telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include <armnn/ArmNN.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 8 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 9 | #include <test/TensorHelpers.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 10 | #include "QuantizeHelper.hpp" |
| 11 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 12 | #include <backends/CpuTensorHandle.hpp> |
| 13 | #include <backends/WorkloadFactory.hpp> |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 14 | #include <backends/WorkloadInfo.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 15 | #include <algorithm> |
| 16 | |
| 17 | template<typename T> |
| 18 | LayerTestResult<T, 4> SimplePooling2dTestImpl( |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 19 | armnn::IWorkloadFactory& workloadFactory, |
| 20 | armnn::Pooling2dDescriptor descriptor, |
| 21 | float qScale, |
| 22 | int32_t qOffset, |
| 23 | const boost::multi_array<T, 4>& input, |
| 24 | const boost::multi_array<T, 4>& outputExpected) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 25 | { |
| 26 | unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[2]); |
| 27 | unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[3]); |
| 28 | unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[1]); |
| 29 | unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); |
| 30 | |
| 31 | unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]); |
| 32 | unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]); |
| 33 | unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]); |
| 34 | unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); |
| 35 | |
| 36 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 37 | armnn::GetDataType<T>()); |
| 38 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 39 | armnn::GetDataType<T>()); |
| 40 | |
| 41 | // Set quantization parameters if the requested type is a quantized type. |
| 42 | if(armnn::IsQuantizedType<T>()) |
| 43 | { |
| 44 | inputTensorInfo.SetQuantizationScale(qScale); |
| 45 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 46 | outputTensorInfo.SetQuantizationScale(qScale); |
| 47 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 48 | } |
| 49 | |
| 50 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 51 | |
| 52 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 53 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 54 | |
| 55 | armnn::Pooling2dQueueDescriptor queueDescriptor; |
| 56 | queueDescriptor.m_Parameters = descriptor; |
| 57 | armnn::WorkloadInfo workloadInfo; |
| 58 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 59 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 60 | |
| 61 | // Don't execute if Pooling is not supported, as an exception will be raised. |
| 62 | armnn::Compute compute = workloadFactory.GetCompute(); |
| 63 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 64 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
| 65 | result.supported = armnn::IsPooling2dSupported(compute, inputTensorInfo, outputTensorInfo, |
| 66 | queueDescriptor.m_Parameters, |
| 67 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 68 | if (!result.supported) |
| 69 | { |
| 70 | return result; |
| 71 | } |
| 72 | |
| 73 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); |
| 74 | |
| 75 | inputHandle->Allocate(); |
| 76 | outputHandle->Allocate(); |
| 77 | |
| 78 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 79 | |
| 80 | workload->Execute(); |
| 81 | |
| 82 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 83 | |
| 84 | result.outputExpected = outputExpected; |
| 85 | |
| 86 | return result; |
| 87 | } |
| 88 | |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 89 | template<typename T> |
| 90 | LayerTestResult<T, 4> SimplePooling2dNhwcTestImpl( |
| 91 | armnn::IWorkloadFactory& workloadFactory, |
| 92 | armnn::Pooling2dDescriptor descriptor, |
| 93 | float qScale, |
| 94 | int32_t qOffset, |
| 95 | const boost::multi_array<T, 4>& input, |
| 96 | const boost::multi_array<T, 4>& outputExpected) |
| 97 | { |
| 98 | unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[1]); |
| 99 | unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[2]); |
| 100 | unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[3]); |
| 101 | unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); |
| 102 | |
| 103 | unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]); |
| 104 | unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]); |
| 105 | unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]); |
| 106 | unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); |
| 107 | |
| 108 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputHeight, inputWidth, inputChannels }, |
| 109 | armnn::GetDataType<T>()); |
| 110 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputHeight, outputWidth, outputChannels }, |
| 111 | armnn::GetDataType<T>()); |
| 112 | |
| 113 | // Set quantization parameters if the requested type is a quantized type. |
| 114 | if(armnn::IsQuantizedType<T>()) |
| 115 | { |
| 116 | inputTensorInfo.SetQuantizationScale(qScale); |
| 117 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 118 | outputTensorInfo.SetQuantizationScale(qScale); |
| 119 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 120 | } |
| 121 | |
| 122 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 123 | |
| 124 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 125 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 126 | |
| 127 | armnn::Pooling2dQueueDescriptor queueDescriptor; |
| 128 | queueDescriptor.m_Parameters = descriptor; |
| 129 | queueDescriptor.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC; |
| 130 | |
| 131 | armnn::WorkloadInfo workloadInfo; |
| 132 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 133 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 134 | |
| 135 | // Don't execute if Pooling is not supported, as an exception will be raised. |
| 136 | armnn::Compute compute = workloadFactory.GetCompute(); |
| 137 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 138 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
| 139 | result.supported = armnn::IsPooling2dSupported(compute, inputTensorInfo, outputTensorInfo, |
| 140 | queueDescriptor.m_Parameters, |
| 141 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 142 | if (!result.supported) |
| 143 | { |
