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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/INetwork.hpp>
#include <backendsCommon/test/CommonTestUtils.hpp>
#include <ResolveType.hpp>
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);
scoresInfo.SetQuantizationScale(scoreScale);
scoresInfo.SetQuantizationOffset(scoreOffset);
anchorsInfo.SetQuantizationScale(anchorScale);
anchorsInfo.SetQuantizationOffset(anchorOffset);
// Builds up the structure of the network
armnn::INetworkPtr net = CreateDetectionPostProcessNetwork<T>(boxEncodingsInfo, scoresInfo,
anchorsInfo, anchors, useRegularNms);
BOOST_TEST_CHECKPOINT("create a network");
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>(
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
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