清理代码，删除跳连接部分

main_temporal.py

@@ -49,11 +49,6 @@ def get_args_parser():
     # Model parameters
     parser.add_argument('--model', default='SwiftFormerTemporal_XS', type=str, metavar='MODEL',
                         help='Name of model to train')
-    parser.add_argument('--use-representation-head', action='store_true',
-                        help='Use representation head for pose/velocity prediction')
-    parser.add_argument('--representation-dim', default=128, type=int,
-                        help='Dimension of representation vector')
-    parser.add_argument('--use-skip', default=False, type=bool, help='using skip connections')
     
     # Training parameters
     parser.add_argument('--batch-size', default=32, type=int)
@@ -207,9 +202,6 @@ def main(args):
     print(f"Creating model: {args.model}")
     model_kwargs = {
         'num_frames': args.num_frames,
-        'use_representation_head': args.use_representation_head,
-        'representation_dim': args.representation_dim,
-        'use_skip': args.use_skip,
     }
     
     if args.model == 'SwiftFormerTemporal_XS':
@@ -258,7 +250,7 @@ def main(args):
             super().__init__()
             self.mse = nn.MSELoss()
         
-        def forward(self, pred_frame, target_frame, representations=None, temporal_indices=None):
+        def forward(self, pred_frame, target_frame, temporal_indices=None):
             loss = self.mse(pred_frame, target_frame)
             loss_dict = {'mse': loss}
             return loss, loss_dict
@@ -386,10 +378,10 @@ def train_one_epoch(model, criterion, data_loader, optimizer, device, epoch, los
 
         # Forward pass
         with torch.amp.autocast(device_type='cuda'):
-            pred_frames, representations = model(input_frames)
+            pred_frames = model(input_frames)
             loss, loss_dict = criterion(
                 pred_frames, target_frames,
-                representations, temporal_indices
+                temporal_indices
             )
 
         loss_value = loss.item()
@@ -452,7 +444,7 @@ def train_one_epoch(model, criterion, data_loader, optimizer, device, epoch, los
             if args is not None and getattr(args, 'log_images', False) and global_step % getattr(args, 'image_log_freq', 100) == 0:
                 with torch.no_grad():
                     # Take first sample from batch for visualization
-                    pred_vis, _ = model(input_frames[:1])
+                    pred_vis = model(input_frames[:1])
                     # Convert to appropriate format for TensorBoard
                     # Assuming frames are in [B, C, H, W] format
                     writer.add_images('train/input', input_frames[:1], global_step)
@@ -497,10 +489,10 @@ def evaluate(data_loader, model, criterion, device, writer=None, epoch=0):
 
         # Compute output
         with torch.amp.autocast(device_type='cuda'):
-            pred_frames, representations = model(input_frames)
+            pred_frames = model(input_frames)
             loss, loss_dict = criterion(
                 pred_frames, target_frames,
-                representations, temporal_indices
+                temporal_indices
             )
 
         # 计算诊断指标
@@ -555,4 +547,4 @@ if __name__ == '__main__':
     if args.output_dir:
         Path(args.output_dir).mkdir(parents=True, exist_ok=True)
     
-    main(args)
+    main(args)