Implemented LR-scheduling.

pysegcnn/core/trainer.py

@@ -34,6 +34,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch.utils.data import DataLoader
 from torch.optim import Optimizer
+from torch.optim.lr_scheduler import _LRScheduler
 from sklearn.metrics import confusion_matrix, classification_report
 
 # locals
@@ -1071,6 +1072,7 @@ class NetworkTrainer(BaseConfig):
     src_valid_dl: DataLoader
     src_test_dl:  DataLoader = DataLoader(None)
     loss_function: nn.modules.loss._Loss = nn.CrossEntropyLoss()
+    lr_scheduler: (type(None), _LRScheduler) = None
     epochs: int = 1
     nthreads: int = torch.get_num_threads()
     early_stop: bool = False
@@ -1290,6 +1292,10 @@ class NetworkTrainer(BaseConfig):
                 if self.save:
                     self.save_state()
 
+            # decay learning rate, if scheduler is specified
+            if self.lr_scheduler is not None:
+                self.lr_scheduler.step()
+
         return self.training_state
 
     def predict(self, dataloader, return_pred=False):