| 144 | return result; |
| 145 | } |
| 146 | |
| 147 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); |
| 148 | |
| 149 | inputHandle->Allocate(); |
| 150 | outputHandle->Allocate(); |
| 151 | |
| 152 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 153 | |
| 154 | workload->Execute(); |
| 155 | |
| 156 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 157 | |
| 158 | result.outputExpected = outputExpected; |
| 159 | |
| 160 | return result; |
| 161 | } |
| 162 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 163 | // |
| 164 | // Tests max pooling with the following parameters: |
| 165 | // |
| 166 | // Pooling size: 3x3 |
| 167 | // Stride: (2,4) |
| 168 | // input size: 8x13 |
| 169 | // channels: 2 |
| 170 | // batch size: 2 |
| 171 | // |
| 172 | template<typename T> |
| 173 | LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 174 | bool forceNoPadding, |
| 175 | float qScale = 1.0f, |
| 176 | int32_t qOffset = 0) |
| 177 | { |
| 178 | armnn::Pooling2dDescriptor descriptor; |
| 179 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 180 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 181 | descriptor.m_StrideX = 2; |
| 182 | descriptor.m_StrideY = 4; |
| 183 | // forceNoPadding is mainly used for compatibility with ARM Compute. |
| 184 | // As of 16/05/2017, it errors if padX or padY are equal to or greater than the pool size. |
| 185 | descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; |
| 186 | descriptor.m_PadTop = descriptor.m_PadBottom = 0; |
| 187 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 188 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 189 | |
| 190 | unsigned int inputWidth = 8; |
| 191 | unsigned int inputHeight = 13; |
| 192 | unsigned int outputWidth = |
| 193 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 194 | descriptor.m_StrideX; |
| 195 | unsigned int outputHeight = |
| 196 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 197 | descriptor.m_StrideY; |
| 198 | unsigned int channels = 2; |
| 199 | unsigned int batchSize = 2; |
| 200 | |
| 201 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 202 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 203 | |
| 204 | // Set quantization parameters if the requested type is a quantized type. |
| 205 | if(armnn::IsQuantizedType<T>()) |
| 206 | { |
| 207 | inputTensorInfo.SetQuantizationScale(qScale); |
| 208 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 209 | outputTensorInfo.SetQuantizationScale(qScale); |
| 210 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 211 | } |
| 212 | |
| 213 | std::vector<float> singleChannelData({ |
| 214 | 0.0f, 4.0f, 8.0f, 1.0f, 6.0f, 4.0f, 5.0f, 8.0f, |
| 215 | 1.0f, 1.0f, 6.0f, 0.0f, 3.0f, 7.0f, 4.0f, 7.0f, |
| 216 | 8.0f, 5.0f, 0.0f, 0.0f, 8.0f, 3.0f, 4.0f, 3.0f, |
| 217 | 8.0f, 2.0f, 5.0f, 4.0f, 1.0f, 9.0f, 2.0f, 0.0f, |
| 218 | 5.0f, 4.0f, 5.0f, 0.0f, 0.0f, 0.0f, 7.0f, 2.0f, |
| 219 | 1.0f, 2.0f, 6.0f, 2.0f, 7.0f, 9.0f, 5.0f, 2.0f, |
| 220 | 9.0f, 7.0f, 3.0f, 1.0f, 3.0f, 4.0f, 8.0f, 3.0f, |
| 221 | 1.0f, 0.0f, 0.0f, 5.0f, 5.0f, 4.0f, 2.0f, 0.0f, |
| 222 | 6.0f, 4.0f, 3.0f, 6.0f, 9.0f, 5.0f, 5.0f, 6.0f, |
| 223 | 8.0f, 7.0f, 9.0f, 6.0f, 1.0f, 4.0f, 1.0f, 9.0f, |
| 224 | 7.0f, 1.0f, 9.0f, 2.0f, 9.0f, 9.0f, 8.0f, 1.0f, |
| 225 | 4.0f, 4.0f, 5.0f, 9.0f, 2.0f, 6.0f, 6.0f, 4.0f, |
| 226 | 3.0f, 5.0f, 4.0f, 0.0f, 1.0f, 5.0f, 9.0f, 7.0f, |
| 227 | }); |
| 228 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 229 | // Constructs input data. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 230 | std::vector<float> inputData; |
| 231 | auto negator = [](float f) { return -f; }; |
| 232 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 233 | // First image (two channels where the second channel is the negative of the first one). |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 234 | inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); |
| 235 | std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); |
| 236 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 237 | // Second image (same as first image). |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 238 | inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); |
| 239 | std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); |
| 240 | |
| 241 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 242 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 243 | // These were calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 244 | auto shape(GetTensorShapeAsArray<4>(outputTensorInfo)); |
| 245 | boost::multi_array<T, 4> outputExpected(shape); |
| 246 | if (forceNoPadding) |
| 247 | { |
| 248 | outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 249 | QuantizedVector<T>(qScale, qOffset, { |
| 250 | 8.0f, 8.0f, 8.0f, |
| 251 | 9.0f, 7.0f, 9.0f, |
| 252 | 9.0f, 9.0f, 9.0f, |
| 253 | |
| 254 | 0.0f, 0.0f, -3.0f, |
| 255 | -1.0f, 0.0f, 0.0f, |
| 256 | -1.0f, -1.0f, -1.0f, |
| 257 | |
| 258 | 8.0f, 8.0f, 8.0f, |
| 259 | 9.0f, 7.0f, 9.0f, |
| 260 | 9.0f, 9.0f, 9.0f, |
| 261 | |
| 262 | 0.0f, 0.0f, -3.0f, |
| 263 | -1.0f, 0.0f, 0.0f, |
| 264 | -1.0f, -1.0f, -1.0f |
| 265 | })); |
| 266 | } |
| 267 | else |
| 268 | { |
| 269 | outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 270 | QuantizedVector<T>(qScale, qOffset, { |
| 271 | 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, |
| 272 | 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, |
| 273 | 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, |
| 274 | |
| 275 | 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, |
| 276 | 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 277 | 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f, |
| 278 | |
| 279 | 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, |
| 280 | 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, |
| 281 | 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, |
| 282 | |
| 283 | 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, |
| 284 | 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 285 | 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f |
| 286 | })); |
| 287 | } |
| 288 | |
| 289 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 290 | } |
| 291 | |
| 292 | template<typename T> |
| 293 | LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 294 | float qScale = 1.0f, |
| 295 | int32_t qOffset = 0) |
| 296 | { |
| 297 | armnn::Pooling2dDescriptor descriptor; |
