No implicit learning rate scheduling

examples/glvq_iris.py

@@ -2,10 +2,10 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
-
+from torch.optim.lr_scheduler import ExponentialLR
-import prototorch as pt
 
 if __name__ == "__main__":
     # Command-line arguments
@@ -29,9 +29,16 @@ if __name__ == "__main__":
     )
 
     # Initialize the model
-    model = pt.models.GLVQ(hparams,
+    model = pt.models.GLVQ(
+        hparams,
         optimizer=torch.optim.Adam,
-                           prototype_initializer=pt.components.SMI(train_ds))
+        prototype_initializer=pt.components.SMI(train_ds),
+        lr_scheduler=ExponentialLR,
+        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+    )
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
 
     # Callbacks
     vis = pt.models.VisGLVQ2D(data=train_ds)
@@ -40,6 +47,8 @@ if __name__ == "__main__":
     trainer = pl.Trainer.from_argparse_args(
         args,
         callbacks=[vis],
+        weights_summary="full",
+        accelerator="ddp",
     )
 
     # Training loop

prototorch/models/abstract.py

@@ -1,5 +1,4 @@
 import pytorch_lightning as pl
-from torch.optim.lr_scheduler import ExponentialLR
 
 
 class AbstractPrototypeModel(pl.LightningModule):
@@ -18,15 +17,16 @@ class AbstractPrototypeModel(pl.LightningModule):
 
     def configure_optimizers(self):
         optimizer = self.optimizer(self.parameters(), lr=self.hparams.lr)
-        scheduler = ExponentialLR(optimizer,
+        if self.lr_scheduler is not None:
-                                  gamma=0.99,
+            scheduler = self.lr_scheduler(optimizer,
-                                  last_epoch=-1,
+                                          **self.lr_scheduler_kwargs)
-                                  verbose=False)
             sch = {
                 "scheduler": scheduler,
                 "interval": "step",
             }  # called after each training step
             return [optimizer], [sch]
+        else:
+            return optimizer
 
 
 class PrototypeImageModel(pl.LightningModule):

prototorch/models/glvq.py

@@ -5,9 +5,12 @@ import torchmetrics
 from prototorch.components import LabeledComponents
 from prototorch.functions.activations import get_activation
 from prototorch.functions.competitions import wtac
-from prototorch.functions.distances import (euclidean_distance,
+from prototorch.functions.distances import (
-                                            lomega_distance, omega_distance,
+    euclidean_distance,
-                                            squared_euclidean_distance)
+    lomega_distance,
+    omega_distance,
+    squared_euclidean_distance,
+)
 from prototorch.functions.helper import get_flat
 from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
 from prototorch.modules import LambdaLayer
@@ -47,6 +50,8 @@ class GLVQ(AbstractPrototypeModel):
         self.initialize_prototype_win_ratios()
 
         self.optimizer = kwargs.get("optimizer", torch.optim.Adam)
+        self.lr_scheduler = kwargs.get("lr_scheduler", None)
+        self.lr_scheduler_kwargs = kwargs.get("lr_scheduler_kwargs", dict())
 
     @property
     def prototype_labels(self):
@@ -187,14 +192,25 @@ class SiameseGLVQ(GLVQ):
     def configure_optimizers(self):
         proto_opt = self.optimizer(self.proto_layer.parameters(),
                                    lr=self.hparams.proto_lr)
+        optimizer = None
         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
+            optimizer = [proto_opt, bb_opt]
         else:
-            return proto_opt
+            optimizer = proto_opt
+        if self.lr_scheduler is not None:
+            scheduler = self.lr_scheduler(optimizer,
+                                          **self.lr_scheduler_kwargs)
+            sch = {
+                "scheduler": scheduler,
+                "interval": "step",
+            }  # called after each training step
+            return optimizer, [sch]
+        else:
+            return optimizer
 
     def _forward(self, x):
         protos, _ = self.proto_layer()