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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2021-03-20 18:09:06 +0100
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2021-03-20 18:09:06 +0100
commit7e8e54e84c63171e748bbf09516fd517e6821ace (patch)
tree996093f75a5d488dddf7ea1f159ed343a561ef89 /training/trainer/callbacks/progress_bar.py
parentb0719d84138b6bbe5f04a4982dfca673aea1a368 (diff)
Inital commit for refactoring to lightning
Diffstat (limited to 'training/trainer/callbacks/progress_bar.py')
-rw-r--r--training/trainer/callbacks/progress_bar.py65
1 files changed, 65 insertions, 0 deletions
diff --git a/training/trainer/callbacks/progress_bar.py b/training/trainer/callbacks/progress_bar.py
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+"""Progress bar callback for the training loop."""
+from typing import Dict, Optional
+
+from tqdm import tqdm
+from training.trainer.callbacks import Callback
+
+
+class ProgressBar(Callback):
+ """A TQDM progress bar for the training loop."""
+
+ def __init__(self, epochs: int, log_batch_frequency: int = None) -> None:
+ """Initializes the tqdm callback."""
+ self.epochs = epochs
+ print(epochs, type(epochs))
+ self.log_batch_frequency = log_batch_frequency
+ self.progress_bar = None
+ self.val_metrics = {}
+
+ def _configure_progress_bar(self) -> None:
+ """Configures the tqdm progress bar with custom bar format."""
+ self.progress_bar = tqdm(
+ total=len(self.model.train_dataloader()),
+ leave=False,
+ unit="steps",
+ mininterval=self.log_batch_frequency,
+ bar_format="{desc} |{bar:32}| {n_fmt}/{total_fmt} ETA: {remaining} {rate_fmt}{postfix}",
+ )
+
+ def _key_abbreviations(self, logs: Dict) -> Dict:
+ """Changes the length of keys, so that the progress bar fits better."""
+
+ def rename(key: str) -> str:
+ """Renames accuracy to acc."""
+ return key.replace("accuracy", "acc")
+
+ return {rename(key): value for key, value in logs.items()}
+
+ # def on_fit_begin(self) -> None:
+ # """Creates a tqdm progress bar."""
+ # self._configure_progress_bar()
+
+ def on_epoch_begin(self, epoch: int, logs: Optional[Dict]) -> None:
+ """Updates the description with the current epoch."""
+ if epoch == 1:
+ self._configure_progress_bar()
+ else:
+ self.progress_bar.reset()
+ self.progress_bar.set_description(f"Epoch {epoch}/{self.epochs}")
+
+ def on_epoch_end(self, epoch: int, logs: Dict) -> None:
+ """At the end of each epoch, the validation metrics are updated to the progress bar."""
+ self.val_metrics = logs
+ self.progress_bar.set_postfix(**self._key_abbreviations(logs))
+ self.progress_bar.update()
+
+ def on_train_batch_end(self, batch: int, logs: Dict) -> None:
+ """Updates the progress bar for each training step."""
+ if self.val_metrics:
+ logs.update(self.val_metrics)
+ self.progress_bar.set_postfix(**self._key_abbreviations(logs))
+ self.progress_bar.update()
+
+ def on_fit_end(self) -> None:
+ """Closes the tqdm progress bar."""
+ self.progress_bar.close()