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"]
docstr: Forward features from the upstream network.
  
  Args:
      feats (tuple[Tensor]): Features from the upstream network, each is
          a 4D-tensor.
  
  Returns:
      tuple: Usually a tuple of classification scores and bbox prediction
          cls_scores (list[Tensor]): Classification scores for all scale
              levels, each is a 4D-tensor, the channels number is
              num_anchors * num_classes.
          bbox_preds (list[Tensor]): Box energies / deltas for all scale
              levels, each is a 4D-tensor, the channels number is
              num_anchors * 4.
docstr_tok: ["Forward","features","from","the","upstream","network",".","Args",":","feats","(","tuple","[","Tensor","]",")",":","Features","from","the","upstream","network",",","each","is","a","4D-tensor",".","Returns",":","tuple",":","Usually","a","tuple","of","classification","scores","and","bbox","prediction","cls_scores","(","list","[","Tensor","]",")",":","Classification","scores","for","all","scale","levels",",","each","is","a","4D-tensor",",","the","channels","number","is","num_anchors","*","num_classes",".","bbox_preds","(","list","[","Tensor","]",")",":","Box","energies","\/","deltas","for","all","scale","levels",",","each","is","a","4D-tensor",",","the","channels","number","is","num_anchors","*","4","."]
code: def forward(self, feats):
      """Forward features from the upstream network.
  
      Args:
          feats (tuple[Tensor]): Features from the upstream network, each is
              a 4D-tensor.
  
      Returns:
          tuple: Usually a tuple of classification scores and bbox prediction
              cls_scores (list[Tensor]): Classification scores for all scale
                  levels, each is a 4D-tensor, the channels number is
                  num_anchors * num_classes.
              bbox_preds (list[Tensor]): Box energies / deltas for all scale
                  levels, each is a 4D-tensor, the channels number is
                  num_anchors * 4.
      """
      return multi_apply(self.forward_single, feats, self.scales)
  
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 4 for calling the ATSSHead obj's underlying member method code with example usage: obj.forward(feats) and returns: multi_apply