Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "ConcatTestImpl.hpp" |
| 7 | |
| 8 | #include <Permute.hpp> |
| 9 | #include <ResolveType.hpp> |
| 10 | |
| 11 | #include <armnn/ArmNN.hpp> |
| 12 | |
| 13 | #include <backendsCommon/test/TensorCopyUtils.hpp> |
| 14 | #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| 15 | |
| 16 | #include <test/TensorHelpers.hpp> |
| 17 | |
| 18 | // |
| 19 | // Helper functions and templates |
| 20 | // |
| 21 | |
| 22 | armnn::OriginsDescriptor CreateDescriptorForConcat( |
| 23 | const std::vector<armnn::TensorInfo> & inputTensorInfos, |
| 24 | unsigned int concatDim) |
| 25 | { |
| 26 | std::vector<armnn::TensorShape> shapes; |
| 27 | shapes.reserve(inputTensorInfos.size()); |
| 28 | for (const armnn::TensorInfo& it: inputTensorInfos) |
| 29 | { |
| 30 | shapes.push_back(it.GetShape()); |
| 31 | } |
| 32 | |
| 33 | return armnn::CreateDescriptorForConcatenation(shapes.begin(), shapes.end(), concatDim); |
| 34 | } |
| 35 | |
| 36 | // |
| 37 | // Concat is only supported for N and C dimensions for NCHW and the inner most dimension |
| 38 | // In case of <4 dimensions we need to make sure that the concat dimensions are at least |
| 39 | // the 3rd slowest iterating one or the inner most dimension. |
| 40 | // |
| 41 | |
| 42 | bool NeedPermuteForConcat( |
| 43 | const std::vector<armnn::TensorInfo> & inputTensorInfos, |
| 44 | unsigned int concatDim) |
| 45 | { |
| 46 | // See note above. Additionally we expect the input shapes to have the |
| 47 | // same number of dimensions. |
| 48 | unsigned int nDimensions = 0; |
| 49 | |
| 50 | // Determine the number of dimensions as well as sanity check them |
| 51 | // agains test implementation issues. |
| 52 | for (auto && tensorInfo : inputTensorInfos) |
| 53 | { |
| 54 | if (!nDimensions) |
| 55 | { |
| 56 | nDimensions = tensorInfo.GetShape().GetNumDimensions(); |
| 57 | } |
| 58 | else |
| 59 | { |
| 60 | BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(), |
| 61 | "Input shapes must have the same number of dimensions"); |
| 62 | } |
| 63 | } |
| 64 | |
| 65 | return (nDimensions < 3 || (nDimensions == 3 && (nDimensions-concatDim) < 3 && (nDimensions-concatDim) != 1)); |
| 66 | } |
| 67 | |
| 68 | armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & inputShape) |
| 69 | { |
| 70 | unsigned int numDims = inputShape.GetNumDimensions(); |
| 71 | if (numDims >= 3) |
| 72 | { |
| 73 | // Nothing to do if the inputShape has at least 3 dimensions. |
| 74 | return inputShape; |
| 75 | } |
| 76 | |
| 77 | std::vector<unsigned int> newDims(size_t(3), 1u); |
| 78 | unsigned int expandedBy = 3 - numDims; |
| 79 | for (unsigned int i=0; i<numDims; ++i) |
| 80 | { |
| 81 | newDims[expandedBy+i] = inputShape[i]; |
| 82 | } |
| 83 | return armnn::TensorShape(3u, &newDims[0]); |
| 84 | } |
| 85 | |
| 86 | void Generate3dPermuteVectorForConcat( |
| 87 | unsigned int numDimensions, |
| 88 | unsigned int & concatDim, |
| 89 | std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutations) |
| 90 | { |
| 91 | BOOST_ASSERT_MSG(numDimensions <= 3, |
| 92 | "Only dimensions 1,2 and 3 are supported by this helper"); |
| 93 | unsigned int expandedBy = 3 - numDimensions; |
| 94 | unsigned int expandedConcatAxis = concatDim + expandedBy; |
| 95 | |
| 96 | if (expandedConcatAxis == 2) |
| 97 | { |
| 98 | concatDim = 0; |
| 99 | armnn::PermutationVector forwardPermutation({1, 2, 0}); |
| 100 | armnn::PermutationVector reversePermutation({2, 0, 1}); |
| 101 | permutations = std::make_pair(forwardPermutation, reversePermutation); |
| 102 | } |
| 103 | else if (expandedConcatAxis == 1) |
| 104 | { |
| 105 | concatDim = 0; |
| 106 | armnn::PermutationVector forwardPermutation({2, 0, 1}); |
| 107 | armnn::PermutationVector reversePermutation({1, 2, 0}); |
| 108 | permutations = std::make_pair(forwardPermutation, reversePermutation); |
| 109 | } |
| 110 | else |
| 111 | { |
| 112 | BOOST_ASSERT(expandedConcatAxis == 0); |
| 113 | concatDim = 0; |
| 114 | } |
| 115 | } |
| 116 | |
| 117 | template<typename T> void PermuteTensorData( |
| 118 | armnn::IWorkloadFactory& workloadFactory, |
| 119 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 120 | const armnn::PermutationVector& mappings, |
| 121 | armnn::TensorInfo & inputTensorInfo, |
| 122 | const T * inputData, |
| 123 | std::vector<T>& outputData) |
| 124 | { |
| 125 | BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null"); |
| 126 | if (inputData == nullptr) |
| 127 | { |
| 128 | // Nullptr is an error in the test. By returning without doing the concatenation |
| 129 | // I expect the caller to fail the test. It still makes sense to report this as |
| 130 | // an assert for Debug builds. |
| 131 | return; |
| 132 | } |
| 133 | |
| 134 | armnn::TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings); |
| 135 | |
| 136 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 137 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 138 | |
| 139 | armnn::PermuteQueueDescriptor queueDescriptor; |
| 140 | queueDescriptor.m_Parameters = armnn::PermuteDescriptor{mappings}; |
| 141 | armnn::WorkloadInfo workloadInfo; |
| 142 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 143 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 144 | |
| 145 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo); |
| 146 | |
| 147 | inputHandle->Allocate(); |
| 148 | outputHandle->Allocate(); |
| 149 | |
| 150 | CopyDataToITensorHandle(inputHandle.get(), inputData); |
| 151 | |
| 152 | workload->PostAllocationConfigure(); |
| 153 | workload->Execute(); |
| 154 | |
| 155 | outputData.resize(outputTensorInfo.GetNumElements()); |
| 156 | CopyDataFromITensorHandle(&outputData[0], outputHandle.get()); |
| 157 | inputTensorInfo = outputTensorInfo; |
| 158 | } |
| 159 | |
| 160 | // |
| 161 | // Permute the input tensors so we can do a supported concatenation. |
| 162 | // Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions |
| 163 | // at the front. Finally this function tells what the output shape |
| 164 | // of the permuted concatenated tensor is going to be. |
| 165 | // |
| 166 | template<typename T> void PermuteInputsForConcat( |
| 167 | armnn::IWorkloadFactory& workloadFactory, |
| 168 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 169 | std::vector<armnn::TensorInfo> & inputTensorInfos, |
| 170 | std::vector<T *> & inputData, |
| 171 | std::vector<std::vector<T>> & inputDataStorage, |
| 172 | armnn::PermutationVector & permuteVector, |
| 173 | unsigned int & concatDim, |
| 174 | armnn::TensorInfo & outputTensorInfo) |
| 175 | { |
| 176 | BOOST_ASSERT_MSG(inputTensorInfos.size() > 1, |
| 177 | "Expecting more than one tensor to be concatenated here"); |
| 178 | |
| 179 | unsigned int numDims = 0; |
| 180 | unsigned int nthInput = 0; |
| 181 | const armnn::PermutationVector identity({0, 1, 2}); |
| 182 | |
| 183 | std::pair<armnn::PermutationVector, armnn::PermutationVector> permutations = |
| 184 | std::make_pair(identity, identity); |
| 185 | |
| 186 | inputDataStorage.resize(inputData.size()); |
| 187 | |
| 188 | for (auto && tensorInfo : inputTensorInfos) |
| 189 | { |
| 190 | if (numDims == 0) |
| 191 | { |
| 192 | numDims = tensorInfo.GetShape().GetNumDimensions(); |
| 193 | Generate3dPermuteVectorForConcat(numDims, concatDim, permutations); |
| 194 | |
| 195 | // Store the reverese permutation. |
| 196 | permuteVector = permutations.second; |
| 197 | BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity), |
| 198 | "Test logic error, we don't need permutation, so we shouldn't arrive here"); |
| 199 | } |
| 200 | else |
| 201 | { |
| 202 | BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(), |
| 203 | "All inputs must have the same number of dimensions"); |
| 204 | } |
| 205 | |
| 206 | armnn::TensorInfo newTensorInfo = tensorInfo; |
| 207 | newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape())); |
| 208 | |
| 209 | PermuteTensorData<T>(workloadFactory, |
| 210 | memoryManager, |
| 211 | permutations.first, |
| 212 | newTensorInfo, |
| 213 | inputData[nthInput], |
| 214 | inputDataStorage[nthInput]); |
| 215 | |
| 216 | inputData[nthInput] = inputDataStorage[nthInput].data(); |
| 217 | inputTensorInfos[nthInput] = newTensorInfo; |
| 218 | |
| 219 | ++nthInput; |
| 220 | } |
| 221 | |
| 222 | outputTensorInfo.SetShape( |
| 223 | armnnUtils::Permuted( |
| 224 | ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()), |
| 225 | permutations.first)); |
| 226 | } |
| 227 | |
| 228 | // |
| 229 | // This is the pair of PermuteInputsForConcat(...) which permutes back |
| 230 | // the output of the concatenation so we can check it against an expected |
| 231 | // output. |
| 232 | // |
| 233 | template <typename T> void PermuteOutputForConcat( |
| 234 | armnn::IWorkloadFactory& workloadFactory, |
| 235 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 236 | const armnn::TensorInfo & tensorInfo, |
| 237 | const armnn::PermutationVector & permuteVector, |
| 238 | std::unique_ptr<armnn::ITensorHandle> && inputDataHandle, |
| 239 | T * data) |
| 240 | { |
| 241 | BOOST_ASSERT_MSG(data != nullptr, "data must not be null"); |
| 242 | if (data == nullptr) |
| 243 | { |
| 244 | // Nullptr is an error in the test. By returning without doing the permutation |
| 245 | // I expect the caller to fail the test. It still makes sense to report this as |
| 246 | // an assert for Debug builds. |
| 247 | return; |
| 248 | } |
| 249 | |
| 250 | armnn::TensorInfo resultTensorInfo = tensorInfo; |
| 251 | std::vector<T> inputData(tensorInfo.GetNumElements()); |
| 252 | std::vector<T> outputData; |
| 253 | |
| 254 | CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get()); |
| 255 | |
| 256 | PermuteTensorData<T>(workloadFactory, |
| 257 | memoryManager, |
| 258 | permuteVector, |
| 259 | resultTensorInfo, |
| 260 | &inputData[0], |
| 261 | outputData); |
| 262 | |
| 263 | ::memcpy(data, &outputData[0], sizeof(T)*outputData.size()); |
| 264 | } |
| 265 | |
| 266 | template<typename T> void Concatenate( |
| 267 | armnn::IWorkloadFactory& workloadFactory, |
| 268 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 269 | std::initializer_list<const armnn::TensorInfo> inputTensorInfosOrig, |
| 270 | std::initializer_list<T *> inputsOrig, |
| 271 | const armnn::TensorInfo& outputTensorInfoOrig, |
| 272 | T * output, |
| 273 | unsigned int concatDim, |
| 274 | bool useSubtensor) |
| 275 | { |
| 276 | BOOST_ASSERT_MSG(output != nullptr, "output must not be null"); |
| 277 | if (output == nullptr) |
| 278 | { |
| 279 | // Nullptr is an error in the test. By returning without doing the permutation |
| 280 | // I expect the caller to fail the test. It still makes sense to report this as |
| 281 | // an assert for Debug builds. |
| 282 | return; |
| 283 | } |
| 284 | |
| 285 | // Saves a copy of the parameters which we might need to change. |
| 286 | std::vector<armnn::TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); |
| 287 | std::vector<T *> inputs = inputsOrig; |
| 288 | armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig; |
| 289 | |
| 290 | armnn::PermutationVector permuteVector{0, 1, 2}; |
| 291 | |
| 292 | // Holds and automatically releases memory for the reshaped input data. |
| 293 | std::vector<std::vector<T>> tmpInputDataStorage; |
| 294 | |
| 295 | const size_t inputCount = inputTensorInfos.size(); |
| 296 | |
| 297 | bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim); |
| 298 | |
| 299 | if (needPermuteForConcat) |
| 300 | { |
| 301 | // |
| 302 | // We need to permute the inputs, because concatenation along |
| 303 | // the requested axis is not supported. |
| 304 | // |
| 305 | PermuteInputsForConcat<T>(workloadFactory, |
| 306 | memoryManager, |
| 307 | inputTensorInfos, |
| 308 | inputs, |
