text: /*M ///////////////////////////////////////////////////////////////////////////////////////
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  #include "../precomp.hpp"
  #include <opencv2/dnn/shape_utils.hpp>
  #include <opencv2/dnn/all_layers.hpp>
  #include "../nms.inl.hpp"
  
  #ifdef HAVE_OPENCL
  #include "opencl_kernels_dnn.hpp"
  #endif
  
  #ifdef HAVE_DNN_NGRAPH
  #include "../ie_ngraph.hpp"
  #endif
  
  
  namespace cv
  {
  namespace dnn
  {
  
  class RegionLayerImpl CV_FINAL : public RegionLayer
  {
  public:
      int coords, classes, anchors, classfix;
      float thresh, nmsThreshold, scale_x_y;
      bool useSoftmax, useLogistic;
  #ifdef HAVE_OPENCL
      UMat blob_umat;
  #endif
  
      RegionLayerImpl(const LayerParams& params)
      {
          setParamsFrom(params);
          CV_Assert(blobs.size() == 1);
  
          thresh = params.get<float>("thresh", 0.2);
          coords = params.get<int>("coords", 4);
          classes = params.get<int>("classes", 0);
          anchors = params.get<int>("anchors", 5);
          classfix = params.get<int>("classfix", 0);
          useSoftmax = params.get<bool>("softmax", false);
          useLogistic = params.get<bool>("logistic", false);
          nmsThreshold = params.get<float>("nms_threshold", 0.4);
          scale_x_y = params.get<float>("scale_x_y", 1.0); // Yolov4
  
          CV_Assert(nmsThreshold >= 0.);
          CV_Assert(coords == 4);
          CV_Assert(classes >= 1);
          CV_Assert(anchors >= 1);
          CV_Assert(useLogistic || useSoftmax);
          if (params.get<bool>("softmax_tree", false))
              CV_Error(cv::Error::StsNotImplemented, "Yolo9000 is not implemented");
      }
  
      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);
          // channels == cell_size*anchors
          CV_Assert(inputs[0][3] == (1 + coords + classes)*anchors);
          int batch_size = inputs[0][0];
          if(batch_size > 1)
              outputs = std::vector<MatShape>(1, shape(batch_size, inputs[0][1] * inputs[0][2] * anchors, inputs[0][3] / anchors));
          else
              outputs = std::vector<MatShape>(1, shape(inputs[0][1] * inputs[0][2] * anchors, inputs[0][3] / anchors));
          return false;
      }
  
      virtual bool supportBackend(int backendId) CV_OVERRIDE
      {
  #ifdef HAVE_DNN_NGRAPH
      if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
          return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_2) && preferableTarget != DNN_TARGET_MYRIAD;
  #endif
          return backendId == DNN_BACKEND_OPENCV;
      }
  
      float logistic_activate(float x) { return 1.F / (1.F + exp(-x)); }
  
      void softmax_activate(const float* input, const int n, const float temp, float* output)
      {
          int i;
          float sum = 0;
          float largest = -FLT_MAX;
          for (i = 0; i < n; ++i) {
              if (input[i] > largest) largest = input[i];
          }
          for (i = 0; i < n; ++i) {
              float e = exp((input[i] - largest) / temp);
              sum += e;
              output[i] = e;
          }
          for (i = 0; i < n; ++i) {
              output[i] /= sum;
          }
      }
  
  #ifdef HAVE_OPENCL
      bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
      {
          if (blob_umat.empty())
              blobs[0].copyTo(blob_umat);
  
          std::vector<UMat> inputs;
          std::vector<UMat> outputs;
  
          // TODO: implement a logistic activation to classification scores.
          if (useLogistic || inps.depth() == CV_16S)
              return false;
  
          inps.getUMatVector(inputs);
          outs.getUMatVector(outputs);
  
          CV_Assert(inputs.size() >= 1);
          int const cell_size = classes + coords + 1;
  
          for (size_t ii = 0; ii < outputs.size(); ii++)
          {
              UMat& inpBlob = inputs[ii];
              UMat& outBlob = outputs[ii];
  
              int batch_size = inpBlob.size[0];
              int rows = inpBlob.size[1];
              int cols = inpBlob.size[2];
  
