class_docstr: DLA/DLA-X Bottleneck
class_docstr_tok: ["DLA\/DLA-X","Bottleneck"]
code: def __init__(
      self,
      inplanes,
      outplanes,
      stride=1,
      dilation=1,
      cardinality=1,
      base_width=64,
  ):
      super(DlaBottleneck, self).__init__()
      self.stride = stride
      mid_planes = int(
          math.floor(outplanes * (base_width / 64)) * cardinality
      )
      mid_planes = mid_planes // self.expansion
  
      self.conv1 = nn.Conv2d(
          inplanes, mid_planes, kernel_size=1, bias=False
      )
      self.bn1 = nn.BatchNorm2d(mid_planes)
      self.conv2 = nn.Conv2d(
          mid_planes,
          mid_planes,
          kernel_size=3,
          stride=stride,
          padding=dilation,
          bias=False,
          dilation=dilation,
          groups=cardinality,
      )
      self.bn2 = nn.BatchNorm2d(mid_planes)
      self.conv3 = nn.Conv2d(
          mid_planes, outplanes, kernel_size=1, bias=False
      )
      self.bn3 = nn.BatchNorm2d(outplanes)
      self.relu = nn.ReLU(inplace=True)
  
imports: import math
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new DlaBottleneck object from inherited base classes: nn with example: obj = DlaBottleneck(inplanes, outplanes, stride, dilation, cardinality, base_width)