| 309 | tmpInputDataStorage, |
| 310 | permuteVector, |
| 311 | concatDim, |
| 312 | outputTensorInfo); |
| 313 | } |
| 314 | |
| 315 | armnn::WorkloadInfo workloadInfo; |
| 316 | |
| 317 | std::vector<std::unique_ptr<armnn::ITensorHandle>> inputHandles; |
| 318 | inputHandles.reserve(inputCount); |
| 319 | |
| 320 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 321 | |
| 322 | armnn::ConcatQueueDescriptor queueDescriptor; |
| 323 | armnn::OriginsDescriptor viewsDescriptor = CreateDescriptorForConcat(inputTensorInfos, concatDim); |
| 324 | queueDescriptor.m_Parameters = viewsDescriptor; |
| 325 | |
| 326 | if (useSubtensor) |
| 327 | { |
| 328 | queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews()); |
| 329 | for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i) |
| 330 | { |
| 331 | queueDescriptor.m_ViewOrigins.emplace_back(std::vector<unsigned int>(viewsDescriptor.GetViewOrigin(i), |
| 332 | viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions())); |
| 333 | } |
| 334 | |
| 335 | outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 336 | |
| 337 | const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 338 | for (unsigned int i = 0; i < inputCount; ++i) |
| 339 | { |
| 340 | const armnn::TensorInfo& inputTensorInfo = inputTensorInfos[i]; |
| 341 | std::unique_ptr<armnn::ITensorHandle> inputHandle = |
| 342 | subTensorsSupported ? |
| 343 | workloadFactory.CreateSubTensorHandle(*outputHandle, |
| 344 | inputTensorInfo.GetShape(), |
| 345 | queueDescriptor.m_ViewOrigins[i].m_Origin.data()) : |
| 346 | workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 347 | |
| 348 | inputHandles.emplace_back(std::move(inputHandle)); |
| 349 | } |
| 350 | |
| 351 | } |
| 352 | else |
| 353 | { |
| 354 | for (unsigned int i = 0; i < inputCount; ++i) |
| 355 | { |
| 356 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfos[i]); |
| 357 | inputHandles.emplace_back(std::move(inputHandle)); |
| 358 | } |
| 359 | } |
| 360 | |
| 361 | for (unsigned int i = 0; i < inputCount; ++i) |
| 362 | { |
| 363 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get()); |
| 364 | } |
| 365 | |
| 366 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 367 | |
| 368 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(queueDescriptor, workloadInfo); |
| 369 | |
| 370 | for (auto& inputHandle : inputHandles) |
| 371 | { |
| 372 | inputHandle->Allocate(); |
| 373 | } |
| 374 | |
| 375 | outputHandle->Allocate(); |
| 376 | |
| 377 | unsigned int nextInputId = 0; |
| 378 | for (auto& inputHandle : inputHandles) |
| 379 | { |
| 380 | CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]); |
| 381 | ++nextInputId; |
| 382 | } |
| 383 | |
| 384 | workload->PostAllocationConfigure(); |
| 385 | workload->Execute(); |
| 386 | |
| 387 | if (needPermuteForConcat) |
| 388 | { |
| 389 | PermuteOutputForConcat<T>(workloadFactory, |
| 390 | memoryManager, |
| 391 | outputTensorInfo, |
| 392 | permuteVector, |
| 393 | std::move(outputHandle), |
| 394 | output); |
| 395 | } |
| 396 | else |
| 397 | { |
| 398 | CopyDataFromITensorHandle(output, outputHandle.get()); |
| 399 | } |
| 400 | } |
| 401 | |
| 402 | // |
| 403 | // Implementation templates |
| 404 | // |
| 405 | |
| 406 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 407 | LayerTestResult<T, 1> Concat1dTestImpl( |
| 408 | armnn::IWorkloadFactory& workloadFactory, |
| 409 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 410 | float qScale, |
| 411 | int32_t qOffset) |
| 412 | { |
| 413 | armnn::TensorInfo inputTensorInfo({ 3 }, ArmnnType, qScale, qOffset); |
| 414 | |
| 415 | auto input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 3.0f })); |
| 416 | auto input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 4.0f, 5.0f, 6.0f })); |
| 417 | auto input2 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 7.0f, 8.0f, 9.0f })); |
| 418 | |
| 419 | armnn::TensorInfo outputTensorInfo({ 9 }, ArmnnType, qScale, qOffset); |
| 420 | |
| 421 | LayerTestResult<T, 1> result(outputTensorInfo); |
| 422 | |
| 423 | std::vector<T> output; |
| 424 | output.resize(outputTensorInfo.GetNumElements()); |
| 425 | Concatenate<T>(workloadFactory, memoryManager, |
| 426 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 427 | { input0.data(), input1.data(), input2.data() }, |
| 428 | outputTensorInfo, |
| 429 | output.data(), |
| 430 | 0, |
| 431 | true); |
| 432 | |
| 433 | result.output = MakeTensor<T, 1>(outputTensorInfo, output); |
| 434 | result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 435 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f |
| 436 | })); |
| 437 | |
| 438 | return result; |
| 439 | } |
| 440 | |
| 441 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 442 | LayerTestResult<T, 2> Concat2dTestImpl( |
| 443 | armnn::IWorkloadFactory& workloadFactory, |
| 444 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 445 | const armnn::TensorInfo& outputTensorInfo, |
| 446 | unsigned int dimension, |
| 447 | const float qScale, |
| 448 | const int32_t qOffset) |
| 449 | { |
| 450 | armnn::TensorInfo inputTensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); |
| 451 | |
| 452 | auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 453 | // Batch 0 |
| 454 | 1.0f, 2.0f, 3.0f, |
| 455 | |
| 456 | // Batch 1 |
| 457 | 10.0f, 11.0f, 12.0f, |
| 458 | })); |
| 459 | |
| 460 | auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 461 | // Batch 0 |
| 462 | 4.0f, 5.0f, 6.0f, |
| 463 | |
| 464 | // Batch 1 |
| 465 | 13.0f, 14.0f, 15.0f, |
| 466 | })); |
| 467 | |
| 468 | auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 469 | // Batch 0 |
| 470 | 7.0f, 8.0f, 9.0f, |
| 471 | |
| 472 | // Batch 1 |
| 473 | 16.0f, 17.0f, 18.0f, |
| 474 | })); |
| 475 | |
| 476 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 477 | |
| 478 | std::vector<T> output; |
| 479 | output.resize(outputTensorInfo.GetNumElements()); |
| 480 | Concatenate<T>(workloadFactory, memoryManager, |
| 481 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 482 | { input0.data(), input1.data(), input2.data() }, |
| 483 | outputTensorInfo, |
| 484 | output.data(), |
| 485 | dimension, |
| 486 | true); |
| 487 | |
| 488 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
| 489 | return result; |
| 490 | } |
| 491 | |
| 492 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 493 | LayerTestResult<T, 2> Concat2dDim0TestImpl( |
| 494 | armnn::IWorkloadFactory& workloadFactory, |
| 495 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 496 | float qScale, |
| 497 | int32_t qOffset) |
| 498 | { |
| 499 | armnn::TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); |
| 500 | |
| 501 | LayerTestResult<T, 2> result = Concat2dTestImpl<ArmnnType>( |
| 502 | workloadFactory, memoryManager, outputTensorInfo, 0, qScale, qOffset); |
| 503 | |
| 504 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 505 | // Batch 0 |
| 506 | 1.0f, 2.0f, 3.0f, |
| 507 | |
| 508 | // Batch 1 |
| 509 | 10.0f, 11.0f, 12.0f, |
| 510 | |
| 511 | // Batch 2 |
| 512 | 4.0f, 5.0f, 6.0f, |
| 513 | |
| 514 | // Batch 3 |
| 515 | 13.0f, 14.0f, 15.0f, |
| 516 | |
| 517 | // Batch 4 |
| 518 | 7.0f, 8.0f, 9.0f, |
| 519 | |
| 520 | // Batch 5 |
| 521 | 16.0f, 17.0f, 18.0f, |
| 522 | })); |
| 523 | |
| 524 | return result; |
| 525 | } |
| 526 | |
| 527 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 528 | LayerTestResult<T, 2> Concat2dDim1TestImpl( |
| 529 | armnn::IWorkloadFactory& workloadFactory, |
| 530 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 531 | float qScale, |
| 532 | int32_t qOffset) |
| 533 | { |
| 534 | armnn::TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); |
| 535 | |
| 536 | LayerTestResult<T, 2> result = Concat2dTestImpl<ArmnnType>( |
| 537 | workloadFactory, memoryManager, outputTensorInfo, 1, qScale, qOffset); |
| 538 | |
| 539 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 540 | // Batch 0 |
| 541 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
| 542 | |
| 543 | // Batch 1 |
| 544 | 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f |
| 545 | })); |
| 546 | |
| 547 | return result; |
| 548 | } |
| 549 | |
| 550 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 551 | LayerTestResult<T, 2> Concat2dDim0DiffInputDimsTestImpl( |
| 552 | armnn::IWorkloadFactory& workloadFactory, |
| 553 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 554 | float qScale, |
| 555 | int32_t qOffset) |
| 556 | { |
| 557 | armnn::TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); |
| 558 | auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 559 | // Batch 0 |
| 560 | 1.0f, 2.0f, 3.0f, |
| 561 | |
| 562 | // Batch 1 |
| 563 | 10.0f, 11.0f, 12.0f, |
| 564 | })); |
| 565 | |
| 566 | armnn::TensorInfo input1TensorInfo({ 3, 3 }, ArmnnType, qScale, qOffset); |
| 567 | auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 568 | // Batch 0 |
| 569 | 4.0f, 5.0f, 6.0f, |
| 570 | |
| 571 | // Batch 1 |
| 572 | 13.0f, 14.0f, 15.0f, |
| 573 | |
| 574 | // Batch 0 |
| 575 | 7.0f, 8.0f, 9.0f, |
| 576 | })); |
| 577 | |
| 578 | armnn::TensorInfo input2TensorInfo({ 1, 3 }, ArmnnType, qScale, qOffset); |
| 579 | auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 580 | // Batch 1 |
| 581 | 16.0f, 17.0f, 18.0f, |
| 582 | })); |
| 583 | |
| 584 | armnn::TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); |
| 585 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 586 | |
| 587 | std::vector<T> output; |
| 588 | output.resize(outputTensorInfo.GetNumElements()); |
| 589 | Concatenate<T>(workloadFactory, memoryManager, |
| 590 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 591 | { input0.data(), input1.data(), input2.data() }, |
| 592 | outputTensorInfo, |
| 593 | output.data(), |
| 594 | 0, |
| 595 | true); |
| 596 | |
| 597 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
| 598 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 599 | // Batch 0 |
| 600 | 1.0f, 2.0f, 3.0f, |
| 601 | |
| 602 | // Batch 1 |
| 603 | 10.0f, 11.0f, 12.0f, |
| 604 | |
| 605 | // Batch 2 |
| 606 | 4.0f, 5.0f, 6.0f, |
| 607 | |
| 608 | // Batch 3 |
| 609 | 13.0f, 14.0f, 15.0f, |
| 610 | |
| 611 | // Batch 4 |
| 612 | 7.0f, 8.0f, 9.0f, |
| 613 | |
| 614 | // Batch 5 |
| 615 | 16.0f, 17.0f, 18.0f, |
| 616 | })); |
| 617 | |
| 618 | return result; |
| 619 | } |
| 620 | |
| 621 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 622 | LayerTestResult<T, 2> Concat2dDim1DiffInputDimsTestImpl( |
| 623 | armnn::IWorkloadFactory& workloadFactory, |
| 624 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 625 | float qScale, |
| 626 | int32_t qOffset) |
| 627 | { |
| 628 | armnn::TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); |
| 629 | auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 630 | // Batch 0 |
| 631 | 1.0f, 2.0f, 3.0f, |
| 632 | |
| 633 | // Batch 1 |
| 634 | 10.0f, 11.0f, 12.0f, |
| 635 | })); |
| 636 | |
| 637 | armnn::TensorInfo input1TensorInfo({ 2, 5 }, ArmnnType, qScale, qOffset); |
| 638 | auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 639 | // Batch 0 |
| 640 | 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, |
| 641 | |
| 642 | // Batch 1 |
| 643 | 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, |
| 644 | })); |
| 645 | |
| 646 | armnn::TensorInfo input2TensorInfo({ 2, 1 }, ArmnnType, qScale, qOffset); |
| 647 | auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 648 | // Batch 0 |
| 649 | 9.0f, |
| 650 | |
| 651 | // Batch 1 |
| 652 | 18.0f |
| 653 | })); |
| 654 | |
| 655 | armnn::TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); |
| 656 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 657 | |
| 658 | std::vector<T> output; |
| 659 | output.resize(outputTensorInfo.GetNumElements()); |
