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 | |
Matteo Martincigh | 2135015 | 2018-11-28 16:22:22 +0000 | [diff] [blame] | 23 | #include <DataLayoutIndexed.hpp> |
| 24 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 25 | #include <boost/numeric/conversion/cast.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 26 | |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 27 | #include <algorithm> |
| 28 | #include <string> |
| 29 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 30 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 31 | LayerTestResult<T, 4> SimplePooling2dTestImpl( |
| 32 | armnn::IWorkloadFactory& workloadFactory, |
| 33 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 34 | armnn::Pooling2dDescriptor descriptor, |
| 35 | float qScale, |
| 36 | int32_t qOffset, |
| 37 | const boost::multi_array<T, 4>& input, |
| 38 | const boost::multi_array<T, 4>& outputExpected) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 39 | { |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 40 | const armnn::DataLayout dataLayout = descriptor.m_DataLayout; |
Matteo Martincigh | 2135015 | 2018-11-28 16:22:22 +0000 | [diff] [blame] | 41 | const armnnUtils::DataLayoutIndexed dimensionIndices = dataLayout; |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 42 | auto heightIndex = dimensionIndices.GetHeightIndex(); |
| 43 | auto widthIndex = dimensionIndices.GetWidthIndex(); |
| 44 | auto channelsIndex = dimensionIndices.GetChannelsIndex(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 45 | |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 46 | unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[heightIndex]); |
| 47 | unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[widthIndex]); |
| 48 | unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[channelsIndex]); |
| 49 | unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); |
| 50 | |
| 51 | unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[heightIndex]); |
| 52 | unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[widthIndex]); |
| 53 | unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[channelsIndex]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 54 | unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); |
| 55 | |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame] | 56 | armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<T>(inputBatchSize, inputChannels, inputHeight, |
| 57 | inputWidth, dataLayout); |
| 58 | armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(outputBatchSize, outputChannels, outputHeight, |
| 59 | outputWidth, dataLayout); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 60 | |
| 61 | // Set quantization parameters if the requested type is a quantized type. |
| 62 | if(armnn::IsQuantizedType<T>()) |
| 63 | { |
| 64 | inputTensorInfo.SetQuantizationScale(qScale); |
| 65 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 66 | outputTensorInfo.SetQuantizationScale(qScale); |
| 67 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 68 | } |
| 69 | |
| 70 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 71 | |
| 72 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 73 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 74 | |
| 75 | armnn::Pooling2dQueueDescriptor queueDescriptor; |
| 76 | queueDescriptor.m_Parameters = descriptor; |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 77 | queueDescriptor.m_Parameters.m_DataLayout = dataLayout; |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 78 | |
| 79 | armnn::WorkloadInfo workloadInfo; |
| 80 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 81 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 82 | |
| 83 | // 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] | 84 | armnn::BackendId backend = workloadFactory.GetBackendId(); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 85 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 86 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 87 | result.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 88 | queueDescriptor.m_Parameters, |
| 89 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 90 | if (!result.supported) |
| 91 | { |
| 92 | return result; |
| 93 | } |
| 94 | |
| 95 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); |
| 96 | |
| 97 | inputHandle->Allocate(); |
| 98 | outputHandle->Allocate(); |
| 99 | |
| 100 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 101 | |
| 102 | workload->Execute(); |
| 103 | |
| 104 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 105 | |
| 106 | result.outputExpected = outputExpected; |
| 107 | |
| 108 | return result; |
| 109 | } |
| 110 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 111 | // |
| 112 | // Tests max pooling with the following parameters: |
| 113 | // |
| 114 | // Pooling size: 3x3 |
| 115 | // Stride: (2,4) |
| 116 | // input size: 8x13 |
| 117 | // channels: 2 |
| 118 | // batch size: 2 |
| 119 | // |
| 120 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 121 | LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon( |
| 122 | armnn::IWorkloadFactory& workloadFactory, |
| 123 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 124 | bool forceNoPadding, |
| 125 | float qScale = 1.0f, |
| 126 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 127 | { |
| 128 | armnn::Pooling2dDescriptor descriptor; |
| 129 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 130 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 131 | descriptor.m_StrideX = 2; |
| 132 | descriptor.m_StrideY = 4; |
| 133 | // forceNoPadding is mainly used for compatibility with ARM Compute. |
| 134 | // As of 16/05/2017, it errors if padX or padY are equal to or greater than the pool size. |
| 135 | descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; |
| 136 | descriptor.m_PadTop = descriptor.m_PadBottom = 0; |
| 137 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 138 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 139 | |
| 140 | unsigned int inputWidth = 8; |
| 141 | unsigned int inputHeight = 13; |
| 142 | unsigned int outputWidth = |
| 143 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 144 | descriptor.m_StrideX; |
| 145 | unsigned int outputHeight = |
| 146 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 147 | descriptor.m_StrideY; |
| 148 | unsigned int channels = 2; |
| 149 | unsigned int batchSize = 2; |
| 150 | |
| 151 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 152 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 153 | |
| 154 | // Set quantization parameters if the requested type is a quantized type. |
| 155 | if(armnn::IsQuantizedType<T>()) |
| 156 | { |
| 157 | inputTensorInfo.SetQuantizationScale(qScale); |
| 158 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 159 | outputTensorInfo.SetQuantizationScale(qScale); |
| 160 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 161 | } |
| 162 | |
| 163 | std::vector<float> singleChannelData({ |
| 164 | 0.0f, 4.0f, 8.0f, 1.0f, 6.0f, 4.0f, 5.0f, 8.0f, |
| 165 | 1.0f, 1.0f, 6.0f, 0.0f, 3.0f, 7.0f, 4.0f, 7.0f, |
| 166 | 8.0f, 5.0f, 0.0f, 0.0f, 8.0f, 3.0f, 4.0f, 3.0f, |
| 167 | 8.0f, 2.0f, 5.0f, 4.0f, 1.0f, 9.0f, 2.0f, 0.0f, |
