check for fix prediction

hot_fair_utilities/inference/predict.py

@@ -76,12 +76,13 @@ def predict(
         for idx in range(0, len(image_paths), BATCH_SIZE):
             batch = image_paths[idx:idx + BATCH_SIZE]
             for i, r in enumerate(model(batch, stream=True, conf=confidence, verbose=False)):
-                if r.masks is None:
-                    preds = np.zeros((IMAGE_SIZE, IMAGE_SIZE,), dtype=np.float32)
-                else:
-                    preds = r.masks.data.max(dim=0)[0]  # dim=0 means to take only footprint
+                if hasattr(r, 'masks') and r.masks is not None:  # Check for segmentation output
+                    preds = r.masks.data.max(dim=0)[0]  # Take only footprint
                     preds = torch.where(preds > confidence, torch.tensor(1), torch.tensor(0))
                     preds = preds.detach().cpu().numpy()
+                else:
+                    preds = np.zeros((IMAGE_SIZE, IMAGE_SIZE,), dtype=np.float32)  # Default if no masks
+
                 save_mask(preds, str(f"{prediction_path}/{Path(batch[i]).stem}.png"))
     else:
         raise RuntimeError("Loaded model is not supported")