Source code for torchvision.datasets.ucf101

import glob
import os

from .utils import list_dir
from .folder import 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``. 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 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: 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, annotation_path, frames_per_clip, step_between_clips=1, frame_rate=None, fold=1, train=True, transform=None, _precomputed_metadata=None, num_workers=1, _video_width=0, _video_height=0, _video_min_dimension=0, _audio_samples=0): 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 classes = list(sorted(list_dir(root))) class_to_idx = {classes[i]: i for i in range(len(classes))} self.samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file=None) self.classes = classes 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, ) self.video_clips_metadata = video_clips.metadata 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): return self.video_clips_metadata def _select_fold(self, video_list, annotation_path, fold, train): name = "train" if train else "test" name = "{}list{:02d}.txt".format(name, fold) f = os.path.join(annotation_path, name) selected_files = [] with open(f, "r") as fid: data = fid.readlines() data = [x.strip().split(" ") for x in data] data = [os.path.join(self.root, x[0]) for x in data] selected_files.extend(data) selected_files = set(selected_files) indices = [i for i in range(len(video_list)) if video_list[i] in selected_files] return indices def __len__(self): return self.video_clips.num_clips() def __getitem__(self, idx): 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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