Narumol Prangnawarat | e0a4ad8 | 2019-02-04 19:05:27 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include "TensorCopyUtils.hpp" |
Aron Virginas-Tar | d4f0fea | 2019-04-09 14:08:06 +0100 | [diff] [blame] | 8 | #include <ResolveType.hpp> |
Narumol Prangnawarat | e0a4ad8 | 2019-02-04 19:05:27 +0000 | [diff] [blame] | 9 | #include "WorkloadTestUtils.hpp" |
| 10 | |
| 11 | #include <armnn/Types.hpp> |
| 12 | #include <backendsCommon/CpuTensorHandle.hpp> |
| 13 | #include <backendsCommon/IBackendInternal.hpp> |
| 14 | #include <backendsCommon/WorkloadFactory.hpp> |
| 15 | #include <backendsCommon/test/WorkloadFactoryHelper.hpp> |
| 16 | #include <test/TensorHelpers.hpp> |
| 17 | |
| 18 | template <typename FactoryType, armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 19 | void DetectionPostProcessImpl(const armnn::TensorInfo& boxEncodingsInfo, |
| 20 | const armnn::TensorInfo& scoresInfo, |
| 21 | const armnn::TensorInfo& anchorsInfo, |
| 22 | const std::vector<T>& boxEncodingsData, |
| 23 | const std::vector<T>& scoresData, |
| 24 | const std::vector<T>& anchorsData, |
| 25 | const std::vector<float>& expectedDetectionBoxes, |
| 26 | const std::vector<float>& expectedDetectionClasses, |
| 27 | const std::vector<float>& expectedDetectionScores, |
| 28 | const std::vector<float>& expectedNumDetections, |
| 29 | bool useRegularNms) |
| 30 | { |
| 31 | std::unique_ptr<armnn::Profiler> profiler = std::make_unique<armnn::Profiler>(); |
| 32 | armnn::ProfilerManager::GetInstance().RegisterProfiler(profiler.get()); |
| 33 | |
| 34 | auto memoryManager = WorkloadFactoryHelper<FactoryType>::GetMemoryManager(); |
| 35 | FactoryType workloadFactory = WorkloadFactoryHelper<FactoryType>::GetFactory(memoryManager); |
| 36 | |
| 37 | auto boxEncodings = MakeTensor<T, 3>(boxEncodingsInfo, boxEncodingsData); |
| 38 | auto scores = MakeTensor<T, 3>(scoresInfo, scoresData); |
| 39 | auto anchors = MakeTensor<T, 2>(anchorsInfo, anchorsData); |
| 40 | |
| 41 | armnn::TensorInfo detectionBoxesInfo({ 1, 3, 4 }, armnn::DataType::Float32); |
| 42 | armnn::TensorInfo detectionScoresInfo({ 1, 3 }, armnn::DataType::Float32); |
| 43 | armnn::TensorInfo detectionClassesInfo({ 1, 3 }, armnn::DataType::Float32); |
| 44 | armnn::TensorInfo numDetectionInfo({ 1 }, armnn::DataType::Float32); |
| 45 | |
| 46 | LayerTestResult<float, 3> detectionBoxesResult(detectionBoxesInfo); |
| 47 | detectionBoxesResult.outputExpected = MakeTensor<float, 3>(detectionBoxesInfo, expectedDetectionBoxes); |
| 48 | LayerTestResult<float, 2> detectionClassesResult(detectionClassesInfo); |
| 49 | detectionClassesResult.outputExpected = MakeTensor<float, 2>(detectionClassesInfo, expectedDetectionClasses); |
| 50 | LayerTestResult<float, 2> detectionScoresResult(detectionScoresInfo); |
| 51 | detectionScoresResult.outputExpected = MakeTensor<float, 2>(detectionScoresInfo, expectedDetectionScores); |
| 52 | LayerTestResult<float, 1> numDetectionsResult(numDetectionInfo); |
| 53 | numDetectionsResult.outputExpected = MakeTensor<float, 1>(numDetectionInfo, expectedNumDetections); |
| 54 | |
| 55 | std::unique_ptr<armnn::ITensorHandle> boxedHandle = workloadFactory.CreateTensorHandle(boxEncodingsInfo); |
| 56 | std::unique_ptr<armnn::ITensorHandle> scoreshandle = workloadFactory.CreateTensorHandle(scoresInfo); |
| 57 | std::unique_ptr<armnn::ITensorHandle> anchorsHandle = workloadFactory.CreateTensorHandle(anchorsInfo); |
