Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "Conv3dTestImpl.hpp" |
| 7 | |
| 8 | #include <QuantizeHelper.hpp> |
| 9 | |
| 10 | #include <armnnUtils/DataLayoutIndexed.hpp> |
| 11 | |
Colm Donelan | 0c47974 | 2021-12-10 12:43:54 +0000 | [diff] [blame] | 12 | #include <armnn/backends/TensorHandle.hpp> |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 13 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 14 | #include <armnnTestUtils/DataLayoutUtils.hpp> |
| 15 | #include <armnnTestUtils/TensorCopyUtils.hpp> |
Colm Donelan | 0c47974 | 2021-12-10 12:43:54 +0000 | [diff] [blame] | 16 | #include <armnnTestUtils/WorkloadTestUtils.hpp> |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 17 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 18 | #include <TensorHelpers.hpp> |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 19 | |
| 20 | using namespace armnnUtils; |
| 21 | |
| 22 | // |
| 23 | // Helper templates |
| 24 | // |
| 25 | |
| 26 | // Helper template that returns a quantized bias depending on the number of output channels. |
| 27 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 28 | std::vector<T> GetBiasData(bool biasEnabled, float qScale, armnn::TensorInfo outputInfo, armnn::DataLayout layout) |
| 29 | { |
| 30 | if(!biasEnabled) |
| 31 | { |
| 32 | return std::vector<T>(); |
| 33 | } |
| 34 | else |
| 35 | { |
| 36 | const armnnUtils::DataLayoutIndexed dataLayoutIndexed(layout); |
| 37 | const unsigned int outputChannels = outputInfo.GetShape()[dataLayoutIndexed.GetChannelsIndex()]; |
| 38 | |
| 39 | switch (outputChannels) |
| 40 | { |
| 41 | case 1: |
| 42 | { |
| 43 | return QuantizedVector<T>({2}, qScale, 0); |
| 44 | } |
| 45 | case 2: |
| 46 | default: |
| 47 | { |
| 48 | return QuantizedVector<T>({0, 2}, qScale, 0); |
| 49 | } |
| 50 | } |
| 51 | } |
| 52 | } |
| 53 | |
| 54 | // Modifies a std::vector in-place using a specified bias. |
| 55 | template<typename T, typename B> |
| 56 | void ApplyBiasToData(std::vector<T>& v, const std::vector<B>& bias, |
| 57 | float vScale, int32_t vOffset, |
| 58 | float bScale, int32_t bOffset) |
| 59 | { |
| 60 | ARMNN_ASSERT_MSG((armnn::IsQuantizedType<T>() && vScale != 0.0f) || (!armnn::IsQuantizedType<T>()), |
| 61 | "Invalid type and parameter combination."); |
| 62 | ARMNN_ASSERT_MSG((armnn::IsQuantizedType<B>() && bScale != 0.0f) || (!armnn::IsQuantizedType<B>()), |
| 63 | "Invalid type and parameter combination."); |
| 64 | |
| 65 | for (uint32_t i = 0; i < bias.size(); ++i) |
| 66 | { |
Rob Hughes | eb70c91 | 2021-10-07 08:53:58 +0100 | [diff] [blame] | 67 | for (size_t j = i; j < v.size(); j+=bias.size()) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 68 | { |
| 69 | // Note we need to dequantize and re-quantize the image value and the bias. |
| 70 | float dBias = SelectiveDequantize(bias[i], bScale, bOffset); |
| 71 | |
| 72 | T& outRef = v[j]; |
| 73 | float dOutput = SelectiveDequantize(outRef, vScale, vOffset); |
| 74 | outRef = SelectiveQuantize<T>(dOutput + dBias, vScale, vOffset); |
| 75 | } |
| 76 | } |
| 77 | } |
| 78 | |
| 79 | // Set the quantization scale and offset values for data types. |
| 80 | template<armnn::DataType ArmnnType> |
| 81 | void SetScaleOffset(float& qScale, int32_t& qOffset) |
| 82 | { |
| 83 | switch (ArmnnType) |
| 84 | { |
| 85 | case armnn::DataType::QAsymmU8: |
| 86 | { |
| 87 | qScale = 0.1f; |
| 88 | qOffset = 128; |
| 89 | break; |
| 90 | } |
| 91 | case armnn::DataType::QAsymmS8: |
Teresa Charlin | ec5f7d1 | 2021-10-22 17:15:00 +0100 | [diff] [blame] | 92 | { |
| 93 | qScale = 0.1f; |
| 94 | qOffset = 64; |
| 95 | break; |
| 96 | } |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 97 | case armnn::DataType::QSymmS16: |
| 98 | { |
| 99 | qScale = 0.1f; |
| 100 | qOffset = 0; |
| 101 | break; |
| 102 | } |
| 103 | case armnn::DataType::BFloat16: |
| 104 | case armnn::DataType::Float16: |
| 105 | case armnn::DataType::Float32: |
| 106 | default: |
| 107 | { |
Teresa Charlin | ec5f7d1 | 2021-10-22 17:15:00 +0100 | [diff] [blame] | 108 | qScale = 1.f; |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 109 | qOffset = 0; |
| 110 | break; |
| 111 | } |
| 112 | } |
| 113 | } |
| 114 | |
| 115 | // Create a vector from 0 to size and quantize (if required). |
| 116 | template <typename T> |
| 117 | std::vector<T> CreateQuantizedData(int32_t size, float qScale, int32_t qOffset) |
| 118 | { |
| 119 | std::vector<float> data; |
| 120 | for (int32_t i = 0; i < size; ++i) |
| 121 | { |
| 122 | data.push_back(static_cast<float>(i)); |
| 123 | } |
| 124 | |
| 125 | return QuantizedVector<T>(data, qScale, qOffset); |
| 126 | } |
| 127 | |
| 128 | // Create a vector from 0 to size divided and then quantized (if required) to create smaller floating point values. |
| 129 | template <typename T> |
| 130 | std::vector<T> CreateSmallQuantizedData(int32_t size, float divisor, float qScale, int32_t qOffset) |
| 131 | { |
| 132 | std::vector<float> data; |
| 133 | for (int32_t i = 0; i < size; ++i) |
| 134 | { |
| 135 | float value = static_cast<float>(i); |
| 136 | data.push_back(value/divisor); |
| 137 | } |
| 138 | |
| 139 | return QuantizedVector<T>(data, qScale, qOffset);; |
| 140 | } |
| 141 | |
| 142 | // |
| 143 | // Convolution3d implementations |
| 144 | // |
| 145 | |
| 146 | template<armnn::DataType ArmnnType, |
| 147 | armnn::DataType ArmnnBType, |
| 148 | typename T = armnn::ResolveType<ArmnnType>, |
