text: # Copyright (C) 2018-2021 Intel Corporation
  # SPDX-License-Identifier: Apache-2.0
  
  from math import log
  
  import numpy as np
  
  from openvino.tools.mo.front.extractor import FrontExtractorOp
  from openvino.tools.mo.front.onnx.extractors.utils import onnx_attr
  from openvino.tools.mo.ops.detection_output_onnx import ExperimentalDetectronDetectionOutput
  
  
  class ExperimentalDetectronDetectionOutputFrontExtractor(FrontExtractorOp):
      op = 'ExperimentalDetectronDetectionOutput'
      enabled = True
  
      @classmethod
      def extract(cls, node):
          attrs = dict(class_agnostic_box_regression=onnx_attr(node, 'class_agnostic_box_regression', 'i', 0),
                       max_detections_per_image=onnx_attr(node, 'max_detections_per_image', 'i', 100),
                       nms_threshold=onnx_attr(node, 'nms_threshold', 'f', 0.5),
                       num_classes=onnx_attr(node, 'num_classes', 'i', 81),
                       post_nms_count=onnx_attr(node, 'post_nms_count', 'i', 2000),
                       score_threshold=onnx_attr(node, 'score_threshold', 'f', 0.05),
                       max_delta_log_wh=onnx_attr(node, 'max_delta_log_wh', 'f', log(1000. / 16.)),
                       deltas_weights=np.array(onnx_attr(node, 'deltas_weights', 'floats', [10., 10., 5., 5.]),
                                               dtype=np.float32)
                       )
          ExperimentalDetectronDetectionOutput.update_node_stat(node, attrs)
          return cls.enabled
  