Major BiC fix/reimplementation.

avalanche/models/bic_model.py

@@ -1,3 +1,4 @@
+from typing import Iterable, SupportsInt
 import torch
 
 
@@ -10,25 +11,28 @@ class BiasLayer(torch.nn.Module):
     Recognition. 2019"
     """
 
-    def __init__(self, device, clss):
+    def __init__(self, clss: Iterable[SupportsInt]):
         """
-        :param device: device used by the main model. 'cpu' or 'cuda'
         :param clss: list of classes of the current layer. This are use
             to identify the columns which are multiplied by the Bias
             correction Layer.
         """
         super().__init__()
-        self.alpha = torch.nn.Parameter(torch.ones(1, device=device))
-        self.beta = torch.nn.Parameter(torch.zeros(1, device=device))
+        self.alpha = torch.nn.Parameter(torch.ones(1))
+        self.beta = torch.nn.Parameter(torch.zeros(1))
 
-        self.clss = torch.Tensor(list(clss)).long().to(device)
-        self.not_clss = None
+        unique_classes = list(sorted(set(int(x) for x in clss)))
+
+        self.register_buffer("clss", torch.tensor(unique_classes, dtype=torch.long))
 
     def forward(self, x):
         alpha = torch.ones_like(x)
-        beta = torch.ones_like(x)
+        beta = torch.zeros_like(x)
 
         alpha[:, self.clss] = self.alpha
         beta[:, self.clss] = self.beta
 
         return alpha * x + beta
+
+
+__all__ = ["BiasLayer"]