| 149 | typename B = armnn::ResolveType<ArmnnBType>> |
| 150 | LayerTestResult<T, 5> SimpleConvolution3dTestImpl( |
| 151 | armnn::IWorkloadFactory& workloadFactory, |
| 152 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 153 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 154 | const std::vector<T>& input, |
| 155 | const std::vector<T>& kernel, |
| 156 | const std::vector<B>& bias, |
| 157 | const std::vector<T>& outputExpected, |
| 158 | const armnn::TensorShape& inputShape, |
| 159 | const armnn::TensorShape& kernelShape, |
| 160 | const armnn::TensorShape& outputExpectedShape, |
| 161 | const armnn::DataLayout dataLayout, |
| 162 | float qScale, |
| 163 | int32_t qOffset, |
| 164 | uint32_t strideX = 1, |
| 165 | uint32_t strideY = 1, |
| 166 | uint32_t strideZ = 1, |
| 167 | uint32_t dilationX = 1, |
| 168 | uint32_t dilationY = 1, |
| 169 | uint32_t dilationZ = 1, |
| 170 | uint32_t padLeft = 0, |
| 171 | uint32_t padTop = 0, |
| 172 | uint32_t padRight = 0, |
| 173 | uint32_t padBottom = 0, |
| 174 | uint32_t padFront = 0, |
| 175 | uint32_t padBack = 0) |
| 176 | { |
| 177 | unsigned int inputNum = armnn::numeric_cast<unsigned int>(inputShape[0]); |
| 178 | unsigned int inputDepth = armnn::numeric_cast<unsigned int>(inputShape[1]); |
| 179 | unsigned int inputHeight = armnn::numeric_cast<unsigned int>(inputShape[2]); |
| 180 | unsigned int inputWidth = armnn::numeric_cast<unsigned int>(inputShape[3]); |
| 181 | unsigned int inputChannels = armnn::numeric_cast<unsigned int>(inputShape[4]); |
| 182 | |
| 183 | // Conv3d weights/kernel layout: [D,H,W,I,O] |
| 184 | unsigned int kernelDepth = armnn::numeric_cast<unsigned int>(kernelShape[0]); |
| 185 | unsigned int kernelHeight = armnn::numeric_cast<unsigned int>(kernelShape[1]); |
| 186 | unsigned int kernelWidth = armnn::numeric_cast<unsigned int>(kernelShape[2]); |
| 187 | unsigned int kernelInChannels = armnn::numeric_cast<unsigned int>(kernelShape[3]); |
| 188 | unsigned int kernelOutChannels = armnn::numeric_cast<unsigned int>(kernelShape[4]); |
| 189 | |
| 190 | unsigned int outputNum = armnn::numeric_cast<unsigned int>(outputExpectedShape[0]); |
| 191 | unsigned int outputDepth = armnn::numeric_cast<unsigned int>(outputExpectedShape[1]); |
| 192 | unsigned int outputHeight = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]); |
| 193 | unsigned int outputWidth = armnn::numeric_cast<unsigned int>(outputExpectedShape[3]); |
| 194 | unsigned int outputChannels = armnn::numeric_cast<unsigned int>(outputExpectedShape[4]); |
| 195 | |
| 196 | bool biasEnabled = bias.size() > 0; |
| 197 | |
| 198 | // If a bias is used, its size must equal the number of output channels. |
| 199 | ARMNN_ASSERT(!biasEnabled || bias.size() == outputChannels); |
| 200 | |
| 201 | // Creates the tensors. |
| 202 | armnn::TensorInfo inputTensorInfo({inputNum, inputDepth, inputHeight, inputWidth, inputChannels}, ArmnnType); |
| 203 | armnn::TensorInfo outputTensorInfo({outputNum, outputDepth, outputHeight, outputWidth, outputChannels}, ArmnnType); |
| 204 | armnn::TensorInfo kernelDesc({kernelDepth, kernelHeight, kernelWidth, kernelInChannels, kernelOutChannels}, |
| 205 | ArmnnType); |
| 206 | armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType); |
| 207 | |
| 208 | // Set quantization parameters if the requested type is a quantized type. |
| 209 | if(armnn::IsQuantizedType<T>()) |
| 210 | { |
| 211 | inputTensorInfo.SetQuantizationScale(qScale); |
| 212 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 213 | outputTensorInfo.SetQuantizationScale(qScale); |
| 214 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 215 | kernelDesc.SetQuantizationScale(qScale); |
| 216 | kernelDesc.SetQuantizationOffset(qOffset); |
| 217 | biasDesc.SetQuantizationScale(qScale*qScale); |
| 218 | biasDesc.SetQuantizationOffset(0); |
| 219 | } |
| 220 | |
| 221 | // Construct the input data. |
| 222 | std::vector<T> inputData; |
| 223 | inputData.assign(input.data(), input.data() + inputNum*inputDepth*inputHeight*inputWidth*inputChannels); |
| 224 | |
| 225 | // Construct the output data and apply bias if needed. |
| 226 | std::vector<T> outputData; |
| 227 | outputData.assign(outputExpected.data(), outputExpected.data() + |
| 228 | outputNum*outputDepth*outputHeight*outputWidth*outputChannels); |
| 229 | |
| 230 | if (biasEnabled) |
| 231 | { |
| 232 | ApplyBiasToData(outputData, bias, |
| 233 | outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), |
| 234 | biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset()); |
| 235 | } |
| 236 | |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 237 | // Permute input and output if data layout is NCDHW. |
| 238 | if (dataLayout == armnn::DataLayout::NCDHW) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 239 | { |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 240 | PermuteTensorNdhwcToNcdhw(inputTensorInfo, inputData); |
| 241 | PermuteTensorNdhwcToNcdhw(outputTensorInfo, outputData); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 242 | } |
| 243 | |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 244 | std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); |
| 245 | |
| 246 | std::unique_ptr<armnn::ITensorHandle> input0Handle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 247 | std::unique_ptr<armnn::ITensorHandle> input1Handle = tensorHandleFactory.CreateTensorHandle(kernelDesc); |
