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.

examples/glvq_iris.py

@@ -21,12 +21,13 @@ if __name__ == "__main__":
     prototypes_per_class = 2
     hparams = dict(
         distribution=(nclasses, prototypes_per_class),
-        prototype_initializer=pt.components.SMI(train_ds),
         lr=0.01,
     )
 
     # Initialize the model
-    model = pt.models.GLVQ(hparams, optimizer=torch.optim.Adam)
+    model = pt.models.GLVQ(hparams,
+                           optimizer=torch.optim.Adam,
+                           prototype_initializer=pt.components.SMI(train_ds))
 
     # Callbacks
     vis = pt.models.VisGLVQ2D(data=(x_train, y_train))

examples/glvq_spiral.py

@@ -29,14 +29,15 @@ if __name__ == "__main__":
     prototypes_per_class = 20
     hparams = dict(
         distribution=(nclasses, prototypes_per_class),
-        prototype_initializer=pt.components.SSI(train_ds, noise=1e-1),
         transfer_function="sigmoid_beta",
         transfer_beta=10.0,
         lr=0.01,
     )
 
     # Initialize the model
-    model = pt.models.GLVQ(hparams)
+    model = pt.models.GLVQ(hparams,
+                           prototype_initializer=pt.components.SSI(train_ds,
+                                                                   noise=1e-1))
 
     # Callbacks
     vis = pt.models.VisGLVQ2D(train_ds, show_last_only=True, block=True)

examples/gmlvq_iris.py

@@ -21,12 +21,12 @@ if __name__ == "__main__":
         distribution=(nclasses, prototypes_per_class),
         input_dim=x_train.shape[1],
         latent_dim=x_train.shape[1],
-        prototype_initializer=pt.components.SMI(train_ds),
         lr=0.01,
     )
 
     # Initialize the model
-    model = pt.models.GMLVQ(hparams)
+    model = pt.models.GMLVQ(hparams,
+                            prototype_initializer=pt.components.SMI(train_ds))
 
     # Setup trainer
     trainer = pl.Trainer(max_epochs=100)

examples/gmlvq_mnist.py

@@ -0,0 +1,68 @@
+"""GMLVQ example using the MNIST dataset."""
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+from torchvision import transforms
+from torchvision.datasets import MNIST
+
+if __name__ == "__main__":
+    # Dataset
+    train_ds = MNIST(
+        "~/datasets",
+        train=True,
+        download=True,
+        transform=transforms.Compose([
+            transforms.ToTensor(),
+        ]),
+    )
+    test_ds = MNIST(
+        "~/datasets",
+        train=False,
+        download=True,
+        transform=transforms.Compose([
+            transforms.ToTensor(),
+        ]),
+    )
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds,
+                                               num_workers=0,
+                                               batch_size=256)
+    test_loader = torch.utils.data.DataLoader(test_ds,
+                                              num_workers=0,
+                                              batch_size=256)
+
+    # Hyperparameters
+    nclasses = 10
+    prototypes_per_class = 2
+    hparams = dict(
+        input_dim=28 * 28,
+        latent_dim=28 * 28,
+        distribution=(nclasses, prototypes_per_class),
+        lr=0.01,
+    )
+
+    # Initialize the model
+    model = pt.models.ImageGMLVQ(
+        hparams,
+        optimizer=torch.optim.Adam,
+        prototype_initializer=pt.components.SMI(train_ds),
+    )
+
+    # Callbacks
+    vis = pt.models.VisImgComp(data=train_ds,
+                               nrow=5,
+                               show=False,
+                               tensorboard=True)
+
+    # Setup trainer
+    trainer = pl.Trainer(
+        max_epochs=50,
+        callbacks=[vis],
+        # overfit_batches=1,
+        # fast_dev_run=3,
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)

examples/liramlvq_tecator.py

@@ -23,12 +23,12 @@ if __name__ == "__main__":
         distribution=(nclasses, prototypes_per_class),
         input_dim=100,
         latent_dim=2,
-        prototype_initializer=pt.components.SMI(train_ds),
         lr=0.001,
     )
 
     # Initialize the model
-    model = pt.models.GMLVQ(hparams)
+    model = pt.models.GMLVQ(hparams,
+                            prototype_initializer=pt.components.SMI(train_ds))
 
     # Callbacks
     vis = pt.models.VisSiameseGLVQ2D(train_ds, border=0.1)

examples/siamese_glvq_iris.py

@@ -37,7 +37,6 @@ if __name__ == "__main__":
     # Hyperparameters
     hparams = dict(
         distribution=[1, 2, 3],
-        prototype_initializer=pt.components.SMI(train_ds),
         proto_lr=0.01,
         bb_lr=0.01,
     )
@@ -45,6 +44,7 @@ if __name__ == "__main__":
     # Initialize the model
     model = pt.models.SiameseGLVQ(
         hparams,
+        prototype_initializer=pt.components.SMI(train_ds),
         backbone_module=Backbone,
     )