Create model.py

model.py

@@ -0,0 +1,32 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ConvEncoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 32, 3, stride=1, padding=1), nn.ReLU(),
+            nn.Conv2d(32, 64, 3, stride=2, padding=1), nn.ReLU(),
+            nn.Conv2d(64, 128, 3, stride=2, padding=1), nn.ReLU(),
+        )
+
+    def forward(self, x):
+        return self.features(x)
+
+
+class GenConViT(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.encoder = ConvEncoder()
+        self.classifier = nn.Sequential(
+            nn.Linear(128 * 56 * 56, 256),
+            nn.ReLU(),
+            nn.Linear(256, 2)
+        )
+
+    def forward(self, x):
+        feat = self.encoder(x)
+        feat = feat.view(feat.size(0), -1)
+        out = self.classifier(feat)
+        return out