class_docstr: The GCNet implementation based on PaddlePaddle.
  
  The original article refers to
  Cao, Yue, et al. "GCnet: Non-local networks meet squeeze-excitation networks and beyond"
  (https://arxiv.org/pdf/1904.11492.pdf).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): Backbone network, currently support Resnet50/101.
      backbone_indices (tuple, optional): Two values in the tuple indicate the indices of output of backbone.
      gc_channels (int, optional): The input channels to Global Context Block. Default: 512.
      ratio (float, optional): It indicates the ratio of attention channels and gc_channels. Default: 0.25.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
      align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the feature size is even,
          e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
class_docstr_tok: ["The","GCNet","implementation","based","on","PaddlePaddle",".","The","original","article","refers","to","Cao",",","Yue",",","et","al",".","``","GCnet",":","Non-local","networks","meet","squeeze-excitation","networks","and","beyond","''","(","https",":","\/\/arxiv.org\/pdf\/1904.11492.pdf",")",".","Args",":","num_classes","(","int",")",":","The","unique","number","of","target","classes",".","backbone","(","Paddle.nn.Layer",")",":","Backbone","network",",","currently","support","Resnet50\/101",".","backbone_indices","(","tuple",",","optional",")",":","Two","values","in","the","tuple","indicate","the","indices","of","output","of","backbone",".","gc_channels","(","int",",","optional",")",":","The","input","channels","to","Global","Context","Block",".","Default",":","512.","ratio","(","float",",","optional",")",":","It","indicates","the","ratio","of","attention","channels","and","gc_channels",".","Default",":","0.25.","enable_auxiliary_loss","(","bool",",","optional",")",":","A","bool","value","indicates","whether","adding","auxiliary","loss",".","Default",":","True",".","align_corners","(","bool",",","optional",")",":","An","argument","of","F.interpolate",".","It","should","be","set","to","False","when","the","feature","size","is","even",",","e.g",".","1024x512",",","otherwise","it","is","True",",","e.g",".","769x769",".","Default",":","False",".","pretrained","(","str",",","optional",")",":","The","path","or","url","of","pretrained","model",".","Default",":","None","."]
code: def __init__(
      self,
      num_classes,
      backbone,
      backbone_indices=(2, 3),
      gc_channels=512,
      ratio=0.25,
      enable_auxiliary_loss=True,
      align_corners=False,
      pretrained=None,
  ):
      super().__init__()
  
      self.backbone = backbone
      backbone_channels = [
          backbone.feat_channels[i] for i in backbone_indices
      ]
  
      self.head = GCNetHead(
          num_classes,
          backbone_indices,
          backbone_channels,
          gc_channels,
          ratio,
          enable_auxiliary_loss,
      )
      self.align_corners = align_corners
      self.pretrained = pretrained
      self.init_weight()
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new GCNet object from inherited base classes: nn with example: obj = GCNet(num_classes, backbone, backbone_indices, gc_channels, ratio, enable_auxiliary_loss, align_corners, pretrained)