update feature_pipeline accordingly

openfold/data/feature_pipeline.py

@@ -93,14 +93,22 @@ def np_example_to_features(
 
     with torch.no_grad():
         if is_multimer:
-            features,gt_features = input_pipeline_multimer.process_tensors_from_config(
-                tensor_dict,
-                cfg.common,
-                cfg[mode],
-            )
             if mode == 'train':
+                features,gt_features = input_pipeline_multimer.process_tensors_from_config(
+                    tensor_dict,
+                    cfg.common,
+                    cfg[mode],
+                    is_training=True
+                )
+            
                 return {k: v for k, v in features.items()}, gt_features
             else:
+                features = input_pipeline_multimer.process_tensors_from_config(
+                    tensor_dict,
+                    cfg.common,
+                    cfg[mode],
+                    is_training=False
+                )
                 return {k: v for k, v in features.items()}
         
         else: