format used to look up the raw tensor. |
| 170 | * @param[in] channel Channel used to look up the raw tensor. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 171 | * |
| 172 | * @return a raw tensor for the specified image channel. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 173 | */ |
| 174 | const RawTensor &get(const std::string &name, Format format, Channel channel) const; |
| 175 | |
| 176 | /** Provides a raw tensor for the specified channel after it has been |
| 177 | * extracted form the given image formatted to @p format. |
| 178 | * |
| 179 | * @param[in] name Image file used to look up the raw tensor. |
| 180 | * @param[in] format Format used to look up the raw tensor. |
| 181 | * @param[in] channel Channel used to look up the raw tensor. |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 182 | * |
| 183 | * @return a raw tensor for the specified image channel. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 184 | */ |
| 185 | RawTensor get(const std::string &name, Format format, Channel channel); |
| 186 | |
Giorgio Arena | a261181 | 2017-07-21 10:08:48 +0100 | [diff] [blame] | 187 | /** Puts garbage values all around the tensor for testing purposes |
| 188 | * |
| 189 | * @param[in, out] tensor To be filled tensor. |
| 190 | * @param[in] distribution Distribution used to fill the tensor's surroundings. |
| 191 | * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. |
| 192 | */ |
| 193 | template <typename T, typename D> |
| 194 | void fill_borders_with_garbage(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const; |
| 195 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 196 | /** Fills the specified @p tensor with random values drawn from @p |
| 197 | * distribution. |
| 198 | * |
| 199 | * @param[in, out] tensor To be filled tensor. |
| 200 | * @param[in] distribution Distribution used to fill the tensor. |
| 201 | * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. |
| 202 | * |
| 203 | * @note The @p distribution has to provide operator(Generator &) which |
| 204 | * will be used to draw samples. |
| 205 | */ |
| 206 | template <typename T, typename D> |
| 207 | void fill(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const; |
| 208 | |
Pablo Tello | e96e4f0 | 2018-12-21 16:47:23 +0000 | [diff] [blame] | 209 | template <typename T, typename D> |
| 210 | void fill_boxes(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const; |
| 211 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 212 | /** Fills the specified @p raw tensor with random values drawn from @p |
| 213 | * distribution. |
| 214 | * |
Vidhya Sudhan Loganathan | 951b8a4 | 2019-11-04 14:42:08 +0000 | [diff] [blame] | 215 | * @param[in, out] vec To be filled vector. |
| 216 | * @param[in] distribution Distribution used to fill the tensor. |
| 217 | * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. |
| 218 | * |
| 219 | * @note The @p distribution has to provide operator(Generator &) which |
| 220 | * will be used to draw samples. |
| 221 | */ |
| 222 | template <typename T, typename D> |
| 223 | void fill(std::vector<T> &vec, D &&distribution, std::random_device::result_type seed_offset) const; |
| 224 | |
| 225 | /** Fills the specified @p raw tensor with random values drawn from @p |
| 226 | * distribution. |
| 227 | * |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 228 | * @param[in, out] raw To be filled raw. |
| 229 | * @param[in] distribution Distribution used to fill the tensor. |
| 230 | * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. |
| 231 | * |
| 232 | * @note The @p distribution has to provide operator(Generator &) which |
| 233 | * will be used to draw samples. |
| 234 | */ |
| 235 | template <typename D> |
| 236 | void fill(RawTensor &raw, D &&distribution, std::random_device::result_type seed_offset) const; |
| 237 | |
| 238 | /** Fills the specified @p tensor with the content of the specified image |
| 239 | * converted to the given format. |
| 240 | * |
| 241 | * @param[in, out] tensor To be filled tensor. |
| 242 | * @param[in] name Image file used to fill the tensor. |
| 243 | * @param[in] format Format of the image used to fill the tensor. |
| 244 | * |
| 245 | * @warning No check is performed that the specified format actually |
| 246 | * matches the format of the tensor. |
| 247 | */ |
| 248 | template <typename T> |
| 249 | void fill(T &&tensor, const std::string &name, Format format) const; |
| 250 | |
| 251 | /** Fills the raw tensor with the content of the specified image |
| 252 | * converted to the given format. |
| 253 | * |
| 254 | * @param[in, out] raw To be filled raw tensor. |
| 255 | * @param[in] name Image file used to fill the tensor. |
| 256 | * @param[in] format Format of the image used to fill the tensor. |
| 257 | * |
| 258 | * @warning No check is performed that the specified format actually |
| 259 | * matches the format of the tensor. |
| 260 | */ |
| 261 | void fill(RawTensor &raw, const std::string &name, Format format) const; |
| 262 | |
| 263 | /** Fills the specified @p tensor with the content of the specified channel |
| 264 | * extracted from the given image. |
| 265 | * |
| 266 | * @param[in, out] tensor To be filled tensor. |
| 267 | * @param[in] name Image file used to fill the tensor. |
| 268 | * @param[in] channel Channel of the image used to fill the tensor. |
| 269 | * |
| 270 | * @note The channel has to be unambiguous so that the format can be |
| 271 | * inferred automatically. |
| 272 | * |
| 273 | * @warning No check is performed that the specified format actually |
| 274 | * matches the format of the tensor. |
| 275 | */ |
| 276 | template <typename T> |
| 277 | void fill(T &&tensor, const std::string &name, Channel channel) const; |
| 278 | |
| 279 | /** Fills the raw tensor with the content of the specified channel |
| 280 | * extracted from the given image. |
| 281 | * |
| 282 | * @param[in, out] raw To be filled raw tensor. |
