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 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 7 | #include <string> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 8 | #include <armnn/ArmNN.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 9 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 10 | #include <test/TensorHelpers.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 11 | #include "QuantizeHelper.hpp" |
| 12 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 13 | #include <backends/CpuTensorHandle.hpp> |
| 14 | #include <backends/WorkloadFactory.hpp> |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 15 | #include <backends/WorkloadInfo.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 16 | #include <algorithm> |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 17 | #include "Permute.hpp" |
| 18 | #include <boost/numeric/conversion/cast.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 19 | |
| 20 | template<typename T> |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 21 | LayerTestResult<T, 4> SimplePooling2dTestImpl(armnn::IWorkloadFactory& workloadFactory, |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 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) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 27 | { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 28 | const armnn::DataLayoutIndexed dataLayout = descriptor.m_DataLayout; |
| 29 | auto heightIndex = dataLayout.GetHeightIndex(); |
| 30 | auto widthIndex = dataLayout.GetWidthIndex(); |
| 31 | auto channelsIndex = dataLayout.GetChannelsIndex(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 32 | |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 33 | unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[heightIndex]); |
| 34 | unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[widthIndex]); |
| 35 | unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[channelsIndex]); |
| 36 | unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); |
| 37 | |
| 38 | unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[heightIndex]); |
| 39 | unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[widthIndex]); |
| 40 | unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[channelsIndex]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 41 | unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); |
| 42 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 43 | armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(inputBatchSize, inputChannels, inputHeight, |
| 44 | inputWidth, dataLayout); |
| 45 | armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(outputBatchSize, outputChannels, outputHeight, |
| 46 | outputWidth, dataLayout); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 47 | |
| 48 | // Set quantization parameters if the requested type is a quantized type. |
| 49 | if(armnn::IsQuantizedType<T>()) |
| 50 | { |
| 51 | inputTensorInfo.SetQuantizationScale(qScale); |
| 52 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 53 | outputTensorInfo.SetQuantizationScale(qScale); |
| 54 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 55 | } |
| 56 | |
| 57 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 58 | |
| 59 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 60 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 61 | |
| 62 | armnn::Pooling2dQueueDescriptor queueDescriptor; |
| 63 | queueDescriptor.m_Parameters = descriptor; |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 64 | queueDescriptor.m_Parameters.m_DataLayout = dataLayout; |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 65 | |
| 66 | armnn::WorkloadInfo workloadInfo; |
| 67 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 68 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 69 | |
| 70 | // Don't execute if Pooling is not supported, as an exception will be raised. |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 71 | armnn::BackendId backend = workloadFactory.GetBackendId(); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 72 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 73 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 74 | result.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 75 | queueDescriptor.m_Parameters, |
| 76 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 77 | if (!result.supported) |
| 78 | { |
| 79 | return result; |
| 80 | } |
| 81 | |
| 82 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); |
| 83 | |
| 84 | inputHandle->Allocate(); |
| 85 | outputHandle->Allocate(); |
| 86 | |
| 87 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 88 | |
| 89 | workload->Execute(); |
| 90 | |
| 91 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 92 | |
| 93 | result.outputExpected = outputExpected; |
| 94 | |
| 95 | return result; |
| 96 | } |
| 97 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 98 | // |
| 99 | // Tests max pooling with the following parameters: |
| 100 | // |
| 101 | // Pooling size: 3x3 |
| 102 | // Stride: (2,4) |
| 103 | // input size: 8x13 |
| 104 | // channels: 2 |
| 105 | // batch size: 2 |
| 106 | // |
| 107 | template<typename T> |
| 108 | LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 109 | bool forceNoPadding, |
| 110 | float qScale = 1.0f, |
| 111 | int32_t qOffset = 0) |
| 112 | { |
| 113 | armnn::Pooling2dDescriptor descriptor; |
| 114 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 115 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 116 | descriptor.m_StrideX = 2; |
| 117 | descriptor.m_StrideY = 4; |
| 118 | // forceNoPadding is mainly used for compatibility with ARM Compute. |
| 119 | // As of 16/05/2017, it errors if padX or padY are equal to or greater than the pool size. |
| 120 | descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; |
| 121 | descriptor.m_PadTop = descriptor.m_PadBottom = 0; |
| 122 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 123 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 124 | |
| 125 | unsigned int inputWidth = 8; |
| 126 | unsigned int inputHeight = 13; |
| 127 | unsigned int outputWidth = |
| 128 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 129 | descriptor.m_StrideX; |
| 130 | unsigned int outputHeight = |
| 131 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 132 | descriptor.m_StrideY; |
| 133 | unsigned int channels = 2; |
| 134 | unsigned int batchSize = 2; |
| 135 | |
| 136 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 137 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 138 | |
| 139 | // Set quantization parameters if the requested type is a quantized type. |
