class_docstr: Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
  Detector.<https://arxiv.org/abs/2008.13367>`_.
  
  The VFNet predicts IoU-aware classification scores which mix the
  object presence confidence and object localization accuracy as the
  detection score. It is built on the FCOS architecture and uses ATSS
  for defining positive/negative training examples. The VFNet is trained
  with Varifocal Loss and empolys star-shaped deformable convolution to
  extract features for a bbox.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
          level points.
      center_sampling (bool): If true, use center sampling. Defaults to False.
      center_sample_radius (float): Radius of center sampling. Defaults to 1.5.
      sync_num_pos (bool): If true, synchronize the number of positive
          examples across GPUs. Defaults to True
      gradient_mul (float): The multiplier to gradients from bbox refinement
          and recognition. Defaults to 0.1.
      bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
          'stride'. Defaults to reg_denom
      loss_cls_fl (:obj:`ConfigDict` or dict): Config of focal loss.
      use_vfl (bool): If true, use varifocal loss for training.
          Defaults to True.
      loss_cls (:obj:`ConfigDict` or dict): Config of varifocal loss.
      loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss,
          GIoU Loss.
      loss_bbox (:obj:`ConfigDict` or dict): Config of localization
          refinement loss, GIoU Loss.
      norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
          config norm layer. Defaults to norm_cfg=dict(type='GN',
          num_groups=32, requires_grad=True).
      use_atss (bool): If true, use ATSS to define positive/negative
          examples. Defaults to True.
      anchor_generator (:obj:`ConfigDict` or dict): Config of anchor
          generator for ATSS.
      init_cfg (:obj:`ConfigDict` or dict or list[dict] or
          list[:obj:`ConfigDict`]): Initialization config dict.
  
  Example:
      >>> self = VFNetHead(11, 7)
      >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
      >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
      >>> assert len(cls_score) == len(self.scales)
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docstr: Forward features from the upstream network.
  
  Args:
      x (tuple[Tensor]): Features from the upstream network, each is
          a 4D-tensor.
  
  Returns:
      tuple:
  
      - cls_scores (list[Tensor]): Box iou-aware scores for each scale
        level, each is a 4D-tensor, the channel number is
        num_points * num_classes.
      - bbox_preds (list[Tensor]): Box offsets for each
        scale level, each is a 4D-tensor, the channel number is
        num_points * 4.
      - bbox_preds_refine (list[Tensor]): Refined Box offsets for
        each scale level, each is a 4D-tensor, the channel
        number is num_points * 4.
docstr_tok: ["Forward","features","from","the","upstream","network",".","Args",":","x","(","tuple","[","Tensor","]",")",":","Features","from","the","upstream","network",",","each","is","a","4D-tensor",".","Returns",":","tuple",":","-","cls_scores","(","list","[","Tensor","]",")",":","Box","iou-aware","scores","for","each","scale","level",",","each","is","a","4D-tensor",",","the","channel","number","is","num_points","*","num_classes",".","-","bbox_preds","(","list","[","Tensor","]",")",":","Box","offsets","for","each","scale","level",",","each","is","a","4D-tensor",",","the","channel","number","is","num_points","*","4",".","-","bbox_preds_refine","(","list","[","Tensor","]",")",":","Refined","Box","offsets","for","each","scale","level",",","each","is","a","4D-tensor",",","the","channel","number","is","num_points","*","4","."]
code: def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:
      """Forward features from the upstream network.
  
      Args:
          x (tuple[Tensor]): Features from the upstream network, each is
              a 4D-tensor.
  
      Returns:
          tuple:
  
          - cls_scores (list[Tensor]): Box iou-aware scores for each scale
            level, each is a 4D-tensor, the channel number is
            num_points * num_classes.
          - bbox_preds (list[Tensor]): Box offsets for each
            scale level, each is a 4D-tensor, the channel number is
            num_points * 4.
          - bbox_preds_refine (list[Tensor]): Refined Box offsets for
            each scale level, each is a 4D-tensor, the channel
            number is num_points * 4.
      """
      return multi_apply(
          self.forward_single,
          x,
          self.scales,
          self.scales_refine,
          self.strides,
          self.reg_denoms,
      )
  
imports: from typing import List, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, Scale
  from mmcv.ops import DeformConv2d
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import bbox_overlaps
  from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptInstanceList, RangeType, reduce_mean
  from ..task_modules.prior_generators import MlvlPointGenerator
  from ..task_modules.samplers import PseudoSampler
  from ..utils import multi_apply
  from .atss_head import ATSSHead
  from .fcos_head import FCOSHead
label_desc: Use image node_id 3 for calling the VFNetHead obj's underlying member method code with example usage: obj.forward(x) and returns: multi_apply