class_docstr: Bridging the Gap Between Anchor-based and Anchor-free Detection via
  Adaptive Training Sample Selection.
  
  ATSS head structure is similar with FCOS, however ATSS use anchor boxes
  and assign label by Adaptive Training Sample Selection instead max-iou.
  
  https://arxiv.org/abs/1912.02424
class_docstr_tok: ["Bridging","the","Gap","Between","Anchor-based","and","Anchor-free","Detection","via","Adaptive","Training","Sample","Selection",".","ATSS","head","structure","is","similar","with","FCOS",",","however","ATSS","use","anchor","boxes","and","assign","label","by","Adaptive","Training","Sample","Selection","instead","max-iou",".","https",":","\/\/arxiv.org\/abs\/1912.02424"]
code: def __init__(
      self,
      num_classes,
      in_channels,
      stacked_convs=4,
      conv_cfg=None,
      norm_cfg=dict(type="GN", num_groups=32, requires_grad=True),
      loss_centerness=dict(
          type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
      ),
      **kwargs,
  ):
      self.stacked_convs = stacked_convs
      self.conv_cfg = conv_cfg
      self.norm_cfg = norm_cfg
      super(ATSSHead, self).__init__(num_classes, in_channels, **kwargs)
  
      self.sampling = False
      if self.train_cfg:
          self.assigner = build_assigner(self.train_cfg.assigner)
          # SSD sampling=False so use PseudoSampler
          sampler_cfg = dict(type="PseudoSampler")
          self.sampler = build_sampler(sampler_cfg, context=self)
      self.loss_centerness = build_loss(loss_centerness)
  
imports: import torch
  import torch.nn
  from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
  from mmcv.runner import force_fp32
  from mmdet.core import anchor_inside_flags, build_assigner, build_sampler, images_to_levels, multi_apply, multiclass_nms, reduce_mean, unmap
  from ..builder import HEADS, build_loss
  from .anchor_head import AnchorHead
label_desc: Use image node_id 1 to create a new ATSSHead object from inherited base classes: AnchorHead with example: obj = ATSSHead(num_classes, in_channels, stacked_convs, conv_cfg, norm_cfg, loss_centerness)