Cleanup models

Siamese architectures no longer accept a `backbone_module`. They have to be
initialized with an pre-initialized backbone object instead. This is so that the
visualization callbacks could use the very same object for visualization
purposes. Also, there's no longer a dependent copy of the backbone. It is
managed simply with `requires_grad` instead.

prototorch/models/abstract.py

@@ -1,5 +1,6 @@
 import pytorch_lightning as pl
 import torch
+from prototorch.functions.competitions import wtac
 from torch.optim.lr_scheduler import ExponentialLR
 
 
@@ -29,3 +30,33 @@ class AbstractPrototypeModel(pl.LightningModule):
 class PrototypeImageModel(pl.LightningModule):
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
         self.proto_layer.components.data.clamp_(0.0, 1.0)
+
+
+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.
+
+        """
+        # model.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