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"""Log model graph to respective logger."""
# Copyright (C) 2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import torch
from lightning.pytorch import Callback, LightningModule, Trainer
from anomalib.loggers import AnomalibCometLogger, AnomalibTensorBoardLogger, AnomalibWandbLogger
class GraphLogger(Callback):
"""Log model graph to respective logger.
Examples:
Log model graph to Tensorboard
>>> from anomalib.callbacks import GraphLogger
>>> from anomalib.loggers import AnomalibTensorBoardLogger
>>> from anomalib.engine import Engine
...
>>> logger = AnomalibTensorBoardLogger()
>>> callbacks = [GraphLogger()]
>>> engine = Engine(logger=logger, callbacks=callbacks)
Log model graph to Comet
>>> from anomalib.loggers import AnomalibCometLogger
>>> from anomalib.engine import Engine
...
>>> logger = AnomalibCometLogger()
>>> callbacks = [GraphLogger()]
>>> engine = Engine(logger=logger, callbacks=callbacks)
"""
def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Log model graph to respective logger.
Args:
trainer: Trainer object which contans reference to loggers.
pl_module: LightningModule object which is logged.
"""
for logger in trainer.loggers:
if isinstance(logger, AnomalibWandbLogger):
# NOTE: log graph gets populated only after one backward pass. This won't work for models which do not
# require training such as Padim
logger.watch(pl_module, log_graph=True, log="all")
break
def on_train_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Unwatch model if configured for wandb and log it model graph in Tensorboard if specified.
Args:
trainer: Trainer object which contans reference to loggers.
pl_module: LightningModule object which is logged.
"""
for logger in trainer.loggers:
if isinstance(logger, AnomalibCometLogger | AnomalibTensorBoardLogger):
logger.log_graph(pl_module, input_array=torch.ones((1, 3, 256, 256)))
elif isinstance(logger, AnomalibWandbLogger):
logger.experiment.unwatch(pl_module)
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