merlin.dataloader.torch.DLDataLoader

class merlin.dataloader.torch.DLDataLoader(dataset: torch.utils.data.dataset.Dataset[T_co], batch_size: Optional[int] = 1, shuffle: Optional[bool] = None, sampler: Optional[Union[torch.utils.data.sampler.Sampler, Iterable]] = None, batch_sampler: Optional[Union[torch.utils.data.sampler.Sampler[List], Iterable[List]]] = None, num_workers: int = 0, collate_fn: Optional[Callable[[List[T]], Any]] = None, pin_memory: bool = False, drop_last: bool = False, timeout: float = 0, worker_init_fn: Optional[Callable[[int], None]] = None, multiprocessing_context=None, generator=None, *, prefetch_factor: Optional[int] = None, persistent_workers: bool = False, pin_memory_device: str = '')[source]

Bases: Generic[torch.utils.data.dataloader.T_co]

This class is an extension of the torch dataloader. It is required to support the FastAI framework.

__init__(dataset: torch.utils.data.dataset.Dataset[T_co], batch_size: Optional[int] = 1, shuffle: Optional[bool] = None, sampler: Optional[Union[torch.utils.data.sampler.Sampler, Iterable]] = None, batch_sampler: Optional[Union[torch.utils.data.sampler.Sampler[List], Iterable[List]]] = None, num_workers: int = 0, collate_fn: Optional[Callable[[List[T]], Any]] = None, pin_memory: bool = False, drop_last: bool = False, timeout: float = 0, worker_init_fn: Optional[Callable[[int], None]] = None, multiprocessing_context=None, generator=None, *, prefetch_factor: Optional[int] = None, persistent_workers: bool = False, pin_memory_device: str = '')

Methods

`__init__`(dataset[, batch_size, shuffle, …])
`check_worker_number_rationality`()

Attributes

`device`
`multiprocessing_context`

property device

dataset: torch.utils.data.dataset.Dataset[T_co]

batch_size: Optional[int]

num_workers: int

pin_memory: bool

drop_last: bool

timeout: float

sampler: Union[torch.utils.data.sampler.Sampler, Iterable]

pin_memory_device: str

prefetch_factor: Optional[int]