123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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r"""
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BasePredictionWriter
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====================
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Aids in saving predictions
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"""
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from typing import Any, Optional, Sequence
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.utilities import LightningEnum
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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class WriteInterval(LightningEnum):
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BATCH = "batch"
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EPOCH = "epoch"
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BATCH_AND_EPOCH = "batch_and_epoch"
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@property
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def on_batch(self) -> bool:
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return self in (self.BATCH, self.BATCH_AND_EPOCH)
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@property
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def on_epoch(self) -> bool:
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return self in (self.EPOCH, self.BATCH_AND_EPOCH)
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class BasePredictionWriter(Callback):
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"""
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Base class to implement how the predictions should be stored.
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Args:
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write_interval: When to write.
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Example::
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import torch
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from pytorch_lightning.callbacks import BasePredictionWriter
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class CustomWriter(BasePredictionWriter):
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def __init__(self, output_dir: str, write_interval: str):
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super().__init__(write_interval)
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self.output_dir
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def write_on_batch_end(
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self, trainer, pl_module: 'LightningModule', prediction: Any, batch_indices: List[int], batch: Any,
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batch_idx: int, dataloader_idx: int
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):
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torch.save(prediction, os.path.join(self.output_dir, dataloader_idx, f"{batch_idx}.pt"))
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def write_on_epoch_end(
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self, trainer, pl_module: 'LightningModule', predictions: List[Any], batch_indices: List[Any]
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):
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torch.save(predictions, os.path.join(self.output_dir, "predictions.pt"))
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"""
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def __init__(self, write_interval: str = "batch") -> None:
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if write_interval not in list(WriteInterval):
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raise MisconfigurationException(f"`write_interval` should be one of {[i.value for i in WriteInterval]}.")
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self.interval = WriteInterval(write_interval)
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def write_on_batch_end(
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self,
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trainer: "pl.Trainer",
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pl_module: "pl.LightningModule",
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prediction: Any,
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batch_indices: Optional[Sequence[int]],
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batch: Any,
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batch_idx: int,
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dataloader_idx: int,
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) -> None:
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"""Override with the logic to write a single batch."""
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raise NotImplementedError()
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def write_on_epoch_end(
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self,
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trainer: "pl.Trainer",
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pl_module: "pl.LightningModule",
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predictions: Sequence[Any],
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batch_indices: Optional[Sequence[Any]],
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) -> None:
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"""Override with the logic to write all batches."""
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raise NotImplementedError()
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def on_predict_batch_end(
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self,
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trainer: "pl.Trainer",
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pl_module: "pl.LightningModule",
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outputs: Any,
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batch: Any,
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batch_idx: int,
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dataloader_idx: int,
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) -> None:
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if not self.interval.on_batch:
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return
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is_distributed = trainer.accelerator_connector.is_distributed
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batch_indices = trainer.predict_loop.epoch_loop.current_batch_indices if is_distributed else None
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self.write_on_batch_end(trainer, pl_module, outputs, batch_indices, batch, batch_idx, dataloader_idx)
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def on_predict_epoch_end(
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self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: Sequence[Any]
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) -> None:
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if not self.interval.on_epoch:
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return
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is_distributed = trainer.accelerator_connector.is_distributed
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epoch_batch_indices = trainer.predict_loop.epoch_batch_indices if is_distributed else None
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self.write_on_epoch_end(trainer, pl_module, trainer.predict_loop.predictions, epoch_batch_indices)
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