| 283 | * @param[in] name Image file used to fill the tensor. |
| 284 | * @param[in] channel Channel of the image used to fill the tensor. |
| 285 | * |
| 286 | * @note The channel has to be unambiguous so that the format can be |
| 287 | * inferred automatically. |
| 288 | * |
| 289 | * @warning No check is performed that the specified format actually |
| 290 | * matches the format of the tensor. |
| 291 | */ |
| 292 | void fill(RawTensor &raw, const std::string &name, Channel channel) const; |
| 293 | |
| 294 | /** Fills the specified @p tensor with the content of the specified channel |
| 295 | * extracted from the given image after it has been converted to the given |
| 296 | * format. |
| 297 | * |
| 298 | * @param[in, out] tensor To be filled tensor. |
| 299 | * @param[in] name Image file used to fill the tensor. |
| 300 | * @param[in] format Format of the image used to fill the tensor. |
| 301 | * @param[in] channel Channel of the image used to fill the tensor. |
| 302 | * |
| 303 | * @warning No check is performed that the specified format actually |
| 304 | * matches the format of the tensor. |
| 305 | */ |
| 306 | template <typename T> |
| 307 | void fill(T &&tensor, const std::string &name, Format format, Channel channel) const; |
| 308 | |
| 309 | /** Fills the raw tensor with the content of the specified channel |
| 310 | * extracted from the given image after it has been converted to the given |
| 311 | * format. |
| 312 | * |
| 313 | * @param[in, out] raw To be filled raw tensor. |
| 314 | * @param[in] name Image file used to fill the tensor. |
| 315 | * @param[in] format Format of the image used to fill the tensor. |
| 316 | * @param[in] channel Channel of the image used to fill the tensor. |
| 317 | * |
| 318 | * @warning No check is performed that the specified format actually |
| 319 | * matches the format of the tensor. |
| 320 | */ |
| 321 | void fill(RawTensor &raw, const std::string &name, Format format, Channel channel) const; |
| 322 | |
Alex Gilday | 345ab18 | 2018-01-09 11:40:19 +0000 | [diff] [blame] | 323 | /** Fills the specified @p tensor with the content of the raw tensor. |
| 324 | * |
| 325 | * @param[in, out] tensor To be filled tensor. |
| 326 | * @param[in] raw Raw tensor used to fill the tensor. |
| 327 | * |
| 328 | * @warning No check is performed that the specified format actually |
| 329 | * matches the format of the tensor. |
| 330 | */ |
| 331 | template <typename T> |
| 332 | void fill(T &&tensor, RawTensor raw) const; |
| 333 | |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 334 | /** Fill a tensor with uniform distribution |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 335 | * |
| 336 | * @param[in, out] tensor To be filled tensor. |
| 337 | * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. |
| 338 | */ |
| 339 | template <typename T> |
| 340 | void fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset) const; |
| 341 | |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 342 | /** Fill a tensor with uniform distribution |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 343 | * |
| 344 | * @param[in, out] tensor To be filled tensor. |
| 345 | * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. |
| 346 | * @param[in] low lowest value in the range (inclusive) |
| 347 | * @param[in] high highest value in the range (inclusive) |
| 348 | * |
| 349 | * @note @p low and @p high must be of the same type as the data type of @p tensor |
| 350 | */ |
| 351 | template <typename T, typename D> |
| 352 | void fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset, D low, D high) const; |
| 353 | |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 354 | /** Fill a tensor with uniform distribution across the specified range |
| 355 | * |
| 356 | * @param[in, out] tensor To be filled tensor. |
| 357 | * @param[in] seed_offset The offset will be added to the global seed before initialising the random generator. |
| 358 | * @param[in] excluded_range_pairs Ranges to exclude from the generator |
| 359 | */ |
| 360 | template <typename T> |
| 361 | void fill_tensor_uniform_ranged(T &&tensor, |
| 362 | std::random_device::result_type seed_offset, |
| 363 | const std::vector<AssetsLibrary::RangePair> &excluded_range_pairs) const; |
| 364 | |
SiCong Li | 86b5333 | 2017-08-23 11:02:43 +0100 | [diff] [blame] | 365 | /** Fills the specified @p tensor with data loaded from .npy (numpy binary) in specified path. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 366 | * |
| 367 | * @param[in, out] tensor To be filled tensor. |
| 368 | * @param[in] name Data file. |
SiCong Li | 86b5333 | 2017-08-23 11:02:43 +0100 | [diff] [blame] | 369 | * |
| 370 | * @note The numpy array stored in the binary .npy file must be row-major in the sense that it |
| 371 | * must store elements within a row consecutively in the memory, then rows within a 2D slice, |
| 372 | * then 2D slices within a 3D slice and so on. Note that it imposes no restrictions on what |
| 373 | * indexing convention is used in the numpy array. That is, the numpy array can be either fortran |
| 374 | * style or C style as long as it adheres to the rule above. |
| 375 | * |
| 376 | * More concretely, the orders of dimensions for each style are as follows: |
| 377 | * C-style (numpy default): |
| 378 | * array[HigherDims..., Z, Y, X] |
| 379 | * Fortran style: |
| 380 | * array[X, Y, Z, HigherDims...] |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 381 | */ |
| 382 | template <typename T> |
| 383 | void fill_layer_data(T &&tensor, std::string name) const; |
| 384 | |
Michele Di Giorgio | 4d33630 | 2018-03-02 09:43:54 +0000 | [diff] [blame] | 385 | /** Fill a tensor with a constant value |
| 386 | * |
| 387 | * @param[in, out] tensor To be filled tensor. |
| 388 | * @param[in] value Value to be assigned to all elements of the input tensor. |
| 389 | * |
