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 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 7 | #include "WorkloadTestUtils.hpp" |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame^] | 8 | #include "TensorUtils.hpp" |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 9 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 10 | #include "QuantizeHelper.hpp" |
| 11 | |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 12 | #include <armnn/ArmNN.hpp> |
| 13 | |
| 14 | #include <Permute.hpp> |
| 15 | |
| 16 | #include <backendsCommon/CpuTensorHandle.hpp> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 17 | #include <backendsCommon/IBackendInternal.hpp> |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 18 | #include <backendsCommon/WorkloadFactory.hpp> |
| 19 | #include <backendsCommon/WorkloadInfo.hpp> |
| 20 | |
| 21 | #include <test/TensorHelpers.hpp> |
| 22 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 23 | #include <boost/numeric/conversion/cast.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 24 | |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 25 | #include <algorithm> |
| 26 | #include <string> |
| 27 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 28 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 29 | LayerTestResult<T, 4> SimplePooling2dTestImpl( |
| 30 | armnn::IWorkloadFactory& workloadFactory, |
| 31 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 32 | armnn::Pooling2dDescriptor descriptor, |
| 33 | float qScale, |
| 34 | int32_t qOffset, |
| 35 | const boost::multi_array<T, 4>& input, |
| 36 | const boost::multi_array<T, 4>& outputExpected) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 37 | { |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 38 | const armnn::DataLayout dataLayout = descriptor.m_DataLayout; |
| 39 | const armnn::DataLayoutIndexed dimensionIndices = dataLayout; |
| 40 | auto heightIndex = dimensionIndices.GetHeightIndex(); |
| 41 | auto widthIndex = dimensionIndices.GetWidthIndex(); |
| 42 | auto channelsIndex = dimensionIndices.GetChannelsIndex(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 43 | |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 44 | unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[heightIndex]); |
| 45 | unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[widthIndex]); |
| 46 | unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[channelsIndex]); |
| 47 | unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); |
| 48 | |
| 49 | unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[heightIndex]); |
| 50 | unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[widthIndex]); |
| 51 | unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[channelsIndex]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 52 | unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); |
| 53 | |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame^] | 54 | armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<T>(inputBatchSize, inputChannels, inputHeight, |
| 55 | inputWidth, dataLayout); |
| 56 | armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(outputBatchSize, outputChannels, outputHeight, |
| 57 | outputWidth, dataLayout); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 58 | |
| 59 | // Set quantization parameters if the requested type is a quantized type. |
| 60 | if(armnn::IsQuantizedType<T>()) |
| 61 | { |
| 62 | inputTensorInfo.SetQuantizationScale(qScale); |
| 63 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 64 | outputTensorInfo.SetQuantizationScale(qScale); |
| 65 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 66 | } |
| 67 | |
| 68 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 69 | |
| 70 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 71 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 72 | |
| 73 | armnn::Pooling2dQueueDescriptor queueDescriptor; |
| 74 | queueDescriptor.m_Parameters = descriptor; |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 75 | queueDescriptor.m_Parameters.m_DataLayout = dataLayout; |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 76 | |
| 77 | armnn::WorkloadInfo workloadInfo; |
| 78 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 79 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 80 | |
| 81 | // 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] | 82 | armnn::BackendId backend = workloadFactory.GetBackendId(); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 83 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 84 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 85 | result.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 86 | queueDescriptor.m_Parameters, |
| 87 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 88 | if (!result.supported) |
| 89 | { |
| 90 | return result; |
| 91 | } |
| 92 | |
| 93 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); |
| 94 | |
| 95 | inputHandle->Allocate(); |
| 96 | outputHandle->Allocate(); |
| 97 | |
| 98 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 99 | |
| 100 | workload->Execute(); |
| 101 | |
| 102 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 103 | |
| 104 | result.outputExpected = outputExpected; |
| 105 | |
| 106 | return result; |
| 107 | } |
| 108 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 109 | // |
| 110 | // Tests max pooling with the following parameters: |
| 111 | // |
| 112 | // Pooling size: 3x3 |
| 113 | // Stride: (2,4) |
| 114 | // input size: 8x13 |
| 115 | // channels: 2 |
| 116 | // batch size: 2 |
| 117 | // |
| 118 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 119 | LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon( |
| 120 | armnn::IWorkloadFactory& workloadFactory, |
| 121 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 122 | bool forceNoPadding, |
| 123 | float qScale = 1.0f, |
| 124 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 125 | { |
| 126 | armnn::Pooling2dDescriptor descriptor; |
| 127 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 128 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 129 | descriptor.m_StrideX = 2; |
| 130 | descriptor.m_StrideY = 4; |
| 131 | // forceNoPadding is mainly used for compatibility with ARM Compute. |
| 132 | // As of 16/05/2017, it errors if padX or padY are equal to or greater than the pool size. |
| 133 | descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; |
| 134 | descriptor.m_PadTop = descriptor.m_PadBottom = 0; |
| 135 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 136 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 137 | |
| 138 | unsigned int inputWidth = 8; |
| 139 | unsigned int inputHeight = 13; |
| 140 | unsigned int outputWidth = |
| 141 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 142 | descriptor.m_StrideX; |
| 143 | unsigned int outputHeight = |
| 144 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 145 | descriptor.m_StrideY; |
| 146 | unsigned int channels = 2; |
| 147 | unsigned int batchSize = 2; |
| 148 | |
| 149 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 150 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 151 | |
| 152 | // Set quantization parameters if the requested type is a quantized type. |
