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.
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code: def forward(self, x):
      feats = self.backbone(x)
      x = feats[-1]
      dcm_outs = [x]
      for dcm_module in self.dcm_modules:
          dcm_outs.append(dcm_module(x))
      dcm_outs = paddle.concat(dcm_outs, axis=1)
      x = self.bottleneck(dcm_outs)
      x = self.cls(x)
      x = F.interpolate(
          x, scale_factor=8, mode="bilinear", align_corners=True
      )
      output = [x]
      if self.training:
          fcn_out = self.fcn_head(feats[2])
          fcn_out = F.interpolate(
              fcn_out,
              scale_factor=8,
              mode="bilinear",
              align_corners=True,
          )
          output.append(fcn_out)
          return output
      return output
  
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 3 for calling the DMNet obj's underlying member method code with example usage: obj.forward(x) and returns: output, output