Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2019 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_UNIT_DYNAMIC_TENSOR |
| 25 | #define ARM_COMPUTE_TEST_UNIT_DYNAMIC_TENSOR |
| 26 | |
| 27 | #include "arm_compute/core/TensorShape.h" |
| 28 | #include "arm_compute/core/Types.h" |
| 29 | #include "tests/AssetsLibrary.h" |
| 30 | #include "tests/Globals.h" |
| 31 | #include "tests/IAccessor.h" |
| 32 | #include "tests/framework/Asserts.h" |
| 33 | #include "tests/framework/Fixture.h" |
| 34 | #include "tests/validation/Helpers.h" |
Georgios Pinitas | 2ff0009 | 2019-09-30 16:50:08 +0100 | [diff] [blame] | 35 | #include "tests/validation/reference/ConvolutionLayer.h" |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 36 | #include "tests/validation/reference/NormalizationLayer.h" |
| 37 | |
| 38 | namespace arm_compute |
| 39 | { |
| 40 | namespace test |
| 41 | { |
| 42 | namespace validation |
| 43 | { |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 44 | template <typename AllocatorType, |
| 45 | typename LifetimeMgrType, |
| 46 | typename PoolMgrType, |
| 47 | typename MemoryMgrType> |
| 48 | struct MemoryManagementService |
| 49 | { |
| 50 | public: |
Georgios Pinitas | 2ff0009 | 2019-09-30 16:50:08 +0100 | [diff] [blame] | 51 | using LftMgrType = LifetimeMgrType; |
| 52 | |
| 53 | public: |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 54 | MemoryManagementService() |
| 55 | : allocator(), lifetime_mgr(nullptr), pool_mgr(nullptr), mm(nullptr), mg(), num_pools(0) |
| 56 | { |
| 57 | lifetime_mgr = std::make_shared<LifetimeMgrType>(); |
| 58 | pool_mgr = std::make_shared<PoolMgrType>(); |
| 59 | mm = std::make_shared<MemoryMgrType>(lifetime_mgr, pool_mgr); |
| 60 | mg = MemoryGroup(mm); |
| 61 | } |
| 62 | |
| 63 | void populate(size_t pools) |
| 64 | { |
| 65 | mm->populate(allocator, pools); |
| 66 | num_pools = pools; |
| 67 | } |
| 68 | |
| 69 | void clear() |
| 70 | { |
| 71 | mm->clear(); |
| 72 | num_pools = 0; |
| 73 | } |
| 74 | |
| 75 | void validate(bool validate_finalized) const |
| 76 | { |
| 77 | ARM_COMPUTE_EXPECT(mm->pool_manager() != nullptr, framework::LogLevel::ERRORS); |
| 78 | ARM_COMPUTE_EXPECT(mm->lifetime_manager() != nullptr, framework::LogLevel::ERRORS); |
| 79 | |
| 80 | if(validate_finalized) |
| 81 | { |
| 82 | ARM_COMPUTE_EXPECT(mm->lifetime_manager()->are_all_finalized(), framework::LogLevel::ERRORS); |
| 83 | } |
| 84 | ARM_COMPUTE_EXPECT(mm->pool_manager()->num_pools() == num_pools, framework::LogLevel::ERRORS); |
| 85 | } |
| 86 | |
| 87 | AllocatorType allocator; |
| 88 | std::shared_ptr<LifetimeMgrType> lifetime_mgr; |
| 89 | std::shared_ptr<PoolMgrType> pool_mgr; |
| 90 | std::shared_ptr<MemoryMgrType> mm; |
| 91 | MemoryGroup mg; |
| 92 | size_t num_pools; |
| 93 | }; |
Georgios Pinitas | b785dd4 | 2019-09-19 12:09:32 +0100 | [diff] [blame] | 94 | |
| 95 | template <typename MemoryMgrType, typename FuncType, typename ITensorType> |
| 96 | class SimpleFunctionWrapper |
| 97 | { |
| 98 | public: |
| 99 | SimpleFunctionWrapper(std::shared_ptr<MemoryMgrType> mm) |
| 100 | : _func(mm) |
| 101 | { |
| 102 | } |
| 103 | void configure(ITensorType *src, ITensorType *dst) |
| 104 | { |
Michalis Spyrou | 6bff195 | 2019-10-02 17:22:11 +0100 | [diff] [blame] | 105 | ARM_COMPUTE_UNUSED(src, dst); |
Georgios Pinitas | b785dd4 | 2019-09-19 12:09:32 +0100 | [diff] [blame] | 106 | } |
| 107 | void run() |
| 108 | { |
| 109 | _func.run(); |
| 110 | } |
| 111 | |
| 112 | private: |
| 113 | FuncType _func; |
| 114 | }; |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 115 | |
| 116 | /** Simple test case to run a single function with different shapes twice. |
| 117 | * |
| 118 | * Runs a specified function twice, where the second time the size of the input/output is different |