| 153 | if(armnn::IsQuantizedType<T>()) |
| 154 | { |
| 155 | inputTensorInfo.SetQuantizationScale(qScale); |
| 156 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 157 | outputTensorInfo.SetQuantizationScale(qScale); |
| 158 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 159 | } |
| 160 | |
| 161 | std::vector<float> singleChannelData({ |
| 162 | 0.0f, 4.0f, 8.0f, 1.0f, 6.0f, 4.0f, 5.0f, 8.0f, |
| 163 | 1.0f, 1.0f, 6.0f, 0.0f, 3.0f, 7.0f, 4.0f, 7.0f, |
| 164 | 8.0f, 5.0f, 0.0f, 0.0f, 8.0f, 3.0f, 4.0f, 3.0f, |
| 165 | 8.0f, 2.0f, 5.0f, 4.0f, 1.0f, 9.0f, 2.0f, 0.0f, |
| 166 | 5.0f, 4.0f, 5.0f, 0.0f, 0.0f, 0.0f, 7.0f, 2.0f, |
| 167 | 1.0f, 2.0f, 6.0f, 2.0f, 7.0f, 9.0f, 5.0f, 2.0f, |
| 168 | 9.0f, 7.0f, 3.0f, 1.0f, 3.0f, 4.0f, 8.0f, 3.0f, |
| 169 | 1.0f, 0.0f, 0.0f, 5.0f, 5.0f, 4.0f, 2.0f, 0.0f, |
| 170 | 6.0f, 4.0f, 3.0f, 6.0f, 9.0f, 5.0f, 5.0f, 6.0f, |
| 171 | 8.0f, 7.0f, 9.0f, 6.0f, 1.0f, 4.0f, 1.0f, 9.0f, |
| 172 | 7.0f, 1.0f, 9.0f, 2.0f, 9.0f, 9.0f, 8.0f, 1.0f, |
| 173 | 4.0f, 4.0f, 5.0f, 9.0f, 2.0f, 6.0f, 6.0f, 4.0f, |
| 174 | 3.0f, 5.0f, 4.0f, 0.0f, 1.0f, 5.0f, 9.0f, 7.0f, |
| 175 | }); |
| 176 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 177 | // Constructs input data. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 178 | std::vector<float> inputData; |
| 179 | auto negator = [](float f) { return -f; }; |
| 180 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 181 | // 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] | 182 | inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); |
| 183 | std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); |
| 184 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 185 | // Second image (same as first image). |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 186 | inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); |
| 187 | std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); |
| 188 | |
| 189 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 190 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 191 | // These were calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 192 | auto shape(GetTensorShapeAsArray<4>(outputTensorInfo)); |
| 193 | boost::multi_array<T, 4> outputExpected(shape); |
| 194 | if (forceNoPadding) |
| 195 | { |
| 196 | outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 197 | QuantizedVector<T>(qScale, qOffset, { |
| 198 | 8.0f, 8.0f, 8.0f, |
| 199 | 9.0f, 7.0f, 9.0f, |
| 200 | 9.0f, 9.0f, 9.0f, |
| 201 | |
| 202 | 0.0f, 0.0f, -3.0f, |
| 203 | -1.0f, 0.0f, 0.0f, |
| 204 | -1.0f, -1.0f, -1.0f, |
| 205 | |
| 206 | 8.0f, 8.0f, 8.0f, |
| 207 | 9.0f, 7.0f, 9.0f, |
| 208 | 9.0f, 9.0f, 9.0f, |
| 209 | |
| 210 | 0.0f, 0.0f, -3.0f, |
| 211 | -1.0f, 0.0f, 0.0f, |
| 212 | -1.0f, -1.0f, -1.0f |
| 213 | })); |
| 214 | } |
| 215 | else |
| 216 | { |
| 217 | outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 218 | QuantizedVector<T>(qScale, qOffset, { |
| 219 | 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, |
| 220 | 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, |
| 221 | 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, |
| 222 | |
| 223 | 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, |
| 224 | 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 225 | 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f, |
| 226 | |
| 227 | 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, |
| 228 | 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, |
| 229 | 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, |
| 230 | |
| 231 | 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, |
| 232 | 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 233 | 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f |
| 234 | })); |
| 235 | } |
| 236 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 237 | return SimplePooling2dTestImpl<T>( |
| 238 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 239 | } |
| 240 | |
| 241 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 242 | LayerTestResult<T, 4> SimpleMaxPooling2dTestCommon( |
| 243 | armnn::IWorkloadFactory& workloadFactory, |
| 244 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 245 | const armnn::DataLayout dataLayout = armnn::DataLayout::NCHW, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 246 | float qScale = 1.0f, |
| 247 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 248 | { |
| 249 | armnn::Pooling2dDescriptor descriptor; |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 250 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 251 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 252 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 253 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 254 | descriptor.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 255 | |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame^] | 256 | armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 4, 4, dataLayout); |
| 257 | armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 2, 2, dataLayout); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 258 | |
| 259 | // Set quantization parameters if the requested type is a quantized type. |
| 260 | if(armnn::IsQuantizedType<T>()) |
| 261 | { |
| 262 | inputTensorInfo.SetQuantizationScale(qScale); |
| 263 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 264 | outputTensorInfo.SetQuantizationScale(qScale); |
| 265 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 266 | } |
| 267 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 268 | std::vector<T> inputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 269 | QuantizedVector<T>(qScale, qOffset, { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 270 | 1.0f, 2.0f, 5.0f, 6.0f, |
| 271 | 3.0f, 4.0f, 7.0f, 8.0f, |
| 272 | 9.0f, 10.0f, 13.0f, 14.0f, |
| 273 | 11.0f, 12.0f, 15.0f, 16.0f, |
| 274 | |
| 275 | 17.0f, 18.0f, 21.0f, 22.0f, |
| 276 | 19.0f, 20.0f, 23.0f, 24.0f, |
| 277 | 25.0f, 26.0f, 29.0f, 30.0f, |
| 278 | 27.0f, 28.0f, 31.0f, 32.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 279 | })); |
| 280 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 281 | std::vector<T> outputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 282 | QuantizedVector<T>(qScale, qOffset, { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 283 | 4.0f, 8.0f, |
| 284 | 12.0f, 16.0f, |
| 285 | |
| 286 | 20.0f, 24.0f, |
| 287 | 28.0f, 32.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 288 | })); |
| 289 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 290 | const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 291 | if (dataLayout == armnn::DataLayout::NHWC) |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 292 | { |
| 293 | std::vector<T> tmp(inputData.size()); |
| 294 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); |
| 295 | inputData = tmp; |
| 296 | |
| 297 | std::vector<T> tmp1(outputData.size()); |
| 298 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); |
| 299 | outputData = tmp1; |
| 300 | } |
| 301 | |
| 302 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 303 | |
| 304 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); |
| 305 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 306 | return SimplePooling2dTestImpl<T>( |
