text: import math
  
  from mmcv.cnn import build_conv_layer, build_norm_layer
  
  from ..builder import BACKBONES
  from .detectors_resnet import Bottleneck as _Bottleneck
  from .detectors_resnet import DetectoRS_ResNet
  
  
  class Bottleneck(_Bottleneck):
      expansion = 4
  
      def __init__(self,
                   inplanes,
                   planes,
                   groups=1,
                   base_width=4,
                   base_channels=64,
                   **kwargs):
          """Bottleneck block for ResNeXt.
  
          If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
          it is "caffe", the stride-two layer is the first 1x1 conv layer.
          """
          super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
  
          if groups == 1:
              width = self.planes
          else:
              width = math.floor(self.planes *
                                 (base_width / base_channels)) * groups
  
          self.norm1_name, norm1 = build_norm_layer(
              self.norm_cfg, width, postfix=1)
          self.norm2_name, norm2 = build_norm_layer(
              self.norm_cfg, width, postfix=2)
          self.norm3_name, norm3 = build_norm_layer(
              self.norm_cfg, self.planes * self.expansion, postfix=3)
  
          self.conv1 = build_conv_layer(
              self.conv_cfg,
              self.inplanes,
              width,
              kernel_size=1,
              stride=self.conv1_stride,
              bias=False)
          self.add_module(self.norm1_name, norm1)
          fallback_on_stride = False
          self.with_modulated_dcn = False
          if self.with_dcn:
              fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
          if self.with_sac:
              self.conv2 = build_conv_layer(
                  self.sac,
                  width,
                  width,
                  kernel_size=3,
                  stride=self.conv2_stride,
                  padding=self.dilation,
                  dilation=self.dilation,
                  groups=groups,
                  bias=False)
          elif not self.with_dcn or fallback_on_stride:
              self.conv2 = build_conv_layer(
                  self.conv_cfg,
                  width,
                  width,
                  kernel_size=3,
                  stride=self.conv2_stride,
                  padding=self.dilation,
                  dilation=self.dilation,
                  groups=groups,
                  bias=False)
          else:
              assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
              self.conv2 = build_conv_layer(
                  self.dcn,
                  width,
                  width,
                  kernel_size=3,
                  stride=self.conv2_stride,
                  padding=self.dilation,
                  dilation=self.dilation,
                  groups=groups,
                  bias=False)
  
          self.add_module(self.norm2_name, norm2)
          self.conv3 = build_conv_layer(
              self.conv_cfg,
              width,
              self.planes * self.expansion,
              kernel_size=1,
              bias=False)
          self.add_module(self.norm3_name, norm3)
  
  
  @BACKBONES.register_module()
  class DetectoRS_ResNeXt(DetectoRS_ResNet):
      """ResNeXt backbone for DetectoRS.
  
      Args:
          groups (int): The number of groups in ResNeXt.
          base_width (int): The base width of ResNeXt.
      """
  
      arch_settings = {
          50: (Bottleneck, (3, 4, 6, 3)),
          101: (Bottleneck, (3, 4, 23, 3)),
          152: (Bottleneck, (3, 8, 36, 3))
      }
  
      def __init__(self, groups=1, base_width=4, **kwargs):
          self.groups = groups
          self.base_width = base_width
          super(DetectoRS_ResNeXt, self).__init__(**kwargs)
  
      def make_res_layer(self, **kwargs):
          return super().make_res_layer(
              groups=self.groups,
              base_width=self.base_width,
              base_channels=self.base_channels,
              **kwargs)
  