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# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data pipeline elements which wrap the data N times
A RepeatedDataLoader resets its iterator less frequently. This saves time
on multi-GPU platforms and is invisible to the training loop.
NOTE: Repeating puts a block of (len(dataset) * repeats) int64s into RAM.
Do not use more repeats than necessary (e.g., 10**6 to simulate infinity).
"""
import itertools
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
class RepeatedDataLoader(DataLoader):
def __init__(self, repeats, *args, **kwargs):
self.repeats = repeats
super().__init__(*args, **kwargs)
def __iter__(self):
if self._iterator is None or self.repeats_done >= self.repeats:
self.repeats_done = 1
return super().__iter__()
else:
self.repeats_done += 1
return self._iterator
class RepeatedDistributedSampler(DistributedSampler):
def __init__(self, repeats, *args, **kwargs):
self.repeats = repeats
assert self.repeats <= 10000, "Too many repeats overload RAM."
super().__init__(*args, **kwargs)
def __iter__(self):
# Draw indices for `self.repeats` epochs forward
start_epoch = self.epoch
iters = []
for r in range(self.repeats):
self.set_epoch(start_epoch + r)
iters.append(super().__iter__())
self.set_epoch(start_epoch)
return itertools.chain.from_iterable(iters)
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