modules/dnn/src/layers/reorg_layer.cpp

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  42
  43 #include "../precomp.hpp"
  44 #include "../op_inf_engine.hpp"
  45 #include <opencv2/dnn/shape_utils.hpp>
  46 #include <opencv2/dnn/all_layers.hpp>
  47
  48 #ifdef HAVE_OPENCL
  49 #include "opencl_kernels_dnn.hpp"
  50 #endif
  51
  52 namespace cv
  53 {
  54 namespace dnn
  55 {
  56
  57 class ReorgLayerImpl CV_FINAL : public ReorgLayer
  58 {
  59     int reorgStride;
  60 public:
  61
  62     ReorgLayerImpl(const LayerParams& params)
  63     {
  64         setParamsFrom(params);
  65
  66         reorgStride = params.get<int>("reorg_stride", 2);
  67         CV_Assert(reorgStride > 0);
  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         CV_Assert(inputs.size() > 0);
  76         outputs = std::vector<MatShape>(inputs.size(), shape(
  77             inputs[0][0],
  78             inputs[0][1] * reorgStride * reorgStride,
  79             inputs[0][2] / reorgStride,
  80             inputs[0][3] / reorgStride));
  81
  82         CV_Assert(outputs[0][0] > 0 && outputs[0][1] > 0 && outputs[0][2] > 0 && outputs[0][3] > 0);
  83         CV_Assert(total(outputs[0]) == total(inputs[0]));
  84
  85         return false;
  86     }
  87
  88     virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
  89     {
  90         std::vector<Mat> inputs, outputs;
  91         inputs_arr.getMatVector(inputs);
  92         outputs_arr.getMatVector(outputs);
  93
  94         Mat inp = inputs[0];
  95         Mat out = outputs[0];
  96         int batchSize = inp.size[0];
  97
  98         LayerParams permParams;
  99         if (batchSize == 1)
 100         {
 101             int order[] = {1, 3, 0, 2};
 102             permParams.set("order", DictValue::arrayInt(&order[0], 4));
 103
 104             permuteInpShape.resize(4);
 105             permuteInpShape[0] = inp.size[1] * inp.size[2] / (reorgStride * reorgStride);  // (channels*height)/(r*r)
 106             permuteInpShape[1] = reorgStride;
 107             permuteInpShape[2] = inp.size[3];  // width
 108             permuteInpShape[3] = reorgStride;
 109
 110             permuteOutShape.resize(4);
 111             for (int i = 0; i < 4; ++i)
 112                 permuteOutShape[i] = permuteInpShape[order[i]];
 113         }
 114         else
 115         {
 116             int order[] = {0, 2, 4, 1, 3};
 117             permParams.set("order", DictValue::arrayInt(&order[0], 5));
 118
 119             permuteInpShape.resize(5);
 120             permuteInpShape[0] = batchSize;
 121             permuteInpShape[1] = inp.size[1] * inp.size[2] / (reorgStride * reorgStride);  // (channels*height)/(r*r)
 122             permuteInpShape[2] = reorgStride;
 123             permuteInpShape[3] = inp.size[3];  // width
 124             permuteInpShape[4] = reorgStride;
 125
 126             permuteOutShape.resize(5);
 127             for (int i = 0; i < 5; ++i)
 128                 permuteOutShape[i] = permuteInpShape[order[i]];
 129         }
 130         permute = PermuteLayer::create(permParams);
 131         std::vector<Mat> permuteInputs(1, inp.reshape(1, permuteInpShape));
 132         std::vector<Mat> permuteOutputs(1, out.reshape(1, permuteOutShape));
 133         permute->finalize(permuteInputs, permuteOutputs);
 134     }
 135
 136     virtual bool supportBackend(int backendId) CV_OVERRIDE
 137     {
 138         return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
 139     }
 140
 141 #ifdef HAVE_OPENCL
 142     bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
 143     {
 144         std::vector<UMat> inputs;
 145         std::vector<UMat> outputs;
 146
 147         inps.getUMatVector(inputs);
 148         outs.getUMatVector(outputs);
 149
 150         inputs[0] = inputs[0].reshape(1, permuteInpShape.size(), &permuteInpShape[0]);
 151         outputs[0] = outputs[0].reshape(1, permuteOutShape.size(), &permuteOutShape[0]);
 152         permute->preferableTarget = preferableTarget;
 153         permute->forward(inputs, outputs, internals);
 154         return true;
 155     }
 156 #endif
 157
 158     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
 159     {
 160         CV_TRACE_FUNCTION();
 161         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 162
 163         CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
 164                    forward_ocl(inputs_arr, outputs_arr, internals_arr))
 165
 166         if (inputs_arr.depth() == CV_16S)
 167         {
 168             forward_fallback(inputs_arr, outputs_arr, internals_arr);
 169             return;
 170         }
 171
 172         std::vector<Mat> inputs, outputs;
 173         inputs_arr.getMatVector(inputs);
 174         outputs_arr.getMatVector(outputs);
 175
 176         inputs[0] = inputs[0].reshape(1, permuteInpShape);
 177         outputs[0] = outputs[0].reshape(1, permuteOutShape);
 178         permute->forward(inputs, outputs, internals_arr);
 179     }
 180
 181 #ifdef HAVE_INF_ENGINE
 182     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
 183     {
 184         InferenceEngine::Builder::ReorgYoloLayer ieLayer(name);
 185         ieLayer.setStride(reorgStride);
 186         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
 187     }
 188 #endif  // HAVE_INF_ENGINE
 189
 190     virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
 191                            const std::vector<MatShape> &outputs) const CV_OVERRIDE
 192     {
 193         CV_UNUSED(outputs); // suppress unused variable warning
 194
 195         int64 flops = 0;
 196         for(int i = 0; i < inputs.size(); i++)
 197         {
 198             flops += 21*total(inputs[i]);
 199         }
 200         return flops;
 201     }
 202
 203 private:
 204     Ptr<PermuteLayer> permute;
 205     std::vector<int> permuteInpShape, permuteOutShape;
 206 };
 207
 208 Ptr<ReorgLayer> ReorgLayer::create(const LayerParams& params)
 209 {
 210     return Ptr<ReorgLayer>(new ReorgLayerImpl(params));
 211 }
 212
 213 }  // namespace dnn
 214 }  // namespace cv