Refactor code

prototorch/models/abstract.py

@@ -37,33 +37,3 @@ class PrototypeImageModel(pl.LightningModule):
         if return_channels_last:
             grid = grid.permute((1, 2, 0))
         return grid.cpu()
-
-
-class SiamesePrototypeModel(pl.LightningModule):
-    def configure_optimizers(self):
-        proto_opt = self.optimizer(self.proto_layer.parameters(),
-                                   lr=self.hparams.proto_lr)
-        if list(self.backbone.parameters()):
-            # only add an optimizer is the backbone has trainable parameters
-            # otherwise, the next line fails
-            bb_opt = self.optimizer(self.backbone.parameters(),
-                                    lr=self.hparams.bb_lr)
-            return proto_opt, bb_opt
-        else:
-            return proto_opt
-
-    def predict_latent(self, x, map_protos=True):
-        """Predict `x` assuming it is already embedded in the latent space.
-
-        Only the prototypes are embedded in the latent space using the
-        backbone.
-
-        """
-        self.eval()
-        with torch.no_grad():
-            protos, plabels = self.proto_layer()
-            if map_protos:
-                protos = self.backbone(protos)
-            d = self.distance_fn(x, protos)
-            y_pred = wtac(d, plabels)
-        return y_pred