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 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 47 | std::vector<float> input = |
| 48 | { |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 49 | // Batch #0 |
| 50 | 1.0f, 2.0f, |
| 51 | 3.0f, 4.0f, |
| 52 | // Batch #1 |
| 53 | 5.0f, 6.0f, |
| 54 | 7.0f, 8.0f |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 55 | }; |
| 56 | |
| 57 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 58 | std::vector<float> expectedOutput(outputTensorInfo.GetNumElements()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 59 | |
| 60 | float alpha = 1.f; |
| 61 | float beta = 1.f; |
| 62 | float kappa = 1.f; |
| 63 | uint32_t normSize = 3; |
| 64 | |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 65 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 66 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 67 | |
| 68 | armnn::NormalizationQueueDescriptor data; |
| 69 | armnn::WorkloadInfo info; |
| 70 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 71 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 72 | data.m_Parameters.m_NormChannelType = normChannel; |
| 73 | data.m_Parameters.m_NormMethodType = normMethod; |
| 74 | data.m_Parameters.m_NormSize = normSize; |
| 75 | data.m_Parameters.m_Alpha = alpha; |
| 76 | data.m_Parameters.m_Beta = beta; |
| 77 | data.m_Parameters.m_K = kappa; |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 78 | data.m_Parameters.m_DataLayout = armnn::DataLayout::NCHW; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 79 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 80 | armnn::PassthroughTensorHandle refHandle(outputTensorInfo, expectedOutput.data()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 81 | armnn::NormalizationQueueDescriptor refData = data; |
| 82 | armnn::WorkloadInfo refInfo = info; |
| 83 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle); |
| 84 | |
| 85 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| 86 | |
| 87 | inputHandle->Allocate(); |
| 88 | outputHandle->Allocate(); |
| 89 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 90 | CopyDataToITensorHandle(inputHandle.get(), input.data()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 91 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 92 | ExecuteWorkload(*workload, memoryManager); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 93 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 94 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 95 | |
| 96 | switch (normMethod) |
| 97 | { |
| 98 | case armnn::NormalizationAlgorithmMethod::LocalBrightness: |
| 99 | { |
| 100 | switch (normChannel) |
| 101 | { |
| 102 | case armnn::NormalizationAlgorithmChannel::Within: |
| 103 | { |
| 104 | // When normalising within channels, the 3x3 kernel covers the entire 2x2 input at every index. |
| 105 | // Therefore, all output values should equal the inputs, but divided by: |
| 106 | // pow((kappa + (accumulatedScale * alpha)), beta) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 107 | // ...where accumulatedScale is the sum of every element squared. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 108 | float divisor[inputNum]; |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 109 | |
| 110 | float accumulatedScale1 = 0.0f; |
| 111 | for (size_t i = 0; i < input.size()/2; ++i) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 112 | { |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 113 | accumulatedScale1 += input[i]*input[i]; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 114 | } |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 115 | |
| 116 | float accumulatedScale2 = 0.0f; |
| 117 | for (size_t i = input.size()/2; i < input.size(); ++i) |
| 118 | { |
| 119 | accumulatedScale2 += input[i]*input[i]; |
| 120 | } |
| 121 | |
| 122 | divisor[0] = powf((kappa + accumulatedScale1 * alpha), beta); |
| 123 | divisor[1] = powf((kappa + accumulatedScale2 * alpha), beta); |
| 124 | |
| 125 | std::vector<float> output; |
| 126 | unsigned int divisorIndex = 0; |
| 127 | for (size_t i = 0; i < input.size(); ++i) |
| 128 | { |
| 129 | if (i == input.size()/2) |
| 130 | { |
| 131 | divisorIndex++; |
| 132 | } |
| 133 | output.emplace_back(input[i]/divisor[divisorIndex]); |
| 134 | } |
| 135 | |
| 136 | expectedOutput = output; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 137 | break; |
| 138 | } |
| 139 | case armnn::NormalizationAlgorithmChannel::Across: |
| 140 | { |
| 141 | // When normalising across channels, all output values should equal the inputs, but multiplied by: |
| 142 | // pow((kappa + (accumulatedScale * alpha)), -beta) |
| 143 | // ...where accumulatedScale is the sum of the inputs for adjacent channels for this element squared |
| 144 | // ...where adjacent channels means within half the normSize for the channel |
