[BUGFIX] Fix reasonings initializer dimension bug

prototorch/core/competitions.py

@@ -42,7 +42,6 @@ def cbcc(detections: torch.Tensor, reasonings: torch.Tensor):
     nk = (1 - A) * B
     numerator = (detections @ (pk - nk).T) + nk.sum(1)
     probs = numerator / (pk + nk).sum(1)
-    # probs = probs.squeeze(0)
     return probs
 
 

prototorch/core/components.py

@@ -13,6 +13,7 @@ from .initializers import (
     AbstractLabelsInitializer,
     AbstractReasoningsInitializer,
     LabelsInitializer,
+    PurePositiveReasoningsInitializer,
     RandomReasoningsInitializer,
 )
 
@@ -308,10 +309,13 @@ class ReasoningComponents(AbstractComponents):
     three element probability distribution.
 
     """
-    def __init__(self, distribution: Union[dict, list, tuple],
-                 components_initializer: AbstractComponentsInitializer,
-                 reasonings_initializer: AbstractReasoningsInitializer,
-                 **kwargs):
+    def __init__(
+            self,
+            distribution: Union[dict, list, tuple],
+            components_initializer: AbstractComponentsInitializer,
+            reasonings_initializer:
+        AbstractReasoningsInitializer = PurePositiveReasoningsInitializer(),
+            **kwargs):
         super().__init__(**kwargs)
         self.add_components(distribution, components_initializer,
                             reasonings_initializer)

prototorch/core/initializers.py

@@ -296,7 +296,7 @@ class OneHotLabelsInitializer(LabelsInitializer):
 # Reasonings
 class AbstractReasoningsInitializer(ABC):
     """Abstract class for all reasonings initializers."""
-    def __init__(self, components_first=True):
+    def __init__(self, components_first: bool = True):
         self.components_first = components_first
 
     def compute_shape(self, distribution):
@@ -375,7 +375,7 @@ class PurePositiveReasoningsInitializer(AbstractReasoningsInitializer):
         num_components, num_classes, _ = self.compute_shape(distribution)
         A = OneHotLabelsInitializer().generate(distribution)
         B = torch.zeros(num_components, num_classes)
-        reasonings = torch.stack([A, B]).permute(2, 1, 0)
+        reasonings = torch.stack([A, B], dim=-1)
         reasonings = self.generate_end_hook(reasonings)
         return reasonings