| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include "TensorCopyUtils.hpp" |
| #include <ResolveType.hpp> |
| #include "WorkloadTestUtils.hpp" |
| |
| #include <armnn/Types.hpp> |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| #include <backendsCommon/IBackendInternal.hpp> |
| #include <backendsCommon/WorkloadFactory.hpp> |
| #include <backendsCommon/test/WorkloadFactoryHelper.hpp> |
| #include <test/TensorHelpers.hpp> |
| |
| template <typename FactoryType, armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void DetectionPostProcessImpl(const armnn::TensorInfo& boxEncodingsInfo, |
| const armnn::TensorInfo& scoresInfo, |
| const armnn::TensorInfo& anchorsInfo, |
| const std::vector<T>& boxEncodingsData, |
| const std::vector<T>& scoresData, |
| const std::vector<T>& anchorsData, |
| const std::vector<float>& expectedDetectionBoxes, |
| const std::vector<float>& expectedDetectionClasses, |
| const std::vector<float>& expectedDetectionScores, |
| const std::vector<float>& expectedNumDetections, |
| bool useRegularNms) |
| { |
| std::unique_ptr<armnn::Profiler> profiler = std::make_unique<armnn::Profiler>(); |
| armnn::ProfilerManager::GetInstance().RegisterProfiler(profiler.get()); |
| |
| auto memoryManager = WorkloadFactoryHelper<FactoryType>::GetMemoryManager(); |
| FactoryType workloadFactory = WorkloadFactoryHelper<FactoryType>::GetFactory(memoryManager); |
| |
| auto boxEncodings = MakeTensor<T, 3>(boxEncodingsInfo, boxEncodingsData); |
| auto scores = MakeTensor<T, 3>(scoresInfo, scoresData); |
| auto anchors = MakeTensor<T, 2>(anchorsInfo, anchorsData); |
| |
| armnn::TensorInfo detectionBoxesInfo({ 1, 3, 4 }, armnn::DataType::Float32); |
| armnn::TensorInfo detectionScoresInfo({ 1, 3 }, armnn::DataType::Float32); |
| armnn::TensorInfo detectionClassesInfo({ 1, 3 }, armnn::DataType::Float32); |
| armnn::TensorInfo numDetectionInfo({ 1 }, armnn::DataType::Float32); |
| |
| LayerTestResult<float, 3> detectionBoxesResult(detectionBoxesInfo); |
| detectionBoxesResult.outputExpected = MakeTensor<float, 3>(detectionBoxesInfo, expectedDetectionBoxes); |
| LayerTestResult<float, 2> detectionClassesResult(detectionClassesInfo); |
| detectionClassesResult.outputExpected = MakeTensor<float, 2>(detectionClassesInfo, expectedDetectionClasses); |
| LayerTestResult<float, 2> detectionScoresResult(detectionScoresInfo); |
| detectionScoresResult.outputExpected = MakeTensor<float, 2>(detectionScoresInfo, expectedDetectionScores); |
| LayerTestResult<float, 1> numDetectionsResult(numDetectionInfo); |
| numDetectionsResult.outputExpected = MakeTensor<float, 1>(numDetectionInfo, expectedNumDetections); |
| |
| std::unique_ptr<armnn::ITensorHandle> boxedHandle = workloadFactory.CreateTensorHandle(boxEncodingsInfo); |
| std::unique_ptr<armnn::ITensorHandle> scoreshandle = workloadFactory.CreateTensorHandle(scoresInfo); |
| std::unique_ptr<armnn::ITensorHandle> anchorsHandle = workloadFactory.CreateTensorHandle(anchorsInfo); |
| std::unique_ptr<armnn::ITensorHandle> outputBoxesHandle = workloadFactory.CreateTensorHandle(detectionBoxesInfo); |
| std::unique_ptr<armnn::ITensorHandle> classesHandle = workloadFactory.CreateTensorHandle(detectionClassesInfo); |
| std::unique_ptr<armnn::ITensorHandle> outputScoresHandle = workloadFactory.CreateTensorHandle(detectionScoresInfo); |
| std::unique_ptr<armnn::ITensorHandle> numDetectionHandle = workloadFactory.CreateTensorHandle(numDetectionInfo); |
| |
