chainer.iterators.MultithreadIterator

class chainer.iterators.MultithreadIterator(dataset, batch_size, repeat=True, shuffle=None, n_threads=1, order_sampler=None)

Dataset iterator that loads examples in parallel.

This is an implementation of Iterator that loads examples with worker threads. It uses the standard threading module to parallelize the loading.

Note that this iterator effectively prefetches the examples for the next batch asynchronously after the current batch is returned.

This iterator saves -1 instead of None in snapshots since some serializers do not support None.

Parameters

• dataset (Dataset) – Dataset to iterate.

• batch_size (int) – Number of examples within each batch.

• repeat (bool) – If True, it infinitely loops over the dataset. Otherwise, it stops iteration at the end of the first epoch.

• shuffle (bool) – If True, the order of examples is shuffled at the beginning of each epoch. Otherwise, examples are extracted in the order of indexes. If None and no order_sampler is given, the behavior is the same as the case with shuffle=True.

• n_threads (int) – Number of worker threads.

• order_sampler (callable) – A callable that generates the order of the indices to sample in the next epoch when a epoch finishes. This function should take two arguments: the current order and the current position of the iterator. This should return the next order. The size of the order should remain constant. This option cannot be used when shuffle is not None.

Methods

__enter__()

With statement context manager method

This method does nothing by default. Implementation may override it to better handle the internal resources by with statement.

__exit__(exc_type, exc_value, traceback)

With statement context manager method

This method does nothing by default. Implementation may override it to better handle the internal resources by with statement.

__next__()

Returns the next batch.

This is a part of the iterator protocol of Python. It may raise the StopIteration exception when it stops the iteration.

__iter__()

Returns self.

finalize()

Finalizes the iterator and possibly releases the resources.

This method does nothing by default. Implementation may override it to better handle the internal resources.

next()

Returns the next batch.

This is a part of the iterator protocol of Python. It may raise the StopIteration exception when it stops the iteration.

reset()

serialize(serializer)

Serializes the internal state of the iterator.

This is a method to support the serializer protocol of Chainer.

Note

It should only serialize the internal state that changes over the iteration. It should not serialize what is set manually by users such as the batch size.

Attributes

current_position

epoch

epoch_detail

is_new_epoch

previous_epoch_detail

repeat