class_docstr: Detection block in YOLO neck.
  
  Let out_channels = n, the DetectionBlock contains:
  Six ConvLayers, 1 Conv2D Layer and 1 YoloLayer.
  The first 6 ConvLayers are formed the following way:
      1x1xn, 3x3x2n, 1x1xn, 3x3x2n, 1x1xn, 3x3x2n.
  The Conv2D layer is 1x1x255.
  Some block will have branch after the fifth ConvLayer.
  The input channel is arbitrary (in_channels)
  
  Args:
      in_channels (int): The number of input channels.
      out_channels (int): The number of output channels.
      conv_cfg (dict): Config dict for convolution layer. Default: None.
      norm_cfg (dict): Dictionary to construct and config norm layer.
          Default: dict(type='BN', requires_grad=True)
      act_cfg (dict): Config dict for activation layer.
          Default: dict(type='LeakyReLU', negative_slope=0.1).
code: def __init__(
      self,
      in_channels,
      out_channels,
      conv_cfg=None,
      norm_cfg=dict(type="BN", requires_grad=True),
      act_cfg=dict(type="LeakyReLU", negative_slope=0.1),
  ):
      super(DetectionBlock, self).__init__()
      double_out_channels = out_channels * 2
  
      # shortcut
      cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
      self.conv1 = ConvModule(in_channels, out_channels, 1, **cfg)
      self.conv2 = ConvModule(
          out_channels, double_out_channels, 3, padding=1, **cfg
      )
      self.conv3 = ConvModule(
          double_out_channels, out_channels, 1, **cfg
      )
      self.conv4 = ConvModule(
          out_channels, double_out_channels, 3, padding=1, **cfg
      )
      self.conv5 = ConvModule(
          double_out_channels, out_channels, 1, **cfg
      )
  
file_path: insightface/detection/scrfd/mmdet/models/necks/yolo_neck.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from ..builder import NECKS
label_desc: Use image node_id 1 to create a new DetectionBlock object from inherited base classes: nn with example: obj = DetectionBlock(in_channels, out_channels, conv_cfg, norm_cfg, act_cfg)