feat: ImageGTLVQ and SiameseGTLVQ with examples

prototorch/models/__init__.py

@@ -13,8 +13,10 @@ from .glvq import (
     LVQMLN,
     ImageGLVQ,
     ImageGMLVQ,
+    ImageGTLVQ,
     SiameseGLVQ,
     SiameseGMLVQ,
+    SiameseGTLVQ,
 )
 from .knn import KNN
 from .lvq import LVQ1, LVQ21, MedianLVQ

prototorch/models/glvq.py

@@ -284,22 +284,28 @@ class LGMLVQ(GMLVQ):
 
 
 class GTLVQ(LGMLVQ):
-    """Localized and Generalized Matrix Learning Vector Quantization."""
+    """Localized and Generalized Tangent Learning Vector Quantization."""
     def __init__(self, hparams, **kwargs):
         distance_fn = kwargs.pop("distance_fn", ltangent_distance)
         super().__init__(hparams, distance_fn=distance_fn, **kwargs)
 
         omega_initializer = kwargs.get("omega_initializer")
-        omega = omega_initializer.generate(self.hparams.input_dim,
-                                           self.hparams.latent_dim)
+
+        if omega_initializer is not None:
+            subspace = omega_initializer.generate(self.hparams.input_dim,
+                                                  self.hparams.latent_dim)
+            omega = torch.repeat_interleave(subspace.unsqueeze(0),
+                                            self.num_prototypes,
+                                            dim=0)
+        else:
+            omega = torch.rand(
+                self.num_prototypes,
+                self.hparams.input_dim,
+                self.hparams.latent_dim,
+                device=self.device,
+            )
 
         # Re-register `_omega` to override the one from the super class.
-        omega = torch.rand(
-            self.num_prototypes,
-            self.hparams.input_dim,
-            self.hparams.latent_dim,
-            device=self.device,
-        )
         self.register_parameter("_omega", Parameter(omega))
 
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
@@ -307,6 +313,14 @@ class GTLVQ(LGMLVQ):
             self._omega.copy_(orthogonalization(self._omega))
 
 
+class SiameseGTLVQ(SiameseGLVQ, GTLVQ):
+    """Generalized Tangent Learning Vector Quantization.
+
+    Implemented as a Siamese network with a linear transformation backbone.
+
+    """
+
+
 class GLVQ1(GLVQ):
     """Generalized Learning Vector Quantization 1."""
     def __init__(self, hparams, **kwargs):
@@ -339,3 +353,17 @@ class ImageGMLVQ(ImagePrototypesMixin, GMLVQ):
     after updates.
 
     """
+
+
+class ImageGTLVQ(ImagePrototypesMixin, GTLVQ):
+    """GTLVQ for training on image data.
+
+    GTLVQ model that constrains the prototypes to the range [0, 1] by clamping
+    after updates.
+
+    """
+    def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
+        """Constrain the components to the range [0, 1] by clamping after updates."""
+        self.proto_layer.components.data.clamp_(0.0, 1.0)
+        with torch.no_grad():
+            self._omega.copy_(orthogonalization(self._omega))