modules/dnn/src/layers/blank_layer.cpp

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  42 #include "../precomp.hpp"
  43 #include "../op_inf_engine.hpp"
  44
  45 namespace cv
  46 {
  47 namespace dnn
  48 {
  49 class BlankLayerImpl CV_FINAL : public BlankLayer
  50 {
  51 public:
  52     BlankLayerImpl(const LayerParams& params)
  53     {
  54         setParamsFrom(params);
  55     }
  56
  57     virtual bool supportBackend(int backendId) CV_OVERRIDE
  58     {
  59         return backendId == DNN_BACKEND_OPENCV ||
  60                (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
  61     }
  62
  63     bool getMemoryShapes(const std::vector<MatShape> &inputs,
  64                          const int requiredOutputs,
  65                          std::vector<MatShape> &outputs,
  66                          std::vector<MatShape> &internals) const CV_OVERRIDE
  67     {
  68         Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
  69         return true;
  70     }
  71
  72 #ifdef HAVE_OPENCL
  73     bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
  74     {
  75         std::vector<UMat> inputs;
  76         std::vector<UMat> outputs;
  77
  78         inputs_.getUMatVector(inputs);
  79         outputs_.getUMatVector(outputs);
  80
  81         for (int i = 0, n = outputs.size(); i < n; ++i)
  82         {
  83             void *src_handle = inputs[i].handle(ACCESS_READ);
  84             void *dst_handle = outputs[i].handle(ACCESS_WRITE);
  85             if (src_handle != dst_handle)
  86                 inputs[i].copyTo(outputs[i]);
  87         }
  88
  89         return true;
  90     }
  91 #endif
  92
  93     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
  94     {
  95         CV_TRACE_FUNCTION();
  96         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
  97
  98         CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
  99                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 100
 101         std::vector<Mat> inputs, outputs;
 102         inputs_arr.getMatVector(inputs);
 103         outputs_arr.getMatVector(outputs);
 104
 105         for (int i = 0, n = outputs.size(); i < n; ++i)
 106             if (outputs[i].data != inputs[i].data)
 107                 inputs[i].copyTo(outputs[i]);
 108     }
 109
 110 #ifdef HAVE_INF_ENGINE
 111     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
 112     {
 113         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
 114         std::vector<size_t> dims = input->getDims();
 115         CV_Assert(!dims.empty());
 116
 117         InferenceEngine::Builder::Layer ieLayer(name);
 118         ieLayer.setName(name);
 119         if (preferableTarget == DNN_TARGET_MYRIAD)
 120         {
 121             ieLayer.setType("Copy");
 122         }
 123         else
 124         {
 125             ieLayer.setType("Split");
 126             ieLayer.getParameters()["axis"] = dims.size() - 1;
 127             ieLayer.getParameters()["out_sizes"] = dims[0];
 128         }
 129         ieLayer.setInputPorts({InferenceEngine::Port(dims)});
 130         ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
 131         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
 132     }
 133 #endif  // HAVE_INF_ENGINE
 134 };
 135
 136 Ptr<Layer> BlankLayer::create(const LayerParams& params)
 137 {
 138     // In case of Caffe's Dropout layer from Faster-RCNN framework,
 139     // https://github.com/rbgirshick/caffe-fast-rcnn/tree/faster-rcnn
 140     // return Power layer.
 141     if (!params.get<bool>("scale_train", true))
 142     {
 143         float scale = 1 - params.get<float>("dropout_ratio", 0.5f);
 144         CV_Assert(scale > 0);
 145
 146         LayerParams powerParams;
 147         powerParams.name = params.name;
 148         powerParams.type = "Power";
 149         powerParams.set("scale", scale);
 150
 151         return PowerLayer::create(powerParams);
 152     }
 153     else
 154         return Ptr<BlankLayer>(new BlankLayerImpl(params));
 155 }
 156
 157 }
 158 }