| 119 | * Internal memory of the function and input/output are managed by different services |
| 120 | */ |
| 121 | template <typename TensorType, |
| 122 | typename AccessorType, |
Georgios Pinitas | 2ff0009 | 2019-09-30 16:50:08 +0100 | [diff] [blame] | 123 | typename MemoryManagementServiceType, |
Georgios Pinitas | b785dd4 | 2019-09-19 12:09:32 +0100 | [diff] [blame] | 124 | typename SimpleFunctionWrapperType> |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 125 | class DynamicTensorType3SingleFunction : public framework::Fixture |
| 126 | { |
Georgios Pinitas | 2ff0009 | 2019-09-30 16:50:08 +0100 | [diff] [blame] | 127 | using T = float; |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 128 | |
| 129 | public: |
| 130 | template <typename...> |
| 131 | void setup(TensorShape input_level0, TensorShape input_level1) |
| 132 | { |
| 133 | input_l0 = input_level0; |
| 134 | input_l1 = input_level1; |
| 135 | run(); |
| 136 | } |
| 137 | |
| 138 | protected: |
| 139 | void run() |
| 140 | { |
| 141 | MemoryManagementServiceType serv_internal; |
| 142 | MemoryManagementServiceType serv_cross; |
| 143 | const size_t num_pools = 1; |
| 144 | const bool validate_finalized = true; |
| 145 | |
| 146 | // Create Tensor shapes. |
| 147 | TensorShape level_0 = TensorShape(input_l0); |
| 148 | TensorShape level_1 = TensorShape(input_l1); |
| 149 | |
| 150 | // Level 0 |
| 151 | // Create tensors |
Georgios Pinitas | b785dd4 | 2019-09-19 12:09:32 +0100 | [diff] [blame] | 152 | TensorType src = create_tensor<TensorType>(level_0, DataType::F32, 1); |
| 153 | TensorType dst = create_tensor<TensorType>(level_0, DataType::F32, 1); |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 154 | |
| 155 | serv_cross.mg.manage(&src); |
| 156 | serv_cross.mg.manage(&dst); |
| 157 | |
| 158 | // Create and configure function |
Georgios Pinitas | b785dd4 | 2019-09-19 12:09:32 +0100 | [diff] [blame] | 159 | SimpleFunctionWrapperType layer(serv_internal.mm); |
| 160 | layer.configure(&src, &dst); |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 161 | |
| 162 | ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 163 | ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 164 | |
| 165 | // Allocate tensors |
| 166 | src.allocator()->allocate(); |
| 167 | dst.allocator()->allocate(); |
| 168 | |
| 169 | ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 170 | ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 171 | |
| 172 | // Populate and validate memory manager |
| 173 | serv_cross.populate(num_pools); |
| 174 | serv_internal.populate(num_pools); |
| 175 | serv_cross.validate(validate_finalized); |
| 176 | serv_internal.validate(validate_finalized); |
| 177 | |
| 178 | // Extract lifetime manager meta-data information |
| 179 | internal_l0 = serv_internal.lifetime_mgr->info(); |
| 180 | cross_l0 = serv_cross.lifetime_mgr->info(); |
| 181 | |
| 182 | // Acquire memory manager, fill tensors and compute functions |
| 183 | serv_cross.mg.acquire(); |
Georgios Pinitas | b785dd4 | 2019-09-19 12:09:32 +0100 | [diff] [blame] | 184 | arm_compute::test::library->fill_tensor_value(AccessorType(src), 12.f); |
| 185 | layer.run(); |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 186 | serv_cross.mg.release(); |
| 187 | |
| 188 | // Clear manager |
| 189 | serv_cross.clear(); |
| 190 | serv_internal.clear(); |
| 191 | serv_cross.validate(validate_finalized); |
| 192 | serv_internal.validate(validate_finalized); |
| 193 | |
| 194 | // Level 1 |
| 195 | // Update the tensor shapes |
| 196 | src.info()->set_tensor_shape(level_1); |
| 197 | dst.info()->set_tensor_shape(level_1); |
| 198 | src.info()->set_is_resizable(true); |
| 199 | dst.info()->set_is_resizable(true); |
| 200 | |
| 201 | serv_cross.mg.manage(&src); |
| 202 | serv_cross.mg.manage(&dst); |
| 203 | |
