text: """
   Copyright (c) 2018-2019 Intel Corporation
  
   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at
  
        http://www.apache.org/licenses/LICENSE-2.0
  
   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
  """
  
  import logging as log
  
  from mo.front.caffe.collect_attributes import merge_attrs
  from mo.front.common.partial_infer.multi_box_detection import multi_box_detection_infer
  from mo.front.extractor import FrontExtractorOp
  from mo.ops.op import Op
  
  
  class DetectionOutputFrontExtractor(FrontExtractorOp):
      op = 'DetectionOutput'
      enabled = True
  
      @staticmethod
      def extract(node):
          pl = node.pb
          assert pl, 'Protobuf layer can not be empty'
  
          param = pl.detection_output_param
  
          # TODO rewrite params as complex structures
          if hasattr(param, 'nms_param'):
              nms_threshold = param.nms_param.nms_threshold
              eta = param.nms_param.eta
              if param.nms_param.top_k == 0:
                  top_k = -1
              else:
                  top_k = param.nms_param.top_k
  
          code_type_values = [
              "",
              "caffe.PriorBoxParameter.CORNER",
              "caffe.PriorBoxParameter.CENTER_SIZE",
              "caffe.PriorBoxParameter.CORNER_SIZE"
          ]
  
          code_type = code_type_values[1]
          if hasattr(param, 'code_type'):
              if param.code_type < 1 or param.code_type > 3:
                  log.error("Incorrect value of code_type parameter")
                  return
              code_type = code_type_values[param.code_type]
  
          visualize_threshold = param.visualize_threshold if param.visualize_threshold else 0.6
  
          resize_mode_values = [
              "",
              "caffe.ResizeParameter.WARP",
              "caffe.ResizeParameter.FIT_SMALL_SIZE",
              "caffe.ResizeParameter.FIT_LARGE_SIZE_AND_PAD"
          ]
  
          if param.save_output_param.resize_param.resize_mode < 1 or param.save_output_param.resize_param.resize_mode > 3:
              log.error("Incorrect value of resize_mode parameter")
              return
          resize_mode = resize_mode_values[param.save_output_param.resize_param.resize_mode]
  
          pad_mode_values = [
              "",
              "caffe.ResizeParameter.CONSTANT",
              "caffe.ResizeParameter.MIRRORED",
              "caffe.ResizeParameter.REPEAT_NEAREST"
          ]
  
          if param.save_output_param.resize_param.pad_mode < 1 or param.save_output_param.resize_param.pad_mode > 3:
              log.error("Incorrect value of pad_mode parameter")
          else:
              pad_mode = pad_mode_values[param.save_output_param.resize_param.pad_mode]
  
          interp_mode_values = [
              "",
              "caffe.ResizeParameter.LINEAR",
              "caffe.ResizeParameter.AREA",
              "caffe.ResizeParameter.NEAREST",
              "caffe.ResizeParameter.CUBIC",
              "caffe.ResizeParameter.LANCZOS4"
          ]
          interp_mode = ""
          for x in param.save_output_param.resize_param.interp_mode:
              if x < 1 or x > 5:
                  log.error("Incorrect value of interp_mode parameter")
                  return
              interp_mode += interp_mode_values[x]
  
          attrs = {
              'num_classes': param.num_classes,
              'share_location': int(param.share_location),
              'background_label_id': param.background_label_id,
              'code_type': code_type,
              'variance_encoded_in_target': int(param.variance_encoded_in_target),
              'keep_top_k': param.keep_top_k,
              'confidence_threshold': param.confidence_threshold,
              'visualize': param.visualize,
              'visualize_threshold': visualize_threshold,
              'save_file': param.save_file,
              # nms_param
              'nms_threshold': nms_threshold,
              'top_k': top_k,
              'eta': eta,
              # save_output_param
              'output_directory': param.save_output_param.output_directory,
              'output_name_prefix': param.save_output_param.output_name_prefix,
              'output_format': param.save_output_param.output_format,
              'label_map_file': param.save_output_param.label_map_file,
              'name_size_file': param.save_output_param.name_size_file,
              'num_test_image': param.save_output_param.num_test_image,
              # save_output_param.resize_param
              'prob': param.save_output_param.resize_param.prob,
              'resize_mode': resize_mode,
              'height': param.save_output_param.resize_param.height,
              'width': param.save_output_param.resize_param.width,
              'height_scale': param.save_output_param.resize_param.height_scale,
              'width_scale': param.save_output_param.resize_param.width_scale,
              'pad_mode': pad_mode,
              'pad_value': ','.join(str(x) for x in param.save_output_param.resize_param.pad_value),
              'interp_mode': interp_mode,
          }
  
          # these params can be omitted in caffe.proto and in param as consequence,
          # so check if it is set or set to default
          fields = [field[0].name for field in param.ListFields()]
          if 'input_width' in fields:
              attrs['input_width'] = param.input_width
          if 'input_height' in fields:
              attrs['input_height'] = param.input_height
          if 'normalized' in fields:
              attrs['normalized'] = int(param.normalized)
          if 'objectness_score' in fields:
              attrs['objectness_score'] = param.objectness_score
  
          mapping_rule = merge_attrs(param, attrs)
  
          # force setting infer function because it doesn't exist in proto so merge_attrs will not set it
          mapping_rule.update({'infer': multi_box_detection_infer})
  
          # update the attributes of the node
          Op.get_op_class_by_name(__class__.op).update_node_stat(node, mapping_rule)
          return __class__.enabled
  