| 168 | 5.0f, 4.0f, 5.0f, 0.0f, 0.0f, 0.0f, 7.0f, 2.0f, |
| 169 | 1.0f, 2.0f, 6.0f, 2.0f, 7.0f, 9.0f, 5.0f, 2.0f, |
| 170 | 9.0f, 7.0f, 3.0f, 1.0f, 3.0f, 4.0f, 8.0f, 3.0f, |
| 171 | 1.0f, 0.0f, 0.0f, 5.0f, 5.0f, 4.0f, 2.0f, 0.0f, |
| 172 | 6.0f, 4.0f, 3.0f, 6.0f, 9.0f, 5.0f, 5.0f, 6.0f, |
| 173 | 8.0f, 7.0f, 9.0f, 6.0f, 1.0f, 4.0f, 1.0f, 9.0f, |
| 174 | 7.0f, 1.0f, 9.0f, 2.0f, 9.0f, 9.0f, 8.0f, 1.0f, |
| 175 | 4.0f, 4.0f, 5.0f, 9.0f, 2.0f, 6.0f, 6.0f, 4.0f, |
| 176 | 3.0f, 5.0f, 4.0f, 0.0f, 1.0f, 5.0f, 9.0f, 7.0f, |
| 177 | }); |
| 178 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 179 | // Constructs input data. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 180 | std::vector<float> inputData; |
| 181 | auto negator = [](float f) { return -f; }; |
| 182 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 183 | // 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] | 184 | inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); |
| 185 | std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); |
| 186 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 187 | // Second image (same as first image). |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 188 | inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); |
| 189 | std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); |
| 190 | |
| 191 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 192 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 193 | // These were calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 194 | auto shape(GetTensorShapeAsArray<4>(outputTensorInfo)); |
| 195 | boost::multi_array<T, 4> outputExpected(shape); |
| 196 | if (forceNoPadding) |
| 197 | { |
| 198 | outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 199 | QuantizedVector<T>(qScale, qOffset, { |
| 200 | 8.0f, 8.0f, 8.0f, |
| 201 | 9.0f, 7.0f, 9.0f, |
| 202 | 9.0f, 9.0f, 9.0f, |
| 203 | |
| 204 | 0.0f, 0.0f, -3.0f, |
| 205 | -1.0f, 0.0f, 0.0f, |
| 206 | -1.0f, -1.0f, -1.0f, |
| 207 | |
| 208 | 8.0f, 8.0f, 8.0f, |
| 209 | 9.0f, 7.0f, 9.0f, |
| 210 | 9.0f, 9.0f, 9.0f, |
| 211 | |
| 212 | 0.0f, 0.0f, -3.0f, |
| 213 | -1.0f, 0.0f, 0.0f, |
| 214 | -1.0f, -1.0f, -1.0f |
| 215 | })); |
| 216 | } |
| 217 | else |
| 218 | { |
| 219 | outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 220 | QuantizedVector<T>(qScale, qOffset, { |
| 221 | 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, |
| 222 | 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, |
| 223 | 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, |
| 224 | |
| 225 | 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, |
| 226 | 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 227 | 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f, |
| 228 | |
| 229 | 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, |
| 230 | 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, |
| 231 | 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, |
| 232 | |
| 233 | 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, |
| 234 | 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 235 | 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f |
| 236 | })); |
| 237 | } |
| 238 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 239 | return SimplePooling2dTestImpl<T>( |
| 240 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 241 | } |
| 242 | |
| 243 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 244 | LayerTestResult<T, 4> SimpleMaxPooling2dTestCommon( |
| 245 | armnn::IWorkloadFactory& workloadFactory, |
| 246 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 247 | const armnn::DataLayout dataLayout = armnn::DataLayout::NCHW, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 248 | float qScale = 1.0f, |
| 249 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 250 | { |
| 251 | armnn::Pooling2dDescriptor descriptor; |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 252 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 253 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 254 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 255 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 256 | descriptor.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 257 | |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame] | 258 | armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 4, 4, dataLayout); |
| 259 | armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 2, 2, dataLayout); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 260 | |
| 261 | // Set quantization parameters if the requested type is a quantized type. |
| 262 | if(armnn::IsQuantizedType<T>()) |
| 263 | { |
| 264 | inputTensorInfo.SetQuantizationScale(qScale); |
| 265 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 266 | outputTensorInfo.SetQuantizationScale(qScale); |
| 267 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 268 | } |
| 269 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 270 | std::vector<T> inputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 271 | QuantizedVector<T>(qScale, qOffset, { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 272 | 1.0f, 2.0f, 5.0f, 6.0f, |
| 273 | 3.0f, 4.0f, 7.0f, 8.0f, |
| 274 | 9.0f, 10.0f, 13.0f, 14.0f, |
| 275 | 11.0f, 12.0f, 15.0f, 16.0f, |
| 276 | |
| 277 | 17.0f, 18.0f, 21.0f, 22.0f, |
| 278 | 19.0f, 20.0f, 23.0f, 24.0f, |
| 279 | 25.0f, 26.0f, 29.0f, 30.0f, |
| 280 | 27.0f, 28.0f, 31.0f, 32.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 281 | })); |
| 282 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 283 | std::vector<T> outputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 284 | QuantizedVector<T>(qScale, qOffset, { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 285 | 4.0f, 8.0f, |
| 286 | 12.0f, 16.0f, |
| 287 | |
| 288 | 20.0f, 24.0f, |
| 289 | 28.0f, 32.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 290 | })); |
| 291 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 292 | const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 293 | if (dataLayout == armnn::DataLayout::NHWC) |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 294 | { |
| 295 | std::vector<T> tmp(inputData.size()); |
| 296 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); |
| 297 | inputData = tmp; |
| 298 | |
| 299 | std::vector<T> tmp1(outputData.size()); |
| 300 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); |
| 301 | outputData = tmp1; |
| 302 | } |
| 303 | |
| 304 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 305 | |
| 306 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); |
| 307 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 308 | return SimplePooling2dTestImpl<T>( |
| 309 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 310 | } |
| 311 | |
| 312 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 313 | LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon( |
| 314 | armnn::IWorkloadFactory& workloadFactory, |
| 315 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 316 | armnn::DataLayout dataLayout = armnn::DataLayout::NCHW, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 317 | float qScale = 1.0f, |
| 318 | int32_t qOffset = 0) |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 319 | { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 320 | armnn::Pooling2dDescriptor descriptor; |
