class_docstr: Detection Head of `ATSS <https://arxiv.org/abs/1912.02424>`_.
  
  ATSS head structure is similar with FCOS, however ATSS use anchor boxes
  and assign label by Adaptive Training Sample Selection instead max-iou.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      pred_kernel_size (int): Kernel size of ``nn.Conv2d``
      stacked_convs (int): Number of stacking convs of the head.
      conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
          convolution layer. Defaults to None.
      norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization
          layer. Defaults to ``dict(type='GN', num_groups=32,
          requires_grad=True)``.
      reg_decoded_bbox (bool): If true, the regression loss would be
          applied directly on decoded bounding boxes, converting both
          the predicted boxes and regression targets to absolute
          coordinates format. Defaults to False. It should be `True` when
          using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
      loss_centerness (:obj:`ConfigDict` or dict): Config of centerness loss.
          Defaults to ``dict(type='CrossEntropyLoss', use_sigmoid=True,
          loss_weight=1.0)``.
      init_cfg (:obj:`ConfigDict` or dict or list[dict] or
          list[:obj:`ConfigDict`]): Initialization config dict.
docstr: Get targets for ATSS head.
  
  This method is almost the same as `AnchorHead.get_targets()`. Besides
  returning the targets as the parent method does, it also returns the
  anchors as the first element of the returned tuple.
docstr_tok: ["Get","targets","for","ATSS","head",".","This","method","is","almost","the","same","as","`","AnchorHead.get_targets","(",")","`",".","Besides","returning","the","targets","as","the","parent","method","does",",","it","also","returns","the","anchors","as","the","first","element","of","the","returned","tuple","."]
code: def get_targets(
      self,
      anchor_list: List[List[Tensor]],
      valid_flag_list: List[List[Tensor]],
      batch_gt_instances: InstanceList,
      batch_img_metas: List[dict],
      batch_gt_instances_ignore: OptInstanceList = None,
      unmap_outputs: bool = True,
  ) -> tuple:
      """Get targets for ATSS head.
  
      This method is almost the same as `AnchorHead.get_targets()`. Besides
      returning the targets as the parent method does, it also returns the
      anchors as the first element of the returned tuple.
      """
      num_imgs = len(batch_img_metas)
      assert len(anchor_list) == len(valid_flag_list) == num_imgs
  
      # anchor number of multi levels
      num_level_anchors = [
          anchors.size(0) for anchors in anchor_list[0]
      ]
      num_level_anchors_list = [num_level_anchors] * num_imgs
  
      # concat all level anchors and flags to a single tensor
      for i in range(num_imgs):
          assert len(anchor_list[i]) == len(valid_flag_list[i])
          anchor_list[i] = torch.cat(anchor_list[i])
          valid_flag_list[i] = torch.cat(valid_flag_list[i])
  
      # compute targets for each image
      if batch_gt_instances_ignore is None:
          batch_gt_instances_ignore = [None] * num_imgs
      (
          all_anchors,
          all_labels,
          all_label_weights,
          all_bbox_targets,
          all_bbox_weights,
          pos_inds_list,
          neg_inds_list,
          sampling_results_list,
      ) = multi_apply(
          self._get_targets_single,
          anchor_list,
          valid_flag_list,
          num_level_anchors_list,
          batch_gt_instances,
          batch_img_metas,
          batch_gt_instances_ignore,
          unmap_outputs=unmap_outputs,
      )
      # Get `avg_factor` of all images, which calculate in `SamplingResult`.
      # When using sampling method, avg_factor is usually the sum of
      # positive and negative priors. When using `PseudoSampler`,
      # `avg_factor` is usually equal to the number of positive priors.
      avg_factor = sum(
          [results.avg_factor for results in sampling_results_list]
      )
      # split targets to a list w.r.t. multiple levels
      anchors_list = images_to_levels(all_anchors, num_level_anchors)
      labels_list = images_to_levels(all_labels, num_level_anchors)
      label_weights_list = images_to_levels(
          all_label_weights, num_level_anchors
      )
      bbox_targets_list = images_to_levels(
          all_bbox_targets, num_level_anchors
      )
      bbox_weights_list = images_to_levels(
          all_bbox_weights, num_level_anchors
      )
      return (
          anchors_list,
          labels_list,
          label_weights_list,
          bbox_targets_list,
          bbox_weights_list,
          avg_factor,
      )
  
imports: from typing import List, Optional, Sequence, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, Scale
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptConfigType, OptInstanceList, reduce_mean
  from ..task_modules.prior_generators import anchor_inside_flags
  from ..utils import images_to_levels, multi_apply, unmap
  from .anchor_head import AnchorHead
label_desc: Use image node_id 8 for calling the ATSSHead obj's underlying member method code with example usage: obj.get_targets(anchor_list, valid_flag_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore, unmap_outputs) and returns: anchors_list,  labels_list,  label_weights_list,  bbox_targets_list,  bbox_weights_list,  avg_factor