mirror of https://github.com/alibaba/MNN.git
51 lines
1.8 KiB
C++
51 lines
1.8 KiB
C++
//
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// ShapeNonMaxSuppressionV2.cpp
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// MNN
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//
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// Created by MNN on 2019/01/10.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "shape/SizeComputer.hpp"
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#include "core/Macro.h"
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namespace MNN {
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class NonMaxSuppressionV2Computer : public SizeComputer {
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virtual 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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// boxes: [num_boxes, 4]
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const Tensor* boxes = inputs[0];
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// scores: [num_boxes]
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const Tensor* scores = inputs[1];
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// iou_threshold: scalar
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if (inputs.size() > 3 && inputs[3]->host<float>() != nullptr) {
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auto iou_threshold_val = inputs[3]->host<float>()[0];
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MNN_ASSERT(iou_threshold_val >= 0 && iou_threshold_val <= 1);
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}
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int num_boxes = 0;
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MNN_ASSERT(boxes->buffer().dimensions == 2);
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num_boxes = boxes->buffer().dim[0].extent;
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MNN_ASSERT(boxes->buffer().dimensions == 2 && scores->buffer().dim[0].extent == num_boxes &&
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boxes->buffer().dim[1].extent == 4 && scores->buffer().dimensions == 1);
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int output_size = num_boxes;
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if (inputs.size() > 2 && inputs[2]->host<int32_t>() != nullptr) {
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output_size = std::min(inputs[2]->host<int32_t>()[0], num_boxes);
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}
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// TODO ramdom output shape only for fast rcnn
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outputs[0]->buffer().dimensions = 1;
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outputs[0]->setType(MNN::DataType_DT_INT32);
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outputs[0]->buffer().dim[0].extent = output_size;
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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return true;
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}
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};
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REGISTER_SHAPE_INPUTS(NonMaxSuppressionV2Computer, OpType_NonMaxSuppressionV2, (std::vector<int>{2, 3}));
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} // namespace MNN
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