Aron Virginas-Tar | 735a450 | 2019-06-26 15:02:47 +0100 | [diff] [blame^] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include "QuantizeHelper.hpp" |
| 8 | |
| 9 | #include <armnn/ArmNN.hpp> |
| 10 | |
| 11 | #include <ResolveType.hpp> |
| 12 | |
| 13 | #include <backendsCommon/CpuTensorHandle.hpp> |
| 14 | #include <backendsCommon/test/CommonTestUtils.hpp> |
| 15 | #include <backendsCommon/test/TensorCopyUtils.hpp> |
| 16 | #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| 17 | |
| 18 | #include <reference/RefWorkloadFactory.hpp> |
| 19 | |
| 20 | #include <boost/test/unit_test.hpp> |
| 21 | |
| 22 | #include <string> |
| 23 | #include <utility> |
| 24 | #include <vector> |
| 25 | |
| 26 | namespace |
| 27 | { |
| 28 | |
| 29 | template<typename T> |
| 30 | using TensorData = std::pair<armnn::TensorInfo, std::vector<T>>; |
| 31 | |
| 32 | template<typename T> |
| 33 | void VerifyInputTensorData(const TensorData<T>& data, const std::string& tensorName) |
| 34 | { |
| 35 | if (data.first.GetNumElements() > data.second.size()) |
| 36 | { |
| 37 | throw armnn::InvalidArgumentException("Size of data too small for " + tensorName + ": expected " + |
| 38 | std::to_string(data.first.GetNumElements()) + "but got " + std::to_string(data.second.size())); |
| 39 | } |
| 40 | } |
| 41 | |
| 42 | template<typename T, typename BT> |
| 43 | void TransposeConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 44 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 45 | const armnn::TransposeConvolution2dDescriptor& descriptor, |
| 46 | const TensorData<T>& input, |
| 47 | TensorData<T>& output, |
| 48 | const TensorData<T>& weights, |
| 49 | const armnn::Optional<TensorData<BT>>& biases) |
| 50 | { |
| 51 | using namespace armnn; |
| 52 | |
| 53 | VerifyInputTensorData(input, "input"); |
| 54 | VerifyInputTensorData(weights, "biases"); |
| 55 | |
| 56 | if (descriptor.m_BiasEnabled) |
| 57 | { |
| 58 | if (!biases.has_value()) |
| 59 | { |
| 60 | throw InvalidArgumentException("Bias enabled but no bias data provided"); |
| 61 | } |
| 62 | VerifyInputTensorData(biases.value(), "biases"); |
| 63 | } |
| 64 | |
| 65 | // set up weights |
| 66 | ScopedCpuTensorHandle weightsTensor(weights.first); |
| 67 | |
| 68 | TransposeConvolution2dQueueDescriptor queueDescriptor; |
| 69 | queueDescriptor.m_Parameters = descriptor; |
| 70 | queueDescriptor.m_Weight = &weightsTensor; |
| 71 | |
| 72 | AllocateAndCopyDataToITensorHandle(&weightsTensor, weights.second.data()); |
| 73 | |
| 74 | std::unique_ptr<ScopedCpuTensorHandle> biasesTensor; |
| 75 | if (descriptor.m_BiasEnabled) |
| 76 | { |
| 77 | // set up biases |
| 78 | biasesTensor = std::make_unique<ScopedCpuTensorHandle>(biases.value().first); |
| 79 | queueDescriptor.m_Bias = biasesTensor.get(); |
| 80 | |
| 81 | AllocateAndCopyDataToITensorHandle(biasesTensor.get(), biases.value().second.data()); |
| 82 | } |
| 83 | |
| 84 | // set up input and output handles |
| 85 | std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(input.first); |
