class_docstr: "RetinaHead with separate BN.
  
  In RetinaHead, conv/norm layers are shared across different FPN levels,
  while in RetinaSepBNHead, conv layers are shared across different FPN
  levels, but BN layers are separated.
class_docstr_tok: ["``","RetinaHead","with","separate","BN",".","In","RetinaHead",",","conv\/norm","layers","are","shared","across","different","FPN","levels",",","while","in","RetinaSepBNHead",",","conv","layers","are","shared","across","different","FPN","levels",",","but","BN","layers","are","separated","."]
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: Tuple[Tensor]) -> tuple:
      """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.
      """
      cls_scores = []
      bbox_preds = []
      for i, x in enumerate(feats):
          cls_feat = feats[i]
          reg_feat = feats[i]
          for cls_conv in self.cls_convs[i]:
              cls_feat = cls_conv(cls_feat)
          for reg_conv in self.reg_convs[i]:
              reg_feat = reg_conv(reg_feat)
          cls_score = self.retina_cls(cls_feat)
          bbox_pred = self.retina_reg(reg_feat)
          cls_scores.append(cls_score)
          bbox_preds.append(bbox_pred)
      return cls_scores, bbox_preds
  
file_path: mmdetection/mmdet/models/dense_heads/retina_sepbn_head.py
imports: from typing import Tuple
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmengine.model import bias_init_with_prob, normal_init
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.utils import OptConfigType, OptMultiConfig
  from .anchor_head import AnchorHead
label_desc: Use image node_id 4 for calling the RetinaSepBNHead obj's underlying member method code with example usage: obj.forward(feats) and returns: cls_scores,  bbox_preds