| 390 | * @note @p value must be of the same type as the data type of @p tensor |
| 391 | */ |
| 392 | template <typename T, typename D> |
| 393 | void fill_tensor_value(T &&tensor, D value) const; |
| 394 | |
Sang-Hoon Park | a8a7c1d | 2020-05-12 22:01:23 +0100 | [diff] [blame] | 395 | /** Fill a tensor with a given vector with static values. |
| 396 | * |
| 397 | * @param[in, out] tensor To be filled tensor. |
| 398 | * @param[in] values A vector containing values |
| 399 | * |
| 400 | * To cope with various size tensors, the vector size doens't have to be |
| 401 | * the same as tensor's size. If the size of the tensor is larger than the vector, |
| 402 | * the iterator the vector will keep iterating and wrap around. If the vector is |
| 403 | * larger, values located after the required size won't be used. |
| 404 | */ |
| 405 | template <typename T, typename DataType> |
| 406 | void fill_static_values(T &&tensor, const std::vector<DataType> &values) const; |
| 407 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 408 | private: |
| 409 | // Function prototype to convert between image formats. |
| 410 | using Converter = void (*)(const RawTensor &src, RawTensor &dst); |
| 411 | // Function prototype to extract a channel from an image. |
| 412 | using Extractor = void (*)(const RawTensor &src, RawTensor &dst); |
| 413 | // Function prototype to load an image file. |
| 414 | using Loader = RawTensor (*)(const std::string &path); |
Sang-Hoon Park | a8a7c1d | 2020-05-12 22:01:23 +0100 | [diff] [blame] | 415 | // Function type to generate a number to fill tensors. |
| 416 | template <typename ResultType> |
| 417 | using GeneratorFunctionType = std::function<ResultType(void)>; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 418 | |
| 419 | const Converter &get_converter(Format src, Format dst) const; |
| 420 | const Converter &get_converter(DataType src, Format dst) const; |
| 421 | const Converter &get_converter(Format src, DataType dst) const; |
| 422 | const Converter &get_converter(DataType src, DataType dst) const; |
| 423 | const Extractor &get_extractor(Format format, Channel) const; |
| 424 | const Loader &get_loader(const std::string &extension) const; |
| 425 | |
| 426 | /** Creates a raw tensor from the specified image. |
| 427 | * |
| 428 | * @param[in] name To be loaded image file. |
| 429 | * |
| 430 | * @note If use_single_image is true @p name is ignored and the user image |
| 431 | * is loaded instead. |
| 432 | */ |
| 433 | RawTensor load_image(const std::string &name) const; |
| 434 | |
| 435 | /** Provides a raw tensor for the specified image and format. |
| 436 | * |
| 437 | * @param[in] name Image file used to look up the raw tensor. |
| 438 | * @param[in] format Format used to look up the raw tensor. |
| 439 | * |
| 440 | * If the tensor has already been requested before the cached version will |
| 441 | * be returned. Otherwise the tensor will be added to the cache. |
| 442 | * |
| 443 | * @note If use_single_image is true @p name is ignored and the user image |
| 444 | * is loaded instead. |
| 445 | */ |
| 446 | const RawTensor &find_or_create_raw_tensor(const std::string &name, Format format) const; |
| 447 | |
| 448 | /** Provides a raw tensor for the specified image, format and channel. |
| 449 | * |
| 450 | * @param[in] name Image file used to look up the raw tensor. |
| 451 | * @param[in] format Format used to look up the raw tensor. |
| 452 | * @param[in] channel Channel used to look up the raw tensor. |
| 453 | * |
| 454 | * If the tensor has already been requested before the cached version will |
| 455 | * be returned. Otherwise the tensor will be added to the cache. |
| 456 | * |
| 457 | * @note If use_single_image is true @p name is ignored and the user image |
| 458 | * is loaded instead. |
| 459 | */ |
| 460 | const RawTensor &find_or_create_raw_tensor(const std::string &name, Format format, Channel channel) const; |
| 461 | |
Sang-Hoon Park | a8a7c1d | 2020-05-12 22:01:23 +0100 | [diff] [blame] | 462 | /** Fill a tensor with a value generator function. |
| 463 | * |
| 464 | * @param[in, out] tensor To be filled tensor. |
| 465 | * @param[in] generate_value A function that generates values. |
| 466 | */ |
| 467 | template <typename T, typename ResultType> |
| 468 | void fill_with_generator(T &&tensor, const GeneratorFunctionType<ResultType> &generate_value) const; |
| 469 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 470 | mutable TensorCache _cache{}; |
Georgios Pinitas | 421405b | 2018-10-26 19:05:32 +0100 | [diff] [blame] | 471 | mutable arm_compute::Mutex _format_lock{}; |
| 472 | mutable arm_compute::Mutex _channel_lock{}; |
Anthony Barbier | ac69aa1 | 2017-07-03 17:39:37 +0100 | [diff] [blame] | 473 | const std::string _library_path; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 474 | std::random_device::result_type _seed; |
| 475 | }; |
| 476 | |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 477 | namespace detail |
| 478 | { |
| 479 | template <typename T> |
| 480 | inline std::vector<std::pair<T, T>> convert_range_pair(const std::vector<AssetsLibrary::RangePair> &excluded_range_pairs) |
| 481 | { |
| 482 | std::vector<std::pair<T, T>> converted; |
| 483 | std::transform(excluded_range_pairs.begin(), |
| 484 | excluded_range_pairs.end(), |
| 485 | std::back_inserter(converted), |
| 486 | [](const AssetsLibrary::RangePair & p) |
| 487 | { |
| 488 | return std::pair<T, T>(static_cast<T>(p.first), static_cast<T>(p.second)); |
| 489 | }); |
| 490 | return converted; |
| 491 | } |
Matthew Bentham | 470bc1e | 2020-03-09 10:55:40 +0000 | [diff] [blame] | 492 | |
| 493 | /* Read npy header and check the payload is suitable for the specified type and shape |
| 494 | * |
| 495 | * @param[in] stream ifstream of the npy file |
| 496 | * @param[in] expect_typestr Expected typestr |
| 497 | * @param[in] expect_shape Shape of tensor expected to receive the data |
| 498 | * |