| 248 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 249 | |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 250 | armnn::Convolution3dQueueDescriptor data; |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 251 | data.m_Parameters.m_StrideX = strideX; |
| 252 | data.m_Parameters.m_StrideY = strideY; |
| 253 | data.m_Parameters.m_StrideZ = strideZ; |
| 254 | data.m_Parameters.m_PadLeft = padLeft; |
| 255 | data.m_Parameters.m_PadRight = padRight; |
| 256 | data.m_Parameters.m_PadTop = padTop; |
| 257 | data.m_Parameters.m_PadBottom = padBottom; |
| 258 | data.m_Parameters.m_PadFront = padFront; |
| 259 | data.m_Parameters.m_PadBack = padBack; |
| 260 | data.m_Parameters.m_DilationX = dilationX; |
| 261 | data.m_Parameters.m_DilationY = dilationY; |
| 262 | data.m_Parameters.m_DilationZ = dilationZ; |
| 263 | data.m_Parameters.m_DataLayout = dataLayout; |
| 264 | data.m_Parameters.m_BiasEnabled = biasEnabled; |
| 265 | |
| 266 | armnn::WorkloadInfo info; |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 267 | AddInputToWorkload(data, info, inputTensorInfo, input0Handle.get()); |
| 268 | AddInputToWorkload(data, info, kernelDesc, input1Handle.get()); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 269 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 270 | |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 271 | std::unique_ptr<armnn::ITensorHandle> input2Handle = nullptr; |
| 272 | if (biasEnabled) |
| 273 | { |
| 274 | input2Handle = tensorHandleFactory.CreateTensorHandle(biasDesc); |
| 275 | AddInputToWorkload(data, info, biasDesc, input2Handle.get()); |
| 276 | } |
| 277 | |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 278 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution3d(data, info); |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 279 | input0Handle->Allocate(); |
| 280 | input1Handle->Allocate(); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 281 | outputHandle->Allocate(); |
| 282 | |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 283 | CopyDataToITensorHandle(input0Handle.get(), inputData.data()); |
| 284 | CopyDataToITensorHandle(input1Handle.get(), kernel.data()); |
| 285 | if (biasEnabled) |
| 286 | { |
| 287 | input2Handle->Allocate(); |
| 288 | CopyDataToITensorHandle(input2Handle.get(), bias.data()); |
| 289 | } |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 290 | |
| 291 | ExecuteWorkload(*workload, memoryManager); |
| 292 | |
| 293 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 294 | |
| 295 | return LayerTestResult<T, 5>(actualOutput, |
| 296 | outputData, |
| 297 | outputHandle->GetShape(), |
| 298 | outputTensorInfo.GetShape()); |
| 299 | } |
| 300 | |
| 301 | template<armnn::DataType ArmnnType, |
| 302 | armnn::DataType ArmnnBType, |
| 303 | typename T = armnn::ResolveType<ArmnnType>> |
| 304 | LayerTestResult<T, 5> SimpleConvolution3d3x3x3TestCommon( |
| 305 | armnn::IWorkloadFactory& workloadFactory, |
| 306 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 307 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 308 | bool biasEnabled, |
| 309 | armnn::DataLayout dataLayout) |
| 310 | { |
| 311 | float qScale; |
| 312 | int32_t qOffset; |
| 313 | SetScaleOffset<ArmnnType>(qScale, qOffset); |
| 314 | |
| 315 | armnn::TensorInfo inputDesc({ 1, 5, 5, 5, 1 }, ArmnnType); |
| 316 | std::vector<T> input = CreateQuantizedData<T>(125, qScale, qOffset); |
| 317 | |
| 318 | armnn::TensorInfo kernelDesc({ 3, 3, 3, 1, 1 }, ArmnnType); |
| 319 | std::vector<T> kernel = QuantizedVector<T>( |
| 320 | { |
| 321 | 1, 1, 1, |
| 322 | 1, 1, 1, |
| 323 | 1, 1, 1, |
| 324 | |
| 325 | 0, 0, 0, |
| 326 | 0, 1, 0, |
| 327 | 0, 0, 0, |
| 328 | |
| 329 | 1, 1, 1, |
| 330 | 1, 1, 1, |
| 331 | 1, 1, 1, |
| 332 | }, |
| 333 | qScale, qOffset); |
| 334 | |
| 335 | armnn::TensorInfo outputDesc({ 1, 3, 3, 3, 1 }, ArmnnType); |
| 336 | std::vector<T> outputData = QuantizedVector<T>( |
| 337 | { |
| 338 | 589, 608, 627, |
| 339 | 684, 703, 722, |
| 340 | 779, 798, 817, |
| 341 | |
| 342 | 1064, 1083, 1102, |
| 343 | 1159, 1178, 1197, |
| 344 | 1254, 1273, 1292, |
| 345 | |
| 346 | 1539, 1558, 1577, |
| 347 | 1634, 1653, 1672, |
| 348 | 1729, 1748, 1767 |
| 349 | }, |
| 350 | qScale, qOffset); |
| 351 | |
| 352 | return SimpleConvolution3dTestImpl<ArmnnType, ArmnnBType>( |
| 353 | workloadFactory, |
| 354 | memoryManager, |
| 355 | tensorHandleFactory, |
| 356 | input, |
| 357 | kernel, |
| 358 | GetBiasData<ArmnnBType>(biasEnabled, qScale * qScale, outputDesc, dataLayout), |
| 359 | outputData, |
| 360 | inputDesc.GetShape(), |
| 361 | kernelDesc.GetShape(), |
| 362 | outputDesc.GetShape(), |
| 363 | dataLayout, |
| 364 | qScale, |
| 365 | qOffset |
| 366 | ); |
| 367 | } |
| 368 | |
| 369 | template<armnn::DataType ArmnnType, |
| 370 | armnn::DataType ArmnnBType, |
| 371 | typename T = armnn::ResolveType<ArmnnType>> |
| 372 | LayerTestResult<T, 5> Convolution3d2x2x2Strides3x5x5TestCommon( |
| 373 | armnn::IWorkloadFactory& workloadFactory, |
| 374 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 375 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 376 | bool biasEnabled, |
| 377 | armnn::DataLayout dataLayout) |
| 378 | { |
| 379 | float qScale; |
| 380 | int32_t qOffset; |
| 381 | SetScaleOffset<ArmnnType>(qScale, qOffset); |
| 382 | |
| 383 | armnn::TensorInfo inputDesc({ 1, 3, 10, 10, 1 }, ArmnnType); |
