Move callbacks to training folder, refactor

2 files changed, 0 insertions, 212 deletions

diff --git a/text_recognizer/callbacks/__init__.py b/text_recognizer/callbacks/__init__.py
deleted file mode 100644
index 82d8ce3..0000000
--- a/text_recognizer/callbacks/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Module for PyTorch Lightning callbacks."""
diff --git a/text_recognizer/callbacks/wandb_callbacks.py b/text_recognizer/callbacks/wandb_callbacks.py
deleted file mode 100644
index d9d81f6..0000000
--- a/text_recognizer/callbacks/wandb_callbacks.py
+++ /dev/null
@@ -1,211 +0,0 @@
-"""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)