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"""Weights and Biases callbacks."""
from pathlib import Path
from typing import List
import attr
import wandb
from pytorch_lightning import Callback, LightningModule, Trainer
from pytorch_lightning.loggers import LoggerCollection, WandbLogger
def get_wandb_logger(trainer: Trainer) -> WandbLogger:
"""Safely get W&B logger from Trainer."""
if isinstance(trainer.logger, WandbLogger):
return trainer.logger
if isinstance(trainer.logger, LoggerCollection):
for logger in trainer.logger:
if isinstance(logger, WandbLogger):
return logger
raise Exception("Weight and Biases logger not found for some reason...")
@attr.s
class WatchModel(Callback):
"""Make W&B watch the model at the beginning of the run."""
log: str = attr.ib(default="gradients")
log_freq: int = attr.ib(default=100)
def __attrs_pre_init__(self) -> None:
super().__init__()
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, log_freq=self.log_freq)
@attr.s
class UploadCodeAsArtifact(Callback):
"""Upload all *.py files to W&B as an artifact, at the beginning of the run."""
project_dir: Path = attr.ib(converter=Path)
def __attrs_pre_init__(self) -> None:
super().__init__()
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("project-source", type="code")
for filepath in self.project_dir.glob("**/*.py"):
artifact.add_file(filepath)
experiment.use_artifact(artifact)
@attr.s
class UploadCheckpointAsArtifact(Callback):
"""Upload checkpoint to wandb as an artifact, at the end of a run."""
ckpt_dir: Path = attr.ib(converter=Path)
upload_best_only: bool = attr.ib()
def __attrs_pre_init__(self) -> None:
super().__init__()
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).glob("**/*.ckpt"):
ckpts.add_file(ckpt)
experiment.use_artifact(ckpts)
@attr.s
class LogTextPredictions(Callback):
"""Logs a validation batch with image to text transcription."""
num_samples: int = attr.ib(default=8)
ready: bool = attr.ib(default=True)
def __attrs_pre_init__(self) -> None:
super().__init__()
def _log_predictions(
stage: str, trainer: Trainer, pl_module: LightningModule
) -> 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(trainer.datamodule.val_dataloader()))
imgs, labels = samples
imgs = imgs.to(device=pl_module.device)
logits = pl_module(imgs)
mapping = pl_module.mapping
experiment.log(
{
f"OCR/{experiment.name}/{stage}": [
wandb.Image(
img,
caption=f"Pred: {mapping.get_text(pred)}, Label: {mapping.get_text(label)}",
)
for img, pred, label in zip(
imgs[: self.num_samples],
logits[: self.num_samples],
labels[: self.num_samples],
)
]
}
)
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."""
self._log_predictions(stage="val", trainer=trainer, pl_module=pl_module)
def on_train_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Logs predictions on train epoch end."""
self._log_predictions(stage="test", trainer=trainer, pl_module=pl_module)
@attr.s
class LogReconstuctedImages(Callback):
"""Log reconstructions of images."""
num_samples: int = attr.ib(default=8)
ready: bool = attr.ib(default=True)
def __attrs_pre_init__(self) -> None:
super().__init__()
def _log_reconstruction(
self, stage: str, trainer: Trainer, pl_module: LightningModule
) -> None:
"""Logs the reconstructions."""
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(trainer.datamodule.val_dataloader()))
imgs, _ = samples
imgs = imgs.to(device=pl_module.device)
reconstructions = pl_module(imgs)
experiment.log(
{
f"Reconstructions/{experiment.name}/{stage}": [
[
wandb.Image(img),
wandb.Image(rec),
]
for img, rec in zip(
imgs[: self.num_samples],
reconstructions[: self.num_samples],
)
]
}
)
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."""
self._log_reconstruction(stage="val", trainer=trainer, pl_module=pl_module)
def on_train_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
"""Logs predictions on train epoch end."""
self._log_reconstruction(stage="test", trainer=trainer, pl_module=pl_module)
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