| 307 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 308 | } |
| 309 | |
| 310 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 311 | LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon( |
| 312 | armnn::IWorkloadFactory& workloadFactory, |
| 313 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 314 | armnn::DataLayout dataLayout = armnn::DataLayout::NCHW, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 315 | float qScale = 1.0f, |
| 316 | int32_t qOffset = 0) |
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 | armnn::Pooling2dDescriptor descriptor; |
| 319 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 320 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 321 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 322 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 323 | descriptor.m_DataLayout = dataLayout; |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 324 | |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame^] | 325 | armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 4, 4, dataLayout); |
| 326 | armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 2, 2, dataLayout); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 327 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 328 | // Set quantization parameters if the requested type is a quantized type. |
| 329 | if(armnn::IsQuantizedType<T>()) |
| 330 | { |
| 331 | inputTensorInfo.SetQuantizationScale(qScale); |
| 332 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 333 | outputTensorInfo.SetQuantizationScale(qScale); |
| 334 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 335 | } |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 336 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 337 | std::vector<T> inputData( |
| 338 | QuantizedVector<T>(qScale, qOffset, { |
| 339 | 2.0f, 2.0f, 6.0f, 6.0f, |
| 340 | 4.0f, 4.0f, 8.0f, 8.0f, |
| 341 | 10.0f, 12.0f, 14.0f, 16.0f, |
| 342 | 10.0f, 12.0f, 16.0f, 14.0f, |
| 343 | |
| 344 | 18.0f, 20.0f, 24.0f, 22.0f, |
| 345 | 20.0f, 18.0f, 22.0f, 24.0f, |
| 346 | 26.0f, 28.0f, 0.0f, 0.0f, |
| 347 | 26.0f, 28.0f, 0.0f, 0.0f, |
| 348 | })); |
| 349 | |
| 350 | std::vector<T> outputData( |
| 351 | QuantizedVector<T>(qScale, qOffset, { |
| 352 | 3.0f, 7.0f, |
| 353 | 11.0f, 15.0f, |
| 354 | |
| 355 | 19.0f, 23.0f, |
| 356 | 27.0f, 0.0f, |
| 357 | })); |
| 358 | |
| 359 | const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 360 | if (dataLayout == armnn::DataLayout::NHWC) |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 361 | { |
| 362 | std::vector<T> tmp(inputData.size()); |
| 363 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); |
| 364 | inputData = tmp; |
| 365 | |
| 366 | std::vector<T> tmp1(outputData.size()); |
| 367 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); |
| 368 | outputData = tmp1; |
| 369 | } |
| 370 | |
| 371 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 372 | |
| 373 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); |
| 374 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 375 | return SimplePooling2dTestImpl<T>( |
| 376 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 377 | } |
| 378 | |
| 379 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 380 | LayerTestResult<T, 4> LargeTensorsAveragePooling2dTestCommon( |
| 381 | armnn::IWorkloadFactory& workloadFactory, |
| 382 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 383 | float qScale = 1.0f, |
| 384 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 385 | { |
| 386 | armnn::Pooling2dDescriptor descriptor; |
| 387 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 388 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 100; |
| 389 | descriptor.m_StrideX = descriptor.m_StrideY = 5; |
| 390 | descriptor.m_PadLeft = 50; |
| 391 | descriptor.m_PadRight = 50; |
| 392 | descriptor.m_PadTop = 50; |
| 393 | descriptor.m_PadBottom = 50; |
| 394 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 395 | |
| 396 | armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60 }, armnn::GetDataType<T>()); |
| 397 | armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13 }, armnn::GetDataType<T>()); |
| 398 | |
| 399 | // Set quantization parameters if the requested type is a quantized type. |
| 400 | if(armnn::IsQuantizedType<T>()) |
| 401 | { |
| 402 | inputTensorInfo.SetQuantizationScale(qScale); |
| 403 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 404 | outputTensorInfo.SetQuantizationScale(qScale); |
| 405 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 406 | } |
| 407 | |
| 408 | std::vector<T> inputVec; |
| 409 | |
| 410 | for (unsigned int i = 0 ; i < inputTensorInfo.GetShape().GetNumElements(); ++i) |
| 411 | { |
| 412 | inputVec.push_back(1); |
| 413 | } |
| 414 | |
| 415 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputVec); |
| 416 | |
| 417 | std::vector<T> outputVec; |
| 418 | |
| 419 | for (unsigned int i = 0 ; i < outputTensorInfo.GetShape().GetNumElements(); ++i) |
| 420 | { |
| 421 | outputVec.push_back(1); |
| 422 | } |
| 423 | |
| 424 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputVec); |
| 425 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 426 | return SimplePooling2dTestImpl<T>( |
| 427 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 428 | } |
| 429 | |
| 430 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 431 | LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon( |
| 432 | armnn::IWorkloadFactory& workloadFactory, |
| 433 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 434 | armnn::DataLayout dataLayout = armnn::DataLayout::NCHW, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 435 | float qScale = 1.0f, |
| 436 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 437 | { |
| 438 | armnn::Pooling2dDescriptor descriptor; |
| 439 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 440 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 441 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 442 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 443 | descriptor.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 444 | |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame^] | 445 | armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 4, 4, dataLayout); |
| 446 | armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 2, 2, dataLayout); |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 447 | |
| 448 | std::vector<T> inputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 449 | QuantizedVector<T>(qScale, qOffset, { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 450 | 1.0f, 7.0f, 5.0f, 5.0f, |
| 451 | 1.0f, 7.0f, 5.0f, 5.0f, |
| 452 | 3.0f, 3.0f, 1.0f, 1.0f, |
| 453 | 3.0f, 3.0f, 1.0f, 1.0f, |
| 454 | |
| 455 | 1.0f, 7.0f, 0.0f, 0.0f, |
| 456 | 1.0f, 7.0f, 2.0f, 0.0f, |
| 457 | 0.0f, 2.0f, 1.0f, 1.0f, |
| 458 | 0.0f, 0.0f, 1.0f, 1.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 459 | })); |
| 460 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 461 | std::vector<T> outputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 462 | QuantizedVector<T>(qScale, qOffset, { |
| 463 | 5.0f, 5.0f, |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 464 | 3.0f, 1.0f, |
| 465 | |
| 466 | 5.0f, 1.0f, |
