content: #!/usr/bin/env python3
  # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  
  import json
  import numpy as np
  import os
  import random
  from itertools import chain as chain
  import torch
  from torchvision import transforms
  import torch.utils.data
  
  import slowfast.utils.logging as logging
  
  from . import utils as utils
  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 Sth(torch.utils.data.Dataset):
      """
      Something-Something video loader. Construct the Sth 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 Something-Something data (frame paths, labels, etc. ) to a given
          Dataset object. The dataset could be downloaded from Something-Something
          official website (https://20bn.com/datasets/something-something).
          Please see datasets/DATASET.md for more information about the data format.
          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 for reading frames from disk.
          """
          # Only support train, val, and test mode.
          assert mode in [
              "train",
              "val",
              "test",
          ], "Split '{}' not supported for Something-Something V1".format(mode)
          self.mode = mode
          self.cfg = cfg
  
          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 Something-Something {}...".format(mode))
          self._construct_loader()
  
          self.aug = False
          self.rand_erase = False
          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,
              self.cfg.DATA.LABEL_PATH_TEMPLATE.format(
                  "train" if self.mode == "train" else "validation"
              ),
          )
          tmp = [x.strip().split(' ') for x in open(path_to_file)]
          self._path_to_videos = list()
          self._labels = list()
          for item in tmp:
              path = item[0]
              num_frames = int(item[1])
              label = int(item[2])
              # path_list = list()
              # for i in range(num_frames):
              #     path_list.append(os.path.join(
              #         self.cfg.DATA.PATH_PREFIX,
              #         path,
              #         self.cfg.DATA.IMAGE_TEMPLATE.format(i+1)
              #     ))
              # 0: path, 1: num_frames
              self._path_to_videos.append([path, num_frames])
              self._labels.append(label)
  
          # Extend self when self._num_clips > 1 (during testing).
          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._path_to_videos))
                  ]
              )
          )
          logger.info(
              "Something-Something dataloader constructed "
              " (size: {}) from {}".format(
                  len(self._path_to_videos), path_to_file
              )
          )
  
      def get_seq_frames(self, index, temporal_sample_index):
          """
          Given the video index, return the list of sampled frame indexes.
          Args:
              index (int): the video index.
              temporal_sample_index (int): temporal sample index.
          Returns:
              seq (list): the indexes of frames of sampled from the video.
          """
          num_frames = self.cfg.DATA.NUM_FRAMES
          video_length = self._path_to_videos[index][1]
  
          seg_size = float(video_length - 1) / num_frames
          seq = []
          # index from 1, must add 1
          if self.mode == "train":
              for i in range(num_frames):
                  start = int(np.round(seg_size * i))
                  end = int(np.round(seg_size * (i + 1)))
                  seq.append(random.randint(start, end) + 1)
          else:
              duration = seg_size / (self.cfg.TEST.NUM_ENSEMBLE_VIEWS + 1)
              for i in range(num_frames):
                  start = int(np.round(seg_size * i))
                  end = int(np.round(seg_size * (i + 1)))
                  frame_index = start + int(duration * (temporal_sample_index + 1))
                  seq.append(frame_index + 1)
          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"]:
              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
              )
              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)
              )
  
          if self.mode in ["test"]:
              seq = self.get_seq_frames(index, temporal_sample_index)
          else:
              seq = self.get_seq_frames(index, 0)
  
          path_template = os.path.join(
                              self.cfg.DATA.PATH_PREFIX,
                              self._path_to_videos[index][0],
                              self.cfg.DATA.IMAGE_TEMPLATE
                          )
          frames = torch.as_tensor(
              utils.retry_load_images(
                  [path_template.format(frame) for frame in seq],
                  self._num_retries,
          ))
  
          label = self._labels[index]
  
          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,
                      )
                      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,
              )
  
          frames = utils.pack_pathway_output(self.cfg, frames)
          return frames, label, index, {}
  
      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)
  