diff --git a/train.py b/train.py
index b386606..b5c5479 100644
--- a/train.py
+++ b/train.py
@@ -16,7 +16,7 @@ from torch.utils.data.distributed import DistributedSampler
 
 device = torch.device("cuda")
 
-exp = os.path.abspath('.').split('/')[-1]
+log_path = 'train_log'
 
 def get_learning_rate(step):
     if step < 2000:
@@ -85,11 +85,11 @@ def train(model, local_rank):
             step += 1
         nr_eval += 1
         if nr_eval % 5 == 0:
-            evaluate(model, val_data, step)
+            evaluate(model, val_data, step, local_rank, writer_val)
         model.save_model(log_path, local_rank)    
         dist.barrier()
 
-def evaluate(model, val_data, nr_eval):
+def evaluate(model, val_data, nr_eval, local_rank, writer_val):
     loss_l1_list = []
     loss_distill_list = []
     loss_tea_list = []
@@ -119,7 +119,7 @@ def evaluate(model, val_data, nr_eval):
         if i == 0 and local_rank == 0:
             for j in range(10):
                 imgs = np.concatenate((merged_img[j], pred[j], gt[j]), 1)[:, :, ::-1]
-                writer_val.add_image(str(j) + '/img', imgs.copy(), nr_eval, dataformats='HWC')
+                ..add_image(str(j) + '/img', imgs.copy(), nr_eval, dataformats='HWC')
                 writer_val.add_image(str(j) + '/flow', flow2rgb(flow0[j][:, :, ::-1]), nr_eval, dataformats='HWC')
     
     eval_time_interval = time.time() - time_stamp