fix(convexadam): layers_mask as a 1/0 bitmask string, like elastix

ModelSpec.layers_mask is now a str ('1', '0000001' — one char per model layer, '1'=use) matching the elastix ModelSpec, instead of a list[bool]. The engine converts it to the bool list itk-impact's ModelConfiguration expects; '1' -> [True] keeps the current single-layer configs byte-identical.

ConvexAdam_Coarse/Model.py

@@ -100,7 +100,7 @@ class ModelSpec:
 
     ref: Annotated[str, Choices(registry_choices)]
     voxel_size: list[float] = field(default_factory=lambda: [3.0, 3.0, 3.0])
-    layers_mask: list[bool] = field(default_factory=lambda: [True])
+    layers_mask: str = "1"  # per-layer bitmask, one char per model layer ('1' = use, '0' = skip), like elastix
     layers_weight: float = 1.0
     pca: Annotated[int, Range(0, 100)] = 0
     distance: Literal["L1", "L2", "Dice", "Cosine", "NCC"] = "L1"
@@ -508,7 +508,7 @@ class RegistrationNet(network.Network):
             [spec.ref for spec in specs],
             [list(spec.voxel_size) for spec in specs],
             overlap,
-            [list(spec.layers_mask) for spec in specs],
+            [[c == "1" for c in spec.layers_mask] for spec in specs],
             mixed_precision,
             grid_spacing,
             displacement_half_width,

ConvexAdam_Coarse/Prediction.yml

@@ -9,8 +9,7 @@ Predictor:
           - 3.0
           - 3.0
           - 3.0
-          layers_mask:
-          - true
+          layers_mask: '1'
           layers_weight: 1.0
           pca: 0
           distance: L1

ConvexAdam_Composite/Model.py

@@ -100,7 +100,7 @@ class ModelSpec:
 
     ref: Annotated[str, Choices(registry_choices)]
     voxel_size: list[float] = field(default_factory=lambda: [3.0, 3.0, 3.0])
-    layers_mask: list[bool] = field(default_factory=lambda: [True])
+    layers_mask: str = "1"  # per-layer bitmask, one char per model layer ('1' = use, '0' = skip), like elastix
     layers_weight: float = 1.0
     pca: Annotated[int, Range(0, 100)] = 0
     distance: Literal["L1", "L2", "Dice", "Cosine", "NCC"] = "L1"
@@ -508,7 +508,7 @@ class RegistrationNet(network.Network):
             [spec.ref for spec in specs],
             [list(spec.voxel_size) for spec in specs],
             overlap,
-            [list(spec.layers_mask) for spec in specs],
+            [[c == "1" for c in spec.layers_mask] for spec in specs],
             mixed_precision,
             grid_spacing,
             displacement_half_width,

ConvexAdam_Composite/Prediction.yml

@@ -9,8 +9,7 @@ Predictor:
           - 3.0
           - 3.0
           - 3.0
-          layers_mask:
-          - true
+          layers_mask: '1'
           layers_weight: 1.0
           pca: 0
           distance: L1

ConvexAdam_Fine/Model.py

@@ -100,7 +100,7 @@ class ModelSpec:
 
     ref: Annotated[str, Choices(registry_choices)]
     voxel_size: list[float] = field(default_factory=lambda: [3.0, 3.0, 3.0])
-    layers_mask: list[bool] = field(default_factory=lambda: [True])
+    layers_mask: str = "1"  # per-layer bitmask, one char per model layer ('1' = use, '0' = skip), like elastix
     layers_weight: float = 1.0
     pca: Annotated[int, Range(0, 100)] = 0
     distance: Literal["L1", "L2", "Dice", "Cosine", "NCC"] = "L1"
@@ -508,7 +508,7 @@ class RegistrationNet(network.Network):
             [spec.ref for spec in specs],
             [list(spec.voxel_size) for spec in specs],
             overlap,
-            [list(spec.layers_mask) for spec in specs],
+            [[c == "1" for c in spec.layers_mask] for spec in specs],
             mixed_precision,
             grid_spacing,
             displacement_half_width,

ConvexAdam_Fine/Prediction.yml

@@ -9,8 +9,7 @@ Predictor:
           - 3.0
           - 3.0
           - 3.0
-          layers_mask:
-          - true
+          layers_mask: '1'
           layers_weight: 1.0
           pca: 0
           distance: L1