minor improvements in predict script

scripts/predict.py

@@ -36,6 +36,7 @@ def run(cfg: dict,
         process: bool = True,
         num_models: int = None,
         num_tta_transforms: int = None,
+        test_split: bool = False,
         ):
     """
     Run inference pipeline
@@ -48,6 +49,10 @@ def run(cfg: dict,
             are used
         num_tta_transforms: number of tta transformation; if None the maximum
             number of transformation is used
+        test_split: Typical usage of nnDetection will never require
+            this option! Predict an already preprocessed split of the original
+            training data. The 'test' split needs to be located in fold 0 
+            of a manually created split file.
     """
     plan = load_pickle(training_dir / "plan_inference.pkl")
 
@@ -68,7 +73,13 @@ def run(cfg: dict,
         )
 
     prediction_dir.mkdir(parents=True, exist_ok=True)
+    if test_split:
+        source_dir = preprocessed_output_dir / plan["data_identifier"] / "imagesTr"
+        case_ids = load_pickle(training_dir / "splits.pkl")[0]["test"]
+    else:
         source_dir = preprocessed_output_dir / plan["data_identifier"] / "imagesTs"
+        case_ids = None
+    
     predict_dir(source_dir=source_dir,
                 target_dir=prediction_dir,
                 cfg=cfg,
@@ -78,7 +89,8 @@ def run(cfg: dict,
                 num_tta_transforms=num_tta_transforms,
                 model_fn=load_all_models,
                 restore=True,
-                # do_seg=True, # TODO: change this...
+                case_ids=case_ids,
+                **cfg.get("inference_kwargs", {}),
                 )
 
 
@@ -129,7 +141,10 @@ def main():
                         help="number of tta transforms (per default most tta are chosen)",
                         required=False)
     parser.add_argument('-o', '--overwrites', type=str, nargs='+',
-                        help="overwrites for config file", default=None,
+                        help=("overwrites for config file. "
+                              "inference_kwargs can be used to add additional "
+                              "keyword arguments to inference."),
+                        default=None,
                         required=False)
     parser.add_argument('--no_preprocess', help="Preprocess test data", action='store_false')
     parser.add_argument('--force_args',
@@ -137,6 +152,12 @@ def main():
                         "and fold might differ from the original one. "
                         "This forces an overwrite to the passed in arguments of"
                         " this function. This can be dangerous!"), action='store_true')
+    parser.add_argument('--test_split',
+                        help=("Typical usage of nnDetection will never require "
+                              "this option! Predict an already preprocessed "
+                              "split of the original training data. "
+                              "The 'test' split needs to be located in fold 0 "
+                              "of a manually created split file."))
 
     args = parser.parse_args()
     model = args.model
@@ -146,12 +167,16 @@ def main():
     num_tta_transforms = args.num_tta
     ov = args.overwrites
     force_args = args.force_args
+    test_split = args.test_split
 
     task_name = get_task(task, name=True)
     task_model_dir = Path(os.getenv("det_models"))
     training_dir = get_training_dir(task_model_dir / task_name / model, fold)
 
     process = args.no_preprocess
+    if test_split:
+        raise ValueError("When using the test split option raw data is not "
+                         "supported. Need to add --no_preprocess flag!")
 
     cfg = OmegaConf.load(str(training_dir / "config.yaml"))
 
@@ -170,6 +195,7 @@ def main():
         process=process,
         num_models=num_models,
         num_tta_transforms=num_tta_transforms,
+        test_split=test_split,
         )