| 384 | std::vector<T> input = CreateQuantizedData<T>(300, qScale, qOffset); |
| 385 | |
| 386 | armnn::TensorInfo kernelDesc({ 3, 5, 5, 1, 1 }, ArmnnType); |
| 387 | std::vector<T> kernel = QuantizedVector<T>( |
| 388 | { |
| 389 | 1, 1, 1, 1, 1, |
| 390 | 1, 1, 1, 1, 1, |
| 391 | 1, 1, 1, 1, 1, |
| 392 | 1, 1, 1, 1, 1, |
| 393 | 1, 1, 1, 1, 1, |
| 394 | |
| 395 | 0, 0, 0, 0, 0, |
| 396 | 0, 0, 0, 0, 0, |
| 397 | 0, 0, 0, 0, 0, |
| 398 | 0, 0, 0, 0, 0, |
| 399 | 0, 0, 0, 0, 0, |
| 400 | |
| 401 | 2, 2, 2, 2, 2, |
| 402 | 2, 2, 2, 2, 2, |
| 403 | 2, 2, 2, 2, 2, |
| 404 | 2, 2, 2, 2, 2, |
| 405 | 2, 2, 2, 2, 2, |
| 406 | }, |
| 407 | qScale, qOffset); |
| 408 | |
| 409 | armnn::TensorInfo outputDesc({ 1, 1, 3, 3, 1 }, ArmnnType); |
| 410 | std::vector<T> outputData = QuantizedVector<T>( |
| 411 | { |
| 412 | 11650, 11800, 11950, |
| 413 | |
| 414 | 13150, 13300, 13450, |
| 415 | |
| 416 | 14650, 14800, 14950 |
| 417 | }, |
| 418 | qScale, qOffset); |
| 419 | |
| 420 | return SimpleConvolution3dTestImpl<ArmnnType, ArmnnBType>( |
| 421 | workloadFactory, |
| 422 | memoryManager, |
| 423 | tensorHandleFactory, |
| 424 | input, |
| 425 | kernel, |
| 426 | GetBiasData<ArmnnBType>(biasEnabled, qScale * qScale, outputDesc, dataLayout), |
| 427 | outputData, |
| 428 | inputDesc.GetShape(), |
| 429 | kernelDesc.GetShape(), |
| 430 | outputDesc.GetShape(), |
| 431 | dataLayout, |
| 432 | qScale, |
| 433 | qOffset, |
| 434 | 2, // strideX |
| 435 | 2, // strideY |
| 436 | 2 // strideZ |
| 437 | ); |
| 438 | } |
| 439 | |
| 440 | template<armnn::DataType ArmnnType, |
| 441 | armnn::DataType ArmnnBType, |
| 442 | typename T = armnn::ResolveType<ArmnnType>> |
| 443 | LayerTestResult<T, 5> Convolution3d2x2x2Dilation2x2x2TestCommon( |
| 444 | armnn::IWorkloadFactory& workloadFactory, |
| 445 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 446 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 447 | bool biasEnabled, |
| 448 | armnn::DataLayout dataLayout) |
| 449 | { |
| 450 | float qScale; |
| 451 | int32_t qOffset; |
| 452 | SetScaleOffset<ArmnnType>(qScale, qOffset); |
| 453 | |
| 454 | armnn::TensorInfo inputDesc({ 1, 5, 5, 5, 2 }, ArmnnType); |
| 455 | std::vector<T> input = CreateQuantizedData<T>(250, qScale, qOffset); |
| 456 | |
| 457 | armnn::TensorInfo kernelDesc({ 2, 2, 2, 2, 2 }, ArmnnType); |
| 458 | std::vector<T> kernel = QuantizedVector<T>( |
| 459 | { |
| 460 | -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, |
| 461 | 1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, |
| 462 | }, |
| 463 | qScale, qOffset); |
| 464 | |
| 465 | // Since the dilation rate is 3 this will dilate the kernel to be 4x4, |
| 466 | // therefore the output will be 2x2 |
| 467 | armnn::TensorInfo outputDesc({ 1, 2, 2, 2, 2 }, ArmnnType); |
| 468 | std::vector<T> outputData = QuantizedVector<T>( |
| 469 | { |
| 470 | -1124, 974, |
| 471 | -1148, 978, |
| 472 | |
| 473 | -1244, 994, |
| 474 | -1268, 998, |
| 475 | |
| 476 | -1724, 1074, |
| 477 | -1748, 1078, |
| 478 | |
| 479 | -1844, 1094, |
| 480 | -1868, 1098 |
| 481 | }, |
| 482 | qScale, qOffset); |
| 483 | |
| 484 | return SimpleConvolution3dTestImpl<ArmnnType, ArmnnBType>( |
| 485 | workloadFactory, |
| 486 | memoryManager, |
| 487 | tensorHandleFactory, |
| 488 | input, |
| 489 | kernel, |
| 490 | GetBiasData<ArmnnBType>(biasEnabled, qScale * qScale, outputDesc, dataLayout), |
| 491 | outputData, |
| 492 | inputDesc.GetShape(), |
| 493 | kernelDesc.GetShape(), |
| 494 | outputDesc.GetShape(), |
| 495 | dataLayout, |
| 496 | qScale, |
| 497 | qOffset, |
| 498 | 1, // strideX |
| 499 | 1, // strideY |
| 500 | 1, // strideZ |
| 501 | 3, // dilationX |
| 502 | 3, // dilationY |
| 503 | 3 // dilationZ |
| 504 | ); |
| 505 | } |
| 506 | |
| 507 | template<armnn::DataType ArmnnType, |
| 508 | armnn::DataType ArmnnBType, |
| 509 | typename T = armnn::ResolveType<ArmnnType>> |
| 510 | LayerTestResult<T, 5> Convolution3dPaddingSame3x3x3TestCommon( |
| 511 | armnn::IWorkloadFactory& workloadFactory, |
| 512 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 513 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 514 | bool biasEnabled, |
| 515 | armnn::DataLayout dataLayout) |
| 516 | { |
| 517 | float qScale; |
| 518 | int32_t qOffset; |
| 519 | SetScaleOffset<ArmnnType>(qScale, qOffset); |
| 520 | |
| 521 | armnn::TensorInfo inputDesc({ 1, 5, 5, 5, 1 }, ArmnnType); |
| 522 | std::vector<T> input = CreateQuantizedData<T>(125, qScale, qOffset); |
| 523 | |
| 524 | armnn::TensorInfo kernelDesc({ 3, 3, 3, 1, 1 }, ArmnnType); |
| 525 | std::vector<T> kernel = QuantizedVector<T>( |
| 526 | { |
| 527 | 1, 1, 1, |
| 528 | 1, 1, 1, |
| 529 | 1, 1, 1, |
| 530 | |
| 531 | 0, 0, 0, |
| 532 | 0, 0, 0, |
| 533 | 0, 0, 0, |
| 534 | |
| 535 | 1, 1, 1, |
| 536 | 1, 1, 1, |
| 537 | 1, 1, 1, |
| 538 | }, |
| 539 | qScale, qOffset); |
| 540 | |
| 541 | armnn::TensorInfo outputDesc({ 1, 5, 5, 5, 1 }, ArmnnType); |
| 542 | std::vector<T> outputData = QuantizedVector<T>( |
| 543 | { |
| 544 | 112, 171, 177, 183, 124, |
| 545 | 183, 279, 288, 297, 201, |
| 546 | 213, 324, 333, 342, 231, |
| 547 | 243, 369, 378, 387, 261, |
| 548 | 172, 261, 267, 273, 184, |
| 549 | |
| 550 | 224, 342, 354, 366, 248, |
| 551 | 366, 558, 576, 594, 402, |
| 552 | 426, 648, 666, 684, 462, |
| 553 | 486, 738, 756, 774, 522, |
| 554 | 344, 522, 534, 546, 368, |
| 555 | |
| 556 | 424, 642, 654, 666, 448, |
