content: import os.path as osp
  
  import torch
  
  from .base import BaseDataset
  from .registry import DATASETS
  
  
  @DATASETS.register_module()
  class VideoDataset(BaseDataset):
      """Video dataset for action recognition.
  
      The dataset loads raw videos and apply specified transforms to return a
      dict containing the frame tensors and other information.
  
      The ann_file is a text file with multiple lines, and each line indicates
      a sample video with the filepath and label, which are split with a
      whitespace. Example of a annotation file:
  
      .. code-block:: txt
  
          some/path/000.mp4 1
          some/path/001.mp4 1
          some/path/002.mp4 2
          some/path/003.mp4 2
          some/path/004.mp4 3
          some/path/005.mp4 3
  
  
      Args:
          ann_file (str): Path to the annotation file.
          pipeline (list[dict | callable]): A sequence of data transforms.
          start_index (int): Specify a start index for frames in consideration of
              different filename format. However, when taking videos as input,
              it should be set to 0, since frames loaded from videos count
              from 0. Default: 0.
          **kwargs: Keyword arguments for ``BaseDataset``.
      """
  
      def __init__(self, ann_file, pipeline, start_index=0, **kwargs):
          super().__init__(ann_file, pipeline, start_index=start_index, **kwargs)
  
      def load_annotations(self):
          """Load annotation file to get video information."""
          if self.ann_file.endswith('.json'):
              return self.load_json_annotations()
  
          video_infos = []
          with open(self.ann_file, 'r') as fin:
              for line in fin:
                  line_split = line.strip().split()
                  if self.multi_class:
                      assert self.num_classes is not None
                      filename, label = line_split[0], line_split[1:]
                      label = list(map(int, label))
                      onehot = torch.zeros(self.num_classes)
                      onehot[label] = 1.0
                  else:
                      filename, label = line_split
                      label = int(label)
                  if self.data_prefix is not None:
                      filename = osp.join(self.data_prefix, filename)
                  video_infos.append(
                      dict(
                          filename=filename,
                          label=onehot if self.multi_class else label))
          return video_infos
  