Do not report accuracy metrics for benchmark tests (#6757)

* Do not report metrics in performance benchmarks

* Rename flag

official/resnet/keras/keras_common.py

@@ -333,6 +333,8 @@ def define_keras_flags():
   flags.DEFINE_boolean(name='skip_eval', default=False, help='Skip evaluation?')
   flags.DEFINE_boolean(name='use_trivial_model', default=False,
                        help='Whether to use a trivial Keras model.')
+  flags.DEFINE_boolean(name='report_accuracy_metrics', default=True,
+                       help='Report metrics during training and evaluation.')
   flags.DEFINE_boolean(name='use_tensor_lr', default=False,
                        help='Use learning rate tensor instead of a callback.')
   flags.DEFINE_boolean(

official/resnet/keras/keras_imagenet_benchmark.py

@@ -650,6 +650,7 @@ class Resnet50KerasBenchmarkSynth(Resnet50KerasBenchmarkBase):
   def __init__(self, output_dir=None, root_data_dir=None, **kwargs):
     def_flags = {}
     def_flags['skip_eval'] = True
+    def_flags['report_accuracy_metrics'] = False
     def_flags['use_synthetic_data'] = True
     def_flags['train_steps'] = 110
     def_flags['log_steps'] = 10
@@ -664,6 +665,7 @@ class Resnet50KerasBenchmarkReal(Resnet50KerasBenchmarkBase):
   def __init__(self, output_dir=None, root_data_dir=None, **kwargs):
     def_flags = {}
     def_flags['skip_eval'] = True
+    def_flags['report_accuracy_metrics'] = False
     def_flags['data_dir'] = os.path.join(root_data_dir, 'imagenet')
     def_flags['train_steps'] = 110
     def_flags['log_steps'] = 10
@@ -682,6 +684,7 @@ class TrivialKerasBenchmarkReal(keras_benchmark.KerasBenchmark):
     ]
     def_flags = {}
     def_flags['skip_eval'] = True
+    def_flags['report_accuracy_metrics'] = False
     def_flags['dtype'] = 'fp16'
     def_flags['enable_xla'] = True
     def_flags['data_dir'] = os.path.join(root_data_dir, 'imagenet')

official/resnet/keras/keras_imagenet_main.py

@@ -210,7 +210,8 @@ def run(flags_obj):
 
     model.compile(loss='sparse_categorical_crossentropy',
                   optimizer=optimizer,
-                  metrics=['sparse_categorical_accuracy'],
+                  metrics=(['sparse_categorical_accuracy']
+                           if flags_obj.report_accuracy_metrics else None),
                   cloning=flags_obj.clone_model_in_keras_dist_strat)
 
   callbacks = keras_common.get_callbacks(