GLVQ with configurable distance.

examples/siamese_glvq_iris.py

@@ -4,11 +4,11 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
-from prototorch.datasets.abstract import NumpyDataset
-from prototorch.models.glvq import SiameseGLVQ
 from sklearn.datasets import load_iris
 from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
+
+from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.glvq import SiameseGLVQ
 
 
 class VisualizationCallback(pl.Callback):
@@ -57,10 +57,12 @@ class VisualizationCallback(pl.Callback):
         ax.set_xlim(left=x_min + 0, right=x_max - 0)
         ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
         tb = pl_module.logger.experiment
-        tb.add_figure(tag=f"{self.title}",
-                      figure=self.fig,
-                      global_step=trainer.current_epoch,
-                      close=False)
+        tb.add_figure(
+            tag=f"{self.title}",
+            figure=self.fig,
+            global_step=trainer.current_epoch,
+            close=False,
+        )
         plt.pause(0.1)
 
 

prototorch/models/callbacks/visualization.py

@@ -7,8 +7,7 @@ from matplotlib.offsetbox import AnchoredText
 
 from prototorch.utils.celluloid import Camera
 from prototorch.utils.colors import color_scheme
-from prototorch.utils.utils import (gif_from_dir, make_directory,
-                                    prettify_string)
+from prototorch.utils.utils import gif_from_dir, make_directory, prettify_string
 
 
 class VisWeights(Callback):

prototorch/models/glvq.py

@@ -12,13 +12,19 @@ class GLVQ(pl.LightningModule):
     """Generalized Learning Vector Quantization."""
     def __init__(self, hparams, **kwargs):
         super().__init__()
+
         self.save_hyperparameters(hparams)
+
+        # Default Values
+        self.hparams.setdefault("distance", euclidean_distance)
+
         self.proto_layer = Prototypes1D(
             input_dim=self.hparams.input_dim,
             nclasses=self.hparams.nclasses,
             prototypes_per_class=self.hparams.prototypes_per_class,
             prototype_initializer=self.hparams.prototype_initializer,
             **kwargs)
+
         self.train_acc = torchmetrics.Accuracy()
 
     @property
@@ -35,7 +41,7 @@ class GLVQ(pl.LightningModule):
 
     def forward(self, x):
         protos = self.proto_layer.prototypes
-        dis = euclidean_distance(x, protos)
+        dis = self.hparams.distance(x, protos)
         return dis
 
     def training_step(self, train_batch, batch_idx):