refactor: use LinearTransform instead of torch.nn.Linear

prototorch/models/glvq.py

@@ -7,6 +7,7 @@ from ..core.competitions import wtac
 from ..core.distances import lomega_distance, omega_distance, squared_euclidean_distance
 from ..core.initializers import EyeTransformInitializer
 from ..core.losses import glvq_loss, lvq1_loss, lvq21_loss
+from ..core.transforms import LinearTransform
 from ..nn.activations import get_activation
 from ..nn.wrappers import LambdaLayer, LossLayer
 from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
@@ -208,18 +209,22 @@ class SiameseGMLVQ(SiameseGLVQ):
         super().__init__(hparams, **kwargs)
 
         # Override the backbone
-        self.backbone = torch.nn.Linear(self.hparams.input_dim,
+        omega_initializer = kwargs.get("omega_initializer",
-                                        self.hparams.latent_dim,
+                                       EyeTransformInitializer())
-                                        bias=False)
+        self.backbone = LinearTransform(
+            self.hparams.input_dim,
+            self.hparams.output_dim,
+            initializer=omega_initializer,
+        )
 
     @property
     def omega_matrix(self):
-        return self.backbone.weight.detach().cpu()
+        return self.backbone.weights
 
     @property
     def lambda_matrix(self):
-        omega = self.backbone.weight  # (latent_dim, input_dim)
+        omega = self.backbone.weight  # (input_dim, latent_dim)
-        lam = omega.T @ omega
+        lam = omega @ omega.T
         return lam.detach().cpu()