| 321 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 322 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 323 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 324 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 325 | descriptor.m_DataLayout = dataLayout; |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 326 | |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame] | 327 | armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 4, 4, dataLayout); |
| 328 | armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 2, 2, dataLayout); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 329 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 330 | // Set quantization parameters if the requested type is a quantized type. |
| 331 | if(armnn::IsQuantizedType<T>()) |
| 332 | { |
| 333 | inputTensorInfo.SetQuantizationScale(qScale); |
| 334 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 335 | outputTensorInfo.SetQuantizationScale(qScale); |
| 336 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 337 | } |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 338 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 339 | std::vector<T> inputData( |
| 340 | QuantizedVector<T>(qScale, qOffset, { |
| 341 | 2.0f, 2.0f, 6.0f, 6.0f, |
| 342 | 4.0f, 4.0f, 8.0f, 8.0f, |
| 343 | 10.0f, 12.0f, 14.0f, 16.0f, |
| 344 | 10.0f, 12.0f, 16.0f, 14.0f, |
| 345 | |
| 346 | 18.0f, 20.0f, 24.0f, 22.0f, |
| 347 | 20.0f, 18.0f, 22.0f, 24.0f, |
| 348 | 26.0f, 28.0f, 0.0f, 0.0f, |
| 349 | 26.0f, 28.0f, 0.0f, 0.0f, |
| 350 | })); |
| 351 | |
| 352 | std::vector<T> outputData( |
| 353 | QuantizedVector<T>(qScale, qOffset, { |
| 354 | 3.0f, 7.0f, |
| 355 | 11.0f, 15.0f, |
| 356 | |
| 357 | 19.0f, 23.0f, |
| 358 | 27.0f, 0.0f, |
| 359 | })); |
| 360 | |
| 361 | const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 362 | if (dataLayout == armnn::DataLayout::NHWC) |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 363 | { |
| 364 | std::vector<T> tmp(inputData.size()); |
| 365 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); |
| 366 | inputData = tmp; |
| 367 | |
| 368 | std::vector<T> tmp1(outputData.size()); |
| 369 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); |
| 370 | outputData = tmp1; |
| 371 | } |
| 372 | |
| 373 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 374 | |
| 375 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); |
| 376 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 377 | return SimplePooling2dTestImpl<T>( |
| 378 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 379 | } |
| 380 | |
| 381 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 382 | LayerTestResult<T, 4> LargeTensorsAveragePooling2dTestCommon( |
| 383 | armnn::IWorkloadFactory& workloadFactory, |
| 384 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 385 | float qScale = 1.0f, |
| 386 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 387 | { |
| 388 | armnn::Pooling2dDescriptor descriptor; |
| 389 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 390 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 100; |
| 391 | descriptor.m_StrideX = descriptor.m_StrideY = 5; |
| 392 | descriptor.m_PadLeft = 50; |
| 393 | descriptor.m_PadRight = 50; |
| 394 | descriptor.m_PadTop = 50; |
| 395 | descriptor.m_PadBottom = 50; |
| 396 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 397 | |
| 398 | armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60 }, armnn::GetDataType<T>()); |
| 399 | armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13 }, armnn::GetDataType<T>()); |
| 400 | |
| 401 | // Set quantization parameters if the requested type is a quantized type. |
| 402 | if(armnn::IsQuantizedType<T>()) |
| 403 | { |
| 404 | inputTensorInfo.SetQuantizationScale(qScale); |
| 405 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 406 | outputTensorInfo.SetQuantizationScale(qScale); |
| 407 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 408 | } |
| 409 | |
| 410 | std::vector<T> inputVec; |
| 411 | |
| 412 | for (unsigned int i = 0 ; i < inputTensorInfo.GetShape().GetNumElements(); ++i) |
| 413 | { |
| 414 | inputVec.push_back(1); |
| 415 | } |
| 416 | |
| 417 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputVec); |
| 418 | |
| 419 | std::vector<T> outputVec; |
| 420 | |
| 421 | for (unsigned int i = 0 ; i < outputTensorInfo.GetShape().GetNumElements(); ++i) |
| 422 | { |
| 423 | outputVec.push_back(1); |
| 424 | } |
| 425 | |
| 426 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputVec); |
| 427 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 428 | return SimplePooling2dTestImpl<T>( |
| 429 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 430 | } |
| 431 | |
| 432 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 433 | LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon( |
| 434 | armnn::IWorkloadFactory& workloadFactory, |
| 435 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 436 | armnn::DataLayout dataLayout = armnn::DataLayout::NCHW, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 437 | float qScale = 1.0f, |
| 438 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 439 | { |
| 440 | armnn::Pooling2dDescriptor descriptor; |
| 441 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 442 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 443 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 444 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 445 | descriptor.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 446 | |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame] | 447 | armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 4, 4, dataLayout); |
| 448 | armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(1, 2, 2, 2, dataLayout); |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 449 | |
| 450 | std::vector<T> inputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 451 | QuantizedVector<T>(qScale, qOffset, { |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 452 | 1.0f, 7.0f, 5.0f, 5.0f, |
| 453 | 1.0f, 7.0f, 5.0f, 5.0f, |
| 454 | 3.0f, 3.0f, 1.0f, 1.0f, |
| 455 | 3.0f, 3.0f, 1.0f, 1.0f, |
| 456 | |
| 457 | 1.0f, 7.0f, 0.0f, 0.0f, |
| 458 | 1.0f, 7.0f, 2.0f, 0.0f, |
| 459 | 0.0f, 2.0f, 1.0f, 1.0f, |
| 460 | 0.0f, 0.0f, 1.0f, 1.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 461 | })); |
| 462 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 463 | std::vector<T> outputData( |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 464 | QuantizedVector<T>(qScale, qOffset, { |
| 465 | 5.0f, 5.0f, |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 466 | 3.0f, 1.0f, |
| 467 | |
| 468 | 5.0f, 1.0f, |
| 469 | 1.0f, 1.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 470 | })); |
| 471 | |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 472 | const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; |
Matthew Bentham | 8800c00 | 2018-11-19 13:19:28 +0000 | [diff] [blame] | 473 | if (dataLayout == armnn::DataLayout::NHWC) |
James Conroy | 45a9b77 | 2018-10-31 11:47:53 +0000 | [diff] [blame] | 474 | { |
| 475 | std::vector<T> tmp(inputData.size()); |
