Source code for torchvision.datasets.hmdb51

import glob
import os
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

from torch import Tensor

from .folder import find_classes, 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 (str or ``pathlib.Path``): 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. output_format (str, optional): The format of the output video tensors (before transforms). Can be either "THWC" (default) or "TCHW". Returns: tuple: A 3-tuple with the following entries: - video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): 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: Union[str, Path], 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, output_format: str = "THWC", ) -> None: super().__init__(root) if fold not in (1, 2, 3): raise ValueError(f"fold should be between 1 and 3, got {fold}") extensions = ("avi",) self.classes, class_to_idx = find_classes(self.root) 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, output_format=output_format, ) # we bookkeep the full version of video clips because we want to be able # to return the metadata of full version rather than the subset version of # video clips self.full_video_clips = video_clips self.fold = fold self.train = train 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) -> Dict[str, Any]: return self.full_video_clips.metadata def _select_fold(self, video_list: List[str], annotations_dir: str, fold: int, train: bool) -> List[int]: target_tag = self.TRAIN_TAG if train else self.TEST_TAG split_pattern_name = f"*test_split{fold}.txt" split_pattern_path = os.path.join(annotations_dir, split_pattern_name) annotation_paths = glob.glob(split_pattern_path) selected_files = set() 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.add(video_filename) 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) -> int: return self.video_clips.num_clips()
[docs] def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]: 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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