From 70c0d5cead17947de979b615f634f16a18e56c08 Mon Sep 17 00:00:00 2001
From: Serein <serein7z@163.com>
Date: Tue, 11 Apr 2023 13:27:10 +0800
Subject: [PATCH 1/2] replace out+=residual with out=out+residual

---
 model.py | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/model.py b/model.py
index 80d94db..7343d5f 100644
--- a/model.py
+++ b/model.py
@@ -12,13 +12,14 @@
 nonlinearity = partial(F.relu, inplace=True)
 
 
-
-
 def downsample():
     return nn.MaxPool3d(kernel_size=2, stride=2)
+
+
 def deconv(in_channels, out_channels):
     return nn.ConvTranspose3d(in_channels, out_channels, kernel_size=2, stride=2)
 
+
 def initialize_weights(*models):
     for model in models:
         for m in model.modules():
@@ -32,6 +33,7 @@ def initialize_weights(*models):
 
 
 class ResDecoder(nn.Module):
+
     def __init__(self, in_channels):
         super(ResDecoder, self).__init__()
         self.conv1 = nn.Conv3d(in_channels, in_channels, kernel_size=3, padding=1)
@@ -45,10 +47,13 @@ def forward(self, x):
         residual = self.conv1x1(x)
         out = self.relu(self.bn1(self.conv1(x)))
         out = self.relu(self.bn2(self.conv2(out)))
-        out += residual
+        out = out + residual
         out = self.relu(out)
         return out
+
+
 class SFConv(nn.Module):
+
     def __init__(self, features, M=2, r=4, L=32):
         """ Constructor
         Args:
@@ -75,10 +80,9 @@ def __init__(self, features, M=2, r=4, L=32):
         self.fc = nn.Linear(features, d)
         self.fcs = nn.ModuleList([])
         for i in range(M):
-            self.fcs.append(
-                nn.Linear(d, features)
-            )
+            self.fcs.append(nn.Linear(d, features))
         self.softmax = nn.Softmax(dim=1)
+
     def forward(self, x1, x2):
         # for i, conv in enumerate(self.convs):
         #     fea = conv(x).unsqueeze_(dim=1)
@@ -101,10 +105,10 @@ def forward(self, x1, x2):
         attention_vectors = attention_vectors.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
         fea_v = (feas * attention_vectors).sum(dim=1)
         return fea_v
-        
 
 
 class SF_Decoder(nn.Module):
+
     def __init__(self, out_channels):
         super(SF_Decoder, self).__init__()
         self.conv1 = SFConv(out_channels)
@@ -125,7 +129,9 @@ def forward(self, x1, x2):
         # out = self.relu(out)
         return out
 
+
 class ResEncoder(nn.Module):
+
     def __init__(self, in_channels, out_channels):
         super(ResEncoder, self).__init__()
         self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size=3, padding=1)
@@ -139,10 +145,13 @@ def forward(self, x):
         residual = self.conv1x1(x)
         out = self.relu(self.bn1(self.conv1(x)))
         out = self.relu(self.bn2(self.conv2(out)))
-        out += residual
+        out = out + residual
         out = self.relu(out)
         return out
+
+
 class ER_Net(nn.Module):
+
     def __init__(self, classes, channels):
         # def __init__(self):
 

From b16baf2ad70dd7fa39a78b9d4d9885458f470a3f Mon Sep 17 00:00:00 2001
From: Serein <serein7z@163.com>
Date: Tue, 11 Apr 2023 13:29:20 +0800
Subject: [PATCH 2/2] replace out+=residual with out=out+residual

---
 model.py | 21 ++++++---------------
 1 file changed, 6 insertions(+), 15 deletions(-)

diff --git a/model.py b/model.py
index 7343d5f..b0e7c10 100644
--- a/model.py
+++ b/model.py
@@ -12,14 +12,13 @@
 nonlinearity = partial(F.relu, inplace=True)
 
 
-def downsample():
-    return nn.MaxPool3d(kernel_size=2, stride=2)
 
 
+def downsample():
+    return nn.MaxPool3d(kernel_size=2, stride=2)
 def deconv(in_channels, out_channels):
     return nn.ConvTranspose3d(in_channels, out_channels, kernel_size=2, stride=2)
 
-
 def initialize_weights(*models):
     for model in models:
         for m in model.modules():
@@ -33,7 +32,6 @@ def initialize_weights(*models):
 
 
 class ResDecoder(nn.Module):
-
     def __init__(self, in_channels):
         super(ResDecoder, self).__init__()
         self.conv1 = nn.Conv3d(in_channels, in_channels, kernel_size=3, padding=1)
@@ -50,10 +48,7 @@ def forward(self, x):
         out = out + residual
         out = self.relu(out)
         return out
-
-
 class SFConv(nn.Module):
-
     def __init__(self, features, M=2, r=4, L=32):
         """ Constructor
         Args:
@@ -80,9 +75,10 @@ def __init__(self, features, M=2, r=4, L=32):
         self.fc = nn.Linear(features, d)
         self.fcs = nn.ModuleList([])
         for i in range(M):
-            self.fcs.append(nn.Linear(d, features))
+            self.fcs.append(
+                nn.Linear(d, features)
+            )
         self.softmax = nn.Softmax(dim=1)
-
     def forward(self, x1, x2):
         # for i, conv in enumerate(self.convs):
         #     fea = conv(x).unsqueeze_(dim=1)
@@ -105,10 +101,10 @@ def forward(self, x1, x2):
         attention_vectors = attention_vectors.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
         fea_v = (feas * attention_vectors).sum(dim=1)
         return fea_v
+        
 
 
 class SF_Decoder(nn.Module):
-
     def __init__(self, out_channels):
         super(SF_Decoder, self).__init__()
         self.conv1 = SFConv(out_channels)
@@ -129,9 +125,7 @@ def forward(self, x1, x2):
         # out = self.relu(out)
         return out
 
-
 class ResEncoder(nn.Module):
-
     def __init__(self, in_channels, out_channels):
         super(ResEncoder, self).__init__()
         self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size=3, padding=1)
@@ -148,10 +142,7 @@ def forward(self, x):
         out = out + residual
         out = self.relu(out)
         return out
-
-
 class ER_Net(nn.Module):
-
     def __init__(self, classes, channels):
         # def __init__(self):