Dataloaders
Reference information for the Dataloader classes.
eva.data.DataLoader
dataclass
The DataLoader combines a dataset and a sampler.
It provides an iterable over the given dataset.
batch_size: int | None = 1
class-attribute
instance-attribute
How many samples per batch to load.
Set to None for iterable dataset where dataset produces batches.
shuffle: bool = False
class-attribute
instance-attribute
Whether to shuffle the data at every epoch.
sampler: samplers.Sampler | None = None
class-attribute
instance-attribute
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: samplers.Sampler | None = None
class-attribute
instance-attribute
Like sampler, but returns a batch of indices at a time.
Mutually exclusive with batch_size, shuffle, sampler and drop_last.
num_workers: int = multiprocessing.cpu_count()
class-attribute
instance-attribute
How many workers to use for loading the data.
By default, it will use the number of CPUs available.
collate_fn: Callable | None = None
class-attribute
instance-attribute
The batching process.
pin_memory: bool = True
class-attribute
instance-attribute
Will copy Tensors into CUDA pinned memory before returning them.
drop_last: bool = False
class-attribute
instance-attribute
Drops the last incomplete batch.
persistent_workers: bool = True
class-attribute
instance-attribute
Will keep the worker processes after a dataset has been consumed once.
prefetch_factor: int | None = 2
class-attribute
instance-attribute
Number of batches loaded in advance by each worker.