| 499 | * @note Advances stream to the beginning of the data payload |
| 500 | */ |
| 501 | void validate_npy_header(std::ifstream &stream, const std::string &expect_typestr, const TensorShape &expect_shape); |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 502 | } // namespace detail |
| 503 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 504 | template <typename T, typename D> |
Giorgio Arena | a261181 | 2017-07-21 10:08:48 +0100 | [diff] [blame] | 505 | void AssetsLibrary::fill_borders_with_garbage(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const |
| 506 | { |
| 507 | const PaddingSize padding_size = tensor.padding(); |
| 508 | |
| 509 | Window window; |
| 510 | window.set(0, Window::Dimension(-padding_size.left, tensor.shape()[0] + padding_size.right, 1)); |
Gian Marco | 5420b28 | 2017-11-29 10:41:38 +0000 | [diff] [blame] | 511 | if(tensor.shape().num_dimensions() > 1) |
| 512 | { |
| 513 | window.set(1, Window::Dimension(-padding_size.top, tensor.shape()[1] + padding_size.bottom, 1)); |
| 514 | } |
Giorgio Arena | a261181 | 2017-07-21 10:08:48 +0100 | [diff] [blame] | 515 | |
Michalis Spyrou | 6bff195 | 2019-10-02 17:22:11 +0100 | [diff] [blame] | 516 | std::mt19937 gen(_seed + seed_offset); |
Giorgio Arena | a261181 | 2017-07-21 10:08:48 +0100 | [diff] [blame] | 517 | |
| 518 | execute_window_loop(window, [&](const Coordinates & id) |
| 519 | { |
| 520 | TensorShape shape = tensor.shape(); |
| 521 | |
| 522 | // If outside of valid region |
| 523 | if(id.x() < 0 || id.x() >= static_cast<int>(shape.x()) || id.y() < 0 || id.y() >= static_cast<int>(shape.y())) |
| 524 | { |
| 525 | using ResultType = typename std::remove_reference<D>::type::result_type; |
| 526 | const ResultType value = distribution(gen); |
| 527 | void *const out_ptr = tensor(id); |
| 528 | store_value_with_data_type(out_ptr, value, tensor.data_type()); |
| 529 | } |
| 530 | }); |
| 531 | } |
| 532 | |
| 533 | template <typename T, typename D> |
Pablo Tello | e96e4f0 | 2018-12-21 16:47:23 +0000 | [diff] [blame] | 534 | void AssetsLibrary::fill_boxes(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const |
| 535 | { |
| 536 | using ResultType = typename std::remove_reference<D>::type::result_type; |
Michalis Spyrou | fae513c | 2019-10-16 17:41:33 +0100 | [diff] [blame] | 537 | std::mt19937 gen(_seed + seed_offset); |
| 538 | TensorShape shape(tensor.shape()); |
| 539 | const uint32_t num_boxes = tensor.num_elements() / 4; |
Pablo Tello | e96e4f0 | 2018-12-21 16:47:23 +0000 | [diff] [blame] | 540 | // Iterate over all elements |
| 541 | std::uniform_real_distribution<> size_dist(0.f, 1.f); |
Michalis Spyrou | fae513c | 2019-10-16 17:41:33 +0100 | [diff] [blame] | 542 | for(uint32_t element_idx = 0; element_idx < num_boxes * 4; element_idx += 4) |
Pablo Tello | e96e4f0 | 2018-12-21 16:47:23 +0000 | [diff] [blame] | 543 | { |
| 544 | const ResultType delta = size_dist(gen); |
| 545 | const ResultType epsilon = size_dist(gen); |
| 546 | const ResultType left = distribution(gen); |
| 547 | const ResultType top = distribution(gen); |
| 548 | const ResultType right = left + delta; |
| 549 | const ResultType bottom = top + epsilon; |
| 550 | const std::tuple<ResultType, ResultType, ResultType, ResultType> box(left, top, right, bottom); |
| 551 | Coordinates x1 = index2coord(shape, element_idx); |
| 552 | Coordinates y1 = index2coord(shape, element_idx + 1); |
| 553 | Coordinates x2 = index2coord(shape, element_idx + 2); |
| 554 | Coordinates y2 = index2coord(shape, element_idx + 3); |
| 555 | ResultType &target_value_x1 = reinterpret_cast<ResultType *>(tensor(x1))[0]; |
| 556 | ResultType &target_value_y1 = reinterpret_cast<ResultType *>(tensor(y1))[0]; |
| 557 | ResultType &target_value_x2 = reinterpret_cast<ResultType *>(tensor(x2))[0]; |
| 558 | ResultType &target_value_y2 = reinterpret_cast<ResultType *>(tensor(y2))[0]; |
| 559 | store_value_with_data_type(&target_value_x1, std::get<0>(box), tensor.data_type()); |
| 560 | store_value_with_data_type(&target_value_y1, std::get<1>(box), tensor.data_type()); |
| 561 | store_value_with_data_type(&target_value_x2, std::get<2>(box), tensor.data_type()); |
| 562 | store_value_with_data_type(&target_value_y2, std::get<3>(box), tensor.data_type()); |
| 563 | } |
| 564 | fill_borders_with_garbage(tensor, distribution, seed_offset); |
| 565 | } |
| 566 | |
| 567 | template <typename T, typename D> |
Vidhya Sudhan Loganathan | 951b8a4 | 2019-11-04 14:42:08 +0000 | [diff] [blame] | 568 | void AssetsLibrary::fill(std::vector<T> &vec, D &&distribution, std::random_device::result_type seed_offset) const |
| 569 | { |
| 570 | ARM_COMPUTE_ERROR_ON_MSG(vec.empty(), "Vector must not be empty"); |
| 571 | |
| 572 | using ResultType = typename std::remove_reference<D>::type::result_type; |
| 573 | |
| 574 | std::mt19937 gen(_seed + seed_offset); |
| 575 | for(size_t i = 0; i < vec.size(); ++i) |
| 576 | { |
| 577 | const ResultType value = distribution(gen); |
| 578 | |
| 579 | vec[i] = value; |
| 580 | } |
| 581 | } |
| 582 | |
Sang-Hoon Park | a8a7c1d | 2020-05-12 22:01:23 +0100 | [diff] [blame] | 583 | template <typename T, typename ResultType> |
| 584 | void AssetsLibrary::fill_with_generator(T &&tensor, const GeneratorFunctionType<ResultType> &generate_value) const |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 585 | { |
Giorgio Arena | 563494c | 2018-04-30 17:29:41 +0100 | [diff] [blame] | 586 | const bool is_nhwc = tensor.data_layout() == DataLayout::NHWC; |
| 587 | TensorShape shape(tensor.shape()); |
| 588 | |
| 589 | if(is_nhwc) |
| 590 | { |
| 591 | // Ensure that the equivalent tensors will be filled for both data layouts |
| 592 | permute(shape, PermutationVector(1U, 2U, 0U)); |
| 593 | } |
| 594 | |
Ioan-Cristian Szabo | 9414f64 | 2017-10-27 17:35:40 +0100 | [diff] [blame] | 595 | // Iterate over all elements |
Michalis Spyrou | fae513c | 2019-10-16 17:41:33 +0100 | [diff] [blame] | 596 | const uint32_t num_elements = tensor.num_elements(); |
