code: def __init__(
      self,
      in_channels,
      module_loss=dict(type="ABCNetLoss"),
      postprocessor=dict(type="ABCNetDetPostprocessor"),
      num_classes=1,
      strides=(4, 8, 16, 32, 64),
      feat_channels=256,
      stacked_convs=4,
      dcn_on_last_conv=False,
      conv_bias="auto",
      norm_on_bbox=False,
      centerness_on_reg=False,
      use_sigmoid_cls=True,
      with_bezier=False,
      use_scale=False,
      conv_cfg=None,
      norm_cfg=dict(type="GN", num_groups=32, requires_grad=True),
      init_cfg=dict(
          type="Normal",
          layer="Conv2d",
          std=0.01,
          override=dict(
              type="Normal", name="conv_cls", std=0.01, bias_prob=0.01
          ),
      ),
  ):
      super().__init__(
          module_loss=module_loss,
          postprocessor=postprocessor,
          init_cfg=init_cfg,
      )
      self.num_classes = num_classes
      self.in_channels = in_channels
      self.strides = strides
      self.feat_channels = feat_channels
      self.stacked_convs = stacked_convs
      self.dcn_on_last_conv = dcn_on_last_conv
      assert conv_bias == "auto" or isinstance(conv_bias, bool)
      self.conv_bias = conv_bias
      self.norm_on_bbox = norm_on_bbox
      self.centerness_on_reg = centerness_on_reg
      self.conv_cfg = conv_cfg
      self.norm_cfg = norm_cfg
      self.with_bezier = with_bezier
      self.use_scale = use_scale
      self.use_sigmoid_cls = use_sigmoid_cls
      if self.use_sigmoid_cls:
          self.cls_out_channels = num_classes
      else:
          self.cls_out_channels = num_classes + 1
  
      self._init_layers()
  
file_path: mmocr/projects/ABCNet/abcnet/model/abcnet_det_head.py
imports: import torch.nn
  from mmcv.cnn import ConvModule, Scale
  from mmdet.models.utils import multi_apply
  from mmocr.models.textdet.heads.base import BaseTextDetHead
  from mmocr.registry import MODELS
label_desc: Use image node_id 1 to create a new ABCNetDetHead object from inherited base classes: BaseTextDetHead with example: obj = ABCNetDetHead(in_channels, module_loss, postprocessor, num_classes, strides, feat_channels, stacked_convs, dcn_on_last_conv, conv_bias, norm_on_bbox, centerness_on_reg, use_sigmoid_cls, with_bezier, use_scale, conv_cfg, norm_cfg, init_cfg)