Fix TF2 3D Unet to standard model garden recommended style.

PiperOrigin-RevId: 306752053

official/nlp/nhnet/trainer.py

@@ -33,6 +33,7 @@ from official.nlp.nhnet import models
 from official.nlp.nhnet import optimizer
 from official.nlp.transformer import metrics as transformer_metrics
 from official.utils.misc import distribution_utils
+from official.utils.misc import keras_utils
 
 FLAGS = flags.FLAGS
 
@@ -122,18 +123,6 @@ class Trainer(tf.keras.Model):
     }
 
 
-class SimpleCheckpoint(tf.keras.callbacks.Callback):
-  """Keras callback to save tf.train.Checkpoints."""
-
-  def __init__(self, checkpoint_manager):
-    super(SimpleCheckpoint, self).__init__()
-    self.checkpoint_manager = checkpoint_manager
-
-  def on_epoch_end(self, epoch, logs=None):
-    step_counter = self.checkpoint_manager._step_counter.numpy()
-    self.checkpoint_manager.save(checkpoint_number=step_counter)
-
-
 def train(params, strategy, dataset=None):
   """Runs training."""
 
@@ -168,7 +157,7 @@ def train(params, strategy, dataset=None):
     if checkpoint_manager.restore_or_initialize():
       logging.info("Training restored from the checkpoints in: %s",
                    FLAGS.model_dir)
-    checkpoint_callback = SimpleCheckpoint(checkpoint_manager)
+    checkpoint_callback = keras_utils.SimpleCheckpoint(checkpoint_manager)
 
   # Trains the model.
   steps_per_epoch = min(FLAGS.train_steps, FLAGS.checkpoint_interval)

official/utils/misc/keras_utils.py

@@ -164,6 +164,18 @@ def get_profiler_callback(model_dir, profile_steps, enable_tensorboard,
   return ProfilerCallback(model_dir, start_step, stop_step, steps_per_epoch)
 
 
+class SimpleCheckpoint(tf.keras.callbacks.Callback):
+  """Keras callback to save tf.train.Checkpoints."""
+
+  def __init__(self, checkpoint_manager):
+    super(SimpleCheckpoint, self).__init__()
+    self.checkpoint_manager = checkpoint_manager
+
+  def on_epoch_end(self, epoch, logs=None):
+    step_counter = self.checkpoint_manager._step_counter.numpy()  # pylint: disable=protected-access
+    self.checkpoint_manager.save(checkpoint_number=step_counter)
+
+
 class ProfilerCallback(tf.keras.callbacks.Callback):
   """Save profiles in specified step range to log directory."""