mirror of https://github.com/alibaba/MNN.git
57 lines
2.1 KiB
C++
57 lines
2.1 KiB
C++
//
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// ShapeDetectionPostProcess.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 "core/SizeComputer.hpp"
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namespace MNN {
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class DetectionPostProcessSize : public SizeComputer {
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public:
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bool onComputeSize(const MNN::Op *op, const std::vector<Tensor *> &inputs,
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const std::vector<Tensor *> &outputs) const override {
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MNN_CHECK(inputs.size() == 3, "DetectionPostProcess should have 3 inputs!");
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MNN_CHECK(outputs.size() == 4, "DetectionPostProcess should have 4 outputs!");
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auto postProcess = op->main_as_DetectionPostProcessParam();
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const int numDetectedBoxes = postProcess->maxDetections() * postProcess->maxClassesPerDetection();
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const int bathSize = inputs[0]->batch();
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// Outputs: detection_boxes, detection_scores, detection_classes,
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// num_detections
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auto detectionBoxes = outputs[0];
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detectionBoxes->buffer().dimensions = 3;
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detectionBoxes->setLength(0, bathSize);
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detectionBoxes->setLength(1, numDetectedBoxes);
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detectionBoxes->setLength(2, 4);
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detectionBoxes->buffer().type = halide_type_of<float>();
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auto detectionClasses = outputs[1];
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detectionClasses->buffer().dimensions = 2;
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detectionClasses->setLength(0, bathSize);
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detectionClasses->setLength(1, numDetectedBoxes);
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detectionClasses->buffer().type = halide_type_of<float>();
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auto detectionScores = outputs[2];
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detectionScores->buffer().dimensions = 2;
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detectionScores->setLength(0, bathSize);
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detectionScores->setLength(1, numDetectedBoxes);
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detectionScores->buffer().type = halide_type_of<float>();
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auto numDetections = outputs[3];
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numDetections->buffer().dimensions = 1;
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numDetections->setLength(0, 1);
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numDetections->buffer().type = halide_type_of<float>();
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return true;
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}
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};
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REGISTER_SHAPE(DetectionPostProcessSize, OpType_DetectionPostProcess);
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} // namespace MNN
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