mirror of https://github.com/alibaba/MNN.git
81 lines
2.3 KiB
C++
81 lines
2.3 KiB
C++
//
|
|
// ShapePriorbox.cpp
|
|
// MNN
|
|
//
|
|
// Created by MNN on 2019/01/10.
|
|
// Copyright © 2018, Alibaba Group Holding Limited
|
|
//
|
|
|
|
#include "Macro.h"
|
|
#include "SizeComputer.hpp"
|
|
#include "TensorUtils.hpp"
|
|
|
|
namespace MNN {
|
|
class PriorBoxComputer : public SizeComputer {
|
|
public:
|
|
virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
|
|
const std::vector<Tensor*>& outputs) const override {
|
|
MNN_ASSERT(2 == inputs.size());
|
|
MNN_ASSERT(1 == outputs.size());
|
|
|
|
auto layer = op->main_as_PriorBox();
|
|
|
|
auto inputTensor = inputs[0];
|
|
auto inputTensor1 = inputs[1];
|
|
|
|
int w = inputTensor->width();
|
|
int h = inputTensor->height();
|
|
|
|
auto minSizes = layer->minSizes();
|
|
auto maxSizes = layer->maxSizes();
|
|
auto aspectRatios = layer->aspectRatios();
|
|
|
|
int flip = layer->flip();
|
|
int imageWidth = layer->imageWidth();
|
|
int imageHeight = layer->imageHeight();
|
|
float stepWidth = layer->stepWidth();
|
|
float stepHeight = layer->stepHeight();
|
|
|
|
int imageW = imageWidth;
|
|
int imageH = imageHeight;
|
|
if (imageW <= 0) {
|
|
imageW = inputTensor1->width();
|
|
}
|
|
if (imageH <= 0) {
|
|
imageH = inputTensor1->height();
|
|
}
|
|
|
|
float stepW = stepWidth;
|
|
float stepH = stepHeight;
|
|
if (stepW <= 0) {
|
|
stepW = (float)imageW / w;
|
|
}
|
|
|
|
if (stepH <= 0) {
|
|
stepH = (float)imageH / h;
|
|
}
|
|
|
|
int minSizeCount = minSizes ? minSizes->size() : 0;
|
|
int maxSizeCount = maxSizes ? maxSizes->size() : 0;
|
|
int aspectRatioCount = aspectRatios ? aspectRatios->size() : 0;
|
|
|
|
int priorCount = minSizeCount * aspectRatioCount + minSizeCount + maxSizeCount;
|
|
if (flip) {
|
|
priorCount += minSizeCount * aspectRatioCount;
|
|
}
|
|
|
|
auto& outputTensorBuffer = outputs[0]->buffer();
|
|
outputTensorBuffer.dim[0].extent = 1;
|
|
outputTensorBuffer.dim[1].extent = 2;
|
|
outputTensorBuffer.dim[2].extent = 4 * w * h * priorCount;
|
|
outputTensorBuffer.dim[3].extent = 1;
|
|
|
|
TensorUtils::getDescribe(outputs[0])->isConst = true;
|
|
|
|
return true;
|
|
}
|
|
};
|
|
|
|
REGISTER_SHAPE(PriorBoxComputer, OpType_PriorBox);
|
|
} // namespace MNN
|