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Added PriorBox layer implementation

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Anna Petrovicheva 9 years ago
parent abd998d1af
commit acc55b24ab
2 changed files with 346 additions and 0 deletions
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  1. 259
      modules/dnn/src/layers/prior_box_layer.cpp
  2. 87
      modules/dnn/src/layers/prior_box_layer.hpp

259
modules/dnn/src/layers/prior_box_layer.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "prior_box_layer.hpp"
#include <float.h>
#include <algorithm>
#include <cmath>
namespace cv
{
namespace dnn
{
void PriorBoxLayer::checkParameter(const LayerParams &params, const string &parameterName)
{
if (!params.has(parameterName))
{
CV_Error(Error::StsBadArg, "PriorBox layer parameter does not contain " + parameterName + " index.");
}
}
PriorBoxLayer::PriorBoxLayer(LayerParams &params) : Layer(params)
{
checkParameter(params, "min_size");
_minSize = params.min_size();
CV_Assert(_minSize > 0);
_aspectRatios.clear();
_aspectRatios.push_back(1.);
_flip = params.flip();
for (int i = 0; i < params.aspect_ratio_size(); ++i)
{
float aspectRatio = params.aspect_ratio(i);
bool already_exist = false;
for (int j = 0; j < _aspectRatios.size(); ++j)
{
if (fabs(aspectRatio - _aspectRatios[j]) < 1e-6)
{
already_exist = true;
break;
}
}
if (!already_exist)
{
_aspectRatios.push_back(aspectRatio);
if (_flip)
{
_aspectRatios.push_back(1./aspectRatio);
}
}
}
_numPriors = _aspectRatios.size();
_maxSize = -1;
if (params.has(max_size))
{
_maxSize = params.max_size();
CV_Assert(_maxSize > _minSize);
_numPriors += 1;
}
_clip = params.clip();
int varianceSize = params.variance_size();
if (varianceSize > 1)
{
// Must and only provide 4 variance.
CV_Assert(varianceSize == 4);
for (int i = 0; i < varianceSize; ++i)
{
float variance = params.variance(i);
CV_Assert(variance > 0);
_variance.push_back(variance);
}
}
else
{
if (varianceSize == 1)
{
float variance = params.variance(0);
CV_Assert(variance > 0);
_variance.push_back(variance);
}
else
{
// Set default to 0.1.
_variance.push_back(0.1);
}
}
}
void PriorBoxLayer::allocate(const std::vector<Blob*> &inputs, std::vector<Blob> &outputs)
{
CV_Assert(inputs.size() == 2);
_layerWidth = inputs[0]->width();
_layerHeight = inputs[0]->height();
_imageWidth = inputs[1]->width();
_imageHeight = inputs[1]->height();
_stepX = static_cast<float>(_imageWidth) / _layerWidth;
_stepY = static_cast<float>(_imageHeight) / _layerHeight;
// Since all images in a batch has same height and width, we only need to
// generate one set of priors which can be shared across all images.
size_t outNum = 1;
// 2 channels. First channel stores the mean of each prior coordinate.
// Second channel stores the variance of each prior coordinate.
size_t outChannels = 2;
size_t outHeight = _layerHeight;
size_t outWidth = _layerWidth * _numPriors * 4;
_outChannelSize = _layerHeight * _layerWidth * _numPriors * 4;
outputs[0].create(BlobShape(outNum, outChannels, outHeight, outWidth));
}
void PriorBoxLayer::forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs)
{
float* outputPtr = outputs[0].ptrf();
// first prior: aspect_ratio = 1, size = min_size
_boxWidth = _boxHeight = _minSize;
int idx = 0;
for (int h = 0; h < _layerHeight; ++h)
{
for (int w = 0; w < _layerWidth; ++w)
{
float center_x = (w + 0.5) * _stepX;
float center_y = (h + 0.5) * _stepY;
// xmin
outputPtr[idx++] = (center_x - _boxWidth / 2.) / _imageWidth;
// ymin
outputPtr[idx++] = (center_y - _boxHeight / 2.) / _imageHeight;
// xmax
outputPtr[idx++] = (center_x + _boxWidth / 2.) / _imageWidth;
// ymax
outputPtr[idx++] = (center_y + _boxHeight / 2.) / _imageHeight;
if (_maxSize > 0)
{
// second prior: aspect_ratio = 1, size = sqrt(min_size * max_size)
_boxWidth = _boxHeight = sqrt(_minSize * _maxSize);
// xmin
outputPtr[idx++] = (center_x - _boxWidth / 2.) / _imageWidth;
// ymin
outputPtr[idx++] = (center_y - _boxHeight / 2.) / _imageHeight;
// xmax
outputPtr[idx++] = (center_x + _boxWidth / 2.) / _imageWidth;
// ymax
outputPtr[idx++] = (center_y + _boxHeight / 2.) / _imageHeight;
}
// rest of priors
for (int r = 0; r < _aspectRatios.size(); ++r)
{
float ar = _aspectRatios[r];
if (fabs(ar - 1.) < 1e-6)
{
continue;
}
_boxWidth = _minSize * sqrt(ar);
_boxHeight = _minSize / sqrt(ar);
// xmin
outputPtr[idx++] = (center_x - _boxWidth / 2.) / _imageWidth;
// ymin
outputPtr[idx++] = (center_y - _boxHeight / 2.) / _imageHeight;
// xmax
outputPtr[idx++] = (center_x + _boxWidth / 2.) / _imageWidth;
// ymax
outputPtr[idx++] = (center_y + _boxHeight / 2.) / _imageHeight;
}
}
}
// clip the prior's coordidate such that it is within [0, 1]
if (_clip)
{
for (int d = 0; d < _outChannelSize; ++d)
{
outputPtr[d] = std::min<float>(std::max<float>(outputPtr[d], 0.), 1.);
}
}
// set the variance.
outputPtr = outputs[0].ptrf(0, 1);
if(_variance.size() == 1)
{
Mat secondChannel(outputs[0].height(), outputs[0].width(), CV_32F, outputPtr);
secondChannel.setTo(Scalar(_variance[0]));
}
else
{
int count = 0;
for (int h = 0; h < _layerHeight; ++h)
{
for (int w = 0; w < _layerWidth; ++w)
{
for (int i = 0; i < _numPriors; ++i)
{
for (int j = 0; j < 4; ++j)
{
outputPtr[count] = _variance[j];
++count;
}
}
}
}
}
}
}
}

87
modules/dnn/src/layers/prior_box_layer.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_DNN_LAYERS_PRIOR_BOX_LAYER_HPP__
#define __OPENCV_DNN_LAYERS_PRIOR_BOX_LAYER_HPP__
#include "../precomp.hpp"
namespace cv
{
namespace dnn
{
class PriorBoxLayer : public Layer
{
size_t _layerWidth;
size_t _layerHeight;
size_t _imageWidth;
size_t _imageHeight;
size_t _outChannelSize;
float _stepX;
float _stepY;
float _minSize;
float _maxSize;
float _boxWidth;
float _boxHeight;
std::vector<float> _aspectRatios;
std::vector<float> _variance;
bool _flip;
bool _clip;
size_t _numPriors;
static const size_t _numAxes = 4;
public:
PriorBoxLayer(LayerParams &params);
void allocate(const std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
void forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
void checkParameter(const LayerParams &params, const std::string &parameterName);
};
}
}
#endif
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