| 298 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 299 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 300 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 301 | descriptor.m_PadLeft = 1; |
| 302 | descriptor.m_PadRight = 1; |
| 303 | descriptor.m_PadTop = 1; |
| 304 | descriptor.m_PadBottom = 1; |
| 305 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 306 | |
| 307 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 308 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 309 | |
| 310 | // Set quantization parameters if the requested type is a quantized type. |
| 311 | if(armnn::IsQuantizedType<T>()) |
| 312 | { |
| 313 | inputTensorInfo.SetQuantizationScale(qScale); |
| 314 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 315 | outputTensorInfo.SetQuantizationScale(qScale); |
| 316 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 317 | } |
| 318 | |
| 319 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 320 | QuantizedVector<T>(qScale, qOffset, { |
| 321 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 322 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 323 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 324 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 325 | })); |
| 326 | |
| 327 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 328 | QuantizedVector<T>(qScale, qOffset, { |
| 329 | 1.0f, 2.5f, 4.0f, |
| 330 | 1.0f, 2.5f, 4.0f, |
| 331 | 1.0f, 2.5f, 4.0f, |
| 332 | })); |
| 333 | |
| 334 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 335 | } |
| 336 | |
| 337 | template<typename T> |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 338 | LayerTestResult<T, 4> SimpleAveragePooling2dNhwcTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 339 | float qScale = 1.0f, |
| 340 | int32_t qOffset = 0) |
| 341 | { |
| 342 | armnn::Pooling2dDescriptor descriptor; |
| 343 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 344 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 345 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 346 | descriptor.m_PadLeft = 1; |
| 347 | descriptor.m_PadRight = 1; |
| 348 | descriptor.m_PadTop = 1; |
| 349 | descriptor.m_PadBottom = 1; |
| 350 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 351 | |
| 352 | armnn::TensorInfo inputTensorInfo({ 1, 4, 4, 1 }, armnn::GetDataType<T>()); |
| 353 | armnn::TensorInfo outputTensorInfo({ 1, 3, 3, 1 }, armnn::GetDataType<T>()); |
| 354 | |
| 355 | // Set quantization parameters if the requested type is a quantized type. |
| 356 | if(armnn::IsQuantizedType<T>()) |
| 357 | { |
| 358 | inputTensorInfo.SetQuantizationScale(qScale); |
| 359 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 360 | outputTensorInfo.SetQuantizationScale(qScale); |
| 361 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 362 | } |
| 363 | |
| 364 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 365 | QuantizedVector<T>(qScale, qOffset, { |
| 366 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 367 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 368 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 369 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 370 | })); |
| 371 | |
| 372 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 373 | QuantizedVector<T>(qScale, qOffset, { |
| 374 | 1.0f, 2.5f, 4.0f, |
| 375 | 1.0f, 2.5f, 4.0f, |
| 376 | 1.0f, 2.5f, 4.0f, |
| 377 | })); |
| 378 | |
| 379 | return SimplePooling2dNhwcTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 380 | } |
| 381 | |
| 382 | template<typename T> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 383 | LayerTestResult<T, 4> LargeTensorsAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 384 | float qScale = 1.0f, |
| 385 | int32_t qOffset = 0) |
| 386 | { |
| 387 | armnn::Pooling2dDescriptor descriptor; |
| 388 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 389 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 100; |
| 390 | descriptor.m_StrideX = descriptor.m_StrideY = 5; |
| 391 | descriptor.m_PadLeft = 50; |
| 392 | descriptor.m_PadRight = 50; |
| 393 | descriptor.m_PadTop = 50; |
| 394 | descriptor.m_PadBottom = 50; |
| 395 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 396 | |
| 397 | armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60 }, armnn::GetDataType<T>()); |
| 398 | armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13 }, armnn::GetDataType<T>()); |
| 399 | |
| 400 | // Set quantization parameters if the requested type is a quantized type. |
| 401 | if(armnn::IsQuantizedType<T>()) |
| 402 | { |
| 403 | inputTensorInfo.SetQuantizationScale(qScale); |
| 404 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 405 | outputTensorInfo.SetQuantizationScale(qScale); |
| 406 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 407 | } |
| 408 | |
| 409 | std::vector<T> inputVec; |
| 410 | |
| 411 | for (unsigned int i = 0 ; i < inputTensorInfo.GetShape().GetNumElements(); ++i) |
| 412 | { |
| 413 | inputVec.push_back(1); |
| 414 | } |
| 415 | |
| 416 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputVec); |
| 417 | |
| 418 | std::vector<T> outputVec; |
| 419 | |
| 420 | for (unsigned int i = 0 ; i < outputTensorInfo.GetShape().GetNumElements(); ++i) |
| 421 | { |
| 422 | outputVec.push_back(1); |
| 423 | } |
| 424 | |
| 425 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputVec); |
| 426 | |
| 427 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 428 | } |
| 429 | |
| 430 | template<typename T> |
| 431 | LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 432 | float qScale = 1.0f, |
| 433 | int32_t qOffset = 0) |
| 434 | { |
| 435 | armnn::Pooling2dDescriptor descriptor; |
| 436 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 437 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 438 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 439 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 440 | |
| 441 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 442 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 443 | QuantizedVector<T>(qScale, qOffset, { |
| 444 | 1.0f, 7.0f, 1.0f, 7.0f, |
| 445 | 1.0f, 7.0f, 1.0f, 7.0f, |
| 446 | 1.0f, 7.0f, 1.0f, 7.0f, |
| 447 | 1.0f, 7.0f, 1.0f, 7.0f, |
| 448 | })); |
| 449 | |
| 450 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 451 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 452 | QuantizedVector<T>(qScale, qOffset, { |