| 140 | if(armnn::IsQuantizedType<T>()) |
| 141 | { |
| 142 | inputTensorInfo.SetQuantizationScale(qScale); |
| 143 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 144 | outputTensorInfo.SetQuantizationScale(qScale); |
| 145 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 146 | } |
| 147 | |
| 148 | std::vector<float> singleChannelData({ |
| 149 | 0.0f, 4.0f, 8.0f, 1.0f, 6.0f, 4.0f, 5.0f, 8.0f, |
| 150 | 1.0f, 1.0f, 6.0f, 0.0f, 3.0f, 7.0f, 4.0f, 7.0f, |
| 151 | 8.0f, 5.0f, 0.0f, 0.0f, 8.0f, 3.0f, 4.0f, 3.0f, |
| 152 | 8.0f, 2.0f, 5.0f, 4.0f, 1.0f, 9.0f, 2.0f, 0.0f, |
| 153 | 5.0f, 4.0f, 5.0f, 0.0f, 0.0f, 0.0f, 7.0f, 2.0f, |
| 154 | 1.0f, 2.0f, 6.0f, 2.0f, 7.0f, 9.0f, 5.0f, 2.0f, |
| 155 | 9.0f, 7.0f, 3.0f, 1.0f, 3.0f, 4.0f, 8.0f, 3.0f, |
| 156 | 1.0f, 0.0f, 0.0f, 5.0f, 5.0f, 4.0f, 2.0f, 0.0f, |
| 157 | 6.0f, 4.0f, 3.0f, 6.0f, 9.0f, 5.0f, 5.0f, 6.0f, |
| 158 | 8.0f, 7.0f, 9.0f, 6.0f, 1.0f, 4.0f, 1.0f, 9.0f, |
| 159 | 7.0f, 1.0f, 9.0f, 2.0f, 9.0f, 9.0f, 8.0f, 1.0f, |
| 160 | 4.0f, 4.0f, 5.0f, 9.0f, 2.0f, 6.0f, 6.0f, 4.0f, |
| 161 | 3.0f, 5.0f, 4.0f, 0.0f, 1.0f, 5.0f, 9.0f, 7.0f, |
| 162 | }); |
| 163 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 164 | // Constructs input data. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 165 | std::vector<float> inputData; |
| 166 | auto negator = [](float f) { return -f; }; |
| 167 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 168 | // 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] | 169 | inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); |
| 170 | std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); |
| 171 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 172 | // Second image (same as first image). |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 173 | inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); |
| 174 | std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); |
| 175 | |
| 176 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 177 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 178 | // These were calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 179 | auto shape(GetTensorShapeAsArray<4>(outputTensorInfo)); |
| 180 | boost::multi_array<T, 4> outputExpected(shape); |
| 181 | if (forceNoPadding) |
| 182 | { |
| 183 | outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 184 | QuantizedVector<T>(qScale, qOffset, { |
| 185 | 8.0f, 8.0f, 8.0f, |
| 186 | 9.0f, 7.0f, 9.0f, |
| 187 | 9.0f, 9.0f, 9.0f, |
| 188 | |
| 189 | 0.0f, 0.0f, -3.0f, |
| 190 | -1.0f, 0.0f, 0.0f, |
| 191 | -1.0f, -1.0f, -1.0f, |
| 192 | |
| 193 | 8.0f, 8.0f, 8.0f, |
| 194 | 9.0f, 7.0f, 9.0f, |
| 195 | 9.0f, 9.0f, 9.0f, |
| 196 | |
| 197 | 0.0f, 0.0f, -3.0f, |
| 198 | -1.0f, 0.0f, 0.0f, |
| 199 | -1.0f, -1.0f, -1.0f |
| 200 | })); |
| 201 | } |
| 202 | else |
| 203 | { |
| 204 | outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 205 | QuantizedVector<T>(qScale, qOffset, { |
| 206 | 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, |
| 207 | 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, |
| 208 | 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, |
| 209 | |
| 210 | 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, |
| 211 | 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 212 | 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f, |
| 213 | |
| 214 | 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, |
| 215 | 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, |
| 216 | 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, |
| 217 | |
| 218 | 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, |
| 219 | 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 220 | 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f |
| 221 | })); |
| 222 | } |
| 223 | |
| 224 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 225 | } |
| 226 | |
| 227 | template<typename T> |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 228 | LayerTestResult<T, 4> SimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 229 | const armnn::DataLayoutIndexed& dataLayout = armnn::DataLayout::NCHW, |
| 230 | float qScale = 1.0f, |
| 231 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 232 | { |
| 233 | armnn::Pooling2dDescriptor descriptor; |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 234 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 235 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 236 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 237 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 238 | descriptor.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 239 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 240 | armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(1, 2, 4, 4, dataLayout); |
| 241 | armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(1, 2, 2, 2, dataLayout); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 242 | |
| 243 | // Set quantization parameters if the requested type is a quantized type. |
| 244 | if(armnn::IsQuantizedType<T>()) |
| 245 | { |
| 246 | inputTensorInfo.SetQuantizationScale(qScale); |
| 247 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 248 | outputTensorInfo.SetQuantizationScale(qScale); |
| 249 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 250 | } |
| 251 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 252 | std::vector<T> inputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 253 | QuantizedVector<T>(qScale, qOffset, { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 254 | 1.0f, 2.0f, 5.0f, 6.0f, |
| 255 | 3.0f, 4.0f, 7.0f, 8.0f, |
| 256 | 9.0f, 10.0f, 13.0f, 14.0f, |
| 257 | 11.0f, 12.0f, 15.0f, 16.0f, |
| 258 | |
| 259 | 17.0f, 18.0f, 21.0f, 22.0f, |
| 260 | 19.0f, 20.0f, 23.0f, 24.0f, |
| 261 | 25.0f, 26.0f, 29.0f, 30.0f, |
| 262 | 27.0f, 28.0f, 31.0f, 32.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 263 | })); |
| 264 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 265 | std::vector<T> outputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 266 | QuantizedVector<T>(qScale, qOffset, { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 267 | 4.0f, 8.0f, |
| 268 | 12.0f, 16.0f, |
| 269 | |
| 270 | 20.0f, 24.0f, |
| 271 | 28.0f, 32.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 272 | })); |
| 273 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 274 | const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
| 275 | if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) |
| 276 | { |
| 277 | std::vector<T> tmp(inputData.size()); |
| 278 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); |
