| |
|
|
| 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) |
| |
| while True: |
| if not indices: |
| |
| |
| 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 |
|
|