Source code for torchvision.datasets.hmdb51

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 HMDB51(VisionDataset): """ `HMDB51 <>`_ dataset. HMDB51 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 HMDB51 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): 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 """ data_url = "" splits = { "url": "", "md5": "15e67781e70dcfbdce2d7dbb9b3344b5" } TRAIN_TAG = 1 TEST_TAG = 2 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(HMDB51, self).__init__(root) if fold not in (1, 2, 3): raise ValueError("fold should be between 1 and 3, got {}".format(fold)) extensions = ('avi',) classes = sorted(list_dir(root)) class_to_idx = {class_: i for (i, class_) in enumerate(classes)} self.samples = make_dataset( self.root, class_to_idx, extensions, ) video_paths = [path for (path, _) in self.samples] video_clips = VideoClips( video_paths, 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.fold = fold self.train = train self.classes = classes self.video_clips_metadata = video_clips.metadata self.indices = self._select_fold(video_paths, 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, annotations_dir, fold, train): target_tag = self.TRAIN_TAG if train else self.TEST_TAG split_pattern_name = "*test_split{}.txt".format(fold) split_pattern_path = os.path.join(annotations_dir, split_pattern_name) annotation_paths = glob.glob(split_pattern_path) selected_files = [] for filepath in annotation_paths: with open(filepath) as fid: lines = fid.readlines() for line in lines: video_filename, tag_string = line.split() tag = int(tag_string) if tag == target_tag: selected_files.append(video_filename) selected_files = set(selected_files) indices = [] for video_index, video_path in enumerate(video_list): if os.path.basename(video_path) in selected_files: indices.append(video_index) return indices def __len__(self): return self.video_clips.num_clips() def __getitem__(self, idx): video, audio, _, video_idx = self.video_clips.get_clip(idx) sample_index = self.indices[video_idx] _, class_index = self.samples[sample_index] if self.transform is not None: video = self.transform(video) return video, audio, class_index


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