| 58 | std::unique_ptr<armnn::ITensorHandle> outputBoxesHandle = workloadFactory.CreateTensorHandle(detectionBoxesInfo); |
| 59 | std::unique_ptr<armnn::ITensorHandle> classesHandle = workloadFactory.CreateTensorHandle(detectionClassesInfo); |
| 60 | std::unique_ptr<armnn::ITensorHandle> outputScoresHandle = workloadFactory.CreateTensorHandle(detectionScoresInfo); |
| 61 | std::unique_ptr<armnn::ITensorHandle> numDetectionHandle = workloadFactory.CreateTensorHandle(numDetectionInfo); |
| 62 | |
| 63 | armnn::ScopedCpuTensorHandle anchorsTensor(anchorsInfo); |
| 64 | AllocateAndCopyDataToITensorHandle(&anchorsTensor, &anchors[0][0]); |
| 65 | |
| 66 | armnn::DetectionPostProcessQueueDescriptor data; |
| 67 | data.m_Parameters.m_UseRegularNms = useRegularNms; |
| 68 | data.m_Parameters.m_MaxDetections = 3; |
| 69 | data.m_Parameters.m_MaxClassesPerDetection = 1; |
| 70 | data.m_Parameters.m_DetectionsPerClass =1; |
| 71 | data.m_Parameters.m_NmsScoreThreshold = 0.0; |
| 72 | data.m_Parameters.m_NmsIouThreshold = 0.5; |
| 73 | data.m_Parameters.m_NumClasses = 2; |
| 74 | data.m_Parameters.m_ScaleY = 10.0; |
| 75 | data.m_Parameters.m_ScaleX = 10.0; |
| 76 | data.m_Parameters.m_ScaleH = 5.0; |
| 77 | data.m_Parameters.m_ScaleW = 5.0; |
| 78 | data.m_Anchors = &anchorsTensor; |
| 79 | |
| 80 | armnn::WorkloadInfo info; |
| 81 | AddInputToWorkload(data, info, boxEncodingsInfo, boxedHandle.get()); |
| 82 | AddInputToWorkload(data, info, scoresInfo, scoreshandle.get()); |
| 83 | AddOutputToWorkload(data, info, detectionBoxesInfo, outputBoxesHandle.get()); |
| 84 | AddOutputToWorkload(data, info, detectionClassesInfo, classesHandle.get()); |
| 85 | AddOutputToWorkload(data, info, detectionScoresInfo, outputScoresHandle.get()); |
| 86 | AddOutputToWorkload(data, info, numDetectionInfo, numDetectionHandle.get()); |
| 87 | |
| 88 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDetectionPostProcess(data, info); |
| 89 | |
| 90 | boxedHandle->Allocate(); |
| 91 | scoreshandle->Allocate(); |
| 92 | outputBoxesHandle->Allocate(); |
| 93 | classesHandle->Allocate(); |
| 94 | outputScoresHandle->Allocate(); |
| 95 | numDetectionHandle->Allocate(); |
| 96 | |
| 97 | CopyDataToITensorHandle(boxedHandle.get(), boxEncodings.origin()); |
| 98 | CopyDataToITensorHandle(scoreshandle.get(), scores.origin()); |
| 99 | |
| 100 | workload->Execute(); |
| 101 | |
| 102 | CopyDataFromITensorHandle(detectionBoxesResult.output.origin(), outputBoxesHandle.get()); |
| 103 | CopyDataFromITensorHandle(detectionClassesResult.output.origin(), classesHandle.get()); |
| 104 | CopyDataFromITensorHandle(detectionScoresResult.output.origin(), outputScoresHandle.get()); |
| 105 | CopyDataFromITensorHandle(numDetectionsResult.output.origin(), numDetectionHandle.get()); |
| 106 | |
| 107 | BOOST_TEST(CompareTensors(detectionBoxesResult.output, detectionBoxesResult.outputExpected)); |
| 108 | BOOST_TEST(CompareTensors(detectionClassesResult.output, detectionClassesResult.outputExpected)); |
| 109 | BOOST_TEST(CompareTensors(detectionScoresResult.output, detectionScoresResult.outputExpected)); |
| 110 | BOOST_TEST(CompareTensors(numDetectionsResult.output, numDetectionsResult.outputExpected)); |
| 111 | } |
| 112 | |
| 113 | inline void QuantizeData(uint8_t* quant, const float* dequant, const armnn::TensorInfo& info) |
| 114 | { |
| 115 | for (size_t i = 0; i < info.GetNumElements(); i++) |
