Add option to omit the checkpoint callback in text classifier.

PiperOrigin-RevId: 580658724

mediapipe/model_maker/python/core/utils/model_util.py

@@ -35,18 +35,23 @@ ESTIMITED_STEPS_PER_EPOCH = 1000
 
 def get_default_callbacks(
     export_dir: str,
+    checkpoint_frequency: int = 5,
 ) -> Sequence[tf.keras.callbacks.Callback]:
   """Gets default callbacks."""
+  callbacks = []
   summary_dir = os.path.join(export_dir, 'summaries')
   summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
+  callbacks.append(summary_callback)
 
+  if checkpoint_frequency > 0:
     checkpoint_path = os.path.join(export_dir, 'checkpoint')
     checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
         os.path.join(checkpoint_path, 'model-{epoch:04d}'),
         save_weights_only=True,
-      period=5,
+        period=checkpoint_frequency,
     )
-  return [summary_callback, checkpoint_callback]
+    callbacks.append(checkpoint_callback)
+  return callbacks
 
 
 def load_keras_model(

mediapipe/model_maker/python/core/utils/model_util_test.py

@@ -25,6 +25,23 @@ from mediapipe.model_maker.python.core.utils import test_util
 
 class ModelUtilTest(tf.test.TestCase, parameterized.TestCase):
 
+  def test_get_default_callbacks(self):
+    callbacks = model_util.get_default_callbacks(
+        'export_dir', checkpoint_frequency=5
+    )
+    self.assertLen(callbacks, 2)
+    self.assertIsInstance(callbacks[0], tf.keras.callbacks.TensorBoard)
+    self.assertEqual(callbacks[0].log_dir, 'export_dir/summaries')
+    self.assertIsInstance(callbacks[1], tf.keras.callbacks.ModelCheckpoint)
+    self.assertEqual(callbacks[1].period, 5)
+
+    callbacks = model_util.get_default_callbacks(
+        'export_dir_2', checkpoint_frequency=0
+    )
+    self.assertLen(callbacks, 1)
+    self.assertIsInstance(callbacks[0], tf.keras.callbacks.TensorBoard)
+    self.assertEqual(callbacks[0].log_dir, 'export_dir_2/summaries')
+
   def test_load_keras_model(self):
     input_dim = 4
     model = test_util.build_model(input_shape=[input_dim], num_classes=2)

mediapipe/model_maker/python/text/text_classifier/hyperparameters.py

@@ -56,6 +56,8 @@ class BertHParams(hp.BaseHParams):
       value to 0. Defaults to 2.0.
     tokenizer: Tokenizer to use for preprocessing. Must be one of the enum
       options of SupportedBertTokenizers. Defaults to FULL_TOKENIZER.
+    checkpoint_frequency: Frequency(in epochs) of saving checkpoints during
+      training. Defaults to 0 which does not save training checkpoints.
   """
 
   learning_rate: float = 3e-5
@@ -75,5 +77,7 @@ class BertHParams(hp.BaseHParams):
       bert_tokenizer.SupportedBertTokenizers.FULL_TOKENIZER
   )
 
+  checkpoint_frequency: int = 0
+
 
 HParams = Union[BertHParams, AverageWordEmbeddingHParams]

mediapipe/model_maker/python/text/text_classifier/text_classifier.py

@@ -372,7 +372,9 @@ class _BertClassifier(TextClassifier):
   ):
     super().__init__(model_spec, label_names, hparams.shuffle)
     self._hparams = hparams
-    self._callbacks = model_util.get_default_callbacks(self._hparams.export_dir)
+    self._callbacks = model_util.get_default_callbacks(
+        self._hparams.export_dir, self._hparams.checkpoint_frequency
+    )
     self._model_options = model_options
     self._text_preprocessor: preprocessor.BertClassifierPreprocessor = None
     with self._hparams.get_strategy().scope():