| 453 | 5.0f, 5.0f, |
| 454 | 5.0f, 5.0f, |
| 455 | })); |
| 456 | |
| 457 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 458 | } |
| 459 | |
| 460 | template<typename T> |
| 461 | LayerTestResult<T, 4> L2Pooling2dSize3Stride1TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 462 | float qScale = 1.0f, |
| 463 | int32_t qOffset = 0) |
| 464 | { |
| 465 | armnn::Pooling2dDescriptor descriptor; |
| 466 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 467 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 468 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 469 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 470 | |
| 471 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 472 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 473 | QuantizedVector<T>(qScale, qOffset, { |
| 474 | 2.0f, 1.0f, 5.0f, 2.0f, |
| 475 | 1.0f, 2.0f, 2.0f, 1.0f, |
| 476 | 5.0f, 4.0f, 1.0f, 5.0f, |
| 477 | 2.0f, 1.0f, 5.0f, 2.0f, |
| 478 | })); |
| 479 | |
| 480 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 481 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 482 | QuantizedVector<T>(qScale, qOffset, { |
| 483 | 3.0f, 3.0f, |
| 484 | 3.0f, 3.0f, |
| 485 | })); |
| 486 | |
| 487 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 488 | } |
| 489 | |
| 490 | template<typename T> |
| 491 | LayerTestResult<T, 4> L2Pooling2dSize3Stride3TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 492 | float qScale = 1.0f, |
| 493 | int32_t qOffset = 0) |
| 494 | { |
| 495 | armnn::Pooling2dDescriptor descriptor; |
| 496 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 497 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 498 | descriptor.m_StrideX = descriptor.m_StrideY = 3; |
| 499 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 500 | |
| 501 | armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); |
| 502 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 503 | QuantizedVector<T>(qScale, qOffset, { |
| 504 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 505 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 506 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 507 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 508 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 509 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 510 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 511 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 512 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 513 | })); |
| 514 | |
| 515 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 516 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 517 | QuantizedVector<T>(qScale, qOffset, { |
| 518 | 3.0f, 3.0f, 3.0f, |
| 519 | 3.0f, 3.0f, 3.0f, |
| 520 | 3.0f, 3.0f, 3.0f, |
| 521 | })); |
| 522 | |
| 523 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 524 | } |
| 525 | |
| 526 | template<typename T> |
| 527 | LayerTestResult<T, 4> L2Pooling2dSize3Stride4TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 528 | float qScale = 1.0f, |
| 529 | int32_t qOffset = 0) |
| 530 | { |
| 531 | armnn::Pooling2dDescriptor descriptor; |
| 532 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 533 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 534 | descriptor.m_StrideX = descriptor.m_StrideY = 4; |
| 535 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 536 | |
| 537 | armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); |
| 538 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 539 | QuantizedVector<T>(qScale, qOffset, { |
| 540 | 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, |
| 541 | 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, |
| 542 | 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, |
| 543 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 544 | 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, |
| 545 | 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, |
| 546 | 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, |
| 547 | })); |
| 548 | |
| 549 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 550 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 551 | QuantizedVector<T>(qScale, qOffset, { |
| 552 | 3.0f, 3.0f, |
| 553 | 3.0f, 3.0f, |
| 554 | })); |
| 555 | |
| 556 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 557 | } |
| 558 | |
| 559 | template<typename T> |
| 560 | LayerTestResult<T, 4> L2Pooling2dSize7TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 561 | float qScale = 1.0f, |
| 562 | int32_t qOffset = 0) |
| 563 | { |
| 564 | armnn::Pooling2dDescriptor descriptor; |
| 565 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 566 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 7; |
| 567 | descriptor.m_StrideX = descriptor.m_StrideY = 7; |
| 568 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 569 | |
| 570 | armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); |
| 571 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 572 | QuantizedVector<T>(qScale, qOffset, { |
| 573 | 1.0f, 0.0f, 2.0f, 0.0f, 3.0f, 0.0f, 4.0f, |
| 574 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 575 | 0.0f, 5.0f, 0.0f, 6.0f, 0.0f, 7.0f, 0.0f, |
| 576 | 8.0f, 0.0f, 9.0f, 0.0f, 10.0f, 0.0f, 5.0f, |
| 577 | 0.0f, 5.0f, 0.0f, 2.0f, 0.0f, 1.0f, 1.0f, |
| 578 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 579 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 580 | })); |
| 581 | |
| 582 | armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); |
| 583 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 584 | QuantizedVector<T>(qScale, qOffset, { |
| 585 | 3.0f, |
| 586 | })); |
| 587 | |
| 588 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 589 | } |
| 590 | |
| 591 | template<typename T> |
| 592 | LayerTestResult<T, 4> L2Pooling2dSize9TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 593 | float qScale = 1.0f, |
| 594 | int32_t qOffset = 0) |
| 595 | { |
| 596 | armnn::Pooling2dDescriptor descriptor; |
| 597 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 598 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 9; |
| 599 | descriptor.m_StrideX = descriptor.m_StrideY = 9; |
| 600 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 601 | |
| 602 | armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); |
| 603 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 604 | QuantizedVector<T>(qScale, qOffset, { |
