content: #!/usr/bin/env python3
  # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  
  import os
  import random
  from itertools import chain as chain
  import torch
  import torch.utils.data
  from iopath.common.file_io import g_pathmgr
  
  import slowfast.utils.logging as logging
  
  from . import utils as utils
  from .build import DATASET_REGISTRY
  
  logger = logging.get_logger(__name__)
  
  
  @DATASET_REGISTRY.register()
  class Charades(torch.utils.data.Dataset):
      """
      Charades video loader. Construct the Charades video loader, then sample
      clips from the videos. For training and validation, a single clip is randomly
      sampled from every video with random cropping, scaling, and flipping. For
      testing, multiple clips are uniformaly sampled from every video with uniform
      cropping. For uniform cropping, we take the left, center, and right crop if
      the width is larger than height, or take top, center, and bottom crop if the
      height is larger than the width.
      """
  
      def __init__(self, cfg, mode, num_retries=10):
          """
          Load Charades data (frame paths, labels, etc. ) to a given Dataset object.
          The dataset could be downloaded from Chrades official website
          (https://allenai.org/plato/charades/).
          Please see datasets/DATASET.md for more information about the data format.
          Args:
              dataset (Dataset): a Dataset object to load Charades data to.
              mode (string): 'train', 'val', or 'test'.
          Args:
              cfg (CfgNode): configs.
              mode (string): Options includes `train`, `val`, or `test` mode.
                  For the train and val mode, the data loader will take data
                  from the train or val set, and sample one clip per video.
                  For the test mode, the data loader will take data from test set,
                  and sample multiple clips per video.
              num_retries (int): number of retries.
          """
          # Only support train, val, and test mode.
          assert mode in [
              "train",
              "val",
              "test",
          ], "Split '{}' not supported for Charades ".format(mode)
          self.mode = mode
          self.cfg = cfg
  
          self._video_meta = {}
          self._num_retries = num_retries
          # For training or validation mode, one single clip is sampled from every
          # video. For testing, NUM_ENSEMBLE_VIEWS clips are sampled from every
          # video. For every clip, NUM_SPATIAL_CROPS is cropped spatially from
          # the frames.
          if self.mode in ["train", "val"]:
              self._num_clips = 1
          elif self.mode in ["test"]:
              self._num_clips = (
                  cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS
              )
  
          logger.info("Constructing Charades {}...".format(mode))
          self._construct_loader()
  
      def _construct_loader(self):
          """
          Construct the video loader.
          """
          path_to_file = os.path.join(
              self.cfg.DATA.PATH_TO_DATA_DIR,
              "{}.csv".format("train" if self.mode == "train" else "val"),
          )
          assert g_pathmgr.exists(path_to_file), "{} dir not found".format(
              path_to_file
          )
          (self._path_to_videos, self._labels) = utils.load_image_lists(
              path_to_file, self.cfg.DATA.PATH_PREFIX, return_list=True
          )
  
          if self.mode != "train":
              # Form video-level labels from frame level annotations.
              self._labels = utils.convert_to_video_level_labels(self._labels)
  
          self._path_to_videos = list(
              chain.from_iterable(
                  [[x] * self._num_clips for x in self._path_to_videos]
              )
          )
          self._labels = list(
              chain.from_iterable([[x] * self._num_clips for x in self._labels])
          )
          self._spatial_temporal_idx = list(
              chain.from_iterable(
                  [range(self._num_clips) for _ in range(len(self._labels))]
              )
          )
  
          logger.info(
              "Charades dataloader constructed (size: {}) from {}".format(
                  len(self._path_to_videos), path_to_file
              )
          )
  
      def get_seq_frames(self, index):
          """
          Given the video index, return the list of indexs of sampled frames.
          Args:
              index (int): the video index.
          Returns:
              seq (list): the indexes of sampled frames from the video.
          """
          temporal_sample_index = (
              -1
              if self.mode in ["train", "val"]
              else self._spatial_temporal_idx[index]
              // self.cfg.TEST.NUM_SPATIAL_CROPS
          )
          num_frames = self.cfg.DATA.NUM_FRAMES
          sampling_rate = utils.get_random_sampling_rate(
              self.cfg.MULTIGRID.LONG_CYCLE_SAMPLING_RATE,
              self.cfg.DATA.SAMPLING_RATE,
          )
          video_length = len(self._path_to_videos[index])
          assert video_length == len(self._labels[index])
  