| 279 | inputData = tmp; |
| 280 | |
| 281 | std::vector<T> tmp1(outputData.size()); |
| 282 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); |
| 283 | outputData = tmp1; |
| 284 | } |
| 285 | |
| 286 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 287 | |
| 288 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); |
| 289 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 290 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 291 | } |
| 292 | |
| 293 | template<typename T> |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 294 | LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 295 | armnn::DataLayoutIndexed dataLayout = armnn::DataLayout::NCHW, |
| 296 | float qScale = 1.0f, |
| 297 | int32_t qOffset = 0) |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 298 | { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 299 | armnn::Pooling2dDescriptor descriptor; |
| 300 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 301 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 302 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 303 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 304 | descriptor.m_DataLayout = dataLayout; |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 305 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 306 | armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(1, 2, 4, 4, dataLayout); |
| 307 | armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(1, 2, 2, 2, dataLayout); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 308 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 309 | // Set quantization parameters if the requested type is a quantized type. |
| 310 | if(armnn::IsQuantizedType<T>()) |
| 311 | { |
| 312 | inputTensorInfo.SetQuantizationScale(qScale); |
| 313 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 314 | outputTensorInfo.SetQuantizationScale(qScale); |
| 315 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 316 | } |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 317 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 318 | std::vector<T> inputData( |
| 319 | QuantizedVector<T>(qScale, qOffset, { |
| 320 | 2.0f, 2.0f, 6.0f, 6.0f, |
| 321 | 4.0f, 4.0f, 8.0f, 8.0f, |
| 322 | 10.0f, 12.0f, 14.0f, 16.0f, |
| 323 | 10.0f, 12.0f, 16.0f, 14.0f, |
| 324 | |
| 325 | 18.0f, 20.0f, 24.0f, 22.0f, |
| 326 | 20.0f, 18.0f, 22.0f, 24.0f, |
| 327 | 26.0f, 28.0f, 0.0f, 0.0f, |
| 328 | 26.0f, 28.0f, 0.0f, 0.0f, |
| 329 | })); |
| 330 | |
| 331 | std::vector<T> outputData( |
| 332 | QuantizedVector<T>(qScale, qOffset, { |
| 333 | 3.0f, 7.0f, |
| 334 | 11.0f, 15.0f, |
| 335 | |
| 336 | 19.0f, 23.0f, |
| 337 | 27.0f, 0.0f, |
| 338 | })); |
| 339 | |
| 340 | const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
| 341 | if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) |
| 342 | { |
| 343 | std::vector<T> tmp(inputData.size()); |
| 344 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); |
| 345 | inputData = tmp; |
| 346 | |
| 347 | std::vector<T> tmp1(outputData.size()); |
| 348 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); |
| 349 | outputData = tmp1; |
| 350 | } |
| 351 | |
| 352 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 353 | |
| 354 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); |
| 355 | |
| 356 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 357 | } |
| 358 | |
| 359 | template<typename T> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 360 | LayerTestResult<T, 4> LargeTensorsAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 361 | float qScale = 1.0f, |
| 362 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 363 | { |
| 364 | armnn::Pooling2dDescriptor descriptor; |
| 365 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 366 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 100; |
| 367 | descriptor.m_StrideX = descriptor.m_StrideY = 5; |
| 368 | descriptor.m_PadLeft = 50; |
| 369 | descriptor.m_PadRight = 50; |
| 370 | descriptor.m_PadTop = 50; |
| 371 | descriptor.m_PadBottom = 50; |
| 372 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 373 | |
| 374 | armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60 }, armnn::GetDataType<T>()); |
| 375 | armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13 }, armnn::GetDataType<T>()); |
| 376 | |
| 377 | // Set quantization parameters if the requested type is a quantized type. |
| 378 | if(armnn::IsQuantizedType<T>()) |
| 379 | { |
| 380 | inputTensorInfo.SetQuantizationScale(qScale); |
| 381 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 382 | outputTensorInfo.SetQuantizationScale(qScale); |
| 383 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 384 | } |
| 385 | |
| 386 | std::vector<T> inputVec; |
| 387 | |
| 388 | for (unsigned int i = 0 ; i < inputTensorInfo.GetShape().GetNumElements(); ++i) |
| 389 | { |
| 390 | inputVec.push_back(1); |
| 391 | } |
| 392 | |
| 393 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputVec); |
| 394 | |
| 395 | std::vector<T> outputVec; |
| 396 | |
| 397 | for (unsigned int i = 0 ; i < outputTensorInfo.GetShape().GetNumElements(); ++i) |
| 398 | { |
| 399 | outputVec.push_back(1); |
| 400 | } |
| 401 | |
| 402 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputVec); |
| 403 | |
| 404 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 405 | } |
| 406 | |
| 407 | template<typename T> |
| 408 | LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 409 | armnn::DataLayoutIndexed dataLayout = armnn::DataLayout::NCHW, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 410 | float qScale = 1.0f, |
| 411 | int32_t qOffset = 0) |
| 412 | { |
| 413 | armnn::Pooling2dDescriptor descriptor; |
| 414 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 415 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 416 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 417 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 418 | descriptor.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 419 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 420 | armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(1, 2, 4, 4, dataLayout); |
| 421 | armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(1, 2, 2, 2, dataLayout); |
| 422 | |
| 423 | std::vector<T> inputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 424 | QuantizedVector<T>(qScale, qOffset, { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 425 | 1.0f, 7.0f, 5.0f, 5.0f, |
| 426 | 1.0f, 7.0f, 5.0f, 5.0f, |
| 427 | 3.0f, 3.0f, 1.0f, 1.0f, |
| 428 | 3.0f, 3.0f, 1.0f, 1.0f, |
| 429 | |
| 430 | 1.0f, 7.0f, 0.0f, 0.0f, |
