Stop passing component initializers as hparams

Pass the component initializer as an hparam slows down the script very much. The
API has now been changed to pass it as a kwarg to the models instead.

The example scripts have also been updated to reflect the new changes.

Also, ImageGMLVQ and an example script `gmlvq_mnist.py` that uses it have also
been added.

prototorch/models/glvq.py

@@ -5,9 +5,18 @@ from prototorch.functions.activations import get_activation
 from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import (euclidean_distance, omega_distance,
                                             squared_euclidean_distance)
+from prototorch.functions.helper import get_flat
 from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
+from prototorch.modules.mappings import OmegaMapping
 
-from .abstract import AbstractPrototypeModel
+from .abstract import AbstractPrototypeModel, PrototypeImageModel
+
+
+class GLVQ(AbstractPrototypeModel):
+    """Generalized Learning Vector Quantization."""
+
+
+from .abstract import AbstractPrototypeModel, PrototypeImageModel
 
 
 class GLVQ(AbstractPrototypeModel):
@@ -18,6 +27,7 @@ class GLVQ(AbstractPrototypeModel):
         self.save_hyperparameters(hparams)
 
         self.optimizer = kwargs.get("optimizer", torch.optim.Adam)
+        prototype_initializer = kwargs.get("prototype_initializer", None)
 
         # Default Values
         self.hparams.setdefault("distance", euclidean_distance)
@@ -26,7 +36,7 @@ class GLVQ(AbstractPrototypeModel):
 
         self.proto_layer = LabeledComponents(
             distribution=self.hparams.distribution,
-            initializer=self.hparams.prototype_initializer)
+            initializer=prototype_initializer)
 
         self.transfer_function = get_activation(self.hparams.transfer_function)
         self.train_acc = torchmetrics.Accuracy()
@@ -44,7 +54,6 @@ class GLVQ(AbstractPrototypeModel):
 
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         x, y = train_batch
-        x = x.view(x.size(0), -1)  # flatten
         dis = self(x)
         plabels = self.proto_layer.component_labels
         mu = self.loss(dis, y, prototype_labels=plabels)
@@ -95,15 +104,14 @@ class LVQ21(GLVQ):
         self.optimizer = torch.optim.SGD
 
 
-class ImageGLVQ(GLVQ):
+class ImageGLVQ(GLVQ, PrototypeImageModel):
     """GLVQ for training on image data.
 
     GLVQ 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):
-        self.proto_layer.components.data.clamp_(0.0, 1.0)
+    pass
 
 
 class SiameseGLVQ(GLVQ):
@@ -235,6 +243,7 @@ class GMLVQ(GLVQ):
 
     def forward(self, x):
         protos, _ = self.proto_layer()
+        x, protos = get_flat(x, protos)
         latent_x = self.omega_layer(x)
         latent_protos = self.omega_layer(protos)
         dis = squared_euclidean_distance(latent_x, latent_protos)
@@ -256,6 +265,16 @@ class GMLVQ(GLVQ):
         return y_pred.numpy()
 
 
+class ImageGMLVQ(GMLVQ, PrototypeImageModel):
+    """GMLVQ for training on image data.
+
+    GMLVQ model that constrains the prototypes to the range [0, 1] by clamping
+    after updates.
+
+    """
+    pass
+
+
 class LVQMLN(GLVQ):
     """Learning Vector Quantization Multi-Layer Network.