| 476 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); |
| 477 | inputData = tmp; |
| 478 | |
| 479 | std::vector<T> tmp1(outputData.size()); |
| 480 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); |
| 481 | outputData = tmp1; |
| 482 | } |
| 483 | |
| 484 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 485 | |
| 486 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); |
| 487 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 488 | return SimplePooling2dTestImpl<T>( |
| 489 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 490 | } |
| 491 | |
| 492 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 493 | LayerTestResult<T, 4> L2Pooling2dSize3Stride1TestCommon( |
| 494 | armnn::IWorkloadFactory& workloadFactory, |
| 495 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 496 | float qScale = 1.0f, |
| 497 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 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 = 1; |
| 503 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 504 | |
| 505 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 506 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 507 | QuantizedVector<T>(qScale, qOffset, { |
| 508 | 2.0f, 1.0f, 5.0f, 2.0f, |
| 509 | 1.0f, 2.0f, 2.0f, 1.0f, |
| 510 | 5.0f, 4.0f, 1.0f, 5.0f, |
| 511 | 2.0f, 1.0f, 5.0f, 2.0f, |
| 512 | })); |
| 513 | |
| 514 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 515 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 516 | QuantizedVector<T>(qScale, qOffset, { |
| 517 | 3.0f, 3.0f, |
| 518 | 3.0f, 3.0f, |
| 519 | })); |
| 520 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 521 | return SimplePooling2dTestImpl<T>( |
| 522 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 523 | } |
| 524 | |
| 525 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 526 | LayerTestResult<T, 4> L2Pooling2dSize3Stride3TestCommon( |
| 527 | armnn::IWorkloadFactory& workloadFactory, |
| 528 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 529 | float qScale = 1.0f, |
| 530 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 531 | { |
| 532 | armnn::Pooling2dDescriptor descriptor; |
| 533 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 534 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 535 | descriptor.m_StrideX = descriptor.m_StrideY = 3; |
| 536 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 537 | |
| 538 | armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); |
| 539 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 540 | QuantizedVector<T>(qScale, qOffset, { |
| 541 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 542 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 543 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 544 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 545 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 546 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 547 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 548 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 549 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 550 | })); |
| 551 | |
| 552 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 553 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 554 | QuantizedVector<T>(qScale, qOffset, { |
| 555 | 3.0f, 3.0f, 3.0f, |
| 556 | 3.0f, 3.0f, 3.0f, |
| 557 | 3.0f, 3.0f, 3.0f, |
| 558 | })); |
| 559 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 560 | return SimplePooling2dTestImpl<T>( |
| 561 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 562 | } |
| 563 | |
| 564 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 565 | LayerTestResult<T, 4> L2Pooling2dSize3Stride4TestCommon( |
| 566 | armnn::IWorkloadFactory& workloadFactory, |
| 567 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 568 | float qScale = 1.0f, |
| 569 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 570 | { |
| 571 | armnn::Pooling2dDescriptor descriptor; |
| 572 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 573 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 574 | descriptor.m_StrideX = descriptor.m_StrideY = 4; |
| 575 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 576 | |
| 577 | armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); |
| 578 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 579 | QuantizedVector<T>(qScale, qOffset, { |
| 580 | 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, |
| 581 | 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, |
| 582 | 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, |
| 583 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 584 | 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, |
| 585 | 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, |
| 586 | 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, |
| 587 | })); |
| 588 | |
| 589 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 590 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 591 | QuantizedVector<T>(qScale, qOffset, { |
| 592 | 3.0f, 3.0f, |
| 593 | 3.0f, 3.0f, |
| 594 | })); |
| 595 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 596 | return SimplePooling2dTestImpl<T>( |
| 597 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 598 | } |
| 599 | |
| 600 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 601 | LayerTestResult<T, 4> L2Pooling2dSize7TestCommon( |
| 602 | armnn::IWorkloadFactory& workloadFactory, |
| 603 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 604 | float qScale = 1.0f, |
| 605 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 606 | { |
| 607 | armnn::Pooling2dDescriptor descriptor; |
| 608 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 609 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 7; |
| 610 | descriptor.m_StrideX = descriptor.m_StrideY = 7; |
| 611 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 612 | |
| 613 | armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); |
| 614 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 615 | QuantizedVector<T>(qScale, qOffset, { |
| 616 | 1.0f, 0.0f, 2.0f, 0.0f, 3.0f, 0.0f, 4.0f, |
| 617 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 618 | 0.0f, 5.0f, 0.0f, 6.0f, 0.0f, 7.0f, 0.0f, |
| 619 | 8.0f, 0.0f, 9.0f, 0.0f, 10.0f, 0.0f, 5.0f, |
| 620 | 0.0f, 5.0f, 0.0f, 2.0f, 0.0f, 1.0f, 1.0f, |
| 621 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 622 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 623 | })); |
| 624 | |
| 625 | armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); |
| 626 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 627 | QuantizedVector<T>(qScale, qOffset, { |
| 628 | 3.0f, |
| 629 | })); |
| 630 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 631 | return SimplePooling2dTestImpl<T>( |
| 632 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 633 | } |
| 634 | |
| 635 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 636 | LayerTestResult<T, 4> L2Pooling2dSize9TestCommon( |
| 637 | armnn::IWorkloadFactory& workloadFactory, |
| 638 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 639 | float qScale = 1.0f, |
| 640 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 641 | { |
| 642 | armnn::Pooling2dDescriptor descriptor; |