| 557 | 666, 1008, 1026, 1044, 702, |
| 558 | 726, 1098, 1116, 1134, 762, |
| 559 | 786, 1188, 1206, 1224, 822, |
| 560 | 544, 822, 834, 846, 568, |
| 561 | 624, 942, 954, 966, 648, |
| 562 | |
| 563 | 966, 1458, 1476, 1494, 1002, |
| 564 | 1026, 1548, 1566, 1584, 1062, |
| 565 | 1086, 1638, 1656, 1674, 1122, |
| 566 | 744, 1122, 1134, 1146, 768, |
| 567 | 312, 471, 477, 483, 324, |
| 568 | 483, 729, 738, 747, 501, |
| 569 | 513, 774, 783, 792, 531, |
| 570 | 543, 819, 828, 837, 561, |
| 571 | 372, 561, 567, 573, 384 |
| 572 | }, |
| 573 | qScale, qOffset); |
| 574 | |
| 575 | return SimpleConvolution3dTestImpl<ArmnnType, ArmnnBType>( |
| 576 | workloadFactory, |
| 577 | memoryManager, |
| 578 | tensorHandleFactory, |
| 579 | input, |
| 580 | kernel, |
| 581 | GetBiasData<ArmnnBType>(biasEnabled, qScale * qScale, outputDesc, dataLayout), |
| 582 | outputData, |
| 583 | inputDesc.GetShape(), |
| 584 | kernelDesc.GetShape(), |
| 585 | outputDesc.GetShape(), |
| 586 | dataLayout, |
| 587 | qScale, |
| 588 | qOffset, |
| 589 | 1, // strideX |
| 590 | 1, // strideY |
| 591 | 1, // strideZ |
| 592 | 1, // dilationX |
| 593 | 1, // dilationY |
| 594 | 1, // dilationZ |
| 595 | 1, // padLeft |
| 596 | 1, // padTop |
| 597 | 1, // padRight |
| 598 | 1, // padBottom |
| 599 | 1, // padFront |
| 600 | 1 // padBack |
| 601 | ); |
| 602 | } |
| 603 | |
| 604 | LayerTestResult<float, 5> Convolution3dStrideDilationPadding3x3x3TestCommonFloat32( |
| 605 | armnn::IWorkloadFactory& workloadFactory, |
| 606 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 607 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 608 | bool biasEnabled, |
| 609 | armnn::DataLayout dataLayout) |
| 610 | { |
| 611 | float qScale = 0.f; |
| 612 | int32_t qOffset = 0; |
| 613 | |
| 614 | armnn::TensorInfo inputDesc({ 1, 3, 10, 10, 2 }, armnn::DataType::Float32); |
| 615 | std::vector<float> input = CreateSmallQuantizedData<float>(600, 100.0f, qScale, qOffset); |
| 616 | |
| 617 | armnn::TensorInfo kernelDesc({ 3, 3, 3, 2, 2 }, armnn::DataType::Float32); |
| 618 | std::vector<float> kernel = CreateSmallQuantizedData<float>(108, 100.0f, qScale, qOffset); |
| 619 | |
| 620 | // Since the dilation rate is 2 this will dilate the kernel to be 5x5: d(K-1)+1 --> 2 x (3-1) + 1 = 5, |
| 621 | // therefore the output will be 1x4x4: (I − K + 2P)/S +1 => trunc((10 - 3 + 2x2 )/3 + 1)) |
| 622 | // where, dilation size = d = 2; kernel size = K = 3; input size = I = 10; padding size = P = 2; stride = S = 3 |
| 623 | armnn::TensorInfo outputDesc({ 1, 1, 4, 4, 2 }, armnn::DataType::Float32); |
| 624 | std::vector<float> outputData = |
| 625 | { |
| 626 | 12.0312f, 12.2268f, 17.7512f, 18.0494f, |
| 627 | 18.176f, 18.4814f, 5.6912f, 5.7938f, |
| 628 | 19.1664f, 19.5078f, 28.119f, 28.6383f, |
| 629 | 28.6914f, 29.2215f, 8.9094f, 9.0873f, |
| 630 | |
| 631 | 23.1264f, 23.5398f, 33.843f, 34.4703f, |
| 632 | 34.4154f, 35.0535f, 10.6734f, 10.8873f, |
| 633 | 6.2712f, 6.417f, 9.0718f, 9.2929f, |
| 634 | 9.2194f, 9.4441f, 2.7862f, 2.8615f |
| 635 | }; |
| 636 | |
| 637 | return SimpleConvolution3dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>( |
| 638 | workloadFactory, |
| 639 | memoryManager, |
| 640 | tensorHandleFactory, |
| 641 | input, |
| 642 | kernel, |
| 643 | GetBiasData<armnn::DataType::Float32>(biasEnabled, qScale * qScale, outputDesc, dataLayout), |
| 644 | outputData, |
| 645 | inputDesc.GetShape(), |
| 646 | kernelDesc.GetShape(), |
| 647 | outputDesc.GetShape(), |
| 648 | dataLayout, |
| 649 | qScale, |
| 650 | qOffset, |
| 651 | 3, // strideX |
| 652 | 3, // strideY |
| 653 | 3, // strideZ |
| 654 | 2, // dilationX |
| 655 | 2, // dilationY |
| 656 | 2, // dilationZ |
| 657 | 1, // padLeft |
| 658 | 1, // padTop |
| 659 | 1, // padRight |
| 660 | 1, // padBottom |
| 661 | 1, // padFront |
| 662 | 1 // padBack |
| 663 | ); |
| 664 | } |
| 665 | |
| 666 | LayerTestResult<float, 5> Convolution3d2x2x2Stride3x3x3SmallTestCommonFloat32( |
| 667 | armnn::IWorkloadFactory& workloadFactory, |
| 668 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 669 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 670 | bool biasEnabled, |
| 671 | armnn::DataLayout dataLayout) |
| 672 | { |
| 673 | float qScale = 0.f; |
| 674 | int32_t qOffset = 0; |
| 675 | |
| 676 | armnn::TensorInfo inputDesc({ 1, 3, 10, 10, 1 }, armnn::DataType::Float32); |
| 677 | std::vector<float> input = CreateSmallQuantizedData<float>(300, 100.0f, qScale, qOffset); |
| 678 | |
| 679 | armnn::TensorInfo kernelDesc({ 3, 3, 3, 1, 1 }, armnn::DataType::Float32); |
| 680 | std::vector<float> kernel = |
| 681 | { |
| 682 | 0.125977f, 0.150391f, 0.101562f, |
| 683 | 0.0585938f, 0.0864258f, 0.043457f, |
| 684 | 0.034668f, 0.0322266f, 0.0385742f, |
| 685 | |
| 686 | 0.125977f, 0.150391f, -0.101562f, |
| 687 | -0.0585938f,-0.0864258f,-0.043457f, |
| 688 | -0.0104630f, 0.0154114f, 0.0013768f, |
| 689 | |
| 690 | 0.0344238f, 0.035644f, 0.0495605f, |
| 691 | 0.0683594f, 0.099121f, -0.0461426f, |
| 692 | -0.0996094f,-0.126953f, -0.043457f, |
| 693 | }; |
| 694 | |
| 695 | armnn::TensorInfo outputDesc({ 1, 1, 4, 4, 1 }, armnn::DataType::Float32); |
| 696 | std::vector<float> outputData = |
| 697 | { |
| 698 | -0.08156067f, -0.06891209f, -0.05589598f, -0.04310101f, |
| 699 | 0.04584253f, 0.05855697f, 0.07129729f, 0.08325434f, |
| 700 | 0.17304349f, 0.18521416f, 0.19818866f, 0.21096253f, |
| 701 | 0.29965734f, 0.312698f, 0.32547557f, 0.33818722f |