| 145 | // The test data has only one channel, so this is simplified below. |
| 146 | std::vector<float> outputVector; |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 147 | |
| 148 | for (unsigned int i = 0; i < input.size(); ++i) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 149 | { |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 150 | float accumulatedScale = input[i]*input[i]; |
| 151 | float scale = powf((kappa + accumulatedScale * alpha), -beta); |
| 152 | outputVector.push_back(input[i] * scale); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 153 | } |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 154 | expectedOutput = outputVector; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 155 | break; |
| 156 | } |
| 157 | default: |
| 158 | { |
| 159 | throw armnn::UnimplementedException("Unsupported normalisation channel type, " |
| 160 | "only Across and Within are supported"); |
| 161 | } |
| 162 | } |
| 163 | break; |
| 164 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 165 | case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 166 | default: |
| 167 | { |
| 168 | throw armnn::UnimplementedException("Unsupported normalisation method type, " |
| 169 | "only LocalBrightness is supported"); |
| 170 | } |
| 171 | } |
| 172 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 173 | return LayerTestResult<float, 4>(actualOutput, |
| 174 | expectedOutput, |
| 175 | outputHandle->GetShape(), |
| 176 | outputTensorInfo.GetShape()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 177 | } |
| 178 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 179 | LayerTestResult<float,4> SimpleNormalizationNhwcTestImpl( |
| 180 | armnn::IWorkloadFactory& workloadFactory, |
| 181 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 182 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 183 | armnn::NormalizationAlgorithmChannel normChannel, |
| 184 | armnn::NormalizationAlgorithmMethod normMethod) |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 185 | { |
| 186 | const unsigned int inputHeight = 2; |
| 187 | const unsigned int inputWidth = 2; |
| 188 | const unsigned int inputChannels = 1; |
| 189 | const unsigned int inputNum = 2; |
| 190 | |
| 191 | unsigned int outputHeight = inputHeight; |
| 192 | unsigned int outputWidth = inputWidth; |
| 193 | unsigned int outputChannels = inputChannels; |
| 194 | unsigned int outputNum = inputNum; |
| 195 | |
| 196 | unsigned int inputShape[] = { inputNum, inputHeight, inputWidth, inputChannels }; |
| 197 | unsigned int outputShape[] = { outputNum, outputHeight, outputWidth, outputChannels }; |
| 198 | |
| 199 | auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 200 | auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| 201 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 202 | std::vector<float> input = |
| 203 | { |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 204 | // Batch #0 |
| 205 | 1.0f, 2.0f, |
| 206 | 3.0f, 4.0f, |
| 207 | // Batch #1 |
| 208 | 5.0f, 6.0f, |
| 209 | 7.0f, 8.0f |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 210 | }; |
| 211 | |
| 212 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 213 | std::vector<float> expectedOutput(outputTensorInfo.GetNumElements()); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 214 | |
| 215 | float alpha = 1.f; |
| 216 | float beta = 1.f; |
| 217 | float kappa = 1.f; |
| 218 | uint32_t normSize = 3; |
| 219 | |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 220 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 221 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 222 | |
| 223 | armnn::NormalizationQueueDescriptor data; |
| 224 | armnn::WorkloadInfo info; |
| 225 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 226 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 227 | data.m_Parameters.m_NormChannelType = normChannel; |
| 228 | data.m_Parameters.m_NormMethodType = normMethod; |
| 229 | data.m_Parameters.m_NormSize = normSize; |
| 230 | data.m_Parameters.m_Alpha = alpha; |
| 231 | data.m_Parameters.m_Beta = beta; |
| 232 | data.m_Parameters.m_K = kappa; |
| 233 | data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC; |
| 234 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 235 | armnn::PassthroughTensorHandle refHandle(outputTensorInfo, expectedOutput.data()); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 236 | armnn::NormalizationQueueDescriptor refData = data; |
| 237 | armnn::WorkloadInfo refInfo = info; |
| 238 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle); |
| 239 | |
| 240 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| 241 | |
| 242 | inputHandle->Allocate(); |
| 243 | outputHandle->Allocate(); |