| 467 | 1.0f, 1.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 468 | })); |
| 469 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 470 | const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 471 | if (dataLayout == armnn::DataLayout::NHWC) |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 472 | { |
| 473 | std::vector<T> tmp(inputData.size()); |
| 474 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); |
| 475 | inputData = tmp; |
| 476 | |
| 477 | std::vector<T> tmp1(outputData.size()); |
| 478 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); |
| 479 | outputData = tmp1; |
| 480 | } |
| 481 | |
| 482 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 483 | |
| 484 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); |
| 485 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 486 | return SimplePooling2dTestImpl<T>( |
| 487 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 488 | } |
| 489 | |
| 490 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 491 | LayerTestResult<T, 4> L2Pooling2dSize3Stride1TestCommon( |
| 492 | armnn::IWorkloadFactory& workloadFactory, |
| 493 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 494 | float qScale = 1.0f, |
| 495 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 496 | { |
| 497 | armnn::Pooling2dDescriptor descriptor; |
| 498 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 499 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 500 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 501 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 502 | |
| 503 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 504 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 505 | QuantizedVector<T>(qScale, qOffset, { |
| 506 | 2.0f, 1.0f, 5.0f, 2.0f, |
| 507 | 1.0f, 2.0f, 2.0f, 1.0f, |
| 508 | 5.0f, 4.0f, 1.0f, 5.0f, |
| 509 | 2.0f, 1.0f, 5.0f, 2.0f, |
| 510 | })); |
| 511 | |
| 512 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 513 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 514 | QuantizedVector<T>(qScale, qOffset, { |
| 515 | 3.0f, 3.0f, |
| 516 | 3.0f, 3.0f, |
| 517 | })); |
| 518 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 519 | return SimplePooling2dTestImpl<T>( |
| 520 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 521 | } |
| 522 | |
| 523 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 524 | LayerTestResult<T, 4> L2Pooling2dSize3Stride3TestCommon( |
| 525 | armnn::IWorkloadFactory& workloadFactory, |
| 526 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 527 | float qScale = 1.0f, |
| 528 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 529 | { |
| 530 | armnn::Pooling2dDescriptor descriptor; |
| 531 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 532 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 533 | descriptor.m_StrideX = descriptor.m_StrideY = 3; |
| 534 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 535 | |
| 536 | armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); |
| 537 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 538 | QuantizedVector<T>(qScale, qOffset, { |
| 539 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 540 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 541 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 542 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 543 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 544 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 545 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 546 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 547 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 548 | })); |
| 549 | |
| 550 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 551 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 552 | QuantizedVector<T>(qScale, qOffset, { |
| 553 | 3.0f, 3.0f, 3.0f, |
| 554 | 3.0f, 3.0f, 3.0f, |
| 555 | 3.0f, 3.0f, 3.0f, |
| 556 | })); |
| 557 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 558 | return SimplePooling2dTestImpl<T>( |
| 559 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 560 | } |
| 561 | |
| 562 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 563 | LayerTestResult<T, 4> L2Pooling2dSize3Stride4TestCommon( |
| 564 | armnn::IWorkloadFactory& workloadFactory, |
| 565 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 566 | float qScale = 1.0f, |
| 567 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 568 | { |
| 569 | armnn::Pooling2dDescriptor descriptor; |
| 570 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 571 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 572 | descriptor.m_StrideX = descriptor.m_StrideY = 4; |
| 573 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 574 | |
| 575 | armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); |
| 576 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 577 | QuantizedVector<T>(qScale, qOffset, { |
| 578 | 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, |
| 579 | 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, |
| 580 | 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, |
| 581 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 582 | 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, |
| 583 | 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, |
| 584 | 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, |
| 585 | })); |
| 586 | |
| 587 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 588 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 589 | QuantizedVector<T>(qScale, qOffset, { |
| 590 | 3.0f, 3.0f, |
| 591 | 3.0f, 3.0f, |
| 592 | })); |
| 593 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 594 | return SimplePooling2dTestImpl<T>( |
| 595 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 596 | } |
| 597 | |
| 598 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 599 | LayerTestResult<T, 4> L2Pooling2dSize7TestCommon( |
| 600 | armnn::IWorkloadFactory& workloadFactory, |
| 601 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 602 | float qScale = 1.0f, |
| 603 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 604 | { |
| 605 | armnn::Pooling2dDescriptor descriptor; |
| 606 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 607 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 7; |
| 608 | descriptor.m_StrideX = descriptor.m_StrideY = 7; |
| 609 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 610 | |
| 611 | armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); |
| 612 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 613 | QuantizedVector<T>(qScale, qOffset, { |
| 614 | 1.0f, 0.0f, 2.0f, 0.0f, 3.0f, 0.0f, 4.0f, |
| 615 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 616 | 0.0f, 5.0f, 0.0f, 6.0f, 0.0f, 7.0f, 0.0f, |
| 617 | 8.0f, 0.0f, 9.0f, 0.0f, 10.0f, 0.0f, 5.0f, |
| 618 | 0.0f, 5.0f, 0.0f, 2.0f, 0.0f, 1.0f, 1.0f, |
| 619 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 620 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 621 | })); |
| 622 | |
| 623 | armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); |
| 624 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 625 | QuantizedVector<T>(qScale, qOffset, { |
| 626 | 3.0f, |
| 627 | })); |
| 628 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 629 | return SimplePooling2dTestImpl<T>( |