              // channels == cell_size*anchors, see l. 94
              int sample_size = cell_size*rows*cols*anchors;
  
              ocl::Kernel logistic_kernel("logistic_activ", ocl::dnn::region_oclsrc);
              size_t nanchors = rows*cols*anchors*batch_size;
              logistic_kernel.set(0, (int)nanchors);
              logistic_kernel.set(1, ocl::KernelArg::PtrReadOnly(inpBlob));
              logistic_kernel.set(2, (int)cell_size);
              logistic_kernel.set(3, ocl::KernelArg::PtrWriteOnly(outBlob));
              logistic_kernel.run(1, &nanchors, NULL, false);
  
              if (useSoftmax)
              {
                  // Yolo v2
                  // softmax activation for Probability, for each grid cell (X x Y x Anchor-index)
                  ocl::Kernel softmax_kernel("softmax_activ", ocl::dnn::region_oclsrc);
                  size_t nanchors = rows*cols*anchors*batch_size;
                  softmax_kernel.set(0, (int)nanchors);
                  softmax_kernel.set(1, ocl::KernelArg::PtrReadOnly(inpBlob));
                  softmax_kernel.set(2, ocl::KernelArg::PtrReadOnly(blob_umat));
                  softmax_kernel.set(3, (int)cell_size);
                  softmax_kernel.set(4, (int)classes);
                  softmax_kernel.set(5, (int)classfix);
                  softmax_kernel.set(6, (int)rows);
                  softmax_kernel.set(7, (int)cols);
                  softmax_kernel.set(8, (int)anchors);
                  softmax_kernel.set(9, (float)thresh);
                  softmax_kernel.set(10, ocl::KernelArg::PtrWriteOnly(outBlob));
                  if (!softmax_kernel.run(1, &nanchors, NULL, false))
                      return false;
              }
  
              if (nmsThreshold > 0) {
                  Mat mat = outBlob.getMat(ACCESS_WRITE);
                  float *dstData = mat.ptr<float>();
                  for (int b = 0; b < batch_size; ++b)
                      do_nms_sort(dstData + b*sample_size, rows*cols*anchors, thresh, nmsThreshold);
              }
  
          }
  
          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))
  
          if (inputs_arr.depth() == CV_16S)
          {
              forward_fallback(inputs_arr, outputs_arr, internals_arr);
              return;
          }
  
          std::vector<Mat> inputs, outputs, internals;
          inputs_arr.getMatVector(inputs);
          outputs_arr.getMatVector(outputs);
          internals_arr.getMatVector(internals);
  
          CV_Assert(inputs.size() >= 1);
          CV_Assert(outputs.size() == 1);
          int const cell_size = classes + coords + 1;
  
          const float* biasData = blobs[0].ptr<float>();
  
          for (size_t ii = 0; ii < outputs.size(); ii++)
          {
              Mat &inpBlob = inputs[ii];
              Mat &outBlob = outputs[ii];
  
              int batch_size = inpBlob.size[0];
              int rows = inpBlob.size[1];
              int cols = inpBlob.size[2];
  
              // address length for one image in batch, both for input and output
              int sample_size = cell_size*rows*cols*anchors;
  
              // assert that the comment above is true
              CV_Assert(sample_size*batch_size == inpBlob.total());
              CV_Assert(sample_size*batch_size == outBlob.total());
  
              CV_Assert(inputs.size() < 2 || inputs[1].dims == 4);
              int hNorm = inputs.size() > 1 ? inputs[1].size[2] : rows;
              int wNorm = inputs.size() > 1 ? inputs[1].size[3] : cols;
  
              const float *srcData = inpBlob.ptr<float>();
              float *dstData = outBlob.ptr<float>();
  
              // logistic activation for t0, for each grid cell (X x Y x Anchor-index)
              for (int i = 0; i < batch_size*rows*cols*anchors; ++i) {
                  int index = cell_size*i;
                  float x = srcData[index + 4];
                  dstData[index + 4] = logistic_activate(x);	// logistic activation
              }
  
              if (useSoftmax) {  // Yolo v2
                  for (int i = 0; i < batch_size*rows*cols*anchors; ++i) {
                      int index = cell_size*i;
                      softmax_activate(srcData + index + 5, classes, 1, dstData + index + 5);
                  }
              }
              else if (useLogistic) {  // Yolo v3
                  for (int i = 0; i < batch_size*rows*cols*anchors; ++i){
                      int index = cell_size*i;
                      const float* input = srcData + index + 5;
                      float* output = dstData + index + 5;
                      for (int c = 0; c < classes; ++c)
                          output[c] = logistic_activate(input[c]);
                  }
              }
              for (int b = 0; b < batch_size; ++b)
                  for (int x = 0; x < cols; ++x)
                      for(int y = 0; y < rows; ++y)
                          for (int a = 0; a < anchors; ++a) {
                              // relative start address for image b within the batch data
                              int index_sample_offset = sample_size*b;
                              int index = (y*cols + x)*anchors + a;  // index for each grid-cell & anchor
                              int p_index = index_sample_offset + index * cell_size + 4;
                              float scale = dstData[p_index];
                              if (classfix == -1 && scale < .5) scale = 0;  // if(t0 < 0.5) t0 = 0;
                              int box_index = index_sample_offset + index * cell_size;
  
