Refactor non-gradient-lvq models into lvq.py

prototorch/models/glvq.py

@@ -1,4 +1,5 @@
-"""Models based on the GLVQ Framework"""
+"""Models based on the GLVQ framework."""
+
 import torch
 import torchmetrics
 from prototorch.components import LabeledComponents
@@ -7,8 +8,7 @@ from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import (euclidean_distance, omega_distance,
                                             sed)
 from prototorch.functions.helper import get_flat
-from prototorch.functions.losses import (_get_dp_dm, glvq_loss, lvq1_loss,
-                                         lvq21_loss)
+from prototorch.functions.losses import (glvq_loss, lvq1_loss, lvq21_loss)
 
 from .abstract import AbstractPrototypeModel, PrototypeImageModel
 
@@ -260,78 +260,6 @@ class LVQMLN(SiameseGLVQ):
         return distances
 
 
-class NonGradientGLVQ(GLVQ):
-    """Abstract Model for Models that do not use gradients in their update phase."""
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.automatic_optimization = False
-
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
-        raise NotImplementedError
-
-
-class LVQ1(NonGradientGLVQ):
-    """Learning Vector Quantization 1."""
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
-        protos = self.proto_layer.components
-        plabels = self.proto_layer.component_labels
-
-        x, y = train_batch
-        dis = self._forward(x)
-        # TODO Vectorized implementation
-
-        for xi, yi in zip(x, y):
-            d = self._forward(xi.view(1, -1))
-            preds = wtac(d, plabels)
-            w = d.argmin(1)
-            if yi == preds:
-                shift = xi - protos[w]
-            else:
-                shift = protos[w] - xi
-            updated_protos = protos + 0.0
-            updated_protos[w] = protos[w] + (self.hparams.lr * shift)
-            self.proto_layer.load_state_dict({"_components": updated_protos},
-                                             strict=False)
-
-        # Logging
-        self.log_acc(dis, y, tag="train_acc")
-
-        return None
-
-
-class LVQ21(NonGradientGLVQ):
-    """Learning Vector Quantization 2.1."""
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
-        protos = self.proto_layer.components
-        plabels = self.proto_layer.component_labels
-
-        x, y = train_batch
-        dis = self._forward(x)
-        # TODO Vectorized implementation
-
-        for xi, yi in zip(x, y):
-            xi = xi.view(1, -1)
-            yi = yi.view(1, )
-            d = self._forward(xi)
-            (_, wp), (_, wn) = _get_dp_dm(d, yi, plabels, with_indices=True)
-            shiftp = xi - protos[wp]
-            shiftn = protos[wn] - xi
-            updated_protos = protos + 0.0
-            updated_protos[wp] = protos[wp] + (self.hparams.lr * shiftp)
-            updated_protos[wn] = protos[wn] + (self.hparams.lr * shiftn)
-            self.proto_layer.load_state_dict({"_components": updated_protos},
-                                             strict=False)
-
-        # Logging
-        self.log_acc(dis, y, tag="train_acc")
-
-        return None
-
-
-class MedianLVQ(NonGradientGLVQ):
-    """Median LVQ"""
-
-
 class GLVQ1(GLVQ):
     """Generalized Learning Vector Quantization 1."""
     def __init__(self, hparams, **kwargs):