Update orbit.Controller: do not write training summary when input summary_dir is None.

PiperOrigin-RevId: 325115012

official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py

@@ -167,6 +167,7 @@ def run(flags_obj):
       steps_per_loop=steps_per_loop,
       checkpoint_manager=checkpoint_manager,
       summary_interval=summary_interval,
+      summary_dir=flags_obj.model_dir,
       eval_summary_dir=os.path.join(flags_obj.model_dir, 'eval'))
 
   time_callback.on_train_begin()

orbit/controller.py

@@ -71,9 +71,11 @@ class Controller:
         `trainer.train` function will always be enabled. If set, the value
         should be divisible by steps_per_loop.
       summary_dir: The directory to restore and write checkpoints and summaries.
-        If None, it will be set to `checkpoint_manager.directory`.
+        For example, You can set it to `checkpoint_manager.directory`.
+        If None, it will not write training summarizes.
       eval_summary_dir: The directory to write eval summaries. If None, it will
-        be set to `summary_dir`.
+        be set to `summary_dir`. If both `summary_dir` and `eval_summary_dir`
+        are None, it will not write evaluation summarizes.
 
     Raises:
       ValueError: If both `trainer` and `evaluator` are None.
@@ -108,9 +110,6 @@ class Controller:
     self.global_step = global_step
     self.checkpoint_manager = checkpoint_manager
 
-    if summary_dir is None and checkpoint_manager:
-      summary_dir = checkpoint_manager.directory
-
     if self.trainer is not None:
       self.step_timer = None
       self.steps_per_loop = steps_per_loop
@@ -118,7 +117,6 @@ class Controller:
       self.summary_manager = utils.SummaryManager(
           summary_dir, tf.summary.scalar, global_step=self.global_step)
 
-    eval_summary_writer = None
     if self.evaluator is not None:
       eval_summary_dir = eval_summary_dir or summary_dir
       if eval_summary_dir == summary_dir and self.trainer is not None:

orbit/controller_test.py

@@ -294,6 +294,56 @@ class ControllerTest(tf.test.TestCase, parameterized.TestCase):
         train_steps=10, eval_steps=2, eval_interval=6)
     self.assertEqual(test_runner.global_step, 10)
 
+  def test_has_checkpoint_no_summaries(self):
+    test_runner = TestRunner()
+    # Has checkpoint, but no summary directories.
+    checkpoint = tf.train.Checkpoint(model=test_runner.model)
+    checkpoint_manager = tf.train.CheckpointManager(
+        checkpoint,
+        self.model_dir,
+        max_to_keep=None,
+        step_counter=test_runner.global_step)
+    test_controller = controller.Controller(
+        trainer=test_runner,
+        evaluator=test_runner,
+        global_step=test_runner.global_step,
+        checkpoint_manager=checkpoint_manager,
+        steps_per_loop=2)
+    test_controller.train_and_evaluate(
+        train_steps=10, eval_steps=2, eval_interval=6)
+    self.assertEqual(test_runner.global_step, 10)
+
+    # No summaries are saved.
+    self.assertEmpty(tf.io.gfile.glob(
+        os.path.join(checkpoint_manager.directory, "events.*")))
+
+  def test_has_checkpoint_eval_summary_only(self):
+    test_runner = TestRunner()
+    # Has checkpoint, but no summary directories.
+    checkpoint = tf.train.Checkpoint(model=test_runner.model)
+    checkpoint_manager = tf.train.CheckpointManager(
+        checkpoint,
+        self.model_dir,
+        max_to_keep=None,
+        step_counter=test_runner.global_step)
+    test_controller = controller.Controller(
+        trainer=test_runner,
+        evaluator=test_runner,
+        global_step=test_runner.global_step,
+        checkpoint_manager=checkpoint_manager,
+        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"),
+        steps_per_loop=2)
+    test_controller.train_and_evaluate(
+        train_steps=10, eval_steps=2, eval_interval=6)
+    self.assertEqual(test_runner.global_step, 10)
+
+    # Training summaries are not saved.
+    self.assertEmpty(tf.io.gfile.glob(
+        os.path.join(checkpoint_manager.directory, "events.*")))
+    # Evaluation summaries are saved.
+    self.assertNotEmpty(tf.io.gfile.glob(
+        os.path.join(self.model_dir, "summaries/eval/events.*")))
+
   @parameterized.named_parameters(("return_numpy", True),
                                   ("return_tensor", False))
   def test_train_and_evaluate(self, return_numpy):
@@ -612,7 +662,8 @@ class ControllerTest(tf.test.TestCase, parameterized.TestCase):
         evaluator=test_runner,
         global_step=test_runner.global_step,
         steps_per_loop=10,
-        checkpoint_manager=checkpoint_manager)
+        checkpoint_manager=checkpoint_manager,
+        summary_dir=self.model_dir)
     test_controller.train_and_evaluate(
         train_steps=10, eval_steps=2, eval_interval=5)