modules/dnn/src/layers/blank_layer.cpp

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  42 #include "../precomp.hpp"
  43 #include "../op_cuda.hpp"
  44 #include "../op_inf_engine.hpp"
  45
  46 #ifdef HAVE_CUDA
  47 #include "../cuda4dnn/primitives/reshape.hpp"
  48 using namespace cv::dnn::cuda4dnn;
  49 #endif
  50
  51 namespace cv
  52 {
  53 namespace dnn
  54 {
  55 class BlankLayerImpl CV_FINAL : public BlankLayer
  56 {
  57 public:
  58     BlankLayerImpl(const LayerParams& params)
  59     {
  60         setParamsFrom(params);
  61     }
  62
  63     virtual bool supportBackend(int backendId) CV_OVERRIDE
  64     {
  65         return backendId == DNN_BACKEND_OPENCV ||
  66                backendId == DNN_BACKEND_CUDA ||
  67                (backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
  68     }
  69
  70     bool getMemoryShapes(const std::vector<MatShape> &inputs,
  71                          const int requiredOutputs,
  72                          std::vector<MatShape> &outputs,
  73                          std::vector<MatShape> &internals) const CV_OVERRIDE
  74     {
  75         Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
  76         return true;
  77     }
  78
  79 #ifdef HAVE_OPENCL
  80     bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
  81     {
  82         std::vector<UMat> inputs;
  83         std::vector<UMat> outputs;
  84
  85         inputs_.getUMatVector(inputs);
  86         outputs_.getUMatVector(outputs);
  87
  88         for (int i = 0, n = outputs.size(); i < n; ++i)
  89         {
  90             void *src_handle = inputs[i].handle(ACCESS_READ);
  91             void *dst_handle = outputs[i].handle(ACCESS_WRITE);
  92             if (src_handle != dst_handle)
  93                 inputs[i].copyTo(outputs[i]);
  94         }
  95
  96         return true;
  97     }
  98 #endif
  99
 100     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
 101     {
 102         CV_TRACE_FUNCTION();
 103         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 104
 105         CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
 106                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 107
 108         std::vector<Mat> inputs, outputs;
 109         inputs_arr.getMatVector(inputs);
 110         outputs_arr.getMatVector(outputs);
 111
 112         for (int i = 0, n = outputs.size(); i < n; ++i)
 113             if (outputs[i].data != inputs[i].data)
 114                 inputs[i].copyTo(outputs[i]);
 115     }
 116
 117 #ifdef HAVE_CUDA
 118     Ptr<BackendNode> initCUDA(
 119         void *context_,
 120         const std::vector<Ptr<BackendWrapper>>& inputs,
 121         const std::vector<Ptr<BackendWrapper>>& outputs
 122     ) override
 123     {
 124         auto context = reinterpret_cast<csl::CSLContext*>(context_);
 125         return make_cuda_node<cuda4dnn::ReshapeOp>(preferableTarget, std::move(context->stream));
 126     }
 127 #endif
 128
 129 #ifdef HAVE_INF_ENGINE
 130     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
 131     {
 132         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
 133         std::vector<size_t> dims = input->getDims();
 134         CV_Assert(!dims.empty());
 135
 136         InferenceEngine::Builder::Layer ieLayer(name);
 137         ieLayer.setName(name);
 138         if (preferableTarget == DNN_TARGET_MYRIAD)
 139         {
 140             ieLayer.setType("Copy");
 141         }
 142         else
 143         {
 144             ieLayer.setType("Split");
 145             ieLayer.getParameters()["axis"] = dims.size() - 1;
 146             ieLayer.getParameters()["out_sizes"] = dims[0];
 147         }
 148         ieLayer.setInputPorts({InferenceEngine::Port(dims)});
 149         ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
 150         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
 151     }
 152 #endif  // HAVE_INF_ENGINE
 153 };
 154
 155 Ptr<Layer> BlankLayer::create(const LayerParams& params)
 156 {
 157     // In case of Caffe's Dropout layer from Faster-RCNN framework,
 158     // https://github.com/rbgirshick/caffe-fast-rcnn/tree/faster-rcnn
 159     // return Power layer.
 160     if (!params.get<bool>("scale_train", true))
 161     {
 162         float scale = 1 - params.get<float>("dropout_ratio", 0.5f);
 163         CV_Assert(scale > 0);
 164
 165         LayerParams powerParams;
 166         powerParams.name = params.name;
 167         powerParams.type = "Power";
 168         powerParams.set("scale", scale);
 169
 170         return PowerLayer::create(powerParams);
 171     }
 172     else
 173         return Ptr<BlankLayer>(new BlankLayerImpl(params));
 174 }
 175
 176 }
 177 }