Class DataLoader

yolox/data/dataloading.py:29–161

Lightnet dataloader that enables on the fly resizing of the images. See :class:`torch.utils.data.DataLoader` for more information on the arguments. Check more on the following website: https://gitlab.com/EAVISE/lightnet/-/blob/master/lightnet/data/_dataloading.py Note:

class DataLoader(torchDataLoader):
    """
    Lightnet dataloader that enables on the fly resizing of the images.
    See :class:`torch.utils.data.DataLoader` for more information on the arguments.
    Check more on the following website:
    https://gitlab.com/EAVISE/lightnet/-/blob/master/lightnet/data/_dataloading.py

    Note:
        This dataloader only works with :class:`lightnet.data.Dataset` based datasets.

    Example:
        >>> class CustomSet(ln.data.Dataset):
        ...     def __len__(self):
        ...         return 4
        ...     @ln.data.Dataset.resize_getitem
        ...     def __getitem__(self, index):
        ...         # Should return (image, anno) but here we return (input_dim,)
        ...         return (self.input_dim,)
        >>> dl = ln.data.DataLoader(
        ...     CustomSet((200,200)),
        ...     batch_size = 2,
        ...     collate_fn = ln.data.list_collate   # We want the data to be grouped as a list
        ... )
        >>> dl.dataset.input_dim    # Default input_dim
        (200, 200)
        >>> for d in dl:
        ...     d
        [[(200, 200), (200, 200)]]
        [[(200, 200), (200, 200)]]
        >>> dl.change_input_dim(320, random_range=None)
        (320, 320)
        >>> for d in dl:
        ...     d
        [[(320, 320), (320, 320)]]
        [[(320, 320), (320, 320)]]
        >>> dl.change_input_dim((480, 320), random_range=None)
        (480, 320)
        >>> for d in dl:
        ...     d
        [[(480, 320), (480, 320)]]
        [[(480, 320), (480, 320)]]
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.__initialized = False
        shuffle = False
        batch_sampler = None
        if len(args) > 5:
            shuffle = args[2]
            sampler = args[3]
            batch_sampler = args[4]
        elif len(args) > 4:
            shuffle = args[2]
            sampler = args[3]
            if "batch_sampler" in kwargs:
                batch_sampler = kwargs["batch_sampler"]
        elif len(args) > 3: