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: Compute regression, classification targets for anchors in a single
  image.
  
  Args:
      flat_anchors (Tensor): Multi-level anchors of the image, which are
          concatenated into a single tensor of shape (num_anchors ,4)
      valid_flags (Tensor): Multi level valid flags of the image,
          which are concatenated into a single tensor of
              shape (num_anchors,).
      num_level_anchors (List[int]): Number of anchors of each scale
          level.
      gt_instances (:obj:`InstanceData`): Ground truth of instance
          annotations. It usually includes ``bboxes`` and ``labels``
          attributes.
      img_meta (dict): Meta information for current image.
      gt_instances_ignore (:obj:`InstanceData`, optional): Instances
          to be ignored during training. It includes ``bboxes`` attribute
          data that is ignored during training and testing.
          Defaults to None.
      unmap_outputs (bool): Whether to map outputs back to the original
          set of anchors.
  
  Returns:
      tuple: N is the number of total anchors in the image.
          labels (Tensor): Labels of all anchors in the image with shape
              (N,).
          label_weights (Tensor): Label weights of all anchor in the
              image with shape (N,).
          bbox_targets (Tensor): BBox targets of all anchors in the
              image with shape (N, 4).
          bbox_weights (Tensor): BBox weights of all anchors in the
              image with shape (N, 4)
          pos_inds (Tensor): Indices of positive anchor with shape
              (num_pos,).
          neg_inds (Tensor): Indices of negative anchor with shape
              (num_neg,).
          sampling_result (:obj:`SamplingResult`): Sampling results.
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code: def _get_targets_single(
      self,
      flat_anchors: Tensor,
      valid_flags: Tensor,
      num_level_anchors: List[int],
      gt_instances: InstanceData,
      img_meta: dict,
      gt_instances_ignore: Optional[InstanceData] = None,
      unmap_outputs: bool = True,
  ) -> tuple:
      """Compute regression, classification targets for anchors in a single
      image.
  
      Args:
          flat_anchors (Tensor): Multi-level anchors of the image, which are
              concatenated into a single tensor of shape (num_anchors ,4)
          valid_flags (Tensor): Multi level valid flags of the image,
              which are concatenated into a single tensor of
                  shape (num_anchors,).
          num_level_anchors (List[int]): Number of anchors of each scale
              level.
          gt_instances (:obj:`InstanceData`): Ground truth of instance
              annotations. It usually includes ``bboxes`` and ``labels``
              attributes.
          img_meta (dict): Meta information for current image.
          gt_instances_ignore (:obj:`InstanceData`, optional): Instances
              to be ignored during training. It includes ``bboxes`` attribute
              data that is ignored during training and testing.
              Defaults to None.
          unmap_outputs (bool): Whether to map outputs back to the original
              set of anchors.
  
      Returns:
          tuple: N is the number of total anchors in the image.
              labels (Tensor): Labels of all anchors in the image with shape
                  (N,).
              label_weights (Tensor): Label weights of all anchor in the
                  image with shape (N,).
              bbox_targets (Tensor): BBox targets of all anchors in the
                  image with shape (N, 4).
              bbox_weights (Tensor): BBox weights of all anchors in the
                  image with shape (N, 4)
              pos_inds (Tensor): Indices of positive anchor with shape
                  (num_pos,).
              neg_inds (Tensor): Indices of negative anchor with shape
                  (num_neg,).
              sampling_result (:obj:`SamplingResult`): Sampling results.
      """
      inside_flags = anchor_inside_flags(
          flat_anchors,
          valid_flags,
          img_meta["img_shape"][:2],
          self.train_cfg["allowed_border"],
      )
      if not inside_flags.any():
          raise ValueError(
              "There is no valid anchor inside the image boundary. Please "
              "check the image size and anchor sizes, or set "
              "``allowed_border`` to -1 to skip the condition."
          )
      # assign gt and sample anchors
      anchors = flat_anchors[inside_flags, :]
  
      num_level_anchors_inside = self.get_num_level_anchors_inside(
          num_level_anchors, inside_flags
      )
      pred_instances = InstanceData(priors=anchors)
      assign_result = self.assigner.assign(
          pred_instances,
          num_level_anchors_inside,
          gt_instances,
          gt_instances_ignore,
      )
  
      sampling_result = self.sampler.sample(
          assign_result, pred_instances, gt_instances
      )
  
      num_valid_anchors = anchors.shape[0]
      bbox_targets = torch.zeros_like(anchors)
      bbox_weights = torch.zeros_like(anchors)
      labels = anchors.new_full(
          (num_valid_anchors,), self.num_classes, dtype=torch.long
      )
      label_weights = anchors.new_zeros(
          num_valid_anchors, dtype=torch.float
      )
  
      pos_inds = sampling_result.pos_inds
      neg_inds = sampling_result.neg_inds
      if len(pos_inds) > 0:
          if self.reg_decoded_bbox:
              pos_bbox_targets = sampling_result.pos_gt_bboxes
          else:
              pos_bbox_targets = self.bbox_coder.encode(
                  sampling_result.pos_priors,
                  sampling_result.pos_gt_bboxes,
              )
  
          bbox_targets[pos_inds, :] = pos_bbox_targets
          bbox_weights[pos_inds, :] = 1.0
  
          labels[pos_inds] = sampling_result.pos_gt_labels
          if self.train_cfg["pos_weight"] <= 0:
              label_weights[pos_inds] = 1.0
          else:
              label_weights[pos_inds] = self.train_cfg["pos_weight"]
      if len(neg_inds) > 0:
          label_weights[neg_inds] = 1.0
  
      # map up to original set of anchors
      if unmap_outputs:
          num_total_anchors = flat_anchors.size(0)
          anchors = unmap(anchors, num_total_anchors, inside_flags)
          labels = unmap(
              labels,
              num_total_anchors,
              inside_flags,
              fill=self.num_classes,
          )
          label_weights = unmap(
              label_weights, num_total_anchors, inside_flags
          )
          bbox_targets = unmap(
              bbox_targets, num_total_anchors, inside_flags
          )
          bbox_weights = unmap(
              bbox_weights, num_total_anchors, inside_flags
          )
  
      return (
          anchors,
          labels,
          label_weights,
          bbox_targets,
          bbox_weights,
          pos_inds,
          neg_inds,
          sampling_result,
      )
  
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 9 for calling the ATSSHead obj's underlying member method code with example usage: obj._get_targets_single(flat_anchors, valid_flags, num_level_anchors, gt_instances, img_meta, gt_instances_ignore, unmap_outputs) and returns: anchors,  labels,  label_weights,  bbox_targets,  bbox_weights,  pos_inds,  neg_inds,  sampling_result