Laurent Carlier | 749294b | 2020-06-01 09:03:17 +0100 | [diff] [blame] | 1 | // |
Teresa Charlin | fbf0e5b | 2020-08-17 01:01:06 +0100 | [diff] [blame] | 2 | // Copyright © 2017 Arm Ltd and Contributors. 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 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 6 | #include "NormalizationTestImpl.hpp" |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 7 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 8 | #include <armnn/Exceptions.hpp> |
| 9 | #include <armnn/LayerSupport.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 10 | |
Matthew Sloyan | 171214c | 2020-09-09 09:07:37 +0100 | [diff] [blame] | 11 | #include <armnn/utility/NumericCast.hpp> |
| 12 | |
James Conroy | 1f58f03 | 2021-04-27 17:13:27 +0100 | [diff] [blame] | 13 | #include <backendsCommon/TensorHandle.hpp> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 14 | |
| 15 | #include <backendsCommon/test/TensorCopyUtils.hpp> |
| 16 | #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| 17 | |
| 18 | #include <test/TensorHelpers.hpp> |
| 19 | |
| 20 | namespace |
| 21 | { |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 22 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 23 | LayerTestResult<float,4> SimpleNormalizationTestImpl( |
| 24 | armnn::IWorkloadFactory& workloadFactory, |
| 25 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 26 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 27 | armnn::NormalizationAlgorithmChannel normChannel, |
| 28 | armnn::NormalizationAlgorithmMethod normMethod) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 29 | { |
Jan Eilers | 8eb2560 | 2020-03-09 12:13:48 +0000 | [diff] [blame] | 30 | IgnoreUnused(memoryManager); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 31 | const unsigned int inputHeight = 2; |
| 32 | const unsigned int inputWidth = 2; |
| 33 | const unsigned int inputChannels = 1; |
| 34 | const unsigned int inputNum = 2; |
| 35 | |
| 36 | unsigned int outputHeight = inputHeight; |
| 37 | unsigned int outputWidth = inputWidth; |
| 38 | unsigned int outputChannels = inputChannels; |
| 39 | unsigned int outputNum = inputNum; |
| 40 | |
| 41 | unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; |
| 42 | unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; |
| 43 | |
| 44 | auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 45 | auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| 46 | |
| 47 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 48 | |
| 49 | auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
| 50 | // Batch #0 |
| 51 | 1.0f, 2.0f, |
| 52 | 3.0f, 4.0f, |
| 53 | // Batch #1 |
| 54 | 5.0f, 6.0f, |
| 55 | 7.0f, 8.0f |
| 56 | })); |
| 57 | |
| 58 | float alpha = 1.f; |
| 59 | float beta = 1.f; |
| 60 | float kappa = 1.f; |
| 61 | uint32_t normSize = 3; |
| 62 | |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 63 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 64 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 65 | |
| 66 | armnn::NormalizationQueueDescriptor data; |
| 67 | armnn::WorkloadInfo info; |
| 68 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 69 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 70 | data.m_Parameters.m_NormChannelType = normChannel; |
| 71 | data.m_Parameters.m_NormMethodType = normMethod; |
| 72 | data.m_Parameters.m_NormSize = normSize; |
| 73 | data.m_Parameters.m_Alpha = alpha; |
| 74 | data.m_Parameters.m_Beta = beta; |
| 75 | data.m_Parameters.m_K = kappa; |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 76 | data.m_Parameters.m_DataLayout = armnn::DataLayout::NCHW; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 77 | |
James Conroy | 1f58f03 | 2021-04-27 17:13:27 +0100 | [diff] [blame] | 78 | armnn::PassthroughTensorHandle refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 79 | armnn::NormalizationQueueDescriptor refData = data; |
| 80 | armnn::WorkloadInfo refInfo = info; |
| 81 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle); |
| 82 | |
| 83 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| 84 | |
| 85 | inputHandle->Allocate(); |
| 86 | outputHandle->Allocate(); |
| 87 | |
| 88 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 89 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 90 | ExecuteWorkload(*workload, memoryManager); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 91 | |
| 92 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 93 | |
| 94 | switch (normMethod) |
| 95 | { |
| 96 | case armnn::NormalizationAlgorithmMethod::LocalBrightness: |
| 97 | { |
| 98 | switch (normChannel) |
| 99 | { |
| 100 | case armnn::NormalizationAlgorithmChannel::Within: |
| 101 | { |
| 102 | // When normalising within channels, the 3x3 kernel covers the entire 2x2 input at every index. |
| 103 | // Therefore, all output values should equal the inputs, but divided by: |
| 104 | // pow((kappa + (accumulatedScale * alpha)), beta) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 105 | // ...where accumulatedScale is the sum of every element squared. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 106 | float divisor[inputNum]; |
