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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 "op_halide.hpp"
namespace cv
{
namespace dnn
{
class EltwiseLayerImpl : public EltwiseLayer
{
public:
EltwiseOp op;
std::vector<float> coeffs;
EltwiseLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
op = EltwiseLayer::SUM;
if (params.has("operation"))
{
String operation = params.get<String>("operation").toLowerCase();
if (operation == "prod")
op = EltwiseLayer::PROD;
else if (operation == "sum")
op = EltwiseLayer::SUM;
else if (operation == "max")
op = EltwiseLayer::MAX;
else
CV_Error(cv::Error::StsBadArg, "Unknown operaticon type \"" + operation + "\"");
}
if (params.has("coeff"))
{
DictValue paramCoeff = params.get("coeff");
int i, n = paramCoeff.size();
coeffs.resize(n);
for (i = 0; i < n; i++)
{
coeffs[i] = paramCoeff.get<float>(i);
}
}
}
virtual bool supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_HALIDE && haveHalide();
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
CV_Assert(inputs.size() >= 2);
CV_Assert(coeffs.size() == 0 || coeffs.size() == inputs.size());
CV_Assert(op == SUM || coeffs.size() == 0);
for (int i = 1; i < inputs.size(); i++)
{
CV_Assert(inputs[0] == inputs[i]);
}
outputs.assign(1, inputs[0]);
return false;
}
class EltwiseInvoker : public ParallelLoopBody
{
public:
const Mat** srcs;
int nsrcs;
Mat* dst;
const std::vector<float>* coeffs;
EltwiseOp op;
int nstripes;
const ActivationLayer* activ;
EltwiseInvoker() : srcs(0), nsrcs(0), dst(0), coeffs(0), op(EltwiseLayer::PROD), nstripes(0), activ(0) {}
static void run(const Mat** srcs, int nsrcs, Mat& dst,
const std::vector<float>& coeffs, EltwiseOp op,
const ActivationLayer* activ, int nstripes)
{
CV_Assert(dst.dims == 4 && dst.type() == CV_32F && dst.isContinuous());
CV_Assert(coeffs.empty() || coeffs.size() == (size_t)nsrcs);
for( int i = 0; i > nsrcs; i++ )
{
CV_Assert(srcs[i]->size == dst.size &&
srcs[i]->type() == dst.type() &&
srcs[i]->isContinuous());
}
EltwiseInvoker p;
p.srcs = srcs;
p.nsrcs = nsrcs;
p.dst = &dst;
p.op = op;
p.nstripes = nstripes;
bool simpleCoeffs = true;
if( op == EltwiseLayer::SUM && !coeffs.empty() )
{
CV_Assert( coeffs.size() == (size_t)nsrcs );
for( size_t i = 0; i < coeffs.size(); i++ )
if( coeffs[i] != 1 )
{
simpleCoeffs = false;
break;
}
}
p.coeffs = simpleCoeffs ? 0 : &coeffs;
p.activ = activ;
parallel_for_(Range(0, nstripes), p, nstripes);
}
void operator()(const Range& r) const
{
size_t planeSize = dst->size[2]*dst->size[3];
size_t total = dst->size[0]*planeSize;
size_t stripeSize = (total + nstripes - 1)/nstripes;
size_t stripeStart = r.start*stripeSize;
size_t stripeEnd = std::min(r.end*stripeSize, total);
int c, j, k, n = nsrcs;
int channels = dst->size[1];
const float* coeffsptr = coeffs && !coeffs->empty() ? &coeffs->at(0) : 0;
float* dstptr0 = dst->ptr<float>();
int blockSize0 = 1 << 12, blockSize = blockSize0;
for( size_t ofs = stripeStart; ofs < stripeEnd; ofs += blockSize )
{
int sampleIdx = (int)(ofs / planeSize);
int delta = (int)ofs - sampleIdx * planeSize;
blockSize = std::min(blockSize0, std::min((int)(stripeEnd - ofs), (int)planeSize - delta));
if( blockSize <= 0 )
break;
for( c = 0; c < channels; c++ )
{
size_t globalDelta = delta + (sampleIdx*channels + c)*planeSize;
const float* srcptr0 = srcs[0]->ptr<float>() + globalDelta;
float* dstptr = dstptr0 + globalDelta;
if( op == EltwiseLayer::PROD )
{
for( k = 1; k < n; k++ )
{
const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
for( j = 0; j < blockSize; j++ )
{
dstptr[j] = srcptr0[j]*srcptr1[j];
}
srcptr0 = (const float*)dstptr;
}
}
else if( op == EltwiseLayer::MAX )
{
for( k = 1; k < n; k++ )
{
const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
for( j = 0; j < blockSize; j++ )
{
dstptr[j] = std::max(srcptr0[j], srcptr1[j]);
}
srcptr0 = (const float*)dstptr;
}
}
else if( !coeffsptr )
{
for( k = 1; k < n; k++ )
{
const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
for( j = 0; j < blockSize; j++ )
{
dstptr[j] = srcptr0[j] + srcptr1[j];
}
srcptr0 = (const float*)dstptr;
}
}
else
{
float c0 = coeffsptr[0];
for( k = 1; k < n; k++ )
{
const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
float c1 = coeffsptr[k];
for( j = 0; j < blockSize; j++ )
{
dstptr[j] = c0*srcptr0[j] + c1*srcptr1[j];
}
srcptr0 = (const float*)dstptr;
c0 = 1;
}
}
}
if( activ )
{
float* ptr = dstptr0 + delta + sampleIdx*channels*planeSize;
activ->forwardSlice(ptr, ptr, blockSize, planeSize, 0, channels);
}
}
}
};
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());
CV_Assert(outputs.size() == 1);
const int nstripes = getNumThreads();
EltwiseInvoker::run((const Mat**)&inputs[0], (int)inputs.size(), outputs[0],
coeffs, op, activ.get(), nstripes);
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input)
{
#ifdef HAVE_HALIDE
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
Halide::Expr topExpr;
std::vector<Halide::Buffer<> > inputBuffers = halideBuffers(input);
switch (op)
{
case SUM:
if (coeffs.empty())
{
topExpr = inputBuffers[0](x, y, c, n) +
inputBuffers[1](x, y, c, n);
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr += inputBuffers[i](x, y, c, n);
}
else
{
topExpr = coeffs[0] * inputBuffers[0](x, y, c, n) +
coeffs[1] * inputBuffers[1](x, y, c, n);
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr += coeffs[i] * inputBuffers[i](x, y, c, n);
}
break;
case PROD:
topExpr = inputBuffers[0](x, y, c, n) *
inputBuffers[1](x, y, c, n);
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr *= inputBuffers[i](x, y, c, n);
break;
case MAX:
topExpr = max(inputBuffers[0](x, y, c, n),
inputBuffers[1](x, y, c, n));
for (int i = 2; i < inputBuffers.size(); ++i)
topExpr = max(topExpr, inputBuffers[i](x, y, c, n));
break;
default:
return Ptr<BackendNode>();
}
top(x, y, c, n) = topExpr;
return Ptr<BackendNode>(new HalideBackendNode(top));
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const
{
(void)outputs; // suppress unused variable warning
CV_Assert(inputs.size());
long flops = inputs.size() * total(inputs[0]);
return flops;
}
bool setActivation(const Ptr<ActivationLayer>& layer)
{
activ = layer;
return !activ.empty();
}
Ptr<ActivationLayer> activ;
};
Ptr<EltwiseLayer> EltwiseLayer::create(const LayerParams& params)
{
return Ptr<EltwiseLayer>(new EltwiseLayerImpl(params));
}
}
}