| 86 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(output.first); |
| 87 | |
| 88 | // set up workload |
| 89 | armnn::WorkloadInfo workloadInfo; |
| 90 | AddInputToWorkload(queueDescriptor, workloadInfo, input.first, inputHandle.get()); |
| 91 | AddOutputToWorkload(queueDescriptor, workloadInfo, output.first, outputHandle.get()); |
| 92 | |
| 93 | std::unique_ptr<armnn::IWorkload> workload = |
| 94 | workloadFactory.CreateTransposeConvolution2d(queueDescriptor, workloadInfo); |
| 95 | |
| 96 | inputHandle->Allocate(); |
| 97 | outputHandle->Allocate(); |
| 98 | |
| 99 | CopyDataToITensorHandle(inputHandle.get(), input.second.data()); |
| 100 | |
| 101 | ExecuteWorkload(*workload, nullptr); |
| 102 | |
| 103 | // copy output |
| 104 | output.second = std::vector<T>(output.first.GetNumElements(), 0.0f); |
| 105 | CopyDataFromITensorHandle(output.second.data(), outputHandle.get()); |
| 106 | } |
| 107 | |
| 108 | template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> |
| 109 | LayerTestResult<T, 4> TransposeConvolution2dTestImpl( |
| 110 | armnn::IWorkloadFactory& workloadFactory, |
| 111 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 112 | const armnn::TransposeConvolution2dDescriptor& descriptor, |
| 113 | armnn::TensorInfo& inputInfo, |
| 114 | const std::vector<float>& inputData, |
| 115 | armnn::TensorInfo& outputInfo, |
| 116 | const std::vector<float>& expectedOutputData, |
| 117 | armnn::TensorInfo& weightsInfo, |
| 118 | const std::vector<float>& weightsData, |
| 119 | armnn::TensorInfo& biasesInfo, |
| 120 | const std::vector<float>& biasesData) |
| 121 | { |
| 122 | using namespace armnn; |
| 123 | |
| 124 | // set up quantization parameters |
| 125 | if (armnn::IsQuantizedType<T>()) |
| 126 | { |
| 127 | constexpr float qScale = 0.25f; |
| 128 | constexpr int32_t qOffset = 50; |
| 129 | |
| 130 | inputInfo.SetQuantizationScale(qScale); |
| 131 | inputInfo.SetQuantizationOffset(qOffset); |
| 132 | |
| 133 | outputInfo.SetQuantizationScale(qScale); |
| 134 | outputInfo.SetQuantizationOffset(qOffset); |
| 135 | |
| 136 | weightsInfo.SetQuantizationScale(qScale); |
| 137 | weightsInfo.SetQuantizationOffset(qOffset); |
| 138 | |
| 139 | biasesInfo.SetQuantizationScale(qScale * qScale); |
| 140 | biasesInfo.SetQuantizationOffset(0); |
| 141 | } |
| 142 | |
| 143 | // set up input |
| 144 | TensorData<T> input = |
| 145 | { |
| 146 | inputInfo, |
| 147 | QuantizedVector<T>(inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), inputData) |
| 148 | }; |
| 149 | |
| 150 | // set up weights |
| 151 | TensorData<T> weights = |
| 152 | { |
| 153 | weightsInfo, |
| 154 | QuantizedVector<T>(weightsInfo.GetQuantizationScale(), weightsInfo.GetQuantizationOffset(), weightsData) |
| 155 | }; |
| 156 | |
| 157 | // set up biases |
| 158 | using BT = armnn::ResolveType<ArmnnBType>; |
| 159 | Optional<TensorData<BT>> optionalBiases; |
| 160 | if (descriptor.m_BiasEnabled) |
| 161 | { |
| 162 | TensorData<BT> biases = |
| 163 | { |
| 164 | biasesInfo, |