| 244 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 245 | CopyDataToITensorHandle(inputHandle.get(), input.data()); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 246 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 247 | ExecuteWorkload(*workload, memoryManager); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 248 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 249 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 250 | |
| 251 | switch (normMethod) |
| 252 | { |
| 253 | case armnn::NormalizationAlgorithmMethod::LocalBrightness: |
| 254 | { |
| 255 | switch (normChannel) |
| 256 | { |
| 257 | case armnn::NormalizationAlgorithmChannel::Across: |
| 258 | { |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 259 | expectedOutput = { 0.5f, 0.400000006f, 0.300000012f, 0.235294119f, |
| 260 | 0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f }; |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 261 | break; |
| 262 | } |
| 263 | default: |
| 264 | { |
| 265 | throw armnn::UnimplementedException("Unsupported normalisation channel type, " |
| 266 | "Only Cross-map is supported for NHWC layout"); |
| 267 | } |
| 268 | } |
| 269 | break; |
| 270 | } |
| 271 | case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. |
| 272 | default: |
| 273 | { |
| 274 | throw armnn::UnimplementedException("Unsupported normalisation method type, " |
| 275 | "only LocalBrightness is supported"); |
| 276 | } |
| 277 | } |
| 278 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 279 | return LayerTestResult<float, 4>(actualOutput, |
| 280 | expectedOutput, |
| 281 | outputHandle->GetShape(), |
| 282 | outputTensorInfo.GetShape()); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 283 | } |
| 284 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 285 | LayerTestResult<float,4> CompareNormalizationTestImpl( |
| 286 | armnn::IWorkloadFactory& workloadFactory, |
| 287 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 288 | armnn::IWorkloadFactory& refWorkloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 289 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 290 | const armnn::ITensorHandleFactory& refTensorHandleFactory, |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 291 | armnn::NormalizationAlgorithmChannel normChannel, |
| 292 | armnn::NormalizationAlgorithmMethod normMethod) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 293 | { |
| 294 | constexpr unsigned int inputNum = 5; |
| 295 | constexpr unsigned int inputChannels = 3; |
| 296 | constexpr unsigned int inputHeight = 32; |
| 297 | constexpr unsigned int inputWidth = 24; |
| 298 | |
| 299 | constexpr unsigned int outputNum = inputNum; |
| 300 | constexpr unsigned int outputChannels = inputChannels; |
| 301 | constexpr unsigned int outputHeight = inputHeight; |
| 302 | constexpr unsigned int outputWidth = inputWidth; |
| 303 | |
| 304 | armnn::TensorInfo inputTensorInfo; |
| 305 | armnn::TensorInfo outputTensorInfo; |
| 306 | |
| 307 | unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; |
| 308 | unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; |
| 309 | |
| 310 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 311 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| 312 | |
| 313 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 314 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 315 | auto input = MakeRandomTensor<float>(inputTensorInfo, 111234); |
| 316 | |
| 317 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 318 | std::vector<float> expectedOutput(outputTensorInfo.GetNumElements()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 319 | |
| 320 | constexpr float alpha = 1.f; |
| 321 | constexpr float beta = 1.f; |
| 322 | constexpr float kappa = 1.f; |
| 323 | constexpr uint32_t normSize = 5; |
| 324 | |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 325 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 326 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 327 | |
| 328 | armnn::NormalizationQueueDescriptor data; |
| 329 | armnn::WorkloadInfo info; |
| 330 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 331 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 332 | data.m_Parameters.m_NormChannelType = normChannel; |
| 333 | data.m_Parameters.m_NormMethodType = normMethod; |
| 334 | data.m_Parameters.m_NormSize = normSize; |
| 335 | data.m_Parameters.m_Alpha = alpha; |
| 336 | data.m_Parameters.m_Beta = beta; |
| 337 | data.m_Parameters.m_K = kappa; |
| 338 | |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 339 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refTensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 340 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refTensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 341 | |
| 342 | armnn::NormalizationQueueDescriptor refData = data; |
| 343 | armnn::WorkloadInfo refInfo = info; |
