From 9ae5fa1a88899180f88ddb14d4cef457ceb847e5 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 5 Apr 2021 20:47:55 +0200
Subject: Add new training loop with PyTorch Lightning, remove stale files

---
 training/trainer/callbacks/progress_bar.py | 65 ------------------------------
 1 file changed, 65 deletions(-)
 delete mode 100644 training/trainer/callbacks/progress_bar.py

(limited to 'training/trainer/callbacks/progress_bar.py')

diff --git a/training/trainer/callbacks/progress_bar.py b/training/trainer/callbacks/progress_bar.py
deleted file mode 100644
index 6c4305a..0000000
--- a/training/trainer/callbacks/progress_bar.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""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()
-- 
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