| 431 | 1.0f, 7.0f, 2.0f, 0.0f, |
| 432 | 0.0f, 2.0f, 1.0f, 1.0f, |
| 433 | 0.0f, 0.0f, 1.0f, 1.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 434 | })); |
| 435 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 436 | std::vector<T> outputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 437 | QuantizedVector<T>(qScale, qOffset, { |
| 438 | 5.0f, 5.0f, |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 439 | 3.0f, 1.0f, |
| 440 | |
| 441 | 5.0f, 1.0f, |
| 442 | 1.0f, 1.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 443 | })); |
| 444 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 445 | const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
| 446 | if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) |
| 447 | { |
| 448 | std::vector<T> tmp(inputData.size()); |
| 449 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); |
| 450 | inputData = tmp; |
| 451 | |
| 452 | std::vector<T> tmp1(outputData.size()); |
| 453 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); |
| 454 | outputData = tmp1; |
| 455 | } |
| 456 | |
| 457 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 458 | |
| 459 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); |
| 460 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 461 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 462 | } |
| 463 | |
| 464 | template<typename T> |
| 465 | LayerTestResult<T, 4> L2Pooling2dSize3Stride1TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 466 | float qScale = 1.0f, |
| 467 | int32_t qOffset = 0) |
| 468 | { |
| 469 | armnn::Pooling2dDescriptor descriptor; |
| 470 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 471 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 472 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 473 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 474 | |
| 475 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 476 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 477 | QuantizedVector<T>(qScale, qOffset, { |
| 478 | 2.0f, 1.0f, 5.0f, 2.0f, |
| 479 | 1.0f, 2.0f, 2.0f, 1.0f, |
| 480 | 5.0f, 4.0f, 1.0f, 5.0f, |
| 481 | 2.0f, 1.0f, 5.0f, 2.0f, |
| 482 | })); |
| 483 | |
| 484 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 485 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 486 | QuantizedVector<T>(qScale, qOffset, { |
| 487 | 3.0f, 3.0f, |
| 488 | 3.0f, 3.0f, |
| 489 | })); |
| 490 | |
| 491 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 492 | } |
| 493 | |
| 494 | template<typename T> |
| 495 | LayerTestResult<T, 4> L2Pooling2dSize3Stride3TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 496 | float qScale = 1.0f, |
| 497 | int32_t qOffset = 0) |
| 498 | { |
| 499 | armnn::Pooling2dDescriptor descriptor; |
| 500 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 501 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 502 | descriptor.m_StrideX = descriptor.m_StrideY = 3; |
| 503 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 504 | |
| 505 | armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); |
| 506 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 507 | QuantizedVector<T>(qScale, qOffset, { |
| 508 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 509 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 510 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 511 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 512 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 513 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 514 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 515 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 516 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 517 | })); |
| 518 | |
| 519 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 520 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 521 | QuantizedVector<T>(qScale, qOffset, { |
| 522 | 3.0f, 3.0f, 3.0f, |
| 523 | 3.0f, 3.0f, 3.0f, |
| 524 | 3.0f, 3.0f, 3.0f, |
| 525 | })); |
| 526 | |
| 527 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 528 | } |
| 529 | |
| 530 | template<typename T> |
| 531 | LayerTestResult<T, 4> L2Pooling2dSize3Stride4TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 532 | float qScale = 1.0f, |
| 533 | int32_t qOffset = 0) |
| 534 | { |
| 535 | armnn::Pooling2dDescriptor descriptor; |
| 536 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 537 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 538 | descriptor.m_StrideX = descriptor.m_StrideY = 4; |
| 539 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 540 | |
| 541 | armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); |
| 542 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 543 | QuantizedVector<T>(qScale, qOffset, { |
| 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 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 548 | 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, |
| 549 | 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, |
| 550 | 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, |
| 551 | })); |
| 552 | |
| 553 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 554 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 555 | QuantizedVector<T>(qScale, qOffset, { |
| 556 | 3.0f, 3.0f, |
| 557 | 3.0f, 3.0f, |
| 558 | })); |
| 559 | |
| 560 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 561 | } |
| 562 | |
| 563 | template<typename T> |
| 564 | LayerTestResult<T, 4> L2Pooling2dSize7TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 565 | float qScale = 1.0f, |
| 566 | int32_t qOffset = 0) |
| 567 | { |
| 568 | armnn::Pooling2dDescriptor descriptor; |
| 569 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 570 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 7; |
| 571 | descriptor.m_StrideX = descriptor.m_StrideY = 7; |
| 572 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 573 | |
| 574 | armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); |
| 575 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 576 | QuantizedVector<T>(qScale, qOffset, { |
| 577 | 1.0f, 0.0f, 2.0f, 0.0f, 3.0f, 0.0f, 4.0f, |
| 578 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 579 | 0.0f, 5.0f, 0.0f, 6.0f, 0.0f, 7.0f, 0.0f, |
| 580 | 8.0f, 0.0f, 9.0f, 0.0f, 10.0f, 0.0f, 5.0f, |
| 581 | 0.0f, 5.0f, 0.0f, 2.0f, 0.0f, 1.0f, 1.0f, |
| 582 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 583 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 584 | })); |
| 585 | |
| 586 | armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); |
| 587 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 588 | QuantizedVector<T>(qScale, qOffset, { |
