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"""Weights and Biases callbacks."""
from pathlib import Path
import wandb
from pytorch_lightning import Callback, LightningModule, Trainer
from pytorch_lightning.loggers import Logger, WandbLogger
from pytorch_lightning.utilities import rank_zero_only
from torch.utils.data import DataLoader
def get_wandb_logger(trainer: Trainer) -> WandbLogger:
"""Safely get W&B logger from Trainer."""
for logger in trainer.loggers:
if isinstance(logger, WandbLogger):
return logger
raise Exception("Weight and Biases logger not found for some reason...")
class WatchModel(Callback):
"""Make W&B watch the model at the beginning of the run."""
def __init__(
self,
log_params: str = "gradients",
log_freq: int = 100,
log_graph: bool = False,
) -> None:
self.log_params = log_params
self.log_freq = log_freq
self.log_graph = log_graph
@rank_zero_only
def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Watches model weights with wandb."""
logger = get_wandb_logger(trainer)
logger.watch(
model=trainer.model,
log=self.log_params,
log_freq=self.log_freq,
log_graph=self.log_graph,
)
class UploadConfigAsArtifact(Callback):
"""Upload all *.py files to W&B as an artifact, at the beginning of the run."""
def __init__(self) -> None:
self.config_dir = Path(".hydra/")
@rank_zero_only
def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Uploads project code as an artifact."""
logger = get_wandb_logger(trainer)
experiment = logger.experiment
artifact = wandb.Artifact("experiment-config", type="config")
for filepath in self.config_dir.rglob("*.yaml"):
artifact.add_file(str(filepath))
experiment.use_artifact(artifact)
class UploadCheckpointsAsArtifact(Callback):
"""Upload checkpoint to wandb as an artifact, at the end of a run."""
def __init__(
self, ckpt_dir: str = "checkpoints/", upload_best_only: bool = False
) -> None:
self.ckpt_dir = Path(ckpt_dir)
self.upload_best_only = upload_best_only
@rank_zero_only
def on_train_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Uploads model checkpoint to W&B."""
logger = get_wandb_logger(trainer)
experiment = logger.experiment
ckpts = wandb.Artifact("experiment-ckpts", type="checkpoints")
if self.upload_best_only:
ckpts.add_file(trainer.checkpoint_callback.best_model_path)
else:
for ckpt in (self.ckpt_dir).rglob("*.ckpt"):
ckpts.add_file(ckpt)
experiment.use_artifact(ckpts)
class LogTextPredictions(Callback):
"""Logs a validation batch with image to text transcription."""
def __init__(self, num_samples: int = 8) -> None:
self.num_samples = num_samples
self.ready = False
def _log_predictions(
self,
stage: str,
trainer: Trainer,
pl_module: LightningModule,
dataloader: DataLoader,
) -> None:
"""Logs the predicted text contained in the images."""
if not self.ready:
return None
logger = get_wandb_logger(trainer)
experiment = logger.experiment
# Get a validation batch from the validation dataloader.
samples = next(iter(dataloader))
imgs, labels = samples
imgs = imgs.to(device=pl_module.device)
logits = pl_module.predict(imgs)
tokenizer = pl_module.tokenizer
data = [
wandb.Image(img, caption=tokenizer.decode(pred))
for img, pred, label in zip(
imgs[: self.num_samples],
logits[: self.num_samples],
labels[: self.num_samples],
)
]
experiment.log({f"HTR/{experiment.name}/{stage}": data})
def on_sanity_check_start(
self, trainer: Trainer, pl_module: LightningModule
) -> None:
"""Sets ready attribute."""
self.ready = False
def on_sanity_check_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Start executing this callback only after all validation sanity checks end."""
self.ready = True
def on_validation_epoch_end(
self, trainer: Trainer, pl_module: LightningModule
) -> None:
"""Logs predictions on validation epoch end."""
dataloader = trainer.datamodule.val_dataloader()
self._log_predictions(
stage="val", trainer=trainer, pl_module=pl_module, dataloader=dataloader
)
def on_test_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Logs predictions on train epoch end."""
dataloader = trainer.datamodule.test_dataloader()
self._log_predictions(
stage="test", trainer=trainer, pl_module=pl_module, dataloader=dataloader
)
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