| 643 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 644 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 9; |
| 645 | descriptor.m_StrideX = descriptor.m_StrideY = 9; |
| 646 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 647 | |
| 648 | armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); |
| 649 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 650 | QuantizedVector<T>(qScale, qOffset, { |
| 651 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 652 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 653 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 654 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 655 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 656 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 657 | 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, |
| 658 | 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, |
| 659 | 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, |
| 660 | })); |
| 661 | |
| 662 | armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); |
| 663 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 664 | QuantizedVector<T>(qScale, qOffset, { |
| 665 | 3.0f, |
| 666 | })); |
| 667 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 668 | return SimplePooling2dTestImpl<T>( |
| 669 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 670 | } |
| 671 | |
| 672 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 673 | LayerTestResult<T, 4> AsymmetricNonSquarePooling2dTestCommon( |
| 674 | armnn::IWorkloadFactory& workloadFactory, |
| 675 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 676 | float qScale = 1.0f, |
| 677 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 678 | { |
| 679 | armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3 }, armnn::GetDataType<T>()); |
| 680 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 681 | |
| 682 | armnn::Pooling2dDescriptor descriptor; |
| 683 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 684 | descriptor.m_PoolWidth = 2; |
| 685 | descriptor.m_PoolHeight = 3; |
| 686 | descriptor.m_StrideX = 2; |
| 687 | descriptor.m_StrideY = 1; |
| 688 | descriptor.m_PadLeft = 2; |
| 689 | descriptor.m_PadRight = 0; |
| 690 | descriptor.m_PadTop = 1; |
| 691 | descriptor.m_PadBottom = 2; |
| 692 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 693 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 694 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 695 | // Construct input data. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 696 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 697 | QuantizedVector<T>(qScale, qOffset, { |
| 698 | 1.0f, 3.0f, 4.0f, |
| 699 | })); |
| 700 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 701 | // These were calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 702 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 703 | QuantizedVector<T>(qScale, qOffset, { |
| 704 | 0.0f, 3.0f, 0.0f, 3.0f, |
| 705 | })); |
| 706 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 707 | return SimplePooling2dTestImpl<T>( |
| 708 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 709 | } |
| 710 | |
| 711 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 712 | LayerTestResult<T, 4> ComparePooling2dTestCommon( |
| 713 | armnn::IWorkloadFactory& workloadFactory, |
| 714 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 715 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 716 | armnn::PoolingAlgorithm poolingType, |
| 717 | float qScale = 1.0f, |
| 718 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 719 | { |
| 720 | const unsigned int inputWidth = 16; |
| 721 | const unsigned int inputHeight = 32; |
| 722 | const unsigned int channelCount = 2; |
| 723 | const unsigned int batchSize = 5; |
| 724 | |
| 725 | const unsigned int poolSize = 3; |
| 726 | const unsigned int strideX = 2; |
| 727 | const unsigned int strideY = 4; |
| 728 | const unsigned int padX = 0; |
| 729 | const unsigned int padY = 0; |
| 730 | |
| 731 | const unsigned int outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX; |
| 732 | const unsigned int outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY; |
| 733 | |
| 734 | armnn::TensorInfo inputTensorInfo; |
| 735 | armnn::TensorInfo outputTensorInfo; |
| 736 | |
| 737 | unsigned int inputShape[] = { batchSize, channelCount, inputHeight, inputWidth }; |
| 738 | unsigned int outputShape[] = { batchSize, channelCount, outputHeight, outputWidth }; |
| 739 | |
| 740 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); |
| 741 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); |
| 742 | |
| 743 | // Set quantization parameters if the requested type is a quantized type. |
| 744 | if(armnn::IsQuantizedType<T>()) |
| 745 | { |
| 746 | inputTensorInfo.SetQuantizationScale(qScale); |
| 747 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 748 | outputTensorInfo.SetQuantizationScale(qScale); |
| 749 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 750 | } |
| 751 | |
| 752 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 81715); |
| 753 | |
| 754 | LayerTestResult<T, 4> comparisonResult(outputTensorInfo); |
| 755 | |
| 756 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 757 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 758 | |
| 759 | armnn::Pooling2dQueueDescriptor data; |
| 760 | armnn::WorkloadInfo info; |
| 761 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 762 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 763 | data.m_Parameters.m_PoolType = poolingType; |
| 764 | data.m_Parameters.m_PoolWidth = poolSize; |
| 765 | data.m_Parameters.m_PoolHeight = poolSize; |
| 766 | data.m_Parameters.m_StrideX = strideX; |
| 767 | data.m_Parameters.m_StrideY = strideY; |
| 768 | data.m_Parameters.m_PadLeft = padX; |
| 769 | data.m_Parameters.m_PadRight = padX; |
| 770 | data.m_Parameters.m_PadTop = padY; |
| 771 | data.m_Parameters.m_PadBottom = padY; |
| 772 | data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 773 | |
| 774 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 775 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); |
| 776 | |
| 777 | // 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] | 778 | armnn::BackendId backend = workloadFactory.GetBackendId(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 779 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 780 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 781 | comparisonResult.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 782 | data.m_Parameters, |
| 783 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 784 | if (!comparisonResult.supported) |
| 785 | { |
| 786 | return comparisonResult; |
| 787 | } |
| 788 | |
| 789 | armnn::Pooling2dQueueDescriptor refData = data; |
| 790 | armnn::WorkloadInfo refInfo = info; |
| 791 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 792 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 793 | |
| 794 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(data, info); |
| 795 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreatePooling2d(refData, refInfo); |
| 796 | |
| 797 | outputHandleRef->Allocate(); |
| 798 | inputHandleRef->Allocate(); |
| 799 | inputHandle->Allocate(); |
| 800 | outputHandle->Allocate(); |
