class_docstr: Faster R-CNN Head for VGG-16 based implementation.
  
  This class is used as a head for Faster R-CNN.
  This outputs class-wise localizations and classification based on feature
  maps in the given RoIs.
  
  Args:
      n_class (int): The number of classes possibly including the background.
      roi_size (int): Height and width of the feature maps after RoI-pooling.
      spatial_scale (float): Scale of the roi is resized.
      vgg_initialW (callable): Initializer for the layers corresponding to
          the VGG-16 layers.
      loc_initialW (callable): Initializer for the localization head.
      score_initialW (callable): Initializer for the score head.
class_docstr_tok: ["Faster","R-CNN","Head","for","VGG-16","based","implementation",".","This","class","is","used","as","a","head","for","Faster","R-CNN",".","This","outputs","class-wise","localizations","and","classification","based","on","feature","maps","in","the","given","RoIs",".","Args",":","n_class","(","int",")",":","The","number","of","classes","possibly","including","the","background",".","roi_size","(","int",")",":","Height","and","width","of","the","feature","maps","after","RoI-pooling",".","spatial_scale","(","float",")",":","Scale","of","the","roi","is","resized",".","vgg_initialW","(","callable",")",":","Initializer","for","the","layers","corresponding","to","the","VGG-16","layers",".","loc_initialW","(","callable",")",":","Initializer","for","the","localization","head",".","score_initialW","(","callable",")",":","Initializer","for","the","score","head","."]
code: def __init__(
      self,
      n_class,
      roi_size,
      spatial_scale,
      vgg_initialW=None,
      loc_initialW=None,
      score_initialW=None,
  ):
      # n_class includes the background
      super(VGG16RoIHead, self).__init__()
      with self.init_scope():
          self.fc6 = L.Linear(25088, 4096, initialW=vgg_initialW)
          self.fc7 = L.Linear(4096, 4096, initialW=vgg_initialW)
          self.cls_loc = L.Linear(
              4096, n_class * 4, initialW=loc_initialW
          )
          self.score = L.Linear(4096, n_class, initialW=score_initialW)
  
      self.n_class = n_class
      self.roi_size = roi_size
      self.spatial_scale = spatial_scale
  
file_path: chainercv/chainercv/links/model/faster_rcnn/faster_rcnn_vgg.py
imports: import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainercv.links.model.faster_rcnn.faster_rcnn import FasterRCNN
  from chainercv.links.model.faster_rcnn.region_proposal_network import RegionProposalNetwork
  from chainercv.links.model.vgg.vgg16 import VGG16
  from chainercv import utils
label_desc: Use image node_id 1 to create a new VGG16RoIHead object from inherited base classes: chainer with example: obj = VGG16RoIHead(n_class, roi_size, spatial_scale, vgg_initialW, loc_initialW, score_initialW)