From 4d8708714738d6987b4b1b01260d36b2acc8357c Mon Sep 17 00:00:00 2001
From: czzhangheng <cz189@qq.com>
Date: Thu, 20 Nov 2025 21:21:39 +0800
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---
 trainer/Trainer.py | 26 ++++++++------------------
 1 file changed, 8 insertions(+), 18 deletions(-)

diff --git a/trainer/Trainer.py b/trainer/Trainer.py
index 82c90f5..508230e 100755
--- a/trainer/Trainer.py
+++ b/trainer/Trainer.py
@@ -316,13 +316,9 @@ class Trainer:
     
     def _log_model_params(self):
         """输出模型可训练参数数量"""
-        try:
-            total_params = sum(
-                p.numel() for p in self.model.parameters() if p.requires_grad
-            )
-            self.logger.info(f"Trainable params: {total_params}")
-        except Exception:
-            pass
+        total_params = sum( p.numel() for p in self.model.parameters() if p.requires_grad)
+        self.logger.info(f"Trainable params: {total_params}")
+        
 
     def _finalize_training(self, best_model, best_test_model):
         self.model.load_state_dict(best_model)
@@ -353,8 +349,8 @@ class Trainer:
             for data, target in data_loader:
                 label = target[..., : args["output_dim"]]
                 output = model(data)
-                y_pred.append(output)
-                y_true.append(label)
+                y_pred.append(output.detach().cpu())
+                y_true.append(label.detach().cpu())
 
         
         d_y_pred = scaler.inverse_transform(torch.cat(y_pred, dim=0))
@@ -368,17 +364,11 @@ class Trainer:
                 args["mae_thresh"],
                 args["mape_thresh"],
             )
-            logger.info(
-                f"Horizon {t + 1:02d}, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}"
-            )
+            logger.info(f"Horizon {t + 1:02d}, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}")
 
         # 计算并记录平均指标
-        mae, rmse, mape = all_metrics(
-            d_y_pred, d_y_true, args["mae_thresh"], args["mape_thresh"]
-        )
-        logger.info(
-            f"Average Horizon, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}"
-        )
+        mae, rmse, mape = all_metrics(d_y_pred, d_y_true, args["mae_thresh"], args["mape_thresh"])
+        logger.info( f"Average Horizon, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}")
 
     @staticmethod
     def _compute_sampling_threshold(global_step, k):