- add support for multiple learning rate schedulers in sequential mod…

dreem/inference/eval.py

@@ -52,7 +52,7 @@ def run(cfg: DictConfig) -> dict[int, sio.Labels]:
         "persistent_tracking", False
     )
     logger.info(f"Computing the following metrics:")
-    logger.info(model.metrics.test)
+    logger.info(model.metrics['test'])
     model.test_results["save_path"] = eval_cfg.cfg.runner.save_path
     logger.info(f"Saving results to {model.test_results['save_path']}")
 

dreem/models/gtr_runner.py

@@ -247,7 +247,7 @@ def configure_optimizers(self) -> dict:
                 "scheduler": scheduler,
                 "monitor": "val_loss",
                 "interval": "epoch",
-                "frequency": 10,
+                "frequency": 1,
             },
         }
 

dreem/models/model_utils.py

@@ -150,14 +150,27 @@ def init_scheduler(
     if config is None:
         return None
     scheduler = config.get("name")
+
     if scheduler is None:
         scheduler = "ReduceLROnPlateau"
 
     scheduler_params = {
         param: val for param, val in config.items() if param.lower() != "name"
     }
+
     try:
-        scheduler_class = getattr(torch.optim.lr_scheduler, scheduler)
+        # if a list is provided, apply each one sequentially
+        if isinstance(scheduler, list):
+            schedulers = []
+            milestones = scheduler_params.get("milestones", None)
+            for ix, s in enumerate(scheduler):
+                params = scheduler_params[str(ix)]
+                schedulers.append(getattr(torch.optim.lr_scheduler, s)(optimizer, **params))
+            scheduler_class = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers, milestones)
+            return scheduler_class
+
+        else:
+            scheduler_class = getattr(torch.optim.lr_scheduler, scheduler)
     except AttributeError:
         if scheduler_class is None:
             print(