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]
@@ -597,12 +596,6 @@ class GenericTrainingManager:
         metric_names = self.params["training_params"]["train_metrics"]
 
         display_values = None
-        # init curriculum learning
-        if (
-            "curriculum_learning" in self.params["training_params"].keys()
-            and self.params["training_params"]["curriculum_learning"]
-        ):
-            self.init_curriculum()
         # perform epochs
         for num_epoch in range(self.latest_epoch + 1, nb_epochs):
             # set models trainable
@@ -655,13 +648,11 @@ 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)
@@ -922,9 +913,6 @@ class GenericTrainingManager:
     def evaluate_batch(self, batch_data, metric_names):
         raise NotImplementedError
 
-    def init_curriculum(self):
-        raise NotImplementedError
-
     def load_save_info(self, info_dict):
         """
         Load curriculum info from saved model info