| 660 | Concatenate<T>(workloadFactory, memoryManager, |
| 661 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 662 | { input0.data(), input1.data(), input2.data() }, |
| 663 | outputTensorInfo, |
| 664 | output.data(), |
| 665 | 1, |
| 666 | true); |
| 667 | |
| 668 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
| 669 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 670 | // Batch 0 |
| 671 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
| 672 | |
| 673 | // Batch 1 |
| 674 | 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, |
| 675 | })); |
| 676 | |
| 677 | return result; |
| 678 | } |
| 679 | |
| 680 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 681 | LayerTestResult<T, 3> Concat3dTestImpl( |
| 682 | armnn::IWorkloadFactory& workloadFactory, |
| 683 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 684 | const armnn::TensorInfo& outputTensorInfo, |
| 685 | unsigned int dimension, |
| 686 | bool useSubtensor, |
| 687 | float qScale, |
| 688 | int32_t qOffset) |
| 689 | { |
| 690 | armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); |
| 691 | |
| 692 | auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 693 | // Batch 0, Channel 0 |
| 694 | 1.0f, 2.0f, |
| 695 | |
| 696 | // Batch 0, Channel 1 |
| 697 | 3.0f, 4.0f, |
| 698 | |
| 699 | // Batch 0, Channel 2 |
| 700 | 5.0f, 6.0f, |
| 701 | |
| 702 | // Batch 1, Channel 0 |
| 703 | 19.0f, 20.0f, |
| 704 | |
| 705 | // Batch 1, Channel 1 |
| 706 | 21.0f, 22.0f, |
| 707 | |
| 708 | // Batch 1, Channel 2 |
| 709 | 23.0f, 24.0f |
| 710 | })); |
| 711 | |
| 712 | auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 713 | // Batch 0, Channel 0 |
| 714 | 7.0f, 8.0f, |
| 715 | |
| 716 | // Batch 0, Channel 1 |
| 717 | 9.0f, 10.0f, |
| 718 | |
| 719 | // Batch 0, Channel 2 |
| 720 | 11.0f, 12.0f, |
| 721 | |
| 722 | // Batch 1, Channel 0 |
| 723 | 25.0f, 26.0f, |
| 724 | |
| 725 | // Batch 1, Channel 1 |
| 726 | 27.0f, 28.0f, |
| 727 | |
| 728 | // Batch 1, Channel 2 |
| 729 | 29.0f, 30.0f |
| 730 | })); |
| 731 | |
| 732 | auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 733 | // Batch 0, Channel 0 |
| 734 | 13.0f, 14.0f, |
| 735 | |
| 736 | // Batch 0, Channel 1 |
| 737 | 15.0f, 16.0f, |
| 738 | |
| 739 | // Batch 0, Channel 2 |
| 740 | 17.0f, 18.0f, |
| 741 | |
| 742 | // Batch 1, Channel 0 |
| 743 | 31.0f, 32.0f, |
| 744 | |
| 745 | // Batch 1, Channel 1 |
| 746 | 33.0f, 34.0f, |
| 747 | |
| 748 | // Batch 1, Channel 2 |
| 749 | 35.0f, 36.0f |
| 750 | })); |
| 751 | |
| 752 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 753 | |
| 754 | std::vector<T> output; |
| 755 | output.resize(outputTensorInfo.GetNumElements()); |
| 756 | Concatenate<T>(workloadFactory, memoryManager, |
| 757 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 758 | { input0.data(), input1.data(), input2.data() }, |
| 759 | outputTensorInfo, |
| 760 | output.data(), |
| 761 | dimension, |
| 762 | useSubtensor); |
| 763 | |
| 764 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 765 | return result; |
| 766 | } |
| 767 | |
| 768 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 769 | LayerTestResult<T, 3> Concat3dDim0TestImpl( |
| 770 | armnn::IWorkloadFactory& workloadFactory, |
| 771 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 772 | float qScale, |
| 773 | int32_t qOffset) |
| 774 | { |
| 775 | armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType, qScale, qOffset); |
| 776 | |
| 777 | LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( |
| 778 | workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); |
| 779 | |
| 780 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 781 | // Batch 0, Channel 0 |
| 782 | 1.0f, 2.0f, |
| 783 | |
| 784 | // Batch 0, Channel 1 |
| 785 | 3.0f, 4.0f, |
| 786 | |
| 787 | // Batch 0, Channel 2 |
| 788 | 5.0f, 6.0f, |
| 789 | |
| 790 | // Batch 1, Channel 0 |
| 791 | 19.0f, 20.0f, |
| 792 | |
| 793 | // Batch 1, Channel 1 |
| 794 | 21.0f, 22.0f, |
| 795 | |
| 796 | // Batch 1, Channel 2 |
| 797 | 23.0f, 24.0f, |
| 798 | |
| 799 | // Batch 2, Channel 0 |
| 800 | 7.0f, 8.0f, |
| 801 | |
| 802 | // Batch 2, Channel 1 |
| 803 | 9.0f, 10.0f, |
| 804 | |
| 805 | // Batch 2, Channel 2 |
| 806 | 11.0f, 12.0f, |
| 807 | |
| 808 | // Batch 3, Channel 0 |
| 809 | 25.0f, 26.0f, |
| 810 | |
| 811 | // Batch 3, Channel 1 |
| 812 | 27.0f, 28.0f, |
| 813 | |
| 814 | // Batch 3, Channel 2 |
| 815 | 29.0f, 30.0f, |
| 816 | |
| 817 | // Batch 4, Channel 0 |
| 818 | 13.0f, 14.0f, |
| 819 | |
| 820 | // Batch 4, Channel 1 |
| 821 | 15.0f, 16.0f, |
| 822 | |
| 823 | // Batch 4, Channel 2 |
| 824 | 17.0f, 18.0f, |
| 825 | |
| 826 | // Batch 5, Channel 0 |
| 827 | 31.0f, 32.0f, |
| 828 | |
| 829 | // Batch 5, Channel 1 |
| 830 | 33.0f, 34.0f, |
| 831 | |
| 832 | // Batch 5, Channel 2 |
| 833 | 35.0f, 36.0f |
| 834 | })); |
| 835 | |
| 836 | return result; |
| 837 | } |
| 838 | |
| 839 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 840 | LayerTestResult<T, 3> Concat3dDim1TestImpl( |
| 841 | armnn::IWorkloadFactory& workloadFactory, |
| 842 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 843 | float qScale, |
| 844 | int32_t qOffset) |
| 845 | { |
| 846 | armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, ArmnnType, qScale, qOffset); |
| 847 | |
| 848 | LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( |
| 849 | workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); |
| 850 | |
| 851 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 852 | // Batch 0, Channel 0 |
| 853 | 1.0f, 2.0f, |
| 854 | |
| 855 | // Batch 0, Channel 1 |
| 856 | 3.0f, 4.0f, |
| 857 | |
| 858 | // Batch 0, Channel 2 |
| 859 | 5.0f, 6.0f, |
| 860 | |
| 861 | // Batch 0, Channel 3 |
| 862 | 7.0f, 8.0f, |
| 863 | |
| 864 | // Batch 0, Channel 4 |
| 865 | 9.0f, 10.0f, |
| 866 | |
| 867 | // Batch 0, Channel 5 |
| 868 | 11.0f, 12.0f, |
| 869 | |
| 870 | // Batch 0, Channel 6 |
| 871 | 13.0f, 14.0f, |
| 872 | |
| 873 | // Batch 0, Channel 7 |
| 874 | 15.0f, 16.0f, |
| 875 | |
| 876 | // Batch 0, Channel 8 |
| 877 | 17.0f, 18.0f, |
| 878 | |
| 879 | // Batch 1, Channel 0 |
| 880 | 19.0f, 20.0f, |
| 881 | |
| 882 | // Batch 1, Channel 1 |
| 883 | 21.0f, 22.0f, |
| 884 | |
| 885 | // Batch 1, Channel 2 |
| 886 | 23.0f, 24.0f, |
| 887 | |
| 888 | // Batch 1, Channel 3 |
| 889 | 25.0f, 26.0f, |
| 890 | |
| 891 | // Batch 1, Channel 4 |
| 892 | 27.0f, 28.0f, |
| 893 | |
| 894 | // Batch 1, Channel 5 |
| 895 | 29.0f, 30.0f, |
| 896 | |
| 897 | // Batch 1, Channel 6 |
| 898 | 31.0f, 32.0f, |
| 899 | |
| 900 | // Batch 1, Channel 7 |
| 901 | 33.0f, 34.0f, |
| 902 | |
| 903 | // Batch 1, Channel 8 |
| 904 | 35.0f, 36.0f |
| 905 | })); |
| 906 | |
| 907 | return result; |
| 908 | } |
| 909 | |
| 910 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 911 | LayerTestResult<T, 3> Concat3dDim2TestImpl( |
| 912 | armnn::IWorkloadFactory& workloadFactory, |
| 913 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 914 | bool useSubtensor, |
| 915 | float qScale, |
| 916 | int32_t qOffset) |
| 917 | { |
| 918 | armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); |
| 919 | |
| 920 | LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( |
| 921 | workloadFactory, memoryManager, outputTensorInfo, 2, useSubtensor, qScale, qOffset); |
| 922 | |
| 923 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 924 | // Batch 0, Channel 0 |
| 925 | 1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f, |
| 926 | |
| 927 | // Batch 0, Channel 1 |
| 928 | 3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f, |
| 929 | |
| 930 | // Batch 0, Channel 2 |
| 931 | 5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f, |
| 932 | |
| 933 | // Batch 1, Channel 0 |
| 934 | 19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f, |
| 935 | |
| 936 | // Batch 1, Channel 1 |
| 937 | 21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f, |
| 938 | |
| 939 | // Batch 1, Channel 2 |
| 940 | 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f, |
| 941 | })); |
| 942 | |
| 943 | return result; |
| 944 | } |
| 945 | |
| 946 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 947 | LayerTestResult<T, 3> Concat3dDim0DiffInputDimsTestImpl( |
| 948 | armnn::IWorkloadFactory& workloadFactory, |
| 949 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 950 | float qScale, |
| 951 | int32_t qOffset) |
| 952 | { |
| 953 | armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType); |
| 954 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 955 | // Batch 0, Channel 0 |
| 956 | 1.0f, 2.0f, |
| 957 | |
| 958 | // Batch 0, Channel 1 |
| 959 | 3.0f, 4.0f, |
| 960 | |
| 961 | // Batch 0, Channel 2 |
| 962 | 5.0f, 6.0f, |
| 963 | |
| 964 | // Batch 1, Channel 0 |
| 965 | 19.0f, 20.0f, |
| 966 | |
| 967 | // Batch 1, Channel 1 |
| 968 | 21.0f, 22.0f, |
| 969 | |
| 970 | // Batch 1, Channel 2 |
| 971 | 23.0f, 24.0f |
| 972 | })); |
| 973 | |
| 974 | armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, ArmnnType); |
| 975 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 976 | // Batch 0, Channel 0 |
| 977 | 7.0f, 8.0f, |
| 978 | |
| 979 | // Batch 0, Channel 1 |
| 980 | 9.0f, 10.0f, |
| 981 | |
| 982 | // Batch 0, Channel 2 |
| 983 | 11.0f, 12.0f, |
| 984 | })); |
| 985 | |
| 986 | armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, ArmnnType); |
| 987 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 988 | // Batch 0, Channel 0 |
| 989 | 25.0f, 26.0f, |
| 990 | |
| 991 | // Batch 0, Channel 1 |
| 992 | 27.0f, 28.0f, |
| 993 | |
| 994 | // Batch 0, Channel 2 |
| 995 | 29.0f, 30.0f, |
| 996 | |
| 997 | // Batch 1, Channel 0 |
| 998 | 13.0f, 14.0f, |
| 999 | |
| 1000 | // Batch 1, Channel 1 |
| 1001 | 15.0f, 16.0f, |
| 1002 | |
| 1003 | // Batch 1, Channel 2 |
| 1004 | 17.0f, 18.0f, |
| 1005 | |
| 1006 | // Batch 2, Channel 0 |
| 1007 | 31.0f, 32.0f, |
| 1008 | |
| 1009 | // Batch 2, Channel 1 |
| 1010 | 33.0f, 34.0f, |
| 1011 | |
| 1012 | // Batch 2, Channel 2 |
| 1013 | 35.0f, 36.0f |
| 1014 | })); |
| 1015 | |
| 1016 | armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType); |
| 1017 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 1018 | |
| 1019 | std::vector<T> output; |
| 1020 | output.resize(outputTensorInfo.GetNumElements()); |
| 1021 | Concatenate<T>(workloadFactory, memoryManager, |
| 1022 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 1023 | { input0.data(), input1.data(), input2.data() }, |
| 1024 | outputTensorInfo, |
| 1025 | output.data(), |
| 1026 | 0, |
| 1027 | true); |
| 1028 | |
| 1029 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 1030 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1031 | // Batch 0, Channel 0 |
| 1032 | 1.0f, 2.0f, |
| 1033 | |
| 1034 | // Batch 0, Channel 1 |
| 1035 | 3.0f, 4.0f, |
| 1036 | |
| 1037 | // Batch 0, Channel 2 |
| 1038 | 5.0f, 6.0f, |
| 1039 | |
| 1040 | // Batch 1, Channel 0 |
| 1041 | 19.0f, 20.0f, |
| 1042 | |
| 1043 | // Batch 1, Channel 1 |
| 1044 | 21.0f, 22.0f, |
| 1045 | |
| 1046 | // Batch 1, Channel 2 |
| 1047 | 23.0f, 24.0f, |
| 1048 | |
| 1049 | // Batch 2, Channel 0 |
| 1050 | 7.0f, 8.0f, |
| 1051 | |
| 1052 | // Batch 2, Channel 1 |
| 1053 | 9.0f, 10.0f, |
| 1054 | |
| 1055 | // Batch 2, Channel 2 |
| 1056 | 11.0f, 12.0f, |
| 1057 | |
| 1058 | // Batch 3, Channel 0 |
| 1059 | 25.0f, 26.0f, |
| 1060 | |
| 1061 | // Batch 3, Channel 1 |
| 1062 | 27.0f, 28.0f, |
| 1063 | |
| 1064 | // Batch 3, Channel 2 |
| 1065 | 29.0f, 30.0f, |
| 1066 | |
| 1067 | // Batch 4, Channel 0 |
| 1068 | 13.0f, 14.0f, |
| 1069 | |
| 1070 | // Batch 4, Channel 1 |
| 1071 | 15.0f, 16.0f, |
| 1072 | |
| 1073 | // Batch 4, Channel 2 |
| 1074 | 17.0f, 18.0f, |
| 1075 | |
| 1076 | // Batch 5, Channel 0 |
| 1077 | 31.0f, 32.0f, |
| 1078 | |
| 1079 | // Batch 5, Channel 1 |
| 1080 | 33.0f, 34.0f, |
