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"""Weights and Biases callbacks."""
from pathlib import Path
from typing import Tuple
from pytorch_lightning import Callback, LightningModule, Trainer
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.utilities import rank_zero_only
from torch import Tensor
import wandb
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 ImageToCaption(Callback):
"""Logs the image and output caption."""
def __init__(self, num_samples: int = 8, on_train: bool = True) -> None:
self.num_samples = num_samples
self.on_train = on_train
self._required_keys = ("predictions", "ground_truths")
def _log_captions(
self, trainer: Trainer, batch: Tuple[Tensor, Tensor], outputs: dict, key: str
) -> None:
xs, _ = batch
preds, gts = outputs["predictions"], outputs["ground_truths"]
xs, preds, gts = (
list(xs[: self.num_samples]),
preds[: self.num_samples],
gts[: self.num_samples],
)
trainer.logger.log_image(key, xs, caption=preds)
@rank_zero_only
def on_train_batch_end(
self,
trainer: Trainer,
pl_module: LightningModule,
outputs: dict,
batch: Tuple[Tensor, Tensor],
*args,
) -> None:
"""Logs predictions on validation batch end."""
if self.has_metrics(outputs):
self._log_captions(trainer, batch, outputs, "train/predictions")
@rank_zero_only
def on_validation_batch_end(
self,
trainer: Trainer,
pl_module: LightningModule,
outputs: dict,
batch: Tuple[Tensor, Tensor],
*args,
) -> None:
"""Logs predictions on validation batch end."""
if self.has_metrics(outputs):
self._log_captions(trainer, batch, outputs, "val/predictions")
@rank_zero_only
def on_test_batch_end(
self,
trainer: Trainer,
pl_module: LightningModule,
outputs: dict,
batch: Tuple[Tensor, Tensor],
*args,
) -> None:
"""Logs predictions on train batch end."""
if self.has_metrics(outputs):
self._log_captions(trainer, batch, outputs, "test/predictions")
def has_metrics(self, outputs: dict) -> bool:
return all(k in outputs.keys() for k in self._required_keys)
|
