mirror of https://github.com/alibaba/MNN.git
99 lines
3.2 KiB
C++
99 lines
3.2 KiB
C++
//
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// ShapePriorbox.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 "core/Macro.h"
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#include "core/SizeComputer.hpp"
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#include "core/TensorUtils.hpp"
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namespace MNN {
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class PriorBoxComputer : public SizeComputer {
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public:
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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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MNN_ASSERT(2 == inputs.size());
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MNN_ASSERT(1 == outputs.size());
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auto layer = op->main_as_PriorBox();
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auto inputTensor = inputs[0];
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auto inputTensor1 = inputs[1];
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int w = inputTensor->width();
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int h = inputTensor->height();
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auto minSizes = layer->minSizes();
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auto maxSizes = layer->maxSizes();
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auto aspectRatios = layer->aspectRatios();
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int flip = layer->flip();
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int imageWidth = layer->imageWidth();
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int imageHeight = layer->imageHeight();
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float stepWidth = layer->stepWidth();
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float stepHeight = layer->stepHeight();
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int imageW = imageWidth;
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int imageH = imageHeight;
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if (imageW <= 0) {
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imageW = inputTensor1->width();
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}
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if (imageH <= 0) {
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imageH = inputTensor1->height();
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}
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float stepW = stepWidth;
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float stepH = stepHeight;
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if (stepW <= 0) {
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stepW = (float)imageW / w;
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}
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if (stepH <= 0) {
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stepH = (float)imageH / h;
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}
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int minSizeCount = minSizes ? (int)minSizes->size() : 0;
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int maxSizeCount = maxSizes ? (int)maxSizes->size() : 0;
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std::vector<float> aspectRatiosValue{1.0f};
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if (aspectRatios != nullptr) {
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for (int i = 0; i < aspectRatios->size(); ++i) {
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auto ratio = aspectRatios->data()[i];
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bool exist = false;
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for (auto v : aspectRatiosValue) {
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auto diff = v - ratio;
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if (diff < 0) {
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diff = -diff;
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}
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if (diff < 1e-6) {
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exist = true;
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break;
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}
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}
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if (!exist) {
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aspectRatiosValue.emplace_back(ratio);
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if (flip) {
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aspectRatiosValue.emplace_back(1.0f / ratio);
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}
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}
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}
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}
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int priorCount = minSizeCount * aspectRatiosValue.size() + maxSizeCount;
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auto& outputTensorBuffer = outputs[0]->buffer();
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outputTensorBuffer.dim[0].extent = 1;
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outputTensorBuffer.dim[1].extent = 2;
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outputTensorBuffer.dim[2].extent = 4 * w * h * priorCount;
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outputTensorBuffer.dim[3].extent = 1;
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outputTensorBuffer.type = halide_type_of<float>();
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = MNN_DATA_FORMAT_NC4HW4;
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return true;
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}
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};
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REGISTER_SHAPE(PriorBoxComputer, OpType_PriorBox);
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} // namespace MNN
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