| 204 | // Re-configure the function |
Georgios Pinitas | b785dd4 | 2019-09-19 12:09:32 +0100 | [diff] [blame] | 205 | layer.configure(&src, &dst); |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 206 | |
| 207 | // Allocate tensors |
| 208 | src.allocator()->allocate(); |
| 209 | dst.allocator()->allocate(); |
| 210 | |
| 211 | // Populate and validate memory manager |
| 212 | serv_cross.populate(num_pools); |
| 213 | serv_internal.populate(num_pools); |
| 214 | serv_cross.validate(validate_finalized); |
| 215 | serv_internal.validate(validate_finalized); |
| 216 | |
| 217 | // Extract lifetime manager meta-data information |
| 218 | internal_l1 = serv_internal.lifetime_mgr->info(); |
| 219 | cross_l1 = serv_cross.lifetime_mgr->info(); |
| 220 | |
| 221 | // Compute functions |
| 222 | serv_cross.mg.acquire(); |
| 223 | arm_compute::test::library->fill_tensor_value(AccessorType(src), 12.f); |
Georgios Pinitas | b785dd4 | 2019-09-19 12:09:32 +0100 | [diff] [blame] | 224 | layer.run(); |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 225 | serv_cross.mg.release(); |
| 226 | |
| 227 | // Clear manager |
| 228 | serv_cross.clear(); |
| 229 | serv_internal.clear(); |
| 230 | serv_cross.validate(validate_finalized); |
| 231 | serv_internal.validate(validate_finalized); |
| 232 | } |
| 233 | |
| 234 | public: |
Georgios Pinitas | 2ff0009 | 2019-09-30 16:50:08 +0100 | [diff] [blame] | 235 | TensorShape input_l0{}, input_l1{}; |
| 236 | typename MemoryManagementServiceType::LftMgrType::info_type internal_l0{}, internal_l1{}; |
| 237 | typename MemoryManagementServiceType::LftMgrType::info_type cross_l0{}, cross_l1{}; |
| 238 | }; |
| 239 | |
| 240 | /** Simple test case to run a single function with different shapes twice. |
| 241 | * |
| 242 | * Runs a specified function twice, where the second time the size of the input/output is different |
| 243 | * Internal memory of the function and input/output are managed by different services |
| 244 | */ |
| 245 | template <typename TensorType, |
| 246 | typename AccessorType, |
| 247 | typename MemoryManagementServiceType, |
| 248 | typename ComplexFunctionType> |
| 249 | class DynamicTensorType3ComplexFunction : public framework::Fixture |
| 250 | { |
| 251 | using T = float; |
| 252 | |
| 253 | public: |
| 254 | template <typename...> |
| 255 | void setup(std::vector<TensorShape> input_shapes, TensorShape weights_shape, TensorShape bias_shape, std::vector<TensorShape> output_shapes, PadStrideInfo info) |
| 256 | { |
| 257 | num_iterations = input_shapes.size(); |
| 258 | _data_type = DataType::F32; |
| 259 | _data_layout = DataLayout::NHWC; |
| 260 | _input_shapes = input_shapes; |
| 261 | _output_shapes = output_shapes; |
| 262 | _weights_shape = weights_shape; |
| 263 | _bias_shape = bias_shape; |
| 264 | _info = info; |
| 265 | |
| 266 | // Create function |
| 267 | _f_target = support::cpp14::make_unique<ComplexFunctionType>(_ms.mm); |
| 268 | } |
| 269 | |
| 270 | void run_iteration(unsigned int idx) |
| 271 | { |
| 272 | auto input_shape = _input_shapes[idx]; |
| 273 | auto output_shape = _output_shapes[idx]; |
| 274 | |
| 275 | dst_ref = run_reference(input_shape, _weights_shape, _bias_shape, output_shape, _info); |
| 276 | dst_target = run_target(input_shape, _weights_shape, _bias_shape, output_shape, _info, WeightsInfo()); |
| 277 | } |
| 278 | |
| 279 | protected: |
| 280 | template <typename U> |
| 281 | void fill(U &&tensor, int i) |
| 282 | { |
| 283 | switch(tensor.data_type()) |
| 284 | { |
| 285 | case DataType::F32: |
| 286 | { |
| 287 | std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| 288 | library->fill(tensor, distribution, i); |
| 289 | break; |
| 290 | } |
| 291 | default: |
| 292 | library->fill_tensor_uniform(tensor, i); |
| 293 | } |
| 294 | } |
| 295 | |
