--- a
+++ b/CaraNet/lib/context_module.py
@@ -0,0 +1,100 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug 10 17:18:49 2021
+
+@author: angelou
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from lib.conv_layer import Conv, BNPReLU
+import math
+
+class CFPModule(nn.Module):
+    def __init__(self, nIn, d=1, KSize=3,dkSize=3):
+        super().__init__()
+        
+        self.bn_relu_1 = BNPReLU(nIn)
+        self.bn_relu_2 = BNPReLU(nIn)
+        self.conv1x1_1 = Conv(nIn, nIn // 4, KSize, 1, padding=1, bn_acti=True)
+        
+        self.dconv_4_1 = Conv(nIn //4, nIn //16, (dkSize,dkSize),1,padding = (1*d+1,1*d+1),
+                            dilation=(d+1,d+1), groups = nIn //16, bn_acti=True)
+        
+        self.dconv_4_2 = Conv(nIn //16, nIn //16, (dkSize,dkSize),1,padding = (1*d+1,1*d+1),
+                            dilation=(d+1,d+1), groups = nIn //16, bn_acti=True)
+        
+        self.dconv_4_3 = Conv(nIn //16, nIn //8, (dkSize,dkSize),1,padding = (1*d+1,1*d+1),
+                            dilation=(d+1,d+1), groups = nIn //16, bn_acti=True)
+        
+        
+        
+        self.dconv_1_1 = Conv(nIn //4, nIn //16, (dkSize,dkSize),1,padding = (1,1),
+                            dilation=(1,1), groups = nIn //16, bn_acti=True)
+        
+        self.dconv_1_2 = Conv(nIn //16, nIn //16, (dkSize,dkSize),1,padding = (1,1),
+                            dilation=(1,1), groups = nIn //16, bn_acti=True)
+        
+        self.dconv_1_3 = Conv(nIn //16, nIn //8, (dkSize,dkSize),1,padding = (1,1),
+                            dilation=(1,1), groups = nIn //16, bn_acti=True)
+        
+        
+        
+        self.dconv_2_1 = Conv(nIn //4, nIn //16, (dkSize,dkSize),1,padding = (int(d/4+1),int(d/4+1)),
+                            dilation=(int(d/4+1),int(d/4+1)), groups = nIn //16, bn_acti=True)
+        
+        self.dconv_2_2 = Conv(nIn //16, nIn //16, (dkSize,dkSize),1,padding = (int(d/4+1),int(d/4+1)),
+                            dilation=(int(d/4+1),int(d/4+1)), groups = nIn //16, bn_acti=True)
+        
+        self.dconv_2_3 = Conv(nIn //16, nIn //8, (dkSize,dkSize),1,padding = (int(d/4+1),int(d/4+1)),
+                            dilation=(int(d/4+1),int(d/4+1)), groups = nIn //16, bn_acti=True)
+        
+        
+        self.dconv_3_1 = Conv(nIn //4, nIn //16, (dkSize,dkSize),1,padding = (int(d/2+1),int(d/2+1)),
+                            dilation=(int(d/2+1),int(d/2+1)), groups = nIn //16, bn_acti=True)
+        
+        self.dconv_3_2 = Conv(nIn //16, nIn //16, (dkSize,dkSize),1,padding = (int(d/2+1),int(d/2+1)),
+                            dilation=(int(d/2+1),int(d/2+1)), groups = nIn //16, bn_acti=True)
+        
+        self.dconv_3_3 = Conv(nIn //16, nIn //8, (dkSize,dkSize),1,padding = (int(d/2+1),int(d/2+1)),
+                            dilation=(int(d/2+1),int(d/2+1)), groups = nIn //16, bn_acti=True)
+        
+                      
+        
+        self.conv1x1 = Conv(nIn, nIn, 1, 1, padding=0,bn_acti=False)  
+        
+    def forward(self, input):
+        inp = self.bn_relu_1(input)
+        inp = self.conv1x1_1(inp)
+        
+        o1_1 = self.dconv_1_1(inp)
+        o1_2 = self.dconv_1_2(o1_1)
+        o1_3 = self.dconv_1_3(o1_2)
+        
+        o2_1 = self.dconv_2_1(inp)
+        o2_2 = self.dconv_2_2(o2_1)
+        o2_3 = self.dconv_2_3(o2_2)
+        
+        o3_1 = self.dconv_3_1(inp)
+        o3_2 = self.dconv_3_2(o3_1)
+        o3_3 = self.dconv_3_3(o3_2)
+        
+        o4_1 = self.dconv_4_1(inp)
+        o4_2 = self.dconv_4_2(o4_1)
+        o4_3 = self.dconv_4_3(o4_2)
+        
+        output_1 = torch.cat([o1_1,o1_2,o1_3], 1)
+        output_2 = torch.cat([o2_1,o2_2,o2_3], 1)      
+        output_3 = torch.cat([o3_1,o3_2,o3_3], 1)       
+        output_4 = torch.cat([o4_1,o4_2,o4_3], 1)   
+        
+        
+        ad1 = output_1
+        ad2 = ad1 + output_2
+        ad3 = ad2 + output_3
+        ad4 = ad3 + output_4
+        output = torch.cat([ad1,ad2,ad3,ad4],1)
+        output = self.bn_relu_2(output)
+        output = self.conv1x1(output)
+        
+        return output+input
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