chore: import upstream snapshot with attribution
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wehub-resource-sync
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//
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// CPUDetectionPostProcess.cpp
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// MNN
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//
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// Created by MNN on 2019/10/29.
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// Copyright © 2018, Alibaba Group Holding Limited
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#include <math.h>
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#include <numeric>
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#include "backend/cpu/CPUBackend.hpp"
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#include "backend/cpu/CPUDetectionPostProcess.hpp"
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#include "backend/cpu/CPUNonMaxSuppressionV2.hpp"
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namespace MNN {
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static void _decodeBoxes(const Tensor* boxesEncoding, const Tensor* anchors, const CenterSizeEncoding& scaleValues,
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Tensor* decodeBoxes) {
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const int numBoxes = boxesEncoding->length(1);
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const int boxCoordNum = boxesEncoding->length(2);
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const int numAnchors = anchors->length(0);
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const int anchorsCoordNum = anchors->length(1);
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MNN_CHECK(numBoxes == numAnchors, "the number of input boxes should be equal to the number of anchors!");
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MNN_CHECK(boxCoordNum >= 4, "input box encoding ERROR!");
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MNN_CHECK(anchorsCoordNum == 4, "input anchors ERROR!");
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const auto boxesPtr = boxesEncoding->host<float>();
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const auto anchorsPtr = reinterpret_cast<const CenterSizeEncoding*>(anchors->host<float>());
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auto decodeBoxesPtr = reinterpret_cast<BoxCornerEncoding*>(decodeBoxes->host<float>());
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CenterSizeEncoding boxCenterSize;
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CenterSizeEncoding anchor;
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for (int idx = 0; idx < numBoxes; ++idx) {
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const int boxIndex = idx * boxCoordNum;
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boxCenterSize = *reinterpret_cast<const CenterSizeEncoding*>(boxesPtr + boxIndex);
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anchor = anchorsPtr[idx];
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float ycenter = boxCenterSize.y / scaleValues.y * anchor.h + anchor.y;
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float xcenter = boxCenterSize.x / scaleValues.x * anchor.w + anchor.x;
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float halfh = 0.5f * static_cast<float>(exp(boxCenterSize.h / scaleValues.h)) * anchor.h;
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float halfw = 0.5f * static_cast<float>(exp(boxCenterSize.w / scaleValues.w)) * anchor.w;
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auto& curBox = decodeBoxesPtr[idx];
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curBox.ymin = ycenter - halfh;
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curBox.xmin = xcenter - halfw;
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curBox.ymax = ycenter + halfh;
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curBox.xmax = xcenter + halfw;
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}
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}
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static void _NonMaxSuppressionMultiClassFastImpl(const DetectionPostProcessParamT& postProcessParam,
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const Tensor* decodedBoxes, const Tensor* classPredictions,
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Tensor* detectionBoxes, Tensor* detectionClass,
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Tensor* detectionScores, Tensor* numDetections) {
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// decoded_boxes shape is [numBoxes, 4]
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const int numBoxes = decodedBoxes->length(0);
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const int numClasses = postProcessParam.numClasses;
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const int maxClassesPerAnchor = postProcessParam.maxClassesPerDetection;
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const int numClassWithBackground = classPredictions->length(2);
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const int labelOffset = numClassWithBackground - numClasses;
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MNN_ASSERT(maxClassesPerAnchor > 0);
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const int numCategoriesPerAnchor = std::min(maxClassesPerAnchor, numClasses);
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std::vector<float> maxScores;
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maxScores.resize(numBoxes);
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std::vector<int> sortedClassIndices;
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sortedClassIndices.resize(numBoxes * numClasses);
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const auto scoresStartPtr = classPredictions->host<float>();
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// sort scores on every anchor
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for (int idx = 0; idx < numBoxes; ++idx) {
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const auto boxScores = scoresStartPtr + idx * numClassWithBackground + labelOffset;
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auto classIndices = sortedClassIndices.data() + idx * numClasses;
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std::iota(classIndices, classIndices + numClasses, 0);
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std::partial_sort(classIndices, classIndices + numCategoriesPerAnchor, classIndices + numClasses,