| 1081 | |
| 1082 | // Batch 5, Channel 2 |
| 1083 | 35.0f, 36.0f |
| 1084 | })); |
| 1085 | |
| 1086 | return result; |
| 1087 | } |
| 1088 | |
| 1089 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1090 | LayerTestResult<T, 3> Concat3dDim1DiffInputDimsTestImpl( |
| 1091 | armnn::IWorkloadFactory& workloadFactory, |
| 1092 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1093 | float qScale, |
| 1094 | int32_t qOffset) |
| 1095 | { |
| 1096 | armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); |
| 1097 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1098 | // Batch 0, Channel 0 |
| 1099 | 1.0f, 2.0f, |
| 1100 | |
| 1101 | // Batch 0, Channel 1 |
| 1102 | 3.0f, 4.0f, |
| 1103 | |
| 1104 | // Batch 0, Channel 2 |
| 1105 | 5.0f, 6.0f, |
| 1106 | |
| 1107 | // Batch 1, Channel 0 |
| 1108 | 19.0f, 20.0f, |
| 1109 | |
| 1110 | // Batch 1, Channel 1 |
| 1111 | 21.0f, 22.0f, |
| 1112 | |
| 1113 | // Batch 1, Channel 2 |
| 1114 | 23.0f, 24.0f |
| 1115 | })); |
| 1116 | |
| 1117 | armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, ArmnnType, qScale, qOffset); |
| 1118 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1119 | // Batch 0, Channel 0 |
| 1120 | 7.0f, 8.0f, |
| 1121 | |
| 1122 | // Batch 0, Channel 1 |
| 1123 | 9.0f, 10.0f, |
| 1124 | |
| 1125 | // Batch 0, Channel 2 |
| 1126 | 11.0f, 12.0f, |
| 1127 | |
| 1128 | // Batch 0, Channel 3 |
| 1129 | 25.0f, 26.0f, |
| 1130 | |
| 1131 | // Batch 1, Channel 0 |
| 1132 | 27.0f, 28.0f, |
| 1133 | |
| 1134 | // Batch 1, Channel 1 |
| 1135 | 29.0f, 30.0f, |
| 1136 | |
| 1137 | // Batch 1, Channel 2 |
| 1138 | 13.0f, 14.0f, |
| 1139 | |
| 1140 | // Batch 1, Channel 3 |
| 1141 | 15.0f, 16.0f, |
| 1142 | })); |
| 1143 | |
| 1144 | armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, ArmnnType, qScale, qOffset); |
| 1145 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1146 | // Batch 0, Channel 0 |
| 1147 | 17.0f, 18.0f, |
| 1148 | |
| 1149 | // Batch 1, Channel 0 |
| 1150 | 31.0f, 32.0f, |
| 1151 | })); |
| 1152 | |
| 1153 | armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, ArmnnType, qScale, qOffset); |
| 1154 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 1155 | |
| 1156 | std::vector<T> output; |
| 1157 | output.resize(outputTensorInfo.GetNumElements()); |
| 1158 | Concatenate<T>(workloadFactory, memoryManager, |
| 1159 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 1160 | { input0.data(), input1.data(), input2.data() }, |
| 1161 | outputTensorInfo, |
| 1162 | output.data(), |
| 1163 | 1, |
| 1164 | true); |
| 1165 | |
| 1166 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 1167 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1168 | // Batch 0, Channel 0 |
| 1169 | 1.0f, 2.0f, |
| 1170 | |
| 1171 | // Batch 0, Channel 1 |
| 1172 | 3.0f, 4.0f, |
| 1173 | |
| 1174 | // Batch 0, Channel 2 |
| 1175 | 5.0f, 6.0f, |
| 1176 | |
| 1177 | // Batch 0, Channel 3 |
| 1178 | 7.0f, 8.0f, |
| 1179 | |
| 1180 | // Batch 0, Channel 4 |
| 1181 | 9.0f, 10.0f, |
| 1182 | |
| 1183 | // Batch 0, Channel 5 |
| 1184 | 11.0f, 12.0f, |
| 1185 | |
| 1186 | // Batch 0, Channel 6 |
| 1187 | 25.0f, 26.0f, |
| 1188 | |
| 1189 | // Batch 0, Channel 7 |
| 1190 | 17.0f, 18.0f, |
| 1191 | |
| 1192 | // Batch 1, Channel 0 |
| 1193 | 19.0f, 20.0f, |
| 1194 | |
| 1195 | // Batch 1, Channel 1 |
| 1196 | 21.0f, 22.0f, |
| 1197 | |
| 1198 | // Batch 1, Channel 2 |
| 1199 | 23.0f, 24.0f, |
| 1200 | |
| 1201 | // Batch 1, Channel 3 |
| 1202 | 27.0f, 28.0f, |
| 1203 | |
| 1204 | // Batch 1, Channel 4 |
| 1205 | 29.0f, 30.0f, |
| 1206 | |
| 1207 | // Batch 1, Channel 5 |
| 1208 | 13.0f, 14.0f, |
| 1209 | |
| 1210 | // Batch 1, Channel 6 |
| 1211 | 15.0f, 16.0f, |
| 1212 | |
| 1213 | // Batch 1, Channel 7 |
| 1214 | 31.0f, 32.0f, |
| 1215 | })); |
| 1216 | |
| 1217 | return result; |
| 1218 | } |
| 1219 | |
| 1220 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1221 | LayerTestResult<T, 3> Concat3dDim2DiffInputDimsTestImpl( |
| 1222 | armnn::IWorkloadFactory& workloadFactory, |
| 1223 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1224 | bool useSubtensor, |
| 1225 | float qScale, |
| 1226 | int32_t qOffset) |
| 1227 | { |
| 1228 | armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); |
| 1229 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1230 | // Batch 0, Channel 0 |
| 1231 | 1.0f, 2.0f, |
| 1232 | |
| 1233 | // Batch 0, Channel 1 |
| 1234 | 3.0f, 4.0f, |
| 1235 | |
| 1236 | // Batch 0, Channel 2 |
| 1237 | 5.0f, 6.0f, |
| 1238 | |
| 1239 | // Batch 1, Channel 0 |
| 1240 | 19.0f, 20.0f, |
| 1241 | |
| 1242 | // Batch 1, Channel 1 |
| 1243 | 21.0f, 22.0f, |
| 1244 | |
| 1245 | // Batch 1, Channel 2 |
| 1246 | 23.0f, 24.0f |
| 1247 | })); |
| 1248 | |
| 1249 | armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, ArmnnType, qScale, qOffset); |
| 1250 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1251 | // Batch 0, Channel 0 |
| 1252 | 7.0f, |
| 1253 | |
| 1254 | // Batch 0, Channel 1 |
| 1255 | 9.0f, |
| 1256 | |
| 1257 | // Batch 0, Channel 2 |
| 1258 | 11.0f, |
| 1259 | |
| 1260 | // Batch 1, Channel 0 |
| 1261 | 25.0f, |
| 1262 | |
| 1263 | // Batch 1, Channel 1 |
| 1264 | 27.0f, |
| 1265 | |
| 1266 | // Batch 1, Channel 2 |
| 1267 | 29.0f |
| 1268 | })); |
| 1269 | |
| 1270 | armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, ArmnnType, qScale, qOffset); |
| 1271 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1272 | // Batch 0, Channel 0 |
| 1273 | 13.0f, 14.0f, 50.0f, |
| 1274 | |
| 1275 | // Batch 0, Channel 1 |
| 1276 | 15.0f, 16.0f, 51.0f, |
| 1277 | |
| 1278 | // Batch 0, Channel 2 |
| 1279 | 17.0f, 18.0f, 52.0f, |
| 1280 | |
| 1281 | // Batch 1, Channel 0 |
| 1282 | 31.0f, 32.0f, 53.0f, |
| 1283 | |
| 1284 | // Batch 1, Channel 1 |
| 1285 | 33.0f, 34.0f, 54.0f, |
| 1286 | |
| 1287 | // Batch 1, Channel 2 |
| 1288 | 35.0f, 36.0f, 55.0f, |
| 1289 | })); |
| 1290 | |
| 1291 | armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); |
| 1292 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 1293 | |
| 1294 | std::vector<T> output; |
| 1295 | output.resize(outputTensorInfo.GetNumElements()); |
| 1296 | Concatenate<T>(workloadFactory, memoryManager, |
| 1297 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 1298 | { input0.data(), input1.data(), input2.data() }, |
| 1299 | outputTensorInfo, |
| 1300 | output.data(), |
| 1301 | 2, |
| 1302 | useSubtensor); |
| 1303 | |
| 1304 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 1305 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1306 | // Batch 0, Channel 0 |
| 1307 | 1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f, |
| 1308 | |
| 1309 | // Batch 0, Channel 1 |
| 1310 | 3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f, |
| 1311 | |
| 1312 | // Batch 0, Channel 2 |
| 1313 | 5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f, |
| 1314 | |
| 1315 | // Batch 1, Channel 0 |
| 1316 | 19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f, |
| 1317 | |
| 1318 | // Batch 1, Channel 1 |
| 1319 | 21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f, |
| 1320 | |
| 1321 | // Batch 1, Channel 2 |
| 1322 | 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f, |
| 1323 | })); |
| 1324 | |
| 1325 | return result; |
| 1326 | } |
| 1327 | |
| 1328 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1329 | LayerTestResult<T, 4> Concat4dTestImpl( |
| 1330 | armnn::IWorkloadFactory& workloadFactory, |
| 1331 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1332 | const armnn::TensorInfo& outputTensorInfo, |
| 1333 | unsigned int dimension, |
| 1334 | bool useSubtensor, |
| 1335 | float qScale, |
| 1336 | int32_t qOffset) |
| 1337 | { |
| 1338 | armnn::TensorInfo inputTensorInfo({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1339 | |
| 1340 | auto input0 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1341 | 1.0f, 2.0f, |
| 1342 | 3.0f, 4.0f, |
| 1343 | 5.0f, 6.0f, |
| 1344 | 7.0f, 8.0f, |
| 1345 | 9.0f, 10.0f, |
| 1346 | 11.0f, 12.0f |
| 1347 | })); |
| 1348 | |
| 1349 | auto input1 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1350 | 11.0f, 12.0f, |
| 1351 | 13.0f, 14.0f, |
| 1352 | 15.0f, 16.0f, |
| 1353 | 17.0f, 18.0f, |
| 1354 | 19.0f, 20.0f, |
| 1355 | 21.0f, 22.0f |
| 1356 | })); |
| 1357 | |
| 1358 | auto input2 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1359 | 21.0f, 22.0f, |
| 1360 | 23.0f, 24.0f, |
| 1361 | 25.0f, 26.0f, |
| 1362 | 27.0f, 28.0f, |
| 1363 | 29.0f, 30.0f, |
| 1364 | 31.0f, 32.0f |
| 1365 | })); |
| 1366 | |
| 1367 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1368 | |
| 1369 | std::vector<T> output; |
| 1370 | output.resize(outputTensorInfo.GetNumElements()); |
| 1371 | |
| 1372 | Concatenate<T>(workloadFactory, |
| 1373 | memoryManager, |
| 1374 | {inputTensorInfo, inputTensorInfo, inputTensorInfo}, |
| 1375 | {input0.data(), input1.data(), input2.data()}, |
| 1376 | outputTensorInfo, |
| 1377 | output.data(), |
| 1378 | dimension, |
| 1379 | useSubtensor); |
| 1380 | |
| 1381 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
| 1382 | return result; |
| 1383 | } |
| 1384 | |
| 1385 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1386 | LayerTestResult<T, 4> Concat4dDim0TestImpl( |
| 1387 | armnn::IWorkloadFactory& workloadFactory, |
| 1388 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1389 | float qScale, |
| 1390 | int32_t qOffset) |
| 1391 | { |
| 1392 | armnn::TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1393 | |
| 1394 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1395 | workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); |
| 1396 | |
| 1397 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1398 | 1.0f, 2.0f, |
| 1399 | 3.0f, 4.0f, |
| 1400 | 5.0f, 6.0f, |
| 1401 | 7.0f, 8.0f, |
| 1402 | 9.0f, 10.0f, |
| 1403 | 11.0f, 12.0f, |
| 1404 | |
| 1405 | 11.0f, 12.0f, |
| 1406 | 13.0f, 14.0f, |
| 1407 | 15.0f, 16.0f, |
| 1408 | 17.0f, 18.0f, |
| 1409 | 19.0f, 20.0f, |
| 1410 | 21.0f, 22.0f, |
| 1411 | |
| 1412 | 21.0f, 22.0f, |
| 1413 | 23.0f, 24.0f, |
| 1414 | 25.0f, 26.0f, |
| 1415 | 27.0f, 28.0f, |
| 1416 | 29.0f, 30.0f, |
| 1417 | 31.0f, 32.0f |
| 1418 | })); |
| 1419 | return result; |
| 1420 | } |
| 1421 | |
| 1422 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1423 | LayerTestResult<T, 4> Concat4dDim1TestImpl( |
| 1424 | armnn::IWorkloadFactory& workloadFactory, |
| 1425 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1426 | float qScale, |
| 1427 | int32_t qOffset) |
| 1428 | { |
| 1429 | armnn::TensorInfo outputTensorInfo({ 1, 9, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1430 | |
| 1431 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1432 | workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); |
| 1433 | |
| 1434 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1435 | 1.0f, 2.0f, |
| 1436 | 3.0f, 4.0f, |
| 1437 | 5.0f, 6.0f, |
| 1438 | 7.0f, 8.0f, |
| 1439 | 9.0f, 10.0f, |
| 1440 | 11.0f, 12.0f, |
| 1441 | |
| 1442 | 11.0f, 12.0f, |
| 1443 | 13.0f, 14.0f, |
| 1444 | 15.0f, 16.0f, |
| 1445 | 17.0f, 18.0f, |
| 1446 | 19.0f, 20.0f, |
| 1447 | 21.0f, 22.0f, |
| 1448 | |
| 1449 | 21.0f, 22.0f, |
| 1450 | 23.0f, 24.0f, |
| 1451 | 25.0f, 26.0f, |
| 1452 | 27.0f, 28.0f, |
| 1453 | 29.0f, 30.0f, |
| 1454 | 31.0f, 32.0f |
| 1455 | })); |
| 1456 | |
| 1457 | return result; |
| 1458 | } |
| 1459 | |
| 1460 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1461 | LayerTestResult<T, 4> Concat4dDim2TestImpl( |
| 1462 | armnn::IWorkloadFactory& workloadFactory, |
| 1463 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1464 | float qScale, |
| 1465 | int32_t qOffset) |
| 1466 | { |
| 1467 | armnn::TensorInfo outputTensorInfo({ 1, 3, 6, 2 }, ArmnnType, qScale, qOffset); |
| 1468 | |
