class_docstr: FCIS Head for ResNet101 based implementation.
  
  This class is used as a head for FCIS.
  This outputs class-agnostice segmentation scores, class-agnostic
  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
          Position Sensitive RoI pooling.
      group_size (int): Group height and width for Position Sensitive
          ROI pooling.
      spatial_scale (float): Scale of the roi is resized.
      loc_normalize_mean (tuple of four floats): Mean values of
          localization estimates.
      loc_normalize_std (tupler of four floats): Standard deviation
          of localization estimates.
      iter2 (bool): if the value is set :obj:`True`, Position Sensitive
          ROI pooling is executed twice. In the second time, Position
          Sensitive ROI pooling uses improved ROIs by the localization
          parameters calculated in the first time.
      initialW (callable): Initializer for the layers.
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code: def __init__(
      self,
      n_class,
      roi_size,
      group_size,
      spatial_scale,
      loc_normalize_mean,
      loc_normalize_std,
      iter2,
      initialW=None,
  ):
      super(FCISResNet101Head, self).__init__()
  
      if initialW is None:
          initialW = chainer.initializers.Normal(0.01)
  
      self.n_class = n_class
      self.spatial_scale = spatial_scale
      self.group_size = group_size
      self.roi_size = roi_size
      self.loc_normalize_mean = loc_normalize_mean
      self.loc_normalize_std = loc_normalize_std
      self.iter2 = iter2
  
      with self.init_scope():
          self.conv1 = L.Convolution2D(
              2048, 1024, 1, 1, 0, initialW=initialW
          )
          self.cls_seg = L.Convolution2D(
              1024,
              group_size * group_size * n_class * 2,
              1,
              1,
              0,
              initialW=initialW,
          )
          self.ag_loc = L.Convolution2D(
              1024,
              group_size * group_size * 2 * 4,
              1,
              1,
              0,
              initialW=initialW,
          )
  
imports: from __future__ import division
  import chainer
  import chainer.functions
  import chainer.links
  import numpy
  from chainercv.experimental.links.model.fcis import FCIS
  from chainercv.functions import ps_roi_average_pooling_2d
  from chainercv.links import Conv2DBNActiv
  from chainercv.links.model.faster_rcnn.region_proposal_network import RegionProposalNetwork
  from chainercv.links.model.faster_rcnn.utils.loc2bbox import loc2bbox
  from chainercv.links.model.resnet.resblock import ResBlock
  from chainercv.links import ResNet101
  from chainercv import utils
label_desc: Use image node_id 1 to create a new FCISResNet101Head object from inherited base classes: chainer with example: obj = FCISResNet101Head(n_class, roi_size, group_size, spatial_scale, loc_normalize_mean, loc_normalize_std, iter2, initialW)