| 630 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 631 | } |
| 632 | |
| 633 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 634 | LayerTestResult<T, 4> L2Pooling2dSize9TestCommon( |
| 635 | armnn::IWorkloadFactory& workloadFactory, |
| 636 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 637 | float qScale = 1.0f, |
| 638 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 639 | { |
| 640 | armnn::Pooling2dDescriptor descriptor; |
| 641 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 642 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 9; |
| 643 | descriptor.m_StrideX = descriptor.m_StrideY = 9; |
| 644 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 645 | |
| 646 | armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); |
| 647 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 648 | QuantizedVector<T>(qScale, qOffset, { |
| 649 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 650 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 651 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 652 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 653 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 654 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 655 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 656 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 657 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 658 | })); |
| 659 | |
| 660 | armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); |
| 661 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 662 | QuantizedVector<T>(qScale, qOffset, { |
| 663 | 3.0f, |
| 664 | })); |
| 665 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 666 | return SimplePooling2dTestImpl<T>( |
| 667 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 668 | } |
| 669 | |
| 670 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 671 | LayerTestResult<T, 4> AsymmetricNonSquarePooling2dTestCommon( |
| 672 | armnn::IWorkloadFactory& workloadFactory, |
| 673 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 674 | float qScale = 1.0f, |
| 675 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 676 | { |
| 677 | armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3 }, armnn::GetDataType<T>()); |
| 678 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 679 | |
| 680 | armnn::Pooling2dDescriptor descriptor; |
| 681 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 682 | descriptor.m_PoolWidth = 2; |
| 683 | descriptor.m_PoolHeight = 3; |
| 684 | descriptor.m_StrideX = 2; |
| 685 | descriptor.m_StrideY = 1; |
| 686 | descriptor.m_PadLeft = 2; |
| 687 | descriptor.m_PadRight = 0; |
| 688 | descriptor.m_PadTop = 1; |
| 689 | descriptor.m_PadBottom = 2; |
| 690 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 691 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 692 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 693 | // Construct input data. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 694 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 695 | QuantizedVector<T>(qScale, qOffset, { |
| 696 | 1.0f, 3.0f, 4.0f, |
| 697 | })); |
| 698 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 699 | // These were calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 700 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 701 | QuantizedVector<T>(qScale, qOffset, { |
| 702 | 0.0f, 3.0f, 0.0f, 3.0f, |
| 703 | })); |
| 704 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 705 | return SimplePooling2dTestImpl<T>( |
| 706 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 707 | } |
| 708 | |
| 709 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 710 | LayerTestResult<T, 4> ComparePooling2dTestCommon( |
| 711 | armnn::IWorkloadFactory& workloadFactory, |
| 712 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 713 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 714 | armnn::PoolingAlgorithm poolingType, |
| 715 | float qScale = 1.0f, |
| 716 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 717 | { |
| 718 | const unsigned int inputWidth = 16; |
| 719 | const unsigned int inputHeight = 32; |
| 720 | const unsigned int channelCount = 2; |
| 721 | const unsigned int batchSize = 5; |
| 722 | |
| 723 | const unsigned int poolSize = 3; |
| 724 | const unsigned int strideX = 2; |
| 725 | const unsigned int strideY = 4; |
| 726 | const unsigned int padX = 0; |
| 727 | const unsigned int padY = 0; |
| 728 | |
| 729 | const unsigned int outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX; |
| 730 | const unsigned int outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY; |
| 731 | |
| 732 | armnn::TensorInfo inputTensorInfo; |
| 733 | armnn::TensorInfo outputTensorInfo; |
| 734 | |
| 735 | unsigned int inputShape[] = { batchSize, channelCount, inputHeight, inputWidth }; |
| 736 | unsigned int outputShape[] = { batchSize, channelCount, outputHeight, outputWidth }; |
| 737 | |
| 738 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); |
| 739 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); |
| 740 | |
| 741 | // Set quantization parameters if the requested type is a quantized type. |
| 742 | if(armnn::IsQuantizedType<T>()) |
| 743 | { |
| 744 | inputTensorInfo.SetQuantizationScale(qScale); |
| 745 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 746 | outputTensorInfo.SetQuantizationScale(qScale); |
| 747 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 748 | } |
| 749 | |
| 750 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 81715); |
| 751 | |
| 752 | LayerTestResult<T, 4> comparisonResult(outputTensorInfo); |
| 753 | |
| 754 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 755 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 756 | |
| 757 | armnn::Pooling2dQueueDescriptor data; |
| 758 | armnn::WorkloadInfo info; |
| 759 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 760 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 761 | data.m_Parameters.m_PoolType = poolingType; |
| 762 | data.m_Parameters.m_PoolWidth = poolSize; |
| 763 | data.m_Parameters.m_PoolHeight = poolSize; |
| 764 | data.m_Parameters.m_StrideX = strideX; |
| 765 | data.m_Parameters.m_StrideY = strideY; |
| 766 | data.m_Parameters.m_PadLeft = padX; |
| 767 | data.m_Parameters.m_PadRight = padX; |
| 768 | data.m_Parameters.m_PadTop = padY; |
| 769 | data.m_Parameters.m_PadBottom = padY; |
| 770 | data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 771 | |
| 772 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 773 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); |
| 774 | |
| 775 | // 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] | 776 | armnn::BackendId backend = workloadFactory.GetBackendId(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 777 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 778 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 779 | comparisonResult.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 780 | data.m_Parameters, |
| 781 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 782 | if (!comparisonResult.supported) |
| 783 | { |
| 784 | return comparisonResult; |
| 785 | } |
| 786 | |
| 787 | armnn::Pooling2dQueueDescriptor refData = data; |
| 788 | armnn::WorkloadInfo refInfo = info; |