Matthew Sloyan | 171214c | 2020-09-09 09:07:37 +0100 | [diff] [blame] | 107 | for(int i = 0; i < armnn::numeric_cast<int>(inputNum); i++) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 108 | { |
| 109 | float accumulatedScale = input[i][0][0][0]*input[i][0][0][0] + |
| 110 | input[i][0][0][1]*input[i][0][0][1] + |
| 111 | input[i][0][1][0]*input[i][0][1][0] + |
| 112 | input[i][0][1][1]*input[i][0][1][1]; |
| 113 | divisor[i] = powf((kappa + accumulatedScale * alpha), beta); |
| 114 | } |
| 115 | ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, |
| 116 | std::vector<float>({input[0][0][0][0]/divisor[0], |
| 117 | input[0][0][0][1]/divisor[0], |
| 118 | input[0][0][1][0]/divisor[0], |
| 119 | input[0][0][1][1]/divisor[0], |
| 120 | input[1][0][0][0]/divisor[1], |
| 121 | input[1][0][0][1]/divisor[1], |
| 122 | input[1][0][1][0]/divisor[1], |
| 123 | input[1][0][1][1]/divisor[1]})); |
| 124 | break; |
| 125 | } |
| 126 | case armnn::NormalizationAlgorithmChannel::Across: |
| 127 | { |
| 128 | // When normalising across channels, all output values should equal the inputs, but multiplied by: |
| 129 | // pow((kappa + (accumulatedScale * alpha)), -beta) |
| 130 | // ...where accumulatedScale is the sum of the inputs for adjacent channels for this element squared |
| 131 | // ...where adjacent channels means within half the normSize for the channel |
| 132 | // The test data has only one channel, so this is simplified below. |
| 133 | std::vector<float> outputVector; |
Matthew Sloyan | 171214c | 2020-09-09 09:07:37 +0100 | [diff] [blame] | 134 | for (int n = 0; n < armnn::numeric_cast<int>(inputNum); ++n) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 135 | { |
Matthew Sloyan | 171214c | 2020-09-09 09:07:37 +0100 | [diff] [blame] | 136 | for (int h = 0; h < armnn::numeric_cast<int>(inputHeight); ++h) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 137 | { |
Matthew Sloyan | 171214c | 2020-09-09 09:07:37 +0100 | [diff] [blame] | 138 | for (int w = 0; w < armnn::numeric_cast<int>(inputWidth); ++w) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 139 | { |
| 140 | float accumulatedScale = input[n][0][h][w]*input[n][0][h][w]; |
| 141 | float scale = powf((kappa + accumulatedScale * alpha), -beta); |
| 142 | outputVector.push_back(input[n][0][h][w] * scale); |
| 143 | } |
| 144 | } |
| 145 | } |
| 146 | ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputVector); |
| 147 | break; |
| 148 | } |
| 149 | default: |
| 150 | { |
| 151 | throw armnn::UnimplementedException("Unsupported normalisation channel type, " |
| 152 | "only Across and Within are supported"); |
| 153 | } |
| 154 | } |
| 155 | break; |
| 156 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 157 | case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 158 | default: |
| 159 | { |
| 160 | throw armnn::UnimplementedException("Unsupported normalisation method type, " |
| 161 | "only LocalBrightness is supported"); |
| 162 | } |
| 163 | } |
| 164 | |
| 165 | return ret; |
| 166 | } |
| 167 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 168 | LayerTestResult<float,4> SimpleNormalizationNhwcTestImpl( |
| 169 | armnn::IWorkloadFactory& workloadFactory, |
| 170 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 171 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 172 | armnn::NormalizationAlgorithmChannel normChannel, |
| 173 | armnn::NormalizationAlgorithmMethod normMethod) |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 174 | { |
| 175 | const unsigned int inputHeight = 2; |
| 176 | const unsigned int inputWidth = 2; |
| 177 | const unsigned int inputChannels = 1; |
| 178 | const unsigned int inputNum = 2; |
| 179 | |
| 180 | unsigned int outputHeight = inputHeight; |
| 181 | unsigned int outputWidth = inputWidth; |
| 182 | unsigned int outputChannels = inputChannels; |
| 183 | unsigned int outputNum = inputNum; |
| 184 | |
| 185 | unsigned int inputShape[] = { inputNum, inputHeight, inputWidth, inputChannels }; |
| 186 | unsigned int outputShape[] = { outputNum, outputHeight, outputWidth, outputChannels }; |
| 187 | |
| 188 | auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 189 | auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| 190 | |
| 191 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 192 | |
| 193 | auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
| 194 | // Batch #0 |
| 195 | 1.0f, 2.0f, |
| 196 | 3.0f, 4.0f, |
| 197 | // Batch #1 |
| 198 | 5.0f, 6.0f, |
| 199 | 7.0f, 8.0f |
| 200 | })); |
| 201 | |
| 202 | float alpha = 1.f; |
| 203 | float beta = 1.f; |
| 204 | float kappa = 1.f; |
| 205 | uint32_t normSize = 3; |
| 206 | |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 207 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 208 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 209 | |
| 210 | armnn::NormalizationQueueDescriptor data; |
