opencv/modules/dnn/src/layers/scale_layer.cpp

// 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