修改梯度裁剪的恶性bug，当前能进行训练，但是无论是否使用跳连接，预测帧总是输出对称的的效果，mse收敛到0.10

main_temporal.py

@@ -20,18 +20,14 @@ 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
-from util.frame_losses import MultiTaskLoss
+# from util.frame_losses import MultiTaskLoss
 
 # Try to import TensorBoard
 try:
     from torch.utils.tensorboard import SummaryWriter
     TENSORBOARD_AVAILABLE = True
 except ImportError:
-    try:
-        from tensorboardX import SummaryWriter
-        TENSORBOARD_AVAILABLE = True
-    except ImportError:
-        TENSORBOARD_AVAILABLE = False
+    TENSORBOARD_AVAILABLE = False
 
 
 def get_args_parser():
@@ -57,7 +53,7 @@ def get_args_parser():
                         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=True, type=bool, help='using skip connections')
+    parser.add_argument('--use-skip', default=False, type=bool, help='using skip connections')
     
     # Training parameters
     parser.add_argument('--batch-size', default=32, type=int)
@@ -328,7 +324,7 @@ def main(args):
         lr_scheduler.step(epoch)
         
         # Save checkpoint
-        if args.output_dir and (epoch % 2 == 0 or epoch == args.epochs - 1):
+        if args.output_dir and (epoch % 1 == 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(),
@@ -368,7 +364,7 @@ def main(args):
 
 
 def train_one_epoch(model, criterion, data_loader, optimizer, device, epoch, loss_scaler,
-                    clip_grad=0, clip_mode='norm', model_ema=None, writer=None,
+                    clip_grad=None, clip_mode='norm', model_ema=None, writer=None,
                     global_step=0, args=None, **kwargs):
     model.train()
     metric_logger = utils.MetricLogger(delimiter="  ")
@@ -403,7 +399,6 @@ def train_one_epoch(model, criterion, data_loader, optimizer, device, epoch, los
 
         optimizer.zero_grad()
         
-        # 在反向传播前保存梯度用于诊断
         loss_scaler(loss, optimizer, clip_grad=clip_grad, clip_mode=clip_mode,
                     parameters=model.parameters())
 
@@ -426,14 +421,14 @@ def train_one_epoch(model, criterion, data_loader, optimizer, device, epoch, los
         metric_logger.update(pred_std=pred_std)
         metric_logger.update(grad_norm=total_grad_norm)
         
-        # 每50个批次打印一次BatchNorm统计
+        # # 每50个批次打印一次BatchNorm统计
         if batch_idx % 50 == 0:
             print(f"[诊断] 批次 {batch_idx}: 预测均值={pred_mean:.4f}, 预测标准差={pred_std:.4f}, 梯度范数={total_grad_norm:.4f}")
-            # 检查一个BatchNorm层的运行统计
-            for name, module in model.named_modules():
-                if isinstance(module, torch.nn.BatchNorm2d) and 'decoder.blocks.0.bn' in name:
-                    print(f"[诊断] {name}: 运行均值={module.running_mean[0].item():.6f}, 运行方差={module.running_var[0].item():.6f}")
-                    break
+        #     # 检查一个BatchNorm层的运行统计
+        #     for name, module in model.named_modules():
+        #         if isinstance(module, torch.nn.BatchNorm2d) and 'decoder.blocks.0.bn' in name:
+        #             print(f"[诊断] {name}: 运行均值={module.running_mean[0].item():.6f}, 运行方差={module.running_var[0].item():.6f}")
+        #             break
 
         # Log to TensorBoard
         if writer is not None:
@@ -520,21 +515,21 @@ def evaluate(data_loader, model, criterion, device, writer=None, epoch=0):
         metric_logger.update(target_mean=target_mean)
         metric_logger.update(target_std=target_std)
         
-        # 第一个批次打印详细诊断信息
-        if batch_idx == 0:
-            print(f"[评估诊断] 批次 0:")
-            print(f"  预测范围: [{pred_frames.min().item():.4f}, {pred_frames.max().item():.4f}]")
-            print(f"  预测均值: {pred_mean:.4f}, 预测标准差: {pred_std:.4f}")
-            print(f"  目标范围: [{target_frames.min().item():.4f}, {target_frames.max().item():.4f}]")
-            print(f"  目标均值: {target_mean:.4f}, 目标标准差: {target_std:.4f}")
+        # # 第一个批次打印详细诊断信息
+        # if batch_idx == 0:
+        #     print(f"[评估诊断] 批次 0:")
+        #     print(f"  预测范围: [{pred_frames.min().item():.4f}, {pred_frames.max().item():.4f}]")
+        #     print(f"  预测均值: {pred_mean:.4f}, 预测标准差: {pred_std:.4f}")
+        #     print(f"  目标范围: [{target_frames.min().item():.4f}, {target_frames.max().item():.4f}]")
+        #     print(f"  目标均值: {target_mean:.4f}, 目标标准差: {target_std:.4f}")
             
-            # 检查BatchNorm运行统计
-            for name, module in model.named_modules():
-                if isinstance(module, torch.nn.BatchNorm2d) and 'decoder.blocks.0.bn' in name:
-                    print(f"  {name}: 运行均值={module.running_mean[0].item():.6f}, 运行方差={module.running_var[0].item():.6f}")
-                    if module.running_var[0].item() < 1e-6:
-                        print(f"  警告: BatchNorm运行方差接近零!")
-                    break
+        #     # 检查BatchNorm运行统计
+        #     for name, module in model.named_modules():
+        #         if isinstance(module, torch.nn.BatchNorm2d) and 'decoder.blocks.0.bn' in name:
+        #             print(f"  {name}: 运行均值={module.running_mean[0].item():.6f}, 运行方差={module.running_var[0].item():.6f}")
+        #             if module.running_var[0].item() < 1e-6:
+        #                 print(f"  警告: BatchNorm运行方差接近零!")
+        #             break
 
         # Update metrics
         metric_logger.update(loss=loss.item())