class_docstr: YOLOV3Head Paper link: https://arxiv.org/abs/1804.02767.
  
  Args:
      num_classes (int): The number of object classes (w/o background)
      in_channels (List[int]): Number of input channels per scale.
      out_channels (List[int]): The number of output channels per scale
          before the final 1x1 layer. Default: (1024, 512, 256).
      anchor_generator (dict): Config dict for anchor generator
      bbox_coder (dict): Config of bounding box coder.
      featmap_strides (List[int]): The stride of each scale.
          Should be in descending order. Default: (32, 16, 8).
      one_hot_smoother (float): Set a non-zero value to enable label-smooth
          Default: 0.
      conv_cfg (dict): Config dict for convolution layer. Default: None.
      norm_cfg (dict): Dictionary to construct and config norm layer.
          Default: dict(type='BN', requires_grad=True)
      act_cfg (dict): Config dict for activation layer.
          Default: dict(type='LeakyReLU', negative_slope=0.1).
      loss_cls (dict): Config of classification loss.
      loss_conf (dict): Config of confidence loss.
      loss_xy (dict): Config of xy coordinate loss.
      loss_wh (dict): Config of wh coordinate loss.
      train_cfg (dict): Training config of YOLOV3 head. Default: None.
      test_cfg (dict): Testing config of YOLOV3 head. Default: None.
class_docstr_tok: ["YOLOV3Head","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1804.02767",".","Args",":","num_classes","(","int",")",":","The","number","of","object","classes","(","w\/o","background",")","in_channels","(","List","[","int","]",")",":","Number","of","input","channels","per","scale",".","out_channels","(","List","[","int","]",")",":","The","number","of","output","channels","per","scale","before","the","final","1x1","layer",".","Default",":","(","1024",",","512",",","256",")",".","anchor_generator","(","dict",")",":","Config","dict","for","anchor","generator","bbox_coder","(","dict",")",":","Config","of","bounding","box","coder",".","featmap_strides","(","List","[","int","]",")",":","The","stride","of","each","scale",".","Should","be","in","descending","order",".","Default",":","(","32",",","16",",","8",")",".","one_hot_smoother","(","float",")",":","Set","a","non-zero","value","to","enable","label-smooth","Default",":","0.","conv_cfg","(","dict",")",":","Config","dict","for","convolution","layer",".","Default",":","None",".","norm_cfg","(","dict",")",":","Dictionary","to","construct","and","config","norm","layer",".","Default",":","dict","(","type='BN","'",",","requires_grad=True",")","act_cfg","(","dict",")",":","Config","dict","for","activation","layer",".","Default",":","dict","(","type='LeakyReLU","'",",","negative_slope=0.1",")",".","loss_cls","(","dict",")",":","Config","of","classification","loss",".","loss_conf","(","dict",")",":","Config","of","confidence","loss",".","loss_xy","(","dict",")",":","Config","of","xy","coordinate","loss",".","loss_wh","(","dict",")",":","Config","of","wh","coordinate","loss",".","train_cfg","(","dict",")",":","Training","config","of","YOLOV3","head",".","Default",":","None",".","test_cfg","(","dict",")",":","Testing","config","of","YOLOV3","head",".","Default",":","None","."]
docstr: Forward features from the upstream network.
  
  Args:
      feats (tuple[Tensor]): Features from the upstream network, each is
          a 4D-tensor.
  
  Returns:
      tuple[Tensor]: A tuple of multi-level predication map, each is a
          4D-tensor of shape (batch_size, 5+num_classes, height, width).
docstr_tok: ["Forward","features","from","the","upstream","network",".","Args",":","feats","(","tuple","[","Tensor","]",")",":","Features","from","the","upstream","network",",","each","is","a","4D-tensor",".","Returns",":","tuple","[","Tensor","]",":","A","tuple","of","multi-level","predication","map",",","each","is","a","4D-tensor","of","shape","(","batch_size",",","5+num_classes",",","height",",","width",")","."]
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[Tensor]: A tuple of multi-level predication map, each is a
              4D-tensor of shape (batch_size, 5+num_classes, height, width).
      """
  
      assert len(feats) == self.num_levels
      pred_maps = []
      for i in range(self.num_levels):
          x = feats[i]
          x = self.convs_bridge[i](x)
          pred_map = self.convs_pred[i](x)
          pred_maps.append(pred_map)
  
      return (tuple(pred_maps),)
  
imports: import warnings
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, normal_init
  from mmcv.runner import force_fp32
  from mmdet.core import build_anchor_generator, build_assigner, build_bbox_coder, build_sampler, images_to_levels, multi_apply, multiclass_nms
  from ..builder import HEADS, build_loss
  from .base_dense_head import BaseDenseHead
  from .dense_test_mixins import BBoxTestMixin
label_desc: Use image node_id 6 for calling the YOLOV3Head obj's underlying member method code with example usage: obj.forward(feats) and returns: tuple