text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
  from mmcv.runner import force_fp32
  
  from mmdet.core import (anchor_inside_flags, bbox2distance, bbox_overlaps,
                          build_assigner, build_sampler, distance2bbox,
                          images_to_levels, multi_apply, multiclass_nms,
                          reduce_mean, unmap)
  from ..builder import HEADS, build_loss
  from .anchor_head import AnchorHead
  
  
  class Integral(nn.Module):
      """A fixed layer for calculating integral result from distribution.
  
      This layer calculates the target location by :math: `sum{P(y_i) * y_i}`,
      P(y_i) denotes the softmax vector that represents the discrete distribution
      y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max}
  
      Args:
          reg_max (int): The maximal value of the discrete set. Default: 16. You
              may want to reset it according to your new dataset or related
              settings.
      """
  
      def __init__(self, reg_max=16):
          super(Integral, self).__init__()
          self.reg_max = reg_max
          self.register_buffer('project',
                               torch.linspace(0, self.reg_max, self.reg_max + 1))
  
      def forward(self, x):
          """Forward feature from the regression head to get integral result of
          bounding box location.
  
          Args:
              x (Tensor): Features of the regression head, shape (N, 4*(n+1)),
                  n is self.reg_max.
  
          Returns:
              x (Tensor): Integral result of box locations, i.e., distance
                  offsets from the box center in four directions, shape (N, 4).
          """
          x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)
          x = F.linear(x, self.project.type_as(x)).reshape(-1, 4)
          return x
  
  
  @HEADS.register_module()
  class GFLHead(AnchorHead):
      """Generalized Focal Loss: Learning Qualified and Distributed Bounding
      Boxes for Dense Object Detection.
  
      GFL head structure is similar with ATSS, however GFL uses
      1) joint representation for classification and localization quality, and
      2) flexible General distribution for bounding box locations,
      which are supervised by
      Quality Focal Loss (QFL) and Distribution Focal Loss (DFL), respectively
  
      https://arxiv.org/abs/2006.04388
  
      Args:
          num_classes (int): Number of categories excluding the background
              category.
          in_channels (int): Number of channels in the input feature map.
          stacked_convs (int): Number of conv layers in cls and reg tower.
              Default: 4.
          conv_cfg (dict): dictionary to construct and config conv layer.
              Default: None.
          norm_cfg (dict): dictionary to construct and config norm layer.
              Default: dict(type='GN', num_groups=32, requires_grad=True).
          loss_qfl (dict): Config of Quality Focal Loss (QFL).
          reg_max (int): Max value of integral set :math: `{0, ..., reg_max}`
              in QFL setting. Default: 16.
      Example:
          >>> self = GFLHead(11, 7)
          >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
          >>> cls_quality_score, bbox_pred = self.forward(feats)
          >>> assert len(cls_quality_score) == len(self.scales)
      """
  
      def __init__(self,
                   num_classes,
                   in_channels,
                   stacked_convs=4,
                   conv_cfg=None,
                   norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
                   loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
                   reg_max=16,
                   **kwargs):
          self.stacked_convs = stacked_convs
          self.conv_cfg = conv_cfg
          self.norm_cfg = norm_cfg
          self.reg_max = reg_max
          super(GFLHead, self).__init__(num_classes, in_channels, **kwargs)
  
          self.sampling = False
          if self.train_cfg:
              self.assigner = build_assigner(self.train_cfg.assigner)
              # SSD sampling=False so use PseudoSampler
              sampler_cfg = dict(type='PseudoSampler')
              self.sampler = build_sampler(sampler_cfg, context=self)
  
          self.integral = Integral(self.reg_max)
          self.loss_dfl = build_loss(loss_dfl)
  