| 589 | 3.0f, |
| 590 | })); |
| 591 | |
| 592 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 593 | } |
| 594 | |
| 595 | template<typename T> |
| 596 | LayerTestResult<T, 4> L2Pooling2dSize9TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 597 | float qScale = 1.0f, |
| 598 | int32_t qOffset = 0) |
| 599 | { |
| 600 | armnn::Pooling2dDescriptor descriptor; |
| 601 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 602 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 9; |
| 603 | descriptor.m_StrideX = descriptor.m_StrideY = 9; |
| 604 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 605 | |
| 606 | armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); |
| 607 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 608 | QuantizedVector<T>(qScale, qOffset, { |
| 609 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 610 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 611 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 612 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 613 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 614 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 615 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 616 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 617 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 618 | })); |
| 619 | |
| 620 | armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); |
| 621 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 622 | QuantizedVector<T>(qScale, qOffset, { |
| 623 | 3.0f, |
| 624 | })); |
| 625 | |
| 626 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 627 | } |
| 628 | |
| 629 | template<typename T> |
| 630 | LayerTestResult<T, 4> AsymmetricNonSquarePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 631 | float qScale = 1.0f, |
| 632 | int32_t qOffset = 0) |
| 633 | { |
| 634 | armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3 }, armnn::GetDataType<T>()); |
| 635 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 636 | |
| 637 | armnn::Pooling2dDescriptor descriptor; |
| 638 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 639 | descriptor.m_PoolWidth = 2; |
| 640 | descriptor.m_PoolHeight = 3; |
| 641 | descriptor.m_StrideX = 2; |
| 642 | descriptor.m_StrideY = 1; |
| 643 | descriptor.m_PadLeft = 2; |
| 644 | descriptor.m_PadRight = 0; |
| 645 | descriptor.m_PadTop = 1; |
| 646 | descriptor.m_PadBottom = 2; |
| 647 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 648 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 649 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 650 | // Construct input data. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 651 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 652 | QuantizedVector<T>(qScale, qOffset, { |
| 653 | 1.0f, 3.0f, 4.0f, |
| 654 | })); |
| 655 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 656 | // These were calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 657 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 658 | QuantizedVector<T>(qScale, qOffset, { |
| 659 | 0.0f, 3.0f, 0.0f, 3.0f, |
| 660 | })); |
| 661 | |
| 662 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 663 | } |
| 664 | |
| 665 | template<typename T> |
| 666 | LayerTestResult<T, 4> ComparePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 667 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 668 | armnn::PoolingAlgorithm poolingType, |
| 669 | float qScale = 1.0f, |
| 670 | int32_t qOffset = 0) |
| 671 | { |
| 672 | const unsigned int inputWidth = 16; |
| 673 | const unsigned int inputHeight = 32; |
| 674 | const unsigned int channelCount = 2; |
| 675 | const unsigned int batchSize = 5; |
| 676 | |
| 677 | const unsigned int poolSize = 3; |
| 678 | const unsigned int strideX = 2; |
| 679 | const unsigned int strideY = 4; |
| 680 | const unsigned int padX = 0; |
| 681 | const unsigned int padY = 0; |
| 682 | |
| 683 | const unsigned int outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX; |
| 684 | const unsigned int outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY; |
| 685 | |
| 686 | armnn::TensorInfo inputTensorInfo; |
| 687 | armnn::TensorInfo outputTensorInfo; |
| 688 | |
| 689 | unsigned int inputShape[] = { batchSize, channelCount, inputHeight, inputWidth }; |
| 690 | unsigned int outputShape[] = { batchSize, channelCount, outputHeight, outputWidth }; |
| 691 | |
| 692 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); |
| 693 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); |
| 694 | |
| 695 | // Set quantization parameters if the requested type is a quantized type. |
| 696 | if(armnn::IsQuantizedType<T>()) |
| 697 | { |
| 698 | inputTensorInfo.SetQuantizationScale(qScale); |
| 699 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 700 | outputTensorInfo.SetQuantizationScale(qScale); |
| 701 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 702 | } |
| 703 | |
| 704 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 81715); |
| 705 | |
| 706 | LayerTestResult<T, 4> comparisonResult(outputTensorInfo); |
| 707 | |
| 708 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 709 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 710 | |
| 711 | armnn::Pooling2dQueueDescriptor data; |
| 712 | armnn::WorkloadInfo info; |
| 713 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 714 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 715 | data.m_Parameters.m_PoolType = poolingType; |
| 716 | data.m_Parameters.m_PoolWidth = poolSize; |
| 717 | data.m_Parameters.m_PoolHeight = poolSize; |
| 718 | data.m_Parameters.m_StrideX = strideX; |
| 719 | data.m_Parameters.m_StrideY = strideY; |
| 720 | data.m_Parameters.m_PadLeft = padX; |
| 721 | data.m_Parameters.m_PadRight = padX; |
| 722 | data.m_Parameters.m_PadTop = padY; |
| 723 | data.m_Parameters.m_PadBottom = padY; |
| 724 | data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 725 | |
| 726 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 727 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); |
| 728 | |
| 729 | // Don't execute if Pooling is not supported, as an exception will be raised. |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 730 | armnn::BackendId backend = workloadFactory.GetBackendId(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 731 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 732 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 733 | comparisonResult.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 734 | data.m_Parameters, |
| 735 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 736 | if (!comparisonResult.supported) |
