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

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#include "../precomp.hpp"
#include "layers_common.hpp"
#include <float.h>
#include <algorithm>

namespace cv
{
namespace dnn
{
class PermuteLayerImpl : public PermuteLayer
{
public:
    void checkCurrentOrder(int currentOrder)
    {
        if(currentOrder < 0 || currentOrder > 3)
        {
            CV_Error(
                     Error::StsBadArg,
                     "Orders of dimensions in Permute layer parameter"
                     "must be in [0...3] interval");
        }

        if(std::find(_order.begin(), _order.end(), currentOrder) != _order.end())
        {
            CV_Error(Error::StsBadArg,
                     "Permute layer parameter contains duplicated orders.");
        }
    }

    void checkNeedForPermutation()
    {
        _needsPermute = false;
        for (size_t i = 0; i < _numAxes; ++i)
        {
            if (_order[i] != i)
            {
                _needsPermute = true;
                break;
            }
        }
    }

    PermuteLayerImpl(const LayerParams &params)
    {
        if (!params.has("order"))
        {
            _needsPermute = false;
            return;
        }

        DictValue paramOrder = params.get("order");
        if(paramOrder.size() > 4)
        {
            CV_Error(
                     Error::StsBadArg,
                     "Too many (> 4) orders of dimensions in Permute layer");
        }

        _numAxes = paramOrder.size();

        for (size_t i = 0; i < _numAxes; i++)
        {
            int currentOrder = paramOrder.get<int>(i);
            checkCurrentOrder(currentOrder);
            _order.push_back(currentOrder);
        }

        setParamsFrom(params);
        checkNeedForPermutation();
    }

    bool getMemoryShapes(const std::vector<MatShape> &inputs,
                         const int requiredOutputs,
                         std::vector<MatShape> &outputs,
                         std::vector<MatShape> &internals) const
    {
        if(!_needsPermute)
            return true;

        CV_Assert(inputs.size() > 0);
        CV_Assert((int)_numAxes == inputs[0].size());

        MatShape shapeBefore = inputs[0], shapeAfter;
        for (size_t i = 0; i < _numAxes; i++)
        {
            shapeAfter.push_back(shapeBefore[_order[i]]);
        }

        outputs.clear();

        for (size_t i = 0; i < inputs.size(); i++)
        {
            CV_Assert(inputs[i][2] == shapeBefore[2] && inputs[i][3] == shapeBefore[3]);
            CV_Assert(total(inputs[i]) == total(shapeAfter));
            outputs.push_back(shapeAfter);
        }

        return false;
    }

    void computeStrides(const MatShape &shapeBefore, const MatShape &shapeAfter)
    {
        _oldStride.resize(_numAxes);
        _newStride.resize(_numAxes);

        _oldStride[_numAxes - 1] = 1;
        _newStride[_numAxes - 1] = 1;

        for(int i = _numAxes - 2; i >= 0; i--)
        {
            _oldStride[i] = _oldStride[i + 1] * shapeBefore[i + 1];
            _newStride[i] = _newStride[i + 1] * shapeAfter[i + 1];
        }

        _count = _oldStride[0] * shapeBefore[0];
    }

    void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
    {
        if(!_needsPermute)
        {
            return;
        }

        CV_Assert(inputs.size() > 0);
        const Mat& inp0 = *inputs[0];
        CV_Assert((int)_numAxes == inp0.dims);

        computeStrides(shape(*inputs[0]), shape(outputs[0]));
    }

    void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
    {
        size_t k, ninputs = inputs.size();
        if(!_needsPermute)
        {
            for (k = 0; k < ninputs; k++)
                outputs[k] = *inputs[k];
        }
        else
        {
            size_t i, j, count = _count, numAxes = _numAxes;
            const size_t* newStride = &_newStride[0];
            const size_t* oldStride = &_oldStride[0];
            const size_t* order = &_order[0];

            for (k = 0; k < ninputs; k++)
            {
                const Mat& inp = *inputs[k];
                Mat& out = outputs[k];

                CV_Assert(inp.dims == numAxes && inp.size == inputs[0]->size);
                CV_Assert(out.dims == numAxes && out.size == outputs[0].size);

//                for( i = 0; i < numAxes; i++ )
//                {
//                    CV_Assert(inp.size[i] == _oldDimensionSize[i]);
//                    CV_Assert(out.size[i] == _newDimensionSize[i]);
//                }

                CV_Assert(inp.isContinuous() && out.isContinuous());
                CV_Assert(inp.type() == CV_32F && out.type() == CV_32F);

                const float *srcData = inp.ptr<float>();
                float *dstData = out.ptr<float>();

                for (i = 0; i < count; ++i)
                {
                    size_t oldPosition = 0;
                    size_t newPosition = i;

                    for (j = 0; j < numAxes; ++j)
                    {
                        oldPosition += (newPosition / newStride[j]) * oldStride[order[j]];
                        newPosition %= newStride[j];
                    }
                    dstData[i] = srcData[oldPosition];
                }
            }
        }
    }

    size_t _count;
    std::vector<size_t> _order;

    std::vector<int> _oldDimensionSize;
    std::vector<int> _newDimensionSize;

    std::vector<size_t> _oldStride;
    std::vector<size_t> _newStride;
    bool _needsPermute;

    size_t _numAxes;
};

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

}
}