| 789 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 790 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 791 | |
| 792 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(data, info); |
| 793 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreatePooling2d(refData, refInfo); |
| 794 | |
| 795 | outputHandleRef->Allocate(); |
| 796 | inputHandleRef->Allocate(); |
| 797 | inputHandle->Allocate(); |
| 798 | outputHandle->Allocate(); |
| 799 | |
| 800 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 801 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 802 | |
| 803 | workload->Execute(); |
| 804 | workloadRef->Execute(); |
| 805 | |
| 806 | CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); |
| 807 | CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 808 | |
| 809 | return comparisonResult; |
| 810 | } |
| 811 | |
| 812 | // |
| 813 | // Tests max pooling with the following parameters: |
| 814 | // |
| 815 | // Pooling size: 2x2 |
| 816 | // Stride: (2,2) |
| 817 | // input size: 4x4 |
| 818 | // channels: 1 |
| 819 | // batch size: 1 |
| 820 | // |
| 821 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 822 | LayerTestResult<T, 4> SimpleMaxPooling2dSize2x2Stride2x2TestCommon( |
| 823 | armnn::IWorkloadFactory& workloadFactory, |
| 824 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 825 | bool forceNoPadding, |
| 826 | float qScale = 1.0f, |
| 827 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 828 | { |
| 829 | armnn::Pooling2dDescriptor descriptor; |
| 830 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 831 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 832 | descriptor.m_StrideX = 2; |
| 833 | descriptor.m_StrideY = 2; |
| 834 | descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; |
| 835 | descriptor.m_PadTop = descriptor.m_PadBottom = 0; |
| 836 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 837 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 838 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 839 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 840 | unsigned int inputWidth = 4; |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 841 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 842 | unsigned int inputHeight = 4; |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 843 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 844 | unsigned int outputWidth = |
| 845 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 846 | descriptor.m_StrideX; |
| 847 | unsigned int outputHeight = |
| 848 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 849 | descriptor.m_StrideY; |
| 850 | unsigned int channels = 1; |
| 851 | unsigned int batchSize = 1; |
| 852 | |
| 853 | std::vector<float> inputData = { |
| 854 | 510.0f, 222.0f, 780.0f, 654.0f, |
| 855 | 141.0f, 276.0f, 15.0f, 546.0f, |
| 856 | 303.0f, 618.0f, 582.0f, 339.0f, |
| 857 | 438.0f, 564.0f, 573.0f, 402.0f |
| 858 | }; |
| 859 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 860 | // 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] | 861 | std::vector<float> expectedOutputDataWithPadding = { |
| 862 | 0.0f, 510.0f, 780.0f, 654.0f, 0.0f, |
| 863 | 0.0f, 438.0f, 618.0f, 402.0f, 0.0f |
| 864 | }; |
| 865 | |
| 866 | std::vector<float> expectedOutputDataNoPadding = { |
| 867 | 510.0f, 780.0f, |
| 868 | 618.0f, 582.0f |
| 869 | }; |
| 870 | |
| 871 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 872 | |
| 873 | // Scale and offset should match input - we're just calculating maximum values. |
| 874 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 875 | |
| 876 | // Set quantization parameters if the requested type is a quantized type. |
| 877 | if(armnn::IsQuantizedType<T>()) |
| 878 | { |
| 879 | inputTensorInfo.SetQuantizationScale(qScale); |
| 880 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 881 | outputTensorInfo.SetQuantizationScale(qScale); |
| 882 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 883 | } |
| 884 | |
| 885 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 886 | |
| 887 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 888 | forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : |
| 889 | QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); |
| 890 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 891 | return SimplePooling2dTestImpl<T>( |
| 892 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 893 | } |
| 894 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 895 | // |
| 896 | // Tests max pooling with the following parameters: |
| 897 | // |
| 898 | // Pooling size: 3x2 |
| 899 | // Stride: (2,2) |
| 900 | // input size: 3x2 |
| 901 | // channels: 1 |
| 902 | // batch size: 1 |
| 903 | // |
| 904 | template<typename T> |
| 905 | LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon( |
| 906 | armnn::IWorkloadFactory& workloadFactory, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 907 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 908 | bool forceNoPadding, |
| 909 | float qScale = 1.0f, |
| 910 | int32_t qOffset = 0) |
| 911 | { |
| 912 | armnn::Pooling2dDescriptor descriptor; |
| 913 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 914 | descriptor.m_PoolWidth = 3; |
| 915 | descriptor.m_PoolHeight = 2; |
| 916 | descriptor.m_StrideX = 2; |
| 917 | descriptor.m_StrideY = 2; |
| 918 | descriptor.m_PadLeft = (forceNoPadding) ? 0 : 1; |
| 919 | descriptor.m_PadRight = descriptor.m_PadLeft; |
| 920 | descriptor.m_PadTop = 0; |
| 921 | descriptor.m_PadBottom = 0; |
| 922 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 923 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 924 | |
| 925 | unsigned int inputWidth = 3; |
| 926 | unsigned int inputHeight = 2; |
| 927 | unsigned int outputWidth = |
| 928 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 929 | descriptor.m_StrideX; |
| 930 | unsigned int outputHeight = |
| 931 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 932 | descriptor.m_StrideY; |
| 933 | unsigned int channels = 1; |
| 934 | unsigned int batchSize = 1; |
| 935 | |
| 936 | std::vector<float> inputData = { |
| 937 | 3.0f, 6.0f, 9.0f, |
| 938 | 12.0f, 15.0f, 18.0f, |
| 939 | }; |
| 940 | |
| 941 | std::vector<float> expectedOutputDataWithPadding = { |
| 942 | 6.0f, 8.0f, |
| 943 | }; |
| 944 | |
| 945 | std::vector<float> expectedOutputDataNoPadding = { |
| 946 | 10.5f, |
| 947 | }; |
| 948 | |
| 949 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 950 | |
| 951 | // Scale and offset should match input - we're just calculating average values. |
| 952 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, 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, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 964 | |
| 965 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 966 | forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : |
| 967 | QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); |
| 968 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 969 | return SimplePooling2dTestImpl<T>( |
| 970 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 971 | } |
| 972 | |
| 973 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 974 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 975 | LayerTestResult<T, 4> IgnorePaddingSimpleMaxPooling2dTestCommon( |
