Add error handling for brain age model

GANDLF/models/brain_age.py

@@ -1,7 +1,7 @@
 import torch.nn as nn
 import sys
 import torchvision
-
+import traceback
 
 def brainage(parameters):
     """
@@ -18,11 +18,13 @@
     """
 
     # Check that the input data is 2D
-    if parameters["model"]["dimension"] != 2:
-        sys.exit("Brain Age predictions only works on 2D data")
+    assert parameters["model"]["dimension"] == 2, "Brain Age predictions only work on 2D data"
 
-    # Load the pretrained VGG16 model
-    model = torchvision.models.vgg16(pretrained=True)
+    try:
+        # Load the pretrained VGG16 model
+        model = torchvision.models.vgg16(pretrained=True)
+    except Exception:
+        sys.exit("Error: Failed to load VGG16 model: " + traceback.format_exc())
 
     # Remove the final convolutional layer
     model.final_convolution_layer = None
@@ -36,19 +38,13 @@
     features = list(model.classifier.children())[:-1]  # Remove the last layer
     features.extend(
         [
-            nn.Linear(
-                num_features, 1024
-            ),  # Add a linear layer with 1024 output features
+            nn.Linear(num_features, 1024),  # Add a linear layer with 1024 output features
             nn.ReLU(True),  # Add a ReLU activation function
             nn.Dropout2d(0.8),  # Add a 2D dropout layer with a probability of 0.8
-            nn.Linear(
-                1024, 1
-            ),  # Add a linear layer with 1 output feature (for brain age prediction)
+            nn.Linear(1024, 1),  # Add a linear layer with 1 output feature (for brain age prediction)
         ]
     )
-    model.classifier = nn.Sequential(
-        *features
-    )  # Replace the model classifier with the modified one
+    model.classifier = nn.Sequential(*features)  # Replace the model classifier with the modified one
 
     # Set the "amp" parameter to False (not yet implemented for VGG)
     parameters["model"]["amp"] = False