content: #!/usr/bin/env python3
  
  import os
  import random
  import torch
  import torch.utils.data
  from iopath.common.file_io import g_pathmgr
  from torchvision import transforms
  
  import slowfast.utils.logging as logging
  
  from . import decoder as decoder
  from . import utils as utils
  from . import video_container as container
  from .build import DATASET_REGISTRY
  from .random_erasing import RandomErasing
  from .transform import create_random_augment
  
  logger = logging.get_logger(__name__)
  
  
  @DATASET_REGISTRY.register()
  class Anet(torch.utils.data.Dataset):
      """
      ANet video loader. Construct the ANet 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):
          """
          Construct the ANet video loader with a given csv file. The format of
          the csv file is:
          ```
          path_to_video_1 total_time_1 start_time_1 end_time_1 label_1
          path_to_video_2 total_time_2 start_time_2 end_time_2 label_2
          ...
          path_to_video_N total_time_N start_time_N end_time_N label_N
          ```
          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 ANet".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"]:
              self._num_clips = 1
              cfg.TEST.NUM_ENSEMBLE_VIEWS = 1
              cfg.TEST.NUM_SPATIAL_CROPS = 1
          elif self.mode in ["val", "test"]:
              self._num_clips = (
                  cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS
              )
  
          logger.info("Constructing ANet {}...".format(mode))
          self._construct_loader()
          self.aug = False
          self.rand_erase = False
          self.use_temporal_gradient = False
          self.temporal_gradient_rate = 0.0
  
          if self.mode == "train" and self.cfg.AUG.ENABLE:
              self.aug = True
              if self.cfg.AUG.RE_PROB > 0:
                  self.rand_erase = True
  
      def _construct_loader(self):
          """
          Construct the video loader.
          """
          path_to_file = os.path.join(
              self.cfg.DATA.PATH_TO_DATA_DIR, "{}.csv".format(self.mode)
          )
          assert g_pathmgr.exists(path_to_file), "{} dir not found".format(
              path_to_file
          )
  
          self._path_to_videos = []
          self._total_time = []
          self._start_time = []
          self._end_time = []
          self._labels = []
          self._spatial_temporal_idx = []
          with g_pathmgr.open(path_to_file, "r") as f:
              for clip_idx, path_label in enumerate(f.read().splitlines()):
                  # assert (
                  #     len(path_label.split(self.cfg.DATA.PATH_LABEL_SEPARATOR))
                  #     == 5
                  # )
                  path, total_time, start_time, end_time, label = path_label.split(
                      self.cfg.DATA.PATH_LABEL_SEPARATOR
                  )
                  for idx in range(self._num_clips):
                      self._path_to_videos.append(
                          os.path.join(self.cfg.DATA.PATH_PREFIX, path)
                      )
                      self._total_time.append(float(total_time))
                      self._start_time.append(float(start_time))
                      self._end_time.append(float(end_time))
                      self._labels.append(int(label))
                      self._spatial_temporal_idx.append(idx)
                      self._video_meta[clip_idx * self._num_clips + idx] = {}
          assert (
              len(self._path_to_videos) > 0
          ), "Failed to load ANet split {} from {}".format(
              self._split_idx, path_to_file
          )
          logger.info(
              "Constructing ANet dataloader (size: {}) from {}".format(
                  len(self._path_to_videos), path_to_file
              )
          )
  
      def __getitem__(self, index):
          """
          Given the video index, return the list of frames, label, and video
          index if the video can be fetched and decoded successfully, otherwise
          repeatly find a random video that can be decoded as a replacement.
          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): if the video provided by pytorch sampler can be
                  decoded, then return the index of the video. If not, return the
                  index of the video replacement that can be decoded.
          """
          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"]:
              # -1 indicates random sampling.
              temporal_sample_index = -1
              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 ["val", "test"]:
              temporal_sample_index = (
                  self._spatial_temporal_idx[index]
                  // self.cfg.TEST.NUM_SPATIAL_CROPS
              )
              # 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
                  )
                  if self.cfg.TEST.NUM_SPATIAL_CROPS > 1
                  else 1
              )
              # min_scale, max_scale, crop_size = (
              #     [self.cfg.DATA.TEST_CROP_SIZE] * 3
              #     if self.cfg.TEST.NUM_SPATIAL_CROPS > 1
              #     else [self.cfg.DATA.TRAIN_JITTER_SCALES[0]] * 2
              #     + [self.cfg.DATA.TEST_CROP_SIZE]
              # )
              # # 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}) == 1
  
              min_scale, max_scale, crop_size = ([self.cfg.DATA.TEST_CROP_SIZE] * 3)
          else:
              raise NotImplementedError(
                  "Does not support {} mode".format(self.mode)
              )
          sampling_rate = utils.get_random_sampling_rate(
              self.cfg.MULTIGRID.LONG_CYCLE_SAMPLING_RATE,
              self.cfg.DATA.SAMPLING_RATE,
          )
          # Try to decode and sample a clip from a video. If the video can not be
          # decoded, repeatly find a random video replacement that can be decoded.
          for i_try in range(self._num_retries):
              video_container = None
              try:
                  video_container = container.get_video_container(
                      self._path_to_videos[index],
                      self.cfg.DATA_LOADER.ENABLE_MULTI_THREAD_DECODE,
                      self.cfg.DATA.DECODING_BACKEND,
                  )
              except Exception as e:
                  logger.info(
                      "Failed to load video from {} with error {}".format(
                          self._path_to_videos[index], e
                      )
                  )
              # Select a random video if the current video was not able to access.
              if video_container is None:
                  logger.warning(
                      "Failed to meta load video idx {} from {}; trial {}".format(
                          index, self._path_to_videos[index], i_try
                      )
                  )
                  if self.mode not in ["test"] and i_try > self._num_retries // 2:
                      # let's try another one
                      index = random.randint(0, len(self._path_to_videos) - 1)
                  continue
  