| 165 | QuantizedVector<BT>(biasesInfo.GetQuantizationScale(), biasesInfo.GetQuantizationOffset(), biasesData) |
| 166 | }; |
| 167 | |
| 168 | optionalBiases = Optional<TensorData<BT>>(biases); |
| 169 | } |
| 170 | |
| 171 | // set up output |
| 172 | TensorData<T> output = { outputInfo, {} }; |
| 173 | |
| 174 | // execute test |
| 175 | TransposeConvolution2dTestImpl(workloadFactory, |
| 176 | memoryManager, |
| 177 | descriptor, |
| 178 | input, |
| 179 | output, |
| 180 | weights, |
| 181 | optionalBiases); |
| 182 | |
| 183 | // construct result object |
| 184 | LayerTestResult<T, 4> testResult(outputInfo); |
| 185 | testResult.output = MakeTensor<T, 4>(outputInfo, output.second); |
| 186 | testResult.outputExpected = MakeTensor<T, 4>(outputInfo, |
| 187 | QuantizedVector<T>(outputInfo.GetQuantizationScale(), |
| 188 | outputInfo.GetQuantizationOffset(), |
| 189 | expectedOutputData)); |
| 190 | |
| 191 | return testResult; |
| 192 | } |
| 193 | |
| 194 | } // anonymous namespace |
| 195 | |
| 196 | template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> |
| 197 | LayerTestResult<T, 4> SimpleTransposeConvolution2dTestImpl( |
| 198 | armnn::IWorkloadFactory& workloadFactory, |
| 199 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 200 | bool biasEnabled, |
| 201 | const armnn::DataLayout layout) |
| 202 | { |
| 203 | using namespace armnn; |
| 204 | |
| 205 | constexpr unsigned int batches = 1u; |
| 206 | constexpr unsigned int channels = 1u; |
| 207 | |
| 208 | constexpr unsigned int wInput = 3u; |
| 209 | constexpr unsigned int hInput = wInput; |
| 210 | |
| 211 | constexpr unsigned int wOutput = 5u; |
| 212 | constexpr unsigned int hOutput = wOutput; |
| 213 | |
| 214 | constexpr unsigned int wWeights = 3u; |
| 215 | constexpr unsigned int hWeights = wWeights; |
| 216 | |
| 217 | TensorShape inputShape = MakeTensorShape(batches, channels, hInput, wInput, layout); |
| 218 | TensorShape outputShape = MakeTensorShape(batches, channels, hOutput, wOutput, layout); |
| 219 | TensorShape weightsShape = MakeTensorShape(batches, channels, hWeights, wWeights, layout); |
| 220 | |
| 221 | TensorInfo inputInfo(inputShape, ArmnnType); |
| 222 | TensorInfo outputInfo(outputShape, ArmnnType); |
| 223 | TensorInfo weightsInfo(weightsShape, ArmnnType); |
| 224 | TensorInfo biasesInfo({ channels }, ArmnnBType); |
| 225 | |
| 226 | std::vector<float> inputData = |
| 227 | { |
| 228 | 1.f, 1.f, 1.f, |
| 229 | 1.f, 1.f, 1.f, |
| 230 | 1.f, 1.f, 1.f |
| 231 | }; |
| 232 | |
| 233 | std::vector<float> weightsData = |
| 234 | { |
| 235 | 1.f, 2.f, 3.f, |
| 236 | 4.f, 5.f, 6.f, |
| 237 | 7.f, 8.f, 9.f |
| 238 | }; |
| 239 | |
| 240 | std::vector<float> biasesData = { 1.f }; |
| 241 | |
| 242 | std::vector<float> expectedOutputData = |
| 243 | { |
| 244 | 1.f, 3.f, 6.f, 5.f, 3.f, |
| 245 | 5.f, 12.f, 21.f, 16.f, 9.f, |
| 246 | 12.f, 27.f, 45.f, 33.f, 18.f, |
| 247 | 11.f, 24.f, 39.f, 28.f, 15.f, |
| 248 | 7.f, 15.f, 24.f, 17.f, 9.f |
| 249 | }; |
| 250 | |
| 251 | if (biasEnabled) |
| 252 | { |
