Add name filters for exp-org

lib/procedures/q_exps.py

@@ -100,9 +100,9 @@ def run_exp(
         # Train model
         try:
             if hasattr(model, "to"):  # Recoverable model
-                device = model.device
+                ori_device = model.device
                 model = R.load_object(model_obj_name)
-                model.to(device)
+                model.to(ori_device)
             else:
                 model = R.load_object(model_obj_name)
             logger.info("[Find existing object from {:}]".format(model_obj_name))
@@ -119,9 +119,10 @@ def run_exp(
             model.fit(**model_fit_kwargs)
             # remove model to CPU for saving
             if hasattr(model, "to"):
+                old_device = model.device
                 model.to("cpu")
                 R.save_objects(**{model_obj_name: model})
-                model.to()
+                model.to(old_device)
             else:
                 R.save_objects(**{model_obj_name: model})
         except Exception as e:

lib/trade_models/quant_transformer.py

@@ -114,9 +114,9 @@ class QuantTransformer(Model):
 
     def to(self, device):
         if device is None:
-            self.model.to(self.device)
-        else:
-            self.model.to("cpu")
+            device = "cpu"
+        self.device = device
+        self.model.to(self.device)
 
     def loss_fn(self, pred, label):
         mask = ~torch.isnan(label)
@@ -227,7 +227,7 @@ class QuantTransformer(Model):
         # Pre-fetch the potential checkpoints
         ckp_path = os.path.join(save_dir, "{:}.pth".format(self.__class__.__name__))
         if os.path.exists(ckp_path):
-            ckp_data = torch.load(ckp_path)
+            ckp_data = torch.load(ckp_path, map_location=self.device)
             stop_steps, best_score, best_epoch = (
                 ckp_data["stop_steps"],
                 ckp_data["best_score"],
@@ -298,7 +298,7 @@ class QuantTransformer(Model):
                 results_dict=results_dict,
                 start_epoch=iepoch + 1,
             )
-            torch.save(save_info, ckp_path)
+            torch.save(save_info, ckp_path, map_location="cpu")
         self.logger.info(
             "The best score: {:.6f} @ {:02d}-th epoch".format(best_score, best_epoch)
         )