| armnn::ScopedCpuTensorHandle anchorsTensor(anchorsInfo); |
| AllocateAndCopyDataToITensorHandle(&anchorsTensor, &anchors[0][0]); |
| |
| armnn::DetectionPostProcessQueueDescriptor data; |
| data.m_Parameters.m_UseRegularNms = useRegularNms; |
| data.m_Parameters.m_MaxDetections = 3; |
| data.m_Parameters.m_MaxClassesPerDetection = 1; |
| data.m_Parameters.m_DetectionsPerClass =1; |
| data.m_Parameters.m_NmsScoreThreshold = 0.0; |
| data.m_Parameters.m_NmsIouThreshold = 0.5; |
| data.m_Parameters.m_NumClasses = 2; |
| data.m_Parameters.m_ScaleY = 10.0; |
| data.m_Parameters.m_ScaleX = 10.0; |
| data.m_Parameters.m_ScaleH = 5.0; |
| data.m_Parameters.m_ScaleW = 5.0; |
| data.m_Anchors = &anchorsTensor; |
| |
| armnn::WorkloadInfo info; |
| AddInputToWorkload(data, info, boxEncodingsInfo, boxedHandle.get()); |
| AddInputToWorkload(data, info, scoresInfo, scoreshandle.get()); |
| AddOutputToWorkload(data, info, detectionBoxesInfo, outputBoxesHandle.get()); |
| AddOutputToWorkload(data, info, detectionClassesInfo, classesHandle.get()); |
| AddOutputToWorkload(data, info, detectionScoresInfo, outputScoresHandle.get()); |
| AddOutputToWorkload(data, info, numDetectionInfo, numDetectionHandle.get()); |
| |
| std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDetectionPostProcess(data, info); |
| |
| boxedHandle->Allocate(); |
| scoreshandle->Allocate(); |
| outputBoxesHandle->Allocate(); |
| classesHandle->Allocate(); |
| outputScoresHandle->Allocate(); |
| numDetectionHandle->Allocate(); |
| |
| CopyDataToITensorHandle(boxedHandle.get(), boxEncodings.origin()); |
| CopyDataToITensorHandle(scoreshandle.get(), scores.origin()); |
| |
| workload->Execute(); |
| |
| CopyDataFromITensorHandle(detectionBoxesResult.output.origin(), outputBoxesHandle.get()); |
| CopyDataFromITensorHandle(detectionClassesResult.output.origin(), classesHandle.get()); |
| CopyDataFromITensorHandle(detectionScoresResult.output.origin(), outputScoresHandle.get()); |
| CopyDataFromITensorHandle(numDetectionsResult.output.origin(), numDetectionHandle.get()); |
| |
| BOOST_TEST(CompareTensors(detectionBoxesResult.output, detectionBoxesResult.outputExpected)); |
| BOOST_TEST(CompareTensors(detectionClassesResult.output, detectionClassesResult.outputExpected)); |
| BOOST_TEST(CompareTensors(detectionScoresResult.output, detectionScoresResult.outputExpected)); |
| BOOST_TEST(CompareTensors(numDetectionsResult.output, numDetectionsResult.outputExpected)); |
| } |
| |
| inline void QuantizeData(uint8_t* quant, const float* dequant, const armnn::TensorInfo& info) |
| { |
| for (size_t i = 0; i < info.GetNumElements(); i++) |
| { |
| quant[i] = armnn::Quantize<uint8_t>(dequant[i], info.GetQuantizationScale(), info.GetQuantizationOffset()); |
| } |
| } |
| |
| template <typename FactoryType> |
| void DetectionPostProcessRegularNmsFloatTest() |
| { |
| armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| armnn::TensorInfo scoresInfo({ 1, 6, 3}, armnn::DataType::Float32); |
| armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| |
| std::vector<float> boxEncodingsData({ |
| 0.0f, 0.0f, 0.0f, 0.0f, |
| 0.0f, 1.0f, 0.0f, 0.0f, |
| 0.0f, -1.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f, |
| 0.0f, 1.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f |
| }); |
| std::vector<float> scoresData({ |
| 0.0f, 0.9f, 0.8f, |
| 0.0f, 0.75f, 0.72f, |
| 0.0f, 0.6f, 0.5f, |
| 0.0f, 0.93f, 0.95f, |
| 0.0f, 0.5f, 0.4f, |
| 0.0f, 0.3f, 0.2f |
| }); |