| 1469 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1470 | workloadFactory, memoryManager, outputTensorInfo, 2, true, qScale, qOffset); |
| 1471 | |
| 1472 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1473 | 1.0f, 2.0f, |
| 1474 | 3.0f, 4.0f, |
| 1475 | 11.0f, 12.0f, |
| 1476 | 13.0f, 14.0f, |
| 1477 | 21.0f, 22.0f, |
| 1478 | 23.0f, 24.0f, |
| 1479 | |
| 1480 | 5.0f, 6.0f, |
| 1481 | 7.0f, 8.0f, |
| 1482 | 15.0f, 16.0f, |
| 1483 | 17.0f, 18.0f, |
| 1484 | 25.0f, 26.0f, |
| 1485 | 27.0f, 28.0f, |
| 1486 | |
| 1487 | 9.0f, 10.0f, |
| 1488 | 11.0f, 12.0f, |
| 1489 | 19.0f, 20.0f, |
| 1490 | 21.0f, 22.0f, |
| 1491 | 29.0f, 30.0f, |
| 1492 | 31.0f, 32.0f |
| 1493 | })); |
| 1494 | |
| 1495 | return result; |
| 1496 | } |
| 1497 | |
| 1498 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1499 | LayerTestResult<T, 4> Concat4dDim3TestImpl( |
| 1500 | armnn::IWorkloadFactory& workloadFactory, |
| 1501 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1502 | float qScale, |
| 1503 | int32_t qOffset, |
| 1504 | bool useSubtensor) |
| 1505 | { |
| 1506 | armnn::TensorInfo outputTensorInfo({ 1, 3, 2, 6 }, ArmnnType, qScale, qOffset); |
| 1507 | |
| 1508 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1509 | workloadFactory, memoryManager, outputTensorInfo, 3, useSubtensor, qScale, qOffset); |
| 1510 | |
| 1511 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1512 | 1.0f, 2.0f, |
| 1513 | 11.0f, 12.0f, |
| 1514 | 21.0f, 22.0f, |
| 1515 | 3.0f, 4.0f, |
| 1516 | 13.0f, 14.0f, |
| 1517 | 23.0f, 24.0f, |
| 1518 | |
| 1519 | 5.0f, 6.0f, |
| 1520 | 15.0f, 16.0f, |
| 1521 | 25.0f, 26.0f, |
| 1522 | 7.0f, 8.0f, |
| 1523 | 17.0f, 18.0f, |
| 1524 | 27.0f, 28.0f, |
| 1525 | |
| 1526 | 9.0f, 10.0f, |
| 1527 | 19.0f, 20.0f, |
| 1528 | 29.0f, 30.0f, |
| 1529 | 11.0f, 12.0f, |
| 1530 | 21.0f, 22.0f, |
| 1531 | 31.0f, 32.0f |
| 1532 | })); |
| 1533 | |
| 1534 | return result; |
| 1535 | } |
| 1536 | |
| 1537 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1538 | LayerTestResult<T, 4> Concat4dDiffShapeDim0TestImpl( |
| 1539 | armnn::IWorkloadFactory& workloadFactory, |
| 1540 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1541 | float qScale, |
| 1542 | int32_t qOffset) |
| 1543 | { |
| 1544 | unsigned int dimension = 0; |
| 1545 | armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1546 | |
| 1547 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { |
| 1548 | 1.0f, 2.0f, |
| 1549 | 3.0f, 4.0f, |
| 1550 | 5.0f, 6.0f, |
| 1551 | 7.0f, 8.0f, |
| 1552 | 9.0f, 10.0f, |
| 1553 | 11.0f, 12.0f |
| 1554 | })); |
| 1555 | |
| 1556 | armnn::TensorInfo inputTensorInfo1({ 2, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1557 | |
| 1558 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { |
| 1559 | 11.0f, 12.0f, |
| 1560 | 13.0f, 14.0f, |
| 1561 | 15.0f, 16.0f, |
| 1562 | 17.0f, 18.0f, |
| 1563 | 19.0f, 20.0f, |
| 1564 | 21.0f, 22.0f, |
| 1565 | |
| 1566 | 21.0f, 22.0f, |
| 1567 | 23.0f, 24.0f, |
| 1568 | 25.0f, 26.0f, |
| 1569 | 27.0f, 28.0f, |
| 1570 | 29.0f, 30.0f, |
| 1571 | 31.0f, 32.0f |
| 1572 | |
| 1573 | })); |
| 1574 | |
| 1575 | armnn::TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1576 | |
| 1577 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1578 | |
| 1579 | std::vector<T> output; |
| 1580 | output.resize(outputTensorInfo.GetNumElements()); |
| 1581 | Concatenate<T>(workloadFactory, |
| 1582 | memoryManager, |
| 1583 | {inputTensorInfo0, inputTensorInfo1}, |
| 1584 | {input0.data(), input1.data()}, |
| 1585 | outputTensorInfo, |
| 1586 | output.data(), |
| 1587 | dimension, |
| 1588 | true); |
| 1589 | |
| 1590 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
| 1591 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1592 | 1.0f, 2.0f, |
| 1593 | 3.0f, 4.0f, |
| 1594 | 5.0f, 6.0f, |
| 1595 | 7.0f, 8.0f, |
| 1596 | 9.0f, 10.0f, |
| 1597 | 11.0f, 12.0f, |
| 1598 | |
| 1599 | 11.0f, 12.0f, |
| 1600 | 13.0f, 14.0f, |
| 1601 | 15.0f, 16.0f, |
| 1602 | 17.0f, 18.0f, |
| 1603 | 19.0f, 20.0f, |
| 1604 | 21.0f, 22.0f, |
| 1605 | |
| 1606 | 21.0f, 22.0f, |
| 1607 | 23.0f, 24.0f, |
| 1608 | 25.0f, 26.0f, |
| 1609 | 27.0f, 28.0f, |
| 1610 | 29.0f, 30.0f, |
| 1611 | 31.0f, 32.0f |
| 1612 | })); |
| 1613 | |
| 1614 | return result; |
| 1615 | } |
| 1616 | |
| 1617 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1618 | LayerTestResult<T, 4> Concat4dDiffShapeDim1TestImpl( |
| 1619 | armnn::IWorkloadFactory& workloadFactory, |
| 1620 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1621 | float qScale, |
| 1622 | int32_t qOffset) |
| 1623 | { |
| 1624 | unsigned int dimension = 1; |
| 1625 | armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1626 | |
| 1627 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { |
| 1628 | 1.0f, 2.0f, |
| 1629 | 3.0f, 4.0f, |
| 1630 | 5.0f, 6.0f, |
| 1631 | 7.0f, 8.0f, |
| 1632 | 9.0f, 10.0f, |
| 1633 | 11.0f, 12.0f |
| 1634 | })); |
| 1635 | |
| 1636 | armnn::TensorInfo inputTensorInfo1({ 1, 2, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1637 | |
| 1638 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { |
| 1639 | 11.0f, 12.0f, |
| 1640 | 13.0f, 14.0f, |
| 1641 | 15.0f, 16.0f, |
| 1642 | 17.0f, 18.0f, |
| 1643 | |
| 1644 | })); |
| 1645 | |
| 1646 | armnn::TensorInfo outputTensorInfo({ 1, 5, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1647 | |
| 1648 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1649 | |
| 1650 | std::vector<T> output; |
| 1651 | output.resize(outputTensorInfo.GetNumElements()); |
| 1652 | Concatenate<T>(workloadFactory, |
| 1653 | memoryManager, |
| 1654 | {inputTensorInfo0, inputTensorInfo1}, |
| 1655 | {input0.data(), input1.data()}, |
| 1656 | outputTensorInfo, |
| 1657 | output.data(), |
| 1658 | dimension, |
| 1659 | true); |
| 1660 | |
| 1661 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
| 1662 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1663 | 1.0f, 2.0f, |
| 1664 | 3.0f, 4.0f, |
| 1665 | 5.0f, 6.0f, |
| 1666 | 7.0f, 8.0f, |
| 1667 | 9.0f, 10.0f, |
| 1668 | 11.0f, 12.0f, |
| 1669 | 11.0f, 12.0f, |
| 1670 | 13.0f, 14.0f, |
| 1671 | 15.0f, 16.0f, |
| 1672 | 17.0f, 18.0f |
| 1673 | })); |
| 1674 | |
| 1675 | return result; |
| 1676 | } |
| 1677 | |
| 1678 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1679 | LayerTestResult<T, 4> Concat4dDiffShapeDim2TestImpl( |
| 1680 | armnn::IWorkloadFactory& workloadFactory, |
| 1681 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1682 | float qScale, |
| 1683 | int32_t qOffset) |
| 1684 | { |
| 1685 | unsigned int dimension = 2; |
| 1686 | armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1687 | |
| 1688 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { |
| 1689 | 1.0f, 2.0f, |
| 1690 | 3.0f, 4.0f, |
| 1691 | 5.0f, 6.0f, |
| 1692 | 7.0f, 8.0f, |
| 1693 | 9.0f, 10.0f, |
| 1694 | 11.0f, 12.0f |
| 1695 | })); |
| 1696 | |
| 1697 | armnn::TensorInfo inputTensorInfo1({ 1, 3, 3, 2 }, ArmnnType, qScale, qOffset); |
| 1698 | |
| 1699 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { |
| 1700 | 11.0f, 12.0f, |
| 1701 | 13.0f, 14.0f, |
| 1702 | 15.0f, 16.0f, |
| 1703 | 17.0f, 18.0f, |
| 1704 | 19.0f, 20.0f, |
| 1705 | 21.0f, 22.0f, |
| 1706 | 23.0f, 24.0f, |
| 1707 | 25.0f, 26.0f, |
| 1708 | 27.0f, 28.0f |
| 1709 | })); |
| 1710 | |
| 1711 | armnn::TensorInfo outputTensorInfo({ 1, 3, 5, 2 }, ArmnnType, qScale, qOffset); |
| 1712 | |
| 1713 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1714 | |
| 1715 | std::vector<T> output; |
| 1716 | output.resize(outputTensorInfo.GetNumElements()); |
| 1717 | Concatenate<T>(workloadFactory, |
| 1718 | memoryManager, |
| 1719 | {inputTensorInfo0, inputTensorInfo1}, |
| 1720 | {input0.data(), input1.data()}, |
| 1721 | outputTensorInfo, |
| 1722 | output.data(), |
| 1723 | dimension, |
| 1724 | true); |
| 1725 | |
| 1726 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
| 1727 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1728 | 1.0f, 2.0f, |
| 1729 | 3.0f, 4.0f, |
| 1730 | 11.0f, 12.0f, |
| 1731 | 13.0f, 14.0f, |
| 1732 | 15.0f, 16.0f, |
| 1733 | |
| 1734 | 5.0f, 6.0f, |
| 1735 | 7.0f, 8.0f, |
| 1736 | 17.0f, 18.0f, |
| 1737 | 19.0f, 20.0f, |
| 1738 | 21.0f, 22.0f, |
| 1739 | |
| 1740 | 9.0f, 10.0f, |
| 1741 | 11.0f, 12.0f, |
| 1742 | 23.0f, 24.0f, |
| 1743 | 25.0f, 26.0f, |
| 1744 | 27.0f, 28.0f |
| 1745 | })); |
| 1746 | |
| 1747 | return result; |
| 1748 | } |
| 1749 | |
| 1750 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 1751 | LayerTestResult<T, 4> Concat4dDiffShapeDim3TestImpl( |
| 1752 | armnn::IWorkloadFactory& workloadFactory, |
| 1753 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1754 | float qScale, |
| 1755 | int32_t qOffset, |
| 1756 | bool useSubtensor) |
| 1757 | { |
| 1758 | unsigned int dimension = 3; |
| 1759 | armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1760 | |
| 1761 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { |
| 1762 | 1.0f, 2.0f, |
| 1763 | 3.0f, 4.0f, |
| 1764 | 5.0f, 6.0f, |
| 1765 | 7.0f, 8.0f, |
| 1766 | 9.0f, 10.0f, |
| 1767 | 11.0f, 12.0f |
| 1768 | })); |
| 1769 | |
| 1770 | armnn::TensorInfo inputTensorInfo1({ 1, 3, 2, 3 }, ArmnnType, qScale, qOffset); |
| 1771 | |
| 1772 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { |
| 1773 | 11.0f, 12.0f, 13.0f, |
| 1774 | 14.0f, 15.0f, 16.0f, |
| 1775 | |
| 1776 | 17.0f, 18.0f, 19.0f, |
| 1777 | 20.0f, 21.0f, 22.0f, |
| 1778 | |
| 1779 | 23.0f, 24.0f, 25.0f, |
| 1780 | 26.0f, 27.0f, 28.0f |
| 1781 | })); |
| 1782 | |
| 1783 | armnn::TensorInfo outputTensorInfo({ 1, 3, 2, 5 }, ArmnnType, qScale, qOffset); |
| 1784 | |
| 1785 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1786 | |
| 1787 | std::vector<T> output; |
| 1788 | output.resize(outputTensorInfo.GetNumElements()); |
| 1789 | Concatenate<T>(workloadFactory, |
| 1790 | memoryManager, |
| 1791 | {inputTensorInfo0, inputTensorInfo1}, |
| 1792 | {input0.data(), input1.data()}, |
| 1793 | outputTensorInfo, |
| 1794 | output.data(), |
| 1795 | dimension, |
| 1796 | useSubtensor); |
| 1797 | |
| 1798 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
| 1799 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 1800 | 1.0f, 2.0f, 11.0f, 12.0f, 13.0f, |
| 1801 | 3.0f, 4.0f, 14.0f, 15.0f, 16.0f, |
| 1802 | 5.0f, 6.0f, 17.0f, 18.0f, 19.0f, |
| 1803 | 7.0f, 8.0f, 20.0f, 21.0f, 22.0f, |
| 1804 | 9.0f, 10.0f, 23.0f, 24.0f, 25.0f, |
| 1805 | 11.0f, 12.0f, 26.0f, 27.0f, 28.0f |
| 1806 | })); |
| 1807 | |
| 1808 | return result; |
| 1809 | } |
| 1810 | |
| 1811 | template<armnn::DataType ArmnnType, typename T> |
| 1812 | LayerTestResult<T, 3> ConcatDifferentInputOutputQParamTest( |
| 1813 | armnn::IWorkloadFactory& workloadFactory, |
| 1814 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1815 | bool useSubtensor) |
| 1816 | { |
| 1817 | // Defines the tensor descriptors. |
| 1818 | armnn::TensorInfo outputTensorInfo({ 3, 6, 3 }, ArmnnType); |
| 1819 | armnn::TensorInfo inputTensorInfo1({ 3, 6, 2 }, ArmnnType); |
| 1820 | armnn::TensorInfo inputTensorInfo2({ 3, 6, 1 }, ArmnnType); |
| 1821 | |
| 1822 | std::vector<armnn::TensorShape> inputTensorShapes({inputTensorInfo1.GetShape(), inputTensorInfo2.GetShape()}); |
| 1823 | |
| 1824 | // Quantized input1 tensor. |
| 1825 | const float inputScale1 = 0.5f; |
| 1826 | const int32_t inputOffset1 = 5; |
| 1827 | |
| 1828 | auto input1 = MakeTensor<T, 3>(inputTensorInfo1, std::vector<T>( |
| 1829 | { |
| 1830 | 1, 2, 3, |
| 1831 | 4, 5, 6, |
| 1832 | 7, 8, 9, |
| 1833 | 10, 11, 12, |
| 1834 | 13, 14, 15, |
| 1835 | 16, 17, 18, |
| 1836 | |
| 1837 | 19, 20, 21, |
| 1838 | 22, 23, 24, |
| 1839 | 25, 26, 27, |
| 1840 | 28, 29, 30, |
| 1841 | 31, 32, 33, |
| 1842 | 34, 35, 36 |
| 1843 | })); |
| 1844 | |
| 1845 | // Quatized input2 tensor. |
| 1846 | const float inputScale2 = 0.2f; |
| 1847 | const int32_t inputOffset2 = 10; |
