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

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#include "../precomp.hpp"
#include "layers_common.hpp"
#include "op_halide.hpp"

namespace cv
{
namespace dnn
{

class ConcatLayerImpl : public ConcatLayer
{
public:
    ConcatLayerImpl(const LayerParams& params)
    {
        setParamsFrom(params);
        axis = params.get<int>("axis", 1);
    }

    virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
                                 const int requiredOutputs,
                                 std::vector<MatShape> &outputs,
                                 std::vector<MatShape> &internals) const
    {
        CV_Assert(inputs.size() > 0);
        outputs.clear();
        outputs.push_back(inputs[0]);
        int cAxis = clamp(axis, inputs[0]);

        int axisSum = 0;
        for (size_t i = 0; i < inputs.size(); i++)
        {
            MatShape curShape = inputs[i];

            CV_Assert(curShape.size() == outputs.back().size());
            for (int curAxis = 0; curAxis < outputs.back().size(); curAxis++)
            {
                if (curAxis != cAxis && outputs.back()[curAxis] != curShape[curAxis])
                    CV_Error(Error::StsBadSize, "Inconsitent shape for ConcatLayer");
            }

            axisSum += curShape[cAxis];
        }

        outputs.back()[cAxis] = axisSum;

        return false;
    }

    virtual bool supportBackend(int backendId)
    {
        return backendId == DNN_BACKEND_DEFAULT ||
               backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1;  // By channels
    }

    void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
    {
        int cAxis = clamp(axis, inputs[0]->dims);
        Mat& outMat = outputs[0];
        std::vector<Range> ranges(outputs[0].dims, Range::all());

        ranges[cAxis].start = 0;
        for (size_t i = 0; i < inputs.size(); i++)
        {
            ranges[cAxis].end = ranges[cAxis].start + inputs[i]->size[cAxis];
            inputs[i]->copyTo(outMat(&ranges[0]));
            ranges[cAxis].start = ranges[cAxis].end;
        }
    }

    virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input)
    {
#ifdef HAVE_HALIDE
        std::vector<Halide::Buffer<> > inputBuffers = halideBuffers(input);

        Halide::Var x("x"), y("y"), c("c"), n("n");
        Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
        int offset = inputBuffers[0].channels();
        Halide::Expr topExpr = select(c < offset,
                                      inputBuffers[0](x, y, c, n),
                                      inputBuffers[1](x, y, c - offset, n));
        for (int i = 2; i < input.size(); ++i)
        {
            offset += inputBuffers[i - 1].channels();
            topExpr = select(c < offset, topExpr,
                             inputBuffers[i](x, y, c - offset, n));
        }
        top(x, y, c, n) = topExpr;
        return Ptr<BackendNode>(new HalideBackendNode(top));
#endif  // HAVE_HALIDE
        return Ptr<BackendNode>();
    }
};

Ptr<ConcatLayer> ConcatLayer::create(const LayerParams& params)
{
    return Ptr<ConcatLayer>(new ConcatLayerImpl(params));
}

}
}