use predict_from_files_sequential when nps and npp are zero

Author: LennyN95

nnunetv2/inference/predict_from_raw_data.py

@@ -959,13 +959,26 @@ def predict_entry_point():
         args.f,
         checkpoint_name=args.chk
     )
-    predictor.predict_from_files(args.i, args.o, save_probabilities=args.save_probabilities,
-                                 overwrite=not args.continue_prediction,
-                                 num_processes_preprocessing=args.npp,
-                                 num_processes_segmentation_export=args.nps,
-                                 folder_with_segs_from_prev_stage=args.prev_stage_predictions,
-                                 num_parts=args.num_parts,
-                                 part_id=args.part_id)
+    
+    run_sequential = args.nps == 0 and args.npp == 0
+    
+    if run_sequential:
+        
+        print("Running in non-multiprocessing mode")
+        predictor.predict_from_files_sequential(args.i, args.o, save_probabilities=args.save_probabilities,
+                                                overwrite=not args.continue_prediction,
+                                                folder_with_segs_from_prev_stage=args.prev_stage_predictions)
+    
+    else:
+        
+        predictor.predict_from_files(args.i, args.o, save_probabilities=args.save_probabilities,
+                                    overwrite=not args.continue_prediction,
+                                    num_processes_preprocessing=args.npp,
+                                    num_processes_segmentation_export=args.nps,
+                                    folder_with_segs_from_prev_stage=args.prev_stage_predictions,
+                                    num_parts=args.num_parts,
+                                    part_id=args.part_id)
+    
     # r = predict_from_raw_data(args.i,
     #                           args.o,
     #                           model_folder,