1 files changed, 61 insertions, 0 deletions
diff --git a/src/training/trainer/callbacks/progress_bar.py b/src/training/trainer/callbacks/progress_bar.py
new file mode 100644
index 0000000..1970747
--- /dev/null
+++ b/src/training/trainer/callbacks/progress_bar.py
@@ -0,0 +1,61 @@
+"""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
+        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.data_loaders["train"]),
+            leave=True,
+            unit="step",
+            mininterval=self.log_batch_frequency,
+            bar_format="{desc} |{bar:30}| {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."""
+        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()