Source code for torchvision.datasets.ucf101

import os
from typing import Any, Dict, List, Tuple, Optional, Callable
from torch import Tensor

from .folder import find_classes, make_dataset
from .video_utils import VideoClips
from .vision import VisionDataset

[docs]class UCF101(VisionDataset): """ `UCF101 <>`_ dataset. UCF101 is an action recognition video dataset. This dataset consider every video as a collection of video clips of fixed size, specified by ``frames_per_clip``, where the step in frames between each clip is given by ``step_between_clips``. The dataset itself can be downloaded from the dataset website; annotations that ``annotation_path`` should be pointing to can be downloaded from `here <>`. To give an example, for 2 videos with 10 and 15 frames respectively, if ``frames_per_clip=5`` and ``step_between_clips=5``, the dataset size will be (2 + 3) = 5, where the first two elements will come from video 1, and the next three elements from video 2. Note that we drop clips which do not have exactly ``frames_per_clip`` elements, so not all frames in a video might be present. Internally, it uses a VideoClips object to handle clip creation. Args: root (string): Root directory of the UCF101 Dataset. annotation_path (str): path to the folder containing the split files; see docstring above for download instructions of these files frames_per_clip (int): number of frames in a clip. step_between_clips (int, optional): number of frames between each clip. fold (int, optional): which fold to use. Should be between 1 and 3. train (bool, optional): if ``True``, creates a dataset from the train split, otherwise from the ``test`` split. transform (callable, optional): A function/transform that takes in a TxHxWxC video and returns a transformed version. Returns: tuple: A 3-tuple with the following entries: - video (Tensor[T, H, W, C]): the `T` video frames - audio(Tensor[K, L]): the audio frames, where `K` is the number of channels and `L` is the number of points - label (int): class of the video clip """ def __init__( self, root: str, annotation_path: str, frames_per_clip: int, step_between_clips: int = 1, frame_rate: Optional[int] = None, fold: int = 1, train: bool = True, transform: Optional[Callable] = None, _precomputed_metadata: Optional[Dict[str, Any]] = None, num_workers: int = 1, _video_width: int = 0, _video_height: int = 0, _video_min_dimension: int = 0, _audio_samples: int = 0 ) -> None: super(UCF101, self).__init__(root) if not 1 <= fold <= 3: raise ValueError("fold should be between 1 and 3, got {}".format(fold)) extensions = ('avi',) self.fold = fold self.train = train self.classes, class_to_idx = find_classes(self.root) self.samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file=None) video_list = [x[0] for x in self.samples] video_clips = VideoClips( video_list, frames_per_clip, step_between_clips, frame_rate, _precomputed_metadata, num_workers=num_workers, _video_width=_video_width, _video_height=_video_height, _video_min_dimension=_video_min_dimension, _audio_samples=_audio_samples, ) # we bookkeep the full version of video clips because we want to be able # to return the meta data of full version rather than the subset version of # video clips self.full_video_clips = video_clips self.indices = self._select_fold(video_list, annotation_path, fold, train) self.video_clips = video_clips.subset(self.indices) self.transform = transform @property def metadata(self) -> Dict[str, Any]: return self.full_video_clips.metadata def _select_fold(self, video_list: List[str], annotation_path: str, fold: int, train: bool) -> List[int]: name = "train" if train else "test" name = "{}list{:02d}.txt".format(name, fold) f = os.path.join(annotation_path, name) selected_files = set() with open(f, "r") as fid: data = fid.readlines() data = [x.strip().split(" ")[0] for x in data] data = [os.path.join(self.root, x) for x in data] selected_files.update(data) indices = [i for i in range(len(video_list)) if video_list[i] in selected_files] return indices def __len__(self) -> int: return self.video_clips.num_clips()
[docs] def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]: video, audio, info, video_idx = self.video_clips.get_clip(idx) label = self.samples[self.indices[video_idx]][1] if self.transform is not None: video = self.transform(video) return video, audio, label


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