Add SolveigModel implementation

utils/models/solveig_model.py

@@ -4,8 +4,68 @@
 
 
 class SolveigModel(nn.Module):
-    def __init__(self):
+    """
+        A Convolutional Neural Network model for classification.
+
+        Args:
+        ----
+        in_channels : int
+            Number of input channels (e.g., 3 for RGB images, 1 for grayscale).
+        num_classes : int
+            The number of output classes (e.g., 2 for binary classification).
+
+        Attributes:
+        -----------
+        conv_block1 : nn.Sequential
+            First convolutional block containing a convolutional layer, ReLU activation, and max-pooling.
+        conv_block2 : nn.Sequential
+            Second convolutional block containing a convolutional layer and ReLU activation.
+        conv_block3 : nn.Sequential
+            Third convolutional block containing a convolutional layer and ReLU activation.
+        fc1 : nn.Linear
+            Fully connected layer that outputs the final classification scores.
+        """
+
+    def __init__(self, in_channels, num_classes):
         super().__init__()
 
+        # Define the first convolutional block (conv + relu + maxpool)
+        self.conv_block1 = nn.Sequential(
+            nn.Conv2d(in_channels=in_channels, out_channels=25, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2)
+        )
+
+        # Define the second convolutional block (conv + relu)
+        self.conv_block2 = nn.Sequential(
+            nn.Conv2d(in_channels=25, out_channels=50, kernel_size=3, padding=1),
+            nn.ReLU()
+        )
+
+        # Define the third convolutional block (conv + relu)
+        self.conv_block3 = nn.Sequential(
+            nn.Conv2d(in_channels=50, out_channels=100, kernel_size=3, padding=1),
+            nn.ReLU()
+        )
+
+        self.fc1 = nn.Linear(100 * 8 * 8, num_classes)
+
     def forward(self, x):
-        return
+        x = self.conv_block1(x)
+        x = self.conv_block2(x)
+        x = self.conv_block3(x)
+        x = torch.flatten(x, 1)
+
+        x = self.fc1(x)
+        x = nn.Softmax(x)
+
+        return x
+
+
+if __name__ == "__main__":
+    model = SolveigModel(3, 3)
+
+    x = torch.randn(1, 3, 16, 16)
+    y = model(x)
+
+    print(y)