Add ChristianModel: 2 layer CNN w/maxpooling

main.py

@@ -66,7 +66,7 @@ def main():
         "--modelname",
         type=str,
         default="MagnusModel",
-        choices=["MagnusModel"],
+        choices=["MagnusModel", "ChristianModel"],
         help="Model which to be trained on",
     )
     parser.add_argument(

utils/load_model.py

@@ -1,12 +1,15 @@
 import torch.nn as nn
 
-from .models import MagnusModel
+from .models import ChristianModel, MagnusModel
 
 
-def load_model(modelname: str) -> nn.Module:
-    if modelname == "MagnusModel":
-        return MagnusModel()
-    else:
-        raise ValueError(
-            f"Model: {modelname} has not been implemented. \nCheck the documentation for implemented metrics, or check your spelling"
-        )
+def load_model(modelname: str, *args, **kwargs) -> nn.Module:
+    match modelname.lower():
+        case "magnusmodel":
+            return MagnusModel(*args, **kwargs)
+        case "christianmodel":
+            return ChristianModel(*args, **kwargs)
+        case _:
+            raise ValueError(
+                f"Model: {modelname} has not been implemented. \nCheck the documentation for implemented metrics, or check your spelling"
+            )

utils/models/__init__.py

@@ -1,3 +1,4 @@
-__all__ = ["MagnusModel"]
+__all__ = ["MagnusModel", "ChristianModel"]
 
+from .christian_model import ChristianModel
 from .magnus_model import MagnusModel

utils/models/christian_model.py

@@ -0,0 +1,92 @@
+import pytest
+import torch
+import torch.nn as nn
+
+
+class CNNBlock(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+        )
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.maxpool(x)
+        x = self.relu(x)
+        return x
+
+
+class ChristianModel(nn.Module):
+    """Simple CNN model for image classification.
+
+    Args
+    ----
+    in_channels : int
+        Number of input channels.
+    num_classes : int
+        Number of classes in the dataset.
+
+    Processing Images
+    -----------------
+    Input: (N, C, H, W)
+        N: Batch size
+        C: Number of input channels
+        H: Height of the input image
+        W: Width of the input image
+
+    Example:
+    For grayscale images, C = 1.
+
+    Input Image Shape: (5, 1, 16, 16)
+    CNN1 Output Shape: (5, 50, 8, 8)
+    CNN2 Output Shape: (5, 100, 4, 4)
+    FC Output Shape: (5, num_classes)
+    """
+    def __init__(self, in_channels, num_classes):
+        super().__init__()
+
+        self.cnn1 = CNNBlock(in_channels, 50)
+        self.cnn2 = CNNBlock(50, 100)
+
+        self.fc1 = nn.Linear(100 * 4 * 4, num_classes)
+        self.softmax = nn.Softmax(dim=1)
+
+    def forward(self, x):
+        x = self.cnn1(x)
+        x = self.cnn2(x)
+
+        x = x.view(x.size(0), -1)
+        x = self.fc1(x)
+        x = self.softmax(x)
+
+        return x
+
+
+@pytest.mark.parametrize("in_channels, num_classes", [(1, 6), (3, 6)])
+def test_christian_model(in_channels, num_classes):
+    n, c, h, w = 5, in_channels, 16, 16
+
+    model = ChristianModel(c, num_classes)
+
+    x = torch.randn(n, c, h, w)
+    y = model(x)
+
+    assert y.shape == (n, num_classes), f"Shape: {y.shape}"
+    assert y.sum(dim=1).allclose(torch.ones(n), atol=1e-5), f"Softmax output should sum to 1, but got: {y.sum()}"
+
+
+if __name__ == "__main__":
+
+    model = ChristianModel(3, 7)
+
+    x = torch.randn(3, 3, 16, 16)
+    y = model(x)
+
+    print(y)