FM / data

/usr/local/lib64/python3.6/site-packages/torch/distributed/elastic/utils/data/

/usr/local/lib64/python3.6/site-packages/torch/distributed/elastic/utils/data

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
cycling_iterator.py	1376	0644	edit dl rm
elastic_distributed_sampler.py	2452	0644	edit dl rm
__init__.py	372	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/distributed/elastic/utils/data/cycling_iterator.py (1376B)

#!/usr/bin/env python3

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

class CyclingIterator:
    """
    An iterator decorator that cycles through the
    underlying iterator "n" times. Useful to "unroll"
    the dataset across multiple training epochs.

The generator function is called as ``generator_fn(epoch)``
    to obtain the underlying iterator, where ``epoch`` is a
    number less than or equal to ``n`` representing the ``k``th cycle

For example if ``generator_fn`` always returns ``[1,2,3]``
    then ``CyclingIterator(n=2, generator_fn)`` will iterate through
    ``[1,2,3,1,2,3]``
    """

def __init__(self, n: int, generator_fn, start_epoch=0):
        self._n = n
        self._epoch = start_epoch
        self._generator_fn = generator_fn
        self._iter = generator_fn(self._epoch)

def __iter__(self):
        return self

def __next__(self):
        try:
            return next(self._iter)
        except StopIteration as eod:  # eod == end of data
            if self._epoch < self._n - 1:
                self._epoch += 1
                self._iter = self._generator_fn(self._epoch)
                return self.__next__()
            else:
                raise eod

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