tf.estimator.train_and_evalute doesn't return anything in multi-worker case. (#6582)

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update resnet_run_loop.py

official/resnet/resnet_run_loop.py

@@ -628,21 +628,19 @@ def resnet_main(
   train_epochs = (0 if flags_obj.eval_only or not flags_obj.train_epochs else
                   flags_obj.train_epochs)
 
-  use_train_and_evaluate = flags_obj.use_train_and_evaluate or (
-      distribution_strategy.__class__.__name__ in [
-          'CollectiveAllReduceStrategy', 'MultiWorkerMirroredStrategy'])
+  use_train_and_evaluate = flags_obj.use_train_and_evaluate or num_workers > 1
   if use_train_and_evaluate:
     train_spec = tf.estimator.TrainSpec(
         input_fn=lambda input_context=None: input_fn_train(
             train_epochs, input_context=input_context),
         hooks=train_hooks,
         max_steps=flags_obj.max_train_steps)
-    eval_spec = tf.estimator.EvalSpec(input_fn=input_fn_eval,
-                                      steps=flags_obj.max_train_steps)
+    eval_spec = tf.estimator.EvalSpec(input_fn=input_fn_eval)
     tf.compat.v1.logging.info('Starting to train and evaluate.')
-    eval_results, _ = tf.estimator.train_and_evaluate(classifier, train_spec,
-                                                      eval_spec)
-    benchmark_logger.log_evaluation_result(eval_results)
+    tf.estimator.train_and_evaluate(classifier, train_spec, eval_spec)
+    # tf.estimator.train_and_evalute doesn't return anything in multi-worker
+    # case.
+    return {}
   else:
     if train_epochs == 0:
       # If --eval_only is set, perform a single loop with zero train epochs.