| // |
| // Copyright © 2019,2021,2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <CommonTestUtils.hpp> |
| |
| #include <armnn/INetwork.hpp> |
| #include <ResolveType.hpp> |
| |
| #include <doctest/doctest.h> |
| |
| namespace{ |
| |
| template<typename T> |
| armnn::INetworkPtr CreateDetectionPostProcessNetwork(const armnn::TensorInfo& boxEncodingsInfo, |
| const armnn::TensorInfo& scoresInfo, |
| const armnn::TensorInfo& anchorsInfo, |
| const std::vector<T>& anchors, |
| bool useRegularNms) |
| { |
| 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); |
| |
| armnn::DetectionPostProcessDescriptor desc; |
| desc.m_UseRegularNms = useRegularNms; |
| desc.m_MaxDetections = 3; |
| desc.m_MaxClassesPerDetection = 1; |
| desc.m_DetectionsPerClass =1; |
| desc.m_NmsScoreThreshold = 0.0; |
| desc.m_NmsIouThreshold = 0.5; |
| desc.m_NumClasses = 2; |
| desc.m_ScaleY = 10.0; |
| desc.m_ScaleX = 10.0; |
| desc.m_ScaleH = 5.0; |
| desc.m_ScaleW = 5.0; |
| |
| armnn::INetworkPtr net(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* boxesLayer = net->AddInputLayer(0); |
| armnn::IConnectableLayer* scoresLayer = net->AddInputLayer(1); |
| armnn::ConstTensor anchorsTensor(anchorsInfo, anchors.data()); |
| armnn::IConnectableLayer* detectionLayer = net->AddDetectionPostProcessLayer(desc, anchorsTensor, |
| "DetectionPostProcess"); |
| armnn::IConnectableLayer* detectionBoxesLayer = net->AddOutputLayer(0, "detectionBoxes"); |
| armnn::IConnectableLayer* detectionClassesLayer = net->AddOutputLayer(1, "detectionClasses"); |
| armnn::IConnectableLayer* detectionScoresLayer = net->AddOutputLayer(2, "detectionScores"); |
| armnn::IConnectableLayer* numDetectionLayer = net->AddOutputLayer(3, "numDetection"); |
| Connect(boxesLayer, detectionLayer, boxEncodingsInfo, 0, 0); |
| Connect(scoresLayer, detectionLayer, scoresInfo, 0, 1); |
| Connect(detectionLayer, detectionBoxesLayer, detectionBoxesInfo, 0, 0); |
| Connect(detectionLayer, detectionClassesLayer, detectionClassesInfo, 1, 0); |
| Connect(detectionLayer, detectionScoresLayer, detectionScoresInfo, 2, 0); |
| Connect(detectionLayer, numDetectionLayer, numDetectionInfo, 3, 0); |
| |
| return net; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void DetectionPostProcessEndToEnd(const std::vector<BackendId>& backends, bool useRegularNms, |
| const std::vector<T>& boxEncodings, |
| const std::vector<T>& scores, |
| const std::vector<T>& anchors, |
| const std::vector<float>& expectedDetectionBoxes, |
| const std::vector<float>& expectedDetectionClasses, |
| const std::vector<float>& expectedDetectionScores, |
| const std::vector<float>& expectedNumDetections, |
| float boxScale = 1.0f, |
| int32_t boxOffset = 0, |
| float scoreScale = 1.0f, |
| int32_t scoreOffset = 0, |
| float anchorScale = 1.0f, |
| int32_t anchorOffset = 0) |
| { |
| armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, ArmnnType); |
| armnn::TensorInfo scoresInfo({ 1, 6, 3}, ArmnnType); |
| armnn::TensorInfo anchorsInfo({ 6, 4 }, ArmnnType); |
| |
| boxEncodingsInfo.SetQuantizationScale(boxScale); |
| boxEncodingsInfo.SetQuantizationOffset(boxOffset); |
| boxEncodingsInfo.SetConstant(true); |
| scoresInfo.SetQuantizationScale(scoreScale); |
| scoresInfo.SetQuantizationOffset(scoreOffset); |
| scoresInfo.SetConstant(true); |
| anchorsInfo.SetQuantizationScale(anchorScale); |
| anchorsInfo.SetQuantizationOffset(anchorOffset); |
| anchorsInfo.SetConstant(true); |
| |
| // Builds up the structure of the network |
| armnn::INetworkPtr net = CreateDetectionPostProcessNetwork<T>(boxEncodingsInfo, scoresInfo, |
| anchorsInfo, anchors, useRegularNms); |
| |
| CHECK(net); |
| |
| std::map<int, std::vector<T>> inputTensorData = {{ 0, boxEncodings }, { 1, scores }}; |
| std::map<int, std::vector<float>> expectedOutputData = {{ 0, expectedDetectionBoxes }, |
| { 1, expectedDetectionClasses }, |
| { 2, expectedDetectionScores }, |
| { 3, expectedNumDetections }}; |
| |
| EndToEndLayerTestImpl<ArmnnType, armnn::DataType::Float32>( |
| std::move(net), inputTensorData, expectedOutputData, backends); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void DetectionPostProcessRegularNmsEndToEnd(const std::vector<BackendId>& backends, |
| const std::vector<T>& boxEncodings, |
| const std::vector<T>& scores, |
| const std::vector<T>& anchors, |
| float boxScale = 1.0f, |
| int32_t boxOffset = 0, |
| float scoreScale = 1.0f, |
| int32_t scoreOffset = 0, |
| float anchorScale = 1.0f, |
| int32_t anchorOffset = 0) |
| { |
| 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 }); |
| |
| DetectionPostProcessEndToEnd<ArmnnType>(backends, true, boxEncodings, scores, anchors, |
| expectedDetectionBoxes, expectedDetectionClasses, |
| expectedDetectionScores, expectedNumDetections, |
| boxScale, boxOffset, scoreScale, scoreOffset, |
| anchorScale, anchorOffset); |
| |
| }; |
| |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void DetectionPostProcessFastNmsEndToEnd(const std::vector<BackendId>& backends, |
| const std::vector<T>& boxEncodings, |
| const std::vector<T>& scores, |
| const std::vector<T>& anchors, |
| float boxScale = 1.0f, |
| int32_t boxOffset = 0, |
| float scoreScale = 1.0f, |
| int32_t scoreOffset = 0, |
| float anchorScale = 1.0f, |
| int32_t anchorOffset = 0) |
| { |
| 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 }); |
| |
| DetectionPostProcessEndToEnd<ArmnnType>(backends, false, boxEncodings, scores, anchors, |
| expectedDetectionBoxes, expectedDetectionClasses, |
| expectedDetectionScores, expectedNumDetections, |
| boxScale, boxOffset, scoreScale, scoreOffset, |
| anchorScale, anchorOffset); |
| |
| }; |
| |
| } // anonymous namespace |