| std::vector<float> anchorsData({ |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 10.5f, 1.0f, 1.0f, |
| 0.5f, 10.5f, 1.0f, 1.0f, |
| 0.5f, 100.5f, 1.0f, 1.0f |
| }); |
| |
| std::vector<float> expectedDetectionBoxes({ |
| 0.0f, 10.0f, 1.0f, 11.0f, |
| 0.0f, 10.0f, 1.0f, 11.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f |
| }); |
| std::vector<float> expectedDetectionScores({ 0.95f, 0.93f, 0.0f }); |
| std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f }); |
| std::vector<float> expectedNumDetections({ 2.0f }); |
| |
| return DetectionPostProcessImpl<FactoryType, armnn::DataType::Float32>(boxEncodingsInfo, |
| scoresInfo, |
| anchorsInfo, |
| boxEncodingsData, |
| scoresData, |
| anchorsData, |
| expectedDetectionBoxes, |
| expectedDetectionClasses, |
| expectedDetectionScores, |
| expectedNumDetections, |
| true); |
| } |
| |
| template <typename FactoryType> |
| void DetectionPostProcessRegularNmsUint8Test() |
| { |
| armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::QuantisedAsymm8); |
| armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::QuantisedAsymm8); |
| armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::QuantisedAsymm8); |
| |
| boxEncodingsInfo.SetQuantizationScale(1.0f); |
| boxEncodingsInfo.SetQuantizationOffset(1); |
| scoresInfo.SetQuantizationScale(0.01f); |
| scoresInfo.SetQuantizationOffset(0); |
| anchorsInfo.SetQuantizationScale(0.5f); |
| anchorsInfo.SetQuantizationOffset(0); |
| |
| std::vector<float> boxEncodings({ |
| 0.0f, 0.0f, 0.0f, 0.0f, |
| 0.0f, 1.0f, 0.0f, 0.0f, |
| 0.0f, -1.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f, |
| 0.0f, 1.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f |
| }); |
| std::vector<float> scores({ |
| 0.0f, 0.9f, 0.8f, |
| 0.0f, 0.75f, 0.72f, |
| 0.0f, 0.6f, 0.5f, |
| 0.0f, 0.93f, 0.95f, |
| 0.0f, 0.5f, 0.4f, |
| 0.0f, 0.3f, 0.2f |
| }); |
| std::vector<float> anchors({ |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 10.5f, 1.0f, 1.0f, |
| 0.5f, 10.5f, 1.0f, 1.0f, |
| 0.5f, 100.5f, 1.0f, 1.0f |
| }); |
| |
| std::vector<uint8_t> boxEncodingsData(boxEncodings.size(), 0); |
| std::vector<uint8_t> scoresData(scores.size(), 0); |
| std::vector<uint8_t> anchorsData(anchors.size(), 0); |
| QuantizeData(boxEncodingsData.data(), boxEncodings.data(), boxEncodingsInfo); |
| QuantizeData(scoresData.data(), scores.data(), scoresInfo); |
| QuantizeData(anchorsData.data(), anchors.data(), anchorsInfo); |
| |
| std::vector<float> expectedDetectionBoxes({ |
| 0.0f, 10.0f, 1.0f, 11.0f, |
| 0.0f, 10.0f, 1.0f, 11.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f |
| }); |
| std::vector<float> expectedDetectionScores({ 0.95f, 0.93f, 0.0f }); |
| std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f }); |
| std::vector<float> expectedNumDetections({ 2.0f }); |
| |
| return DetectionPostProcessImpl<FactoryType, armnn::DataType::QuantisedAsymm8>(boxEncodingsInfo, |
| scoresInfo, |
| anchorsInfo, |
| boxEncodingsData, |
| scoresData, |
| anchorsData, |
| expectedDetectionBoxes, |
| expectedDetectionClasses, |
| expectedDetectionScores, |
| expectedNumDetections, |
| true); |
| } |
| |
| template <typename FactoryType> |
| void DetectionPostProcessFastNmsFloatTest() |
| { |
| armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| armnn::TensorInfo scoresInfo({ 1, 6, 3}, armnn::DataType::Float32); |
| armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| |
| std::vector<float> boxEncodingsData({ |
| 0.0f, 0.0f, 0.0f, 0.0f, |
