proposed: Refactor to reformat code

Author: synml

models/proposed.py

@@ -7,54 +7,6 @@
 import models.backbone
 
 
-# ASPP(Atrous Spatial Pyramid Pooling) Module
-class ASPP(nn.Module):
-    def __init__(self, in_channels: int, out_channels: int):
-        super(ASPP, self).__init__()
-
-        self.branch1 = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, kernel_size=1),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True)
-        )
-        self.branch2 = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=3, dilation=3),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True)
-        )
-        self.branch3 = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=6, dilation=6),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True)
-        )
-        self.branch4 = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=9, dilation=9),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True)
-        )
-        self.branch5 = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(in_channels, out_channels, kernel_size=1),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True)
-        )
-        self.final_conv = nn.Sequential(
-            nn.Conv2d(out_channels * 5, out_channels, kernel_size=1),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True)
-        )
-
-    def forward(self, x):
-        branch1 = self.branch1(x)
-        branch2 = self.branch2(x)
-        branch3 = self.branch3(x)
-        branch4 = self.branch4(x)
-        branch5 = F.interpolate(self.branch5(x), size=(x.size()[2], x.size()[3]), mode="bilinear", align_corners=False)
-
-        out = self.final_conv(torch.cat([branch1, branch2, branch3, branch4, branch5], dim=1))
-        return out
-
-
 class Proposed(nn.Module):
     def __init__(self, num_classes: int):
         super(Proposed, self).__init__()
@@ -115,6 +67,54 @@ def make_channel_adjuster(self, in_channels: int, out_channels: int):
         )
 
 
+# ASPP(Atrous Spatial Pyramid Pooling) Module
+class ASPP(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int):
+        super(ASPP, self).__init__()
+
+        self.branch1 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+        self.branch2 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=3, dilation=3),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+        self.branch3 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=6, dilation=6),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+        self.branch4 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=9, dilation=9),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+        self.branch5 = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels, out_channels, kernel_size=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+        self.final_conv = nn.Sequential(
+            nn.Conv2d(out_channels * 5, out_channels, kernel_size=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x):
+        branch1 = self.branch1(x)
+        branch2 = self.branch2(x)
+        branch3 = self.branch3(x)
+        branch4 = self.branch4(x)
+        branch5 = F.interpolate(self.branch5(x), size=(x.size()[2], x.size()[3]), mode="bilinear", align_corners=False)
+
+        out = self.final_conv(torch.cat([branch1, branch2, branch3, branch4, branch5], dim=1))
+        return out
+
+
 if __name__ == '__main__':
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     model = Proposed(20).to(device)