| 702 | }; |
| 703 | |
| 704 | return SimpleConvolution3dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>( |
| 705 | workloadFactory, |
| 706 | memoryManager, |
| 707 | tensorHandleFactory, |
| 708 | input, |
| 709 | kernel, |
| 710 | GetBiasData<armnn::DataType::Float32>(biasEnabled, qScale * qScale, outputDesc, dataLayout), |
| 711 | outputData, |
| 712 | inputDesc.GetShape(), |
| 713 | kernelDesc.GetShape(), |
| 714 | outputDesc.GetShape(), |
| 715 | dataLayout, |
| 716 | qScale, |
| 717 | qOffset, |
| 718 | 2, // strideX |
| 719 | 2, // strideY |
| 720 | 2 // strideZ |
| 721 | ); |
| 722 | } |
| 723 | |
| 724 | LayerTestResult<armnn::Half, 5> Convolution3d2x3x3TestCommonFloat16( |
| 725 | armnn::IWorkloadFactory& workloadFactory, |
| 726 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 727 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 728 | bool biasEnabled, |
| 729 | armnn::DataLayout dataLayout) |
| 730 | { |
| 731 | using namespace half_float::literal; |
| 732 | |
| 733 | float qScale = 0.f; |
| 734 | int32_t qOffset = 0; |
| 735 | |
| 736 | armnn::TensorInfo inputDesc({ 1, 2, 3, 3, 2 }, armnn::DataType::Float16); |
| 737 | const std::vector<armnn::Half> input = |
| 738 | { |
| 739 | 1._h, 2._h, 3._h, |
| 740 | 4._h, 5._h, 6._h, |
| 741 | |
| 742 | 7._h, 8._h, 9._h, |
| 743 | 10._h, 11._h, 12._h, |
| 744 | |
| 745 | 13._h, 14._h, 15._h, |
| 746 | 16._h, 17._h, 18._h, |
| 747 | |
| 748 | 19._h, 20._h, 21._h, |
| 749 | 22._h, 23._h, 24._h, |
| 750 | |
| 751 | 25._h, 26._h, 27._h, |
| 752 | 28._h, 29._h, 30._h, |
| 753 | |
| 754 | 31._h, 32._h, 33._h, |
| 755 | 34._h, 35._h, 36._h |
| 756 | }; |
| 757 | |
| 758 | armnn::TensorInfo kernelDesc({ 2, 2, 2, 2, 2 }, armnn::DataType::Float16); |
| 759 | std::vector<armnn::Half> kernel = |
| 760 | { |
| 761 | -1._h, -1._h, -1._h, -1._h, -1._h, -1._h, -1._h, -1._h, |
| 762 | -1._h, -1._h, -1._h, 1._h, 1._h, 1._h, -1._h, -1._h, |
| 763 | 1._h, 1._h, -1._h, 1._h, -1._h, 1._h, -1._h, 1._h, |
| 764 | -1._h, -1._h, -1._h, 1._h, -1._h, 1._h, -1._h, 1._h, |
| 765 | }; |
| 766 | |
| 767 | armnn::TensorInfo outputDesc({ 1, 1, 2, 2, 2 }, armnn::DataType::Float16); |
| 768 | std::vector<armnn::Half> outputData = |
| 769 | { |
| 770 | -176._h, 128._h, |
| 771 | -200._h, 132._h, |
| 772 | |
| 773 | -248._h, 140._h, |
| 774 | -272._h, 144._h |
| 775 | }; |
| 776 | |
| 777 | return SimpleConvolution3dTestImpl<armnn::DataType::Float16, armnn::DataType::Float16>( |
| 778 | workloadFactory, |
| 779 | memoryManager, |
| 780 | tensorHandleFactory, |
| 781 | input, |
| 782 | kernel, |
| 783 | GetBiasData<armnn::DataType::Float16>(biasEnabled, qScale * qScale, outputDesc, dataLayout), |
| 784 | outputData, |
| 785 | inputDesc.GetShape(), |
| 786 | kernelDesc.GetShape(), |
| 787 | outputDesc.GetShape(), |
| 788 | dataLayout, |
| 789 | qScale, |
| 790 | qOffset |
| 791 | ); |
| 792 | } |
| 793 | |
| 794 | LayerTestResult<armnn::Half, 5> Convolution3d2x2x2SmallTestCommonFloat16( |
| 795 | armnn::IWorkloadFactory& workloadFactory, |
| 796 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 797 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 798 | bool biasEnabled, |
| 799 | armnn::DataLayout dataLayout) |
| 800 | { |
| 801 | using namespace half_float::literal; |
| 802 | |
| 803 | float qScale = 0.f; |
| 804 | int32_t qOffset = 0; |
| 805 | |
| 806 | armnn::TensorInfo inputDesc({ 1, 2, 4, 4, 1 }, armnn::DataType::Float16); |
| 807 | const std::vector<armnn::Half> input = |
| 808 | { |
| 809 | 0.0367984_h, 0.0380895_h, 0.0420157_h, 0.0675631_h, |
| 810 | 0.0938920_h, 0.0476106_h, 0.1035490_h, 0.1260370_h, |
| 811 | 0.0461647_h, 0.0883828_h, 0.1159540_h, 0.0498519_h, |
| 812 | 0.0104630_h, 0.0154114_h, 0.00137681_h, 0.0344238_h, |
| 813 | |
| 814 | 0.0356445_h, 0.0495605_h, 0.0683594_h, 0.0991211_h, |
| 815 | 0.0461426_h, 0.0996094_h, 0.1269530_h, 0.0393066_h, |
| 816 | 0.103516_h, 0.032544_h, 0.124334_h, 0.0564566_h, |
| 817 | 0.0123544_h, 0.0461647_h, 0.0883828_h, 0.1159540_h, |
| 818 | }; |
| 819 | |
| 820 | armnn::TensorInfo kernelDesc({ 2, 2, 2, 1, 1 }, armnn::DataType::Float16); |
| 821 | std::vector<armnn::Half> kernel = |
| 822 | { |
| 823 | -0.126184_h, -0.150468_h, |
| 824 | -0.101412_h, -0.0586369_h, |
| 825 | |
| 826 | -0.0435089_h, 0.0347555_h, |
| 827 | 0.0323111_h, 0.0385381_h |
| 828 | }; |
| 829 | |
| 830 | armnn::TensorInfo outputDesc({ 1, 1, 3, 3, 1 }, armnn::DataType::Float16); |
| 831 | std::vector<armnn::Half> outputData = |
| 832 | { |
| 833 | -0.01718917_h, -0.01370182_h, -0.02727737_h, |
| 834 | |
| 835 | -0.02282543_h, -0.03144084_h, -0.04468598_h, |
| 836 | |
| 837 | -0.02228982_h, -0.02244923_h, -0.02042268_h |
| 838 | }; |
| 839 | |
| 840 | return SimpleConvolution3dTestImpl<armnn::DataType::Float16, armnn::DataType::Float16>( |
| 841 | workloadFactory, |
| 842 | memoryManager, |
| 843 | tensorHandleFactory, |
| 844 | input, |
| 845 | kernel, |
| 846 | GetBiasData<armnn::DataType::Float16>(biasEnabled, qScale * qScale, outputDesc, dataLayout), |
| 847 | outputData, |
| 848 | inputDesc.GetShape(), |
| 849 | kernelDesc.GetShape(), |
| 850 | outputDesc.GetShape(), |
| 851 | dataLayout, |
| 852 | qScale, |
| 853 | qOffset |
| 854 | ); |
| 855 | } |
| 856 | |
| 857 | LayerTestResult<float, 5> SimpleConvolution3d3x3x3Float32Test( |
| 858 | armnn::IWorkloadFactory& workloadFactory, |
