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