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