| 801 | |
| 802 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 803 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 804 | |
| 805 | workload->Execute(); |
| 806 | workloadRef->Execute(); |
| 807 | |
| 808 | CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); |
| 809 | CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 810 | |
| 811 | return comparisonResult; |
| 812 | } |
| 813 | |
| 814 | // |
| 815 | // Tests max pooling with the following parameters: |
| 816 | // |
| 817 | // Pooling size: 2x2 |
| 818 | // Stride: (2,2) |
| 819 | // input size: 4x4 |
| 820 | // channels: 1 |
| 821 | // batch size: 1 |
| 822 | // |
| 823 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 824 | LayerTestResult<T, 4> SimpleMaxPooling2dSize2x2Stride2x2TestCommon( |
| 825 | armnn::IWorkloadFactory& workloadFactory, |
| 826 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 827 | bool forceNoPadding, |
| 828 | float qScale = 1.0f, |
| 829 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 830 | { |
| 831 | armnn::Pooling2dDescriptor descriptor; |
| 832 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 833 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 834 | descriptor.m_StrideX = 2; |
| 835 | descriptor.m_StrideY = 2; |
| 836 | descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; |
| 837 | descriptor.m_PadTop = descriptor.m_PadBottom = 0; |
| 838 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 839 | descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 840 | |
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 inputWidth = 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 inputHeight = 4; |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 845 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 846 | unsigned int outputWidth = |
| 847 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 848 | descriptor.m_StrideX; |
| 849 | unsigned int outputHeight = |
| 850 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 851 | descriptor.m_StrideY; |
| 852 | unsigned int channels = 1; |
| 853 | unsigned int batchSize = 1; |
| 854 | |
| 855 | std::vector<float> inputData = { |
| 856 | 510.0f, 222.0f, 780.0f, 654.0f, |
| 857 | 141.0f, 276.0f, 15.0f, 546.0f, |
| 858 | 303.0f, 618.0f, 582.0f, 339.0f, |
| 859 | 438.0f, 564.0f, 573.0f, 402.0f |
| 860 | }; |
| 861 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 862 | // 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] | 863 | std::vector<float> expectedOutputDataWithPadding = { |
| 864 | 0.0f, 510.0f, 780.0f, 654.0f, 0.0f, |
| 865 | 0.0f, 438.0f, 618.0f, 402.0f, 0.0f |
| 866 | }; |
| 867 | |
| 868 | std::vector<float> expectedOutputDataNoPadding = { |
| 869 | 510.0f, 780.0f, |
| 870 | 618.0f, 582.0f |
| 871 | }; |
| 872 | |
| 873 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 874 | |
| 875 | // Scale and offset should match input - we're just calculating maximum values. |
| 876 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 877 | |
| 878 | // Set quantization parameters if the requested type is a quantized type. |
| 879 | if(armnn::IsQuantizedType<T>()) |
| 880 | { |
| 881 | inputTensorInfo.SetQuantizationScale(qScale); |
| 882 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 883 | outputTensorInfo.SetQuantizationScale(qScale); |
| 884 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 885 | } |
| 886 | |
| 887 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 888 | |
| 889 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 890 | forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : |
| 891 | QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); |
| 892 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 893 | return SimplePooling2dTestImpl<T>( |
| 894 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 895 | } |
| 896 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 897 | // |
| 898 | // Tests max pooling with the following parameters: |
| 899 | // |
| 900 | // Pooling size: 3x2 |
| 901 | // Stride: (2,2) |
| 902 | // input size: 3x2 |
| 903 | // channels: 1 |
| 904 | // batch size: 1 |
| 905 | // |
| 906 | template<typename T> |
| 907 | LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon( |
| 908 | armnn::IWorkloadFactory& workloadFactory, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 909 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 910 | bool forceNoPadding, |
| 911 | float qScale = 1.0f, |
| 912 | int32_t qOffset = 0) |
| 913 | { |
| 914 | armnn::Pooling2dDescriptor descriptor; |
| 915 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 916 | descriptor.m_PoolWidth = 3; |
| 917 | descriptor.m_PoolHeight = 2; |
| 918 | descriptor.m_StrideX = 2; |
| 919 | descriptor.m_StrideY = 2; |
| 920 | descriptor.m_PadLeft = (forceNoPadding) ? 0 : 1; |
| 921 | descriptor.m_PadRight = descriptor.m_PadLeft; |
| 922 | descriptor.m_PadTop = 0; |
| 923 | descriptor.m_PadBottom = 0; |
| 924 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 925 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 926 | |
| 927 | unsigned int inputWidth = 3; |
| 928 | unsigned int inputHeight = 2; |
| 929 | unsigned int outputWidth = |
| 930 | (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / |
| 931 | descriptor.m_StrideX; |
| 932 | unsigned int outputHeight = |
| 933 | (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / |
| 934 | descriptor.m_StrideY; |
| 935 | unsigned int channels = 1; |
| 936 | unsigned int batchSize = 1; |
| 937 | |
| 938 | std::vector<float> inputData = { |
| 939 | 3.0f, 6.0f, 9.0f, |
| 940 | 12.0f, 15.0f, 18.0f, |
| 941 | }; |
| 942 | |
| 943 | std::vector<float> expectedOutputDataWithPadding = { |
| 944 | 6.0f, 8.0f, |
| 945 | }; |
| 946 | |
| 947 | std::vector<float> expectedOutputDataNoPadding = { |
| 948 | 10.5f, |
| 949 | }; |
| 950 | |
| 951 | armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); |
| 952 | |
| 953 | // Scale and offset should match input - we're just calculating average values. |
| 954 | armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); |
| 955 | |
| 956 | // Set quantization parameters if the requested type is a quantized type. |
| 957 | if(armnn::IsQuantizedType<T>()) |
| 958 | { |
| 959 | inputTensorInfo.SetQuantizationScale(qScale); |
| 960 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 961 | outputTensorInfo.SetQuantizationScale(qScale); |
| 962 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 963 | } |
| 964 | |
| 965 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 966 | |
| 967 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 968 | forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : |
| 969 | QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); |
| 970 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 971 | return SimplePooling2dTestImpl<T>( |
| 972 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 973 | } |
| 974 | |
| 975 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 976 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 977 | LayerTestResult<T, 4> IgnorePaddingSimpleMaxPooling2dTestCommon( |
| 978 | armnn::IWorkloadFactory& workloadFactory, |
| 979 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 980 | float qScale = 1.0f, |
