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All datasets that represent a map from keys to data samples should subclass this class. All subclasses should overwrite the .getitem() method, which supports fetching a data sample for a given key. Subclasses could also optionally overwrite .length(), which is expected to return the size of the dataset (e.g. number of samples) used by many sampler implementations and the default options of dataloader().

Usage

dataset(
  name = NULL,
  inherit = Dataset,
  ...,
  private = NULL,
  active = NULL,
  parent_env = parent.frame()
)

Arguments

name

a name for the dataset. It it's also used as the class for it.

inherit

you can optionally inherit from a dataset when creating a new dataset.

...

public methods for the dataset class

private

passed to R6::R6Class().

active

passed to R6::R6Class().

parent_env

An environment to use as the parent of newly-created objects.

Value

The output is a function f with class dataset_generator. Calling f()creates a new instance of the R6 class dataset. The R6 class is stored in the enclosing environment of f and can also be accessed through fs attribute Dataset.

Note

dataloader() by default constructs a index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

Get a batch of observations

By default datasets are iterated by returning each observation/item individually. Often it's possible to have an optimized implementation to take a batch of observations (eg, subsetting a tensor by multiple indexes at once is faster than subsetting once for each index), in this case you can implement a .getbatch method that will be used instead of .getitem when getting a batch of observations within the dataloader. .getbatch must work for batches of size larger or equal to 1. For more on this see the the vignette("loading-data").