| 597 | for(uint32_t element_idx = 0; element_idx < num_elements; ++element_idx) |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 598 | { |
Giorgio Arena | 563494c | 2018-04-30 17:29:41 +0100 | [diff] [blame] | 599 | Coordinates id = index2coord(shape, element_idx); |
| 600 | |
| 601 | if(is_nhwc) |
| 602 | { |
| 603 | // Write in the correct id for permuted shapes |
| 604 | permute(id, PermutationVector(2U, 0U, 1U)); |
| 605 | } |
Ioan-Cristian Szabo | 9414f64 | 2017-10-27 17:35:40 +0100 | [diff] [blame] | 606 | |
| 607 | // Iterate over all channels |
| 608 | for(int channel = 0; channel < tensor.num_channels(); ++channel) |
| 609 | { |
Sang-Hoon Park | a8a7c1d | 2020-05-12 22:01:23 +0100 | [diff] [blame] | 610 | const ResultType value = generate_value(); |
Kohei Takahashi | cedb78f | 2018-08-23 10:23:52 +0900 | [diff] [blame] | 611 | ResultType &target_value = reinterpret_cast<ResultType *>(tensor(id))[channel]; |
Ioan-Cristian Szabo | 9414f64 | 2017-10-27 17:35:40 +0100 | [diff] [blame] | 612 | |
| 613 | store_value_with_data_type(&target_value, value, tensor.data_type()); |
| 614 | } |
| 615 | } |
Sang-Hoon Park | a8a7c1d | 2020-05-12 22:01:23 +0100 | [diff] [blame] | 616 | } |
Giorgio Arena | a261181 | 2017-07-21 10:08:48 +0100 | [diff] [blame] | 617 | |
Sang-Hoon Park | a8a7c1d | 2020-05-12 22:01:23 +0100 | [diff] [blame] | 618 | template <typename T, typename D> |
| 619 | void AssetsLibrary::fill(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const |
| 620 | { |
| 621 | using ResultType = typename std::remove_reference<D>::type::result_type; |
| 622 | std::mt19937 gen(_seed + seed_offset); |
| 623 | |
| 624 | GeneratorFunctionType<ResultType> number_generator = [&]() |
| 625 | { |
| 626 | const ResultType value = distribution(gen); |
| 627 | return value; |
| 628 | }; |
| 629 | |
| 630 | fill_with_generator(tensor, number_generator); |
Giorgio Arena | a261181 | 2017-07-21 10:08:48 +0100 | [diff] [blame] | 631 | fill_borders_with_garbage(tensor, distribution, seed_offset); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 632 | } |
| 633 | |
Sang-Hoon Park | a8a7c1d | 2020-05-12 22:01:23 +0100 | [diff] [blame] | 634 | template <typename T, typename DataType> |
| 635 | void AssetsLibrary::fill_static_values(T &&tensor, const std::vector<DataType> &values) const |
| 636 | { |
| 637 | auto it = values.begin(); |
| 638 | GeneratorFunctionType<DataType> get_next_value = [&]() |
| 639 | { |
| 640 | const DataType value = *it; |
| 641 | ++it; |
| 642 | |
| 643 | if(it == values.end()) |
| 644 | { |
| 645 | it = values.begin(); |
| 646 | } |
| 647 | |
| 648 | return value; |
| 649 | }; |
| 650 | |
| 651 | fill_with_generator(tensor, get_next_value); |
| 652 | } |
| 653 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 654 | template <typename D> |
Moritz Pflanzer | fb5aabb | 2017-07-18 14:39:55 +0100 | [diff] [blame] | 655 | void AssetsLibrary::fill(RawTensor &raw, D &&distribution, std::random_device::result_type seed_offset) const |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 656 | { |
| 657 | std::mt19937 gen(_seed + seed_offset); |
| 658 | |
| 659 | for(size_t offset = 0; offset < raw.size(); offset += raw.element_size()) |
| 660 | { |
| 661 | using ResultType = typename std::remove_reference<D>::type::result_type; |
| 662 | const ResultType value = distribution(gen); |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 663 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 664 | store_value_with_data_type(raw.data() + offset, value, raw.data_type()); |
| 665 | } |
| 666 | } |
| 667 | |
| 668 | template <typename T> |
Moritz Pflanzer | fb5aabb | 2017-07-18 14:39:55 +0100 | [diff] [blame] | 669 | void AssetsLibrary::fill(T &&tensor, const std::string &name, Format format) const |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 670 | { |
| 671 | const RawTensor &raw = get(name, format); |
| 672 | |
| 673 | for(size_t offset = 0; offset < raw.size(); offset += raw.element_size()) |
| 674 | { |
| 675 | const Coordinates id = index2coord(raw.shape(), offset / raw.element_size()); |
| 676 | |
Moritz Pflanzer | 82e70a1 | 2017-08-08 16:20:45 +0100 | [diff] [blame] | 677 | const RawTensor::value_type *const raw_ptr = raw.data() + offset; |
| 678 | const auto out_ptr = static_cast<RawTensor::value_type *>(tensor(id)); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 679 | std::copy_n(raw_ptr, raw.element_size(), out_ptr); |
| 680 | } |
| 681 | } |
| 682 | |
| 683 | template <typename T> |
Moritz Pflanzer | fb5aabb | 2017-07-18 14:39:55 +0100 | [diff] [blame] | 684 | void AssetsLibrary::fill(T &&tensor, const std::string &name, Channel channel) const |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 685 | { |
| 686 | fill(std::forward<T>(tensor), name, get_format_for_channel(channel), channel); |
| 687 | } |
| 688 | |
| 689 | template <typename T> |
Moritz Pflanzer | fb5aabb | 2017-07-18 14:39:55 +0100 | [diff] [blame] | 690 | void AssetsLibrary::fill(T &&tensor, const std::string &name, Format format, Channel channel) const |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 691 | { |
| 692 | const RawTensor &raw = get(name, format, channel); |
| 693 | |
| 694 | for(size_t offset = 0; offset < raw.size(); offset += raw.element_size()) |
| 695 | { |
| 696 | const Coordinates id = index2coord(raw.shape(), offset / raw.element_size()); |
| 697 | |
Moritz Pflanzer | 82e70a1 | 2017-08-08 16:20:45 +0100 | [diff] [blame] | 698 | const RawTensor::value_type *const raw_ptr = raw.data() + offset; |
| 699 | const auto out_ptr = static_cast<RawTensor::value_type *>(tensor(id)); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 700 | std::copy_n(raw_ptr, raw.element_size(), out_ptr); |
| 701 | } |
| 702 | } |
| 703 | |
| 704 | template <typename T> |
Alex Gilday | 345ab18 | 2018-01-09 11:40:19 +0000 | [diff] [blame] | 705 | void AssetsLibrary::fill(T &&tensor, RawTensor raw) const |
