class_docstr: The class for implementing DRRG loss. This is partially adapted from
  https://github.com/GXYM/DRRG licensed under the MIT license.
  
  DRRG: `Deep Relational Reasoning Graph Network for Arbitrary Shape Text
  Detection <https://arxiv.org/abs/1908.05900>`_.
  
  Args:
      ohem_ratio (float): The negative/positive ratio in ohem. Defaults to
          3.0.
      downsample_ratio (float): Downsample ratio. Defaults to 1.0. TODO:
          remove it.
      orientation_thr (float): The threshold for distinguishing between
          head edge and tail edge among the horizontal and vertical edges
          of a quadrangle. Defaults to 2.0.
      resample_step (float): The step size for resampling the text center
          line. Defaults to 8.0.
      num_min_comps (int): The minimum number of text components, which
          should be larger than k_hop1 mentioned in paper. Defaults to 9.
      num_max_comps (int): The maximum number of text components. Defaults
          to 600.
      min_width (float): The minimum width of text components. Defaults to
          8.0.
      max_width (float): The maximum width of text components. Defaults to
          24.0.
      center_region_shrink_ratio (float): The shrink ratio of text center
          regions. Defaults to 0.3.
      comp_shrink_ratio (float): The shrink ratio of text components.
          Defaults to 1.0.
      comp_w_h_ratio (float): The width to height ratio of text components.
          Defaults to 0.3.
      min_rand_half_height(float): The minimum half-height of random text
          components. Defaults to 8.0.
      max_rand_half_height (float): The maximum half-height of random
          text components. Defaults to 24.0.
      jitter_level (float): The jitter level of text component geometric
          features. Defaults to 0.2.
      loss_text (dict): The loss config used to calculate the text loss.
          Defaults to ``dict(type='MaskedBalancedBCEWithLogitsLoss',
          fallback_negative_num=100, eps=1e-5)``.
      loss_center (dict): The loss config used to calculate the center loss.
          Defaults to ``dict(type='MaskedBCEWithLogitsLoss')``.
      loss_top (dict): The loss config used to calculate the top loss, which
          is a part of the height loss. Defaults to
          ``dict(type='SmoothL1Loss', reduction='none')``.
      loss_btm (dict): The loss config used to calculate the bottom loss,
          which is a part of the height loss. Defaults to
          ``dict(type='SmoothL1Loss', reduction='none')``.
      loss_sin (dict): The loss config used to calculate the sin loss.
          Defaults to ``dict(type='MaskedSmoothL1Loss')``.
      loss_cos (dict): The loss config used to calculate the cos loss.
          Defaults to ``dict(type='MaskedSmoothL1Loss')``.
      loss_gcn (dict): The loss config used to calculate the GCN loss.
          Defaults to ``dict(type='CrossEntropyLoss')``.
docstr: Generate loss targets from data samples.
  
  Args:
      data_samples (list(TextDetDataSample)): Ground truth data samples.
  
  Returns:
      tuple: A tuple of 8 lists of tensors as DRRG targets. Read
      docstring of ``_get_target_single`` for more details.
docstr_tok: ["Generate","loss","targets","from","data","samples",".","Args",":","data_samples","(","list","(","TextDetDataSample",")",")",":","Ground","truth","data","samples",".","Returns",":","tuple",":","A","tuple","of","8","lists","of","tensors","as","DRRG","targets",".","Read","docstring","of","``","_get_target_single","``","for","more","details","."]
code: def get_targets(self, data_samples: List[TextDetDataSample]) -> Tuple:
      """Generate loss targets from data samples.
  
      Args:
          data_samples (list(TextDetDataSample)): Ground truth data samples.
  
      Returns:
          tuple: A tuple of 8 lists of tensors as DRRG targets. Read
          docstring of ``_get_target_single`` for more details.
      """
  
      # If data_samples points to same object as self.cached_data_samples, it
      # means that get_targets is called more than once in the same train
      # iteration, and pre-computed targets can be reused.
      if (
          hasattr(self, "targets")
          and self.cache_data_samples is data_samples
      ):
          return self.targets
  
      self.cache_data_samples = data_samples
      self.targets = multi_apply(self._get_target_single, data_samples)
      return self.targets
  
imports: from typing import Dict, List, Sequence, Tuple
  import cv2
  import numpy
  import torch
  from mmcv.image import imrescale
  from mmdet.models.utils import multi_apply
  from numpy import ndarray
  from numpy.linalg import norm
  from torch import Tensor
  from mmocr.registry import MODELS
  from mmocr.structures import TextDetDataSample
  from mmocr.utils import check_argument
  from .textsnake_module_loss import TextSnakeModuleLoss
label_desc: Use image node_id 3 for calling the DRRGModuleLoss obj's underlying member method code with example usage: obj.get_targets(data_samples) and returns: self, self