add final evaluation after training

scripts/train.py

@@ -59,6 +59,14 @@ def get_args():
         help="Run validation every n batches."
     )
 
+    parser.add_argument(
+        '-p',
+        '--patience',
+        default=8,
+        type=int,
+        help="Patience for early stopping."
+    )
+
     parser.add_argument('--no-wandb', action='store_true',
                         help='Disables wandb, for testing.')
 
@@ -191,7 +199,7 @@ def main():
     early_stopping = EarlyStopping(
         monitor="val_loss",
         mode="min",
-        patience=5)
+        patience=args.patience)
 
     batch_size = config['batch_size']
     logging.info(f"Training batch size {batch_size}.")
@@ -223,6 +231,13 @@ def main():
                 batch_size=batch_size, train=False, num_workers=3
             ))
 
+    # Run validation with best model to log min val_loss
+    # TODO: Maybe use different metric like min_val_loss ?
+    ckpt_path = trainer.checkpoint_callback.best_model_path or None
+    trainer.validate(model, dataloaders=valid.to_dataloader(
+        batch_size=batch_size, train=False, num_workers=3),
+        ckpt_path=ckpt_path)
+
 
 if __name__ == '__main__':
     main()