| 605 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 606 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 607 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 608 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 609 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 610 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 611 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 612 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 613 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 614 | })); |
| 615 | |
| 616 | armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); |
| 617 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 618 | QuantizedVector<T>(qScale, qOffset, { |
| 619 | 3.0f, |
| 620 | })); |
| 621 | |
| 622 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 623 | } |
| 624 | |
| 625 | template<typename T> |
| 626 | LayerTestResult<T, 4> AsymmetricNonSquarePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 627 | float qScale = 1.0f, |
| 628 | int32_t qOffset = 0) |
| 629 | { |
| 630 | armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3 }, armnn::GetDataType<T>()); |
| 631 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 632 | |
| 633 | armnn::Pooling2dDescriptor descriptor; |
| 634 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 635 | descriptor.m_PoolWidth = 2; |
| 636 | descriptor.m_PoolHeight = 3; |
| 637 | descriptor.m_StrideX = 2; |
| 638 | descriptor.m_StrideY = 1; |
| 639 | descriptor.m_PadLeft = 2; |
| 640 | descriptor.m_PadRight = 0; |
| 641 | descriptor.m_PadTop = 1; |
| 642 | descriptor.m_PadBottom = 2; |
| 643 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 644 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 645 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 646 | // Construct input data. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 647 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 648 | QuantizedVector<T>(qScale, qOffset, { |
| 649 | 1.0f, 3.0f, 4.0f, |
| 650 | })); |
| 651 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 652 | // These were calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 653 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 654 | QuantizedVector<T>(qScale, qOffset, { |
| 655 | 0.0f, 3.0f, 0.0f, 3.0f, |
| 656 | })); |
| 657 | |
| 658 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 659 | } |
| 660 | |
| 661 | template<typename T> |
| 662 | LayerTestResult<T, 4> ComparePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 663 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 664 | armnn::PoolingAlgorithm poolingType, |
| 665 | float qScale = 1.0f, |
| 666 | int32_t qOffset = 0) |
| 667 | { |
| 668 | const unsigned int inputWidth = 16; |
| 669 | const unsigned int inputHeight = 32; |
| 670 | const unsigned int channelCount = 2; |
| 671 | const unsigned int batchSize = 5; |
| 672 | |
| 673 | const unsigned int poolSize = 3; |
| 674 | const unsigned int strideX = 2; |
| 675 | const unsigned int strideY = 4; |
| 676 | const unsigned int padX = 0; |
| 677 | const unsigned int padY = 0; |
| 678 | |
| 679 | const unsigned int outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX; |
| 680 | const unsigned int outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY; |
| 681 | |
| 682 | armnn::TensorInfo inputTensorInfo; |
| 683 | armnn::TensorInfo outputTensorInfo; |
| 684 | |
| 685 | unsigned int inputShape[] = { batchSize, channelCount, inputHeight, inputWidth }; |
| 686 | unsigned int outputShape[] = { batchSize, channelCount, outputHeight, outputWidth }; |
| 687 | |
| 688 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); |
| 689 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); |
| 690 | |
| 691 | // Set quantization parameters if the requested type is a quantized type. |
| 692 | if(armnn::IsQuantizedType<T>()) |
| 693 | { |
| 694 | inputTensorInfo.SetQuantizationScale(qScale); |
| 695 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 696 | outputTensorInfo.SetQuantizationScale(qScale); |
| 697 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 698 | } |
| 699 | |
| 700 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 81715); |
| 701 | |
| 702 | LayerTestResult<T, 4> comparisonResult(outputTensorInfo); |
| 703 | |
| 704 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 705 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 706 | |
| 707 | armnn::Pooling2dQueueDescriptor data; |
| 708 | armnn::WorkloadInfo info; |
| 709 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 710 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 711 | data.m_Parameters.m_PoolType = poolingType; |
| 712 | data.m_Parameters.m_PoolWidth = poolSize; |
| 713 | data.m_Parameters.m_PoolHeight = poolSize; |
| 714 | data.m_Parameters.m_StrideX = strideX; |
| 715 | data.m_Parameters.m_StrideY = strideY; |
| 716 | data.m_Parameters.m_PadLeft = padX; |
| 717 | data.m_Parameters.m_PadRight = padX; |
| 718 | data.m_Parameters.m_PadTop = padY; |
| 719 | data.m_Parameters.m_PadBottom = padY; |
| 720 | data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 721 | |
| 722 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 723 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); |
| 724 | |
| 725 | // Don't execute if Pooling is not supported, as an exception will be raised. |
| 726 | armnn::Compute compute = workloadFactory.GetCompute(); |
| 727 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 728 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
| 729 | comparisonResult.supported = armnn::IsPooling2dSupported(compute, inputTensorInfo, outputTensorInfo, |
| 730 | data.m_Parameters, |
| 731 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 732 | if (!comparisonResult.supported) |
| 733 | { |
| 734 | return comparisonResult; |
| 735 | } |
| 736 | |
| 737 | armnn::Pooling2dQueueDescriptor refData = data; |
| 738 | armnn::WorkloadInfo refInfo = info; |
| 739 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 740 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 741 | |
| 742 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(data, info); |
| 743 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreatePooling2d(refData, refInfo); |
| 744 | |
| 745 | outputHandleRef->Allocate(); |
| 746 | inputHandleRef->Allocate(); |
| 747 | inputHandle->Allocate(); |
| 748 | outputHandle->Allocate(); |
| 749 | |
