Create model_def.py

model_def.py

@@ -0,0 +1,42 @@
+import torch
+import torch.nn as nn
+
+
+class EuroSATCNN(nn.Module):
+    def __init__(self, num_classes, img_height=64, img_width=64):
+        super(EuroSATCNN, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(13, 128, kernel_size=4, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2),
+
+            nn.Conv2d(128, 64, kernel_size=4, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2),
+
+            nn.Conv2d(64, 32, kernel_size=4, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2),
+
+            nn.Conv2d(32, 16, kernel_size=4, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2),
+        )
+
+        with torch.no_grad():
+            dummy_input = torch.randn(1, 13, img_height, img_width)
+            out = self.features(dummy_input)
+            fc1_input_size = out.view(1, -1).shape[1]
+
+        self.classifier = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(fc1_input_size, 64),
+            nn.ReLU(),
+            nn.Linear(64, num_classes)
+
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.classifier(x)
+        return x