diff --git a/ppcls/engine/engine.py b/ppcls/engine/engine.py
index b36aeb70cf5ceb1917e50a7c51d4abcc9c8d1a65..f3b30c9d682907375971005fb4d88cb93fec4d06 100644
--- a/ppcls/engine/engine.py
+++ b/ppcls/engine/engine.py
@@ -99,8 +99,8 @@ class Engine(object):
             paddle.__version__, self.device))
 
         # AMP training and evaluating
-        self.amp = "AMP" in self.config
-        if self.amp and self.config["AMP"] is not None:
+        self.amp = "AMP" in self.config and self.config["AMP"] is not None
+        if self.amp:
             self.scale_loss = self.config["AMP"].get("scale_loss", 1.0)
             self.use_dynamic_loss_scaling = self.config["AMP"].get(
                 "use_dynamic_loss_scaling", False)
@@ -228,7 +228,7 @@ class Engine(object):
                 len(self.train_dataloader),
                 [self.model, self.train_loss_func])
 
-        # for amp training
+        # for amp
         if self.amp:
             self.scaler = paddle.amp.GradScaler(
                 init_loss_scaling=self.scale_loss,
@@ -239,12 +239,13 @@ class Engine(object):
                 logger.warning(msg)
                 self.config['AMP']["level"] = "O1"
                 amp_level = "O1"
-            self.model, self.optimizer = paddle.amp.decorate(
-                models=self.model,
-                optimizers=self.optimizer,
-                level=amp_level,
-                save_dtype='float32')
-            if len(self.train_loss_func.parameters()) > 0:
+            self.model = paddle.amp.decorate(
+                models=self.model, level=amp_level, save_dtype='float32')
+            # TODO(gaotingquan): to compatible with Paddle develop and 2.2
+            if isinstance(self.model, tuple):
+                self.model = self.model[0]
+            if self.mode == "train" and len(self.train_loss_func.parameters(
+            )) > 0:
                 self.train_loss_func = paddle.amp.decorate(
                     models=self.train_loss_func,
                     level=amp_level,
diff --git a/ppcls/engine/evaluation/classification.py b/ppcls/engine/evaluation/classification.py
index 6e7fc1a76fe8c3bc4402d9428d372b9c2b50a17b..5f8407e5d90d4ebc72d595781a2fef9b4b81271f 100644
--- a/ppcls/engine/evaluation/classification.py
+++ b/ppcls/engine/evaluation/classification.py
@@ -32,6 +32,15 @@ def classification_eval(engine, epoch_id=0):
     }
     print_batch_step = engine.config["Global"]["print_batch_step"]
 
+    if engine.amp:
+        amp_level = engine.config['AMP'].get("level", "O1").upper()
+        if amp_level == "O2" and engine.config["AMP"].get("use_fp16_test",
+                                                          False):
+            engine.config["AMP"]["use_fp16_test"] = True
+            msg = "Only support FP16 evaluation when AMP O2 is enabled."
+            logger.warning(msg)
+        amp_eval = engine.config["AMP"].get("use_fp16_test", False)
+
     metric_key = None
     tic = time.time()
     accum_samples = 0
@@ -58,15 +67,7 @@ def classification_eval(engine, epoch_id=0):
             batch[1] = batch[1].reshape([-1, 1]).astype("int64")
 
         # image input
-        if engine.amp and (
-                engine.config['AMP'].get("level", "O1").upper() == "O2" or
-                engine.config["AMP"].get("use_fp16_test", False)):
-            amp_level = engine.config['AMP'].get("level", "O1").upper()
-
-            if amp_level == "O2":
-                msg = "Only support FP16 evaluation when AMP O2 is enabled."
-                logger.warning(msg)
-
+        if engine.amp and amp_eval:
             with paddle.amp.auto_cast(
                     custom_black_list={
                         "flatten_contiguous_range", "greater_than"
@@ -119,8 +120,7 @@ def classification_eval(engine, epoch_id=0):
 
         # calc loss
         if engine.eval_loss_func is not None:
-            if engine.amp and engine.config["AMP"].get("use_fp16_test", False):
-                amp_level = engine.config['AMP'].get("level", "O1").upper()
+            if engine.amp and amp_eval:
                 with paddle.amp.auto_cast(
                         custom_black_list={
                             "flatten_contiguous_range", "greater_than"