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- # Copyright (c) OpenMMLab. All rights reserved.
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- from mmcv.cnn import ConvModule, Scale
- from mmcv.runner import force_fp32
-
- from mmdet.core import (anchor_inside_flags, bbox_overlaps, build_assigner,
- build_sampler, images_to_levels, multi_apply,
- reduce_mean, unmap)
- from mmdet.core.utils import filter_scores_and_topk
- 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).
- bbox_coder (dict): Config of bbox coder. Defaults
- 'DistancePointBBoxCoder'.
- reg_max (int): Max value of integral set :math: `{0, ..., reg_max}`
- in QFL setting. Default: 16.
- init_cfg (dict or list[dict], optional): Initialization config dict.
- 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),
- bbox_coder=dict(type='DistancePointBBoxCoder'),
- reg_max=16,
- init_cfg=dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='gfl_cls',
- std=0.01,
- bias_prob=0.01)),
- **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,
- bbox_coder=bbox_coder,
- init_cfg=init_cfg,
- **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.prior_generator.strides])
-
- 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
- weight 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 = self.bbox_coder.decode(
- 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 = self.bbox_coder.encode(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.prior_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.prior_generator.strides,
- num_total_samples=num_total_samples)
-
- avg_factor = sum(avg_factor)
- avg_factor = reduce_mean(avg_factor).clamp_(min=1).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_single(self,
- cls_score_list,
- bbox_pred_list,
- score_factor_list,
- mlvl_priors,
- img_meta,
- cfg,
- rescale=False,
- with_nms=True,
- **kwargs):
- """Transform outputs of a single image into bbox predictions.
-
- Args:
- cls_score_list (list[Tensor]): Box scores from all scale
- levels of a single image, each item has shape
- (num_priors * num_classes, H, W).
- bbox_pred_list (list[Tensor]): Box energies / deltas from
- all scale levels of a single image, each item has shape
- (num_priors * 4, H, W).
- score_factor_list (list[Tensor]): Score factor from all scale
- levels of a single image. GFL head does not need this value.
- mlvl_priors (list[Tensor]): Each element in the list is
- the priors of a single level in feature pyramid, has shape
- (num_priors, 4).
- img_meta (dict): Image meta info.
- 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:
- tuple[Tensor]: Results of detected bboxes and labels. If with_nms
- is False and mlvl_score_factor is None, return mlvl_bboxes and
- mlvl_scores, else return mlvl_bboxes, mlvl_scores and
- mlvl_score_factor. Usually with_nms is False is used for aug
- test. If with_nms is True, then return the following format
-
- - det_bboxes (Tensor): Predicted bboxes with shape \
- [num_bboxes, 5], where the first 4 columns are bounding \
- box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
- column are scores between 0 and 1.
- - det_labels (Tensor): Predicted labels of the corresponding \
- box with shape [num_bboxes].
- """
- cfg = self.test_cfg if cfg is None else cfg
- img_shape = img_meta['img_shape']
- nms_pre = cfg.get('nms_pre', -1)
-
- mlvl_bboxes = []
- mlvl_scores = []
- mlvl_labels = []
- for level_idx, (cls_score, bbox_pred, stride, priors) in enumerate(
- zip(cls_score_list, bbox_pred_list,
- self.prior_generator.strides, mlvl_priors)):
- assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
- assert stride[0] == stride[1]
-
- bbox_pred = bbox_pred.permute(1, 2, 0)
- bbox_pred = self.integral(bbox_pred) * stride[0]
-
- scores = cls_score.permute(1, 2, 0).reshape(
- -1, self.cls_out_channels).sigmoid()
-
- # After https://github.com/open-mmlab/mmdetection/pull/6268/,
- # this operation keeps fewer bboxes under the same `nms_pre`.
- # There is no difference in performance for most models. If you
- # find a slight drop in performance, you can set a larger
- # `nms_pre` than before.
- results = filter_scores_and_topk(
- scores, cfg.score_thr, nms_pre,
- dict(bbox_pred=bbox_pred, priors=priors))
- scores, labels, _, filtered_results = results
-
- bbox_pred = filtered_results['bbox_pred']
- priors = filtered_results['priors']
-
- bboxes = self.bbox_coder.decode(
- self.anchor_center(priors), bbox_pred, max_shape=img_shape)
- mlvl_bboxes.append(bboxes)
- mlvl_scores.append(scores)
- mlvl_labels.append(labels)
-
- return self._bbox_post_process(
- mlvl_scores,
- mlvl_labels,
- mlvl_bboxes,
- img_meta['scale_factor'],
- cfg,
- rescale=rescale,
- with_nms=with_nms)
-
- 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
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