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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 Scale layer.
*/
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "op_halide.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
{
namespace dnn
{
class ScaleLayerImpl : public ScaleLayer
{
public:
ScaleLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
hasBias = params.get<bool>("bias_term", false);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
CV_Assert(blobs.size() == 1 + hasBias);
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
return true;
}
virtual bool supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_HALIDE && haveHalide();
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
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());
for (size_t ii = 0; ii < outputs.size(); ii++)
{
Mat &inpBlob = *inputs[ii];
Mat &outBlob = outputs[ii];
CV_Assert(inpBlob.size[1] == blobs[0].total());
if (hasBias)
CV_Assert(inpBlob.size[1] == blobs[1].total());
CV_Assert(inpBlob.type() == CV_32F && outBlob.type() == CV_32F);
for( int cn = 0; cn < inpBlob.size[0]; cn++ )
{
for (int n = 0; n < inpBlob.size[1]; n++)
{
float w = blobs[0].at<float>(n);
float b = hasBias ? blobs[1].at<float>(n) : 0;
Mat outBlobPlane = getPlane(outBlob, cn, n);
Mat inpBlobPlane = getPlane(inpBlob, cn, n);
inpBlobPlane.convertTo(outBlobPlane, CV_32F, w, b);
}
}
}
}
virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node)
{
switch (node->backendId)
{
case DNN_BACKEND_HALIDE:
{
#ifdef HAVE_HALIDE
auto base = node.dynamicCast<HalideBackendNode>();
Halide::Func& input = base->funcs.back();
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = attachHalide(input(x, y, c, n));
return Ptr<BackendNode>(new HalideBackendNode(base, top));
#endif // HAVE_HALIDE
break;
}
}
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
{
#ifdef HAVE_HALIDE
Halide::Buffer<float> input = halideBuffer(inputs[0]);
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = attachHalide(input(x, y, c, n));
return Ptr<BackendNode>(new HalideBackendNode(top));
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
}
#ifdef HAVE_HALIDE
// attachHalide can work both with Halide::Buffer and Halide::Func. In the
// second case it will be a fusion.
Halide::Func attachHalide(const Halide::Expr& input)
{
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
Halide::Var x("x"), y("y"), c("c"), n("n");
const int numChannels = blobs[0].total();
auto weights = wrapToHalideBuffer(blobs[0], {numChannels});
Halide::Expr topExpr = input * weights(c);
if (hasBias)
{
auto bias = wrapToHalideBuffer(blobs[1], {numChannels});
topExpr += bias(c);
}
top(x, y, c, n) = topExpr;
return top;
}
#endif // HAVE_HALIDE
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const
{
(void)outputs; // suppress unused variable warning
long flops = 0;
for(int i = 0; i < inputs.size(); i++)
{
flops += 2*total(inputs[i]);
}
return flops;
}
};
Ptr<ScaleLayer> ScaleLayer::create(const LayerParams& params)
{
return Ptr<ScaleLayer>(new ScaleLayerImpl(params));
}
} // namespace dnn
} // namespace cv