[REFACTOR] Remove CustomLabelsInitializer

prototorch/components/components.py

@@ -5,7 +5,6 @@ import warnings
 import torch
 from prototorch.components.initializers import (ClassAwareInitializer,
                                                 ComponentsInitializer,
-                                                CustomLabelsInitializer,
                                                 EqualLabelsInitializer,
                                                 UnequalLabelsInitializer,
                                                 ZeroReasoningsInitializer)
@@ -21,7 +20,9 @@ def get_labels_object(distribution):
                 distribution["num_classes"],
                 distribution["prototypes_per_class"])
         else:
-            labels = CustomLabelsInitializer(distribution)
+            clabels = list(distribution.keys())
+            dist = list(distribution.values())
+            labels = UnequalLabelsInitializer(dist, clabels)
     elif isinstance(distribution, tuple):
         num_classes, prototypes_per_class = distribution
         labels = EqualLabelsInitializer(num_classes, prototypes_per_class)
@@ -156,7 +157,7 @@ class LabeledComponents(Components):
 
         # Components
         if isinstance(initializer, ClassAwareInitializer):
-            _new = initializer.generate(len(new_labels), labels.distribution)
+            _new = initializer.generate(len(new_labels), distribution)
         else:
             _new = initializer.generate(len(new_labels))
         _components = torch.cat([self._components, _new])

prototorch/components/initializers.py

@@ -174,19 +174,17 @@ class LabelsInitializer:
 
 
 class UnequalLabelsInitializer(LabelsInitializer):
-    def __init__(self, dist):
+    def __init__(self, dist, clabels=None):
         self.dist = dist
+        self.clabels = clabels or range(len(self.dist))
 
     @property
     def distribution(self):
         return self.dist
 
-    def generate(self, clabels=None, dist=None):
+    def generate(self):
-        if not clabels:
+        targets = list(
-            clabels = range(len(self.dist))
+            chain(*[[i] * n for i, n in zip(self.clabels, self.dist)]))
-        if not dist:
-            dist = self.dist
-        targets = list(chain(*[[i] * n for i, n in zip(clabels, dist)]))
         return torch.LongTensor(targets)
 
 
@@ -203,13 +201,6 @@ class EqualLabelsInitializer(LabelsInitializer):
         return torch.arange(self.classes).repeat(self.per_class, 1).T.flatten()
 
 
-class CustomLabelsInitializer(UnequalLabelsInitializer):
-    def generate(self):
-        clabels = list(self.dist.keys())
-        dist = list(self.dist.values())
-        return super().generate(clabels, dist)
-
-
 # Reasonings
 class ReasoningsInitializer:
     def generate(self, length):

prototorch/modules/competitions.py

@@ -10,6 +10,7 @@ class WTAC(torch.nn.Module):
     Thin wrapper over the `wtac` function.
 
     """
+
     def forward(self, distances, labels):
         return wtac(distances, labels)
 
@@ -20,6 +21,7 @@ class LTAC(torch.nn.Module):
     Thin wrapper over the `wtac` function.
 
     """
+
     def forward(self, probs, labels):
         return wtac(-1.0 * probs, labels)
 
@@ -30,6 +32,7 @@ class KNNC(torch.nn.Module):
     Thin wrapper over the `knnc` function.
 
     """
+
     def __init__(self, k=1, **kwargs):
         super().__init__(**kwargs)
         self.k = k