更新归一化方式，当前直接映射，不利用均值标准差进行标准化

main_temporal.py

@@ -19,7 +19,7 @@ from timm.utils import NativeScaler, get_state_dict, ModelEma
 from util import *
 from models import *
 from models.swiftformer_temporal import SwiftFormerTemporal_XS, SwiftFormerTemporal_S, SwiftFormerTemporal_L1, SwiftFormerTemporal_L3
-from util.video_dataset import VideoFrameDataset, SyntheticVideoDataset
+from util.video_dataset import VideoFrameDataset
 from util.frame_losses import MultiTaskLoss
 
 # Try to import TensorBoard
@@ -47,7 +47,7 @@ def get_args_parser():
                         help='Number of input frames (T)')
     parser.add_argument('--frame-size', default=224, type=int,
                         help='Input frame size')
-    parser.add_argument('--max-interval', default=1, type=int,
+    parser.add_argument('--max-interval', default=4, type=int,
                         help='Maximum interval between consecutive frames')
     
     # Model parameters
@@ -109,10 +109,10 @@ def get_args_parser():
                         help='Weight for frame prediction loss')
     parser.add_argument('--contrastive-weight', type=float, default=0.1,
                         help='Weight for contrastive loss')
-    parser.add_argument('--l1-weight', type=float, default=1.0,
-                        help='Weight for L1 loss')
-    parser.add_argument('--ssim-weight', type=float, default=0.1,
-                        help='Weight for SSIM loss')
+    # parser.add_argument('--l1-weight', type=float, default=1.0,
+    #                     help='Weight for L1 loss')
+    # parser.add_argument('--ssim-weight', type=float, default=0.1,
+    #                     help='Weight for SSIM loss')
     parser.add_argument('--no-contrastive', action='store_true',
                         help='Disable contrastive loss')
     parser.add_argument('--no-ssim', action='store_true',
@@ -326,7 +326,7 @@ def main(args):
         lr_scheduler.step(epoch)
         
         # Save checkpoint
-        if args.output_dir and (epoch % 10 == 0 or epoch == args.epochs - 1):
+        if args.output_dir and (epoch % 2 == 0 or epoch == args.epochs - 1):
             checkpoint_path = output_dir / f'checkpoint_epoch{epoch}.pth'
             utils.save_on_master({
                 'model': model_without_ddp.state_dict(),