| 0.0f, 1.0f, 0.0f, 0.0f, |
| 0.0f, -1.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f, |
| 0.0f, 1.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f |
| }); |
| std::vector<float> scoresData({ |
| 0.0f, 0.9f, 0.8f, |
| 0.0f, 0.75f, 0.72f, |
| 0.0f, 0.6f, 0.5f, |
| 0.0f, 0.93f, 0.95f, |
| 0.0f, 0.5f, 0.4f, |
| 0.0f, 0.3f, 0.2f |
| }); |
| std::vector<float> anchorsData({ |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 10.5f, 1.0f, 1.0f, |
| 0.5f, 10.5f, 1.0f, 1.0f, |
| 0.5f, 100.5f, 1.0f, 1.0f |
| }); |
| |
| std::vector<float> expectedDetectionBoxes({ |
| 0.0f, 10.0f, 1.0f, 11.0f, |
| 0.0f, 0.0f, 1.0f, 1.0f, |
| 0.0f, 100.0f, 1.0f, 101.0f |
| }); |
| std::vector<float> expectedDetectionScores({ 0.95f, 0.9f, 0.3f }); |
| std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f }); |
| std::vector<float> expectedNumDetections({ 3.0f }); |
| |
| return DetectionPostProcessImpl<FactoryType, armnn::DataType::Float32>(boxEncodingsInfo, |
| scoresInfo, |
| anchorsInfo, |
| boxEncodingsData, |
| scoresData, |
| anchorsData, |
| expectedDetectionBoxes, |
| expectedDetectionClasses, |
| expectedDetectionScores, |
| expectedNumDetections, |
| false); |
| } |
| |
| template <typename FactoryType> |
| void DetectionPostProcessFastNmsUint8Test() |
| { |
| armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::QuantisedAsymm8); |
| armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::QuantisedAsymm8); |
| armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::QuantisedAsymm8); |
| |
| boxEncodingsInfo.SetQuantizationScale(1.0f); |
| boxEncodingsInfo.SetQuantizationOffset(1); |
| scoresInfo.SetQuantizationScale(0.01f); |
| scoresInfo.SetQuantizationOffset(0); |
| anchorsInfo.SetQuantizationScale(0.5f); |
| anchorsInfo.SetQuantizationOffset(0); |
| |
| std::vector<float> boxEncodings({ |
| 0.0f, 0.0f, 0.0f, 0.0f, |
| 0.0f, 1.0f, 0.0f, 0.0f, |
| 0.0f, -1.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f, |
| 0.0f, 1.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, 0.0f |
| }); |
| std::vector<float> scores({ |
| 0.0f, 0.9f, 0.8f, |
| 0.0f, 0.75f, 0.72f, |
| 0.0f, 0.6f, 0.5f, |
| 0.0f, 0.93f, 0.95f, |
| 0.0f, 0.5f, 0.4f, |
| 0.0f, 0.3f, 0.2f |
| }); |
| std::vector<float> anchors({ |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 0.5f, 1.0f, 1.0f, |
| 0.5f, 10.5f, 1.0f, 1.0f, |
| 0.5f, 10.5f, 1.0f, 1.0f, |
| 0.5f, 100.5f, 1.0f, 1.0f |
| }); |
| |
| std::vector<uint8_t> boxEncodingsData(boxEncodings.size(), 0); |
| std::vector<uint8_t> scoresData(scores.size(), 0); |
| std::vector<uint8_t> anchorsData(anchors.size(), 0); |
| QuantizeData(boxEncodingsData.data(), boxEncodings.data(), boxEncodingsInfo); |
| QuantizeData(scoresData.data(), scores.data(), scoresInfo); |
| QuantizeData(anchorsData.data(), anchors.data(), anchorsInfo); |
| |
| std::vector<float> expectedDetectionBoxes({ |
| 0.0f, 10.0f, 1.0f, 11.0f, |
| 0.0f, 0.0f, 1.0f, 1.0f, |
| 0.0f, 100.0f, 1.0f, 101.0f |
| }); |
| std::vector<float> expectedDetectionScores({ 0.95f, 0.9f, 0.3f }); |
| std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f }); |
| std::vector<float> expectedNumDetections({ 3.0f }); |
| |
| return DetectionPostProcessImpl<FactoryType, armnn::DataType::QuantisedAsymm8>(boxEncodingsInfo, |
| scoresInfo, |
| anchorsInfo, |
| boxEncodingsData, |
| scoresData, |
| anchorsData, |
| expectedDetectionBoxes, |
| expectedDetectionClasses, |
| expectedDetectionScores, |
| expectedNumDetections, |
| false); |
| } |
