DataLoader

class tensorplay.utils.data.DataLoader(dataset: Dataset[_T_co], batch_size: int | None = 1, shuffle: bool | None = None, sampler: Sampler | Iterable | None = None, batch_sampler: Sampler | Iterable | None = None, num_workers: int = 0, collate_fn: Callable[[List[Any]], Any] | None = None, pin_memory: bool = False, drop_last: bool = False, timeout: float = 0, worker_init_fn: Callable[[int], None] | None = None, multiprocessing_context=None, generator: Generator | None = None, *, prefetch_factor: int | None = None, persistent_workers: bool = False, in_order: bool = True, device: str | None = None)[source]

Data loader combines a dataset and a sampler, and provides an iterable over the given dataset.

The DataLoader supports both map-style and iterable-style datasets with single- or multi-process loading, customizing loading order and optional automatic batching (collation).

Parameters:

• dataset (Dataset) – dataset from which to load the data.

• batch_size (int, optional) – how many samples per batch to load (default: 1).

• shuffle (bool, optional) – set to True to have the data reshuffled at every epoch (default: False).

• sampler (Sampler or Iterable, optional) – defines the strategy to draw samples from the dataset. Can be any Iterable with __len__ implemented. If specified, shuffle must not be specified.

• batch_sampler (Sampler or Iterable, optional) – like sampler, but returns a batch of indices at a time. Mutually exclusive with batch_size, shuffle, sampler, and drop_last.

• num_workers (int, optional) – how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0)

• collate_fn (Callable, optional) – merges a list of samples to form a mini-batch of Tensor(s). Used when using batched loading from a map-style dataset.

• pin_memory (bool, optional) – If True, the data loader will copy Tensors into CUDA page-locked host memory before returning them.

• drop_last (bool, optional) – set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. (default: False)

• timeout (numeric, optional) – if positive, the timeout value for collecting a batch from workers. Should always be non-negative. (default: 0)

• worker_init_fn (Callable, optional) – If not None, this will be called on each worker subprocess with the worker id (an int in [0, num_workers - 1]) as input, before data loading. (default: None)

• multiprocessing_context (str or context, optional) – start method or multiprocessing context used to spawn the workers, e.g., "fork" or "spawn". If None, the default context of the platform is used. (default: None)

• generator (Generator, optional) – If not None, this RNG will be used by RandomSampler to generate random indexes. (default: None)

• prefetch_factor (int, optional) – Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. (default: 2 when num_workers > 0; otherwise must be None)

• persistent_workers (bool, optional) – If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. (default: False)

• in_order (bool, optional) – If False, the data loader will not enforce that batches returned from multiprocessing workers are provided in the order the sampler produced them. This enables faster delivery of batches that complete early, at the cost of batch order no longer being deterministic. (default: True)

• device (str, optional) – device to move batches to after collation.