Internal change

PiperOrigin-RevId: 366879385

official/core/config_definitions.py

@@ -201,6 +201,7 @@ class TrainerConfig(base_config.Config):
     best_checkpoint_metric_comp: for exporting the best checkpoint, how the
       trainer should compare the evaluation metrics. This can be either `higher`
       (higher the better) or `lower` (lower the better).
+    validation_summary_subdir: A 'str', sub directory for saving eval summary.
   """
   optimizer_config: OptimizationConfig = OptimizationConfig()
   # Orbit settings.
@@ -232,6 +233,7 @@ class TrainerConfig(base_config.Config):
   # the condition and fail the job if the condition happens; max trials > 0,
   # we will retore the model states.
   recovery_max_trials: int = 0
+  validation_summary_subdir: str = "validation"
 
 
 @dataclasses.dataclass

official/core/train_lib.py

@@ -88,7 +88,8 @@ def run_experiment(distribution_strategy: tf.distribute.Strategy,
       steps_per_loop=params.trainer.steps_per_loop,
       checkpoint_manager=checkpoint_manager,
       summary_dir=os.path.join(model_dir, 'train') if (save_summary) else None,
-      eval_summary_dir=os.path.join(model_dir, 'validation') if
+      eval_summary_dir=os.path.join(model_dir,
+                                    params.trainer.validation_summary_subdir) if
       (save_summary) else None,
       summary_interval=params.trainer.summary_interval if
       (save_summary) else None)

official/core/train_lib_test.py

@@ -49,6 +49,7 @@ class TrainTest(tf.test.TestCase, parameterized.TestCase):
             'validation_steps': 5,
             'validation_interval': 10,
             'continuous_eval_timeout': 1,
+            'validation_summary_subdir': 'validation',
             'optimizer_config': {
                 'optimizer': {
                     'type': 'sgd',
@@ -90,6 +91,11 @@ class TrainTest(tf.test.TestCase, parameterized.TestCase):
           model_dir=model_dir,
           run_post_eval=run_post_eval)
 
+    if 'eval' in flag_mode:
+      self.assertTrue(
+          tf.io.gfile.exists(
+              os.path.join(model_dir,
+                           params.trainer.validation_summary_subdir)))
     if run_post_eval:
       self.assertNotEmpty(logs)
     else: