class_docstr: The DMNet implementation based on PaddlePaddle.
  
  The original article refers to
   Junjun He, Zhongying Deng, Yu Qiao. "Dynamic Multi-scale Filters for Semantic Segmentation"
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (paddle.nn.Layer): Backbone network, currently support Resnet50_vd/Resnet101_vd.
      mid_channels (int): The middle channels of convolution layer. Default: 512.
      filter_sizes (list, tuple): The filter size of generated convolution kernel used in Dynamic Convolutional Module. Default: [1, 3, 5, 7].
      fusion (bool): Add one conv to fuse DCM output feature. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
class_docstr_tok: ["The","DMNet","implementation","based","on","PaddlePaddle",".","The","original","article","refers","to","Junjun","He",",","Zhongying","Deng",",","Yu","Qiao",".","``","Dynamic","Multi-scale","Filters","for","Semantic","Segmentation","''","Args",":","num_classes","(","int",")",":","The","unique","number","of","target","classes",".","backbone","(","paddle.nn.Layer",")",":","Backbone","network",",","currently","support","Resnet50_vd\/Resnet101_vd",".","mid_channels","(","int",")",":","The","middle","channels","of","convolution","layer",".","Default",":","512.","filter_sizes","(","list",",","tuple",")",":","The","filter","size","of","generated","convolution","kernel","used","in","Dynamic","Convolutional","Module",".","Default",":","[","1",",","3",",","5",",","7","]",".","fusion","(","bool",")",":","Add","one","conv","to","fuse","DCM","output","feature",".","Default",":","False",".","pretrained","(","str",",","optional",")",":","The","path","or","url","of","pretrained","model",".","Default",":","None","."]
code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
file_path: PaddleSeg/paddleseg/models/dmnet.py
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 2 for calling the DMNet obj's underlying member method code with example usage: obj.init_weight() without return types