| 296 | TensorType run_target(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, |
| 297 | PadStrideInfo info, WeightsInfo weights_info) |
| 298 | { |
| 299 | if(_data_layout == DataLayout::NHWC) |
| 300 | { |
| 301 | permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| 302 | permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| 303 | permute(output_shape, PermutationVector(2U, 0U, 1U)); |
| 304 | } |
| 305 | |
| 306 | _weights_target = create_tensor<TensorType>(weights_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| 307 | _bias_target = create_tensor<TensorType>(bias_shape, _data_type, 1); |
| 308 | |
| 309 | // Create tensors |
| 310 | TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| 311 | TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| 312 | |
| 313 | // Create and configure function |
| 314 | _f_target->configure(&src, &_weights_target, &_bias_target, &dst, info, weights_info); |
| 315 | |
| 316 | ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 317 | ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 318 | |
| 319 | // Allocate tensors |
| 320 | src.allocator()->allocate(); |
| 321 | dst.allocator()->allocate(); |
| 322 | _weights_target.allocator()->allocate(); |
| 323 | _bias_target.allocator()->allocate(); |
| 324 | |
| 325 | ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 326 | ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 327 | |
| 328 | // Fill tensors |
| 329 | fill(AccessorType(src), 0); |
| 330 | fill(AccessorType(_weights_target), 1); |
| 331 | fill(AccessorType(_bias_target), 2); |
| 332 | |
| 333 | // Populate and validate memory manager |
| 334 | _ms.clear(); |
| 335 | _ms.populate(1); |
| 336 | _ms.mg.acquire(); |
| 337 | |
| 338 | // Compute NEConvolutionLayer function |
| 339 | _f_target->run(); |
| 340 | _ms.mg.release(); |
| 341 | |
| 342 | return dst; |
| 343 | } |
| 344 | |
| 345 | SimpleTensor<T> run_reference(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info) |
| 346 | { |
| 347 | // Create reference |
| 348 | SimpleTensor<T> src{ input_shape, _data_type, 1 }; |
| 349 | SimpleTensor<T> weights{ weights_shape, _data_type, 1 }; |
| 350 | SimpleTensor<T> bias{ bias_shape, _data_type, 1 }; |
| 351 | |
| 352 | // Fill reference |
| 353 | fill(src, 0); |
| 354 | fill(weights, 1); |
| 355 | fill(bias, 2); |
| 356 | |
| 357 | return reference::convolution_layer<T>(src, weights, bias, output_shape, info); |
| 358 | } |
| 359 | |
| 360 | public: |
| 361 | unsigned int num_iterations{ 0 }; |
| 362 | SimpleTensor<T> dst_ref{}; |
| 363 | TensorType dst_target{}; |
| 364 | |
| 365 | private: |
| 366 | DataType _data_type{ DataType::UNKNOWN }; |
| 367 | DataLayout _data_layout{ DataLayout::UNKNOWN }; |
| 368 | PadStrideInfo _info{}; |
| 369 | std::vector<TensorShape> _input_shapes{}; |
| 370 | std::vector<TensorShape> _output_shapes{}; |
| 371 | TensorShape _weights_shape{}; |
| 372 | TensorShape _bias_shape{}; |
| 373 | MemoryManagementServiceType _ms{}; |
| 374 | TensorType _weights_target{}; |
| 375 | TensorType _bias_target{}; |
| 376 | std::unique_ptr<ComplexFunctionType> _f_target{}; |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 377 | }; |
Georgios Pinitas | 3d426c5 | 2019-10-10 19:35:43 +0100 | [diff] [blame] | 378 | |
| 379 | /** Fixture that create a pipeline of Convolutions and changes the inputs dynamically |
| 380 | * |
| 381 | * Runs a list of convolutions and then resizes the inputs and reruns. |
| 382 | * Updates the memory manager and allocated memory. |
| 383 | */ |
| 384 | template <typename TensorType, |
| 385 | typename AccessorType, |
| 386 | typename MemoryManagementServiceType, |
| 387 | typename ComplexFunctionType> |
| 388 | class DynamicTensorType2PipelineFunction : public framework::Fixture |
| 389 | { |
| 390 | using T = float; |
| 391 | |
| 392 | public: |
