diff --git a/model/attention/BAM.py b/model/attention/BAM.py
index 2f51f0b..9f1d921 100644
--- a/model/attention/BAM.py
+++ b/model/attention/BAM.py
@@ -39,7 +39,7 @@ def __init__(self,channel,reduction=16,num_layers=3,dia_val=2):
         self.sa.add_module('bn_reduce1',nn.BatchNorm2d(channel//reduction))
         self.sa.add_module('relu_reduce1',nn.ReLU())
         for i in range(num_layers):
-            self.sa.add_module('conv_%d'%i,nn.Conv2d(kernel_size=3,in_channels=channel//reduction,out_channels=channel//reduction,padding=1,dilation=dia_val))
+            self.sa.add_module('conv_%d'%i,nn.Conv2d(kernel_size=3,in_channels=channel//reduction,out_channels=channel//reduction,padding="same",dilation=dia_val))
             self.sa.add_module('bn_%d'%i,nn.BatchNorm2d(channel//reduction))
             self.sa.add_module('relu_%d'%i,nn.ReLU())
         self.sa.add_module('last_conv',nn.Conv2d(channel//reduction,1,kernel_size=1))
@@ -76,7 +76,6 @@ def init_weights(self):
                     init.constant_(m.bias, 0)
 
     def forward(self, x):
-        b, c, _, _ = x.size()
         sa_out=self.sa(x)
         ca_out=self.ca(x)
         weight=self.sigmoid(sa_out+ca_out)
@@ -85,8 +84,9 @@ def forward(self, x):
 
 
 if __name__ == '__main__':
-    input=torch.randn(50,512,7,7)
-    bam = BAMBlock(channel=512,reduction=16,dia_val=2)
+    input=torch.randn(3,32,512,512)
+    bam = BAMBlock(channel=32,reduction=16,dia_val=2)
+    bam.eval()
     output=bam(input)
     print(output.shape)