| 859 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 860 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 861 | bool biasEnabled, |
| 862 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 863 | { |
| 864 | return SimpleConvolution3d3x3x3TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 865 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 866 | } |
| 867 | |
| 868 | LayerTestResult<int8_t, 5> SimpleConvolution3d3x3x3Int8Test( |
| 869 | armnn::IWorkloadFactory& workloadFactory, |
| 870 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 871 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 872 | bool biasEnabled, |
| 873 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 874 | { |
| 875 | return SimpleConvolution3d3x3x3TestCommon<armnn::DataType::QAsymmS8, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 876 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 877 | } |
| 878 | |
| 879 | LayerTestResult<uint8_t, 5> SimpleConvolution3d3x3x3Uint8Test( |
| 880 | armnn::IWorkloadFactory& workloadFactory, |
| 881 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 882 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 883 | bool biasEnabled, |
| 884 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 885 | { |
| 886 | return SimpleConvolution3d3x3x3TestCommon<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 887 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 888 | } |
| 889 | |
| 890 | LayerTestResult<int16_t, 5> SimpleConvolution3d3x3x3Int16Test( |
| 891 | armnn::IWorkloadFactory& workloadFactory, |
| 892 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 893 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 894 | bool biasEnabled, |
| 895 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 896 | { |
| 897 | return SimpleConvolution3d3x3x3TestCommon<armnn::DataType::QSymmS16, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 898 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 899 | } |
| 900 | |
| 901 | |
| 902 | LayerTestResult<float, 5> Convolution3d2x2x2Strides3x5x5Float32Test( |
| 903 | armnn::IWorkloadFactory& workloadFactory, |
| 904 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 905 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 906 | bool biasEnabled, |
| 907 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 908 | { |
| 909 | return Convolution3d2x2x2Strides3x5x5TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 910 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 911 | } |
| 912 | |
| 913 | LayerTestResult<int8_t, 5> Convolution3d2x2x2Strides3x5x5Int8Test( |
| 914 | armnn::IWorkloadFactory& workloadFactory, |
| 915 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 916 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 917 | bool biasEnabled, |
| 918 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 919 | { |
| 920 | return Convolution3d2x2x2Strides3x5x5TestCommon<armnn::DataType::QAsymmS8, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 921 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 922 | } |
| 923 | |
| 924 | LayerTestResult<uint8_t, 5> Convolution3d2x2x2Strides3x5x5Uint8Test( |
| 925 | armnn::IWorkloadFactory& workloadFactory, |
| 926 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 927 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 928 | bool biasEnabled, |
| 929 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 930 | { |
| 931 | return Convolution3d2x2x2Strides3x5x5TestCommon<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 932 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 933 | } |
| 934 | |
| 935 | LayerTestResult<int16_t, 5> Convolution3d2x2x2Strides3x5x5Int16Test( |
| 936 | armnn::IWorkloadFactory& workloadFactory, |
| 937 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 938 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 939 | bool biasEnabled, |
| 940 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 941 | { |
| 942 | return Convolution3d2x2x2Strides3x5x5TestCommon<armnn::DataType::QSymmS16, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 943 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 944 | } |
| 945 | |
| 946 | LayerTestResult<float, 5> Convolution3d2x2x2Dilation2x2x2Float32Test( |
| 947 | armnn::IWorkloadFactory& workloadFactory, |
| 948 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 949 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 950 | bool biasEnabled, |
| 951 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 952 | { |
| 953 | return Convolution3d2x2x2Dilation2x2x2TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 954 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 955 | } |
| 956 | |
| 957 | LayerTestResult<int8_t, 5> Convolution3d2x2x2Dilation2x2x2Int8Test( |
| 958 | armnn::IWorkloadFactory& workloadFactory, |
| 959 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 960 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 961 | bool biasEnabled, |
| 962 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 963 | { |
| 964 | return Convolution3d2x2x2Dilation2x2x2TestCommon<armnn::DataType::QAsymmS8, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 965 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 966 | } |
| 967 | |
| 968 | LayerTestResult<uint8_t, 5> Convolution3d2x2x2Dilation2x2x2Uint8Test( |
| 969 | armnn::IWorkloadFactory& workloadFactory, |
