diff --git a/tools/program.py b/tools/program.py
index 696700c031eda416212e3fd96747badd24cf2c1d..33bf1adc98fc48289e9873d073501d76547c011d 100755
--- a/tools/program.py
+++ b/tools/program.py
@@ -152,7 +152,6 @@ def train(config,
           pre_best_model_dict,
           logger,
           vdl_writer=None):
-
     cal_metric_during_train = config['Global'].get('cal_metric_during_train',
                                                    False)
     log_smooth_window = config['Global']['log_smooth_window']
@@ -185,14 +184,13 @@ def train(config,
 
     for epoch in range(start_epoch, epoch_num):
         if epoch > 0:
-            train_loader = build_dataloader(config, 'Train', device)
+            train_dataloader = build_dataloader(config, 'Train', device, logger)
 
         for idx, batch in enumerate(train_dataloader):
             if idx >= len(train_dataloader):
                 break
             lr = optimizer.get_lr()
             t1 = time.time()
-            batch = [paddle.to_tensor(x) for x in batch]
             images = batch[0]
             preds = model(images)
             loss = loss_class(preds, batch)
@@ -301,11 +299,11 @@ def eval(model, valid_dataloader, post_process_class, eval_class, logger,
     with paddle.no_grad():
         total_frame = 0.0
         total_time = 0.0
-        #         pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
+        pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
         for idx, batch in enumerate(valid_dataloader):
             if idx >= len(valid_dataloader):
                 break
-            images = paddle.to_tensor(batch[0])
+            images = batch[0]
             start = time.time()
             preds = model(images)
 
@@ -315,15 +313,15 @@ def eval(model, valid_dataloader, post_process_class, eval_class, logger,
             total_time += time.time() - start
             # Evaluate the results of the current batch
             eval_class(post_result, batch)
-            #             pbar.update(1)
+            pbar.update(1)
             total_frame += len(images)
-            if idx % print_batch_step == 0 and dist.get_rank() == 0:
-                logger.info('tackling images for eval: {}/{}'.format(
-                    idx, len(valid_dataloader)))
+            # if idx % print_batch_step == 0 and dist.get_rank() == 0:
+            #     logger.info('tackling images for eval: {}/{}'.format(
+            #         idx, len(valid_dataloader)))
         # Get final metirc，eg. acc or hmean
         metirc = eval_class.get_metric()
 
-#         pbar.close()
+    pbar.close()
     model.train()
     metirc['fps'] = total_frame / total_time
     return metirc