class_docstr: YOLOv3.
  
  This is a model of YOLOv3 [#]_.
  This model uses :class:`~chainercv.links.model.yolo.Darknet53Extractor` as
  its feature extractor.
  
  .. [#] Joseph Redmon, Ali Farhadi.
     YOLOv3: An Incremental Improvement. arXiv 2018.
  
  Args:
     n_fg_class (int): The number of classes excluding the background.
     pretrained_model (string): The weight file to be loaded.
         This can take :obj:`'voc0712'`, `filepath` or :obj:`None`.
         The default value is :obj:`None`.
  
          * :obj:`'voc0712'`: Load weights trained on trainval split of                 PASCAL VOC 2007 and 2012.                 The weight file is downloaded and cached automatically.                 :obj:`n_fg_class` must be :obj:`20` or :obj:`None`.                 These weights were converted from the darknet model.                 The conversion code is                 `chainercv/examples/yolo/darknet2npz.py`.
          * `filepath`: A path of npz file. In this case, :obj:`n_fg_class`                 must be specified properly.
          * :obj:`None`: Do not load weights.
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code: def _decode(self, loc, obj, conf):
      raw_bbox = self._default_bbox.copy()
      raw_bbox[:, :2] += 1 / (1 + self.xp.exp(-loc[:, :2]))
      raw_bbox[:, :2] *= self._step[:, None]
      raw_bbox[:, 2:] *= self.xp.exp(loc[:, 2:])
      raw_bbox[:, :2] -= raw_bbox[:, 2:] / 2
      raw_bbox[:, 2:] += raw_bbox[:, :2]
  
      obj = 1 / (1 + self.xp.exp(-obj))
      conf = 1 / (1 + self.xp.exp(-conf))
      raw_score = obj[:, None] * conf
  
      bbox = []
      label = []
      score = []
      for l in range(self.n_fg_class):
          bbox_l = raw_bbox
          score_l = raw_score[:, l]
  
          mask = score_l >= self.score_thresh
          bbox_l = bbox_l[mask]
          score_l = score_l[mask]
  
          indices = utils.non_maximum_suppression(
              bbox_l, self.nms_thresh, score_l
          )
          bbox_l = bbox_l[indices]
          score_l = score_l[indices]
  
          bbox.append(bbox_l)
          label.append(self.xp.array((l,) * len(bbox_l)))
          score.append(score_l)
  
      bbox = self.xp.vstack(bbox).astype(np.float32)
      label = self.xp.hstack(label).astype(np.int32)
      score = self.xp.hstack(score).astype(np.float32)
  
      return bbox, label, score
  
imports: from __future__ import division
  import itertools
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainer.links import Convolution2D
  from chainercv.links import Conv2DBNActiv
  from chainercv import utils
  from chainercv.links.model.yolo.yolo_base import YOLOBase
label_desc: Use image node_id 5 for calling the YOLOv3 obj's underlying member method code with example usage: obj._decode(loc, obj, conf) and returns: bbox,  label,  score