Modify run_prediction so the user is able to "deselect" view to not include.

- parser_defaults.py: Allow empty or none for --cfg_ax, --cfg_sag, --cfg_cor.
- run_prediction.py: If cgf_ax/sag/cor is none/empty, do not use that view
- some formatting cleanup

Author: dkuegler

FastSurferCNN/inference.py

@@ -382,9 +382,7 @@ def eval(
 
                     # check if we need a special mapping (e.g. as for sagittal)
                     if self.get_plane() == "sagittal":
-                        pred = map_prediction_sagittal2full(
-                            pred, num_classes=self.get_num_classes(), lut=self.lut
-                        )
+                        pred = map_prediction_sagittal2full(pred, num_classes=self.get_num_classes(), lut=self.lut)
 
                     # permute the prediction into the out slice order
                     pred = pred.permute(*self.permute_order[plane]).to(

FastSurferCNN/run_prediction.py

@@ -254,34 +254,29 @@ def __init__(
         self.labels = self.lut["ID"].values
         self.torch_labels = torch.from_numpy(self.lut["ID"].values)
         self.names = ["SubjectName", "Average", "Subcortical", "Cortical"]
-        self.cfg_fin, cfg_cor, cfg_sag, cfg_ax = args2cfg(
-            cfg_ax, cfg_cor, cfg_sag, batch_size=batch_size,
-        )
+        self.cfg_fin, cfg_cor, cfg_sag, cfg_ax = args2cfg(cfg_ax, cfg_cor, cfg_sag, batch_size=batch_size)
         # the order in this dictionary dictates the order in the view aggregation
         self.view_ops = {
             "coronal": {"cfg": cfg_cor, "ckpt": ckpt_cor},
             "sagittal": {"cfg": cfg_sag, "ckpt": ckpt_sag},
             "axial": {"cfg": cfg_ax, "ckpt": ckpt_ax},
         }
-        self.num_classes = max(
-            view["cfg"].MODEL.NUM_CLASSES for view in self.view_ops.values()
-        )
+        # self.num_classes = max(view["cfg"].MODEL.NUM_CLASSES for view in self.view_ops.values() if view["cfg"])
+        # currently, num_classes must be 79 in all cases. This seems like it is a config option here, but in reality it
+        # is not, so we hard-code it here. Only sagittal has < 79 classes, but num_classes is only used to set the
+        # dimensions of the view aggregation tensor, which is after splitting the classes from sagittal to all.
+        self.num_classes = 79
         self.models = {}
         for plane, view in self.view_ops.items():
             if all(view[key] is not None for key in ("cfg", "ckpt")):
-                self.models[plane] = Inference(
-                    view["cfg"], ckpt=view["ckpt"], device=self.device, lut=self.lut,
-                )
+                self.models[plane] = Inference(view["cfg"], ckpt=view["ckpt"], device=self.device, lut=self.lut)
 
         if vox_size == "min":
             self.vox_size = "min"
         elif 0.0 < float(vox_size) <= 1.0:
             self.vox_size = float(vox_size)
         else:
-            raise ValueError(
-                f"Invalid value for vox_size, must be between 0 and 1 or 'min', was "
-                f"{vox_size}."
-            )
+            raise ValueError(f"Invalid value for vox_size, must be between 0 and 1 or 'min', was {vox_size}.")
         self.conform_to_1mm_threshold = conform_to_1mm_threshold
 
     @property
@@ -571,15 +566,15 @@ def make_parser():
         parser,
         "checkpoint",
         files,
-        CHECKPOINT_PATHS_FILE
+        CHECKPOINT_PATHS_FILE,
     )
 
     # 4. CFG-file with default options for network
     parser = parser_defaults.add_plane_flags(
         parser,
         "config",
         files,
-        CHECKPOINT_PATHS_FILE
+        CHECKPOINT_PATHS_FILE,
     )
 
     # 5. technical parameters

FastSurferCNN/utils/parser_defaults.py

@@ -407,6 +407,9 @@ def add_plane_flags(
     if configtype not in PLANE_SHORT:
         raise ValueError("type must be either config or checkpoint.")
 
+    def cast_type(__value: str) -> Path | None:
+        return Path(__value) if configtype == "checkpoint" and __value and __value.lower() != "none" else None
+
     from FastSurferCNN.utils.checkpoint import load_checkpoint_config_defaults
     defaults = load_checkpoint_config_defaults(configtype, defaults_path)
 
@@ -422,7 +425,7 @@ def add_plane_flags(
         plane_short = plane[: index + 2]
         parser.add_argument(
             f"--{PLANE_SHORT[configtype]}_{plane_short}",
-            type=Path,
+            type=cast_type,
             dest=f"{PLANE_SHORT[configtype]}_{plane_short}",
             help=PLANE_HELP[configtype].format(plane),
             default=path,