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path: root/src/backends/reference/workloads/DetectionPostProcess.cpp
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-rw-r--r--src/backends/reference/workloads/DetectionPostProcess.cpp111
1 files changed, 84 insertions, 27 deletions
diff --git a/src/backends/reference/workloads/DetectionPostProcess.cpp b/src/backends/reference/workloads/DetectionPostProcess.cpp
index d3790f22ab..d475dd8ac0 100644
--- a/src/backends/reference/workloads/DetectionPostProcess.cpp
+++ b/src/backends/reference/workloads/DetectionPostProcess.cpp
@@ -13,7 +13,7 @@
#include <algorithm>
#include <numeric>
-namespace
+namespace armnn
{
std::vector<unsigned int> GenerateRangeK(unsigned int k)
@@ -48,9 +48,12 @@ float IntersectionOverUnion(const float* boxI, const float* boxJ)
return areaIntersection / areaUnion;
}
-std::vector<unsigned int> NonMaxSuppression(unsigned int numBoxes, const std::vector<float>& boxCorners,
- const std::vector<float>& scores, float nmsScoreThreshold,
- unsigned int maxDetection, float nmsIouThreshold)
+std::vector<unsigned int> NonMaxSuppression(unsigned int numBoxes,
+ const std::vector<float>& boxCorners,
+ const std::vector<float>& scores,
+ float nmsScoreThreshold,
+ unsigned int maxDetection,
+ float nmsIouThreshold)
{
// Select boxes that have scores above a given threshold.
std::vector<float> scoresAboveThreshold;
@@ -67,7 +70,7 @@ std::vector<unsigned int> NonMaxSuppression(unsigned int numBoxes, const std::ve
// Sort the indices based on scores.
unsigned int numAboveThreshold = boost::numeric_cast<unsigned int>(scoresAboveThreshold.size());
std::vector<unsigned int> sortedIndices = GenerateRangeK(numAboveThreshold);
- TopKSort(numAboveThreshold,sortedIndices.data(), scoresAboveThreshold.data(), numAboveThreshold);
+ TopKSort(numAboveThreshold, sortedIndices.data(), scoresAboveThreshold.data(), numAboveThreshold);
// Number of output cannot be more than max detections specified in the option.
unsigned int numOutput = std::min(maxDetection, numAboveThreshold);
@@ -98,10 +101,17 @@ std::vector<unsigned int> NonMaxSuppression(unsigned int numBoxes, const std::ve
return outputIndices;
}
-void AllocateOutputData(unsigned int numOutput, unsigned int numSelected, const std::vector<float>& boxCorners,
- const std::vector<unsigned int>& outputIndices, const std::vector<unsigned int>& selectedBoxes,
- const std::vector<unsigned int>& selectedClasses, const std::vector<float>& selectedScores,
- float* detectionBoxes, float* detectionScores, float* detectionClasses, float* numDetections)
+void AllocateOutputData(unsigned int numOutput,
+ unsigned int numSelected,
+ const std::vector<float>& boxCorners,
+ const std::vector<unsigned int>& outputIndices,
+ const std::vector<unsigned int>& selectedBoxes,
+ const std::vector<unsigned int>& selectedClasses,
+ const std::vector<float>& selectedScores,
+ float* detectionBoxes,
+ float* detectionScores,
+ float* detectionClasses,
+ float* numDetections)
{
for (unsigned int i = 0; i < numOutput; ++i)
{
@@ -129,11 +139,6 @@ void AllocateOutputData(unsigned int numOutput, unsigned int numSelected, const
numDetections[0] = boost::numeric_cast<float>(numSelected);
}
-} // anonymous namespace
-
-namespace armnn
-{
-
void DetectionPostProcess(const TensorInfo& boxEncodingsInfo,
const TensorInfo& scoresInfo,
const TensorInfo& anchorsInfo,
@@ -142,9 +147,9 @@ void DetectionPostProcess(const TensorInfo& boxEncodingsInfo,
const TensorInfo& detectionScoresInfo,
const TensorInfo& numDetectionsInfo,
const DetectionPostProcessDescriptor& desc,
- const float* boxEncodings,
- const float* scores,
- const float* anchors,
+ Decoder<float>& boxEncodings,
+ Decoder<float>& scores,
+ Decoder<float>& anchors,
float* detectionBoxes,
float* detectionClasses,
float* detectionScores,
@@ -153,17 +158,51 @@ void DetectionPostProcess(const TensorInfo& boxEncodingsInfo,
// Transform center-size format which is (ycenter, xcenter, height, width) to box-corner format,
