diff --git a/ppgan/engine/trainer.py b/ppgan/engine/trainer.py
index 0944087a7855d858bbd20a8f75ff1457e79a5cba..b518c6ba85044ded0dc105d8d2c29f6a28d08d0a 100644
--- a/ppgan/engine/trainer.py
+++ b/ppgan/engine/trainer.py
@@ -55,11 +55,8 @@ class Trainer:
 
     def distributed_data_parallel(self):
         strategy = paddle.distributed.prepare_context()
-        for name in self.model.model_names:
-            if isinstance(name, str):
-                net = getattr(self.model, 'net' + name)
-                setattr(self.model, 'net' + name,
-                        paddle.DataParallel(net, strategy))
+        for net_name, net in self.model.nets.items():
+            self.model.nets[net_name] = paddle.DataParallel(net, strategy)
 
     def train(self):
         reader_cost_averager = TimeAverager()
@@ -77,9 +74,9 @@ class Trainer:
                 self.model.set_input(data)
                 self.model.optimize_parameters()
 
-                batch_cost_averager.record(
-                    time.time() - step_start_time,
-                    num_samples=self.cfg.get('batch_size', 1))
+                batch_cost_averager.record(time.time() - step_start_time,
+                                           num_samples=self.cfg.get(
+                                               'batch_size', 1))
                 if i % self.log_interval == 0:
                     self.data_time = reader_cost_averager.get_average()
                     self.step_time = batch_cost_averager.get_average()
@@ -94,8 +91,8 @@ class Trainer:
 
                 step_start_time = time.time()
 
-            self.logger.info(
-                'train one epoch time: {}'.format(time.time() - start_time))
+            self.logger.info('train one epoch time: {}'.format(time.time() -
+                                                               start_time))
             if self.validate_interval > -1 and epoch % self.validate_interval:
                 self.validate()
             self.model.lr_scheduler.step()
@@ -105,8 +102,8 @@ class Trainer:
 
     def validate(self):
         if not hasattr(self, 'val_dataloader'):
-            self.val_dataloader = build_dataloader(
-                self.cfg.dataset.val, is_train=False)
+            self.val_dataloader = build_dataloader(self.cfg.dataset.val,
+                                                   is_train=False)
 
         metric_result = {}
 
@@ -152,8 +149,8 @@ class Trainer:
             self.visual('visual_val', visual_results=visual_results)
 
             if i % self.log_interval == 0:
-                self.logger.info(
-                    'val iter: [%d/%d]' % (i, len(self.val_dataloader)))
+                self.logger.info('val iter: [%d/%d]' %
+                                 (i, len(self.val_dataloader)))
 
         for metric_name in metric_result.keys():
             metric_result[metric_name] /= len(self.val_dataloader.dataset)
@@ -163,8 +160,8 @@ class Trainer:
 
     def test(self):
         if not hasattr(self, 'test_dataloader'):
-            self.test_dataloader = build_dataloader(
-                self.cfg.dataset.test, is_train=False)
+            self.test_dataloader = build_dataloader(self.cfg.dataset.test,
+                                                    is_train=False)
 
         # data[0]: img, data[1]: img path index
         # test batch size must be 1
@@ -188,8 +185,8 @@ class Trainer:
             self.visual('visual_test', visual_results=visual_results)
 
             if i % self.log_interval == 0:
-                self.logger.info(
-                    'Test iter: [%d/%d]' % (i, len(self.test_dataloader)))
+                self.logger.info('Test iter: [%d/%d]' %
+                                 (i, len(self.test_dataloader)))
 
     def print_log(self):
         losses = self.model.get_current_losses()
@@ -277,13 +274,13 @@ class Trainer:
             self.start_epoch = state_dicts['epoch'] + 1
 
         for net_name, net in self.model.nets.items():
-            net.set_dict(state_dicts[net_name])
+            net.set_state_dict(state_dicts[net_name])
 
         for opt_name, opt in self.model.optimizers.items():
-            opt.set_dict(state_dicts[opt_name])
+            opt.set_state_dict(state_dicts[opt_name])
 
     def load(self, weight_path):
         state_dicts = load(weight_path)
 
         for net_name, net in self.model.nets.items():
-            net.set_dict(state_dicts[net_name])
+            net.set_state_dict(state_dicts[net_name])
diff --git a/ppgan/utils/filesystem.py b/ppgan/utils/filesystem.py
index a4f94636cc221abbd0e859b2aa969a492b7d1034..98495b23f6bf1f12b4c464d87f3438f695b6ae04 100644
--- a/ppgan/utils/filesystem.py
+++ b/ppgan/utils/filesystem.py
@@ -6,7 +6,11 @@ import paddle
 
 def makedirs(dir):
     if not os.path.exists(dir):
-        os.makedirs(dir)
+        # avoid error when train with multiple gpus
+        try:
+            os.makedirs(dir)
+        except:
+            pass
 
 
 def save(state_dicts, file_name):