                              float x_tmp = (logistic_activate(srcData[box_index + 0]) - 0.5f) * scale_x_y + 0.5f;
                              float y_tmp = (logistic_activate(srcData[box_index + 1]) - 0.5f) * scale_x_y + 0.5f;
                              dstData[box_index + 0] = (x + x_tmp) / cols;
                              dstData[box_index + 1] = (y + y_tmp) / rows;
                              dstData[box_index + 2] = exp(srcData[box_index + 2]) * biasData[2 * a] / wNorm;
                              dstData[box_index + 3] = exp(srcData[box_index + 3]) * biasData[2 * a + 1] / hNorm;
  
                              int class_index = index_sample_offset + index * cell_size + 5;
                              for (int j = 0; j < classes; ++j) {
                                  float prob = scale*dstData[class_index + j];  // prob = IoU(box, object) = t0 * class-probability
                                  dstData[class_index + j] = (prob > thresh) ? prob : 0;  // if (IoU < threshold) IoU = 0;
                              }
                          }
              if (nmsThreshold > 0) {
                  for (int b = 0; b < batch_size; ++b){
                      do_nms_sort(dstData+b*sample_size, rows*cols*anchors, thresh, nmsThreshold);
                  }
              }
          }
      }
  
      void do_nms_sort(float *detections, int total, float score_thresh, float nms_thresh)
      {
          std::vector<Rect2d> boxes(total);
          std::vector<float> scores(total);
  
          for (int i = 0; i < total; ++i)
          {
              Rect2d &b = boxes[i];
              int box_index = i * (classes + coords + 1);
              b.width = detections[box_index + 2];
              b.height = detections[box_index + 3];
              b.x = detections[box_index + 0] - b.width / 2;
              b.y = detections[box_index + 1] - b.height / 2;
          }
  
          std::vector<int> indices;
          for (int k = 0; k < classes; ++k)
          {
              for (int i = 0; i < total; ++i)
              {
                  int box_index = i * (classes + coords + 1);
                  int class_index = box_index + 5;
                  scores[i] = detections[class_index + k];
                  detections[class_index + k] = 0;
              }
              NMSBoxes(boxes, scores, score_thresh, nms_thresh, indices);
              for (int i = 0, n = indices.size(); i < n; ++i)
              {
                  int box_index = indices[i] * (classes + coords + 1);
                  int class_index = box_index + 5;
                  detections[class_index + k] = scores[indices[i]];
              }
          }
      }
  
      virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
                             const std::vector<MatShape> &outputs) const CV_OVERRIDE
      {
          CV_UNUSED(outputs); // suppress unused variable warning
  
          int64 flops = 0;
          for(int i = 0; i < inputs.size(); i++)
          {
              flops += 60*total(inputs[i]);
          }
          return flops;
      }
  
  #ifdef HAVE_DNN_NGRAPH
      virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
                                          const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
      {
          auto& input = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
          auto parent_shape = input->get_shape();
          int64_t b = parent_shape[0];
          int64_t h = parent_shape[1];
          int64_t w = parent_shape[2];
          int64_t c = parent_shape[3];
  
          int64_t cols = b * h * w * anchors;
          int64_t rows = c / anchors;
          auto shape_node = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2},  std::vector<int64_t>{cols, rows});
          auto tr_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, 0});
  
          std::shared_ptr<ngraph::Node> input2d;
          {
              input2d = std::make_shared<ngraph::op::v1::Reshape>(input, shape_node, true);
              input2d = std::make_shared<ngraph::op::Transpose>(input2d, tr_axes);
          }
  
          std::shared_ptr<ngraph::Node> region;
          {
              auto new_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{4}, std::vector<int64_t>{0, 3, 1, 2});
              auto tr_input = std::make_shared<ngraph::op::Transpose>(input, new_axes);
  
              std::vector<float> anchors_vec(blobs[0].ptr<float>(), blobs[0].ptr<float>() + blobs[0].total());
              std::vector<int64_t> mask(anchors, 1);
              region = std::make_shared<ngraph::op::RegionYolo>(tr_input, coords, classes, anchors, useSoftmax, mask, 1, 3, anchors_vec);
  
