diff --git a/core/trainers/framework/runner.py b/core/trainers/framework/runner.py
index db00c7b09e53538c3dea328421a50fb8b85a5136..aeab7c4ab365a6eb9ca025b9e030b0696bd7b2c1 100644
--- a/core/trainers/framework/runner.py
+++ b/core/trainers/framework/runner.py
@@ -77,9 +77,10 @@ class RunnerBase(object):
         name = "dataset." + reader_name + "."
 
         if envs.get_global_env(name + "type") == "DataLoader":
-            self._executor_dataloader_train(model_dict, context)
+            return self._executor_dataloader_train(model_dict, context)
         else:
             self._executor_dataset_train(model_dict, context)
+            return None
 
     def _executor_dataset_train(self, model_dict, context):
         reader_name = model_dict["dataset_name"]
@@ -137,8 +138,10 @@ class RunnerBase(object):
 
         metrics_varnames = []
         metrics_format = []
+        metrics_names = ["total_batch"]
         metrics_format.append("{}: {{}}".format("batch"))
         for name, var in metrics.items():
+            metrics_names.append(name)
             metrics_varnames.append(var.name)
             metrics_format.append("{}: {{}}".format(name))
         metrics_format = ", ".join(metrics_format)
@@ -147,6 +150,7 @@ class RunnerBase(object):
         reader.start()
         batch_id = 0
         scope = context["model"][model_name]["scope"]
+        result = None
         with fluid.scope_guard(scope):
             try:
                 while True:
@@ -169,7 +173,8 @@ class RunnerBase(object):
                 reader.reset()
 
         if batch_id > 0:
-            print(metrics_format.format(*metrics))
+            result = dict(zip(metrics_names, metrics))
+        return result
 
     def _get_dataloader_program(self, model_dict, context):
         model_name = model_dict["name"]
@@ -340,10 +345,16 @@ class SingleRunner(RunnerBase):
         for epoch in range(epochs):
             for model_dict in context["phases"]:
                 begin_time = time.time()
-                self._run(context, model_dict)
+                result = self._run(context, model_dict)
                 end_time = time.time()
                 seconds = end_time - begin_time
-                print("epoch {} done, use time: {}".format(epoch, seconds))
+                message = "epoch {} done, use time: {}".format(epoch, seconds)
+                if not result is None:
+                    for key in result:
+                        if key.upper().startswith("BATCH_"):
+                            continue
+                        message += ", {}: {}".format(key, result[key])
+                print(message)
                 with fluid.scope_guard(context["model"][model_dict["name"]][
                         "scope"]):
                     train_prog = context["model"][model_dict["name"]][
@@ -477,16 +488,24 @@ class SingleInferRunner(RunnerBase):
     def run(self, context):
         self._dir_check(context)
 
+        self.epoch_model_name_list.sort()
         for index, epoch_name in enumerate(self.epoch_model_name_list):
             for model_dict in context["phases"]:
                 self._load(context, model_dict,
                            self.epoch_model_path_list[index])
                 begin_time = time.time()
-                self._run(context, model_dict)
+                result = self._run(context, model_dict)
                 end_time = time.time()
                 seconds = end_time - begin_time
-                print("Infer {} of {} done, use time: {}".format(model_dict[
-                    "name"], epoch_name, seconds))
+                message = "Infer {} of epoch {} done, use time: {}".format(
+                    model_dict["name"], epoch_name, seconds)
+                if not result is None:
+                    for key in result:
+                        if key.upper().startswith("BATCH_"):
+                            continue
+                        message += ", {}: {}".format(key, result[key])
+                print(message)
+
         context["status"] = "terminal_pass"
 
     def _load(self, context, model_dict, model_path):