slice_layer.cpp

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

#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
#endif

namespace cv
{
namespace dnn
{

class SliceLayerImpl CV_FINAL : public SliceLayer
{
public:
    SliceLayerImpl(const LayerParams& params)
    {
        setParamsFrom(params);
        axis = params.get<int>("axis", 1);
        if (params.has("slice_point"))
        {
            CV_Assert(!params.has("begin") && !params.has("size") && !params.has("end"));
            const DictValue &indicesValue = params.get("slice_point");
            sliceRanges.resize(indicesValue.size() + 1,
                               std::vector<Range>(axis + 1, Range::all()));
            int prevSlice = 0;
            for (int i = 0; i < indicesValue.size(); ++i)
            {
                sliceRanges[i][axis].start = prevSlice;
                sliceRanges[i][axis].end = indicesValue.get<int>(i);
                prevSlice = sliceRanges[i][axis].end;
            }
            sliceRanges.back()[axis].start = prevSlice;
        }
        else if (params.has("begin"))
        {
            CV_Assert(params.has("size") ^ params.has("end"));
            const DictValue &begins = params.get("begin");
            const DictValue &sizesOrEnds = params.has("size") ? params.get("size") : params.get("end");
            CV_Assert(begins.size() == sizesOrEnds.size());

            sliceRanges.resize(1);
            sliceRanges[0].resize(begins.size(), Range::all());
            for (int i = 0; i < begins.size(); ++i)
            {
                int start = begins.get<int>(i);
                int sizeOrEnd = sizesOrEnds.get<int>(i);  // It may be negative to reverse indexation.
                CV_Assert(start >= 0);

                sliceRanges[0][i].start = start;
                if (params.has("size"))
                {
                    int size = sizeOrEnd;
                    CV_Assert(size == -1 || size > 0);  // -1 value means range [start, axis_size).
                    sliceRanges[0][i].end = size > 0 ? (start + size) : -1;  // We'll finalize a negative value later.
                }
                else
                {
                    int end = sizeOrEnd;
                    CV_Assert(end < 0 || end > start);  // End index is excluded.
                    sliceRanges[0][i].end = end;  // We'll finalize a negative value later.
                }
            }
        }
    }

    virtual bool supportBackend(int backendId) CV_OVERRIDE
    {
        return backendId == DNN_BACKEND_OPENCV ||
               backendId == DNN_BACKEND_INFERENCE_ENGINE && sliceRanges.size() == 1 && sliceRanges[0].size() == 4;
    }

    bool getMemoryShapes(const std::vector<MatShape> &inputs,
                            const int requiredOutputs,
                            std::vector<MatShape> &outputs,
                            std::vector<MatShape> &internals) const CV_OVERRIDE
    {
        CV_Assert(inputs.size() == 1);
        MatShape inpShape = inputs[0];

        if (!sliceRanges.empty())
        {
            outputs.resize(sliceRanges.size(), inpShape);
            for (int i = 0; i < outputs.size(); ++i)
            {
                CV_Assert(sliceRanges[i].size() <= inpShape.size());
                for (int j = 0; j < sliceRanges[i].size(); ++j)
                {
                    outputs[i][j] = clamp(sliceRanges[i][j], inpShape[j]).size();
                }
            }
        }
        else  // Divide input blob on equal parts by axis.
        {
            CV_Assert(0 <= axis && axis < inpShape.size());
            CV_Assert(requiredOutputs > 0 && inpShape[axis] % requiredOutputs == 0);
            inpShape[axis] /= requiredOutputs;
            outputs.resize(requiredOutputs, inpShape);
        }
        return false;
    }

    void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
    {
        std::vector<Mat> inputs, outputs;
        inputs_arr.getMatVector(inputs);
        outputs_arr.getMatVector(outputs);

