class_docstr: A Darknet53 based feature extractor for YOLOv3.
  
  This is a feature extractor for :class:`~chainercv.links.model.yolo.YOLOv3`
class_docstr_tok: ["A","Darknet53","based","feature","extractor","for","YOLOv3",".","This","is","a","feature","extractor","for",":","class",":","`","~chainercv.links.model.yolo.YOLOv3","`"]
docstr: Compute feature maps from a batch of images.
  
  This method extracts feature maps from 3 layers.
  
  Args:
      x (ndarray): An array holding a batch of images.
          The images should be resized to :math:`416\times 416`.
  
  Returns:
      list of Variable:
      Each variable contains a feature map.
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code: def forward(self, x):
      """Compute feature maps from a batch of images.
  
      This method extracts feature maps from 3 layers.
  
      Args:
          x (ndarray): An array holding a batch of images.
              The images should be resized to :math:`416\\times 416`.
  
      Returns:
          list of Variable:
          Each variable contains a feature map.
      """
  
      ys = []
      h = x
      hs = []
      for i, link in enumerate(self):
          h = link(h)
          if i in {33, 39, 45}:
              ys.append(h)
          elif i in {14, 23}:
              hs.append(h)
          elif i in {34, 40}:
              h = F.concat((_upsample(h), hs.pop()))
      return ys
  
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 2 for calling the Darknet53Extractor obj's underlying member method code with example usage: obj.forward(x) and returns: ys