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e.g. resetting the state properly after ending a generator, saving state of sample buffer, skipping state of watchdog, log, gc dataset. Adapt required changes in tests. Add typing to specific saved states (instead of FlexState). Make debug output conditional.
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TODO: Check if we can add a check to detect users utilizing global RNG in taskencoder.
| def _pin_memory(self, sample: TSample) -> TSample: | ||
| return PinMemoryMixin.sample_pin_memory(sample, self._device) | ||
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| def __call__(self, sample: Future[TSample]) -> Future[TSample]: |
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I believe this method is never called, because instead _pin_memory is directly being accessed. What's the idea behind the __call__ pattern?
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See line 229 of dataloader.py:
self._pin_memory(
CallableFuture(functools.partial(self.restore_sample, sample_key))
)and line 317:
self._prefetching_samples[worker_idx].append(
self._pin_memory(worker.prefetch_next())
)… multiple shutdown/close functionalities, especially worker_init/_close
…custom_dataloader
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Implementing a custom dataloader instead of
torch.data.utils.DataLoader: