avalanche.benchmarks.utils.data_loader.GroupBalancedInfiniteDataLoader

class avalanche.benchmarks.utils.data_loader.GroupBalancedInfiniteDataLoader(datasets: ~typing.Sequence[~avalanche.benchmarks.utils.classification_dataset.make_classification_dataset], collate_mbatches=<function classification_collate_mbatches_fn>, distributed_sampling: bool = True, **kwargs)[source]

Data loader that balances data from multiple datasets emitting an infinite stream.

__init__(datasets: ~typing.Sequence[~avalanche.benchmarks.utils.classification_dataset.make_classification_dataset], collate_mbatches=<function classification_collate_mbatches_fn>, distributed_sampling: bool = True, **kwargs)[source]

Data loader that balances data from multiple datasets emitting an infinite stream. Mini-batches emitted by this dataloader are created by collating together mini-batches from each group. It may be used to balance data among classes, experiences, tasks, and so on. :param datasets: an instance of AvalancheDataset. :param collate_mbatches: function that given a sequence of mini-batches

(one for each task) combines them into a single mini-batch. Used to combine the mini-batches obtained separately from each task.

Parameters:

kwargs – data loader arguments used to instantiate the loader for each group separately. See pytorch DataLoader.

Methods

__init__(datasets[, collate_mbatches, ...])

Data loader that balances data from multiple datasets emitting an infinite stream. Mini-batches emitted by this dataloader are created by collating together mini-batches from each group. It may be used to balance data among classes, experiences, tasks, and so on. :param datasets: an instance of AvalancheDataset. :param collate_mbatches: function that given a sequence of mini-batches (one for each task) combines them into a single mini-batch. Used to combine the mini-batches obtained separately from each task. :param kwargs: data loader arguments used to instantiate the loader for each group separately. See pytorch DataLoader.