      def _init_layers(self):
          """Initialize layers of the head."""
          self.relu = nn.ReLU(inplace=True)
          self.cls_convs = nn.ModuleList()
          self.reg_convs = nn.ModuleList()
          for i in range(self.stacked_convs):
              chn = self.in_channels if i == 0 else self.feat_channels
              self.cls_convs.append(
                  ConvModule(
                      chn,
                      self.feat_channels,
                      3,
                      stride=1,
                      padding=1,
                      conv_cfg=self.conv_cfg,
                      norm_cfg=self.norm_cfg))
              self.reg_convs.append(
                  ConvModule(
                      chn,
                      self.feat_channels,
                      3,
                      stride=1,
                      padding=1,
                      conv_cfg=self.conv_cfg,
                      norm_cfg=self.norm_cfg))
          assert self.num_anchors == 1, 'anchor free version'
          self.gfl_cls = nn.Conv2d(
              self.feat_channels, self.cls_out_channels, 3, padding=1)
          self.gfl_reg = nn.Conv2d(
              self.feat_channels, 4 * (self.reg_max + 1), 3, padding=1)
          self.scales = nn.ModuleList(
              [Scale(1.0) for _ in self.anchor_generator.strides])
  
      def init_weights(self):
          """Initialize weights of the head."""
          for m in self.cls_convs:
              normal_init(m.conv, std=0.01)
          for m in self.reg_convs:
              normal_init(m.conv, std=0.01)
          bias_cls = bias_init_with_prob(0.01)
          normal_init(self.gfl_cls, std=0.01, bias=bias_cls)
          normal_init(self.gfl_reg, std=0.01)
  
      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: Usually a tuple of classification scores and bbox prediction
                  cls_scores (list[Tensor]): Classification and quality (IoU)
                      joint scores for all scale levels, each is a 4D-tensor,
                      the channel number is num_classes.
                  bbox_preds (list[Tensor]): Box distribution logits for all
                      scale levels, each is a 4D-tensor, the channel number is
                      4*(n+1), n is max value of integral set.
          """
          return multi_apply(self.forward_single, feats, self.scales)
  
      def forward_single(self, x, scale):
          """Forward feature of a single scale level.
  
          Args:
              x (Tensor): Features of a single scale level.
              scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
                  the bbox prediction.
  
          Returns:
              tuple:
                  cls_score (Tensor): Cls and quality joint scores for a single
                      scale level the channel number is num_classes.
                  bbox_pred (Tensor): Box distribution logits for a single scale
                      level, the channel number is 4*(n+1), n is max value of
                      integral set.
          """
          cls_feat = x
          reg_feat = x
          for cls_conv in self.cls_convs:
              cls_feat = cls_conv(cls_feat)
          for reg_conv in self.reg_convs:
              reg_feat = reg_conv(reg_feat)
          cls_score = self.gfl_cls(cls_feat)
          bbox_pred = scale(self.gfl_reg(reg_feat)).float()
          return cls_score, bbox_pred
  
      def anchor_center(self, anchors):
          """Get anchor centers from anchors.
  
          Args:
              anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format.
  
          Returns:
              Tensor: Anchor centers with shape (N, 2), "xy" format.
          """
          anchors_cx = (anchors[..., 2] + anchors[..., 0]) / 2
          anchors_cy = (anchors[..., 3] + anchors[..., 1]) / 2
          return torch.stack([anchors_cx, anchors_cy], dim=-1)
  
      def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
                      bbox_targets, stride, num_total_samples):
          """Compute loss of a single scale level.
  
          Args:
              anchors (Tensor): Box reference for each scale level with shape
                  (N, num_total_anchors, 4).
              cls_score (Tensor): Cls and quality joint scores for each scale
                  level has shape (N, num_classes, H, W).
              bbox_pred (Tensor): Box distribution logits for each scale
                  level with shape (N, 4*(n+1), H, W), n is max value of integral
                  set.
              labels (Tensor): Labels of each anchors with shape
                  (N, num_total_anchors).
              label_weights (Tensor): Label weights of each anchor with shape
                  (N, num_total_anchors)
              bbox_targets (Tensor): BBox regression targets of each anchor wight
                  shape (N, num_total_anchors, 4).
              stride (tuple): Stride in this scale level.
              num_total_samples (int): Number of positive samples that is
                  reduced over all GPUs.
  
