Add glvq model

prototorch/models/glvq.py

@@ -0,0 +1,57 @@
+import pytorch_lightning as pl
+import torch
+from prototorch.functions.competitions import wtac
+from prototorch.functions.distances import euclidean_distance
+from prototorch.functions.initializers import get_initializer
+from prototorch.functions.losses import glvq_loss
+from prototorch.modules.prototypes import Prototypes1D
+
+
+class GLVQ(pl.LightningModule):
+    """Generalized Learning Vector Quantization."""
+    def __init__(self, lr=1e-3, **kwargs):
+        super().__init__()
+        self.lr = lr
+        self.proto_layer = Prototypes1D(**kwargs)
+
+    @property
+    def prototypes(self):
+        return self.proto_layer.prototypes.detach().numpy()
+
+    @property
+    def prototype_labels(self):
+        return self.proto_layer.prototype_labels.detach().numpy()
+
+    def forward(self, x):
+        protos = self.proto_layer.prototypes
+        dis = euclidean_distance(x, protos)
+        return dis
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
+        return optimizer
+
+    def training_step(self, train_batch, batch_idx):
+        x, y = train_batch
+        x = x.view(x.size(0), -1)
+        dis = self(x)
+        plabels = self.proto_layer.prototype_labels
+        mu = glvq_loss(dis, y, prototype_labels=plabels)
+        loss = mu.sum(dim=0)
+        self.log("train_loss", loss)
+        return loss
+
+    def predict(self, x):
+        with torch.no_grad():
+            d = self(x)
+            plabels = self.proto_layer.prototype_labels
+            y_pred = wtac(d, plabels)
+        return y_pred.numpy()
+
+
+class ImageGLVQ(GLVQ):
+    """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.prototypes.data.clamp_(0., 1.)