aboutsummaryrefslogtreecommitdiff
path: root/src/backends/reference/workloads/DetectionPostProcess.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'src/backends/reference/workloads/DetectionPostProcess.cpp')
-rw-r--r--src/backends/reference/workloads/DetectionPostProcess.cpp323
1 files changed, 0 insertions, 323 deletions
diff --git a/src/backends/reference/workloads/DetectionPostProcess.cpp b/src/backends/reference/workloads/DetectionPostProcess.cpp
deleted file mode 100644
index 96e57803a1..0000000000
--- a/src/backends/reference/workloads/DetectionPostProcess.cpp
+++ /dev/null
@@ -1,323 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "DetectionPostProcess.hpp"
-
-
-#include <boost/assert.hpp>
-#include <boost/numeric/conversion/cast.hpp>
-
-#include <algorithm>
-#include <numeric>
-
-namespace armnn
-{
-
-std::vector<unsigned int> GenerateRangeK(unsigned int k)
-{
- std::vector<unsigned int> range(k);
- std::iota(range.begin(), range.end(), 0);
- return range;
-}
-
-void TopKSort(unsigned int k, unsigned int* indices, const float* values, unsigned int numElement)
-{
- std::partial_sort(indices, indices + k, indices + numElement,
- [&values](unsigned int i, unsigned int j) { return values[i] > values[j]; });
-}
-
-float IntersectionOverUnion(const float* boxI, const float* boxJ)
-{
- // Box-corner format: ymin, xmin, ymax, xmax.
- const int yMin = 0;
- const int xMin = 1;
- const int yMax = 2;
- const int xMax = 3;
- float areaI = (boxI[yMax] - boxI[yMin]) * (boxI[xMax] - boxI[xMin]);
- float areaJ = (boxJ[yMax] - boxJ[yMin]) * (boxJ[xMax] - boxJ[xMin]);
- float yMinIntersection = std::max(boxI[yMin], boxJ[yMin]);
- float xMinIntersection = std::max(boxI[xMin], boxJ[xMin]);
- float yMaxIntersection = std::min(boxI[yMax], boxJ[yMax]);
- float xMaxIntersection = std::min(boxI[xMax], boxJ[xMax]);
- float areaIntersection = std::max(yMaxIntersection - yMinIntersection, 0.0f) *
- std::max(xMaxIntersection - xMinIntersection, 0.0f);
- float areaUnion = areaI + areaJ - areaIntersection;
- 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)
-{
- // Select boxes that have scores above a given threshold.
- std::vector<float> scoresAboveThreshold;
- std::vector<unsigned int> indicesAboveThreshold;
- for (unsigned int i = 0; i < numBoxes; ++i)
- {
- if (scores[i] >= nmsScoreThreshold)
- {
- scoresAboveThreshold.push_back(scores[i]);
- indicesAboveThreshold.push_back(i);
- }
- }
-
- // 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);
-
- // Number of output cannot be more than max detections specified in the option.
- unsigned int numOutput = std::min(maxDetection, numAboveThreshold);
- std::vector<unsigned int> outputIndices;
- std::vector<bool> visited(numAboveThreshold, false);
-
- // Prune out the boxes with high intersection over union by keeping the box with higher score.
- for (unsigned int i = 0; i < numAboveThreshold; ++i)
- {
- if (outputIndices.size() >= numOutput)
- {
- break;
- }
- if (!visited[sortedIndices[i]])
- {
- outputIndices.push_back(indicesAboveThreshold[sortedIndices[i]]);
- }
- for (unsigned int j = i + 1; j < numAboveThreshold; ++j)
- {
- unsigned int iIndex = indicesAboveThreshold[sortedIndices[i]] * 4;
- unsigned int jIndex = indicesAboveThreshold[sortedIndices[j]] * 4;
- if (IntersectionOverUnion(&boxCorners[iIndex], &boxCorners[jIndex]) > nmsIouThreshold)
- {
- visited[sortedIndices[j]] = true;
- }
- }
- }
- 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)
-{
- for (unsigned int i = 0; i < numOutput; ++i)
- {
- unsigned int boxIndex = i * 4;
- if (i < numSelected)
- {
- unsigned int boxCornorIndex = selectedBoxes[outputIndices[i]] * 4;
- detectionScores[i] = selectedScores[outputIndices[i]];
- detectionClasses[i] = boost::numeric_cast<float>(selectedClasses[outputIndices[i]]);
- detectionBoxes[boxIndex] = boxCorners[boxCornorIndex];
- detectionBoxes[boxIndex + 1] = boxCorners[boxCornorIndex + 1];
- detectionBoxes[boxIndex + 2] = boxCorners[boxCornorIndex + 2];
- detectionBoxes[boxIndex + 3] = boxCorners[boxCornorIndex + 3];
- }
- else
- {
- detectionScores[i] = 0.0f;
- detectionClasses[i] = 0.0f;
- detectionBoxes[boxIndex] = 0.0f;
- detectionBoxes[boxIndex + 1] = 0.0f;
- detectionBoxes[boxIndex + 2] = 0.0f;
