class_docstr: Pyramid Scene Parsing Network.
  
  This is a PSPNet [#]_ model for semantic segmentation. This is based on
  the implementation found here_.
  
  .. [#] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang     Jiaya Jia "Pyramid Scene Parsing Network"     CVPR, 2017
  
  .. _here: https://github.com/hszhao/PSPNet
  
  Args:
      n_class (int): The number of channels in the last convolution layer.
      pretrained_model (string): The weight file to be loaded.
          This can take :obj:`'cityscapes'`, `filepath` or :obj:`None`.
          The default value is :obj:`None`.
          * :obj:`'cityscapes'`: Load weights trained on the train split of                 Cityscapes dataset.                 :obj:`n_class` must be :obj:`19` or :obj:`None`.
          * :obj:`'ade20k'`: Load weights trained on the train split of                 ADE20K dataset.                 :obj:`n_class` must be :obj:`150` or :obj:`None`.
          * :obj:`'imagenet'`: Load ImageNet pretrained weights for                 the extractor.
          * `filepath`: A path of npz file. In this case, :obj:`n_class`                 must be specified properly.
          * :obj:`None`: Do not load weights.
      input_size (tuple): The size of the input.
          This value is :math:`(height, width)`.
      initialW (callable): Initializer for the weights of
          convolution kernels.
class_docstr_tok: ["Pyramid","Scene","Parsing","Network",".","This","is","a","PSPNet","[","#","]","_","model","for","semantic","segmentation",".","This","is","based","on","the","implementation","found","here_",".","..","[","#","]","Hengshuang","Zhao",",","Jianping","Shi",",","Xiaojuan","Qi",",","Xiaogang","Wang","Jiaya","Jia","``","Pyramid","Scene","Parsing","Network","''","CVPR",",","2017","..","_here",":","https",":","\/\/github.com\/hszhao\/PSPNet","Args",":","n_class","(","int",")",":","The","number","of","channels","in","the","last","convolution","layer",".","pretrained_model","(","string",")",":","The","weight","file","to","be","loaded",".","This","can","take",":","obj",":","`","'cityscapes","'","`",",","`","filepath","`","or",":","obj",":","`","None","`",".","The","default","value","is",":","obj",":","`","None","`",".","*",":","obj",":","`","'cityscapes","'","`",":","Load","weights","trained","on","the","train","split","of","Cityscapes","dataset",".",":","obj",":","`","n_class","`","must","be",":","obj",":","`","19","`","or",":","obj",":","`","None","`",".","*",":","obj",":","`","'ade20k","'","`",":","Load","weights","trained","on","the","train","split","of","ADE20K","dataset",".",":","obj",":","`","n_class","`","must","be",":","obj",":","`","150","`","or",":","obj",":","`","None","`",".","*",":","obj",":","`","'imagenet","'","`",":","Load","ImageNet","pretrained","weights","for","the","extractor",".","*","`","filepath","`",":","A","path","of","npz","file",".","In","this","case",",",":","obj",":","`","n_class","`","must","be","specified","properly",".","*",":","obj",":","`","None","`",":","Do","not","load","weights",".","input_size","(","tuple",")",":","The","size","of","the","input",".","This","value","is",":","math",":","`","(","height",",","width",")","`",".","initialW","(","callable",")",":","Initializer","for","the","weights","of","convolution","kernels","."]
code: def _predict(self, imgs):
      xs = chainer.Variable(self.xp.asarray(imgs))
      with chainer.using_config("train", False):
          scores = F.softmax(self.forward(xs)).array
      return scores
  
imports: from __future__ import division
  from math import ceil
  import numpy
  import chainer
  import chainer.functions
  from chainer import initializers
  import chainer.links
  from chainercv.experimental.links.model.pspnet.transforms import convolution_crop
  from chainercv.links import Conv2DBNActiv
  from chainercv.links.model.resnet import ResBlock
  from chainercv.links import PickableSequentialChain
  from chainercv import transforms
  from chainercv import utils
label_desc: Use image node_id 5 for calling the PSPNet obj's underlying member method code with example usage: obj._predict(imgs) and returns: scores