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

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#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"

#include <float.h>
#include <algorithm>
#include <opencv2/dnn/shape_utils.hpp>

namespace cv
{
namespace dnn
{

class FlattenLayerImpl CV_FINAL : public FlattenLayer
{
public:
    FlattenLayerImpl(const LayerParams &params)
    {
        _startAxis = params.get<int>("axis", 1);
        _endAxis = params.get<int>("end_axis", -1);
        setParamsFrom(params);
    }

    virtual bool supportBackend(int backendId) CV_OVERRIDE
    {
        return backendId == DNN_BACKEND_OPENCV ||
               ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
    }

    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() > 0);
        for (size_t i = 1; i < inputs.size(); i++)
        {
            CV_Assert(inputs[i] == inputs[0]);
        }

        int numAxes = inputs[0].size();
        int startAxis = clamp(_startAxis, numAxes);
        int endAxis = clamp(_endAxis, numAxes);

        CV_Assert(startAxis >= 0);
        CV_Assert(endAxis >= startAxis && endAxis < (int)numAxes);

        size_t flattenedDimensionSize = total(inputs[0], startAxis, endAxis + 1);

        MatShape outputShapeVec;
        for (int i = 0; i < startAxis; i++)
        {
            outputShapeVec.push_back(inputs[0][i]);
        }
        outputShapeVec.push_back(flattenedDimensionSize);
        for (size_t i = endAxis + 1; i < numAxes; i++)
        {
            outputShapeVec.push_back(inputs[0][i]);
        }
        CV_Assert(outputShapeVec.size() <= 4);

        outputs.resize(inputs.size(), outputShapeVec);

        return true;
    }

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

        int numAxes = inputs[0].dims;
        _startAxis = clamp(_startAxis, numAxes);
        _endAxis = clamp(_endAxis, numAxes);
    }

#ifdef HAVE_OPENCL
    bool forward_ocl(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
    {
        std::vector<UMat> inpvec;
        std::vector<UMat> outputs;

        inputs_arr.getUMatVector(inpvec);
        outputs_arr.getUMatVector(outputs);

        std::vector<UMat*> inputs(inpvec.size());
        for (int i = 0; i < inpvec.size(); i++)
            inputs[i] = &inpvec[i];

        for (size_t i = 0; i < inputs.size(); i++)
        {
            MatShape outShape = shape(outputs[i]);
            UMat& output = outputs_arr.getUMatRef(i);
            output = inputs[i]->reshape(1, (int)outShape.size(), &outShape[0]);
        }

        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) &&
                   outputs_arr.isUMatVector(),
                   forward_ocl(inputs_arr, outputs_arr, internals_arr))

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

        for (size_t i = 0; i < inputs.size(); i++)
        {
            MatShape outShape = shape(outputs[i]);
            if (inputs[i].data != outputs[i].data)
            {
                inputs[i].reshape(1, (int)outShape.size(), &outShape[0]).copyTo(outputs[i]);
            }
        }
    }

#ifdef HAVE_DNN_IE_NN_BUILDER_2019
    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
    {
        InferenceEngine::Builder::Layer ieLayer(name);
        ieLayer.setName(name);
        ieLayer.setType("Flatten");
        ieLayer.getParameters()["axis"] = (size_t)_startAxis;
        ieLayer.getParameters()["end_axis"] = _endAxis;  // Do not cast to size_t because it might be negative.
        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
        ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
    }
#endif  // HAVE_DNN_IE_NN_BUILDER_2019

#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                    const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
        auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
        std::vector<size_t> dims = ieInpNode->get_shape();

        int numAxes = dims.size();
        int startAxis = clamp(_startAxis, numAxes);
        int endAxis = clamp(_endAxis, numAxes);

        CV_Assert(startAxis >= 0);
        CV_Assert(endAxis >= startAxis && endAxis < numAxes);
        int64_t flattenedDimensionSize = std::accumulate(dims.begin() + startAxis,
                                         dims.begin() + endAxis + 1, 1, std::multiplies<size_t>());

        std::vector<int64_t> outputShapeVec(dims.begin(), dims.begin() + startAxis);
        outputShapeVec.push_back(flattenedDimensionSize);
        outputShapeVec.insert(outputShapeVec.end(), dims.begin() + endAxis + 1, dims.end());

        auto shape   = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
                       ngraph::Shape({outputShapeVec.size()}), outputShapeVec.data());
        auto reshape = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, shape, true);
        return Ptr<BackendNode>(new InfEngineNgraphNode(reshape));
    }
#endif  // HAVE_DNN_NGRAPH

    int _startAxis;
    int _endAxis;
};

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

}
}