| 706 | { |
| 707 | for(size_t offset = 0; offset < raw.size(); offset += raw.element_size()) |
| 708 | { |
| 709 | const Coordinates id = index2coord(raw.shape(), offset / raw.element_size()); |
| 710 | |
| 711 | const RawTensor::value_type *const raw_ptr = raw.data() + offset; |
| 712 | const auto out_ptr = static_cast<RawTensor::value_type *>(tensor(id)); |
| 713 | std::copy_n(raw_ptr, raw.element_size(), out_ptr); |
| 714 | } |
| 715 | } |
| 716 | |
| 717 | template <typename T> |
Moritz Pflanzer | fb5aabb | 2017-07-18 14:39:55 +0100 | [diff] [blame] | 718 | void AssetsLibrary::fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset) const |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 719 | { |
| 720 | switch(tensor.data_type()) |
| 721 | { |
| 722 | case DataType::U8: |
Anton Lokhmotov | af6204c | 2017-11-08 09:34:19 +0000 | [diff] [blame] | 723 | case DataType::QASYMM8: |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 724 | { |
| 725 | std::uniform_int_distribution<uint8_t> distribution_u8(std::numeric_limits<uint8_t>::lowest(), std::numeric_limits<uint8_t>::max()); |
| 726 | fill(tensor, distribution_u8, seed_offset); |
| 727 | break; |
| 728 | } |
| 729 | case DataType::S8: |
Georgios Pinitas | 3d13af8 | 2019-06-04 13:04:16 +0100 | [diff] [blame] | 730 | case DataType::QSYMM8: |
Georgios Pinitas | 8217c8e | 2019-11-11 18:24:22 +0000 | [diff] [blame] | 731 | case DataType::QSYMM8_PER_CHANNEL: |
Georgios Pinitas | dbdea0d | 2019-10-16 19:21:40 +0100 | [diff] [blame] | 732 | case DataType::QASYMM8_SIGNED: |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 733 | { |
| 734 | std::uniform_int_distribution<int8_t> distribution_s8(std::numeric_limits<int8_t>::lowest(), std::numeric_limits<int8_t>::max()); |
| 735 | fill(tensor, distribution_s8, seed_offset); |
| 736 | break; |
| 737 | } |
| 738 | case DataType::U16: |
| 739 | { |
| 740 | std::uniform_int_distribution<uint16_t> distribution_u16(std::numeric_limits<uint16_t>::lowest(), std::numeric_limits<uint16_t>::max()); |
| 741 | fill(tensor, distribution_u16, seed_offset); |
| 742 | break; |
| 743 | } |
| 744 | case DataType::S16: |
Manuel Bottini | 3689fcd | 2019-06-14 17:18:12 +0100 | [diff] [blame] | 745 | case DataType::QSYMM16: |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 746 | { |
| 747 | std::uniform_int_distribution<int16_t> distribution_s16(std::numeric_limits<int16_t>::lowest(), std::numeric_limits<int16_t>::max()); |
| 748 | fill(tensor, distribution_s16, seed_offset); |
| 749 | break; |
| 750 | } |
| 751 | case DataType::U32: |
| 752 | { |
| 753 | std::uniform_int_distribution<uint32_t> distribution_u32(std::numeric_limits<uint32_t>::lowest(), std::numeric_limits<uint32_t>::max()); |
| 754 | fill(tensor, distribution_u32, seed_offset); |
| 755 | break; |
| 756 | } |
| 757 | case DataType::S32: |
| 758 | { |
| 759 | std::uniform_int_distribution<int32_t> distribution_s32(std::numeric_limits<int32_t>::lowest(), std::numeric_limits<int32_t>::max()); |
| 760 | fill(tensor, distribution_s32, seed_offset); |
| 761 | break; |
| 762 | } |
| 763 | case DataType::U64: |
| 764 | { |
| 765 | std::uniform_int_distribution<uint64_t> distribution_u64(std::numeric_limits<uint64_t>::lowest(), std::numeric_limits<uint64_t>::max()); |
| 766 | fill(tensor, distribution_u64, seed_offset); |
| 767 | break; |
| 768 | } |
| 769 | case DataType::S64: |
| 770 | { |
| 771 | std::uniform_int_distribution<int64_t> distribution_s64(std::numeric_limits<int64_t>::lowest(), std::numeric_limits<int64_t>::max()); |
| 772 | fill(tensor, distribution_s64, seed_offset); |
| 773 | break; |
| 774 | } |
Georgios Pinitas | e8291ac | 2020-02-26 09:58:13 +0000 | [diff] [blame] | 775 | case DataType::BFLOAT16: |
| 776 | { |
| 777 | // It doesn't make sense to check [-inf, inf], so hard code it to a big number |
| 778 | std::uniform_real_distribution<float> distribution_bf16(-1000.f, 1000.f); |
| 779 | fill(tensor, distribution_bf16, seed_offset); |
| 780 | break; |
| 781 | } |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 782 | case DataType::F16: |
SiCong Li | 02dfb2c | 2017-07-27 17:59:20 +0100 | [diff] [blame] | 783 | { |
| 784 | // It doesn't make sense to check [-inf, inf], so hard code it to a big number |
| 785 | std::uniform_real_distribution<float> distribution_f16(-100.f, 100.f); |
| 786 | fill(tensor, distribution_f16, seed_offset); |
| 787 | break; |
| 788 | } |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 789 | case DataType::F32: |
| 790 | { |
| 791 | // It doesn't make sense to check [-inf, inf], so hard code it to a big number |
| 792 | std::uniform_real_distribution<float> distribution_f32(-1000.f, 1000.f); |
| 793 | fill(tensor, distribution_f32, seed_offset); |
| 794 | break; |
| 795 | } |
| 796 | case DataType::F64: |
| 797 | { |
| 798 | // It doesn't make sense to check [-inf, inf], so hard code it to a big number |
| 799 | std::uniform_real_distribution<double> distribution_f64(-1000.f, 1000.f); |
| 800 | fill(tensor, distribution_f64, seed_offset); |
| 801 | break; |
| 802 | } |
| 803 | case DataType::SIZET: |
| 804 | { |
| 805 | std::uniform_int_distribution<size_t> distribution_sizet(std::numeric_limits<size_t>::lowest(), std::numeric_limits<size_t>::max()); |
| 806 | fill(tensor, distribution_sizet, seed_offset); |
| 807 | break; |
| 808 | } |
| 809 | default: |
| 810 | ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| 811 | } |
| 812 | } |
| 813 | |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 814 | template <typename T> |
| 815 | void AssetsLibrary::fill_tensor_uniform_ranged(T &&tensor, |
| 816 | std::random_device::result_type seed_offset, |
| 817 | const std::vector<AssetsLibrary::RangePair> &excluded_range_pairs) const |
| 818 | { |
| 819 | using namespace arm_compute::utils::random; |
| 820 | |
| 821 | switch(tensor.data_type()) |
| 822 | { |
| 823 | case DataType::U8: |