| 1848 | |
| 1849 | auto input2 = MakeTensor<T, 3>(inputTensorInfo2, std::vector<T>( |
| 1850 | { |
| 1851 | 37, 38, 39, |
| 1852 | 40, 41, 42, |
| 1853 | 43, 44, 45, |
| 1854 | 46, 47, 48, |
| 1855 | 49, 50, 51, |
| 1856 | 52, 53, 54 |
| 1857 | })); |
| 1858 | |
| 1859 | // Quantized output tensor. |
| 1860 | const float outputScale = 0.1f; |
| 1861 | const int32_t outputOffset = 20; |
| 1862 | |
| 1863 | LayerTestResult<T, 3> ret(outputTensorInfo); |
| 1864 | |
| 1865 | ret.outputExpected = MakeTensor<T, 3>(outputTensorInfo, std::vector<T>( |
| 1866 | { |
| 1867 | 0, 5, 74, |
| 1868 | 10, 15, 76, |
| 1869 | 20, 25, 78, |
| 1870 | 30, 35, 80, |
| 1871 | 40, 45, 82, |
| 1872 | 50, 55, 84, |
| 1873 | |
| 1874 | 60, 65, 86, |
| 1875 | 70, 75, 88, |
| 1876 | 80, 85, 90, |
| 1877 | 90, 95, 92, |
| 1878 | 100, 105, 94, |
| 1879 | 110, 115, 96, |
| 1880 | |
| 1881 | 120, 125, 98, |
| 1882 | 130, 135, 100, |
| 1883 | 140, 145, 102, |
| 1884 | 150, 155, 104, |
| 1885 | 160, 165, 106, |
| 1886 | 170, 175, 108 |
| 1887 | })); |
| 1888 | |
| 1889 | outputTensorInfo.SetQuantizationScale(outputScale); |
| 1890 | outputTensorInfo.SetQuantizationOffset(outputOffset); |
| 1891 | inputTensorInfo1.SetQuantizationScale(inputScale1); |
| 1892 | inputTensorInfo1.SetQuantizationOffset(inputOffset1); |
| 1893 | inputTensorInfo2.SetQuantizationScale(inputScale2); |
| 1894 | inputTensorInfo2.SetQuantizationOffset(inputOffset2); |
| 1895 | |
| 1896 | std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. |
| 1897 | armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
| 1898 | |
| 1899 | std::vector<unsigned int> wOrigin2 = { 0, 0, 2 }; //Extent of the window is defined by size of input[1]. |
| 1900 | armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
| 1901 | |
| 1902 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1903 | |
| 1904 | bool subTensorsSupported = useSubtensor && workloadFactory.SupportsSubTensors(); |
| 1905 | |
| 1906 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = |
| 1907 | subTensorsSupported ? |
| 1908 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 1909 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 1910 | |
| 1911 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = |
| 1912 | subTensorsSupported ? |
| 1913 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 1914 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 1915 | |
| 1916 | armnn::ConcatQueueDescriptor data; |
| 1917 | armnn::OriginsDescriptor desc = armnn::CreateDescriptorForConcatenation( |
| 1918 | inputTensorShapes.begin(),inputTensorShapes.end(), 2); |
| 1919 | data.m_Parameters = desc; |
| 1920 | |
| 1921 | armnn::WorkloadInfo info; |
| 1922 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 1923 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 1924 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1925 | |
| 1926 | data.m_ViewOrigins.push_back(window1); |
| 1927 | data.m_ViewOrigins.push_back(window2); |
| 1928 | |
| 1929 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); |
| 1930 | |
| 1931 | inputHandle1->Allocate(); |
| 1932 | inputHandle2->Allocate(); |
| 1933 | outputHandle->Allocate(); |
| 1934 | |
| 1935 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 1936 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 1937 | |
| 1938 | workload->PostAllocationConfigure(); |
| 1939 | workload->Execute(); |
| 1940 | |
| 1941 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 1942 | |
| 1943 | return ret; |
| 1944 | } |
| 1945 | |
| 1946 | // |
| 1947 | // Explicit template specializations |
| 1948 | // |
| 1949 | |
| 1950 | template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 3> |
| 1951 | ConcatDifferentInputOutputQParamTest<armnn::DataType::QuantisedAsymm8>( |
| 1952 | armnn::IWorkloadFactory& workloadFactory, |
| 1953 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1954 | bool useSubtensor); |
| 1955 | |
| 1956 | template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 3> |
| 1957 | ConcatDifferentInputOutputQParamTest<armnn::DataType::QuantisedSymm16>( |
| 1958 | armnn::IWorkloadFactory& workloadFactory, |
| 1959 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1960 | bool useSubtensor); |
| 1961 | |
| 1962 | // |
| 1963 | // Implementation functions |
| 1964 | // |
| 1965 | |
| 1966 | LayerTestResult<float,3> ConcatTest( |
| 1967 | armnn::IWorkloadFactory& workloadFactory, |
| 1968 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 1969 | { |
| 1970 | unsigned int outputWidth = 3; |
| 1971 | unsigned int outputHeight = 6; |
| 1972 | unsigned int outputChannels = 3; |
| 1973 | |
| 1974 | unsigned int inputWidth1 = 3; |
| 1975 | unsigned int inputHeight1 = 6; |
| 1976 | unsigned int inputChannels1 = 2; |
| 1977 | |
| 1978 | unsigned int inputWidth2 = 3; |
| 1979 | unsigned int inputHeight2 = 6; |
| 1980 | unsigned int inputChannels2 = 1; |
| 1981 | |
| 1982 | // Define the tensor descriptors. |
| 1983 | armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::Float32); |
| 1984 | armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::Float32); |
| 1985 | armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::Float32); |
| 1986 | |
| 1987 | LayerTestResult<float,3> ret(outputTensorInfo); |
| 1988 | |
| 1989 | ret.outputExpected = MakeTensor<float, 3>(outputTensorInfo, std::vector<float>( |
| 1990 | { |
| 1991 | 1.0f, 2.0f, 3.0f, |
| 1992 | 4.0f, 5.0f, 6.0f, |
| 1993 | 7.0f, 8.0f, 9.0f, |
| 1994 | 10.0f, 11.0f, 12.0f, |
| 1995 | 13.0f, 14.0f, 15.0f, |
| 1996 | 16.0f, 17.0f, 18.0f, |
| 1997 | |
| 1998 | 19.0f, 20.0f, 21.0f, |
| 1999 | 22.0f, 23.0f, 24.0f, |
| 2000 | 25.0f, 26.0f, 27.0f, |
| 2001 | 28.0f, 29.0f, 30.0f, |
| 2002 | 31.0f, 32.0f, 33.0f, |
| 2003 | 34.0f, 35.0f, 36.0f, |
| 2004 | |
| 2005 | 37.0f, 38.0f, 39.0f, |
| 2006 | 40.0f, 41.0f, 42.0f, |
| 2007 | 43.0f, 44.0f, 45.0f, |
| 2008 | 46.0f, 47.0f, 48.0f, |
| 2009 | 49.0f, 50.0f, 51.0f, |
| 2010 | 52.0f, 53.0f, 54.0f, |
| 2011 | }) |
| 2012 | ); |
| 2013 | |
| 2014 | auto input1 = MakeTensor<float, 3>(inputTensorInfo1, std::vector<float>( |
| 2015 | { |
| 2016 | 1.0f, 2.0f, 3.0f, |
| 2017 | 4.0f, 5.0f, 6.0f, |
| 2018 | 7.0f, 8.0f, 9.0f, |
| 2019 | 10.0f, 11.0f, 12.0f, |
| 2020 | 13.0f, 14.0f, 15.0f, |
| 2021 | 16.0f, 17.0f, 18.0f, |
| 2022 | |
| 2023 | 19.0f, 20.0f, 21.0f, |
| 2024 | 22.0f, 23.0f, 24.0f, |
| 2025 | 25.0f, 26.0f, 27.0f, |
| 2026 | 28.0f, 29.0f, 30.0f, |
| 2027 | 31.0f, 32.0f, 33.0f, |
| 2028 | 34.0f, 35.0f, 36.0f, |
| 2029 | }) |
| 2030 | ); |
| 2031 | |
| 2032 | auto input2 = MakeTensor<float, 3>(inputTensorInfo2, std::vector<float>( |
| 2033 | { |
| 2034 | 37.0f, 38.0f, 39.0f, |
| 2035 | 40.0f, 41.0f, 42.0f, |
| 2036 | 43.0f, 44.0f, 45.0f, |
| 2037 | 46.0f, 47.0f, 48.0f, |
| 2038 | 49.0f, 50.0f, 51.0f, |
| 2039 | 52.0f, 53.0f, 54.0f, |
| 2040 | }) |
| 2041 | ); |
| 2042 | |
| 2043 | std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0]. |
| 2044 | armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
| 2045 | |
| 2046 | std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1]. |
| 2047 | armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
| 2048 | |
| 2049 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 2050 | |
| 2051 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2052 | |
| 2053 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = |
| 2054 | subTensorsSupported ? |
| 2055 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2056 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2057 | |
| 2058 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = |
| 2059 | subTensorsSupported ? |
| 2060 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2061 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2062 | |
| 2063 | armnn::ConcatQueueDescriptor data; |
| 2064 | armnn::WorkloadInfo info; |
| 2065 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2066 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2067 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2068 | |
| 2069 | data.m_ViewOrigins.push_back(window1); |
| 2070 | data.m_ViewOrigins.push_back(window2); |
| 2071 | |
| 2072 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); |
| 2073 | |
| 2074 | inputHandle1->Allocate(); |
| 2075 | inputHandle2->Allocate(); |
| 2076 | outputHandle->Allocate(); |
| 2077 | |
| 2078 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2079 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2080 | |
| 2081 | workload->PostAllocationConfigure(); |
| 2082 | workload->Execute(); |
| 2083 | |
| 2084 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2085 | |
| 2086 | return ret; |
| 2087 | } |
| 2088 | |
| 2089 | LayerTestResult<float, 1> Concat1dTest( |
| 2090 | armnn::IWorkloadFactory& workloadFactory, |
| 2091 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2092 | { |
| 2093 | return Concat1dTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2094 | } |
| 2095 | |
| 2096 | LayerTestResult<float, 2> Concat2dDim0Test( |
| 2097 | armnn::IWorkloadFactory& workloadFactory, |
| 2098 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2099 | { |
| 2100 | return Concat2dDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2101 | } |
| 2102 | |
| 2103 | LayerTestResult<float, 2> Concat2dDim1Test( |
| 2104 | armnn::IWorkloadFactory& workloadFactory, |
| 2105 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2106 | { |
| 2107 | return Concat2dDim1TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2108 | } |
| 2109 | |
| 2110 | LayerTestResult<float, 2> Concat2dDim0DiffInputDimsTest( |
| 2111 | armnn::IWorkloadFactory& workloadFactory, |
| 2112 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2113 | { |
| 2114 | return Concat2dDim0DiffInputDimsTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2115 | } |
| 2116 | |
| 2117 | LayerTestResult<float, 2> Concat2dDim1DiffInputDimsTest( |
| 2118 | armnn::IWorkloadFactory& workloadFactory, |
| 2119 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2120 | { |
| 2121 | return Concat2dDim1DiffInputDimsTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2122 | } |
| 2123 | |
| 2124 | LayerTestResult<float, 3> Concat3dDim0Test( |
| 2125 | armnn::IWorkloadFactory& workloadFactory, |
| 2126 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2127 | { |
| 2128 | return Concat3dDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2129 | } |
| 2130 | |
| 2131 | LayerTestResult<float, 3> Concat3dDim1Test( |
| 2132 | armnn::IWorkloadFactory& workloadFactory, |
| 2133 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2134 | { |
| 2135 | return Concat3dDim1TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2136 | } |
| 2137 | |
| 2138 | LayerTestResult<float, 3> Concat3dDim2Test( |
| 2139 | armnn::IWorkloadFactory& workloadFactory, |
| 2140 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 2141 | bool useSubtensor) |
| 2142 | { |
| 2143 | return Concat3dDim2TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, useSubtensor, 0.0f, 0); |
| 2144 | } |
| 2145 | |
| 2146 | LayerTestResult<float, 3> Concat3dDim0DiffInputDimsTest( |
| 2147 | armnn::IWorkloadFactory& workloadFactory, |
| 2148 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2149 | { |
| 2150 | return Concat3dDim0DiffInputDimsTestImpl<armnn::DataType::Float32>( |
| 2151 | workloadFactory, memoryManager, 0.0f, 0); |
| 2152 | } |
| 2153 | |
| 2154 | LayerTestResult<float, 3> Concat3dDim1DiffInputDimsTest( |
| 2155 | armnn::IWorkloadFactory& workloadFactory, |
| 2156 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2157 | { |
| 2158 | return Concat3dDim1DiffInputDimsTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2159 | } |
| 2160 | |
| 2161 | LayerTestResult<float, 3> Concat3dDim2DiffInputDimsTest( |
| 2162 | armnn::IWorkloadFactory& workloadFactory, |