          Returns:
              dict[str, Tensor]: A dictionary of loss components.
          """
          assert stride[0] == stride[1], 'h stride is not equal to w stride!'
          anchors = anchors.reshape(-1, 4)
          cls_score = cls_score.permute(0, 2, 3,
                                        1).reshape(-1, self.cls_out_channels)
          bbox_pred = bbox_pred.permute(0, 2, 3,
                                        1).reshape(-1, 4 * (self.reg_max + 1))
          bbox_targets = bbox_targets.reshape(-1, 4)
          labels = labels.reshape(-1)
          label_weights = label_weights.reshape(-1)
  
          # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
          bg_class_ind = self.num_classes
          pos_inds = ((labels >= 0)
                      & (labels < bg_class_ind)).nonzero().squeeze(1)
          score = label_weights.new_zeros(labels.shape)
  
          if len(pos_inds) > 0:
              pos_bbox_targets = bbox_targets[pos_inds]
              pos_bbox_pred = bbox_pred[pos_inds]
              pos_anchors = anchors[pos_inds]
              pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
  
              weight_targets = cls_score.detach().sigmoid()
              weight_targets = weight_targets.max(dim=1)[0][pos_inds]
              pos_bbox_pred_corners = self.integral(pos_bbox_pred)
              pos_decode_bbox_pred = distance2bbox(pos_anchor_centers,
                                                   pos_bbox_pred_corners)
              pos_decode_bbox_targets = pos_bbox_targets / stride[0]
              score[pos_inds] = bbox_overlaps(
                  pos_decode_bbox_pred.detach(),
                  pos_decode_bbox_targets,
                  is_aligned=True)
              pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
              target_corners = bbox2distance(pos_anchor_centers,
                                             pos_decode_bbox_targets,
                                             self.reg_max).reshape(-1)
  
              # regression loss
              loss_bbox = self.loss_bbox(
                  pos_decode_bbox_pred,
                  pos_decode_bbox_targets,
                  weight=weight_targets,
                  avg_factor=1.0)
  
              # dfl loss
              loss_dfl = self.loss_dfl(
                  pred_corners,
                  target_corners,
                  weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
                  avg_factor=4.0)
          else:
              loss_bbox = bbox_pred.sum() * 0
              loss_dfl = bbox_pred.sum() * 0
              weight_targets = bbox_pred.new_tensor(0)
  
          # cls (qfl) loss
          loss_cls = self.loss_cls(
              cls_score, (labels, score),
              weight=label_weights,
              avg_factor=num_total_samples)
  
          return loss_cls, loss_bbox, loss_dfl, weight_targets.sum()
  
      @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
      def loss(self,
               cls_scores,
               bbox_preds,
               gt_bboxes,
               gt_labels,
               img_metas,
               gt_bboxes_ignore=None):
          """Compute losses of the head.
  
          Args:
              cls_scores (list[Tensor]): Cls and quality scores for each scale
                  level has shape (N, num_classes, H, W).
              bbox_preds (list[Tensor]): Box distribution logits for each scale
                  level with shape (N, 4*(n+1), H, W), n is max value of integral
                  set.
              gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
                  shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
              gt_labels (list[Tensor]): class indices corresponding to each box
              img_metas (list[dict]): Meta information of each image, e.g.,
                  image size, scaling factor, etc.
              gt_bboxes_ignore (list[Tensor] | None): specify which bounding
                  boxes can be ignored when computing the loss.
  
