DataExtractor

class lightning_pose.data.utils.DataExtractor[source]

Bases: object

Helper class to extract all data from a data module.

Attributes Summary

dataset_length

Methods Summary

__call__()

Call self as a function.

get_loader()

iterate_over_dataloader(loader)

verify_labeled_loader(loader)

Attributes Documentation

dataset_length

Methods Documentation

__call__() → tuple[~torch.Annotated[~torch.Tensor, {'__torchtyping__': True, 'details': ('num_examples', -1,), 'cls_name': 'TensorType'}], ~torch.Annotated[~torch.Tensor, {'__torchtyping__': True, 'details': ('num_examples', 3, 'image_width', 'image_height',), 'cls_name': 'TensorType'}] | ~torch.Annotated[~torch.Tensor, {'__torchtyping__': True, 'details': ('num_examples', 'frames', 3, 'image_width', 'image_height',), 'cls_name': 'TensorType'}] | None][source]

Call self as a function.

get_loader() → DataLoader | SemiSupervisedDataLoaderDict[source]
iterate_over_dataloader(loader: DataLoader) → tuple[~torch.Annotated[~torch.Tensor, {'__torchtyping__': True, 'details': ('num_examples', -1,), 'cls_name': 'TensorType'}], ~torch.Annotated[~torch.Tensor, {'__torchtyping__': True, 'details': ('num_examples', 3, 'image_width', 'image_height',), 'cls_name': 'TensorType'}] | ~torch.Annotated[~torch.Tensor, {'__torchtyping__': True, 'details': ('num_examples', 'frames', 3, 'image_width', 'image_height',), 'cls_name': 'TensorType'}] | None][source]
static verify_labeled_loader(loader: DataLoader | SemiSupervisedDataLoaderDict) → DataLoader[source]
__init__(data_module: LightningDataModule, cond: Literal['train', 'test', 'val'] = 'train', extract_images: bool = False, remove_augmentations: bool = True) → None[source]
__new__(**kwargs)