| 750 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 751 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 752 | |
| 753 | workload->Execute(); |
| 754 | workloadRef->Execute(); |
| 755 | |
| 756 | CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); |
| 757 | CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 758 | |
| 759 | return comparisonResult; |
| 760 | } |
| 761 | |
| 762 | // |
| 763 | // Tests max pooling with the following parameters: |
| 764 | // |
| 765 | // Pooling size: 2x2 |
| 766 | // Stride: (2,2) |
| 767 | // input size: 4x4 |
| 768 | // channels: 1 |
| 769 | // batch size: 1 |
| 770 | // |
| 771 | template<typename T> |
| 772 | LayerTestResult<T, 4> SimpleMaxPooling2dSize2x2Stride2x2TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 773 | bool forceNoPadding, |
| 774 | float qScale = 1.0f, |
| 775 | int32_t qOffset = 0) |
| 776 | { |
| 777 | armnn::Pooling2dDescriptor descriptor; |
| 778 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 779 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 780 | descriptor.m_StrideX = 2; |
| 781 | descriptor.m_StrideY = 2; |
| 782 | descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; |
| 783 | descriptor.m_PadTop = descriptor.m_PadBottom = 0; |
| 784 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 785 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 786 | |
| 787 | unsigned int inputWidth = 4; |
| 788 | unsigned int inputHeight = 4; |
| 789 | unsigned int outputWidth = |
| 790 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 791 | descriptor.m_StrideX; |
| 792 | unsigned int outputHeight = |
| 793 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 794 | descriptor.m_StrideY; |
| 795 | unsigned int channels = 1; |
| 796 | unsigned int batchSize = 1; |
| 797 | |
| 798 | std::vector<float> inputData = { |
| 799 | 510.0f, 222.0f, 780.0f, 654.0f, |
| 800 | 141.0f, 276.0f, 15.0f, 546.0f, |
| 801 | 303.0f, 618.0f, 582.0f, 339.0f, |
| 802 | 438.0f, 564.0f, 573.0f, 402.0f |
| 803 | }; |
| 804 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 805 | // Note that left and right edges will be 0.f, due to the 2x2 max pooling only accessing zeros here. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 806 | std::vector<float> expectedOutputDataWithPadding = { |
| 807 | 0.0f, 510.0f, 780.0f, 654.0f, 0.0f, |
| 808 | 0.0f, 438.0f, 618.0f, 402.0f, 0.0f |
| 809 | }; |
| 810 | |
| 811 | std::vector<float> expectedOutputDataNoPadding = { |
| 812 | 510.0f, 780.0f, |
| 813 | 618.0f, 582.0f |
| 814 | }; |
| 815 | |
| 816 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 817 | |
| 818 | // Scale and offset should match input - we're just calculating maximum values. |
| 819 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 820 | |
| 821 | // Set quantization parameters if the requested type is a quantized type. |
| 822 | if(armnn::IsQuantizedType<T>()) |
| 823 | { |
| 824 | inputTensorInfo.SetQuantizationScale(qScale); |
| 825 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 826 | outputTensorInfo.SetQuantizationScale(qScale); |
| 827 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 828 | } |
| 829 | |
| 830 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 831 | |
| 832 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 833 | forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : |
| 834 | QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); |
| 835 | |
| 836 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 837 | } |
| 838 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 839 | // |
| 840 | // Tests max pooling with the following parameters: |
| 841 | // |
| 842 | // Pooling size: 3x2 |
| 843 | // Stride: (2,2) |
| 844 | // input size: 3x2 |
| 845 | // channels: 1 |
| 846 | // batch size: 1 |
| 847 | // |
| 848 | template<typename T> |
| 849 | LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon( |
| 850 | armnn::IWorkloadFactory& workloadFactory, |
| 851 | bool forceNoPadding, |
| 852 | float qScale = 1.0f, |
| 853 | int32_t qOffset = 0) |
| 854 | { |
| 855 | armnn::Pooling2dDescriptor descriptor; |
| 856 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 857 | descriptor.m_PoolWidth = 3; |
| 858 | descriptor.m_PoolHeight = 2; |
| 859 | descriptor.m_StrideX = 2; |
| 860 | descriptor.m_StrideY = 2; |
| 861 | descriptor.m_PadLeft = (forceNoPadding) ? 0 : 1; |
| 862 | descriptor.m_PadRight = descriptor.m_PadLeft; |
| 863 | descriptor.m_PadTop = 0; |
| 864 | descriptor.m_PadBottom = 0; |
| 865 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 866 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 867 | |
| 868 | unsigned int inputWidth = 3; |
| 869 | unsigned int inputHeight = 2; |
| 870 | unsigned int outputWidth = |
| 871 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 872 | descriptor.m_StrideX; |
| 873 | unsigned int outputHeight = |
| 874 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 875 | descriptor.m_StrideY; |
| 876 | unsigned int channels = 1; |
| 877 | unsigned int batchSize = 1; |
| 878 | |
| 879 | std::vector<float> inputData = { |
| 880 | 3.0f, 6.0f, 9.0f, |
| 881 | 12.0f, 15.0f, 18.0f, |
| 882 | }; |
| 883 | |
| 884 | std::vector<float> expectedOutputDataWithPadding = { |
| 885 | 6.0f, 8.0f, |
| 886 | }; |
| 887 | |
| 888 | std::vector<float> expectedOutputDataNoPadding = { |
| 889 | 10.5f, |
| 890 | }; |
| 891 | |
| 892 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 893 | |
| 894 | // Scale and offset should match input - we're just calculating average values. |
| 895 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 896 | |
| 897 | // Set quantization parameters if the requested type is a quantized type. |
| 898 | if(armnn::IsQuantizedType<T>()) |
| 899 | { |
| 900 | inputTensorInfo.SetQuantizationScale(qScale); |
| 901 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 902 | outputTensorInfo.SetQuantizationScale(qScale); |
| 903 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 904 | } |
| 905 | |
| 906 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 907 | |
| 908 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 909 | forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : |
| 910 | QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); |
| 911 | |
| 912 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 913 | } |
| 914 | |
| 915 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 916 | template<typename T> |
