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Source code for pytorch_lightning.loops.dataloader.dataloader_loop

# Copyright The PyTorch Lightning team.
#
# 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.

from abc import abstractmethod
from typing import Any, Sequence

from torch.utils.data import DataLoader

from pytorch_lightning.loops.base import Loop
from pytorch_lightning.trainer.progress import DataLoaderProgress


[docs]class DataLoaderLoop(Loop): """Base class to loop over all dataloaders.""" def __init__(self): super().__init__() self.dataloader_progress = DataLoaderProgress() @property @abstractmethod def dataloaders(self) -> Sequence[DataLoader]: """Returns the dataloaders to loop over.""" @property def current_dataloader_idx(self) -> int: """Returns the index of the current dataloader.""" return self.dataloader_progress.current.ready - 1 @property def current_dataloader(self) -> DataLoader: """Returns the current dataloader.""" return self.dataloaders[self.current_dataloader_idx] @property def num_dataloaders(self) -> int: """Returns the number of dataloaders present.""" return len(self.dataloaders) if self.dataloaders is not None else 0 @property def done(self) -> bool: """Returns whether all dataloaders have been processed.""" return self.dataloader_progress.current.completed >= self.num_dataloaders
[docs] def reset(self) -> None: """Resets the internal state.""" if not self.restarting: self.dataloader_progress.reset_on_run() else: self.dataloader_progress.reset_on_restart()
[docs] def on_advance_start(self, *args: Any, **kwargs: Any) -> None: self.dataloader_progress.increment_ready()
[docs] def on_advance_end(self) -> None: self.dataloader_progress.increment_completed()

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