inference-engine/thirdparty/clDNN/src/region_yolo.cpp

   1 /*
   2 // Copyright (c) 2018 Intel Corporation
   3 //
   4 // Licensed under the Apache License, Version 2.0 (the "License");
   5 // you may not use this file except in compliance with the License.
   6 // You may obtain a copy of the License at
   7 //
   8 //      http://www.apache.org/licenses/LICENSE-2.0
   9 //
  10 // Unless required by applicable law or agreed to in writing, software
  11 // distributed under the License is distributed on an "AS IS" BASIS,
  12 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 // See the License for the specific language governing permissions and
  14 // limitations under the License.
  15 */
  16
  17 #include "region_yolo_inst.h"
  18 #include "primitive_type_base.h"
  19 #include "json_object.h"
  20
  21 namespace cldnn
  22 {
  23     primitive_type_id region_yolo_type_id()
  24     {
  25         static primitive_type_base<region_yolo> instance;
  26         return &instance;
  27     }
  28
  29     layout region_yolo_inst::calc_output_layout(region_yolo_node const& node)
  30     {
  31         assert((bool)node.get_primitive()->output_data_type == false
  32                && "Output data type forcing is not supported for "
  33                   "region_yolo_node!");
  34         auto input_layout = node.input().get_output_layout();
  35         auto desc = node.get_primitive();
  36
  37         if (desc->do_softmax)
  38         {
  39             return cldnn::layout(input_layout.data_type, input_layout.format,
  40                                  tensor(input_layout.size.batch[0],
  41                                         input_layout.size.feature[0] * input_layout.size.spatial[0] * input_layout.size.spatial[1],
  42                                         1, 1));
  43         }
  44         else
  45         {
  46             tensor::value_type features = (desc->classes + desc->coords + 1)*desc->mask_size;
  47             return cldnn::layout(input_layout.data_type, input_layout.format,
  48                                  tensor(input_layout.size.batch[0],
  49                                         features,
  50                                         input_layout.size.spatial[0], input_layout.size.spatial[1]));
  51
  52         }
  53     }
  54
  55     std::string region_yolo_inst::to_string(region_yolo_node const& node)
  56     {
  57         auto desc = node.get_primitive();
  58         auto node_info = node.desc_to_json();
  59         auto coords = desc->coords;
  60         auto classes = desc->classes;
  61         auto num = desc->num;
  62         auto do_softmax = desc->do_softmax;
  63         auto mask_size = desc->mask_size;
  64
  65         std::stringstream primitive_description;
  66
  67         json_composite region_yolo_info;
  68         region_yolo_info.add("coords", coords);
  69         region_yolo_info.add("classes", classes);
  70         region_yolo_info.add("num", num);
  71         region_yolo_info.add("do_softmax", do_softmax);
  72         region_yolo_info.add("mask_size", mask_size);
  73
  74
  75         node_info->add("region yolo info", region_yolo_info);
  76         node_info->dump(primitive_description);
  77
  78         return primitive_description.str();
  79     }
  80
  81     region_yolo_inst::typed_primitive_inst(network_impl& network, region_yolo_node const& node)
  82         : parent(network, node)
  83     {
  84     }
  85 }