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# Copyright (c) Facebook, Inc. and its affiliates. | |
import random | |
from collections import deque | |
from typing import Any, Collection, Deque, Iterable, Iterator, List, Sequence | |
Loader = Iterable[Any] | |
def _pooled_next(iterator: Iterator[Any], pool: Deque[Any]): | |
if not pool: | |
pool.extend(next(iterator)) | |
return pool.popleft() | |
class CombinedDataLoader: | |
""" | |
Combines data loaders using the provided sampling ratios | |
""" | |
BATCH_COUNT = 100 | |
def __init__(self, loaders: Collection[Loader], batch_size: int, ratios: Sequence[float]): | |
self.loaders = loaders | |
self.batch_size = batch_size | |
self.ratios = ratios | |
def __iter__(self) -> Iterator[List[Any]]: | |
iters = [iter(loader) for loader in self.loaders] | |
indices = [] | |
pool = [deque()] * len(iters) | |
# infinite iterator, as in D2 | |
while True: | |
if not indices: | |
# just a buffer of indices, its size doesn't matter | |
# as long as it's a multiple of batch_size | |
k = self.batch_size * self.BATCH_COUNT | |
indices = random.choices(range(len(self.loaders)), self.ratios, k=k) | |
try: | |
batch = [_pooled_next(iters[i], pool[i]) for i in indices[: self.batch_size]] | |
except StopIteration: | |
break | |
indices = indices[self.batch_size :] | |
yield batch | |