| 737 | { |
| 738 | return comparisonResult; |
| 739 | } |
| 740 | |
| 741 | armnn::Pooling2dQueueDescriptor refData = data; |
| 742 | armnn::WorkloadInfo refInfo = info; |
| 743 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 744 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 745 | |
| 746 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(data, info); |
| 747 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreatePooling2d(refData, refInfo); |
| 748 | |
| 749 | outputHandleRef->Allocate(); |
| 750 | inputHandleRef->Allocate(); |
| 751 | inputHandle->Allocate(); |
| 752 | outputHandle->Allocate(); |
| 753 | |
| 754 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 755 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 756 | |
| 757 | workload->Execute(); |
| 758 | workloadRef->Execute(); |
| 759 | |
| 760 | CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); |
| 761 | CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 762 | |
| 763 | return comparisonResult; |
| 764 | } |
| 765 | |
| 766 | // |
| 767 | // Tests max pooling with the following parameters: |
| 768 | // |
| 769 | // Pooling size: 2x2 |
| 770 | // Stride: (2,2) |
| 771 | // input size: 4x4 |
| 772 | // channels: 1 |
| 773 | // batch size: 1 |
| 774 | // |
| 775 | template<typename T> |
| 776 | LayerTestResult<T, 4> SimpleMaxPooling2dSize2x2Stride2x2TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 777 | bool forceNoPadding, |
| 778 | float qScale = 1.0f, |
| 779 | int32_t qOffset = 0) |
| 780 | { |
| 781 | armnn::Pooling2dDescriptor descriptor; |
| 782 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 783 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 784 | descriptor.m_StrideX = 2; |
| 785 | descriptor.m_StrideY = 2; |
| 786 | descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; |
| 787 | descriptor.m_PadTop = descriptor.m_PadBottom = 0; |
| 788 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 789 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 790 | |
| 791 | unsigned int inputWidth = 4; |
| 792 | unsigned int inputHeight = 4; |
| 793 | unsigned int outputWidth = |
| 794 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 795 | descriptor.m_StrideX; |
| 796 | unsigned int outputHeight = |
| 797 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 798 | descriptor.m_StrideY; |
| 799 | unsigned int channels = 1; |
| 800 | unsigned int batchSize = 1; |
| 801 | |
| 802 | std::vector<float> inputData = { |
| 803 | 510.0f, 222.0f, 780.0f, 654.0f, |
| 804 | 141.0f, 276.0f, 15.0f, 546.0f, |
| 805 | 303.0f, 618.0f, 582.0f, 339.0f, |
| 806 | 438.0f, 564.0f, 573.0f, 402.0f |
| 807 | }; |
| 808 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 809 | // 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] | 810 | std::vector<float> expectedOutputDataWithPadding = { |
| 811 | 0.0f, 510.0f, 780.0f, 654.0f, 0.0f, |
| 812 | 0.0f, 438.0f, 618.0f, 402.0f, 0.0f |
| 813 | }; |
| 814 | |
| 815 | std::vector<float> expectedOutputDataNoPadding = { |
| 816 | 510.0f, 780.0f, |
| 817 | 618.0f, 582.0f |
| 818 | }; |
| 819 | |
| 820 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 821 | |
| 822 | // Scale and offset should match input - we're just calculating maximum values. |
| 823 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 824 | |
| 825 | // Set quantization parameters if the requested type is a quantized type. |
| 826 | if(armnn::IsQuantizedType<T>()) |
| 827 | { |
| 828 | inputTensorInfo.SetQuantizationScale(qScale); |
| 829 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 830 | outputTensorInfo.SetQuantizationScale(qScale); |
| 831 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 832 | } |
| 833 | |
| 834 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 835 | |
| 836 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 837 | forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : |
| 838 | QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); |
| 839 | |
| 840 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 841 | } |
| 842 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 843 | // |
| 844 | // Tests max pooling with the following parameters: |
| 845 | // |
| 846 | // Pooling size: 3x2 |
| 847 | // Stride: (2,2) |
| 848 | // input size: 3x2 |
| 849 | // channels: 1 |
| 850 | // batch size: 1 |
| 851 | // |
| 852 | template<typename T> |
| 853 | LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon( |
| 854 | armnn::IWorkloadFactory& workloadFactory, |
| 855 | bool forceNoPadding, |
| 856 | float qScale = 1.0f, |
| 857 | int32_t qOffset = 0) |
| 858 | { |
| 859 | armnn::Pooling2dDescriptor descriptor; |
| 860 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 861 | descriptor.m_PoolWidth = 3; |
| 862 | descriptor.m_PoolHeight = 2; |
| 863 | descriptor.m_StrideX = 2; |
| 864 | descriptor.m_StrideY = 2; |
| 865 | descriptor.m_PadLeft = (forceNoPadding) ? 0 : 1; |
| 866 | descriptor.m_PadRight = descriptor.m_PadLeft; |
| 867 | descriptor.m_PadTop = 0; |
| 868 | descriptor.m_PadBottom = 0; |
| 869 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 870 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 871 | |
| 872 | unsigned int inputWidth = 3; |
| 873 | unsigned int inputHeight = 2; |
| 874 | unsigned int outputWidth = |
| 875 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 876 | descriptor.m_StrideX; |
| 877 | unsigned int outputHeight = |
| 878 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 879 | descriptor.m_StrideY; |
| 880 | unsigned int channels = 1; |
| 881 | unsigned int batchSize = 1; |
| 882 | |
| 883 | std::vector<float> inputData = { |
| 884 | 3.0f, 6.0f, 9.0f, |
| 885 | 12.0f, 15.0f, 18.0f, |
| 886 | }; |
| 887 | |
| 888 | std::vector<float> expectedOutputDataWithPadding = { |
| 889 | 6.0f, 8.0f, |
| 890 | }; |
| 891 | |
| 892 | std::vector<float> expectedOutputDataNoPadding = { |
| 893 | 10.5f, |
| 894 | }; |
| 895 | |
| 896 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 897 | |
| 898 | // Scale and offset should match input - we're just calculating average values. |
| 899 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 900 | |
| 901 | // Set quantization parameters if the requested type is a quantized type. |
| 902 | if(armnn::IsQuantizedType<T>()) |
| 903 | { |
| 904 | inputTensorInfo.SetQuantizationScale(qScale); |
| 905 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 906 | outputTensorInfo.SetQuantizationScale(qScale); |
| 907 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 908 | } |
| 909 | |
| 910 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 911 | |
