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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2016, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
/*
Implementation of shift layer, which adds up const values to blob.
*/
#include "../precomp.hpp"
#include "op_blas.hpp"
namespace cv
{
namespace dnn
{
class ShiftLayerImpl : public ShiftLayer
{
public:
ShiftLayerImpl(const LayerParams ¶ms)
{
setParamsFrom(params);
CV_Assert(blobs.size() == 1);
#ifdef HAVE_LAPACK
{
if (getBlasThreads() != cv::getThreadNum())
{
setBlasThreads(cv::getThreadNum());
}
}
#endif
}
virtual void allocate(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
{
CV_Assert(inputs.size() > 0);
CV_Assert(blobs.size() > 0);
const Mat &inpBlob = *inputs[0];
CV_Assert(inpBlob.dims == 4 && inpBlob.type() == CV_32F);
const Mat &biasBlob = blobs[0];
outputs.resize(inputs.size());
if(inpBlob.dims == biasBlob.dims)
{
for (size_t i = 0; i < inputs.size(); i++)
{
CV_Assert(inputs[i]->type() == inpBlob.type());
CV_Assert(inputs[i]->dims == inpBlob.dims);
outputs[i] = *inputs[i];
}
}
else
{
CV_Assert(biasBlob.total() == (size_t)inpBlob.size[1]);
for (size_t i = 0; i < inputs.size(); i++)
{
CV_Assert(inputs[i]->type() == inpBlob.type());
CV_Assert(inputs[i]->dims == 4 && inputs[i]->size[1] == inpBlob.size[1]);
outputs[i] = *inputs[i];
}
biasOnesMat = Mat::ones(1, inpBlob.size[2] * inpBlob.size[3], inpBlob.type());
}
}
virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
{
CV_Assert(inputs.size() > 0);
CV_Assert(blobs.size() > 0);
if(inputs[0]->dims == blobs[0].dims)
{
for (size_t ii = 0; ii < outputs.size(); ii++)
{
Mat &inpBlob = *inputs[ii];
Mat &outBlob = outputs[ii];
outBlob = inpBlob + blobs[0];
}
}
else
{
for (size_t ii = 0; ii < outputs.size(); ii++)
{
Mat &inpBlob = *inputs[ii];
Mat &outBlob = outputs[ii];
inpBlob.copyTo(outBlob);
for (int n = 0; n < inpBlob.size[0]; n++)
{
Mat dstMat(inpBlob.size[1], inpBlob.size[2] * inpBlob.size[3],
outBlob.type(), outBlob.ptr(n));
dnn::gemm(blobs[0], biasOnesMat, 1, dstMat, 1); //TODO: gemv
}
}
}
}
Mat biasOnesMat;
};
Ptr<ShiftLayer> ShiftLayer::create(const LayerParams& params)
{
return Ptr<ShiftLayer>(new ShiftLayerImpl(params));
}
}
}