| 344 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 345 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 346 | |
| 347 | // 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] | 348 | armnn::BackendId backend = workloadFactory.GetBackendId(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 349 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 350 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 351 | ret.m_Supported = armnn::IsNormalizationSupported(backend, inputTensorInfo, outputTensorInfo, data.m_Parameters, |
| 352 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 353 | if (!ret.m_Supported) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 354 | { |
| 355 | return ret; |
| 356 | } |
| 357 | |
| 358 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| 359 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateNormalization(refData, refInfo); |
| 360 | |
| 361 | outputHandleRef->Allocate(); |
| 362 | inputHandleRef->Allocate(); |
| 363 | |
| 364 | inputHandle->Allocate(); |
| 365 | outputHandle->Allocate(); |
| 366 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 367 | CopyDataToITensorHandle(inputHandle.get(), input.data()); |
| 368 | CopyDataToITensorHandle(inputHandleRef.get(), input.data()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 369 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 370 | ExecuteWorkload(*workload, memoryManager); |
Aron Virginas-Tar | 6057895 | 2018-10-31 11:04:01 +0000 | [diff] [blame] | 371 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 372 | workloadRef->Execute(); |
| 373 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 374 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 375 | CopyDataFromITensorHandle(expectedOutput.data(), outputHandleRef.get()); |
| 376 | ret.m_ActualData = actualOutput; |
| 377 | ret.m_ExpectedData = expectedOutput; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 378 | |
| 379 | return ret; |
| 380 | } |
| 381 | |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 382 | LayerTestResult<float,4> AcrossChannelNormalizationTestImpl( |
| 383 | armnn::IWorkloadFactory& workloadFactory, |
| 384 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 385 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 386 | armnn::NormalizationAlgorithmChannel normChannel, |
| 387 | armnn::NormalizationAlgorithmMethod normMethod) |
| 388 | { |
| 389 | const unsigned int inputHeight = 1; |
| 390 | const unsigned int inputWidth = 2; |
| 391 | const unsigned int inputChannels = 3; |
| 392 | const unsigned int inputNum = 2; |
| 393 | |
| 394 | unsigned int outputHeight = inputHeight; |
| 395 | unsigned int outputWidth = inputWidth; |
| 396 | unsigned int outputChannels = inputChannels; |
| 397 | unsigned int outputNum = inputNum; |
| 398 | |
| 399 | unsigned int inputShape[] = { inputNum, inputHeight, inputWidth, inputChannels }; |
| 400 | unsigned int outputShape[] = { outputNum, outputHeight, outputWidth, outputChannels }; |
| 401 | |
| 402 | auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 403 | auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| 404 | |
| 405 | std::vector<float> input = |
| 406 | { |
| 407 | // Batch #0 |
| 408 | -2.1f, 2.6f, 1.7f, 1.2f, -1.0f, 0.7f, |
| 409 | // Batch #1 |
| 410 | -2.1f, 2.6f, 1.7f, 1.2f, -1.0f, 0.7f, |
| 411 | }; |
| 412 | |
| 413 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 414 | std::vector<float> expectedOutput(outputTensorInfo.GetNumElements()); |
| 415 | |
| 416 | float alpha = 4.f; |
| 417 | float beta = 0.5f; |
| 418 | float kappa = 9.f; |
| 419 | uint32_t normSize = 5; |
| 420 | |
| 421 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 422 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 423 | |
| 424 | armnn::NormalizationQueueDescriptor data; |
| 425 | armnn::WorkloadInfo info; |
| 426 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 427 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 428 | data.m_Parameters.m_NormChannelType = normChannel; |
| 429 | data.m_Parameters.m_NormMethodType = normMethod; |
| 430 | data.m_Parameters.m_NormSize = normSize; |
| 431 | data.m_Parameters.m_Alpha = alpha; |
| 432 | data.m_Parameters.m_Beta = beta; |
| 433 | data.m_Parameters.m_K = kappa; |
| 434 | data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC; |
| 435 | |
| 436 | armnn::PassthroughTensorHandle refHandle(outputTensorInfo, expectedOutput.data()); |
| 437 | armnn::NormalizationQueueDescriptor refData = data; |
| 438 | armnn::WorkloadInfo refInfo = info; |
| 439 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle); |
| 440 | |
| 441 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| 442 | |
| 443 | inputHandle->Allocate(); |
| 444 | outputHandle->Allocate(); |
| 445 | |
| 446 | CopyDataToITensorHandle(inputHandle.get(), input.data()); |
| 447 | |