          clip_length = (num_frames - 1) * sampling_rate + 1
          if temporal_sample_index == -1:
              if clip_length > video_length:
                  start = random.randint(video_length - clip_length, 0)
              else:
                  start = random.randint(0, video_length - clip_length)
          else:
              gap = float(max(video_length - clip_length, 0)) / (
                  self.cfg.TEST.NUM_ENSEMBLE_VIEWS - 1
              )
              start = int(round(gap * temporal_sample_index))
  
          seq = [
              max(min(start + i * sampling_rate, video_length - 1), 0)
              for i in range(num_frames)
          ]
  
          return seq
  
      def __getitem__(self, index):
          """
          Given the video index, return the list of frames, label, and video
          index if the video frames can be fetched.
          Args:
              index (int): the video index provided by the pytorch sampler.
          Returns:
              frames (tensor): the frames of sampled from the video. The dimension
                  is `channel` x `num frames` x `height` x `width`.
              label (int): the label of the current video.
              index (int): the index of the video.
          """
          short_cycle_idx = None
          # When short cycle is used, input index is a tupple.
          if isinstance(index, tuple):
              index, short_cycle_idx = index
  
          if self.mode in ["train", "val"]:
              # -1 indicates random sampling.
              spatial_sample_index = -1
              min_scale = self.cfg.DATA.TRAIN_JITTER_SCALES[0]
              max_scale = self.cfg.DATA.TRAIN_JITTER_SCALES[1]
              crop_size = self.cfg.DATA.TRAIN_CROP_SIZE
              if short_cycle_idx in [0, 1]:
                  crop_size = int(
                      round(
                          self.cfg.MULTIGRID.SHORT_CYCLE_FACTORS[short_cycle_idx]
                          * self.cfg.MULTIGRID.DEFAULT_S
                      )
                  )
              if self.cfg.MULTIGRID.DEFAULT_S > 0:
                  # Decreasing the scale is equivalent to using a larger "span"
                  # in a sampling grid.
                  min_scale = int(
                      round(
                          float(min_scale)
                          * crop_size
                          / self.cfg.MULTIGRID.DEFAULT_S
                      )
                  )
          elif self.mode in ["test"]:
              # spatial_sample_index is in [0, 1, 2]. Corresponding to left,
              # center, or right if width is larger than height, and top, middle,
              # or bottom if height is larger than width.
              spatial_sample_index = (
                  self._spatial_temporal_idx[index]
                  % self.cfg.TEST.NUM_SPATIAL_CROPS
              )
              min_scale, max_scale, crop_size = [self.cfg.DATA.TEST_CROP_SIZE] * 3
              # The testing is deterministic and no jitter should be performed.
              # min_scale, max_scale, and crop_size are expect to be the same.
              assert len({min_scale, max_scale, crop_size}) == 1
          else:
              raise NotImplementedError(
                  "Does not support {} mode".format(self.mode)
              )
  
          seq = self.get_seq_frames(index)
          frames = torch.as_tensor(
              utils.retry_load_images(
                  [self._path_to_videos[index][frame] for frame in seq],
                  self._num_retries,
              )
          )
  
          label = utils.aggregate_labels(
              [self._labels[index][i] for i in range(seq[0], seq[-1] + 1)]
          )
          label = torch.as_tensor(
              utils.as_binary_vector(label, self.cfg.MODEL.NUM_CLASSES)
          )
  
          # Perform color normalization.
          frames = utils.tensor_normalize(
              frames, self.cfg.DATA.MEAN, self.cfg.DATA.STD
          )
          # T H W C -> C T H W.
          frames = frames.permute(3, 0, 1, 2)
          # Perform data augmentation.
          frames = utils.spatial_sampling(
              frames,
              spatial_idx=spatial_sample_index,
              min_scale=min_scale,
              max_scale=max_scale,
              crop_size=crop_size,
              random_horizontal_flip=self.cfg.DATA.RANDOM_FLIP,
              inverse_uniform_sampling=self.cfg.DATA.INV_UNIFORM_SAMPLE,
          )
          frames = utils.pack_pathway_output(self.cfg, frames)
          return frames, label, index, {}
  
      def __len__(self):
          """
          Returns:
              (int): the number of videos in the dataset.
          """
          return self.num_videos
  
      @property
      def num_videos(self):
          """
          Returns:
              (int): the number of videos in the dataset.
          """
          return len(self._path_to_videos)
  