              # read video for ANet
              total_time = self._total_time[index]
              start_time = self._start_time[index]
              end_time = self._end_time[index]
              total_frames = len(video_container) / total_time * (end_time - start_time)
              start_index = int(len(video_container) / total_time * start_time)
  
              # Decode video. Meta info is used to perform selective decoding.
              if self.mode in ["val", "test"]:
                  frames = decoder.decode(
                      video_container,
                      sampling_rate,
                      self.cfg.DATA.NUM_FRAMES,
                      temporal_sample_index,
                      self.cfg.TEST.NUM_ENSEMBLE_VIEWS,
                      video_meta=self._video_meta[index],
                      target_fps=self.cfg.DATA.TARGET_FPS,
                      backend=self.cfg.DATA.DECODING_BACKEND,
                      max_spatial_scale=min_scale,
                      use_offset=self.cfg.DATA.USE_OFFSET_SAMPLING,
                      sparse=True,
                  )
              else:
                  frames = decoder.decode(
                      video_container,
                      sampling_rate,
                      self.cfg.DATA.NUM_FRAMES,
                      temporal_sample_index,
                      self.cfg.TEST.NUM_ENSEMBLE_VIEWS,
                      video_meta=self._video_meta[index],
                      target_fps=self.cfg.DATA.TARGET_FPS,
                      backend=self.cfg.DATA.DECODING_BACKEND,
                      max_spatial_scale=min_scale,
                      use_offset=self.cfg.DATA.USE_OFFSET_SAMPLING,
                      sparse=True,
                      total_frames=total_frames,
                      start_index=start_index
                  )
  
              # If decoding failed (wrong format, video is too short, and etc),
              # select another video.
              if frames is None:
                  logger.warning(
                      "Failed to decode video idx {} from {}; trial {}".format(
                          index, self._path_to_videos[index], i_try
                      )
                  )
                  if self.mode not in ["test"] and i_try > self._num_retries // 2:
                      # let's try another one
                      index = random.randint(0, len(self._path_to_videos) - 1)
                  continue
  
              if self.aug:
                  if self.cfg.AUG.NUM_SAMPLE > 1:
  
                      frame_list = []
                      label_list = []
                      index_list = []
                      for _ in range(self.cfg.AUG.NUM_SAMPLE):
                          new_frames = self._aug_frame(
                              frames,
                              spatial_sample_index,
                              min_scale,
                              max_scale,
                              crop_size,
                          )
                          label = self._labels[index]
                          new_frames = utils.pack_pathway_output(
                              self.cfg, new_frames
                          )
                          frame_list.append(new_frames)
                          label_list.append(label)
                          index_list.append(index)
                      return frame_list, label_list, index_list, {}
  
                  else:
                      frames = self._aug_frame(
                          frames,
                          spatial_sample_index,
                          min_scale,
                          max_scale,
                          crop_size,
                      )
  
              else:
                  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,
                  )
  
              label = self._labels[index]
              frames = utils.pack_pathway_output(self.cfg, frames)
              return frames, label, index, {}
          else:
              raise RuntimeError(
                  "Failed to fetch video after {} retries.".format(
                      self._num_retries
                  )
              )
  
      def _aug_frame(
          self,
          frames,
          spatial_sample_index,
          min_scale,
          max_scale,
          crop_size,
      ):
          aug_transform = create_random_augment(
              input_size=(frames.size(1), frames.size(2)),
              auto_augment=self.cfg.AUG.AA_TYPE,
              interpolation=self.cfg.AUG.INTERPOLATION,
          )
          # T H W C -> T C H W.
          frames = frames.permute(0, 3, 1, 2)
          list_img = self._frame_to_list_img(frames)
          list_img = aug_transform(list_img)
          frames = self._list_img_to_frames(list_img)
          frames = frames.permute(0, 2, 3, 1)
  
          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.
          scl, asp = (
              self.cfg.DATA.TRAIN_JITTER_SCALES_RELATIVE,
              self.cfg.DATA.TRAIN_JITTER_ASPECT_RELATIVE,
          )
          relative_scales = (
              None if (self.mode not in ["train"] or len(scl) == 0) else scl
          )
          relative_aspect = (
              None if (self.mode not in ["train"] or len(asp) == 0) else asp
          )
          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,
              aspect_ratio=relative_aspect,
              scale=relative_scales,
              motion_shift=self.cfg.DATA.TRAIN_JITTER_MOTION_SHIFT
              if self.mode in ["train"]
              else False,
          )
  
          if self.rand_erase:
              erase_transform = RandomErasing(
                  self.cfg.AUG.RE_PROB,
                  mode=self.cfg.AUG.RE_MODE,
                  max_count=self.cfg.AUG.RE_COUNT,
                  num_splits=self.cfg.AUG.RE_COUNT,
                  device="cpu",
              )
              frames = frames.permute(1, 0, 2, 3)
              frames = erase_transform(frames)
              frames = frames.permute(1, 0, 2, 3)
  
          return frames
  
      def _frame_to_list_img(self, frames):
          img_list = [
              transforms.ToPILImage()(frames[i]) for i in range(frames.size(0))
          ]
          return img_list
  
      def _list_img_to_frames(self, img_list):
          img_list = [transforms.ToTensor()(img) for img in img_list]
          return torch.stack(img_list)
  
      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)
  