class_docstr: Implementation of loss module for SPTS with CrossEntropy loss.
  
  Args:
      dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
          the instance of `Dictionary`.
      num_bins (int): Number of bins dividing the image. Defaults to 1000.
      seq_eos_coef (float): The loss weight coefficient of seq_eos token.
          Defaults to 0.01.
      max_seq_len (int): Maximum sequence length. In SPTS, a sequence
          encodes all the text instances in a sample. Defaults to 40, which
          will be overridden by SPTSDecoder.
      max_text_len (int): Maximum length for each text instance in a
          sequence. Defaults to 25.
      letter_case (str): There are three options to alter the letter cases
          of gt texts:
          - unchanged: Do not change gt texts.
          - upper: Convert gt texts into uppercase characters.
          - lower: Convert gt texts into lowercase characters.
          Usually, it only works for English characters. Defaults to
          'unchanged'.
      pad_with (str): The padding strategy for ``gt_text.padded_indexes``.
          Defaults to 'auto'. Options are:
          - 'auto': Use dictionary.padding_idx to pad gt texts, or
            dictionary.end_idx if dictionary.padding_idx
            is None.
          - 'padding': Always use dictionary.padding_idx to pad gt texts.
          - 'end': Always use dictionary.end_idx to pad gt texts.
          - 'none': Do not pad gt texts.
      ignore_char (int or str): Specifies a target value that is
          ignored and does not contribute to the input gradient.
          ignore_char can be int or str. If int, it is the index of
          the ignored char. If str, it is the character to ignore.
          Apart from single characters, each item can be one of the
          following reversed keywords: 'padding', 'start', 'end',
          and 'unknown', which refer to their corresponding special
          tokens in the dictionary. It will not ignore any special
          tokens when ignore_char == -1 or 'none'. Defaults to 'padding'.
      flatten (bool): Whether to flatten the output and target before
          computing CE loss. Defaults to False.
      reduction (str): Specifies the reduction to apply to the output,
          should be one of the following: ('none', 'mean', 'sum'). Defaults
          to 'none'.
      ignore_first_char (bool): Whether to ignore the first token in target (
          usually the start token). Defaults to ``True``.
      flatten (bool): Whether to flatten the vectors for loss computation.
          Defaults to False.
docstr: Target generator.
  
  Args:
      data_samples (list[TextSpottingDataSample]): It usually includes
          ``gt_instances`` information.
  
  Returns:
      list[TextSpottingDataSample]: Updated data_samples. Two keys will
      be added to data_sample:
  
      - indexes (torch.LongTensor): Character indexes representing gt
        texts. All special tokens are excluded, except for UKN.
      - padded_indexes (torch.LongTensor): Character indexes
        representing gt texts with BOS and EOS if applicable, following
        several padding indexes until the length reaches ``max_seq_len``.
        In particular, if ``pad_with='none'``, no padding will be
        applied.
docstr_tok: ["Target","generator",".","Args",":","data_samples","(","list","[","TextSpottingDataSample","]",")",":","It","usually","includes","``","gt_instances","``","information",".","Returns",":","list","[","TextSpottingDataSample","]",":","Updated","data_samples",".","Two","keys","will","be","added","to","data_sample",":","-","indexes","(","torch.LongTensor",")",":","Character","indexes","representing","gt","texts",".","All","special","tokens","are","excluded",",","except","for","UKN",".","-","padded_indexes","(","torch.LongTensor",")",":","Character","indexes","representing","gt","texts","with","BOS","and","EOS","if","applicable",",","following","several","padding","indexes","until","the","length","reaches","``","max_seq_len","``",".","In","particular",",","if","``","pad_with='none","'","``",",","no","padding","will","be","applied","."]
code: def get_targets(
      self, data_samples: Sequence[TextSpottingDataSample]
  ) -> Sequence[TextSpottingDataSample]:
      """Target generator.
  
      Args:
          data_samples (list[TextSpottingDataSample]): It usually includes
              ``gt_instances`` information.
  