- detectionBoxes[boxIndex + 3] = 0.0f;
- }
- }
- numDetections[0] = boost::numeric_cast<float>(numSelected);
-}
-
-void DetectionPostProcess(const TensorInfo& boxEncodingsInfo,
- const TensorInfo& scoresInfo,
- const TensorInfo& anchorsInfo,
- const TensorInfo& detectionBoxesInfo,
- const TensorInfo& detectionClassesInfo,
- const TensorInfo& detectionScoresInfo,
- const TensorInfo& numDetectionsInfo,
- const DetectionPostProcessDescriptor& desc,
- Decoder<float>& boxEncodings,
- Decoder<float>& scores,
- Decoder<float>& anchors,
- float* detectionBoxes,
- float* detectionClasses,
- float* detectionScores,
- float* numDetections)
-{
- boost::ignore_unused(anchorsInfo, detectionClassesInfo, detectionScoresInfo, numDetectionsInfo);
-
- // 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());
-
- 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;
-
- // ymin
- boxCorners[indexY] = yCentre - halfH;
- // xmin
- boxCorners[indexX] = xCentre - halfW;
- // ymax
- boxCorners[indexH] = yCentre + halfH;
- // xmax
- boxCorners[indexW] = xCentre + halfW;
-
- BOOST_ASSERT(boxCorners[indexY] < boxCorners[indexH]);
- BOOST_ASSERT(boxCorners[indexX] < boxCorners[indexW]);
- }
-
- 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;
- 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)
- {
- // For each boxes, get scores of the boxes for the class c.
- for (unsigned int i = 0; i < numBoxes; ++i)
- {
- classScores[i] = decodedScores[i * numClassesWithBg + c + 1];
- }
- std::vector<unsigned int> selectedIndices = NonMaxSuppression(numBoxes,
- boxCorners,
- classScores,
- desc.m_NmsScoreThreshold,
- desc.m_DetectionsPerClass,
- desc.m_NmsIouThreshold);
-
- for (unsigned int i = 0; i < selectedIndices.size(); ++i)
- {
- selectedBoxesAfterNms.push_back(selectedIndices[i]);
- selectedScoresAfterNms.push_back(classScores[selectedIndices[i]]);
- selectedClasses.push_back(c);
- }
- }
-
- // Select max detection numbers of the highest score across all classes
- unsigned int numSelected = boost::numeric_cast<unsigned int>(selectedBoxesAfterNms.size());
- unsigned int numOutput = std::min(desc.m_MaxDetections, numSelected);
-
- // Sort the max scores among the selected indices.
- std::vector<unsigned int> outputIndices = GenerateRangeK(numSelected);
- TopKSort(numOutput, outputIndices.data(), selectedScoresAfterNms.data(), numSelected);
-
- AllocateOutputData(detectionBoxesInfo.GetShape()[1], numOutput, boxCorners, outputIndices,
- selectedBoxesAfterNms, selectedClasses, selectedScoresAfterNms,
- detectionBoxes, detectionScores, detectionClasses, numDetections);
- }
- else
- {
- // Perform Fast NMS.
- // Select max scores of boxes and perform NMS on max scores,
- // select max detection numbers of the highest score
- unsigned int numClassesPerBox = std::min(desc.m_MaxClassesPerDetection, desc.m_NumClasses);
- std::vector<float> maxScores;
- std::vector<unsigned int>boxIndices;
- std::vector<unsigned int>maxScoreClasses;
-
- for (unsigned int box = 0; box < numBoxes; ++box)
- {
- unsigned int scoreIndex = box * numClassesWithBg + 1;
-
- // Get the max scores of the box.
- std::vector<unsigned int> maxScoreIndices = GenerateRangeK(desc.m_NumClasses);
- TopKSort(numClassesPerBox, maxScoreIndices.data(),
- decodedScores.data() + scoreIndex, desc.m_NumClasses);
-
- for (unsigned int i = 0; i < numClassesPerBox; ++i)
- {
- maxScores.push_back(decodedScores[scoreIndex + maxScoreIndices[i]]);
- maxScoreClasses.push_back(maxScoreIndices[i]);
- boxIndices.push_back(box);
- }
- }
-
- // Perform NMS on max scores
- std::vector<unsigned int> selectedIndices = NonMaxSuppression(numBoxes, boxCorners, maxScores,
- desc.m_NmsScoreThreshold,
- desc.m_MaxDetections,
- desc.m_NmsIouThreshold);
-
- unsigned int numSelected = boost::numeric_cast<unsigned int>(selectedIndices.size());
- unsigned int numOutput = std::min(desc.m_MaxDetections, numSelected);
-
- AllocateOutputData(detectionBoxesInfo.GetShape()[1], numOutput, boxCorners, selectedIndices,
- boxIndices, maxScoreClasses, maxScores,
- detectionBoxes, detectionScores, detectionClasses, numDetections);
- }
-}
-
-} // namespace armnn
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-02 21:19:11 +0000