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
.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
dataset( name = NULL, inherit = Dataset, ..., private = NULL, active = NULL, parent_env = parent.frame() )
a name for the dataset. It it's also used as the class for it.
you can optionally inherit from a dataset when creating a new dataset.
public methods for the dataset class
An environment to use as the parent of newly-created objects.
The output is a function
f with class
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
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
that will be used instead of
.getitem when getting a batch of observations within
.getbatch must work for batches of size larger or equal to 1. For more
on this see the the
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.
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