opencv/modules/dnn/src/layers/max_unpooling_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 Batch Normalization layer.
*/

#include "../precomp.hpp"
#include "layers_common.hpp"
#include "op_halide.hpp"
#include <opencv2/dnn/shape_utils.hpp>

namespace cv
{
namespace dnn
{

class MaxUnpoolLayerImpl : public MaxUnpoolLayer
{
public:
    MaxUnpoolLayerImpl(const LayerParams& params)
    {
        setParamsFrom(params);
        poolKernel = Size(params.get<int>("pool_k_w"), params.get<int>("pool_k_h"));
        poolPad = Size(params.get<int>("pool_pad_w"), params.get<int>("pool_pad_h"));
        poolStride = Size(params.get<int>("pool_stride_w"), params.get<int>("pool_stride_h"));
    }

    virtual bool supportBackend(int backendId)
    {
        return backendId == DNN_BACKEND_DEFAULT ||
               backendId == DNN_BACKEND_HALIDE && haveHalide() &&
               !poolPad.width && !poolPad.height;
    }

    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(total(inputs[0]) == total(inputs[1]));

        MatShape outShape = inputs[0];
        outShape[2] = (outShape[2] - 1) * poolStride.height + poolKernel.height - 2 * poolPad.height;
        outShape[3] = (outShape[3] - 1) * poolStride.width + poolKernel.width - 2 * poolPad.width;

        outputs.clear();
        outputs.push_back(outShape);

        return false;
    }

    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());

        CV_Assert(inputs.size() == 2);
        Mat& input = *inputs[0];
        Mat& indices = *inputs[1];

        CV_Assert(input.total() == indices.total());
        CV_Assert(input.size[0] == 1);
        CV_Assert(input.isContinuous());

        for(int i_n = 0; i_n < outputs.size(); i_n++)
        {
            Mat& outBlob = outputs[i_n];
            outBlob.setTo(0);
            CV_Assert(input.size[1] == outBlob.size[1]);
            int outPlaneTotal = outBlob.size[2]*outBlob.size[3];

            for (int i_c = 0; i_c < input.size[1]; i_c++)
            {
                Mat outPlane = getPlane(outBlob, 0, i_c);
                int wh_area = input.size[2]*input.size[3];
                const float* inptr = input.ptr<float>(0, i_c);
                const float* idxptr = indices.ptr<float>(0, i_c);
                float* outptr = outPlane.ptr<float>();

                for(int i_wh = 0; i_wh < wh_area; i_wh++)
                {
                    int index = idxptr[i_wh];
                    CV_Assert(0 <= index && index < outPlaneTotal);
                    outptr[index] = inptr[i_wh];
                }
            }
        }
    }

    virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input)
    {
#ifdef HAVE_HALIDE
        // Meaningless operation if false because if kernel > stride
        // it is not deterministic and if kernel < stride we just
        // skip a part of input data (you'd better change your model).
        if (poolKernel.width != poolStride.width ||
            poolKernel.height != poolStride.height)
            CV_Error(cv::Error::StsNotImplemented,
                     "Halide backend for maximum unpooling "
                     "is not support cases when kernel != stride");

        Halide::Var x("x"), y("y"), c("c"), n("n");
        Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
        Halide::Buffer<float> inputBuffer = halideBuffer(input[0]);
        Halide::Buffer<float> indices = halideBuffer(input[1]);

        Halide::Expr pooledX = x / poolKernel.width;
        Halide::Expr pooledY = y / poolKernel.height;

        const int outW = inputBuffer.width() * poolKernel.width;
        top(x, y, c, n) = select(y * outW + x == indices(pooledX, pooledY, c, n),
                                 inputBuffer(pooledX, pooledY, c, n), 0.0f);
        return Ptr<BackendNode>(new HalideBackendNode(top));
#endif  // HAVE_HALIDE
        return Ptr<BackendNode>();
    }
};

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

}
}