| 253 | // apply bias to expected output data |
| 254 | std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(), |
| 255 | [&](float f) -> float { return f + biasesData[0]; }); |
| 256 | } |
| 257 | |
| 258 | TransposeConvolution2dDescriptor descriptor; |
| 259 | descriptor.m_StrideX = 1; |
| 260 | descriptor.m_StrideY = 1; |
| 261 | descriptor.m_BiasEnabled = biasEnabled; |
| 262 | descriptor.m_DataLayout = layout; |
| 263 | |
| 264 | // swizzle data if needed |
| 265 | if (layout == armnn::DataLayout::NHWC) |
| 266 | { |
| 267 | constexpr size_t dataTypeSize = sizeof(float); |
| 268 | const armnn::PermutationVector nchwToNhwc = { 0, 3, 1, 2 }; |
| 269 | |
| 270 | std::vector<float> tmp(inputData.size()); |
| 271 | armnnUtils::Permute(inputInfo.GetShape(), nchwToNhwc, inputData.data(), tmp.data(), dataTypeSize); |
| 272 | inputData = tmp; |
| 273 | |
| 274 | tmp.resize(weightsData.size()); |
| 275 | armnnUtils::Permute(weightsInfo.GetShape(), nchwToNhwc, weightsData.data(), tmp.data(), dataTypeSize); |
| 276 | weightsData = tmp; |
| 277 | |
| 278 | tmp.resize(expectedOutputData.size()); |
| 279 | armnnUtils::Permute(outputInfo.GetShape(), nchwToNhwc, expectedOutputData.data(), tmp.data(), dataTypeSize); |
| 280 | expectedOutputData = tmp; |
| 281 | } |
| 282 | |
| 283 | return TransposeConvolution2dTestImpl<ArmnnType, ArmnnBType>(workloadFactory, |
| 284 | memoryManager, |
| 285 | descriptor, |
| 286 | inputInfo, |
| 287 | inputData, |
| 288 | outputInfo, |
| 289 | expectedOutputData, |
| 290 | weightsInfo, |
| 291 | weightsData, |
| 292 | biasesInfo, |
| 293 | biasesData); |
| 294 | } |
| 295 | |
| 296 | template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> |
| 297 | LayerTestResult<T, 4> PaddedTransposeConvolution2dTestImpl( |
| 298 | armnn::IWorkloadFactory& workloadFactory, |
| 299 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 300 | bool biasEnabled, |
| 301 | const armnn::DataLayout layout) |
| 302 | { |
| 303 | using namespace armnn; |
| 304 | |
| 305 | constexpr unsigned int batches = 1u; |
| 306 | constexpr unsigned int channels = 1u; |
| 307 | |
| 308 | constexpr unsigned int wInput = 4u; |
| 309 | constexpr unsigned int hInput = wInput; |
| 310 | |
| 311 | constexpr unsigned int wOutput = 2u; |
| 312 | constexpr unsigned int hOutput = wOutput; |
| 313 | |
| 314 | constexpr unsigned int wWeights = 3u; |
| 315 | constexpr unsigned int hWeights = wWeights; |
| 316 | |
| 317 | TensorShape inputShape = MakeTensorShape(batches, channels, hInput, wInput, layout); |
| 318 | TensorShape outputShape = MakeTensorShape(batches, channels, hOutput, wOutput, layout); |
| 319 | TensorShape weightsShape = MakeTensorShape(batches, channels, hWeights, wWeights, layout); |
| 320 | |
| 321 | TensorInfo inputInfo(inputShape, ArmnnType); |
| 322 | TensorInfo outputInfo(outputShape, ArmnnType); |
| 323 | TensorInfo weightsInfo(weightsShape, ArmnnType); |
| 324 | TensorInfo biasesInfo({ channels }, ArmnnBType); |
| 325 | |
| 326 | std::vector<float> inputData = |
| 327 | { |