              auto shape_as_inp = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
                                                                         ngraph::Shape{tr_input->get_shape().size()}, tr_input->get_shape().data());
  
              region = std::make_shared<ngraph::op::v1::Reshape>(region, shape_as_inp, true);
              new_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{4}, std::vector<int64_t>{0, 2, 3, 1});
              region = std::make_shared<ngraph::op::Transpose>(region, new_axes);
  
              region = std::make_shared<ngraph::op::v1::Reshape>(region, shape_node, true);
              region = std::make_shared<ngraph::op::Transpose>(region, tr_axes);
          }
  
          auto strides = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, 1});
          std::vector<int64_t> boxes_shape{b, anchors, h, w};
          auto shape_3d = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{boxes_shape.size()}, boxes_shape.data());
  
          ngraph::Shape box_broad_shape{1, (size_t)anchors, (size_t)h, (size_t)w};
          auto scale_x_y_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &scale_x_y);
          auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{0.5});
  
          std::shared_ptr<ngraph::Node> box_x;
          {
              auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{0, 0});
              auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, cols});
              box_x = std::make_shared<ngraph::op::v1::StridedSlice>(input2d, lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
              box_x = std::make_shared<ngraph::op::Sigmoid>(box_x);
              box_x = std::make_shared<ngraph::op::v1::Subtract>(box_x, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
              box_x = std::make_shared<ngraph::op::v1::Multiply>(box_x, scale_x_y_node, ngraph::op::AutoBroadcastType::NUMPY);
              box_x = std::make_shared<ngraph::op::v1::Add>(box_x, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
              box_x = std::make_shared<ngraph::op::v1::Reshape>(box_x, shape_3d, true);
  
              std::vector<float> x_indices(w * h * anchors);
              auto begin = x_indices.begin();
              for (int i = 0; i < h; i++)
              {
                  std::fill(begin + i * anchors, begin + (i + 1) * anchors, i);
              }
  
              for (int j = 1; j < w; j++)
              {
                  std::copy(begin, begin + h * anchors, begin + j * h * anchors);
              }
              auto horiz = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, box_broad_shape, x_indices.data());
              box_x = std::make_shared<ngraph::op::v1::Add>(box_x, horiz, ngraph::op::AutoBroadcastType::NUMPY);
  
              auto cols_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{float(w)});
              box_x = std::make_shared<ngraph::op::v1::Divide>(box_x, cols_node, ngraph::op::AutoBroadcastType::NUMPY);
          }
  
          std::shared_ptr<ngraph::Node> box_y;
          {
              auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, 0});
              auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{2, cols});
              box_y = std::make_shared<ngraph::op::v1::StridedSlice>(input2d, lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
              box_y = std::make_shared<ngraph::op::Sigmoid>(box_y);
              box_y = std::make_shared<ngraph::op::v1::Subtract>(box_y, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
              box_y = std::make_shared<ngraph::op::v1::Multiply>(box_y, scale_x_y_node, ngraph::op::AutoBroadcastType::NUMPY);
              box_y = std::make_shared<ngraph::op::v1::Add>(box_y, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
              box_y = std::make_shared<ngraph::op::v1::Reshape>(box_y, shape_3d, true);
  
              std::vector<float> y_indices(h * anchors);
              for (int i = 0; i < h; i++)
              {
                  std::fill(y_indices.begin() + i * anchors, y_indices.begin() + (i + 1) * anchors, i);
              }
  
              auto vert = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1, (size_t)anchors, (size_t)h, 1}, y_indices.data());
              box_y = std::make_shared<ngraph::op::v1::Add>(box_y, vert, ngraph::op::AutoBroadcastType::NUMPY);
              auto rows_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{float(h)});
              box_y = std::make_shared<ngraph::op::v1::Divide>(box_y, rows_node, ngraph::op::AutoBroadcastType::NUMPY);
          }
  
          std::shared_ptr<ngraph::Node> box_w, box_h;
          {
              int hNorm, wNorm;
              if (nodes.size() > 1)
              {
                  auto node_1_shape = nodes[1].dynamicCast<InfEngineNgraphNode>()->node->get_shape();
                  hNorm = node_1_shape[2];
                  wNorm = node_1_shape[3];
              }
              else
              {
                  hNorm = h;
                  wNorm = w;
              }
  
              std::vector<float> anchors_w(anchors), anchors_h(anchors);
              for (size_t a = 0; a < anchors; ++a)
              {
                  anchors_w[a] = blobs[0].at<float>(0, 2 * a) / wNorm;
                  anchors_h[a] = blobs[0].at<float>(0, 2 * a + 1) / hNorm;
              }
  