| 912 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 913 | forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : |
| 914 | QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); |
| 915 | |
| 916 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 917 | } |
| 918 | |
| 919 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 920 | template<typename T> |
| 921 | LayerTestResult<T, 4> IgnorePaddingSimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 922 | float qScale = 1.0f, |
| 923 | int32_t qOffset = 0) |
| 924 | { |
| 925 | armnn::Pooling2dDescriptor descriptor; |
| 926 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 927 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 928 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 929 | descriptor.m_PadLeft = 1; |
| 930 | descriptor.m_PadRight = 1; |
| 931 | descriptor.m_PadTop = 1; |
| 932 | descriptor.m_PadBottom = 1; |
| 933 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 934 | |
| 935 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 936 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 937 | |
| 938 | // Set quantization parameters if the requested type is a quantized type. |
| 939 | if(armnn::IsQuantizedType<T>()) |
| 940 | { |
| 941 | inputTensorInfo.SetQuantizationScale(qScale); |
| 942 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 943 | outputTensorInfo.SetQuantizationScale(qScale); |
| 944 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 945 | } |
| 946 | |
| 947 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 948 | QuantizedVector<T>(qScale, qOffset, { |
| 949 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 950 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 951 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 952 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 953 | })); |
| 954 | |
| 955 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 956 | QuantizedVector<T>(qScale, qOffset, { |
| 957 | -1.0f, 3.0f, 4.0f, |
| 958 | 1.0f, 3.0f, 4.0f, |
| 959 | 1.0f, 2.0f, -4.0f, |
| 960 | })); |
| 961 | |
| 962 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 963 | } |
| 964 | |
| 965 | template<typename T> |
| 966 | LayerTestResult<T, 4> IgnorePaddingMaxPooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 967 | float qScale = 1.0f, |
| 968 | int32_t qOffset = 0) |
| 969 | { |
| 970 | armnn::Pooling2dDescriptor descriptor; |
| 971 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 972 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 973 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 974 | descriptor.m_PadLeft = 1; |
| 975 | descriptor.m_PadRight = 1; |
| 976 | descriptor.m_PadTop = 1; |
| 977 | descriptor.m_PadBottom = 1; |
| 978 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 979 | |
| 980 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 981 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 982 | |
| 983 | // Set quantization parameters if the requested type is a quantized type. |
| 984 | if(armnn::IsQuantizedType<T>()) |
| 985 | { |
| 986 | inputTensorInfo.SetQuantizationScale(qScale); |
| 987 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 988 | outputTensorInfo.SetQuantizationScale(qScale); |
| 989 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 990 | } |
| 991 | |
| 992 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 993 | QuantizedVector<T>(qScale, qOffset, { |
| 994 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 995 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 996 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 997 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 998 | })); |
| 999 | |
| 1000 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1001 | QuantizedVector<T>(qScale, qOffset, { |
| 1002 | -1.0f, 3.0f, 4.0f, 4.0f, |
| 1003 | 2.0f, 3.0f, 4.0f, 4.0f, |
| 1004 | 2.0f, 3.0f, 4.0f, 4.0f, |
| 1005 | 2.0f, 2.0f, 2.0f, -3.0f, |
| 1006 | })); |
| 1007 | |
| 1008 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1009 | } |
| 1010 | |
| 1011 | template<typename T> |
| 1012 | LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1013 | float qScale = 1.0f, |
| 1014 | int32_t qOffset = 0) |
| 1015 | { |
| 1016 | armnn::Pooling2dDescriptor descriptor; |
| 1017 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1018 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 1019 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1020 | descriptor.m_PadLeft = 1; |
| 1021 | descriptor.m_PadRight = 1; |
| 1022 | descriptor.m_PadTop = 1; |
| 1023 | descriptor.m_PadBottom = 1; |
| 1024 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1025 | |
| 1026 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1027 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 1028 | |
| 1029 | // Set quantization parameters if the requested type is a quantized type. |
| 1030 | if(armnn::IsQuantizedType<T>()) |
| 1031 | { |
| 1032 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1033 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1034 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1035 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1036 | } |
| 1037 | |
| 1038 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1039 | QuantizedVector<T>(qScale, qOffset, { |
| 1040 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1041 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1042 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1043 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1044 | })); |
| 1045 | |
| 1046 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1047 | QuantizedVector<T>(qScale, qOffset, { |
| 1048 | 3.0f, 13.0f, 10.0f, |
| 1049 | 6.0f, 26.0f, 20.0f, |
| 1050 | 3.0f, 13.0f, 10.0f, |
| 1051 | })); |
| 1052 | |
| 1053 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1054 | } |
| 1055 | |
| 1056 | template<typename T> |
| 1057 | LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1058 | float qScale = 1.0f, |
| 1059 | int32_t qOffset = 0) |
| 1060 | { |
| 1061 | armnn::Pooling2dDescriptor descriptor; |
| 1062 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1063 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1064 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1065 | descriptor.m_PadLeft = 0; |
| 1066 | descriptor.m_PadRight = 0; |
| 1067 | descriptor.m_PadTop = 0; |
| 1068 | descriptor.m_PadBottom = 0; |
| 1069 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1070 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling; |
| 1071 | |
| 1072 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4}, armnn::GetDataType<T>()); |