| 824 | case DataType::QASYMM8: |
| 825 | { |
| 826 | const auto converted_pairs = detail::convert_range_pair<uint8_t>(excluded_range_pairs); |
| 827 | RangedUniformDistribution<uint8_t> distribution_u8(std::numeric_limits<uint8_t>::lowest(), |
| 828 | std::numeric_limits<uint8_t>::max(), |
| 829 | converted_pairs); |
| 830 | fill(tensor, distribution_u8, seed_offset); |
| 831 | break; |
| 832 | } |
| 833 | case DataType::S8: |
Georgios Pinitas | 3d13af8 | 2019-06-04 13:04:16 +0100 | [diff] [blame] | 834 | case DataType::QSYMM8: |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 835 | { |
| 836 | const auto converted_pairs = detail::convert_range_pair<int8_t>(excluded_range_pairs); |
| 837 | RangedUniformDistribution<int8_t> distribution_s8(std::numeric_limits<int8_t>::lowest(), |
| 838 | std::numeric_limits<int8_t>::max(), |
| 839 | converted_pairs); |
| 840 | fill(tensor, distribution_s8, seed_offset); |
| 841 | break; |
| 842 | } |
| 843 | case DataType::U16: |
| 844 | { |
| 845 | const auto converted_pairs = detail::convert_range_pair<uint16_t>(excluded_range_pairs); |
| 846 | RangedUniformDistribution<uint16_t> distribution_u16(std::numeric_limits<uint16_t>::lowest(), |
| 847 | std::numeric_limits<uint16_t>::max(), |
| 848 | converted_pairs); |
| 849 | fill(tensor, distribution_u16, seed_offset); |
| 850 | break; |
| 851 | } |
| 852 | case DataType::S16: |
Manuel Bottini | 3689fcd | 2019-06-14 17:18:12 +0100 | [diff] [blame] | 853 | case DataType::QSYMM16: |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 854 | { |
| 855 | const auto converted_pairs = detail::convert_range_pair<int16_t>(excluded_range_pairs); |
| 856 | RangedUniformDistribution<int16_t> distribution_s16(std::numeric_limits<int16_t>::lowest(), |
| 857 | std::numeric_limits<int16_t>::max(), |
| 858 | converted_pairs); |
| 859 | fill(tensor, distribution_s16, seed_offset); |
| 860 | break; |
| 861 | } |
| 862 | case DataType::U32: |
| 863 | { |
| 864 | const auto converted_pairs = detail::convert_range_pair<uint32_t>(excluded_range_pairs); |
| 865 | RangedUniformDistribution<uint32_t> distribution_u32(std::numeric_limits<uint32_t>::lowest(), |
| 866 | std::numeric_limits<uint32_t>::max(), |
| 867 | converted_pairs); |
| 868 | fill(tensor, distribution_u32, seed_offset); |
| 869 | break; |
| 870 | } |
| 871 | case DataType::S32: |
| 872 | { |
| 873 | const auto converted_pairs = detail::convert_range_pair<int32_t>(excluded_range_pairs); |
| 874 | RangedUniformDistribution<int32_t> distribution_s32(std::numeric_limits<int32_t>::lowest(), |
| 875 | std::numeric_limits<int32_t>::max(), |
| 876 | converted_pairs); |
| 877 | fill(tensor, distribution_s32, seed_offset); |
| 878 | break; |
| 879 | } |
Georgios Pinitas | e8291ac | 2020-02-26 09:58:13 +0000 | [diff] [blame] | 880 | case DataType::BFLOAT16: |
| 881 | { |
| 882 | // It doesn't make sense to check [-inf, inf], so hard code it to a big number |
| 883 | const auto converted_pairs = detail::convert_range_pair<float>(excluded_range_pairs); |
| 884 | RangedUniformDistribution<float> distribution_bf16(-1000.f, 1000.f, converted_pairs); |
| 885 | fill(tensor, distribution_bf16, seed_offset); |
| 886 | break; |
| 887 | } |
Georgios Pinitas | 587708b | 2018-12-31 15:43:52 +0000 | [diff] [blame] | 888 | case DataType::F16: |
| 889 | { |
| 890 | // It doesn't make sense to check [-inf, inf], so hard code it to a big number |
| 891 | const auto converted_pairs = detail::convert_range_pair<float>(excluded_range_pairs); |
| 892 | RangedUniformDistribution<float> distribution_f16(-100.f, 100.f, converted_pairs); |
| 893 | fill(tensor, distribution_f16, seed_offset); |
| 894 | break; |
| 895 | } |
| 896 | case DataType::F32: |
| 897 | { |
| 898 | // It doesn't make sense to check [-inf, inf], so hard code it to a big number |
| 899 | const auto converted_pairs = detail::convert_range_pair<float>(excluded_range_pairs); |
| 900 | RangedUniformDistribution<float> distribution_f32(-1000.f, 1000.f, converted_pairs); |
| 901 | fill(tensor, distribution_f32, seed_offset); |
| 902 | break; |
| 903 | } |
| 904 | default: |
| 905 | ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| 906 | } |
| 907 | } |
| 908 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 909 | template <typename T, typename D> |
Moritz Pflanzer | fb5aabb | 2017-07-18 14:39:55 +0100 | [diff] [blame] | 910 | void AssetsLibrary::fill_tensor_uniform(T &&tensor, std::random_device::result_type seed_offset, D low, D high) const |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 911 | { |
| 912 | switch(tensor.data_type()) |
| 913 | { |
| 914 | case DataType::U8: |
Anton Lokhmotov | af6204c | 2017-11-08 09:34:19 +0000 | [diff] [blame] | 915 | case DataType::QASYMM8: |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 916 | { |
| 917 | ARM_COMPUTE_ERROR_ON(!(std::is_same<uint8_t, D>::value)); |
| 918 | std::uniform_int_distribution<uint8_t> distribution_u8(low, high); |
| 919 | fill(tensor, distribution_u8, seed_offset); |
| 920 | break; |
| 921 | } |
| 922 | case DataType::S8: |
Georgios Pinitas | 3d13af8 | 2019-06-04 13:04:16 +0100 | [diff] [blame] | 923 | case DataType::QSYMM8: |
Sang-Hoon Park | d817647 | 2019-12-04 09:46:28 +0000 | [diff] [blame] | 924 | case DataType::QASYMM8_SIGNED: |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 925 | { |
| 926 | ARM_COMPUTE_ERROR_ON(!(std::is_same<int8_t, D>::value)); |
| 927 | std::uniform_int_distribution<int8_t> distribution_s8(low, high); |
| 928 | fill(tensor, distribution_s8, seed_offset); |
| 929 | break; |
| 930 | } |
| 931 | case DataType::U16: |
| 932 | { |
| 933 | ARM_COMPUTE_ERROR_ON(!(std::is_same<uint16_t, D>::value)); |
| 934 | std::uniform_int_distribution<uint16_t> distribution_u16(low, high); |
| 935 | fill(tensor, distribution_u16, seed_offset); |
| 936 | break; |
| 937 | } |
| 938 | case DataType::S16: |
Manuel Bottini | 3689fcd | 2019-06-14 17:18:12 +0100 | [diff] [blame] | 939 | case DataType::QSYMM16: |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 940 | { |
