Only save summaries if configured to do so

Signed-off-by: Jim Martens <github@2martens.de>

src/twomartens/masterthesis/cli.py

@@ -228,9 +228,12 @@ def _ssd_train(args: argparse.Namespace) -> None:
     nr_batches_train = int(math.floor(train_length / batch_size))
     nr_batches_val = int(math.floor(val_length / batch_size))
     
+    if args.debug and conf.get_property("Debug.summaries"):
         tensorboard_callback = tf.keras.callbacks.TensorBoard(
             log_dir=f"{summary_path}/train/{args.network}/{args.iteration}"
         )
+    else:
+        tensorboard_callback = None
     
     history = ssd.train_keras(
         train_generator,

src/twomartens/masterthesis/ssd.py

@@ -447,7 +447,7 @@ def train_keras(train_generator: callable,
                 initial_epoch: int,
                 nr_epochs: int,
                 lr: float,
-                tensorboard_callback: tf.keras.callbacks.TensorBoard) -> tf.keras.callbacks.History:
+                tensorboard_callback: Optional[tf.keras.callbacks.TensorBoard]) -> tf.keras.callbacks.History:
     """
     Trains the SSD on the given data set using Keras functionality.
     
@@ -491,9 +491,10 @@ def train_keras(train_generator: callable,
             save_weights_only=False
         ),
         tf.keras.callbacks.TerminateOnNaN(),
-        tf.keras.callbacks.EarlyStopping(patience=2, min_delta=0.001, monitor="val_loss"),
-        tensorboard_callback
+        tf.keras.callbacks.EarlyStopping(patience=2, min_delta=0.001, monitor="val_loss")
     ]
+    if tensorboard_callback is not None:
+        callbacks.append(tensorboard_callback)
     
     history = ssd_model.model.fit_generator(generator=train_generator,
                                             epochs=nr_epochs,