class_docstr: RTMDetHead with separated BN layers and shared conv layers.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      share_conv (bool): Whether to share conv layers between stages.
          Defaults to True.
      use_depthwise (bool): Whether to use depthwise separable convolution in
          head. Defaults to False.
      norm_cfg (:obj:`ConfigDict` or dict)): Config dict for normalization
          layer. Defaults to dict(type='BN', momentum=0.03, eps=0.001).
      act_cfg (:obj:`ConfigDict` or dict)): Config dict for activation layer.
          Defaults to dict(type='SiLU').
      pred_kernel_size (int): Kernel size of prediction layer. Defaults to 1.
class_docstr_tok: ["RTMDetHead","with","separated","BN","layers","and","shared","conv","layers",".","Args",":","num_classes","(","int",")",":","Number","of","categories","excluding","the","background","category",".","in_channels","(","int",")",":","Number","of","channels","in","the","input","feature","map",".","share_conv","(","bool",")",":","Whether","to","share","conv","layers","between","stages",".","Defaults","to","True",".","use_depthwise","(","bool",")",":","Whether","to","use","depthwise","separable","convolution","in","head",".","Defaults","to","False",".","norm_cfg","(",":","obj",":","`","ConfigDict","`","or","dict",")",")",":","Config","dict","for","normalization","layer",".","Defaults","to","dict","(","type='BN","'",",","momentum=0.03",",","eps=0.001",")",".","act_cfg","(",":","obj",":","`","ConfigDict","`","or","dict",")",")",":","Config","dict","for","activation","layer",".","Defaults","to","dict","(","type='SiLU","'",")",".","pred_kernel_size","(","int",")",":","Kernel","size","of","prediction","layer",".","Defaults","to","1","."]
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 (tuple[Tensor]): Classification scores for all scale
        levels, each is a 4D-tensor, the channels number is
        num_anchors * num_classes.
      - bbox_preds (tuple[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","(","tuple","[","Tensor","]",")",":","Classification","scores","for","all","scale","levels",",","each","is","a","4D-tensor",",","the","channels","number","is","num_anchors","*","num_classes",".","-","bbox_preds","(","tuple","[","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 (tuple[Tensor]): Classification scores for all scale
            levels, each is a 4D-tensor, the channels number is
            num_anchors * num_classes.
          - bbox_preds (tuple[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 idx, (x, stride) in enumerate(
          zip(feats, self.prior_generator.strides)
      ):
          cls_feat = x
          reg_feat = x
  
          for cls_layer in self.cls_convs[idx]:
              cls_feat = cls_layer(cls_feat)
          cls_score = self.rtm_cls[idx](cls_feat)
  
          for reg_layer in self.reg_convs[idx]:
              reg_feat = reg_layer(reg_feat)
  
          if self.with_objectness:
              objectness = self.rtm_obj[idx](reg_feat)
              cls_score = inverse_sigmoid(
                  sigmoid_geometric_mean(cls_score, objectness)
              )
          if self.exp_on_reg:
              reg_dist = self.rtm_reg[idx](reg_feat).exp() * stride[0]
          else:
              reg_dist = self.rtm_reg[idx](reg_feat) * stride[0]
          cls_scores.append(cls_score)
          bbox_preds.append(reg_dist)
      return tuple(cls_scores), tuple(bbox_preds)
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule, Scale, is_norm
  from mmengine.model import bias_init_with_prob, constant_init, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import distance2bbox
  from mmdet.utils import ConfigType, InstanceList, OptInstanceList, reduce_mean
  from ..layers.transformer import inverse_sigmoid
  from ..task_modules import anchor_inside_flags
  from ..utils import images_to_levels, multi_apply, sigmoid_geometric_mean, unmap
  from .atss_head import ATSSHead
label_desc: Use image node_id 4 for calling the RTMDetSepBNHead obj's underlying member method code with example usage: obj.forward(feats) and returns: tuple,  tuple