| 941 | ARM_COMPUTE_ERROR_ON(!(std::is_same<int16_t, D>::value)); |
| 942 | std::uniform_int_distribution<int16_t> distribution_s16(low, high); |
| 943 | fill(tensor, distribution_s16, seed_offset); |
| 944 | break; |
| 945 | } |
| 946 | case DataType::U32: |
| 947 | { |
| 948 | ARM_COMPUTE_ERROR_ON(!(std::is_same<uint32_t, D>::value)); |
| 949 | std::uniform_int_distribution<uint32_t> distribution_u32(low, high); |
| 950 | fill(tensor, distribution_u32, seed_offset); |
| 951 | break; |
| 952 | } |
| 953 | case DataType::S32: |
| 954 | { |
| 955 | ARM_COMPUTE_ERROR_ON(!(std::is_same<int32_t, D>::value)); |
| 956 | std::uniform_int_distribution<int32_t> distribution_s32(low, high); |
| 957 | fill(tensor, distribution_s32, seed_offset); |
| 958 | break; |
| 959 | } |
| 960 | case DataType::U64: |
| 961 | { |
| 962 | ARM_COMPUTE_ERROR_ON(!(std::is_same<uint64_t, D>::value)); |
| 963 | std::uniform_int_distribution<uint64_t> distribution_u64(low, high); |
| 964 | fill(tensor, distribution_u64, seed_offset); |
| 965 | break; |
| 966 | } |
| 967 | case DataType::S64: |
| 968 | { |
| 969 | ARM_COMPUTE_ERROR_ON(!(std::is_same<int64_t, D>::value)); |
| 970 | std::uniform_int_distribution<int64_t> distribution_s64(low, high); |
| 971 | fill(tensor, distribution_s64, seed_offset); |
| 972 | break; |
| 973 | } |
Georgios Pinitas | e8291ac | 2020-02-26 09:58:13 +0000 | [diff] [blame] | 974 | case DataType::BFLOAT16: |
| 975 | { |
| 976 | std::uniform_real_distribution<float> distribution_bf16(low, high); |
| 977 | fill(tensor, distribution_bf16, seed_offset); |
| 978 | break; |
| 979 | } |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 980 | case DataType::F16: |
| 981 | { |
Moritz Pflanzer | e49e266 | 2017-07-21 15:55:28 +0100 | [diff] [blame] | 982 | std::uniform_real_distribution<float> distribution_f16(low, high); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 983 | fill(tensor, distribution_f16, seed_offset); |
| 984 | break; |
| 985 | } |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 986 | case DataType::F32: |
| 987 | { |
| 988 | ARM_COMPUTE_ERROR_ON(!(std::is_same<float, D>::value)); |
| 989 | std::uniform_real_distribution<float> distribution_f32(low, high); |
| 990 | fill(tensor, distribution_f32, seed_offset); |
| 991 | break; |
| 992 | } |
| 993 | case DataType::F64: |
| 994 | { |
| 995 | ARM_COMPUTE_ERROR_ON(!(std::is_same<double, D>::value)); |
| 996 | std::uniform_real_distribution<double> distribution_f64(low, high); |
| 997 | fill(tensor, distribution_f64, seed_offset); |
| 998 | break; |
| 999 | } |
| 1000 | case DataType::SIZET: |
| 1001 | { |
| 1002 | ARM_COMPUTE_ERROR_ON(!(std::is_same<size_t, D>::value)); |
| 1003 | std::uniform_int_distribution<size_t> distribution_sizet(low, high); |
| 1004 | fill(tensor, distribution_sizet, seed_offset); |
| 1005 | break; |
| 1006 | } |
| 1007 | default: |
| 1008 | ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| 1009 | } |
| 1010 | } |
| 1011 | |
| 1012 | template <typename T> |
Moritz Pflanzer | fb5aabb | 2017-07-18 14:39:55 +0100 | [diff] [blame] | 1013 | void AssetsLibrary::fill_layer_data(T &&tensor, std::string name) const |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1014 | { |
| 1015 | #ifdef _WIN32 |
| 1016 | const std::string path_separator("\\"); |
Anthony Barbier | ac69aa1 | 2017-07-03 17:39:37 +0100 | [diff] [blame] | 1017 | #else /* _WIN32 */ |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1018 | const std::string path_separator("/"); |
Anthony Barbier | ac69aa1 | 2017-07-03 17:39:37 +0100 | [diff] [blame] | 1019 | #endif /* _WIN32 */ |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1020 | const std::string path = _library_path + path_separator + name; |
| 1021 | |
| 1022 | // Open file |
SiCong Li | 86b5333 | 2017-08-23 11:02:43 +0100 | [diff] [blame] | 1023 | std::ifstream stream(path, std::ios::in | std::ios::binary); |
Anthony Barbier | f6705ec | 2017-09-28 12:01:10 +0100 | [diff] [blame] | 1024 | if(!stream.good()) |
| 1025 | { |
| 1026 | throw framework::FileNotFound("Could not load npy file: " + path); |
| 1027 | } |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1028 | |
Matthew Bentham | 470bc1e | 2020-03-09 10:55:40 +0000 | [diff] [blame] | 1029 | validate_npy_header(stream, tensor.data_type(), tensor.shape()); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1030 | |
SiCong Li | 86b5333 | 2017-08-23 11:02:43 +0100 | [diff] [blame] | 1031 | // Read data |
| 1032 | if(tensor.padding().empty()) |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1033 | { |
SiCong Li | 86b5333 | 2017-08-23 11:02:43 +0100 | [diff] [blame] | 1034 | // If tensor has no padding read directly from stream. |
| 1035 | stream.read(reinterpret_cast<char *>(tensor.data()), tensor.size()); |
| 1036 | } |
| 1037 | else |
| 1038 | { |
| 1039 | // If tensor has padding accessing tensor elements through execution window. |
| 1040 | Window window; |
| 1041 | window.use_tensor_dimensions(tensor.shape()); |
| 1042 | |
SiCong Li | 86b5333 | 2017-08-23 11:02:43 +0100 | [diff] [blame] | 1043 | execute_window_loop(window, [&](const Coordinates & id) |
| 1044 | { |
| 1045 | stream.read(reinterpret_cast<char *>(tensor(id)), tensor.element_size()); |
| 1046 | }); |
| 1047 | } |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1048 | } |
Michele Di Giorgio | 4d33630 | 2018-03-02 09:43:54 +0000 | [diff] [blame] | 1049 | |
| 1050 | template <typename T, typename D> |
| 1051 | void AssetsLibrary::fill_tensor_value(T &&tensor, D value) const |
| 1052 | { |
| 1053 | fill_tensor_uniform(tensor, 0, value, value); |
| 1054 | } |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1055 | } // namespace test |
| 1056 | } // namespace arm_compute |
Michalis Spyrou | f464337 | 2019-11-29 16:17:13 +0000 | [diff] [blame] | 1057 | #endif /* ARM_COMPUTE_TEST_TENSOR_LIBRARY_H */ |