diff --git a/FAS_challenge_CVPRW2020/Track2 Single-modal/model1_pytorch/train_CDCNpp_model1.py b/FAS_challenge_CVPRW2020/Track2 Single-modal/model1_pytorch/train_CDCNpp_model1.py
index 7c3fc9a..8242118 100644
--- a/FAS_challenge_CVPRW2020/Track2 Single-modal/model1_pytorch/train_CDCNpp_model1.py	
+++ b/FAS_challenge_CVPRW2020/Track2 Single-modal/model1_pytorch/train_CDCNpp_model1.py	
@@ -45,45 +45,47 @@
 #val_list = '/wrk/yuzitong/DONOTREMOVE/CVPRW2020/4@3_dev_res.txt'
 
 
-def contrast_depth_conv(input):
+def contrast_depth_conv(input, kernel_filter):
     ''' compute contrast depth in both of (out, label) '''
     '''
         input  32x32
         output 8x32x32
     '''
-    
 
-    kernel_filter_list =[
-                        [[1,0,0],[0,-1,0],[0,0,0]], [[0,1,0],[0,-1,0],[0,0,0]], [[0,0,1],[0,-1,0],[0,0,0]],
-                        [[0,0,0],[1,-1,0],[0,0,0]], [[0,0,0],[0,-1,1],[0,0,0]],
-                        [[0,0,0],[0,-1,0],[1,0,0]], [[0,0,0],[0,-1,0],[0,1,0]], [[0,0,0],[0,-1,0],[0,0,1]]
-                        ]
-    
-    kernel_filter = np.array(kernel_filter_list, np.float32)
-    
-    kernel_filter = torch.from_numpy(kernel_filter.astype(np.float)).float().cuda()
-    # weights (in_channel, out_channel, kernel, kernel)
-    kernel_filter = kernel_filter.unsqueeze(dim=1)
-    
     input = input.unsqueeze(dim=1).expand(input.shape[0], 8, input.shape[1],input.shape[2])
     
     contrast_depth = F.conv2d(input, weight=kernel_filter, groups=8)  # depthwise conv
-    
+
     return contrast_depth
 
 
 class Contrast_depth_loss(nn.Module):    # Pearson range [-1, 1] so if < 0, abs|loss| ; if >0, 1- loss
     def __init__(self):
         super(Contrast_depth_loss,self).__init__()
+
+        kernel_filter_list =[
+                        [[1,0,0],[0,-1,0],[0,0,0]], [[0,1,0],[0,-1,0],[0,0,0]], [[0,0,1],[0,-1,0],[0,0,0]],
+                        [[0,0,0],[1,-1,0],[0,0,0]], [[0,0,0],[0,-1,1],[0,0,0]],
+                        [[0,0,0],[0,-1,0],[1,0,0]], [[0,0,0],[0,-1,0],[0,1,0]], [[0,0,0],[0,-1,0],[0,0,1]]
+                        ]
+    
+        kernel_filter = np.array(kernel_filter_list, np.float32)
+        kernel_filter = torch.from_numpy(kernel_filter.astype(np.float)).float().cuda()
+        kernel_filter = kernel_filter.unsqueeze(dim=1)
+        
+        self.kernel_filter = kernel_filter
+
         return
+    
+
     def forward(self, out, label): 
         '''
         compute contrast depth in both of (out, label),
         then get the loss of them
         tf.atrous_convd match tf-versions: 1.4
         '''
-        contrast_out = contrast_depth_conv(out)
-        contrast_label = contrast_depth_conv(label)
+        contrast_out = contrast_depth_conv(out, self.kernel_filter) 
+        contrast_label = contrast_depth_conv(label, self.kernel_filter)
         
         
         criterion_MSE = nn.MSELoss().cuda()