清理代码，删除跳连接部分

evaluate_temporal.py

@@ -45,6 +45,15 @@ def denormalize(tensor):
     # [0, 1] -> [0, 255]
     tensor = tensor * 255
     return tensor.clamp(0, 255)
+    # return tensor
+
+def minmax_denormalize(tensor):
+    tensor_min = tensor.min()
+    tensor_max = tensor.max()
+    tensor = (tensor - tensor_min) / (tensor_max - tensor_min)
+    # tensor = tensor*2-1
+    tensor = tensor*255
+    return tensor.clamp(0, 255)
 
 
 def calculate_metrics(pred, target, debug=False):
@@ -134,6 +143,10 @@ def save_comparison_figure(input_frames, target_frame, pred_frame, save_path,
     ax.set_title('Predicted')
     ax.axis('off')
 
+    #debug print 
+    print(target_frame)
+    print(pred_frame)
+
     # debug print - 改进为更有信息量的输出
     if isinstance(pred_frame, np.ndarray):
         print(f"[DEBUG IMAGE] Pred frame shape: {pred_frame.shape}, range: [{pred_frame.min():.2f}, {pred_frame.max():.2f}], mean: {pred_frame.mean():.2f}")
@@ -161,8 +174,8 @@ def evaluate_model(model, data_loader, device, args):
         metrics_dict: 包含所有指标的字典
         sample_results: 示例结果用于可视化
     """
-    # model.eval()
-    model.train() # 临时使用训练模式
+    model.eval()
+    # model.train() # 临时使用训练模式
     
     # 初始化指标累加器
     total_mse = 0.0
@@ -183,10 +196,11 @@ def evaluate_model(model, data_loader, device, args):
             target_frames = target_frames.to(device, non_blocking=True)
             
             # 前向传播
-            pred_frames, _ = model(input_frames)
+            pred_frames = model(input_frames)
             
             # 反归一化用于指标计算
-            pred_denorm = denormalize(pred_frames)  # [B, 1, H, W]
+            # pred_denorm = minmax_denormalize(pred_frames)  # [B, 1, H, W]
+            pred_denorm = denormalize(pred_frames)
             target_denorm = denormalize(target_frames)  # [B, 1, H, W]
             
             batch_size = input_frames.size(0)
@@ -309,8 +323,6 @@ def main(args):
     print(f"创建模型: {args.model}")
     model_kwargs = {
         'num_frames': args.num_frames,
-        'use_representation_head': args.use_representation_head,
-        'representation_dim': args.representation_dim,
     }
     
     if args.model == 'SwiftFormerTemporal_XS':
@@ -335,10 +347,10 @@ def main(args):
         except (pickle.UnpicklingError, TypeError) as e:
             print(f"使用weights_only=False加载失败: {e}")
             print("尝试使用torch.serialization.add_safe_globals...")
-            from argparse import Namespace
-            # 添加安全全局变量
-            torch.serialization.add_safe_globals([Namespace])
-            checkpoint = torch.load(args.resume, map_location='cpu')
+            # from argparse import Namespace
+            # # 添加安全全局变量
+            # torch.serialization.add_safe_globals([Namespace])
+            # checkpoint = torch.load(args.resume, map_location='cpu')
         
         # 处理状态字典（可能包含'module.'前缀）
         if 'model' in checkpoint:
@@ -462,10 +474,6 @@ def get_args_parser():
     # 模型参数
     parser.add_argument('--model', default='SwiftFormerTemporal_XS', type=str, metavar='MODEL',
                         help='要评估的模型名称')
-    parser.add_argument('--use-representation-head', action='store_true',
-                        help='使用表示头进行姿态/速度预测')
-    parser.add_argument('--representation-dim', default=128, type=int,
-                        help='表示向量的维度')
     
     # 评估参数
     parser.add_argument('--batch-size', default=16, type=int,