          Returns:
              dict[str, Tensor]: A dictionary of loss components.
          """
  
          featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
          assert len(featmap_sizes) == self.anchor_generator.num_levels
  
          device = cls_scores[0].device
          anchor_list, valid_flag_list = self.get_anchors(
              featmap_sizes, img_metas, device=device)
          label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
  
          cls_reg_targets = self.get_targets(
              anchor_list,
              valid_flag_list,
              gt_bboxes,
              img_metas,
              gt_bboxes_ignore_list=gt_bboxes_ignore,
              gt_labels_list=gt_labels,
              label_channels=label_channels)
          if cls_reg_targets is None:
              return None
  
          (anchor_list, labels_list, label_weights_list, bbox_targets_list,
           bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
  
          num_total_samples = reduce_mean(
              torch.tensor(num_total_pos, dtype=torch.float,
                           device=device)).item()
          num_total_samples = max(num_total_samples, 1.0)
  
          losses_cls, losses_bbox, losses_dfl,\
              avg_factor = multi_apply(
                  self.loss_single,
                  anchor_list,
                  cls_scores,
                  bbox_preds,
                  labels_list,
                  label_weights_list,
                  bbox_targets_list,
                  self.anchor_generator.strides,
                  num_total_samples=num_total_samples)
  
          avg_factor = sum(avg_factor)
          avg_factor = reduce_mean(avg_factor).item()
          losses_bbox = list(map(lambda x: x / avg_factor, losses_bbox))
          losses_dfl = list(map(lambda x: x / avg_factor, losses_dfl))
          return dict(
              loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dfl=losses_dfl)
  
      def _get_bboxes(self,
                      cls_scores,
                      bbox_preds,
                      mlvl_anchors,
                      img_shapes,
                      scale_factors,
                      cfg,
                      rescale=False,
                      with_nms=True):
          """Transform outputs for a single batch item into labeled boxes.
  
          Args:
              cls_scores (list[Tensor]): Box scores for a single scale level
                  has shape (N, num_classes, H, W).
              bbox_preds (list[Tensor]): Box distribution logits for a single
                  scale level with shape (N, 4*(n+1), H, W), n is max value of
                  integral set.
              mlvl_anchors (list[Tensor]): Box reference for a single scale level
                  with shape (num_total_anchors, 4).
              img_shapes (list[tuple[int]]): Shape of the input image,
                  list[(height, width, 3)].
              scale_factors (list[ndarray]): Scale factor of the image arange as
                  (w_scale, h_scale, w_scale, h_scale).
              cfg (mmcv.Config | None): 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:
              list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
                  The first item is an (n, 5) tensor, where 5 represent
                  (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
                  The shape of the second tensor in the tuple is (n,), and
                  each element represents the class label of the corresponding
                  box.
          """
          cfg = self.test_cfg if cfg is None else cfg
          assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
          batch_size = cls_scores[0].shape[0]
  
          mlvl_bboxes = []
          mlvl_scores = []
          for cls_score, bbox_pred, stride, anchors in zip(
                  cls_scores, bbox_preds, self.anchor_generator.strides,
                  mlvl_anchors):
              assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
              assert stride[0] == stride[1]
              scores = cls_score.permute(0, 2, 3, 1).reshape(
                  batch_size, -1, self.cls_out_channels).sigmoid()
              bbox_pred = bbox_pred.permute(0, 2, 3, 1)
  
              bbox_pred = self.integral(bbox_pred) * stride[0]
              bbox_pred = bbox_pred.reshape(batch_size, -1, 4)
  
              nms_pre = cfg.get('nms_pre', -1)
              if nms_pre > 0 and scores.shape[1] > nms_pre:
                  max_scores, _ = scores.max(-1)
                  _, topk_inds = max_scores.topk(nms_pre)
                  batch_inds = torch.arange(batch_size).view(
                      -1, 1).expand_as(topk_inds).long()
                  anchors = anchors[topk_inds, :]
                  bbox_pred = bbox_pred[batch_inds, topk_inds, :]
                  scores = scores[batch_inds, topk_inds, :]
              else:
                  anchors = anchors.expand_as(bbox_pred)
  