| 448 | ExecuteWorkload(*workload, memoryManager); |
| 449 | |
| 450 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 451 | |
| 452 | switch (normMethod) |
| 453 | { |
| 454 | case armnn::NormalizationAlgorithmMethod::LocalBrightness: |
| 455 | { |
| 456 | switch (normChannel) |
| 457 | { |
| 458 | case armnn::NormalizationAlgorithmChannel::Across: |
| 459 | { |
| 460 | expectedOutput = { -0.259993f, 0.321897f, 0.210471f, 0.263625f, -0.219687f, 0.153781f, |
| 461 | -0.259993f, 0.321897f, 0.210471f, 0.263625f, -0.219687f, 0.153781f, }; |
| 462 | break; |
| 463 | } |
| 464 | default: |
| 465 | { |
| 466 | throw armnn::UnimplementedException("Unsupported normalisation channel type, " |
| 467 | "only Across and Within are supported"); |
| 468 | } |
| 469 | } |
| 470 | break; |
| 471 | } |
| 472 | case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. |
| 473 | default: |
| 474 | { |
| 475 | throw armnn::UnimplementedException("Unsupported normalisation method type, " |
| 476 | "only LocalBrightness is supported"); |
| 477 | } |
| 478 | } |
| 479 | |
| 480 | return LayerTestResult<float, 4>(actualOutput, |
| 481 | expectedOutput, |
| 482 | outputHandle->GetShape(), |
| 483 | outputTensorInfo.GetShape()); |
| 484 | } |
| 485 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 486 | } // anonymous namespace |
| 487 | |
| 488 | LayerTestResult<float,4> SimpleNormalizationAcrossTest( |
| 489 | armnn::IWorkloadFactory& workloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 490 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 491 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 492 | { |
| 493 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 494 | auto normChannel = armnn::NormalizationAlgorithmChannel::Across; |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 495 | return SimpleNormalizationTestImpl(workloadFactory, memoryManager, tensorHandleFactory, normChannel, normMethod); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 496 | } |
| 497 | |
| 498 | LayerTestResult<float,4> SimpleNormalizationWithinTest( |
| 499 | armnn::IWorkloadFactory& workloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 500 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 501 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 502 | { |
| 503 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 504 | auto normChannel = armnn::NormalizationAlgorithmChannel::Within; |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 505 | return SimpleNormalizationTestImpl(workloadFactory, memoryManager, tensorHandleFactory, normChannel, normMethod); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 506 | } |
| 507 | |
| 508 | LayerTestResult<float,4> SimpleNormalizationAcrossNhwcTest( |
| 509 | armnn::IWorkloadFactory& workloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 510 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 511 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 512 | { |
| 513 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 514 | auto normChannel = armnn::NormalizationAlgorithmChannel::Across; |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 515 | return SimpleNormalizationNhwcTestImpl( |
| 516 | workloadFactory, memoryManager, tensorHandleFactory, normChannel, normMethod); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 517 | } |
| 518 | |
| 519 | LayerTestResult<float,4> CompareNormalizationTest( |
| 520 | armnn::IWorkloadFactory& workloadFactory, |
| 521 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 522 | armnn::IWorkloadFactory& refWorkloadFactory, |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 523 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 524 | const armnn::ITensorHandleFactory& refTensorHandleFactory, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 525 | armnn::NormalizationAlgorithmChannel normChannel, |
| 526 | armnn::NormalizationAlgorithmMethod normMethod) |
| 527 | { |
Finn Williams | 826a543 | 2020-08-27 16:15:20 +0100 | [diff] [blame] | 528 | return CompareNormalizationTestImpl( |
| 529 | workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, |
| 530 | normChannel, normMethod); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 531 | } |
Sadik Armagan | 483c811 | 2021-06-01 09:24:52 +0100 | [diff] [blame] | 532 | |
| 533 | LayerTestResult<float,4> AcrossChannelNormalizationTest( |
| 534 | armnn::IWorkloadFactory& workloadFactory, |
| 535 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 536 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
| 537 | { |
| 538 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 539 | auto normChannel = armnn::NormalizationAlgorithmChannel::Across; |
| 540 | return AcrossChannelNormalizationTestImpl(workloadFactory, |
| 541 | memoryManager, |
| 542 | tensorHandleFactory, |
| 543 | normChannel, |
| 544 | normMethod); |
| 545 | } |