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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.
// Copyright (C) 2017, Intel Corporation, 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 <float.h>
#include <algorithm>
namespace cv
{
namespace dnn
{
namespace
{
const std::string layerName = "NormalizeBBox";
}
class NormalizeBBoxLayerImpl : public NormalizeBBoxLayer
{
float _eps;
bool _across_spatial;
bool _channel_shared;
public:
bool getParameterDict(const LayerParams ¶ms,
const std::string ¶meterName,
DictValue& result)
{
if (!params.has(parameterName))
{
return false;
}
result = params.get(parameterName);
return true;
}
template<typename T>
T getParameter(const LayerParams ¶ms,
const std::string ¶meterName,
const size_t &idx=0,
const bool required=true,
const T& defaultValue=T())
{
DictValue dictValue;
bool success = getParameterDict(params, parameterName, dictValue);
if(!success)
{
if(required)
{
std::string message = layerName;
message += " layer parameter does not contain ";
message += parameterName;
message += " parameter.";
CV_Error(Error::StsBadArg, message);
}
else
{
return defaultValue;
}
}
return dictValue.get<T>(idx);
}
NormalizeBBoxLayerImpl(const LayerParams ¶ms)
{
_eps = getParameter<float>(params, "eps", 0, false, 1e-10f);
_across_spatial = getParameter<bool>(params, "across_spatial");
_channel_shared = getParameter<bool>(params, "channel_shared");
setParamsFrom(params);
}
void checkInputs(const std::vector<Mat*> &inputs)
{
CV_Assert(inputs.size() > 0);
CV_Assert(inputs[0]->dims == 4 && inputs[0]->type() == CV_32F);
for (size_t i = 1; i < inputs.size(); i++)
{
CV_Assert(inputs[i]->dims == 4 && inputs[i]->type() == CV_32F);
CV_Assert(inputs[i]->size == inputs[0]->size);
}
CV_Assert(inputs[0]->dims > 2);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
bool inplace = Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
size_t channels = inputs[0][1];
size_t rows = inputs[0][2];
size_t cols = inputs[0][3];
size_t channelSize = rows * cols;
internals.assign(1, shape(channels, channelSize));
internals.push_back(shape(channels, 1));
internals.push_back(shape(1, channelSize));
return inplace;
}
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
checkInputs(inputs);
Mat& buffer = internals[0], sumChannelMultiplier = internals[1],
sumSpatialMultiplier = internals[2];
sumChannelMultiplier.setTo(1.0);
sumSpatialMultiplier.setTo(1.0);
const Mat& inp0 = *inputs[0];
size_t num = inp0.size[0];
size_t channels = inp0.size[1];
size_t channelSize = inp0.size[2] * inp0.size[3];
Mat zeroBuffer(channels, channelSize, CV_32F, Scalar(0));
Mat absDiff;
Mat scale = blobs[0];
for (size_t j = 0; j < inputs.size(); j++)
{
for (size_t n = 0; n < num; ++n)
{
Mat src = Mat(channels, channelSize, CV_32F, inputs[j]->ptr<float>(n));
Mat dst = Mat(channels, channelSize, CV_32F, outputs[j].ptr<float>(n));
buffer = src.mul(src);
if (_across_spatial)
{
absdiff(buffer, zeroBuffer, absDiff);
// add eps to avoid overflow
double absSum = sum(absDiff)[0] + _eps;
float norm = sqrt(absSum);
dst = src / norm;
}
else
{
Mat norm(channelSize, 1, buffer.type()); // 1 x channelSize
// (_channels x channelSize)T * _channels x 1 -> channelSize x 1
gemm(buffer, sumChannelMultiplier, 1, norm, 0, norm, GEMM_1_T);
// compute norm
pow(norm, 0.5f, norm);
// scale the layer
// _channels x 1 * (channelSize x 1)T -> _channels x channelSize
gemm(sumChannelMultiplier, norm, 1, buffer, 0, buffer, GEMM_2_T);
dst = src / buffer;
}
// scale the output
if (_channel_shared)
{
// _scale: 1 x 1
dst *= scale.at<float>(0, 0);
}
else
{
// _scale: _channels x 1
// _channels x 1 * 1 x channelSize -> _channels x channelSize
gemm(scale, sumSpatialMultiplier, 1, buffer, 0, buffer);
dst = dst.mul(buffer);
}
}
}
}
};
Ptr<NormalizeBBoxLayer> NormalizeBBoxLayer::create(const LayerParams ¶ms)
{
return Ptr<NormalizeBBoxLayer>(new NormalizeBBoxLayerImpl(params));
}
}
}