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.

README.md

@@ -51,7 +51,7 @@ To assist in the development process, you may also find it useful to install
 
 ## Available models
 
-- K-Nearest Neighbors (KNN)
+- k-Nearest Neighbors (KNN)
 - Learning Vector Quantization 1 (LVQ1)
 - Generalized Learning Vector Quantization (GLVQ)
 - Generalized Relevance Learning Vector Quantization (GRLVQ)
@@ -68,6 +68,7 @@ To assist in the development process, you may also find it useful to install
 
 ## Planned models
 
+- Median-LVQ
 - Local-Matrix GMLVQ
 - Generalized Tangent Learning Vector Quantization (GTLVQ)
 - Robust Soft Learning Vector Quantization (RSLVQ)

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,
     )
 

prototorch/models/__init__.py

@@ -2,7 +2,7 @@ from importlib.metadata import PackageNotFoundError, version
 
 from .cbc import CBC
 from .glvq import (GLVQ, GMLVQ, GRLVQ, LVQ1, LVQ21, LVQMLN, ImageGLVQ,
-                   SiameseGLVQ)
+                   ImageGMLVQ, SiameseGLVQ)
 from .knn import KNN
 from .neural_gas import NeuralGas
 from .vis import *

prototorch/models/abstract.py

@@ -8,6 +8,11 @@ class AbstractPrototypeModel(pl.LightningModule):
     def prototypes(self):
         return self.proto_layer.components.detach().cpu()
 
+    @property
+    def components(self):
+        """Only an alias for the prototypes."""
+        return self.prototypes
+
     def configure_optimizers(self):
         optimizer = self.optimizer(self.parameters(), lr=self.hparams.lr)
         scheduler = ExponentialLR(optimizer,
@@ -19,3 +24,8 @@ class AbstractPrototypeModel(pl.LightningModule):
             "interval": "step",
         }  # called after each training step
         return [optimizer], [sch]
+
+
+class PrototypeImageModel(pl.LightningModule):
+    def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
+        self.proto_layer.components.data.clamp_(0.0, 1.0)

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):
+    pass
-        self.proto_layer.components.data.clamp_(0.0, 1.0)
 
 
 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.
 

prototorch/models/vis.py

@@ -3,6 +3,7 @@ import os
 import numpy as np
 import pytorch_lightning as pl
 import torch
+import torchvision
 from matplotlib import pyplot as plt
 from matplotlib.offsetbox import AnchoredText
 from prototorch.utils.celluloid import Camera
@@ -270,6 +271,7 @@ class Vis2DAbstract(pl.Callback):
                  border=1,
                  resolution=50,
                  show_protos=True,
+                 show=True,
                  tensorboard=False,
                  show_last_only=False,
                  pause_time=0.1,
@@ -290,6 +292,7 @@ class Vis2DAbstract(pl.Callback):
         self.border = border
         self.resolution = resolution
         self.show_protos = show_protos
+        self.show = show
         self.tensorboard = tensorboard
         self.show_last_only = show_last_only
         self.pause_time = pause_time
@@ -352,6 +355,7 @@ class Vis2DAbstract(pl.Callback):
     def log_and_display(self, trainer, pl_module):
         if self.tensorboard:
             self.add_to_tensorboard(trainer, pl_module)
+        if self.show:
             if not self.block:
                 plt.pause(self.pause_time)
             else:
@@ -458,3 +462,50 @@ class VisNG2D(Vis2DAbstract):
                     )
 
         self.log_and_display(trainer, pl_module)
+
+
+class VisImgComp(Vis2DAbstract):
+    def __init__(self,
+                 *args,
+                 random_data=0,
+                 dataformats="CHW",
+                 nrow=2,
+                 **kwargs):
+        super().__init__(*args, **kwargs)
+        self.random_data = random_data
+        self.dataformats = dataformats
+        self.nrow = nrow
+
+    def on_epoch_end(self, trainer, pl_module):
+        if not self.precheck(trainer):
+            return True
+
+        if self.show:
+            components = pl_module.components
+            grid = torchvision.utils.make_grid(components, nrow=self.nrow)
+            plt.imshow(grid.permute((1, 2, 0)).cpu(), cmap=self.cmap)
+
+        self.log_and_display(trainer, pl_module)
+
+    def add_to_tensorboard(self, trainer, pl_module):
+        tb = pl_module.logger.experiment
+
+        components = pl_module.components
+        grid = torchvision.utils.make_grid(components, nrow=self.nrow)
+        tb.add_image(
+            tag="Components",
+            img_tensor=grid,
+            global_step=trainer.current_epoch,
+            dataformats=self.dataformats,
+        )
+
+        if self.random_data:
+            ind = np.random.choice(len(self.x_train),
+                                   size=self.random_data,
+                                   replace=False)
+            data_img = self.x_train[ind]
+            grid = torchvision.utils.make_grid(data_img, nrow=self.nrow)
+            tb.add_image(tag="Data",
+                         img_tensor=grid,
+                         global_step=trainer.current_epoch,
+                         dataformats=self.dataformats)