| 2163 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 2164 | bool useSubtensor) |
| 2165 | { |
| 2166 | return Concat3dDim2DiffInputDimsTestImpl<armnn::DataType::Float32>( |
| 2167 | workloadFactory, memoryManager, useSubtensor, 0.0f, 0); |
| 2168 | } |
| 2169 | |
| 2170 | LayerTestResult<float, 4> Concat4dDim0Test( |
| 2171 | armnn::IWorkloadFactory& workloadFactory, |
| 2172 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2173 | { |
| 2174 | return Concat4dDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2175 | } |
| 2176 | |
| 2177 | LayerTestResult<float, 4> Concat4dDim1Test( |
| 2178 | armnn::IWorkloadFactory& workloadFactory, |
| 2179 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2180 | { |
| 2181 | return Concat4dDim1TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2182 | } |
| 2183 | |
| 2184 | LayerTestResult<float, 4> Concat4dDim2Test( |
| 2185 | armnn::IWorkloadFactory& workloadFactory, |
| 2186 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2187 | { |
| 2188 | return Concat4dDim2TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2189 | } |
| 2190 | |
| 2191 | LayerTestResult<float, 4> Concat4dDim3Test( |
| 2192 | armnn::IWorkloadFactory& workloadFactory, |
| 2193 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 2194 | bool useSubtensor) |
| 2195 | { |
| 2196 | return Concat4dDim3TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0, useSubtensor); |
| 2197 | } |
| 2198 | |
| 2199 | LayerTestResult<float, 4> Concat4dDiffShapeDim0Test( |
| 2200 | armnn::IWorkloadFactory& workloadFactory, |
| 2201 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2202 | { |
| 2203 | return Concat4dDiffShapeDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2204 | } |
| 2205 | |
| 2206 | LayerTestResult<float, 4> Concat4dDiffShapeDim1Test( |
| 2207 | armnn::IWorkloadFactory& workloadFactory, |
| 2208 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2209 | { |
| 2210 | return Concat4dDiffShapeDim1TestImpl<armnn::DataType::Float32>( |
| 2211 | workloadFactory, memoryManager, 0.0f, 0); |
| 2212 | } |
| 2213 | |
| 2214 | LayerTestResult<float, 4> Concat4dDiffShapeDim2Test( |
| 2215 | armnn::IWorkloadFactory& workloadFactory, |
| 2216 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2217 | { |
| 2218 | return Concat4dDiffShapeDim2TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
| 2219 | } |
| 2220 | |
| 2221 | LayerTestResult<float, 4> Concat4dDiffShapeDim3Test( |
| 2222 | armnn::IWorkloadFactory& workloadFactory, |
| 2223 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 2224 | bool useSubtensor) |
| 2225 | { |
| 2226 | return Concat4dDiffShapeDim3TestImpl<armnn::DataType::Float32>( |
| 2227 | workloadFactory, memoryManager, 0.0f, 0, useSubtensor); |
| 2228 | } |
| 2229 | |
Matthew Jackson | 9bff144 | 2019-09-12 09:08:23 +0100 | [diff] [blame] | 2230 | LayerTestResult<armnn::Half, 3> ConcatFloat16Test( |
| 2231 | armnn::IWorkloadFactory& workloadFactory, |
| 2232 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2233 | { |
| 2234 | return Concat3dDim1TestImpl<armnn::DataType::Float16>(workloadFactory, memoryManager, 0.0f, 0); |
| 2235 | } |
| 2236 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2237 | LayerTestResult<uint8_t, 3> ConcatUint8DifferentQParamsTest( |
| 2238 | armnn::IWorkloadFactory& workloadFactory, |
| 2239 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2240 | { |
| 2241 | unsigned int outputWidth = 3; |
| 2242 | unsigned int outputHeight = 6; |
| 2243 | unsigned int outputChannels = 3; |
| 2244 | |
| 2245 | unsigned int inputWidth1 = 3; |
| 2246 | unsigned int inputHeight1 = 6; |
| 2247 | unsigned int inputChannels1 = 2; |
| 2248 | |
| 2249 | unsigned int inputWidth2 = 3; |
| 2250 | unsigned int inputHeight2 = 6; |
| 2251 | unsigned int inputChannels2 = 1; |
| 2252 | |
| 2253 | // Defines the tensor descriptors. |
| 2254 | armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8); |
| 2255 | armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8); |
| 2256 | armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8); |
| 2257 | |
| 2258 | // Quantized input1 tensor. Range [-3, 1] |
| 2259 | const float inputScale1 = 0.015686f; |
| 2260 | const int32_t inputOffset1 = 192; |
| 2261 | |
| 2262 | auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>( |
| 2263 | { |
| 2264 | 1, 2, 3, |
| 2265 | 4, 5, 6, |
| 2266 | 7, 8, 9, |
| 2267 | 10, 11, 12, |
| 2268 | 13, 14, 15, |
| 2269 | 16, 17, 18, |
| 2270 | |
| 2271 | 19, 20, 21, |
| 2272 | 22, 23, 24, |
| 2273 | 25, 26, 27, |
| 2274 | 28, 29, 30, |
| 2275 | 31, 32, 33, |
| 2276 | 34, 35, 36, |
| 2277 | }) |
| 2278 | ); |
| 2279 | |
| 2280 | // Quatized input2 tensor. Range [-1, 4] |
| 2281 | const float inputScale2 = 0.019608f; |
| 2282 | const int32_t inputOffset2 = 50; |
| 2283 | |
| 2284 | auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>( |
| 2285 | { |
| 2286 | 37, 38, 39, |
| 2287 | 40, 41, 42, |
| 2288 | 43, 44, 45, |
| 2289 | 46, 47, 48, |
| 2290 | 49, 50, 51, |
| 2291 | 52, 53, 54, |
| 2292 | }) |
| 2293 | ); |
| 2294 | |
| 2295 | // Output has the same quantization parameters than input1, |
| 2296 | // so that only the requantization of input2 is required |
| 2297 | const float outputScale = 0.015686f; |
| 2298 | const int32_t outputOffset = 192; |
| 2299 | |
| 2300 | LayerTestResult<uint8_t, 3> ret(outputTensorInfo); |
| 2301 | |
| 2302 | ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>( |
| 2303 | { |
| 2304 | 1, 2, 3, |
| 2305 | 4, 5, 6, |
| 2306 | 7, 8, 9, |
| 2307 | 10, 11, 12, |
| 2308 | 13, 14, 15, |
| 2309 | 16, 17, 18, |
| 2310 | |
| 2311 | 19, 20, 21, |
| 2312 | 22, 23, 24, |
| 2313 | 25, 26, 27, |
| 2314 | 28, 29, 30, |
| 2315 | 31, 32, 33, |
| 2316 | 34, 35, 36, |
| 2317 | |
| 2318 | 176, 177, 178, |
| 2319 | 179, 181, 182, |
| 2320 | 183, 184, 186, |
| 2321 | 187, 188, 189, |
| 2322 | 191, 192, 193, |
| 2323 | 195, 196, 197, |
| 2324 | }) |
| 2325 | ); |
| 2326 | |
| 2327 | outputTensorInfo.SetQuantizationScale(outputScale); |
| 2328 | outputTensorInfo.SetQuantizationOffset(outputOffset); |
| 2329 | inputTensorInfo1.SetQuantizationScale(inputScale1); |
| 2330 | inputTensorInfo1.SetQuantizationOffset(inputOffset1); |
| 2331 | inputTensorInfo2.SetQuantizationScale(inputScale2); |
| 2332 | inputTensorInfo2.SetQuantizationOffset(inputOffset2); |
| 2333 | |
| 2334 | std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. |
| 2335 | armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
| 2336 | |
| 2337 | std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. |
| 2338 | armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
| 2339 | |
| 2340 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 2341 | |
| 2342 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2343 | |
| 2344 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = |
| 2345 | subTensorsSupported ? |
| 2346 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2347 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2348 | |
| 2349 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = |
| 2350 | subTensorsSupported ? |
| 2351 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2352 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2353 | |
| 2354 | armnn::ConcatQueueDescriptor data; |
| 2355 | armnn::WorkloadInfo info; |
| 2356 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2357 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2358 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2359 | |
| 2360 | data.m_ViewOrigins.push_back(window1); |
| 2361 | data.m_ViewOrigins.push_back(window2); |
| 2362 | |
| 2363 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); |
| 2364 | |
| 2365 | inputHandle1->Allocate(); |
| 2366 | inputHandle2->Allocate(); |
| 2367 | outputHandle->Allocate(); |
| 2368 | |
| 2369 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2370 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2371 | |
| 2372 | workload->PostAllocationConfigure(); |
| 2373 | workload->Execute(); |
| 2374 | |
| 2375 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2376 | |
| 2377 | return ret; |
| 2378 | } |
| 2379 | |
| 2380 | LayerTestResult<uint8_t, 3> ConcatUint8Test( |
| 2381 | armnn::IWorkloadFactory& workloadFactory, |
| 2382 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2383 | { |
| 2384 | unsigned int outputWidth = 3; |
| 2385 | unsigned int outputHeight = 6; |
| 2386 | unsigned int outputChannels = 3; |
| 2387 | |
| 2388 | unsigned int inputWidth1 = 3; |
| 2389 | unsigned int inputHeight1 = 6; |
| 2390 | unsigned int inputChannels1 = 2; |
| 2391 | |
| 2392 | unsigned int inputWidth2 = 3; |
| 2393 | unsigned int inputHeight2 = 6; |
| 2394 | unsigned int inputChannels2 = 1; |
| 2395 | |
| 2396 | // Defines the tensor descriptors. |
| 2397 | armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8); |
| 2398 | armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8); |
| 2399 | armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8); |
| 2400 | |
| 2401 | // Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them. |
| 2402 | const float scale = 0.13497836f; |
| 2403 | const int32_t offset = -7; |
| 2404 | |
| 2405 | outputTensorInfo.SetQuantizationScale(scale); |
| 2406 | outputTensorInfo.SetQuantizationOffset(offset); |
| 2407 | inputTensorInfo1.SetQuantizationScale(scale); |
| 2408 | inputTensorInfo1.SetQuantizationOffset(offset); |
| 2409 | inputTensorInfo2.SetQuantizationScale(scale); |
| 2410 | inputTensorInfo2.SetQuantizationOffset(offset); |
| 2411 | |
| 2412 | LayerTestResult<uint8_t, 3> ret(outputTensorInfo); |
| 2413 | |
| 2414 | ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>( |
| 2415 | { |
| 2416 | 1, 2, 3, |
| 2417 | 4, 5, 6, |
| 2418 | 7, 8, 9, |
| 2419 | 10, 11, 12, |
| 2420 | 13, 14, 15, |
| 2421 | 16, 17, 18, |
| 2422 | |
| 2423 | 19, 20, 21, |
| 2424 | 22, 23, 24, |
| 2425 | 25, 26, 27, |
| 2426 | 28, 29, 30, |
| 2427 | 31, 32, 33, |
| 2428 | 34, 35, 36, |
| 2429 | |
| 2430 | 37, 38, 39, |
| 2431 | 40, 41, 42, |
| 2432 | 43, 44, 45, |
| 2433 | 46, 47, 48, |
| 2434 | 49, 50, 51, |
| 2435 | 52, 53, 54, |
| 2436 | }) |
| 2437 | ); |
| 2438 | |
| 2439 | auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>( |
| 2440 | { |
| 2441 | 1, 2, 3, |
| 2442 | 4, 5, 6, |
| 2443 | 7, 8, 9, |
| 2444 | 10, 11, 12, |
| 2445 | 13, 14, 15, |
| 2446 | 16, 17, 18, |
| 2447 | |
| 2448 | 19, 20, 21, |
| 2449 | 22, 23, 24, |
| 2450 | 25, 26, 27, |
| 2451 | 28, 29, 30, |
| 2452 | 31, 32, 33, |
| 2453 | 34, 35, 36, |
| 2454 | }) |
| 2455 | ); |
| 2456 | |
| 2457 | auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>( |
| 2458 | { |
| 2459 | 37, 38, 39, |
| 2460 | 40, 41, 42, |
| 2461 | 43, 44, 45, |
| 2462 | 46, 47, 48, |
| 2463 | 49, 50, 51, |
| 2464 | 52, 53, 54, |
| 2465 | }) |
| 2466 | ); |
| 2467 | |
| 2468 | std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. |
| 2469 | armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
| 2470 | |
| 2471 | std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. |
| 2472 | armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
| 2473 | |
| 2474 | |
| 2475 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 2476 | |
| 2477 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2478 | |
| 2479 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = |
| 2480 | subTensorsSupported ? |
| 2481 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2482 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2483 | |
| 2484 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = |
| 2485 | subTensorsSupported ? |
| 2486 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2487 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2488 | |
| 2489 | |
| 2490 | armnn::ConcatQueueDescriptor data; |