| 976 | armnn::IWorkloadFactory& workloadFactory, |
| 977 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 978 | float qScale = 1.0f, |
| 979 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 980 | { |
| 981 | armnn::Pooling2dDescriptor descriptor; |
| 982 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 983 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 984 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 985 | descriptor.m_PadLeft = 1; |
| 986 | descriptor.m_PadRight = 1; |
| 987 | descriptor.m_PadTop = 1; |
| 988 | descriptor.m_PadBottom = 1; |
| 989 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 990 | |
| 991 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 992 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 993 | |
| 994 | // Set quantization parameters if the requested type is a quantized type. |
| 995 | if(armnn::IsQuantizedType<T>()) |
| 996 | { |
| 997 | inputTensorInfo.SetQuantizationScale(qScale); |
| 998 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 999 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1000 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1001 | } |
| 1002 | |
| 1003 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1004 | QuantizedVector<T>(qScale, qOffset, { |
| 1005 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 1006 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 1007 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 1008 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 1009 | })); |
| 1010 | |
| 1011 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1012 | QuantizedVector<T>(qScale, qOffset, { |
| 1013 | -1.0f, 3.0f, 4.0f, |
| 1014 | 1.0f, 3.0f, 4.0f, |
| 1015 | 1.0f, 2.0f, -4.0f, |
| 1016 | })); |
| 1017 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1018 | return SimplePooling2dTestImpl<T>( |
| 1019 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1020 | } |
| 1021 | |
| 1022 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1023 | LayerTestResult<T, 4> IgnorePaddingMaxPooling2dSize3TestCommon( |
| 1024 | armnn::IWorkloadFactory& workloadFactory, |
| 1025 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1026 | float qScale = 1.0f, |
| 1027 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1028 | { |
| 1029 | armnn::Pooling2dDescriptor descriptor; |
| 1030 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 1031 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1032 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1033 | descriptor.m_PadLeft = 1; |
| 1034 | descriptor.m_PadRight = 1; |
| 1035 | descriptor.m_PadTop = 1; |
| 1036 | descriptor.m_PadBottom = 1; |
| 1037 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1038 | |
| 1039 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1040 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1041 | |
| 1042 | // Set quantization parameters if the requested type is a quantized type. |
| 1043 | if(armnn::IsQuantizedType<T>()) |
| 1044 | { |
| 1045 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1046 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1047 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1048 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1049 | } |
| 1050 | |
| 1051 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1052 | QuantizedVector<T>(qScale, qOffset, { |
| 1053 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 1054 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 1055 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 1056 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 1057 | })); |
| 1058 | |
| 1059 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1060 | QuantizedVector<T>(qScale, qOffset, { |
| 1061 | -1.0f, 3.0f, 4.0f, 4.0f, |
| 1062 | 2.0f, 3.0f, 4.0f, 4.0f, |
| 1063 | 2.0f, 3.0f, 4.0f, 4.0f, |
| 1064 | 2.0f, 2.0f, 2.0f, -3.0f, |
| 1065 | })); |
| 1066 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1067 | return SimplePooling2dTestImpl<T>( |
| 1068 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1069 | } |
| 1070 | |
| 1071 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1072 | LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dTestCommon( |
| 1073 | armnn::IWorkloadFactory& workloadFactory, |
| 1074 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1075 | float qScale = 1.0f, |
| 1076 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1077 | { |
| 1078 | armnn::Pooling2dDescriptor descriptor; |
| 1079 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1080 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 1081 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1082 | descriptor.m_PadLeft = 1; |
| 1083 | descriptor.m_PadRight = 1; |
| 1084 | descriptor.m_PadTop = 1; |
| 1085 | descriptor.m_PadBottom = 1; |
| 1086 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1087 | |
| 1088 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1089 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 1090 | |
| 1091 | // Set quantization parameters if the requested type is a quantized type. |
| 1092 | if(armnn::IsQuantizedType<T>()) |
| 1093 | { |
| 1094 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1095 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1096 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1097 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1098 | } |
| 1099 | |
| 1100 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1101 | QuantizedVector<T>(qScale, qOffset, { |
| 1102 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1103 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1104 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1105 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1106 | })); |
| 1107 | |
| 1108 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1109 | QuantizedVector<T>(qScale, qOffset, { |
| 1110 | 3.0f, 13.0f, 10.0f, |
| 1111 | 6.0f, 26.0f, 20.0f, |
| 1112 | 3.0f, 13.0f, 10.0f, |
| 1113 | })); |
| 1114 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1115 | return SimplePooling2dTestImpl<T>( |
| 1116 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1117 | } |
| 1118 | |
| 1119 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1120 | LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon( |
| 1121 | armnn::IWorkloadFactory& workloadFactory, |
| 1122 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1123 | float qScale = 1.0f, |
| 1124 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1125 | { |
| 1126 | armnn::Pooling2dDescriptor descriptor; |
| 1127 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1128 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1129 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1130 | descriptor.m_PadLeft = 0; |
| 1131 | descriptor.m_PadRight = 0; |
| 1132 | descriptor.m_PadTop = 0; |
| 1133 | descriptor.m_PadBottom = 0; |
| 1134 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1135 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling; |
| 1136 | |
| 1137 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4}, armnn::GetDataType<T>()); |
| 1138 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 1139 | |
| 1140 | // Set quantization parameters if the requested type is a quantized type. |