| 116 | { |
| 117 | quant[i] = armnn::Quantize<uint8_t>(dequant[i], info.GetQuantizationScale(), info.GetQuantizationOffset()); |
| 118 | } |
| 119 | } |
| 120 | |
| 121 | template <typename FactoryType> |
| 122 | void DetectionPostProcessRegularNmsFloatTest() |
| 123 | { |
| 124 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| 125 | armnn::TensorInfo scoresInfo({ 1, 6, 3}, armnn::DataType::Float32); |
| 126 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| 127 | |
| 128 | std::vector<float> boxEncodingsData({ |
| 129 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 130 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 131 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 132 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 133 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 134 | 0.0f, 0.0f, 0.0f, 0.0f |
| 135 | }); |
| 136 | std::vector<float> scoresData({ |
| 137 | 0.0f, 0.9f, 0.8f, |
| 138 | 0.0f, 0.75f, 0.72f, |
| 139 | 0.0f, 0.6f, 0.5f, |
| 140 | 0.0f, 0.93f, 0.95f, |
| 141 | 0.0f, 0.5f, 0.4f, |
| 142 | 0.0f, 0.3f, 0.2f |
| 143 | }); |
| 144 | std::vector<float> anchorsData({ |
| 145 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 146 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 147 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 148 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 149 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 150 | 0.5f, 100.5f, 1.0f, 1.0f |
| 151 | }); |
| 152 | |
| 153 | std::vector<float> expectedDetectionBoxes({ |
| 154 | 0.0f, 10.0f, 1.0f, 11.0f, |
| 155 | 0.0f, 10.0f, 1.0f, 11.0f, |
| 156 | 0.0f, 0.0f, 0.0f, 0.0f |
| 157 | }); |
| 158 | std::vector<float> expectedDetectionScores({ 0.95f, 0.93f, 0.0f }); |
| 159 | std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f }); |
| 160 | std::vector<float> expectedNumDetections({ 2.0f }); |
| 161 | |
| 162 | return DetectionPostProcessImpl<FactoryType, armnn::DataType::Float32>(boxEncodingsInfo, |
| 163 | scoresInfo, |
| 164 | anchorsInfo, |
| 165 | boxEncodingsData, |
| 166 | scoresData, |
| 167 | anchorsData, |
| 168 | expectedDetectionBoxes, |
| 169 | expectedDetectionClasses, |
| 170 | expectedDetectionScores, |
| 171 | expectedNumDetections, |
| 172 | true); |
| 173 | } |
| 174 | |
| 175 | template <typename FactoryType> |
| 176 | void DetectionPostProcessRegularNmsUint8Test() |
| 177 | { |
| 178 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::QuantisedAsymm8); |
| 179 | armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::QuantisedAsymm8); |
| 180 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::QuantisedAsymm8); |
| 181 | |
| 182 | boxEncodingsInfo.SetQuantizationScale(1.0f); |
| 183 | boxEncodingsInfo.SetQuantizationOffset(1); |
| 184 | scoresInfo.SetQuantizationScale(0.01f); |
| 185 | scoresInfo.SetQuantizationOffset(0); |
| 186 | anchorsInfo.SetQuantizationScale(0.5f); |
| 187 | anchorsInfo.SetQuantizationOffset(0); |
| 188 | |
| 189 | std::vector<float> boxEncodings({ |
| 190 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 191 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 192 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 193 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 194 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 195 | 0.0f, 0.0f, 0.0f, 0.0f |
| 196 | }); |
| 197 | std::vector<float> scores({ |