| 1073 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 1074 | |
| 1075 | // Set quantization parameters if the requested type is a quantized type. |
| 1076 | if(armnn::IsQuantizedType<T>()) |
| 1077 | { |
| 1078 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1079 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1080 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1081 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1082 | } |
| 1083 | |
| 1084 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1085 | QuantizedVector<T>(qScale, qOffset, { |
| 1086 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1087 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1088 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1089 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1090 | })); |
| 1091 | |
| 1092 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1093 | QuantizedVector<T>(qScale, qOffset, { |
| 1094 | 2.0f, 3.5f, |
| 1095 | 2.0f, 3.5f |
| 1096 | })); |
| 1097 | |
| 1098 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1099 | } |
| 1100 | |
| 1101 | template<typename T> |
| 1102 | LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1103 | float qScale = 1.0f, |
| 1104 | int32_t qOffset = 0) |
| 1105 | { |
| 1106 | armnn::Pooling2dDescriptor descriptor; |
| 1107 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1108 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1109 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1110 | descriptor.m_PadLeft = 1; |
| 1111 | descriptor.m_PadRight = 1; |
| 1112 | descriptor.m_PadTop = 1; |
| 1113 | descriptor.m_PadBottom = 1; |
| 1114 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1115 | |
| 1116 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1117 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1118 | |
| 1119 | // Set quantization parameters if the requested type is a quantized type. |
| 1120 | if(armnn::IsQuantizedType<T>()) |
| 1121 | { |
| 1122 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1123 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1124 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1125 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1126 | } |
| 1127 | |
| 1128 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1129 | QuantizedVector<T>(qScale, qOffset, { |
| 1130 | 9.0f, 27.0f, 18.0f, 36.0f, |
| 1131 | 18.0f, 9.0f, 18.0f, 9.0f, |
| 1132 | 27.0f, 18.0f, 9.0f, 27.0f, |
| 1133 | 9.0f, 27.0f, 9.0f, 18.0f, |
| 1134 | })); |
| 1135 | |
| 1136 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1137 | QuantizedVector<T>(qScale, qOffset, { |
| 1138 | 7.0f, 11.0f, 13.0f, 9.0f, |
| 1139 | 12.0f, 17.0f, 19.0f, 13.0f, |
| 1140 | 12.0f, 16.0f, 16.0f, 10.0f, |
| 1141 | 9.0f, 11.0f, 12.0f, 7.0f, |
| 1142 | })); |
| 1143 | |
| 1144 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1145 | } |
| 1146 | |
| 1147 | template<typename T> |
| 1148 | LayerTestResult<T, 4> IgnorePaddingSimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1149 | float qScale = 1.0f, |
| 1150 | int32_t qOffset = 0) |
| 1151 | { |
| 1152 | armnn::Pooling2dDescriptor descriptor; |
| 1153 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 1154 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 1155 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1156 | descriptor.m_PadLeft = 1; |
| 1157 | descriptor.m_PadRight = 1; |
| 1158 | descriptor.m_PadTop = 1; |
| 1159 | descriptor.m_PadBottom = 1; |
| 1160 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1161 | |
| 1162 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1163 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 1164 | |
| 1165 | // Set quantization parameters if the requested type is a quantized type. |
| 1166 | if(armnn::IsQuantizedType<T>()) |
| 1167 | { |
| 1168 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1169 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1170 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1171 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1172 | } |
| 1173 | |
| 1174 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1175 | QuantizedVector<T>(qScale, qOffset, { |
| 1176 | 2.0f, 4.0f, 8.0f, 16.0f, |
| 1177 | 4.0f, 2.0f, 2.0f, 4.0f, |
| 1178 | 8.0f, 2.0f, 4.0f, 2.0f, |
| 1179 | 16.0f, 2.0f, 2.0f, 8.0f, |
| 1180 | })); |
| 1181 | |
| 1182 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1183 | QuantizedVector<T>(qScale, qOffset, { |
| 1184 | 1.0f, 4.4721f, 8.0f, |
| 1185 | 4.4721f, 2.6457f, 2.236f, |
| 1186 | 8.0f, 1.4142f, 4.0f, |
| 1187 | })); |
| 1188 | |
| 1189 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1190 | } |
| 1191 | |
| 1192 | template<typename T> |
| 1193 | LayerTestResult<T, 4> IgnorePaddingL2Pooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 1194 | float qScale = 1.0f, |
| 1195 | int32_t qOffset = 0) |
| 1196 | { |
| 1197 | armnn::Pooling2dDescriptor descriptor; |
| 1198 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 1199 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1200 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1201 | descriptor.m_PadLeft = 1; |
| 1202 | descriptor.m_PadRight = 1; |
| 1203 | descriptor.m_PadTop = 1; |
| 1204 | descriptor.m_PadBottom = 1; |
| 1205 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1206 | |
| 1207 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1208 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1209 | |
| 1210 | // Set quantization parameters if the requested type is a quantized type. |
| 1211 | if(armnn::IsQuantizedType<T>()) |
| 1212 | { |
| 1213 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1214 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1215 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1216 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1217 | } |
| 1218 | |
| 1219 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1220 | QuantizedVector<T>(qScale, qOffset, { |
| 1221 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1222 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1223 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1224 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1225 | })); |
| 1226 | |
| 1227 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1228 | QuantizedVector<T>(qScale, qOffset, { |
| 1229 | 1.0540f, 1.7638f, 2.5385f, 2.3570f, |
| 1230 | 1.2909f, 2.1602f, 3.1091f, 2.8867f, |
| 1231 | 1.2909f, 2.1602f, 3.1091f, 2.8867f, |
| 1232 | 1.0540f, 1.7638f, 2.5385f, 2.3570f, |
| 1233 | })); |
| 1234 | |
| 1235 | return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); |
| 1236 | } |