Added the possible_capability_values base method for estimators...

New `possible_capability_values` base method for estimators. The goal
is to return a list of possible values for each non-numerical
capability, as some performance metrics may benefit from this complete
listing.

Additionally, added a new exception to indicate when a capability is
invalid in a given context, e.g., when the model does not support it.

src/facebias/estimators/__init__.py

@@ -16,6 +16,11 @@ class Capability(StrEnum):
     ETHNICITY = "ethinicity"
 
 
+class InvalidCapabilityError(Exception):
+    def __init__(self, msg="Invalid capability for model."):
+        super(InvalidCapability, self).__init__(msg=msg)
+
+
 class BaseEstimator(ABC):
     @abstractmethod
     def predict(self, images: dict[Path, np.ndarray]) -> dict[Path, dict[Capability, Any]]:
@@ -35,3 +40,8 @@ class BaseEstimator(ABC):
         face image, such as `age_group`, `sex`, `skin_color`, etc.
         """
         pass
+
+    @abstractclassmethod
+    def possible_capability_values(cap: Capability) -> list:
+        """Returns all possible values for a given model capability."""
+        pass

src/facebias/estimators/fairface.py

@@ -8,7 +8,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torchvision
-from facebias.estimators import BaseEstimator, Capability
+from facebias.estimators import BaseEstimator, Capability, InvalidCapabilityError
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from torchvision import transforms
 
@@ -96,9 +96,20 @@ class FairFace(BaseEstimator):
     def capabilities() -> list[Capability]:
         return [Capability.AGEGROUP, Capability.SEX] #, Capability.ETHNICITY]
 
+    def possible_capability_values(cap: Capability) -> list:
+        if cap == Capability.AGEGROUP:
+            return ["0-2", "3-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70+"]
+        elif cap == Capability.SEX:
+            return ['m', 'f']
+        # elif cap == Capability.ETHNICITY:
+        #     return ["White", "Black", "Asian", "Indian"]
+        else:
+            raise InvalidCapabilityError()
+
 
 def _to_age_label(age):
-    return "{}-{}".format(age * 10, age * 10 + 9)
+    return FairFace.possible_classes(Capability.AGEGROUP)[age]
+    # return "{}-{}".format(age * 10, age * 10 + 9)
 
 
 def _to_ethno_label(val):

src/facebias/estimators/mivolov1.py

@@ -9,7 +9,7 @@ import torch
 import torch.nn as nn
 import torchvision
 from facebias import load_dataset
-from facebias.estimators import BaseEstimator, Capability
+from facebias.estimators import BaseEstimator, Capability, InvalidCapabilityError
 from facebias.estimators.mivolo.mi_volo import MiVOLO
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from torchvision import transforms
@@ -80,10 +80,28 @@ class MiVOLOv1(BaseEstimator):
     def capabilities() -> list[Capability]:
         return [Capability.AGE, Capability.AGEGROUP, Capability.SEX]
 
+    def possible_capability_values(cap: Capability) -> list[str]:
+        if cap == Capability.AGE:
+            return []
+        elif cap == Capability.AGEGROUP:
+            return ["0-2", "3-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70+"]
+        elif cap == Capability.SEX:
+            return ['m', 'f']
+        else:
+            raise InvalidCapabilityError()
+
 
 def _to_age_label(age):
-    base_age = int(age // 10 * 10)
+    iage = int(age)
-    return "{}-{}".format(base_age, base_age + 9)
+    if iage < 3:
+        return "0-2"
+    elif iage < 10:
+        return "3-9"
+    elif iage > 69:
+        return "70+"
+
+    d = int(iage // 10 * 10)
+    return "{}-{}".format(d, d + 9)
 
 
 if __name__ == '__main__':