| 198 | 0.0f, 0.9f, 0.8f, |
| 199 | 0.0f, 0.75f, 0.72f, |
| 200 | 0.0f, 0.6f, 0.5f, |
| 201 | 0.0f, 0.93f, 0.95f, |
| 202 | 0.0f, 0.5f, 0.4f, |
| 203 | 0.0f, 0.3f, 0.2f |
| 204 | }); |
| 205 | std::vector<float> anchors({ |
| 206 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 207 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 208 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 209 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 210 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 211 | 0.5f, 100.5f, 1.0f, 1.0f |
| 212 | }); |
| 213 | |
| 214 | std::vector<uint8_t> boxEncodingsData(boxEncodings.size(), 0); |
| 215 | std::vector<uint8_t> scoresData(scores.size(), 0); |
| 216 | std::vector<uint8_t> anchorsData(anchors.size(), 0); |
| 217 | QuantizeData(boxEncodingsData.data(), boxEncodings.data(), boxEncodingsInfo); |
| 218 | QuantizeData(scoresData.data(), scores.data(), scoresInfo); |
| 219 | QuantizeData(anchorsData.data(), anchors.data(), anchorsInfo); |
| 220 | |
| 221 | std::vector<float> expectedDetectionBoxes({ |
| 222 | 0.0f, 10.0f, 1.0f, 11.0f, |
| 223 | 0.0f, 10.0f, 1.0f, 11.0f, |
| 224 | 0.0f, 0.0f, 0.0f, 0.0f |
| 225 | }); |
| 226 | std::vector<float> expectedDetectionScores({ 0.95f, 0.93f, 0.0f }); |
| 227 | std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f }); |
| 228 | std::vector<float> expectedNumDetections({ 2.0f }); |
| 229 | |
| 230 | return DetectionPostProcessImpl<FactoryType, armnn::DataType::QuantisedAsymm8>(boxEncodingsInfo, |
| 231 | scoresInfo, |
| 232 | anchorsInfo, |
| 233 | boxEncodingsData, |
| 234 | scoresData, |
| 235 | anchorsData, |
| 236 | expectedDetectionBoxes, |
| 237 | expectedDetectionClasses, |
| 238 | expectedDetectionScores, |
| 239 | expectedNumDetections, |
| 240 | true); |
| 241 | } |
| 242 | |
| 243 | template <typename FactoryType> |
| 244 | void DetectionPostProcessFastNmsFloatTest() |
| 245 | { |
| 246 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| 247 | armnn::TensorInfo scoresInfo({ 1, 6, 3}, armnn::DataType::Float32); |
| 248 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| 249 | |
| 250 | std::vector<float> boxEncodingsData({ |
| 251 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 252 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 253 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 254 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 255 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 256 | 0.0f, 0.0f, 0.0f, 0.0f |
| 257 | }); |
| 258 | std::vector<float> scoresData({ |
| 259 | 0.0f, 0.9f, 0.8f, |
| 260 | 0.0f, 0.75f, 0.72f, |
| 261 | 0.0f, 0.6f, 0.5f, |
| 262 | 0.0f, 0.93f, 0.95f, |
| 263 | 0.0f, 0.5f, 0.4f, |
| 264 | 0.0f, 0.3f, 0.2f |
| 265 | }); |
| 266 | std::vector<float> anchorsData({ |
| 267 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 268 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 269 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 270 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 271 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 272 | 0.5f, 100.5f, 1.0f, 1.0f |
| 273 | }); |
| 274 | |
| 275 | std::vector<float> expectedDetectionBoxes({ |
| 276 | 0.0f, 10.0f, 1.0f, 11.0f, |
| 277 | 0.0f, 0.0f, 1.0f, 1.0f, |
| 278 | 0.0f, 100.0f, 1.0f, 101.0f |
| 279 | }); |
| 280 | std::vector<float> expectedDetectionScores({ 0.95f, 0.9f, 0.3f }); |
