class_docstr: Stage of CascadeRPNHead.
  
  Args:
      in_channels (int): Number of channels in the input feature map.
      anchor_generator (:obj:`ConfigDict` or dict): anchor generator config.
      adapt_cfg (:obj:`ConfigDict` or dict): adaptation config.
      bridged_feature (bool): whether update rpn feature. Defaults to False.
      with_cls (bool): whether use classification branch. Defaults to True.
      init_cfg :obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
          list[dict], optional): Initialization config dict.
          Defaults to None.
class_docstr_tok: ["Stage","of","CascadeRPNHead",".","Args",":","in_channels","(","int",")",":","Number","of","channels","in","the","input","feature","map",".","anchor_generator","(",":","obj",":","`","ConfigDict","`","or","dict",")",":","anchor","generator","config",".","adapt_cfg","(",":","obj",":","`","ConfigDict","`","or","dict",")",":","adaptation","config",".","bridged_feature","(","bool",")",":","whether","update","rpn","feature",".","Defaults","to","False",".","with_cls","(","bool",")",":","whether","use","classification","branch",".","Defaults","to","True",".","init_cfg",":","obj",":","`","ConfigDict","`","or","list","[",":","obj",":","`","ConfigDict","`","]","or","dict","or","list","[","dict","]",",","optional",")",":","Initialization","config","dict",".","Defaults","to","None","."]
docstr: Compute regression and classification targets for anchors.
  
  Args:
      anchor_list (list[list[Tensor]]): Multi level anchors of each
          image.
      valid_flag_list (list[list[Tensor]]): Multi level valid flags of
          each image.
      featmap_sizes (list[Tuple[int, int]]): Feature map size each level.
      batch_gt_instances (list[:obj:`InstanceData`]): Batch of
          gt_instance. It usually includes ``bboxes`` and ``labels``
          attributes.
      batch_img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
          Batch of gt_instances_ignore. It includes ``bboxes`` attribute
          data that is ignored during training and testing.
          Defaults to None.
      return_sampling_results (bool): Whether to return the sampling
          results. Defaults to False.
  
  Returns:
      tuple:
  
          - labels_list (list[Tensor]): Labels of each level.
          - label_weights_list (list[Tensor]): Label weights of each
            level.
          - bbox_targets_list (list[Tensor]): BBox targets of each level.
          - bbox_weights_list (list[Tensor]): BBox weights of each level.
          - avg_factor (int): Average factor that is used to average
            the loss. When using sampling method, avg_factor is usually
            the sum of positive and negative priors. When using
            ``PseudoSampler``, ``avg_factor`` is usually equal to the
            number of positive priors.
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code: def get_targets(
      self,
      anchor_list: List[List[Tensor]],
      valid_flag_list: List[List[Tensor]],
      featmap_sizes: List[Tuple[int, int]],
      batch_gt_instances: InstanceList,
      batch_img_metas: List[dict],
      batch_gt_instances_ignore: OptInstanceList = None,
      return_sampling_results: bool = False,
  ) -> tuple:
      """Compute regression and classification targets for anchors.
  
      Args:
          anchor_list (list[list[Tensor]]): Multi level anchors of each
              image.
          valid_flag_list (list[list[Tensor]]): Multi level valid flags of
              each image.
          featmap_sizes (list[Tuple[int, int]]): Feature map size each level.
          batch_gt_instances (list[:obj:`InstanceData`]): Batch of
              gt_instance. It usually includes ``bboxes`` and ``labels``
              attributes.
          batch_img_metas (list[dict]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
          batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
              Batch of gt_instances_ignore. It includes ``bboxes`` attribute
              data that is ignored during training and testing.
              Defaults to None.
          return_sampling_results (bool): Whether to return the sampling
              results. Defaults to False.
  
      Returns:
          tuple:
  
              - labels_list (list[Tensor]): Labels of each level.
              - label_weights_list (list[Tensor]): Label weights of each
                level.
              - bbox_targets_list (list[Tensor]): BBox targets of each level.
              - bbox_weights_list (list[Tensor]): BBox weights of each level.
              - avg_factor (int): Average factor that is used to average
                the loss. When using sampling method, avg_factor is usually
                the sum of positive and negative priors. When using
                ``PseudoSampler``, ``avg_factor`` is usually equal to the
                number of positive priors.
      """
      if isinstance(self.assigner, RegionAssigner):
          cls_reg_targets = self.region_targets(
              anchor_list,
              valid_flag_list,
              featmap_sizes,
              batch_gt_instances,
              batch_img_metas,
              batch_gt_instances_ignore=batch_gt_instances_ignore,
              return_sampling_results=return_sampling_results,
          )
      else:
          cls_reg_targets = super().get_targets(
              anchor_list,
              valid_flag_list,
              batch_gt_instances,
              batch_img_metas,
              batch_gt_instances_ignore=batch_gt_instances_ignore,
              return_sampling_results=return_sampling_results,
          )
      return cls_reg_targets
  
imports: from __future__ import division
  import copy
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from mmcv.ops import DeformConv2d
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule, ModuleList
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures import SampleList
  from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptInstanceList, OptMultiConfig
  from ..task_modules.assigners import RegionAssigner
  from ..task_modules.samplers import PseudoSampler
  from ..utils import images_to_levels, multi_apply, select_single_mlvl, unpack_gt_instances
  from .base_dense_head import BaseDenseHead
  from .rpn_head import RPNHead
label_desc: Use image node_id 7 for calling the StageCascadeRPNHead obj's underlying member method code with example usage: obj.get_targets(anchor_list, valid_flag_list, featmap_sizes, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore, return_sampling_results) and returns: cls_reg_targets