--- a
+++ b/CaraNet/lib/partial_decoder.py
@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug 10 17:20:30 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 aggregation(nn.Module):
+    # dense aggregation, it can be replaced by other aggregation previous, such as DSS, amulet, and so on.
+    # used after MSF
+    def __init__(self, channel):
+        super(aggregation, self).__init__()
+        self.relu = nn.ReLU(True)
+
+        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
+        self.conv_upsample1 = Conv(32, 32, 3,1, padding=1)
+        self.conv_upsample2 = Conv(32, 32, 3,1, padding=1)
+        self.conv_upsample3 = Conv(32, 32, 3,1, padding=1)
+        self.conv_upsample4 = Conv(32, 32, 3,1, padding=1)
+        self.conv_upsample5 = Conv(2*32, 2*32, 3,1, padding=1)
+
+        self.conv_concat2 = Conv(2*32, 2*32, 3,1, padding=1)
+        self.conv_concat3 = Conv(3*32, 3*32, 3,1, padding=1)
+        self.conv4 = Conv(3*32, 3*32, 3,1, padding=1)
+        self.conv5 = nn.Conv2d(3*32 , 1, 1)
+
+    def forward(self, x1, x2, x3):
+        x1_1 = x1
+        x2_1 = self.conv_upsample1(self.upsample(x1)) * x2
+        x3_1 = self.conv_upsample2(self.upsample(self.upsample(x1))) \
+               * self.conv_upsample3(self.upsample(x2)) * x3
+
+        x2_2 = torch.cat((x2_1, self.conv_upsample4(self.upsample(x1_1))), 1)
+        x2_2 = self.conv_concat2(x2_2)
+
+        x3_2 = torch.cat((x3_1, self.conv_upsample5(self.upsample(x2_2))), 1)
+        x3_2 = self.conv_concat3(x3_2)
+
+        x = self.conv4(x3_2)
+        x = self.conv5(x)
+
+        return x
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