Create 1024_model.py

1024_model.py

@@ -0,0 +1,36 @@
+class UNet(nn.Module):
+    def __init__(self):
+        super(UNet, self).__init__()
+
+        # Encoder
+        self.encoder = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1),  # 256 -> 128
+            nn.ReLU(inplace=True),
+            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),  # 128 -> 64
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),  # 64 -> 32
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1),  # 32 -> 16
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 1024, kernel_size=4, stride=2, padding=1),  # 16 -> 8
+            nn.ReLU(inplace=True)
+        )
+
+        # Decoder
+        self.decoder = nn.Sequential(
+            nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1),  # 8 -> 16
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),  # 16 -> 32
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),  # 32 -> 64
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),  # 64 -> 128
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1),  # 128 -> 256
+            nn.Tanh()  # Output range [-1, 1]
+        )
+
+    def forward(self, x):
+        enc = self.encoder(x)
+        dec = self.decoder(enc)
+        return dec