| 211 | armnn::WorkloadInfo info; |
| 212 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 213 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 214 | data.m_Parameters.m_NormChannelType = normChannel; |
| 215 | data.m_Parameters.m_NormMethodType = normMethod; |
| 216 | data.m_Parameters.m_NormSize = normSize; |
| 217 | data.m_Parameters.m_Alpha = alpha; |
| 218 | data.m_Parameters.m_Beta = beta; |
| 219 | data.m_Parameters.m_K = kappa; |
| 220 | data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC; |
| 221 | |
James Conroy | 1f58f03 | 2021-04-27 17:13:27 +0100 | [diff] [blame] | 222 | armnn::PassthroughTensorHandle refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 223 | armnn::NormalizationQueueDescriptor refData = data; |
| 224 | armnn::WorkloadInfo refInfo = info; |
| 225 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle); |
| 226 | |
| 227 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| 228 | |
| 229 | inputHandle->Allocate(); |
| 230 | outputHandle->Allocate(); |
| 231 | |
| 232 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 233 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 234 | ExecuteWorkload(*workload, memoryManager); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 235 | |
| 236 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 237 | |
| 238 | switch (normMethod) |
| 239 | { |
| 240 | case armnn::NormalizationAlgorithmMethod::LocalBrightness: |
| 241 | { |
| 242 | switch (normChannel) |
| 243 | { |
| 244 | case armnn::NormalizationAlgorithmChannel::Across: |
| 245 | { |
| 246 | std::vector<float> expectedOutput{ 0.5f, 0.400000006f, 0.300000012f, 0.235294119f, |
| 247 | 0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f }; |
| 248 | ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, expectedOutput); |
| 249 | break; |
| 250 | } |
| 251 | default: |
| 252 | { |
| 253 | throw armnn::UnimplementedException("Unsupported normalisation channel type, " |
| 254 | "Only Cross-map is supported for NHWC layout"); |
| 255 | } |
| 256 | } |
| 257 | break; |
| 258 | } |
| 259 | case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. |
| 260 | default: |
| 261 | { |
| 262 | throw armnn::UnimplementedException("Unsupported normalisation method type, " |
| 263 | "only LocalBrightness is supported"); |
| 264 | } |
| 265 | } |
| 266 | |
| 267 | return ret; |
| 268 | } |
| 269 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 270 | LayerTestResult<float,4> CompareNormalizationTestImpl( |
| 271 | armnn::IWorkloadFactory& workloadFactory, |
| 272 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 273 | armnn::IWorkloadFactory& refWorkloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 274 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 275 | const armnn::ITensorHandleFactory& refTensorHandleFactory, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 276 | armnn::NormalizationAlgorithmChannel normChannel, |
| 277 | armnn::NormalizationAlgorithmMethod normMethod) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 278 | { |
| 279 | constexpr unsigned int inputNum = 5; |
| 280 | constexpr unsigned int inputChannels = 3; |
| 281 | constexpr unsigned int inputHeight = 32; |
| 282 | constexpr unsigned int inputWidth = 24; |
| 283 | |
| 284 | constexpr unsigned int outputNum = inputNum; |
| 285 | constexpr unsigned int outputChannels = inputChannels; |
| 286 | constexpr unsigned int outputHeight = inputHeight; |
| 287 | constexpr unsigned int outputWidth = inputWidth; |
| 288 | |
| 289 | armnn::TensorInfo inputTensorInfo; |
| 290 | armnn::TensorInfo outputTensorInfo; |
| 291 | |
| 292 | unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; |
| 293 | unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; |
| 294 | |
| 295 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 296 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| 297 | |
| 298 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 299 | |
| 300 | auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 111234); |
| 301 | |
| 302 | constexpr float alpha = 1.f; |
| 303 | constexpr float beta = 1.f; |
| 304 | constexpr float kappa = 1.f; |
| 305 | constexpr uint32_t normSize = 5; |
| 306 | |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 307 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 308 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 309 | |
| 310 | armnn::NormalizationQueueDescriptor data; |
| 311 | armnn::WorkloadInfo info; |
| 312 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 313 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 314 | data.m_Parameters.m_NormChannelType = normChannel; |
| 315 | data.m_Parameters.m_NormMethodType = normMethod; |
| 316 | data.m_Parameters.m_NormSize = normSize; |
| 317 | data.m_Parameters.m_Alpha = alpha; |
| 318 | data.m_Parameters.m_Beta = beta; |
| 319 | data.m_Parameters.m_K = kappa; |