| 1141 | if(armnn::IsQuantizedType<T>()) |
| 1142 | { |
| 1143 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1144 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1145 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1146 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1147 | } |
| 1148 | |
| 1149 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1150 | QuantizedVector<T>(qScale, qOffset, { |
| 1151 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1152 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1153 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1154 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1155 | })); |
| 1156 | |
| 1157 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1158 | QuantizedVector<T>(qScale, qOffset, { |
| 1159 | 2.0f, 3.5f, |
| 1160 | 2.0f, 3.5f |
| 1161 | })); |
| 1162 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1163 | return SimplePooling2dTestImpl<T>( |
| 1164 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1165 | } |
| 1166 | |
| 1167 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1168 | LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3TestCommon( |
| 1169 | armnn::IWorkloadFactory& workloadFactory, |
| 1170 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1171 | float qScale = 1.0f, |
| 1172 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1173 | { |
| 1174 | armnn::Pooling2dDescriptor descriptor; |
| 1175 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1176 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1177 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1178 | descriptor.m_PadLeft = 1; |
| 1179 | descriptor.m_PadRight = 1; |
| 1180 | descriptor.m_PadTop = 1; |
| 1181 | descriptor.m_PadBottom = 1; |
| 1182 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1183 | |
| 1184 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1185 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1186 | |
| 1187 | // Set quantization parameters if the requested type is a quantized type. |
| 1188 | if(armnn::IsQuantizedType<T>()) |
| 1189 | { |
| 1190 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1191 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1192 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1193 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1194 | } |
| 1195 | |
| 1196 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1197 | QuantizedVector<T>(qScale, qOffset, { |
| 1198 | 9.0f, 27.0f, 18.0f, 36.0f, |
| 1199 | 18.0f, 9.0f, 18.0f, 9.0f, |
| 1200 | 27.0f, 18.0f, 9.0f, 27.0f, |
| 1201 | 9.0f, 27.0f, 9.0f, 18.0f, |
| 1202 | })); |
| 1203 | |
| 1204 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1205 | QuantizedVector<T>(qScale, qOffset, { |
| 1206 | 7.0f, 11.0f, 13.0f, 9.0f, |
| 1207 | 12.0f, 17.0f, 19.0f, 13.0f, |
| 1208 | 12.0f, 16.0f, 16.0f, 10.0f, |
| 1209 | 9.0f, 11.0f, 12.0f, 7.0f, |
| 1210 | })); |
| 1211 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1212 | return SimplePooling2dTestImpl<T>( |
| 1213 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1214 | } |
| 1215 | |
| 1216 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1217 | LayerTestResult<T, 4> IgnorePaddingSimpleL2Pooling2dTestCommon( |
| 1218 | armnn::IWorkloadFactory& workloadFactory, |
| 1219 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1220 | float qScale = 1.0f, |
| 1221 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1222 | { |
| 1223 | armnn::Pooling2dDescriptor descriptor; |
| 1224 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 1225 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 1226 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1227 | descriptor.m_PadLeft = 1; |
| 1228 | descriptor.m_PadRight = 1; |
| 1229 | descriptor.m_PadTop = 1; |
| 1230 | descriptor.m_PadBottom = 1; |
| 1231 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1232 | |
| 1233 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1234 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 1235 | |
| 1236 | // Set quantization parameters if the requested type is a quantized type. |
| 1237 | if(armnn::IsQuantizedType<T>()) |
| 1238 | { |
| 1239 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1240 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1241 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1242 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1243 | } |
| 1244 | |
| 1245 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1246 | QuantizedVector<T>(qScale, qOffset, { |
| 1247 | 2.0f, 4.0f, 8.0f, 16.0f, |
| 1248 | 4.0f, 2.0f, 2.0f, 4.0f, |
| 1249 | 8.0f, 2.0f, 4.0f, 2.0f, |
| 1250 | 16.0f, 2.0f, 2.0f, 8.0f, |
| 1251 | })); |
| 1252 | |
| 1253 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1254 | QuantizedVector<T>(qScale, qOffset, { |
| 1255 | 1.0f, 4.4721f, 8.0f, |
| 1256 | 4.4721f, 2.6457f, 2.236f, |
| 1257 | 8.0f, 1.4142f, 4.0f, |
| 1258 | })); |
| 1259 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1260 | return SimplePooling2dTestImpl<T>( |
| 1261 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1262 | } |
| 1263 | |
| 1264 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1265 | LayerTestResult<T, 4> IgnorePaddingL2Pooling2dSize3TestCommon( |
| 1266 | armnn::IWorkloadFactory& workloadFactory, |
| 1267 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1268 | float qScale = 1.0f, |
| 1269 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1270 | { |
| 1271 | armnn::Pooling2dDescriptor descriptor; |
| 1272 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 1273 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1274 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1275 | descriptor.m_PadLeft = 1; |
| 1276 | descriptor.m_PadRight = 1; |
| 1277 | descriptor.m_PadTop = 1; |
| 1278 | descriptor.m_PadBottom = 1; |
| 1279 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1280 | |
| 1281 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1282 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1283 | |
| 1284 | // Set quantization parameters if the requested type is a quantized type. |
| 1285 | if(armnn::IsQuantizedType<T>()) |
| 1286 | { |
| 1287 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1288 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1289 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1290 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1291 | } |
| 1292 | |
| 1293 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1294 | QuantizedVector<T>(qScale, qOffset, { |
| 1295 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1296 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1297 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1298 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1299 | })); |
| 1300 | |
| 1301 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1302 | QuantizedVector<T>(qScale, qOffset, { |
| 1303 | 1.0540f, 1.7638f, 2.5385f, 2.3570f, |
| 1304 | 1.2909f, 2.1602f, 3.1091f, 2.8867f, |
| 1305 | 1.2909f, 2.1602f, 3.1091f, 2.8867f, |
| 1306 | 1.0540f, 1.7638f, 2.5385f, 2.3570f, |
| 1307 | })); |
| 1308 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1309 | return SimplePooling2dTestImpl<T>( |
| 1310 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1311 | } |