| 281 | std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f }); |
| 282 | std::vector<float> expectedNumDetections({ 3.0f }); |
| 283 | |
| 284 | return DetectionPostProcessImpl<FactoryType, armnn::DataType::Float32>(boxEncodingsInfo, |
| 285 | scoresInfo, |
| 286 | anchorsInfo, |
| 287 | boxEncodingsData, |
| 288 | scoresData, |
| 289 | anchorsData, |
| 290 | expectedDetectionBoxes, |
| 291 | expectedDetectionClasses, |
| 292 | expectedDetectionScores, |
| 293 | expectedNumDetections, |
| 294 | false); |
| 295 | } |
| 296 | |
| 297 | template <typename FactoryType> |
| 298 | void DetectionPostProcessFastNmsUint8Test() |
| 299 | { |
| 300 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::QuantisedAsymm8); |
| 301 | armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::QuantisedAsymm8); |
| 302 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::QuantisedAsymm8); |
| 303 | |
| 304 | boxEncodingsInfo.SetQuantizationScale(1.0f); |
| 305 | boxEncodingsInfo.SetQuantizationOffset(1); |
| 306 | scoresInfo.SetQuantizationScale(0.01f); |
| 307 | scoresInfo.SetQuantizationOffset(0); |
| 308 | anchorsInfo.SetQuantizationScale(0.5f); |
| 309 | anchorsInfo.SetQuantizationOffset(0); |
| 310 | |
| 311 | std::vector<float> boxEncodings({ |
| 312 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 313 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 314 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 315 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 316 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 317 | 0.0f, 0.0f, 0.0f, 0.0f |
| 318 | }); |
| 319 | std::vector<float> scores({ |
| 320 | 0.0f, 0.9f, 0.8f, |
| 321 | 0.0f, 0.75f, 0.72f, |
| 322 | 0.0f, 0.6f, 0.5f, |
| 323 | 0.0f, 0.93f, 0.95f, |
| 324 | 0.0f, 0.5f, 0.4f, |
| 325 | 0.0f, 0.3f, 0.2f |
| 326 | }); |
| 327 | std::vector<float> anchors({ |
| 328 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 329 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 330 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 331 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 332 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 333 | 0.5f, 100.5f, 1.0f, 1.0f |
| 334 | }); |
| 335 | |
| 336 | std::vector<uint8_t> boxEncodingsData(boxEncodings.size(), 0); |
| 337 | std::vector<uint8_t> scoresData(scores.size(), 0); |
| 338 | std::vector<uint8_t> anchorsData(anchors.size(), 0); |
| 339 | QuantizeData(boxEncodingsData.data(), boxEncodings.data(), boxEncodingsInfo); |
| 340 | QuantizeData(scoresData.data(), scores.data(), scoresInfo); |
| 341 | QuantizeData(anchorsData.data(), anchors.data(), anchorsInfo); |
| 342 | |
| 343 | std::vector<float> expectedDetectionBoxes({ |
| 344 | 0.0f, 10.0f, 1.0f, 11.0f, |
| 345 | 0.0f, 0.0f, 1.0f, 1.0f, |
| 346 | 0.0f, 100.0f, 1.0f, 101.0f |
| 347 | }); |
| 348 | std::vector<float> expectedDetectionScores({ 0.95f, 0.9f, 0.3f }); |
| 349 | std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f }); |
| 350 | std::vector<float> expectedNumDetections({ 3.0f }); |
| 351 | |
| 352 | return DetectionPostProcessImpl<FactoryType, armnn::DataType::QuantisedAsymm8>(boxEncodingsInfo, |
| 353 | scoresInfo, |
| 354 | anchorsInfo, |
| 355 | boxEncodingsData, |
| 356 | scoresData, |
| 357 | anchorsData, |
| 358 | expectedDetectionBoxes, |
| 359 | expectedDetectionClasses, |
| 360 | expectedDetectionScores, |
| 361 | expectedNumDetections, |
| 362 | false); |
| 363 | } |