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

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

namespace cv
{
namespace dnn
{
class BlankLayerImpl CV_FINAL : public BlankLayer
{
public:
    BlankLayerImpl(const LayerParams& params)
    {
        setParamsFrom(params);
    }

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

    bool getMemoryShapes(const std::vector<MatShape> &inputs,
                         const int requiredOutputs,
                         std::vector<MatShape> &outputs,
                         std::vector<MatShape> &internals) const CV_OVERRIDE
    {
        Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
        return true;
    }

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

        inputs_.getUMatVector(inputs);
        outputs_.getUMatVector(outputs);

        for (int i = 0, n = outputs.size(); i < n; ++i)
        {
            void *src_handle = inputs[i].handle(ACCESS_READ);
            void *dst_handle = outputs[i].handle(ACCESS_WRITE);
            if (src_handle != dst_handle)
                inputs[i].copyTo(outputs[i]);
        }

        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);

        for (int i = 0, n = outputs.size(); i < n; ++i)
            if (outputs[i].data != inputs[i].data)
                inputs[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]);
        CV_Assert(!input->dims.empty());

        InferenceEngine::LayerParams lp;
        lp.name = name;
        lp.type = "Split";
        lp.precision = InferenceEngine::Precision::FP32;
        std::shared_ptr<InferenceEngine::SplitLayer> ieLayer(new InferenceEngine::SplitLayer(lp));
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R3)
        ieLayer->params["axis"] = format("%d", (int)input->dims.size() - 1);
        ieLayer->params["out_sizes"] = format("%d", (int)input->dims[0]);
#endif
        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif  // HAVE_INF_ENGINE
        return Ptr<BackendNode>();
    }
};

Ptr<Layer> BlankLayer::create(const LayerParams& params)
{
    // In case of Caffe's Dropout layer from Faster-RCNN framework,
    // https://github.com/rbgirshick/caffe-fast-rcnn/tree/faster-rcnn
    // return Power layer.
    if (!params.get<bool>("scale_train", true))
    {
        float scale = 1 - params.get<float>("dropout_ratio", 0.5f);
        CV_Assert(scale > 0);

        LayerParams powerParams;
        powerParams.name = params.name;
        powerParams.type = "Power";
        powerParams.set("scale", scale);

        return PowerLayer::create(powerParams);
    }
    else
        return Ptr<BlankLayer>(new BlankLayerImpl(params));
}

}
}