|
|
|
|
|
from abc import ABCMeta
|
|
|
from typing import Callable, List, Optional, Union
|
|
|
|
|
|
import torch
|
|
|
from mmengine.dataset import BaseDataset
|
|
|
|
|
|
from mmaction.utils import ConfigType
|
|
|
|
|
|
|
|
|
class BaseActionDataset(BaseDataset, metaclass=ABCMeta):
|
|
|
"""Base class for datasets.
|
|
|
|
|
|
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, optional): Path to a directory where
|
|
|
videos are held. Defaults to None.
|
|
|
test_mode (bool): Store True when building test or validation dataset.
|
|
|
Defaults to False.
|
|
|
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``, ``Pose``,
|
|
|
``Audio``. Defaults to ``RGB``.
|
|
|
"""
|
|
|
|
|
|
def __init__(self,
|
|
|
ann_file: str,
|
|
|
pipeline: List[Union[ConfigType, Callable]],
|
|
|
data_prefix: Optional[ConfigType] = dict(prefix=''),
|
|
|
test_mode: bool = False,
|
|
|
multi_class: bool = False,
|
|
|
num_classes: Optional[int] = None,
|
|
|
start_index: int = 0,
|
|
|
modality: str = 'RGB',
|
|
|
**kwargs) -> None:
|
|
|
self.multi_class = multi_class
|
|
|
self.num_classes = num_classes
|
|
|
self.start_index = start_index
|
|
|
self.modality = modality
|
|
|
super().__init__(
|
|
|
ann_file,
|
|
|
pipeline=pipeline,
|
|
|
data_prefix=data_prefix,
|
|
|
test_mode=test_mode,
|
|
|
**kwargs)
|
|
|
|
|
|
def get_data_info(self, idx: int) -> dict:
|
|
|
"""Get annotation by index."""
|
|
|
data_info = super().get_data_info(idx)
|
|
|
data_info['modality'] = self.modality
|
|
|
data_info['start_index'] = self.start_index
|
|
|
|
|
|
if self.multi_class:
|
|
|
onehot = torch.zeros(self.num_classes)
|
|
|
onehot[data_info['label']] = 1.
|
|
|
data_info['label'] = onehot
|
|
|
|
|
|
return data_info
|
|
|
|