              bboxes = distance2bbox(
                  self.anchor_center(anchors), bbox_pred, max_shape=img_shapes)
              mlvl_bboxes.append(bboxes)
              mlvl_scores.append(scores)
  
          batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
          if rescale:
              batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
                  scale_factors).unsqueeze(1)
  
          batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
          # 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 = batch_mlvl_scores.new_zeros(batch_size,
                                                batch_mlvl_scores.shape[1], 1)
          batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
  
          if with_nms:
              det_results = []
              for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes,
                                                    batch_mlvl_scores):
                  det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores,
                                                       cfg.score_thr, cfg.nms,
                                                       cfg.max_per_img)
                  det_results.append(tuple([det_bbox, det_label]))
          else:
              det_results = [
                  tuple(mlvl_bs)
                  for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores)
              ]
          return det_results
  
      def get_targets(self,
                      anchor_list,
                      valid_flag_list,
                      gt_bboxes_list,
                      img_metas,
                      gt_bboxes_ignore_list=None,
                      gt_labels_list=None,
                      label_channels=1,
                      unmap_outputs=True):
          """Get targets for GFL 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(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 gt_bboxes_ignore_list is None:
              gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
          if gt_labels_list is None:
              gt_labels_list = [None for _ in range(num_imgs)]
          (all_anchors, all_labels, all_label_weights, all_bbox_targets,
           all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
               self._get_target_single,
               anchor_list,
               valid_flag_list,
               num_level_anchors_list,
               gt_bboxes_list,
               gt_bboxes_ignore_list,
               gt_labels_list,
               img_metas,
               label_channels=label_channels,
               unmap_outputs=unmap_outputs)
          # no valid anchors
          if any([labels is None for labels in all_labels]):
              return None
          # sampled anchors of all images
          num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
          num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_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, num_total_pos,
                  num_total_neg)
  
      def _get_target_single(self,
                             flat_anchors,
                             valid_flags,
                             num_level_anchors,
                             gt_bboxes,
                             gt_bboxes_ignore,
                             gt_labels,
                             img_meta,
                             label_channels=1,
                             unmap_outputs=True):
          """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 Tensor): Number of anchors of each scale level.
              gt_bboxes (Tensor): Ground truth bboxes of the image,
                  shape (num_gts, 4).
              gt_bboxes_ignore (Tensor): Ground truth bboxes to be
                  ignored, shape (num_ignored_gts, 4).
              gt_labels (Tensor): Ground truth labels of each box,
                  shape (num_gts,).
              img_meta (dict): Meta info of the image.
              label_channels (int): Channel of label.
              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.
                  anchors (Tensor): All anchors in the image with shape (N, 4).
                  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,).
          """
          inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
                                             img_meta['img_shape'][:2],
                                             self.train_cfg.allowed_border)
          if not inside_flags.any():
              return (None, ) * 7
          # 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)
          assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
                                               gt_bboxes, gt_bboxes_ignore,
                                               gt_labels)
  
          sampling_result = self.sampler.sample(assign_result, anchors,
                                                gt_bboxes)
  
          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:
              pos_bbox_targets = sampling_result.pos_gt_bboxes
              bbox_targets[pos_inds, :] = pos_bbox_targets
              bbox_weights[pos_inds, :] = 1.0
              if gt_labels is None:
                  # Only rpn gives gt_labels as None
                  # Foreground is the first class
                  labels[pos_inds] = 0
              else:
                  labels[pos_inds] = gt_labels[
                      sampling_result.pos_assigned_gt_inds]
              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)
  
      def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
          split_inside_flags = torch.split(inside_flags, num_level_anchors)
          num_level_anchors_inside = [
              int(flags.sum()) for flags in split_inside_flags
          ]
          return num_level_anchors_inside
  