Source code for mmaction.datasets.video_dataset

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Callable, List, Optional, Union

from mmengine.fileio import exists, list_from_file

from mmaction.registry import DATASETS
from mmaction.utils import ConfigType
from .base import BaseActionDataset

[docs]@DATASETS.register_module() class VideoDataset(BaseActionDataset): """Video dataset for action recognition. The dataset loads raw videos and apply specified transforms to return a dict containing the frame tensors and other information. The ann_file is a text file with multiple lines, and each line indicates a sample video with the filepath and label, which are split with a whitespace. Example of a annotation file: .. code-block:: txt some/path/000.mp4 1 some/path/001.mp4 1 some/path/002.mp4 2 some/path/003.mp4 2 some/path/004.mp4 3 some/path/005.mp4 3 Args: ann_file (str): Path to the annotation file. pipeline (List[Union[dict, ConfigDict, Callable]]): A sequence of data transforms. data_prefix (dict or ConfigDict): Path to a directory where videos are held. Defaults to ``dict(video='')``. multi_class (bool): Determines whether the dataset is a multi-class dataset. Defaults to False. num_classes (int, optional): Number of classes of the dataset, used in multi-class datasets. Defaults to None. start_index (int): Specify a start index for frames in consideration of different filename format. However, when taking videos as input, it should be set to 0, since frames loaded from videos count from 0. Defaults to 0. modality (str): Modality of data. Support ``'RGB'``, ``'Flow'``. Defaults to ``'RGB'``. test_mode (bool): Store True when building test or validation dataset. Defaults to False. delimiter (str): Delimiter for the annotation file. Defaults to ``' '`` (whitespace). """ def __init__(self, ann_file: str, pipeline: List[Union[dict, Callable]], data_prefix: ConfigType = dict(video=''), multi_class: bool = False, num_classes: Optional[int] = None, start_index: int = 0, modality: str = 'RGB', test_mode: bool = False, delimiter: str = ' ', **kwargs) -> None: self.delimiter = delimiter super().__init__( ann_file, pipeline=pipeline, data_prefix=data_prefix, multi_class=multi_class, num_classes=num_classes, start_index=start_index, modality=modality, test_mode=test_mode, **kwargs)
[docs] def load_data_list(self) -> List[dict]: """Load annotation file to get video information.""" exists(self.ann_file) data_list = [] fin = list_from_file(self.ann_file) for line in fin: line_split = line.strip().split(self.delimiter) if self.multi_class: assert self.num_classes is not None filename, label = line_split[0], line_split[1:] label = list(map(int, label)) # add fake label for inference datalist without label elif len(line_split) == 1: filename, label = line_split[0], -1 else: filename, label = line_split label = int(label) if self.data_prefix['video'] is not None: filename = osp.join(self.data_prefix['video'], filename) data_list.append(dict(filename=filename, label=label)) return data_list
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