class_docstr: Bridging the Gap Between Anchor-based and Anchor-free Detection via
  Adaptive Training Sample Selection.
  
  ATSS head structure is similar with FCOS, however ATSS use anchor boxes
  and assign label by Adaptive Training Sample Selection instead max-iou.
  
  https://arxiv.org/abs/1912.02424
class_docstr_tok: ["Bridging","the","Gap","Between","Anchor-based","and","Anchor-free","Detection","via","Adaptive","Training","Sample","Selection",".","ATSS","head","structure","is","similar","with","FCOS",",","however","ATSS","use","anchor","boxes","and","assign","label","by","Adaptive","Training","Sample","Selection","instead","max-iou",".","https",":","\/\/arxiv.org\/abs\/1912.02424"]
code: 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
  
imports: import torch
  import torch.nn
  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, build_assigner, build_sampler, images_to_levels, multi_apply, multiclass_nms, reduce_mean, unmap
  from ..builder import HEADS, build_loss
  from .anchor_head import AnchorHead
label_desc: Use image node_id 13 for calling the ATSSHead obj's underlying member method code with example usage: obj.get_num_level_anchors_inside(num_level_anchors, inside_flags) and returns: num_level_anchors_inside