Update Examples to new initializer architecture.

Visualization still borken for some examples.

prototorch/models/cbc.py

@@ -1,10 +1,9 @@
 import pytorch_lightning as pl
 import torch
 import torchmetrics
-
+from prototorch.components.components import Components
 from prototorch.functions.distances import euclidean_distance
 from prototorch.functions.similarities import cosine_similarity
-from prototorch.modules.prototypes import Prototypes1D
 
 
 def rescaled_cosine_similarity(x, y):
@@ -93,12 +92,8 @@ class CBC(pl.LightningModule):
         super().__init__()
         self.save_hyperparameters(hparams)
         self.margin = margin
-        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.component_layer = Components(self.hparams.num_components,
+                                          self.hparams.component_initializer)
         # self.similarity = CosineSimilarity()
         self.similarity = similarity
         self.backbone = backbone_class()
@@ -110,7 +105,7 @@ class CBC(pl.LightningModule):
 
     @property
     def components(self):
-        return self.proto_layer.prototypes.detach().cpu()
+        return self.component_layer.components.detach().cpu()
 
     @property
     def reasonings(self):
@@ -126,7 +121,7 @@ class CBC(pl.LightningModule):
 
     def forward(self, x):
         self.sync_backbones()
-        protos, _ = self.proto_layer()
+        protos = self.component_layer()
 
         latent_x = self.backbone(x)
         latent_protos = self.backbone_dependent(protos)
@@ -167,4 +162,4 @@ class ImageCBC(CBC):
     """
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
         # super().on_train_batch_end(outputs, batch, batch_idx, dataloader_idx)
-        self.proto_layer.prototypes.data.clamp_(0.0, 1.0)
+        self.component_layer.prototypes.data.clamp_(0.0, 1.0)

prototorch/models/glvq.py

@@ -1,4 +1,3 @@
-import pytorch_lightning as pl
 import torch
 import torchmetrics
 from prototorch.components import LabeledComponents
@@ -7,7 +6,6 @@ from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import (euclidean_distance,
                                             squared_euclidean_distance)
 from prototorch.functions.losses import glvq_loss
-from prototorch.modules.prototypes import Prototypes1D
 
 from .abstract import AbstractPrototypeModel
 
@@ -55,7 +53,6 @@ class GLVQ(AbstractPrototypeModel):
         with torch.no_grad():
             preds = wtac(dis, plabels)
         # `.int()` because FloatTensors are assumed to be class probabilities
-        self.train_acc(preds.int(), y.int())
 
         # Logging
         self.log("train_loss", loss)

prototorch/models/neural_gas.py

@@ -1,9 +1,7 @@
-import pytorch_lightning as pl
 import torch
 from prototorch.components import Components
 from prototorch.components import initializers as cinit
 from prototorch.functions.distances import euclidean_distance
-from prototorch.modules import Prototypes1D
 from prototorch.modules.losses import NeuralGasEnergy
 
 from .abstract import AbstractPrototypeModel