// which represents the lower left corner and the upper right corner (ymin, xmin, ymax, xmax)
std::vector<float> boxCorners(boxEncodingsInfo.GetNumElements());
- unsigned int numBoxes = boxEncodingsInfo.GetShape()[1];
+
+ const unsigned int numBoxes = boxEncodingsInfo.GetShape()[1];
+ const unsigned int numScores = scoresInfo.GetNumElements();
+
for (unsigned int i = 0; i < numBoxes; ++i)
{
+ // Y
+ float boxEncodingY = boxEncodings.Get();
+ float anchorY = anchors.Get();
+
+ ++boxEncodings;
+ ++anchors;
+
+ // X
+ float boxEncodingX = boxEncodings.Get();
+ float anchorX = anchors.Get();
+
+ ++boxEncodings;
+ ++anchors;
+
+ // H
+ float boxEncodingH = boxEncodings.Get();
+ float anchorH = anchors.Get();
+
+ ++boxEncodings;
+ ++anchors;
+
+ // W
+ float boxEncodingW = boxEncodings.Get();
+ float anchorW = anchors.Get();
+
+ ++boxEncodings;
+ ++anchors;
+
+ float yCentre = boxEncodingY / desc.m_ScaleY * anchorH + anchorY;
+ float xCentre = boxEncodingX / desc.m_ScaleX * anchorW + anchorX;
+
+ float halfH = 0.5f * expf(boxEncodingH / desc.m_ScaleH) * anchorH;
+ float halfW = 0.5f * expf(boxEncodingW / desc.m_ScaleW) * anchorW;
+
unsigned int indexY = i * 4;
unsigned int indexX = indexY + 1;
unsigned int indexH = indexX + 1;
unsigned int indexW = indexH + 1;
- float yCentre = boxEncodings[indexY] / desc.m_ScaleY * anchors[indexH] + anchors[indexY];
- float xCentre = boxEncodings[indexX] / desc.m_ScaleX * anchors[indexW] + anchors[indexX];
- float halfH = 0.5f * expf(boxEncodings[indexH] / desc.m_ScaleH) * anchors[indexH];
- float halfW = 0.5f * expf(boxEncodings[indexW] / desc.m_ScaleW) * anchors[indexW];
+
// ymin
boxCorners[indexY] = yCentre - halfH;
// xmin
@@ -179,14 +218,29 @@ void DetectionPostProcess(const TensorInfo& boxEncodingsInfo,
unsigned int numClassesWithBg = desc.m_NumClasses + 1;
+ // Decode scores
+ std::vector<float> decodedScores;
+ decodedScores.reserve(numScores);
+
+ for (unsigned int i = 0u; i < numScores; ++i)
+ {
+ decodedScores.emplace_back(scores.Get());
+ ++scores;
+ }
+
// Perform Non Max Suppression.
if (desc.m_UseRegularNms)
{
// Perform Regular NMS.
// For each class, perform NMS and select max detection numbers of the highest score across all classes.
std::vector<float> classScores(numBoxes);
- std::vector<unsigned int>selectedBoxesAfterNms;
+
+ std::vector<unsigned int> selectedBoxesAfterNms;
+ selectedBoxesAfterNms.reserve(numBoxes);
+
std::vector<float> selectedScoresAfterNms;
+ selectedBoxesAfterNms.reserve(numScores);
+
std::vector<unsigned int> selectedClasses;
for (unsigned int c = 0; c < desc.m_NumClasses; ++c)
@@ -194,9 +248,11 @@ void DetectionPostProcess(const TensorInfo& boxEncodingsInfo,
// For each boxes, get scores of the boxes for the class c.
for (unsigned int i = 0; i < numBoxes; ++i)
{
- classScores[i] = scores[i * numClassesWithBg + c + 1];
+ classScores[i] = decodedScores[i * numClassesWithBg + c + 1];
}
- std::vector<unsigned int> selectedIndices = NonMaxSuppression(numBoxes, boxCorners, classScores,
+ std::vector<unsigned int> selectedIndices = NonMaxSuppression(numBoxes,
+ boxCorners,
+ classScores,
desc.m_NmsScoreThreshold,
desc.m_DetectionsPerClass,
desc.m_NmsIouThreshold);
@@ -237,11 +293,12 @@ void DetectionPostProcess(const TensorInfo& boxEncodingsInfo,
// Get the max scores of the box.
std::vector<unsigned int> maxScoreIndices = GenerateRangeK(desc.m_NumClasses);
- TopKSort(numClassesPerBox, maxScoreIndices.data(), scores + scoreIndex, desc.m_NumClasses);
+ TopKSort(numClassesPerBox, maxScoreIndices.data(),
+ decodedScores.data() + scoreIndex, desc.m_NumClasses);
for (unsigned int i = 0; i < numClassesPerBox; ++i)
{
- maxScores.push_back(scores[scoreIndex + maxScoreIndices[i]]);
+ maxScores.push_back(decodedScores[scoreIndex + maxScoreIndices[i]]);
maxScoreClasses.push_back(maxScoreIndices[i]);
boxIndices.push_back(box);
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-06 13:22:29 +0000