| 393 | template <typename...> |
| 394 | void setup(std::vector<TensorShape> input_shapes) |
| 395 | { |
| 396 | _data_type = DataType::F32; |
| 397 | _data_layout = DataLayout::NHWC; |
| 398 | _input_shapes = input_shapes; |
| 399 | |
| 400 | run(); |
| 401 | } |
| 402 | |
| 403 | protected: |
| 404 | template <typename U> |
| 405 | void fill(U &&tensor, int i) |
| 406 | { |
| 407 | switch(tensor.data_type()) |
| 408 | { |
| 409 | case DataType::F32: |
| 410 | { |
| 411 | std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| 412 | library->fill(tensor, distribution, i); |
| 413 | break; |
| 414 | } |
| 415 | default: |
| 416 | library->fill_tensor_uniform(tensor, i); |
| 417 | } |
| 418 | } |
| 419 | |
| 420 | void run() |
| 421 | { |
| 422 | const unsigned int num_functions = 5; |
| 423 | const unsigned int num_tensors = num_functions + 1; |
| 424 | const unsigned int num_resizes = _input_shapes.size(); |
| 425 | |
| 426 | for(unsigned int i = 0; i < num_functions; ++i) |
| 427 | { |
| 428 | _functions.emplace_back(support::cpp14::make_unique<ComplexFunctionType>(_ms.mm)); |
| 429 | } |
| 430 | |
| 431 | for(unsigned int i = 0; i < num_resizes; ++i) |
| 432 | { |
| 433 | TensorShape input_shape = _input_shapes[i]; |
| 434 | TensorShape weights_shape = TensorShape(3U, 3U, input_shape[2], input_shape[2]); |
| 435 | TensorShape output_shape = input_shape; |
| 436 | PadStrideInfo info(1U, 1U, 1U, 1U); |
| 437 | |
| 438 | if(_data_layout == DataLayout::NHWC) |
| 439 | { |
| 440 | permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| 441 | permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| 442 | permute(output_shape, PermutationVector(2U, 0U, 1U)); |
| 443 | } |
| 444 | |
| 445 | std::vector<TensorType> tensors(num_tensors); |
| 446 | std::vector<TensorType> ws(num_functions); |
| 447 | std::vector<TensorType> bs(num_functions); |
| 448 | |
| 449 | auto tensor_info = TensorInfo(input_shape, 1, _data_type); |
| 450 | auto weights_info = TensorInfo(weights_shape, 1, _data_type); |
| 451 | tensor_info.set_data_layout(_data_layout); |
| 452 | weights_info.set_data_layout(_data_layout); |
| 453 | |
Georgios Pinitas | 9aaf09e | 2019-11-07 17:22:06 +0000 | [diff] [blame] | 454 | tensors[0].allocator()->init(tensor_info); |
Georgios Pinitas | 3d426c5 | 2019-10-10 19:35:43 +0100 | [diff] [blame] | 455 | for(unsigned int f = 0; f < num_functions; ++f) |
| 456 | { |
Georgios Pinitas | 3d426c5 | 2019-10-10 19:35:43 +0100 | [diff] [blame] | 457 | tensors[f + 1].allocator()->init(tensor_info); |
| 458 | ws[f].allocator()->init(weights_info); |
| 459 | |
| 460 | _functions[f]->configure(&tensors[f], &ws[f], nullptr, &tensors[f + 1], info); |
| 461 | |
| 462 | // Allocate tensors |
| 463 | tensors[f].allocator()->allocate(); |
| 464 | ws[f].allocator()->allocate(); |
| 465 | } |
| 466 | tensors[num_functions].allocator()->allocate(); |
| 467 | |
| 468 | // Populate and validate memory manager |
| 469 | _ms.clear(); |
| 470 | _ms.populate(1); |
| 471 | _ms.mg.acquire(); |
| 472 | |
| 473 | // Run pipeline |
| 474 | for(unsigned int f = 0; f < num_functions; ++f) |
| 475 | { |
| 476 | _functions[f]->run(); |
| 477 | } |
| 478 | |
| 479 | // Release memory group |
| 480 | _ms.mg.release(); |
| 481 | } |
| 482 | } |
| 483 | |
| 484 | private: |
| 485 | DataType _data_type{ DataType::UNKNOWN }; |
| 486 | DataLayout _data_layout{ DataLayout::UNKNOWN }; |
| 487 | std::vector<TensorShape> _input_shapes{}; |
| 488 | MemoryManagementServiceType _ms{}; |
| 489 | std::vector<std::unique_ptr<ComplexFunctionType>> _functions{}; |
| 490 | }; |
Michalis Spyrou | caa7dee | 2019-09-09 19:23:39 +0100 | [diff] [blame] | 491 | } // namespace validation |
| 492 | } // namespace test |
| 493 | } // namespace arm_compute |
| 494 | #endif /* ARM_COMPUTE_TEST_UNIT_DYNAMIC_TENSOR */ |