      Returns:
          list[TextSpottingDataSample]: Updated data_samples. Two keys will
          be added to data_sample:
  
          - indexes (torch.LongTensor): Character indexes representing gt
            texts. All special tokens are excluded, except for UKN.
          - padded_indexes (torch.LongTensor): Character indexes
            representing gt texts with BOS and EOS if applicable, following
            several padding indexes until the length reaches ``max_seq_len``.
            In particular, if ``pad_with='none'``, no padding will be
            applied.
      """
  
      batch_max_len = 0
  
      for data_sample in data_samples:
          if data_sample.get("have_target", False):
              continue
  
          if len(data_sample.gt_instances) > self.max_num_text:
              keep = random.sample(
                  range(len(data_sample.gt_instances)),
                  self.max_num_text,
              )
              data_sample.gt_instances = data_sample.gt_instances[keep]
  
          gt_instances = data_sample.gt_instances
  
          if len(gt_instances) > 0:
              center_pts = []
              # Slightly different from the original implementation
              # which gets the center points from bezier curves
              # for bezier_pt in gt_instances.beziers:
              #     bezier_pt = bezier_pt.reshape(8, 2)
              #     mid_pt1 = sample_bezier_curve(
              #         bezier_pt[:4], mid_point=True)
              #     mid_pt2 = sample_bezier_curve(
              #         bezier_pt[4:], mid_point=True)
              #     center_pt = (mid_pt1 + mid_pt2) / 2
              for polygon in gt_instances.polygons:
                  center_pt = polygon.reshape(-1, 2).mean(0)
                  center_pts.append(center_pt)
              center_pts = np.vstack(center_pts)
              center_pts /= data_sample.img_shape[::-1]
              center_pts = torch.from_numpy(center_pts).type(
                  torch.float32
              )
          else:
              center_pts = (
                  torch.ones(0).reshape(-1, 2).type(torch.float32)
              )
  
          center_pts = (
              (center_pts * self.num_bins).floor().type(torch.long)
          )
          center_pts = torch.clamp(
              center_pts, min=0, max=self.num_bins - 1
          )
  
          gt_indexes = []
          for text in gt_instances.texts:
              if self.letter_case in ["upper", "lower"]:
                  text = getattr(text, self.letter_case)()
  
              indexes = self.dictionary.str2idx(text)
              indexes_tensor = (
                  torch.zeros(self.max_text_len, dtype=torch.long)
                  + self.dictionary.end_idx
              )
              max_len = min(self.max_text_len - 1, len(indexes))
              indexes_tensor[:max_len] = torch.LongTensor(indexes)[
                  :max_len
              ]
              indexes_tensor = indexes_tensor
              gt_indexes.append(indexes_tensor)
  
          if len(gt_indexes) == 0:
              gt_indexes = torch.ones(0).reshape(-1, self.max_text_len)
          else:
              gt_indexes = torch.vstack(gt_indexes)
          gt_indexes = torch.cat([center_pts, gt_indexes], dim=-1)
          gt_indexes = gt_indexes.flatten()
  
          if self.dictionary.start_idx is not None:
              gt_indexes = torch.cat(
                  [
                      torch.LongTensor([self.dictionary.start_idx]),
                      gt_indexes,
                  ]
              )
          if self.dictionary.seq_end_idx is not None:
              gt_indexes = torch.cat(
                  [
                      gt_indexes,
                      torch.LongTensor([self.dictionary.seq_end_idx]),
                  ]
              )
  
          batch_max_len = max(batch_max_len, len(gt_indexes))
  
          gt_instances.set_metainfo(dict(indexes=gt_indexes))
  
      # Here we have to have the second pass as we need to know the max
      # length of the batch to pad the indexes in order to save memory
      for data_sample in data_samples:
          if data_sample.get("have_target", False):
              continue
  
          indexes = data_sample.gt_instances.indexes
  
          padded_indexes = (
              torch.zeros(batch_max_len, dtype=torch.long)
              + self.dictionary.padding_idx
          )
          padded_indexes[: len(indexes)] = indexes
          data_sample.gt_instances.set_metainfo(
              dict(padded_indexes=padded_indexes)
          )
          data_sample.set_metainfo(dict(have_target=True))
  
      return data_samples
  
file_path: mmocr/projects/SPTS/spts/model/spts_module_loss.py
imports: import random
  from typing import Dict, Sequence, Union
  import numpy
  import torch
  from torch import nn
  from mmocr.models.common.dictionary import Dictionary
  from mmocr.models.textrecog.module_losses import CEModuleLoss
  from mmocr.registry import MODELS
  from mmocr.structures import TextSpottingDataSample
label_desc: Use image node_id 2 for calling the SPTSModuleLoss obj's underlying member method code with example usage: obj.get_targets(data_samples) and returns: data_samples