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[&boxScores](const int i, const int j) { return boxScores[i] > boxScores[j]; });
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maxScores[idx] = boxScores[classIndices[0]];
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}
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std::vector<int> seleted;
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NonMaxSuppressionSingleClasssImpl(decodedBoxes, maxScores.data(), postProcessParam.maxDetections,
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postProcessParam.iouThreshold, postProcessParam.nmsScoreThreshold, &seleted);
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const auto decodedBoxesPtr = reinterpret_cast<const BoxCornerEncoding*>(decodedBoxes->host<float>());
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auto detectionBoxesPtr = reinterpret_cast<BoxCornerEncoding*>(detectionBoxes->host<float>());
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auto detectionClassesPtr = detectionClass->host<float>();
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auto detectionScoresPtr = detectionScores->host<float>();
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auto numDetectionsPtr = numDetections->host<float>();
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int outputBoxIndex = 0;
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for (const auto& selectedIndex : seleted) {
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const float* boxScores = scoresStartPtr + selectedIndex * numClassWithBackground + labelOffset;
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const int* classIndices = sortedClassIndices.data() + selectedIndex * numClasses;
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for (int col = 0; col < numCategoriesPerAnchor; ++col) {
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int boxOffset = numCategoriesPerAnchor * outputBoxIndex + col;
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detectionBoxesPtr[boxOffset] = decodedBoxesPtr[selectedIndex];
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detectionClassesPtr[boxOffset] = classIndices[col];
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detectionScoresPtr[boxOffset] = boxScores[classIndices[col]];
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outputBoxIndex++;
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}
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}
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*numDetectionsPtr = outputBoxIndex;
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}
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CPUDetectionPostProcess::CPUDetectionPostProcess(Backend* bn, const MNN::Op* op) : Execution(bn) {
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auto param = op->main_as_DetectionPostProcessParam();
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param->UnPackTo(&mParam);
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if (mParam.useRegularNMS) {
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MNN_ERROR("TODO, use regular NMS to process decoded boxes!");
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return;
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}
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}
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ErrorCode CPUDetectionPostProcess::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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auto boxEncodings = inputs[0];
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const int numAnchors = boxEncodings->length(1);
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mDecodedBoxes.reset(Tensor::createDevice<float>({numAnchors, 4}));
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auto allocRes = backend()->onAcquireBuffer(mDecodedBoxes.get(), Backend::DYNAMIC);
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if (!allocRes) {
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return OUT_OF_MEMORY;
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}
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backend()->onReleaseBuffer(mDecodedBoxes.get(), Backend::DYNAMIC);
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return NO_ERROR;
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}
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ErrorCode CPUDetectionPostProcess::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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CenterSizeEncoding scaleValues;
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scaleValues.y = mParam.centerSizeEncoding[0];
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scaleValues.x = mParam.centerSizeEncoding[1];
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scaleValues.h = mParam.centerSizeEncoding[2];
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scaleValues.w = mParam.centerSizeEncoding[3];
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_decodeBoxes(inputs[0], inputs[2], scaleValues, mDecodedBoxes.get());
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if (mParam.useRegularNMS) {
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return NOT_SUPPORT;
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} else {
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// perform NMS on max scores
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_NonMaxSuppressionMultiClassFastImpl(mParam, mDecodedBoxes.get(), inputs[1], outputs[0], outputs[1], outputs[2],
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outputs[3]);
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}
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return NO_ERROR;
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}
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class CPUDetectionPostProcessCreator : public CPUBackend::Creator {
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public:
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Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, const MNN::Op* op,
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Backend* backend) const override {
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return new CPUDetectionPostProcess(backend, op);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUDetectionPostProcessCreator, OpType_DetectionPostProcess);
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} // namespace MNN
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