diff --git a/ppcls/engine/engine.py b/ppcls/engine/engine.py
index a0bd0376c86685639ce931c45384f6ad4103d0be..cb43b6a9e406e0f8d243e34d8bb14a8b05eb6aa2 100755
--- a/ppcls/engine/engine.py
+++ b/ppcls/engine/engine.py
@@ -50,6 +50,9 @@ class Engine(object):
         assert mode in ["train", "eval", "infer", "export"]
         self.mode = mode
         self.config = config
+        self.start_eval_epoch = self.config["Global"].get("start_eval_epoch",
+                                                          0) - 1
+        self.epochs = self.config["Global"].get("epochs", 1)
 
         # set seed
         self._init_seed()
@@ -113,8 +116,6 @@ class Engine(object):
         assert self.mode == "train"
         print_batch_step = self.config['Global']['print_batch_step']
         save_interval = self.config["Global"]["save_interval"]
-        start_eval_epoch = self.config["Global"].get("start_eval_epoch", 0) - 1
-        epochs = self.config["Global"]["epochs"]
 
         best_metric = {
             "metric": -1.0,
@@ -140,20 +141,20 @@ class Engine(object):
 
         # global iter counter
         self.global_step = 0
-        for epoch_id in range(best_metric["epoch"] + 1, epochs + 1):
+        for epoch_id in range(best_metric["epoch"] + 1, self.epochs + 1):
             # for one epoch train
             self.train_epoch_func(self, epoch_id, print_batch_step)
 
             metric_msg = ", ".join(
                 [self.output_info[key].avg_info for key in self.output_info])
-            logger.info("[Train][Epoch {}/{}][Avg]{}".format(epoch_id, epochs,
-                                                             metric_msg))
+            logger.info("[Train][Epoch {}/{}][Avg]{}".format(
+                epoch_id, self.epochs, metric_msg))
             self.output_info.clear()
 
             acc = 0.0
             if self.config["Global"][
                     "eval_during_train"] and epoch_id % self.config["Global"][
-                        "eval_interval"] == 0 and epoch_id > start_eval_epoch:
+                        "eval_interval"] == 0 and epoch_id > self.start_eval_epoch:
                 acc = self.eval(epoch_id)
 
                 # step lr (by epoch) according to given metric, such as acc
@@ -469,7 +470,6 @@ class Engine(object):
                                      self.model_ema)
             if metric_info is not None:
                 best_metric.update(metric_info)
-        return best_metric
 
 
 class ExportModel(TheseusLayer):