Always use the same dropout scheduler function

Closes #159 (closed)

dan/manager/training.py

@@ -32,7 +32,6 @@ class GenericTrainingManager:
         self.type = None
         self.is_master = False
         self.params = params
-        self.dropout_scheduler = None
         self.models = {}
         self.dataset = None
         self.dataset_name = list(self.params["dataset_params"]["datasets"].values())[0]
@@ -196,10 +195,7 @@ class GenericTrainingManager:
                 )
 
         # Handle curriculum dropout
-        if "dropout_scheduler" in self.params["model_params"]:
-            func = self.params["model_params"]["dropout_scheduler"]["function"]
-            T = self.params["model_params"]["dropout_scheduler"]["T"]
-            self.dropout_scheduler = DropoutScheduler(self.models, func, T)
+        self.dropout_scheduler = DropoutScheduler(self.models)
 
         self.scaler = GradScaler(enabled=self.params["training_params"]["use_amp"])
 
@@ -652,13 +648,9 @@ class GenericTrainingManager:
                                 if "lr" in metric_names:
                                     self.writer.add_scalar(
                                         "lr_{}".format(model_name),
-                                        self.lr_schedulers[model_name].lr,
-                                        self.lr_schedulers[model_name].step_num,
-                                    )
-                    # Update dropout scheduler if used
-                    if self.dropout_scheduler:
-                        self.dropout_scheduler.step(len(batch_metrics["names"]))
-                        self.dropout_scheduler.update_dropout_rate()
+                    # Update dropout scheduler
+                    self.dropout_scheduler.step(len(batch_metrics["names"]))
+                    self.dropout_scheduler.update_dropout_rate()
 
                     # Add batch metrics values to epoch metrics values
                     self.metric_manager["train"].update_metrics(batch_metrics)