Source code for nni.nas.oneshot.pytorch.dataloader

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

from __future__ import annotations

from typing import Any

from pytorch_lightning.trainer.supporters import CombinedLoader, CombinedLoaderIterator

__all__ = ['ConcatLoader']

[docs]class ConcatLoader(CombinedLoader): """This loader is same as CombinedLoader in PyTorch-Lightning, but concatenate sub-loaders instead of loading them in parallel. Parameters ---------- loaders For example, :: { "train": DataLoader(train_dataset), "val": DataLoader(val_dataset) } In this example, the loader will first produce the batches from "train", then "val". mode Only support "min_size" for now. """ def __init__(self, loaders: dict[str, Any], mode: str = 'min_size'): # FIXME: max_cycle will make dataloaders cycle iterators, # causing extra problems. if mode != 'min_size': raise ValueError('Only min_size mode is supported now.') super().__init__(loaders, mode) def __iter__(self) -> Any: """Replace the super-class iterator with ours.""" self._try_to_patch_pytorch_dataloader() iterator = ConcatLoaderIterator(self.loaders) # handle fault tolerant restart. self.on_restart(iterator) self._iterator = iterator return iterator @staticmethod def _try_to_patch_pytorch_dataloader(): """Copied from CombinedLoader.""" from import _BaseDataLoaderIter # prevent `NotImplementedError` from PyTorch: # def __getstate__patch__(*_): return {} _BaseDataLoaderIter.__getstate__ = __getstate__patch__ # type: ignore def __len__(self) -> int: return int(sum(self._calc_num_batches(loader) for loader in self.loaders.values()))
class ConcatLoaderIterator(CombinedLoaderIterator): """Similar to CombinedLoaderIterator in Lightning, but in a concat manner.""" def __next__(self) -> Any: """Fetches the next batch from multiple data loaders, by looking for the first iterator that isn't exhausted yet. """ if not len(self.loader_iters) == len(self.loaders): raise RuntimeError('loader_iters must have the same length as loaders.') for i, (loader_name, iterator) in enumerate(self.loader_iters.items()): try: return (self.request_next_batch(iterator), loader_name) except StopIteration: if i + 1 == len(self.loader_iters): raise