| 328 | 1.f, 3.f, 2.f, 1.f, |
| 329 | 1.f, 3.f, 3.f, 1.f, |
| 330 | 2.f, 1.f, 1.f, 3.f, |
| 331 | 3.f, 2.f, 3.f, 3.f |
| 332 | }; |
| 333 | |
| 334 | std::vector<float> weightsData = |
| 335 | { |
| 336 | 1.f, 2.f, 3.f, |
| 337 | 0.f, 1.f, 0.f, |
| 338 | 2.f, 1.f, 2.f |
| 339 | }; |
| 340 | |
| 341 | std::vector<float> biasesData = { 1.f }; |
| 342 | |
| 343 | std::vector<float> expectedOutputData = |
| 344 | { |
| 345 | 21.f, 21.f, |
| 346 | 28.f, 27.f |
| 347 | }; |
| 348 | |
| 349 | if (biasEnabled) |
| 350 | { |
| 351 | // apply bias to expected output data |
| 352 | std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(), |
| 353 | [&](float f) -> float { return f + biasesData[0]; }); |
| 354 | } |
| 355 | |
| 356 | TransposeConvolution2dDescriptor descriptor; |
| 357 | descriptor.m_PadLeft = 2; |
| 358 | descriptor.m_PadRight = 2; |
| 359 | descriptor.m_PadTop = 2; |
| 360 | descriptor.m_PadBottom = 2; |
| 361 | descriptor.m_StrideX = 1; |
| 362 | descriptor.m_StrideY = 1; |
| 363 | descriptor.m_BiasEnabled = biasEnabled; |
| 364 | descriptor.m_DataLayout = layout; |
| 365 | |
| 366 | // swizzle data if needed |
| 367 | if (layout == armnn::DataLayout::NHWC) |
| 368 | { |
| 369 | constexpr size_t dataTypeSize = sizeof(float); |
| 370 | const armnn::PermutationVector nchwToNhwc = { 0, 3, 1, 2 }; |
| 371 | |
| 372 | std::vector<float> tmp(inputData.size()); |
| 373 | armnnUtils::Permute(inputInfo.GetShape(), nchwToNhwc, inputData.data(), tmp.data(), dataTypeSize); |
| 374 | inputData = tmp; |
| 375 | |
| 376 | tmp.resize(weightsData.size()); |
| 377 | armnnUtils::Permute(weightsInfo.GetShape(), nchwToNhwc, weightsData.data(), tmp.data(), dataTypeSize); |
| 378 | weightsData = tmp; |
| 379 | |
| 380 | tmp.resize(expectedOutputData.size()); |
| 381 | armnnUtils::Permute(outputInfo.GetShape(), nchwToNhwc, expectedOutputData.data(), tmp.data(), dataTypeSize); |
| 382 | expectedOutputData = tmp; |
| 383 | } |
| 384 | |
| 385 | return TransposeConvolution2dTestImpl<ArmnnType, ArmnnBType>(workloadFactory, |
| 386 | memoryManager, |
| 387 | descriptor, |
| 388 | inputInfo, |
| 389 | inputData, |
| 390 | outputInfo, |
| 391 | expectedOutputData, |
| 392 | weightsInfo, |
| 393 | weightsData, |
| 394 | biasesInfo, |
| 395 | biasesData); |
| 396 | } |
| 397 | |
| 398 | template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> |
| 399 | LayerTestResult<T, 4> StridedTransposeConvolution2dTestImpl( |
| 400 | armnn::IWorkloadFactory& workloadFactory, |
| 401 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 402 | bool biasEnabled, |
| 403 | const armnn::DataLayout layout) |
| 404 | { |
| 405 | using namespace armnn; |
| 406 | |
| 407 | constexpr unsigned int batches = 1u; |
| 408 | constexpr unsigned int channels = 1u; |
| 409 | |
| 410 | constexpr unsigned int wInput = 3u; |
| 411 | constexpr unsigned int hInput = wInput; |
| 412 | |
| 413 | constexpr unsigned int wOutput = 7u; |
| 414 | constexpr unsigned int hOutput = wOutput; |
| 415 | |
| 416 | constexpr unsigned int wWeights = 3u; |
