fix engine.py

ppcls/engine/engine.py

@@ -144,22 +144,21 @@ class Engine(object):
                                    self.config["Global"]["eval_during_train"]):
             if self.eval_mode in ["classification", "adaface"]:
                 self.eval_dataloader = build_dataloader(
-                    self.config["DataLoader"], "Eval", self.device,
-                    self.use_dali)
+                    self.config["DataLoader"], "Eval", self.device, False)
             elif self.eval_mode == "retrieval":
                 self.gallery_query_dataloader = None
                 if len(self.config["DataLoader"]["Eval"].keys()) == 1:
                     key = list(self.config["DataLoader"]["Eval"].keys())[0]
                     self.gallery_query_dataloader = build_dataloader(
                         self.config["DataLoader"]["Eval"], key, self.device,
-                        self.use_dali)
+                        False)
                 else:
                     self.gallery_dataloader = build_dataloader(
                         self.config["DataLoader"]["Eval"], "Gallery",
-                        self.device, self.use_dali)
+                        self.device, False)
                     self.query_dataloader = build_dataloader(
                         self.config["DataLoader"]["Eval"], "Query",
-                        self.device, self.use_dali)
+                        self.device, False)
 
         # build loss
         if self.mode == "train":
@@ -339,11 +338,11 @@ class Engine(object):
                 )
                 self.config["Global"]["seed"] = seed = 42
             logger.info(
-                f"Set random seed to ({seed} + $PADDLE_TRAINER_ID) for different trainer"
+                f"Set random seed to ({int(seed)} + $PADDLE_TRAINER_ID) for different trainer"
             )
-            paddle.seed(seed + dist.get_rank())
-            np.random.seed(seed + dist.get_rank())
-            random.seed(seed + dist.get_rank())
+            paddle.seed(int(seed) + dist.get_rank())
+            np.random.seed(int(seed) + dist.get_rank())
+            random.seed(int(seed) + dist.get_rank())
 
         # build postprocess for infer
         if self.mode == 'infer':