nnunet flags hotfix (#64)

imgtools/autopipeline.py

@@ -725,6 +725,13 @@ def run(self):
 
 def main():
     args = parser()
+
+    if args.nnunet:
+        args.is_nnunet=True
+
+    if args.nnunet_inference:
+        args.is_nnunet_inference=True
+
     args_dict = vars(args)
     # args_dict.pop("input_directory")
     if args.continue_processing:
@@ -755,6 +762,7 @@ def main():
     print(f"Outputted data to {args.output_directory}")
     csv_path = pathlib.Path(args.output_directory, "dataset.csv").as_posix()
     print(f"Dataset info found at {csv_path}")
+
     if args.nnunet:
         json_path = pathlib.Path(args.output_directory, "dataset.json").as_posix()
         print(f"dataset.json for nnU-net can be found at {json_path}")

imgtools/utils/args.py

@@ -52,7 +52,7 @@ def parser():
     parser.add_argument("--custom_train_test_split", default=False, action="store_true",
                         help="Whether to use a custom train-test-split, stored in custom_train_test_split.yaml in the input directory.")
 
-    parser.add_argument("--is_nnunet_inference", default=False, action="store_true",
+    parser.add_argument("--nnunet_inference", default=False, action="store_true",
                         help="Whether to generate data for nnUNet inference.")
 
     parser.add_argument("--dataset_json_path", type=str,

setup.py

@@ -8,7 +8,7 @@
 
 setup(
     name="med-imagetools",
-    version="1.0.3",
+    version="1.0.4",
     author="Sejin Kim, Michal Kazmierski, Kevin Qu, Vishwesh Ramanathan, Benjamin Haibe-Kains",
     author_email="benjamin.haibe.kains@utoronto.ca",
     description="Transparent and reproducible image processing pipelines in Python.",