text: # Copyright (c) OpenMMLab. All rights reserved.
  import torch.nn as nn
  from mmcv.cnn import ConvModule
  
  from mmedit.models.common import SimpleGatedConvModule
  from mmedit.models.registry import COMPONENTS
  
  
  @COMPONENTS.register_module()
  class GLDilationNeck(nn.Module):
      """Dilation Backbone used in Global&Local model.
  
      This implementation follows:
      Globally and locally Consistent Image Completion
  
      Args:
          in_channels (int): Channel number of input feature.
          conv_type (str): The type of conv module. In DeepFillv1 model, the
              `conv_type` should be 'conv'. In DeepFillv2 model, the `conv_type`
              should be 'gated_conv'.
          norm_cfg (dict): Config dict to build norm layer.
          act_cfg (dict): Config dict for activation layer, "relu" by default.
          kwargs (keyword arguments).
      """
      _conv_type = dict(conv=ConvModule, gated_conv=SimpleGatedConvModule)
  
      def __init__(self,
                   in_channels=256,
                   conv_type='conv',
                   norm_cfg=None,
                   act_cfg=dict(type='ReLU'),
                   **kwargs):
          super().__init__()
          conv_module = self._conv_type[conv_type]
          dilation_convs_ = []
          for i in range(4):
              dilation_ = int(2**(i + 1))
              dilation_convs_.append(
                  conv_module(
                      in_channels,
                      in_channels,
                      kernel_size=3,
                      padding=dilation_,
                      dilation=dilation_,
                      stride=1,
                      norm_cfg=norm_cfg,
                      act_cfg=act_cfg,
                      **kwargs))
          self.dilation_convs = nn.Sequential(*dilation_convs_)
  
      def forward(self, x):
          """Forward Function.
  
          Args:
              x (torch.Tensor): Input tensor with shape of (n, c, h, w).
  
          Returns:
              torch.Tensor: Output tensor with shape of (n, c, h', w').
          """
          x = self.dilation_convs(x)
          return x
  