class_docstr: Base Model for Semantic Segmentation
  
  Parameters
  ----------
  backbone : string
      Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
      'resnet101' or 'resnet152').
code: def __init__(
      self,
      num_classes,
      pretrained=True,
      aux=False,
      backbone="resnet101",
      **kwargs,
  ):
      super(SegBaseModel, self).__init__()
      self.aux = aux
      self.nclass = num_classes
      if backbone == "resnet50":
          self.pretrained = resnet50_v1s(
              pretrained=pretrained, **kwargs
          )
      elif backbone == "resnet101":
          self.pretrained = resnet101_v1s(
              pretrained=pretrained, **kwargs
          )
      elif backbone == "resnet152":
          self.pretrained = resnet152_v1s(
              pretrained=pretrained, **kwargs
          )
      else:
          raise RuntimeError("unknown backbone: {}".format(backbone))
  
imports: import torch.nn
  from pywick.models.segmentation.da_basenets.resnetv1b import resnet50_v1s, resnet101_v1s, resnet152_v1s
label_desc: Use image node_id 1 to create a new SegBaseModel object from inherited base classes: nn with example: obj = SegBaseModel(num_classes, pretrained, aux, backbone)