              std::vector<float> bias_w(w * h * anchors), bias_h(w * h * anchors);
              for (int j = 0; j < h; j++)
              {
                  std::copy(anchors_w.begin(), anchors_w.end(), bias_w.begin() + j * anchors);
                  std::copy(anchors_h.begin(), anchors_h.end(), bias_h.begin() + j * anchors);
              }
  
              for (int i = 1; i < w; i++)
              {
                  std::copy(bias_w.begin(), bias_w.begin() + h * anchors, bias_w.begin() + i * h * anchors);
                  std::copy(bias_h.begin(), bias_h.begin() + h * anchors, bias_h.begin() + i * h * anchors);
              }
  
              auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{2, 0});
              auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{3, cols});
              box_w = std::make_shared<ngraph::op::v1::StridedSlice>(input2d, lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
              box_w = std::make_shared<ngraph::op::v0::Exp>(box_w);
              box_w = std::make_shared<ngraph::op::v1::Reshape>(box_w, shape_3d, true);
              auto anchor_w_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, box_broad_shape, bias_w.data());
              box_w = std::make_shared<ngraph::op::v1::Multiply>(box_w, anchor_w_node, ngraph::op::AutoBroadcastType::NUMPY);
  
              lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{3, 0});
              upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{4, cols});
              box_h = std::make_shared<ngraph::op::v1::StridedSlice>(input2d, lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
              box_h = std::make_shared<ngraph::op::v0::Exp>(box_h);
              box_h = std::make_shared<ngraph::op::v1::Reshape>(box_h, shape_3d, true);
              auto anchor_h_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, box_broad_shape, bias_h.data());
              box_h = std::make_shared<ngraph::op::v1::Multiply>(box_h, anchor_h_node, ngraph::op::AutoBroadcastType::NUMPY);
          }
  
          std::shared_ptr<ngraph::Node> scale;
          {
              auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{4, 0});
              auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{5, cols});
              scale = std::make_shared<ngraph::op::v1::StridedSlice>(region, lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
  
              if (classfix == -1)
              {
                  auto thresh_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{0.5});
                  auto mask = std::make_shared<ngraph::op::v1::Less>(scale, thresh_node);
                  auto zero_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, mask->get_shape(), std::vector<float>(b * cols, 0));
                  scale = std::make_shared<ngraph::op::v1::Select>(mask, scale, zero_node);
              }
          }
  
          std::shared_ptr<ngraph::Node> probs;
          {
              auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{5, 0});
              auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{rows, cols});
              auto classes = std::make_shared<ngraph::op::v1::StridedSlice>(region, lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
              probs = std::make_shared<ngraph::op::v1::Multiply>(classes, scale, ngraph::op::AutoBroadcastType::NUMPY);
  
              auto thresh_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &thresh);
              auto mask = std::make_shared<ngraph::op::v1::Greater>(probs, thresh_node);
              auto zero_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, mask->get_shape(), std::vector<float>((rows - 5) * cols, 0));
              probs = std::make_shared<ngraph::op::v1::Select>(mask, probs, zero_node);
          }
  
  
          auto concat_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, cols});
          box_x = std::make_shared<ngraph::op::v1::Reshape>(box_x, concat_shape, true);
          box_y = std::make_shared<ngraph::op::v1::Reshape>(box_y, concat_shape, true);
          box_w = std::make_shared<ngraph::op::v1::Reshape>(box_w, concat_shape, true);
          box_h = std::make_shared<ngraph::op::v1::Reshape>(box_h, concat_shape, true);
  
          ngraph::NodeVector inp_nodes{box_x, box_y, box_w, box_h, scale, probs};
          std::shared_ptr<ngraph::Node> result = std::make_shared<ngraph::op::Concat>(inp_nodes, 0);
          result = std::make_shared<ngraph::op::Transpose>(result, tr_axes);
          if (b > 1)
          {
              std::vector<size_t> sizes = {(size_t)b, result->get_shape()[0] / b, result->get_shape()[1]};
              auto shape_node = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{sizes.size()}, sizes.data());
              result = std::make_shared<ngraph::op::v1::Reshape>(result, shape_node, true);
          }
  
          return Ptr<BackendNode>(new InfEngineNgraphNode(result));
      }
  #endif  // HAVE_DNN_NGRAPH
  
  };
  
  Ptr<RegionLayer> RegionLayer::create(const LayerParams& params)
  {
      return Ptr<RegionLayer>(new RegionLayerImpl(params));
  }
  
  }  // namespace dnn
  }  // namespace cv
  