| 417 | constexpr unsigned int hWeights = wWeights; |
| 418 | |
| 419 | TensorShape inputShape = MakeTensorShape(batches, channels, hInput, wInput, layout); |
| 420 | TensorShape outputShape = MakeTensorShape(batches, channels, hOutput, wOutput, layout); |
| 421 | TensorShape weightsShape = MakeTensorShape(batches, channels, hWeights, wWeights, layout); |
| 422 | |
| 423 | TensorInfo inputInfo(inputShape, ArmnnType); |
| 424 | TensorInfo outputInfo(outputShape, ArmnnType); |
| 425 | TensorInfo weightsInfo(weightsShape, ArmnnType); |
| 426 | TensorInfo biasesInfo({ channels }, ArmnnBType); |
| 427 | |
| 428 | std::vector<float> inputData = |
| 429 | { |
| 430 | 1.f, 1.f, 1.f, |
| 431 | 1.f, 1.f, 1.f, |
| 432 | 1.f, 1.f, 1.f |
| 433 | }; |
| 434 | |
| 435 | std::vector<float> weightsData = |
| 436 | { |
| 437 | 1.f, 2.f, 3.f, |
| 438 | 4.f, 5.f, 6.f, |
| 439 | 7.f, 8.f, 9.f |
| 440 | }; |
| 441 | |
| 442 | std::vector<float> biasesData = { 1.f }; |
| 443 | |
| 444 | std::vector<float> expectedOutputData = |
| 445 | { |
| 446 | 1.f, 2.f, 4.f, 2.f, 4.f, 2.f, 3.f, |
| 447 | 4.f, 5.f, 10.f, 5.f, 10.f, 5.f, 6.f, |
| 448 | 8.f, 10.f, 20.f, 10.f, 20.f, 10.f, 12.f, |
| 449 | 4.f, 5.f, 10.f, 5.f, 10.f, 5.f, 6.f, |
| 450 | 8.f, 10.f, 20.f, 10.f, 20.f, 10.f, 12.f, |
| 451 | 4.f, 5.f, 10.f, 5.f, 10.f, 5.f, 6.f, |
| 452 | 7.f, 8.f, 16.f, 8.f, 16.f, 8.f, 9.f |
| 453 | }; |
| 454 | |
| 455 | if (biasEnabled) |
| 456 | { |
| 457 | // apply bias to expected output data |
| 458 | std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(), |
| 459 | [&](float f) -> float { return f + biasesData[0]; }); |
| 460 | } |
| 461 | |
| 462 | TransposeConvolution2dDescriptor descriptor; |
| 463 | descriptor.m_StrideX = 2; |
| 464 | descriptor.m_StrideY = 2; |
| 465 | descriptor.m_BiasEnabled = biasEnabled; |
| 466 | descriptor.m_DataLayout = layout; |
| 467 | |
| 468 | // swizzle data if needed |
| 469 | if (layout == armnn::DataLayout::NHWC) |
| 470 | { |
| 471 | constexpr size_t dataTypeSize = sizeof(float); |
| 472 | const armnn::PermutationVector nchwToNhwc = { 0, 3, 1, 2 }; |
| 473 | |
| 474 | std::vector<float> tmp(inputData.size()); |
| 475 | armnnUtils::Permute(inputInfo.GetShape(), nchwToNhwc, inputData.data(), tmp.data(), dataTypeSize); |
| 476 | inputData = tmp; |
| 477 | |
| 478 | tmp.resize(weightsData.size()); |
| 479 | armnnUtils::Permute(weightsInfo.GetShape(), nchwToNhwc, weightsData.data(), tmp.data(), dataTypeSize); |
| 480 | weightsData = tmp; |
| 481 | |
| 482 | tmp.resize(expectedOutputData.size()); |
| 483 | armnnUtils::Permute(outputInfo.GetShape(), nchwToNhwc, expectedOutputData.data(), tmp.data(), dataTypeSize); |
| 484 | expectedOutputData = tmp; |
| 485 | } |
| 486 | |
| 487 | return TransposeConvolution2dTestImpl<ArmnnType, ArmnnBType>(workloadFactory, |
| 488 | memoryManager, |
| 489 | descriptor, |
| 490 | inputInfo, |
| 491 | inputData, |
| 492 | outputInfo, |
| 493 | expectedOutputData, |
| 494 | weightsInfo, |
| 495 | weightsData, |
| 496 | biasesInfo, |
| 497 | biasesData); |
| 498 | } |