config/REPST/PEMS-BAY.yaml

@@ -43,14 +43,13 @@ train:
   early_stop_patience: 15
   epochs: 100
   grad_norm: false
-  loss_func: mse
-  scheduler: cosineAnnealingLR
-  tmax: 5
+  loss_func: mae
   lr_decay: true
   lr_decay_rate: 0.3
   lr_decay_step: "5,20,40,70"
   lr_init: 0.003
   max_grad_norm: 5
+  real_value: true
   weight_decay: 0
   debug: false
   output_dim: 1

utils/initializer.py

@@ -23,29 +23,21 @@ def init_model(args):
 
 
 def init_optimizer(model, args):
-    optim = args.get("optimizer", "Adam")
-    match optim :
-        case "Adam":
-            optimizer = torch.optim.Adam(
-            params=model.parameters(),
-            lr=args["lr_init"],
-            eps=1.0e-8,
-            weight_decay=args["weight_decay"],
-            amsgrad=False,
+    optimizer = torch.optim.Adam(
+        params=model.parameters(),
+        lr=args["lr_init"],
+        eps=1.0e-8,
+        weight_decay=args["weight_decay"],
+        amsgrad=False,
+    )
+
+    lr_scheduler = None
+    if args["lr_decay"]:
+        lr_decay_steps = [int(step) for step in args["lr_decay_step"].split(",")]
+        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer=optimizer, milestones=lr_decay_steps, gamma=args["lr_decay_rate"]
         )
-    
-    scheduler = args.get("scheduler", "multistepLR")
-    match scheduler:
-        case "multistepLR":
-            lr_scheduler = None
-            if args["lr_decay"]:
-                lr_decay_steps = [int(step) for step in args["lr_decay_step"].split(",")]
-                lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
-                    optimizer=optimizer, milestones=lr_decay_steps, gamma=args["lr_decay_rate"]
-                )
-        case "cosineAnnealingLR":
-            T_max = args.get("tmax", 5)
-            lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer=optimizer, T_max=T_max, eta_min=1e-8)
+
     return optimizer, lr_scheduler