Logs

app.py

@@ -134,14 +134,17 @@ def evaluate_model(model, dataloader, device):
 
             # Get predictions
             preds = torch.argmax(logits, dim=-1).cpu().numpy()
+
+            print("Shape of preds:", preds.shape)
+            print("Shape of labels:", labels.shape)
             
             all_preds.extend(preds)
             all_labels.extend(labels)
 
     # Calculate evaluation metrics
     print("evaluate_model sizes")
-    print("Shape of preds:", all_preds.shape)
-    print("Shape of labels:", all_labels.shape)
+    print(len(all_preds[0]))
+    print(len(all_labels[0]))
     all_preds = np.asarray(all_preds, dtype=np.float32)
     all_labels = np.asarray(all_labels, dtype=np.float32)
     print("Flattened sizes")