| 917 | LayerTestResult<T, 4> IgnorePaddingSimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 918 | float qScale = 1.0f, |
| 919 | int32_t qOffset = 0) |
| 920 | { |
| 921 | armnn::Pooling2dDescriptor descriptor; |
| 922 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 923 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 924 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 925 | descriptor.m_PadLeft = 1; |
| 926 | descriptor.m_PadRight = 1; |
| 927 | descriptor.m_PadTop = 1; |
| 928 | descriptor.m_PadBottom = 1; |
| 929 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 930 | |
| 931 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 932 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 933 | |
| 934 | // Set quantization parameters if the requested type is a quantized type. |
| 935 | if(armnn::IsQuantizedType<T>()) |
| 936 | { |
| 937 | inputTensorInfo.SetQuantizationScale(qScale); |
| 938 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 939 | outputTensorInfo.SetQuantizationScale(qScale); |
| 940 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 941 | } |
| 942 | |
| 943 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 944 | QuantizedVector<T>(qScale, qOffset, { |
| 945 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 946 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 947 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 948 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 949 | })); |
| 950 | |
| 951 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 952 | QuantizedVector<T>(qScale, qOffset, { |
| 953 | -1.0f, 3.0f, 4.0f, |
| 954 | 1.0f, 3.0f, 4.0f, |
| 955 | 1.0f, 2.0f, -4.0f, |
| 956 | })); |
| 957 | |
| 958 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 959 | } |
| 960 | |
| 961 | template<typename T> |
| 962 | LayerTestResult<T, 4> IgnorePaddingMaxPooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 963 | float qScale = 1.0f, |
| 964 | int32_t qOffset = 0) |
| 965 | { |
| 966 | armnn::Pooling2dDescriptor descriptor; |
| 967 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 968 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 969 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 970 | descriptor.m_PadLeft = 1; |
| 971 | descriptor.m_PadRight = 1; |
| 972 | descriptor.m_PadTop = 1; |
| 973 | descriptor.m_PadBottom = 1; |
| 974 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 975 | |
| 976 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 977 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 978 | |
| 979 | // Set quantization parameters if the requested type is a quantized type. |
| 980 | if(armnn::IsQuantizedType<T>()) |
| 981 | { |
| 982 | inputTensorInfo.SetQuantizationScale(qScale); |
| 983 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 984 | outputTensorInfo.SetQuantizationScale(qScale); |
| 985 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 986 | } |
| 987 | |
| 988 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 989 | QuantizedVector<T>(qScale, qOffset, { |
| 990 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 991 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 992 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 993 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 994 | })); |
| 995 | |
| 996 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 997 | QuantizedVector<T>(qScale, qOffset, { |
| 998 | -1.0f, 3.0f, 4.0f, 4.0f, |
| 999 | 2.0f, 3.0f, 4.0f, 4.0f, |
| 1000 | 2.0f, 3.0f, 4.0f, 4.0f, |
| 1001 | 2.0f, 2.0f, 2.0f, -3.0f, |
| 1002 | })); |
| 1003 | |
| 1004 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1005 | } |
| 1006 | |
| 1007 | template<typename T> |
| 1008 | LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1009 | float qScale = 1.0f, |
| 1010 | int32_t qOffset = 0) |
| 1011 | { |
| 1012 | armnn::Pooling2dDescriptor descriptor; |
| 1013 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1014 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 1015 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1016 | descriptor.m_PadLeft = 1; |
| 1017 | descriptor.m_PadRight = 1; |
| 1018 | descriptor.m_PadTop = 1; |
| 1019 | descriptor.m_PadBottom = 1; |
| 1020 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1021 | |
| 1022 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1023 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 1024 | |
| 1025 | // Set quantization parameters if the requested type is a quantized type. |
| 1026 | if(armnn::IsQuantizedType<T>()) |
| 1027 | { |
| 1028 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1029 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1030 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1031 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1032 | } |
| 1033 | |
| 1034 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1035 | QuantizedVector<T>(qScale, qOffset, { |
| 1036 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1037 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1038 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1039 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1040 | })); |
| 1041 | |
| 1042 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1043 | QuantizedVector<T>(qScale, qOffset, { |
| 1044 | 3.0f, 13.0f, 10.0f, |
| 1045 | 6.0f, 26.0f, 20.0f, |
| 1046 | 3.0f, 13.0f, 10.0f, |
| 1047 | })); |
| 1048 | |
| 1049 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1050 | } |
| 1051 | |
| 1052 | template<typename T> |
| 1053 | LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1054 | float qScale = 1.0f, |
| 1055 | int32_t qOffset = 0) |
| 1056 | { |
| 1057 | armnn::Pooling2dDescriptor descriptor; |
| 1058 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1059 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1060 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1061 | descriptor.m_PadLeft = 0; |
| 1062 | descriptor.m_PadRight = 0; |
| 1063 | descriptor.m_PadTop = 0; |
| 1064 | descriptor.m_PadBottom = 0; |
| 1065 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1066 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling; |
| 1067 | |
| 1068 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4}, armnn::GetDataType<T>()); |
| 1069 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 1070 | |
| 1071 | // Set quantization parameters if the requested type is a quantized type. |