        CV_Assert(inputs.size() == 1);
        const MatSize& inpShape = inputs[0].size;

        if (sliceRanges.empty())
        {
            // Divide input blob on equal parts by axis.
            int outAxisSize = inpShape[axis] / outputs.size();
            sliceRanges.resize(outputs.size(),
                               std::vector<Range>(axis + 1, Range::all()));
            int prevSlice = 0;
            for (int i = 0; i < outputs.size(); ++i)
            {
                sliceRanges[i][axis].start = prevSlice;
                sliceRanges[i][axis].end = sliceRanges[i][axis].start + outAxisSize;
                prevSlice = sliceRanges[i][axis].end;
            }
        }
        else
            CV_Assert(outputs.size() == sliceRanges.size());

        for (int i = 0; i < outputs.size(); ++i)
        {
            CV_Assert(sliceRanges[i].size() <= inpShape.dims());
            // Clamp.
            for (int j = 0; j < sliceRanges[i].size(); ++j)
            {
                sliceRanges[i][j] = clamp(sliceRanges[i][j], inpShape[j]);
            }
            // Fill the rest of ranges.
            for (int j = sliceRanges[i].size(); j < inpShape.dims(); ++j)
            {
                sliceRanges[i].push_back(Range::all());
            }
        }
    }

#ifdef HAVE_OPENCL
    bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
    {
        std::vector<UMat> inputs;
        std::vector<UMat> outputs;

        bool use_half = (inputs_.depth() == CV_16S);
        inputs_.getUMatVector(inputs);
        outputs_.getUMatVector(outputs);

        if (inputs[0].dims < 4 || (total(shape(outputs[0]), 0, 2) % 4 != 0) ||
            (total(shape(outputs[0]), 2) % 4 != 0))
            return false;

        String opts;
        if (use_half)
            opts = "-DDtype=half -DDtype4=half4 -DDtype8=half8";
        else
            opts = "-DDtype=float -DDtype4=float4 -DDtype8=float8";
        const UMat& inpMat = inputs[0];
        for (size_t i = 0; i < outputs.size(); i++)
        {
            int groups = outputs[i].size[0];
            int channels = outputs[i].size[1];
            int rows = outputs[i].size[2];
            int cols = outputs[i].size[3];

            ocl::Kernel kernel("slice", ocl::dnn::slice_oclsrc, opts);
            size_t local[] = { 128 };
            size_t global[] = { (size_t)groups * channels / 4 * local[0] };
            int idx = 0;
            kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inpMat));
            kernel.set(idx++, (int)(inpMat.size[2] * inpMat.size[3]));
            kernel.set(idx++, (int)(rows * cols));
            kernel.set(idx++, (int)inpMat.size[3]);
            kernel.set(idx++, (int)cols);
            kernel.set(idx++, (int)sliceRanges[i][2].start);
            kernel.set(idx++, (int)sliceRanges[i][3].start);
            kernel.set(idx++, ocl::KernelArg::PtrWriteOnly(outputs[i]));
            bool ret = kernel.run(1, global, local, false);
            if (!ret)
                return false;
        }

        return true;
    }
#endif

    void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
    {
        CV_TRACE_FUNCTION();
        CV_TRACE_ARG_VALUE(name, "name", name.c_str());

        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
                   forward_ocl(inputs_arr, outputs_arr, internals_arr))

        std::vector<Mat> inputs, outputs;
        inputs_arr.getMatVector(inputs);
        outputs_arr.getMatVector(outputs);

        const Mat& inpMat = inputs[0];
        CV_Assert(outputs.size() == sliceRanges.size());
        for (size_t i = 0; i < outputs.size(); i++)
        {
            inpMat(sliceRanges[i]).copyTo(outputs[i]);
        }
    }

    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
    {
#ifdef HAVE_INF_ENGINE
        InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
        InferenceEngine::LayerParams lp;
        lp.name = name;
        lp.type = "Crop";
        lp.precision = InferenceEngine::Precision::FP32;
        std::shared_ptr<InferenceEngine::CropLayer> ieLayer(new InferenceEngine::CropLayer(lp));

        CV_Assert(sliceRanges.size() == 1);

        int from, to, step;
        if (preferableTarget == DNN_TARGET_MYRIAD)
        {
            from = 1;
            to = sliceRanges[0].size() + 1;
            step = 1;
        }
        else
        {
            from = sliceRanges[0].size() - 1;
            to = -1;
            step = -1;
        }
        for (int i = from; i != to; i += step)
        {
            ieLayer->axis.push_back(i);
            ieLayer->offset.push_back(sliceRanges[0][i].start);
            ieLayer->dim.push_back(sliceRanges[0][i].end - sliceRanges[0][i].start);
        }
        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));

#endif  // HAVE_INF_ENGINE
        return Ptr<BackendNode>();
    }
};

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

}
}