| 970 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 971 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 972 | bool biasEnabled, |
| 973 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 974 | { |
| 975 | return Convolution3d2x2x2Dilation2x2x2TestCommon<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 976 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 977 | } |
| 978 | |
| 979 | LayerTestResult<int16_t, 5> Convolution3d2x2x2Dilation2x2x2Int16Test( |
| 980 | armnn::IWorkloadFactory& workloadFactory, |
| 981 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 982 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 983 | bool biasEnabled, |
| 984 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 985 | { |
| 986 | return Convolution3d2x2x2Dilation2x2x2TestCommon<armnn::DataType::QSymmS16, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 987 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 988 | } |
| 989 | |
| 990 | LayerTestResult<float, 5> Convolution3dPaddingSame3x3x3Float32Test( |
| 991 | armnn::IWorkloadFactory& workloadFactory, |
| 992 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 993 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 994 | bool biasEnabled, |
| 995 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 996 | { |
| 997 | return Convolution3dPaddingSame3x3x3TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 998 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 999 | } |
| 1000 | |
| 1001 | LayerTestResult<int8_t, 5> Convolution3dPaddingSame3x3x3Int8Test( |
| 1002 | armnn::IWorkloadFactory& workloadFactory, |
| 1003 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1004 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1005 | bool biasEnabled, |
| 1006 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1007 | { |
| 1008 | return Convolution3dPaddingSame3x3x3TestCommon<armnn::DataType::QAsymmS8, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1009 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1010 | } |
| 1011 | |
| 1012 | LayerTestResult<uint8_t, 5> Convolution3dPaddingSame3x3x3Uint8Test( |
| 1013 | armnn::IWorkloadFactory& workloadFactory, |
| 1014 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1015 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1016 | bool biasEnabled, |
| 1017 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1018 | { |
| 1019 | return Convolution3dPaddingSame3x3x3TestCommon<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1020 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1021 | } |
| 1022 | |
| 1023 | LayerTestResult<int16_t, 5> Convolution3dPaddingSame3x3x3Int16Test( |
| 1024 | armnn::IWorkloadFactory& workloadFactory, |
| 1025 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1026 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1027 | bool biasEnabled, |
| 1028 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1029 | { |
| 1030 | return Convolution3dPaddingSame3x3x3TestCommon<armnn::DataType::QSymmS16, armnn::DataType::Signed32>( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1031 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1032 | } |
| 1033 | |
| 1034 | LayerTestResult<float, 5> Convolution3dStrideDilationPadding3x3x3Float32Test( |
| 1035 | armnn::IWorkloadFactory& workloadFactory, |
| 1036 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1037 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1038 | bool biasEnabled, |
| 1039 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1040 | { |
| 1041 | return Convolution3dStrideDilationPadding3x3x3TestCommonFloat32( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1042 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1043 | } |
| 1044 | |
| 1045 | LayerTestResult<float, 5> Convolution3d2x2x2Stride3x3x3SmallFloat32Test( |
| 1046 | armnn::IWorkloadFactory& workloadFactory, |
| 1047 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1048 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1049 | bool biasEnabled, |
| 1050 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1051 | { |
| 1052 | return Convolution3d2x2x2Stride3x3x3SmallTestCommonFloat32( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1053 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1054 | } |
| 1055 | |
| 1056 | LayerTestResult<armnn::Half, 5> Convolution3d2x3x3Float16Test( |
| 1057 | armnn::IWorkloadFactory& workloadFactory, |
| 1058 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1059 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1060 | bool biasEnabled, |
| 1061 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1062 | { |
| 1063 | return Convolution3d2x3x3TestCommonFloat16( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1064 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1065 | } |
| 1066 | |
| 1067 | LayerTestResult<armnn::Half, 5> Convolution3d2x2x2SmallFloat16Test( |
| 1068 | armnn::IWorkloadFactory& workloadFactory, |
| 1069 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1070 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1071 | bool biasEnabled, |
| 1072 | armnn::DataLayout dataLayout) |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1073 | { |
| 1074 | return Convolution3d2x2x2SmallTestCommonFloat16( |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1075 | workloadFactory, memoryManager, tensorHandleFactory, biasEnabled, dataLayout); |
Matthew Sloyan | b63a311 | 2021-09-08 13:05:51 +0100 | [diff] [blame] | 1076 | } |