fix static type annotations

src/segmantic/commands/monai_unet_cli.py

@@ -174,7 +174,7 @@ def predict(
         None, "--tissue-list", "-t", help="label descriptors in iSEG format"
     ),
     results_dir: Path = typer.Option(
-        None, "--results-dir", "-r", help="output directory"
+        ..., "--results-dir", "-r", help="output directory"
     ),
     spacing: list[float] = typer.Option(
         [], "--spacing", help="if specified, the image is first resampled"

src/segmantic/seg/monai_unet.py

@@ -142,7 +142,7 @@ def __init__(
         self.best_val_dice = 0.0
         self.best_val_epoch = 0.0
         self.validation_step_outputs: list[dict] = []
-        self.training_step_outputs = []
+        self.training_step_outputs: list[torch.Tensor] = []
 
     @property
     def num_channels(self):
@@ -591,8 +591,8 @@ def train(
 def predict(
     model_file: Path,
     test_images: list[Path],
+    output_dir: Path,
     test_labels: Optional[list[Path]] = None,
-    output_dir: Path = None,
     tissue_dict: dict[str, int] = None,
     channels: tuple[int, ...] = (16, 32, 64, 128, 256),
     strides: tuple[int, ...] = (2, 2, 2, 2),
@@ -750,7 +750,7 @@ def to_one_hot(x):
             delimiter=",",
         )
 
-        if test_labels:
+        if test_labels and confusion is not None:
             plot_confusion_matrix(
                 confusion,
                 tissue_names,