| 981 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 982 | { |
| 983 | armnn::Pooling2dDescriptor descriptor; |
| 984 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 985 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 986 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 987 | descriptor.m_PadLeft = 1; |
| 988 | descriptor.m_PadRight = 1; |
| 989 | descriptor.m_PadTop = 1; |
| 990 | descriptor.m_PadBottom = 1; |
| 991 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 992 | |
| 993 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 994 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 995 | |
| 996 | // Set quantization parameters if the requested type is a quantized type. |
| 997 | if(armnn::IsQuantizedType<T>()) |
| 998 | { |
| 999 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1000 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1001 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1002 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1003 | } |
| 1004 | |
| 1005 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1006 | QuantizedVector<T>(qScale, qOffset, { |
| 1007 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 1008 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 1009 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 1010 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 1011 | })); |
| 1012 | |
| 1013 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1014 | QuantizedVector<T>(qScale, qOffset, { |
| 1015 | -1.0f, 3.0f, 4.0f, |
| 1016 | 1.0f, 3.0f, 4.0f, |
| 1017 | 1.0f, 2.0f, -4.0f, |
| 1018 | })); |
| 1019 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1020 | return SimplePooling2dTestImpl<T>( |
| 1021 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1022 | } |
| 1023 | |
| 1024 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1025 | LayerTestResult<T, 4> IgnorePaddingMaxPooling2dSize3TestCommon( |
| 1026 | armnn::IWorkloadFactory& workloadFactory, |
| 1027 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1028 | float qScale = 1.0f, |
| 1029 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1030 | { |
| 1031 | armnn::Pooling2dDescriptor descriptor; |
| 1032 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 1033 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1034 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1035 | descriptor.m_PadLeft = 1; |
| 1036 | descriptor.m_PadRight = 1; |
| 1037 | descriptor.m_PadTop = 1; |
| 1038 | descriptor.m_PadBottom = 1; |
| 1039 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1040 | |
| 1041 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1042 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1043 | |
| 1044 | // Set quantization parameters if the requested type is a quantized type. |
| 1045 | if(armnn::IsQuantizedType<T>()) |
| 1046 | { |
| 1047 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1048 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1049 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1050 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1051 | } |
| 1052 | |
| 1053 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1054 | QuantizedVector<T>(qScale, qOffset, { |
| 1055 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 1056 | -1.0f, -2.0f, 3.0f, 4.0f, |
| 1057 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 1058 | 1.0f, 2.0f, -3.0f, -4.0f, |
| 1059 | })); |
| 1060 | |
| 1061 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1062 | QuantizedVector<T>(qScale, qOffset, { |
| 1063 | -1.0f, 3.0f, 4.0f, 4.0f, |
| 1064 | 2.0f, 3.0f, 4.0f, 4.0f, |
| 1065 | 2.0f, 3.0f, 4.0f, 4.0f, |
| 1066 | 2.0f, 2.0f, 2.0f, -3.0f, |
| 1067 | })); |
| 1068 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1069 | return SimplePooling2dTestImpl<T>( |
| 1070 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1071 | } |
| 1072 | |
| 1073 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1074 | LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dTestCommon( |
| 1075 | armnn::IWorkloadFactory& workloadFactory, |
| 1076 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1077 | float qScale = 1.0f, |
| 1078 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1079 | { |
| 1080 | armnn::Pooling2dDescriptor descriptor; |
| 1081 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1082 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 1083 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1084 | descriptor.m_PadLeft = 1; |
| 1085 | descriptor.m_PadRight = 1; |
| 1086 | descriptor.m_PadTop = 1; |
| 1087 | descriptor.m_PadBottom = 1; |
| 1088 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1089 | |
| 1090 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1091 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 1092 | |
| 1093 | // Set quantization parameters if the requested type is a quantized type. |
| 1094 | if(armnn::IsQuantizedType<T>()) |
| 1095 | { |
| 1096 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1097 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1098 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1099 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1100 | } |
| 1101 | |
| 1102 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1103 | QuantizedVector<T>(qScale, qOffset, { |
| 1104 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1105 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1106 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1107 | 12.0f, 20.0f, 32.0f, 40.0f, |
| 1108 | })); |
| 1109 | |
| 1110 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1111 | QuantizedVector<T>(qScale, qOffset, { |
| 1112 | 3.0f, 13.0f, 10.0f, |
| 1113 | 6.0f, 26.0f, 20.0f, |
| 1114 | 3.0f, 13.0f, 10.0f, |
| 1115 | })); |
| 1116 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1117 | return SimplePooling2dTestImpl<T>( |
| 1118 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1119 | } |
| 1120 | |
| 1121 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1122 | LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon( |
| 1123 | armnn::IWorkloadFactory& workloadFactory, |
| 1124 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1125 | float qScale = 1.0f, |
| 1126 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1127 | { |
| 1128 | armnn::Pooling2dDescriptor descriptor; |
| 1129 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1130 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1131 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1132 | descriptor.m_PadLeft = 0; |
| 1133 | descriptor.m_PadRight = 0; |
| 1134 | descriptor.m_PadTop = 0; |
| 1135 | descriptor.m_PadBottom = 0; |
| 1136 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1137 | descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling; |
| 1138 | |
| 1139 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4}, armnn::GetDataType<T>()); |
| 1140 | armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); |
| 1141 | |
| 1142 | // Set quantization parameters if the requested type is a quantized type. |
| 1143 | if(armnn::IsQuantizedType<T>()) |
| 1144 | { |
| 1145 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1146 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1147 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1148 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1149 | } |