| 1072 | if(armnn::IsQuantizedType<T>()) |
| 1073 | { |
| 1074 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1075 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1076 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1077 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1078 | } |
| 1079 | |
| 1080 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1081 | QuantizedVector<T>(qScale, qOffset, { |
| 1082 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1083 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1084 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1085 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1086 | })); |
| 1087 | |
| 1088 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1089 | QuantizedVector<T>(qScale, qOffset, { |
| 1090 | 2.0f, 3.5f, |
| 1091 | 2.0f, 3.5f |
| 1092 | })); |
| 1093 | |
| 1094 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1095 | } |
| 1096 | |
| 1097 | template<typename T> |
| 1098 | LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1099 | float qScale = 1.0f, |
| 1100 | int32_t qOffset = 0) |
| 1101 | { |
| 1102 | armnn::Pooling2dDescriptor descriptor; |
| 1103 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1104 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1105 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1106 | descriptor.m_PadLeft = 1; |
| 1107 | descriptor.m_PadRight = 1; |
| 1108 | descriptor.m_PadTop = 1; |
| 1109 | descriptor.m_PadBottom = 1; |
| 1110 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1111 | |
| 1112 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1113 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1114 | |
| 1115 | // Set quantization parameters if the requested type is a quantized type. |
| 1116 | if(armnn::IsQuantizedType<T>()) |
| 1117 | { |
| 1118 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1119 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1120 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1121 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1122 | } |
| 1123 | |
| 1124 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1125 | QuantizedVector<T>(qScale, qOffset, { |
| 1126 | 9.0f, 27.0f, 18.0f, 36.0f, |
| 1127 | 18.0f, 9.0f, 18.0f, 9.0f, |
| 1128 | 27.0f, 18.0f, 9.0f, 27.0f, |
| 1129 | 9.0f, 27.0f, 9.0f, 18.0f, |
| 1130 | })); |
| 1131 | |
| 1132 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1133 | QuantizedVector<T>(qScale, qOffset, { |
| 1134 | 7.0f, 11.0f, 13.0f, 9.0f, |
| 1135 | 12.0f, 17.0f, 19.0f, 13.0f, |
| 1136 | 12.0f, 16.0f, 16.0f, 10.0f, |
| 1137 | 9.0f, 11.0f, 12.0f, 7.0f, |
| 1138 | })); |
| 1139 | |
| 1140 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1141 | } |
| 1142 | |
| 1143 | template<typename T> |
| 1144 | LayerTestResult<T, 4> IgnorePaddingSimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1145 | float qScale = 1.0f, |
| 1146 | int32_t qOffset = 0) |
| 1147 | { |
| 1148 | armnn::Pooling2dDescriptor descriptor; |
| 1149 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 1150 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 1151 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1152 | descriptor.m_PadLeft = 1; |
| 1153 | descriptor.m_PadRight = 1; |
| 1154 | descriptor.m_PadTop = 1; |
| 1155 | descriptor.m_PadBottom = 1; |
| 1156 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1157 | |
| 1158 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1159 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 1160 | |
| 1161 | // Set quantization parameters if the requested type is a quantized type. |
| 1162 | if(armnn::IsQuantizedType<T>()) |
| 1163 | { |
| 1164 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1165 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1166 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1167 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1168 | } |
| 1169 | |
| 1170 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1171 | QuantizedVector<T>(qScale, qOffset, { |
| 1172 | 2.0f, 4.0f, 8.0f, 16.0f, |
| 1173 | 4.0f, 2.0f, 2.0f, 4.0f, |
| 1174 | 8.0f, 2.0f, 4.0f, 2.0f, |
| 1175 | 16.0f, 2.0f, 2.0f, 8.0f, |
| 1176 | })); |
| 1177 | |
| 1178 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1179 | QuantizedVector<T>(qScale, qOffset, { |
| 1180 | 1.0f, 4.4721f, 8.0f, |
| 1181 | 4.4721f, 2.6457f, 2.236f, |
| 1182 | 8.0f, 1.4142f, 4.0f, |
| 1183 | })); |
| 1184 | |
| 1185 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1186 | } |
| 1187 | |
| 1188 | template<typename T> |
| 1189 | LayerTestResult<T, 4> IgnorePaddingL2Pooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1190 | float qScale = 1.0f, |
| 1191 | int32_t qOffset = 0) |
| 1192 | { |
| 1193 | armnn::Pooling2dDescriptor descriptor; |
| 1194 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 1195 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1196 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1197 | descriptor.m_PadLeft = 1; |
| 1198 | descriptor.m_PadRight = 1; |
| 1199 | descriptor.m_PadTop = 1; |
| 1200 | descriptor.m_PadBottom = 1; |
| 1201 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1202 | |
| 1203 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1204 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1205 | |
| 1206 | // Set quantization parameters if the requested type is a quantized type. |
| 1207 | if(armnn::IsQuantizedType<T>()) |
| 1208 | { |
| 1209 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1210 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1211 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1212 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1213 | } |
| 1214 | |
| 1215 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1216 | QuantizedVector<T>(qScale, qOffset, { |
| 1217 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1218 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1219 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1220 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1221 | })); |
| 1222 | |
| 1223 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1224 | QuantizedVector<T>(qScale, qOffset, { |
| 1225 | 1.0540f, 1.7638f, 2.5385f, 2.3570f, |
| 1226 | 1.2909f, 2.1602f, 3.1091f, 2.8867f, |
| 1227 | 1.2909f, 2.1602f, 3.1091f, 2.8867f, |
| 1228 | 1.0540f, 1.7638f, 2.5385f, 2.3570f, |
| 1229 | })); |
| 1230 | |
| 1231 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1232 | } |