dataloader {torch}

Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.

Description

Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.

Usage

dataloader(
  dataset,
  batch_size = 1,
  shuffle = FALSE,
  sampler = NULL,
  batch_sampler = NULL,
  num_workers = 0,
  collate_fn = NULL,
  pin_memory = FALSE,
  drop_last = FALSE,
  timeout = -1,
  worker_init_fn = NULL,
  worker_globals = NULL,
  worker_packages = NULL
)

Arguments

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, optional): defines the strategy to draw samples from the dataset. If specified, shuffle must be False. Custom samplers can be created with sampler().

batch_sampler

(Sampler, optional): like sampler, but returns a batch of indices at a time. Mutually exclusive with batch_size, shuffle, sampler, and drop_last. Custom samplers can be created with sampler().

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.

pin_memory

(bool, optional): If TRUE, the data loader will copy tensors into CUDA pinned memory before returning them. If your data elements are a custom type, or your collate_fn returns a batch that is a custom type see the example below.

drop_last

(bool, optional): set to TRUE to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If FALSE and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: FALSE)

timeout

(numeric, optional): if positive, the timeout value for collecting a batch from workers. -1 means no timeout. (default: -1)

worker_init_fn

(callable, optional): If not NULL, this will be called on each worker subprocess with the worker id (an int in ⁠[1, num_workers]⁠) as input, after seeding and before data loading. (default: NULL)

worker_globals

(list or character vector, optional) only used when num_workers > 0. If a character vector, then objects with those names are copied from the global environment to the workers. If a named list, then this list is copied and attached to the worker global environment. Notice that the objects are copied only once at the worker initialization.

worker_packages

(character vector, optional) Only used if num_workers > 0 optional character vector naming packages that should be loaded in each worker.

Parallel data loading

When using num_workers > 0 data loading will happen in parallel for each worker. Note that batches are taken in parallel and not observations.

The worker initialization process happens in the following order:

• num_workers R sessions are initialized.

Then in each worker we perform the following actions:

• the torch library is loaded.

• a random seed is set both using set.seed() and using torch_manual_seed.

• packages passed to the worker_packages argument are loaded.

• objects passed trough the worker_globals parameters are copied into the global environment.

• the worker_init function is ran with an id argument.

• the dataset fetcher is copied to the worker.

See Also

dataset(), sampler()