| 1150 | |
| 1151 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1152 | QuantizedVector<T>(qScale, qOffset, { |
| 1153 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1154 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1155 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1156 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1157 | })); |
| 1158 | |
| 1159 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1160 | QuantizedVector<T>(qScale, qOffset, { |
| 1161 | 2.0f, 3.5f, |
| 1162 | 2.0f, 3.5f |
| 1163 | })); |
| 1164 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1165 | return SimplePooling2dTestImpl<T>( |
| 1166 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1167 | } |
| 1168 | |
| 1169 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1170 | LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3TestCommon( |
| 1171 | armnn::IWorkloadFactory& workloadFactory, |
| 1172 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1173 | float qScale = 1.0f, |
| 1174 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1175 | { |
| 1176 | armnn::Pooling2dDescriptor descriptor; |
| 1177 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 1178 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1179 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1180 | descriptor.m_PadLeft = 1; |
| 1181 | descriptor.m_PadRight = 1; |
| 1182 | descriptor.m_PadTop = 1; |
| 1183 | descriptor.m_PadBottom = 1; |
| 1184 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1185 | |
| 1186 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1187 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1188 | |
| 1189 | // Set quantization parameters if the requested type is a quantized type. |
| 1190 | if(armnn::IsQuantizedType<T>()) |
| 1191 | { |
| 1192 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1193 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1194 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1195 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1196 | } |
| 1197 | |
| 1198 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1199 | QuantizedVector<T>(qScale, qOffset, { |
| 1200 | 9.0f, 27.0f, 18.0f, 36.0f, |
| 1201 | 18.0f, 9.0f, 18.0f, 9.0f, |
| 1202 | 27.0f, 18.0f, 9.0f, 27.0f, |
| 1203 | 9.0f, 27.0f, 9.0f, 18.0f, |
| 1204 | })); |
| 1205 | |
| 1206 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1207 | QuantizedVector<T>(qScale, qOffset, { |
| 1208 | 7.0f, 11.0f, 13.0f, 9.0f, |
| 1209 | 12.0f, 17.0f, 19.0f, 13.0f, |
| 1210 | 12.0f, 16.0f, 16.0f, 10.0f, |
| 1211 | 9.0f, 11.0f, 12.0f, 7.0f, |
| 1212 | })); |
| 1213 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1214 | return SimplePooling2dTestImpl<T>( |
| 1215 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1216 | } |
| 1217 | |
| 1218 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1219 | LayerTestResult<T, 4> IgnorePaddingSimpleL2Pooling2dTestCommon( |
| 1220 | armnn::IWorkloadFactory& workloadFactory, |
| 1221 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1222 | float qScale = 1.0f, |
| 1223 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1224 | { |
| 1225 | armnn::Pooling2dDescriptor descriptor; |
| 1226 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 1227 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; |
| 1228 | descriptor.m_StrideX = descriptor.m_StrideY = 2; |
| 1229 | descriptor.m_PadLeft = 1; |
| 1230 | descriptor.m_PadRight = 1; |
| 1231 | descriptor.m_PadTop = 1; |
| 1232 | descriptor.m_PadBottom = 1; |
| 1233 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1234 | |
| 1235 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1236 | armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); |
| 1237 | |
| 1238 | // Set quantization parameters if the requested type is a quantized type. |
| 1239 | if(armnn::IsQuantizedType<T>()) |
| 1240 | { |
| 1241 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1242 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1243 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1244 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1245 | } |
| 1246 | |
| 1247 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1248 | QuantizedVector<T>(qScale, qOffset, { |
| 1249 | 2.0f, 4.0f, 8.0f, 16.0f, |
| 1250 | 4.0f, 2.0f, 2.0f, 4.0f, |
| 1251 | 8.0f, 2.0f, 4.0f, 2.0f, |
| 1252 | 16.0f, 2.0f, 2.0f, 8.0f, |
| 1253 | })); |
| 1254 | |
| 1255 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1256 | QuantizedVector<T>(qScale, qOffset, { |
| 1257 | 1.0f, 4.4721f, 8.0f, |
| 1258 | 4.4721f, 2.6457f, 2.236f, |
| 1259 | 8.0f, 1.4142f, 4.0f, |
| 1260 | })); |
| 1261 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1262 | return SimplePooling2dTestImpl<T>( |
| 1263 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1264 | } |
| 1265 | |
| 1266 | template<typename T> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1267 | LayerTestResult<T, 4> IgnorePaddingL2Pooling2dSize3TestCommon( |
| 1268 | armnn::IWorkloadFactory& workloadFactory, |
| 1269 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1270 | float qScale = 1.0f, |
| 1271 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1272 | { |
| 1273 | armnn::Pooling2dDescriptor descriptor; |
| 1274 | descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| 1275 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 1276 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 1277 | descriptor.m_PadLeft = 1; |
| 1278 | descriptor.m_PadRight = 1; |
| 1279 | descriptor.m_PadTop = 1; |
| 1280 | descriptor.m_PadBottom = 1; |
| 1281 | descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| 1282 | |
| 1283 | armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1284 | armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 1285 | |
| 1286 | // Set quantization parameters if the requested type is a quantized type. |
| 1287 | if(armnn::IsQuantizedType<T>()) |
| 1288 | { |
| 1289 | inputTensorInfo.SetQuantizationScale(qScale); |
| 1290 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 1291 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1292 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1293 | } |
| 1294 | |
| 1295 | auto input = MakeTensor<T, 4>(inputTensorInfo, |
| 1296 | QuantizedVector<T>(qScale, qOffset, { |
| 1297 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1298 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1299 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1300 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 1301 | })); |
| 1302 | |
| 1303 | auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, |
| 1304 | QuantizedVector<T>(qScale, qOffset, { |
| 1305 | 1.0540f, 1.7638f, 2.5385f, 2.3570f, |
| 1306 | 1.2909f, 2.1602f, 3.1091f, 2.8867f, |
| 1307 | 1.2909f, 2.1602f, 3.1091f, 2.8867f, |
| 1308 | 1.0540f, 1.7638f, 2.5385f, 2.3570f, |
| 1309 | })); |
| 1310 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 1311 | return SimplePooling2dTestImpl<T>( |
| 1312 | workloadFactory, memoryManager, descriptor, qScale, qOffset, input, outputExpected); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1313 | } |