| 2491 | armnn::WorkloadInfo info; |
| 2492 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2493 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2494 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2495 | |
| 2496 | data.m_ViewOrigins.push_back(window1); |
| 2497 | data.m_ViewOrigins.push_back(window2); |
| 2498 | |
| 2499 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); |
| 2500 | |
| 2501 | inputHandle1->Allocate(); |
| 2502 | inputHandle2->Allocate(); |
| 2503 | outputHandle->Allocate(); |
| 2504 | |
| 2505 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2506 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2507 | |
| 2508 | workload->PostAllocationConfigure(); |
| 2509 | workload->Execute(); |
| 2510 | |
| 2511 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2512 | |
| 2513 | return ret; |
| 2514 | } |
| 2515 | |
| 2516 | LayerTestResult<uint16_t, 3> ConcatUint16Test( |
| 2517 | armnn::IWorkloadFactory& workloadFactory, |
| 2518 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2519 | { |
| 2520 | unsigned int outputWidth = 3; |
| 2521 | unsigned int outputHeight = 6; |
| 2522 | unsigned int outputChannels = 3; |
| 2523 | |
| 2524 | unsigned int inputWidth1 = 3; |
| 2525 | unsigned int inputHeight1 = 6; |
| 2526 | unsigned int inputChannels1 = 2; |
| 2527 | |
| 2528 | unsigned int inputWidth2 = 3; |
| 2529 | unsigned int inputHeight2 = 6; |
| 2530 | unsigned int inputChannels2 = 1; |
| 2531 | |
| 2532 | // Defines the tensor descriptors. |
| 2533 | armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedSymm16); |
| 2534 | armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedSymm16); |
| 2535 | armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedSymm16); |
| 2536 | |
| 2537 | // Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them. |
| 2538 | const float scale = 0.13497836f; |
| 2539 | const int32_t offset = -7; |
| 2540 | |
| 2541 | outputTensorInfo.SetQuantizationScale(scale); |
| 2542 | outputTensorInfo.SetQuantizationOffset(offset); |
| 2543 | inputTensorInfo1.SetQuantizationScale(scale); |
| 2544 | inputTensorInfo1.SetQuantizationOffset(offset); |
| 2545 | inputTensorInfo2.SetQuantizationScale(scale); |
| 2546 | inputTensorInfo2.SetQuantizationOffset(offset); |
| 2547 | |
| 2548 | LayerTestResult<uint16_t, 3> ret(outputTensorInfo); |
| 2549 | |
| 2550 | ret.outputExpected = MakeTensor<uint16_t, 3>(outputTensorInfo, std::vector<uint16_t>( |
| 2551 | { |
| 2552 | 1, 2, 3, |
| 2553 | 4, 5, 6, |
| 2554 | 7, 8, 9, |
| 2555 | 10, 11, 12, |
| 2556 | 13, 14, 15, |
| 2557 | 16, 17, 18, |
| 2558 | |
| 2559 | 19, 20, 21, |
| 2560 | 22, 23, 24, |
| 2561 | 25, 26, 27, |
| 2562 | 28, 29, 30, |
| 2563 | 31, 32, 33, |
| 2564 | 34, 35, 36, |
| 2565 | |
| 2566 | 37, 38, 39, |
| 2567 | 40, 41, 42, |
| 2568 | 43, 44, 45, |
| 2569 | 46, 47, 48, |
| 2570 | 49, 50, 51, |
| 2571 | 52, 53, 54, |
| 2572 | })); |
| 2573 | |
| 2574 | auto input1 = MakeTensor<uint16_t, 3>(inputTensorInfo1, std::vector<uint16_t>( |
| 2575 | { |
| 2576 | 1, 2, 3, |
| 2577 | 4, 5, 6, |
| 2578 | 7, 8, 9, |
| 2579 | 10, 11, 12, |
| 2580 | 13, 14, 15, |
| 2581 | 16, 17, 18, |
| 2582 | |
| 2583 | 19, 20, 21, |
| 2584 | 22, 23, 24, |
| 2585 | 25, 26, 27, |
| 2586 | 28, 29, 30, |
| 2587 | 31, 32, 33, |
| 2588 | 34, 35, 36, |
| 2589 | })); |
| 2590 | |
| 2591 | auto input2 = MakeTensor<uint16_t, 3>(inputTensorInfo2, std::vector<uint16_t>( |
| 2592 | { |
| 2593 | 37, 38, 39, |
| 2594 | 40, 41, 42, |
| 2595 | 43, 44, 45, |
| 2596 | 46, 47, 48, |
| 2597 | 49, 50, 51, |
| 2598 | 52, 53, 54, |
| 2599 | })); |
| 2600 | |
| 2601 | std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. |
| 2602 | armnn::ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
| 2603 | |
| 2604 | std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. |
| 2605 | armnn::ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
| 2606 | |
| 2607 | |
| 2608 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 2609 | |
| 2610 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2611 | |
| 2612 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = |
| 2613 | subTensorsSupported ? |
| 2614 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2615 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2616 | |
| 2617 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = |
| 2618 | subTensorsSupported ? |
| 2619 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2620 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2621 | |
| 2622 | |
| 2623 | armnn::ConcatQueueDescriptor data; |
| 2624 | armnn::WorkloadInfo info; |
| 2625 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2626 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2627 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2628 | |
| 2629 | data.m_ViewOrigins.push_back(window1); |
| 2630 | data.m_ViewOrigins.push_back(window2); |
| 2631 | |
| 2632 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConcat(data, info); |
| 2633 | |
| 2634 | inputHandle1->Allocate(); |
| 2635 | inputHandle2->Allocate(); |
| 2636 | outputHandle->Allocate(); |
| 2637 | |
| 2638 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2639 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2640 | |
| 2641 | workload->PostAllocationConfigure(); |
| 2642 | workload->Execute(); |
| 2643 | |
| 2644 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2645 | |
| 2646 | return ret; |
| 2647 | } |
| 2648 | |
| 2649 | LayerTestResult<uint8_t, 1> Concat1dUint8Test( |
| 2650 | armnn::IWorkloadFactory& workloadFactory, |
| 2651 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2652 | { |
| 2653 | return Concat1dTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
| 2654 | } |
| 2655 | |
| 2656 | LayerTestResult<uint8_t, 2> Concat2dDim0Uint8Test( |
| 2657 | armnn::IWorkloadFactory& workloadFactory, |
| 2658 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2659 | { |
| 2660 | return Concat2dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
| 2661 | } |
| 2662 | |
| 2663 | LayerTestResult<uint8_t, 2> Concat2dDim1Uint8Test( |
| 2664 | armnn::IWorkloadFactory& workloadFactory, |
| 2665 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2666 | { |
| 2667 | return Concat2dDim1TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
| 2668 | } |
| 2669 | |
| 2670 | LayerTestResult<uint8_t, 2> Concat2dDim0DiffInputDimsUint8Test( |
| 2671 | armnn::IWorkloadFactory& workloadFactory, |
| 2672 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2673 | { |
| 2674 | return Concat2dDim0DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2675 | workloadFactory, memoryManager, 0.5f, -1); |
| 2676 | } |
| 2677 | |
| 2678 | LayerTestResult<uint8_t, 2> Concat2dDim1DiffInputDimsUint8Test( |
| 2679 | armnn::IWorkloadFactory& workloadFactory, |
| 2680 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2681 | { |
| 2682 | return Concat2dDim1DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2683 | workloadFactory, memoryManager, 0.5f, -1); |
| 2684 | } |
| 2685 | |
| 2686 | LayerTestResult<uint8_t, 3> Concat3dDim0Uint8Test( |
| 2687 | armnn::IWorkloadFactory& workloadFactory, |
| 2688 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2689 | { |
| 2690 | return Concat3dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
| 2691 | } |
| 2692 | |
| 2693 | LayerTestResult<uint8_t, 3> Concat3dDim1Uint8Test( |
| 2694 | armnn::IWorkloadFactory& workloadFactory, |
| 2695 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2696 | { |
| 2697 | return Concat3dDim1TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
| 2698 | } |
| 2699 | |
| 2700 | LayerTestResult<uint8_t, 3> Concat3dDim2Uint8Test( |
| 2701 | armnn::IWorkloadFactory& workloadFactory, |
| 2702 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 2703 | bool useSubtensor) |
| 2704 | { |
| 2705 | return Concat3dDim2TestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2706 | workloadFactory, memoryManager, useSubtensor, 0.5f, -1); |
| 2707 | } |
| 2708 | |
| 2709 | LayerTestResult<uint8_t, 3> Concat3dDim0DiffInputDimsUint8Test( |
| 2710 | armnn::IWorkloadFactory& workloadFactory, |
| 2711 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2712 | { |
| 2713 | return Concat3dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
| 2714 | } |
| 2715 | |
| 2716 | LayerTestResult<uint8_t, 3> Concat3dDim1DiffInputDimsUint8Test( |
| 2717 | armnn::IWorkloadFactory& workloadFactory, |
| 2718 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2719 | { |
| 2720 | return Concat3dDim1DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2721 | workloadFactory, memoryManager, 0.5f, -1); |
| 2722 | } |
| 2723 | |
| 2724 | LayerTestResult<uint8_t, 3> Concat3dDim2DiffInputDimsUint8Test( |
| 2725 | armnn::IWorkloadFactory& workloadFactory, |
| 2726 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 2727 | bool useSubtensor) |
| 2728 | { |
| 2729 | return Concat3dDim2DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2730 | workloadFactory, memoryManager, useSubtensor, 0.5f, -1); |
| 2731 | } |
| 2732 | |
| 2733 | LayerTestResult<uint8_t, 4> Concat4dDim0Uint8Test( |
| 2734 | armnn::IWorkloadFactory& workloadFactory, |
| 2735 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2736 | { |
| 2737 | return Concat4dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
| 2738 | } |
| 2739 | |
| 2740 | LayerTestResult<uint8_t, 4> Concat4dDim1Uint8Test( |
| 2741 | armnn::IWorkloadFactory& workloadFactory, |
| 2742 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2743 | { |
| 2744 | return Concat4dDim1TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
| 2745 | } |
| 2746 | |
| 2747 | LayerTestResult<uint8_t, 4> Concat4dDim2Uint8Test( |
| 2748 | armnn::IWorkloadFactory& workloadFactory, |
| 2749 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2750 | { |
| 2751 | return Concat4dDim2TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
| 2752 | } |
| 2753 | |
| 2754 | LayerTestResult<uint8_t, 4> Concat4dDim3Uint8Test( |
| 2755 | armnn::IWorkloadFactory& workloadFactory, |
| 2756 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) |
| 2757 | { |
| 2758 | return Concat4dDim3TestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2759 | workloadFactory, memoryManager, 0.5f, -1, useSubtensor); |
| 2760 | } |
| 2761 | |
| 2762 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim0Uint8Test( |
| 2763 | armnn::IWorkloadFactory& workloadFactory, |
| 2764 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2765 | { |
| 2766 | return Concat4dDiffShapeDim0TestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2767 | workloadFactory, memoryManager, 0.5f, -1); |
| 2768 | } |
| 2769 | |
| 2770 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim1Uint8Test( |
| 2771 | armnn::IWorkloadFactory& workloadFactory, |
| 2772 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2773 | { |
| 2774 | return Concat4dDiffShapeDim1TestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2775 | workloadFactory, memoryManager, 0.5f, -1); |
| 2776 | } |
| 2777 | |
| 2778 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim2Uint8Test( |
| 2779 | armnn::IWorkloadFactory& workloadFactory, |
| 2780 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 2781 | { |
| 2782 | return Concat4dDiffShapeDim2TestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2783 | workloadFactory, memoryManager, 0.5f, -1); |
| 2784 | } |
| 2785 | |
| 2786 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim3Uint8Test( |
| 2787 | armnn::IWorkloadFactory& workloadFactory, |
| 2788 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 2789 | bool useSubtensor) |
| 2790 | { |
| 2791 | return Concat4dDiffShapeDim3TestImpl<armnn::DataType::QuantisedAsymm8>( |
| 2792 | workloadFactory, memoryManager, 0.5f, -1, useSubtensor); |
| 2793 | } |