class_docstr: Anchor-based head (RPN, RetinaNet, SSD, etc.).
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      feat_channels (int): Number of hidden channels. Used in child classes.
      anchor_generator (dict): Config dict for anchor generator
      bbox_coder (dict): Config of bounding box coder.
      reg_decoded_bbox (bool): If true, the regression loss would be
          applied on decoded bounding boxes. Default: False
      loss_cls (dict): Config of classification loss.
      loss_bbox (dict): Config of localization loss.
      train_cfg (dict): Training config of anchor head.
      test_cfg (dict): Testing config of anchor head.
docstr: Transform outputs for a single batch item into bbox predictions.
  
  Args:
      cls_score_list (list[Tensor]): Box scores for a single scale level
          Has shape (num_anchors * num_classes, H, W).
      bbox_pred_list (list[Tensor]): Box energies / deltas for a single
          scale level with shape (num_anchors * 4, H, W).
      mlvl_anchors (list[Tensor]): Box reference for a single scale level
          with shape (num_total_anchors, 4).
      img_shape (tuple[int]): Shape of the input image,
          (height, width, 3).
      scale_factor (ndarray): Scale factor of the image arange as
          (w_scale, h_scale, w_scale, h_scale).
      cfg (mmcv.Config): Test / postprocessing configuration,
          if None, test_cfg would be used.
      rescale (bool): If True, return boxes in original image space.
          Default: False.
      with_nms (bool): If True, do nms before return boxes.
          Default: True.
  
  Returns:
      Tensor: Labeled boxes in shape (n, 5), where the first 4 columns
          are bounding box positions (tl_x, tl_y, br_x, br_y) and the
          5-th column is a score between 0 and 1.
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code: def _get_bboxes_single(
      self,
      cls_score_list,
      bbox_pred_list,
      mlvl_anchors,
      img_shape,
      scale_factor,
      cfg,
      rescale=False,
      with_nms=True,
  ):
      """Transform outputs for a single batch item into bbox predictions.
  
      Args:
          cls_score_list (list[Tensor]): Box scores for a single scale level
              Has shape (num_anchors * num_classes, H, W).
          bbox_pred_list (list[Tensor]): Box energies / deltas for a single
              scale level with shape (num_anchors * 4, H, W).
          mlvl_anchors (list[Tensor]): Box reference for a single scale level
              with shape (num_total_anchors, 4).
          img_shape (tuple[int]): Shape of the input image,
              (height, width, 3).
          scale_factor (ndarray): Scale factor of the image arange as
              (w_scale, h_scale, w_scale, h_scale).
          cfg (mmcv.Config): Test / postprocessing configuration,
              if None, test_cfg would be used.
          rescale (bool): If True, return boxes in original image space.
              Default: False.
          with_nms (bool): If True, do nms before return boxes.
              Default: True.
  
      Returns:
          Tensor: Labeled boxes in shape (n, 5), where the first 4 columns
              are bounding box positions (tl_x, tl_y, br_x, br_y) and the
              5-th column is a score between 0 and 1.
      """
      cfg = self.test_cfg if cfg is None else cfg
      assert (
          len(cls_score_list)
          == len(bbox_pred_list)
          == len(mlvl_anchors)
      )
      mlvl_bboxes = []
      mlvl_scores = []
      for cls_score, bbox_pred, anchors in zip(
          cls_score_list, bbox_pred_list, mlvl_anchors
      ):
          assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
          cls_score = cls_score.permute(1, 2, 0).reshape(
              -1, self.cls_out_channels
          )
          if self.use_sigmoid_cls:
              scores = cls_score.sigmoid()
          else:
              scores = cls_score.softmax(-1)
          bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
          nms_pre = cfg.get("nms_pre", -1)
          if nms_pre > 0 and scores.shape[0] > nms_pre:
              # Get maximum scores for foreground classes.
              if self.use_sigmoid_cls:
                  max_scores, _ = scores.max(dim=1)
              else:
                  # remind that we set FG labels to [0, num_class-1]
                  # since mmdet v2.0
                  # BG cat_id: num_class
                  max_scores, _ = scores[:, :-1].max(dim=1)
              _, topk_inds = max_scores.topk(nms_pre)
              anchors = anchors[topk_inds, :]
              bbox_pred = bbox_pred[topk_inds, :]
              scores = scores[topk_inds, :]
          bboxes = self.bbox_coder.decode(
              anchors, bbox_pred, max_shape=img_shape
          )
          mlvl_bboxes.append(bboxes)
          mlvl_scores.append(scores)
      mlvl_bboxes = torch.cat(mlvl_bboxes)
      if rescale:
          mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
      mlvl_scores = torch.cat(mlvl_scores)
      if self.use_sigmoid_cls:
          # Add a dummy background class to the backend when using sigmoid
          # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
          # BG cat_id: num_class
          padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
          mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
  
      if with_nms:
          det_bboxes, det_labels = multiclass_nms(
              mlvl_bboxes,
              mlvl_scores,
              cfg.score_thr,
              cfg.nms,
              cfg.max_per_img,
          )
          return det_bboxes, det_labels
      else:
          return mlvl_bboxes, mlvl_scores
  
imports: import torch
  import torch.nn
  from mmcv.cnn import normal_init
  from mmcv.runner import force_fp32
  from mmdet.core import anchor_inside_flags, build_anchor_generator, build_assigner, build_bbox_coder, build_sampler, images_to_levels, multi_apply, multiclass_nms, unmap
  from ..builder import HEADS, build_loss
  from .base_dense_head import BaseDenseHead
  from .dense_test_mixins import BBoxTestMixin
label_desc: Use image node_id 12 for calling the AnchorHead obj's underlying member method code with example usage: obj._get_bboxes_single(cls_score_list, bbox_pred_list, mlvl_anchors, img_shape, scale_factor, cfg, rescale, with_nms) and returns: det_bboxes,  det_labels, mlvl_bboxes,  mlvl_scores