| 320 | |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 321 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refTensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 322 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refTensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 323 | |
| 324 | armnn::NormalizationQueueDescriptor refData = data; |
| 325 | armnn::WorkloadInfo refInfo = info; |
| 326 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 327 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 328 | |
| 329 | // Don't execute if Normalization is not supported for the method and channel types, as an exception will be raised. |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 330 | armnn::BackendId backend = workloadFactory.GetBackendId(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 331 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 332 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
David Beck | 79141b9 | 2018-10-23 16:09:36 +0100 | [diff] [blame] | 333 | ret.supported = armnn::IsNormalizationSupported(backend, inputTensorInfo, outputTensorInfo, data.m_Parameters, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 334 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 335 | if (!ret.supported) |
| 336 | { |
| 337 | return ret; |
| 338 | } |
| 339 | |
| 340 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| 341 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateNormalization(refData, refInfo); |
| 342 | |
| 343 | outputHandleRef->Allocate(); |
| 344 | inputHandleRef->Allocate(); |
| 345 | |
| 346 | inputHandle->Allocate(); |
| 347 | outputHandle->Allocate(); |
| 348 | |
| 349 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 350 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 351 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 352 | ExecuteWorkload(*workload, memoryManager); |
Aron Virginas-Tar | 6057895 | 2018-10-31 11:04:01 +0000 | [diff] [blame] | 353 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 354 | workloadRef->Execute(); |
| 355 | |
| 356 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 357 | CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 358 | |
| 359 | return ret; |
| 360 | } |
| 361 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 362 | } // anonymous namespace |
| 363 | |
| 364 | LayerTestResult<float,4> SimpleNormalizationAcrossTest( |
| 365 | armnn::IWorkloadFactory& workloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 366 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 367 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 368 | { |
| 369 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 370 | auto normChannel = armnn::NormalizationAlgorithmChannel::Across; |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 371 | return SimpleNormalizationTestImpl(workloadFactory, memoryManager, tensorHandleFactory, normChannel, normMethod); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 372 | } |
| 373 | |
| 374 | LayerTestResult<float,4> SimpleNormalizationWithinTest( |
| 375 | armnn::IWorkloadFactory& workloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 376 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 377 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 378 | { |
| 379 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 380 | auto normChannel = armnn::NormalizationAlgorithmChannel::Within; |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 381 | return SimpleNormalizationTestImpl(workloadFactory, memoryManager, tensorHandleFactory, normChannel, normMethod); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 382 | } |
| 383 | |
| 384 | LayerTestResult<float,4> SimpleNormalizationAcrossNhwcTest( |
| 385 | armnn::IWorkloadFactory& workloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 386 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 387 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 388 | { |
| 389 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 390 | auto normChannel = armnn::NormalizationAlgorithmChannel::Across; |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 391 | return SimpleNormalizationNhwcTestImpl( |
| 392 | workloadFactory, memoryManager, tensorHandleFactory, normChannel, normMethod); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 393 | } |
| 394 | |
| 395 | LayerTestResult<float,4> CompareNormalizationTest( |
| 396 | armnn::IWorkloadFactory& workloadFactory, |
| 397 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 398 | armnn::IWorkloadFactory& refWorkloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 399 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 400 | const armnn::ITensorHandleFactory& refTensorHandleFactory, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 401 | armnn::NormalizationAlgorithmChannel normChannel, |
| 402 | armnn::NormalizationAlgorithmMethod normMethod) |
| 403 | { |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 404 | return CompareNormalizationTestImpl( |
| 405 | workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, |
| 406 | normChannel, normMethod); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 407 | } |