--- a
+++ b/CaraNet/lib/self_attention.py
@@ -0,0 +1,48 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug 10 17:15:44 2021
+
+@author: angelou
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from lib.conv_layer import Conv
+import math
+
+class self_attn(nn.Module):
+    def __init__(self, in_channels, mode='hw'):
+        super(self_attn, self).__init__()
+
+        self.mode = mode
+
+        self.query_conv = Conv(in_channels, in_channels // 8, kSize=(1, 1),stride=1,padding=0)
+        self.key_conv = Conv(in_channels, in_channels // 8, kSize=(1, 1),stride=1,padding=0)
+        self.value_conv = Conv(in_channels, in_channels, kSize=(1, 1),stride=1,padding=0)
+
+        self.gamma = nn.Parameter(torch.zeros(1))
+        self.sigmoid = nn.Sigmoid()
+    def forward(self, x):
+        batch_size, channel, height, width = x.size()
+
+        axis = 1
+        if 'h' in self.mode:
+            axis *= height
+        if 'w' in self.mode:
+            axis *= width
+
+        view = (batch_size, -1, axis)
+
+        projected_query = self.query_conv(x).view(*view).permute(0, 2, 1)
+        projected_key = self.key_conv(x).view(*view)
+
+        attention_map = torch.bmm(projected_query, projected_key)
+        attention = self.sigmoid(attention_map)
+        projected_value = self.value_conv(x).view(*view)
+
+        out = torch.bmm(projected_value, attention.permute(0, 2, 1))
+        out = out.view(batch_size, channel, height, width)
+
+        out = self.gamma * out + x
+        return out
\ No newline at end of file